See the Full List of Posters and Presenters Here

Bio: Lars Ruthotto is a Professor in the Departments of Mathematics and Computer Science at Emory University, where he is a member of the Scientific Computing Group and directs the Emory REU/RET Site for Computational Mathematics for Data Science. He earned his PhD in Mathematics from the University of Muenster in 2012 and was a postdoc at the University of British Columbia before joining Emory. His research develops computational methods at the interface of scientific computing, optimization, machine learning, and inverse problems, with recent emphasis on generative modeling, high-dimensional partial differential equations, and stochastic optimal control. He is a recipient of the NSF CAREER Award and serves as a Section Editor for the SIAM Journal on Scientific Computing.

Access Via Zoom: https://gatech.zoom.us/j/93198655277?pwd=R49ghn4Eacaqbc0YIBa68NdmgbPYYO.1

Meeting ID: 931 9865 5277
Passcode: 938967

For CODA guest access, please contact shatcher8@gatech.edu at least 2 business days prior to the event.

Bio: Ferdous Alam is an Assistant Professor in the George W. Woodruff School of Mechanical Engineering at the Georgia Institute of Technology and an affiliated faculty member of ML@GT. He is also a core faculty member at the Institute for Robotics and Intelligent Machines (IRIM). He leads the Inference Lab, where his research focuses on the computational foundations of AI-driven engineering design and robotic manufacturing, including generative modeling of mechanical systems, representation learning for 3D geometry, and robot learning for manufacturing tasks. Before joining Georgia Tech, Dr. Alam was a postdoctoral researcher at MIT, and he previously received his PhD from The Ohio State University. His research has been recognized with several Best Paper Awards at leading ASME conferences, as well as the Google Research Scholar Award in Applied Science.

Zoom Link: https://gatech.zoom.us/j/99778983822?pwd=O0YsaiaJp6ipUrlfIYSHa9ChwbLwXJ.1
Meeting ID: 997 7898 3822 
Passcode: 524773

For CODA guest access, please contact shatcher8@gatech.edu at least 2 business days prior to the event.

Bio: Andrea Bajcsy is an Assistant Professor in the Robotics Institute at Carnegie Mellon University where she leads the Interactive and Trustworthy Robotics Lab (Intent Lab). She broadly works at the intersection of robotics, machine learning, control theory, and human-AI interaction. Prior to joining CMU, Andrea received her Ph.D. in Electrical Engineering & Computer Science from University of California, Berkeley in 2022. She is the recipient of the DARPA Young Faculty Award (2025), NSF CAREER Award (2025), Amazon Research Award (2025), Google Research Scholar Award (2024), Finalist for Best Paper Award of the IEEE RAS Technical Committee on Robot Control (2024), Rising Stars in EECS Award (2021), Honorable Mention for the T-RO Best Paper Award (2020), NSF Graduate Research Fellowship (2016), and worked at NVIDIA Research for Autonomous Driving.

Abstract: This talk investigates the fundamental differences between low-norm and flat solutions of shallow ReLU networks training problems, particularly in high-dimensional settings. We show that global minima with small weight norms exhibit strong generalization guarantees that are dimension-independent. In contrast, local minima that are “flat” can generalize poorly as the input dimension increases. We attribute this gap to a phenomenon we call neural shattering, where neurons specialize to extremely sparse input regions, resulting in activations that are nearly disjoint across data points. This forces the network to rely on large weight magnitudes, leading to poor generalization. Our theoretical analysis establishes an exponential separation between flat and low-norm minima. In particular, while flatness does imply some degree of generalization, we show that the corresponding convergence rates necessarily deteriorate exponentially with input dimension. These findings suggest that flatness alone does not fully explain the generalization performance of neural networks.

Bio: Rahul Parhi is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of California, San Diego. Prior to joining UCSD, he was a Postdoctoral Researcher at the École Polytechnique Fédérale de Lausanne (EPFL), where he worked from 2022 to 2024. He completed his PhD in Electrical Engineering at the University of Wisconsin-Madison in 2022. His research interests lie at the interface between functional and harmonic analysis and data science.

Zoom link: https://gatech.zoom.us/j/93991261775?pwd=PCAJs5vTANpLGiCcKyyBR50hMuV432.1
Meeting ID: 939 9126 1775 
Passcode: 914902

Digital technologies are reshaping our work and our lives at unprecedented speed. This presentation examines three forces driving this Fourth Industrial Revolution: exponentiality, convergence, and ubiquity, and their implications for higher education and adult learning. Dr. Schatz explores critical questions: How do we leverage AI-enabled personalization to support careers when 40% of worker skills become obsolete every five years? How do we design learning ecosystems that scale adaptive support across institutional boundaries? How do we use data to inform lifelong learning while respecting individuals’ data sovereignty? Drawing on cognitive science and learning engineering research, this presentation offers actionable strategies for implementing AI-augmented adult learning systems that move from pilot projects to institutional practice.

Biosketch

Sae (pronounced like “say”) Schatz works at the intersection of cognition, technology, and data. She specializes in bridging the gap between scientific principles and human-centered practice, and her unique approach to “strategic storytelling” (translating complex topics into memorable messages) has garnered professional awards and seen her work translated into multiple languages. 

In 2023, Sae launched the Knowledge Forge LLC, a boutique consultancy offering keynotes, professional development workshops, and strategic services. Her clients include government agencies, academic institutions, and businesses. Recently, for example, she delivered keynotes at the UK Special Forces Club (Knightsbridge), Prometric board meeting, and Innovations in Testing annual conference. She’s published works in TD Magazine, Defense Dossier, and the Journal of Military Learning, and she helped to produce the Learning Engineering Toolkit (2022), authored Engines of Engagement: A Curious Book About Generative AI (2023), and contributed to the U.S. National Academies’ report on Adult Learning in the Military Context (2024). 

From 2015 to 2022, Sae served as the director of the Advanced Distributed Learning (ADL) Initiative, a U.S. Department of Defense program for research, development, and policy stewardship. She also co-chaired the NATO Training Group (NTG) Individual Training and Educational Developments task group. Under her leadership, the U.S. ADL Initiative sparked the international community’s pursuit of the lifelong “digital learning ecosystem,” in part with her popular book Modernizing Learning: Building the Future Learning Ecosystem. Her efforts were also formally recognized as “a pathfinder for enterprise data services” by then–DoD Chief Data Officer David L. Spirk. 

Before joining the civil service, Sae worked as an applied human-systems scientist in both business and academia. From 2011 to 2014, she was the chief scientist at MESH Solutions (a defense contractor), where she led the U.S. Marine Corps’ Making Good Instructors Great project and the U.S. Joint Staff’s award-winning Blended Learning–Training effort. Prior to that, she held an assistant professorship with the University of Central Florida’s Institute for Simulation and Training. Sae is a prolific writer and professional presenter as well as a graphic designer who often uses those skills to enhance books, presentations, and infographics. 

She holds a doctorate in Modeling and Simulation (M&S), with an emphasis on human systems.

Event Agenda 

• 12:00 – 12:45 PM | Arrival, Lunch, and Networking
• 12:45 – 1:15 PM | Welcome and IRIM Updates - Executive Director Greg Sawicki (Q&A to follow)
• 1:15 – 1:30 PM Break
• 1:30 – 2:45 PM | Research Neighborhood & Research Initiative Updates + All Hands Q&A
• 1:30 – 1:45 PM – Human-Robot Collaborative Teaming | Speaker: Ye Zhao
• 1:45 – 2:00 PM – Agricultural Robotics | Speaker: Colin Usher
• 2:00 – 2:15 PM – Household Robotics | Speaker: Sonia Chernova
• 2:15 – 2:30 PM – Construction Robotics | Speaker: Edvard Bruun
• 2:45 – 4:00 PM  | Poster Session (Atrium) 

REGISTRATION LINK

PLEASE NOTE SEMINAR IS TUESDAY THE 21st!

Abstract: How should we act under incomplete information? We must integrate over missing values to assess the risk of a downstream decision, or the expected risk reduction from gathering further information.  Applications include NLP annotation, LLM workflows, clinical medicine, experimental design, and many others.  The classical approach to predicting missing values involves fitting some generative model to the incomplete data.  Alas, classical methods are slow and often inaccurate due to local optima, poor mixing, or model misspecification.  We propose a low-fuss, efficient alternative with fewer assumptions: given incomplete data, train a neural network to directly predict masked values. Our generic network architecture resembles BERT: it constructs a representation for each random variable's posterior marginal distribution and iteratively refines it through attention on other random variables and entities.  We find that this posterior marginal Transformer ("Marformer") works much better than EM and almost as well as MCMC even when those methods know the true data generating model, and it outperforms them on naturally occurring data. We outline future methods for adaptive masking and a future design for managing data collection efforts.

Bio: Jason Eisner is Professor of Computer Science at Johns Hopkins University and a Fellow of the Association for Computational Linguistics. At Johns Hopkins, he is also affiliated with the Center for Language and Speech Processing, the Mathematical Institute for Data Science, the Cognitive Science Department, and the Data Science and AI Institute. His goal is to develop the probabilistic modeling, inference, and learning techniques needed for a unified model of all kinds of linguistic structure, and to connect existing models (such as LLMs) to commonsense reasoning, formal reasoning, and downstream applications. His 180+ papers have presented various algorithms for parsing, machine translation, and weighted finite-state machines; formalizations, algorithms, theorems, and empirical results in computational phonology; unsupervised or semi-supervised learning methods for syntax, morphology, and word-sense disambiguation; and principled methods for conversational AI, including neural language modeling and semantic parsing. From 2019-2024 he was Director of Research at Microsoft Semantic Machines, which developed new approaches to conversational AI. He is also the lead designer of Dyna, a declarative programming language that provides an infrastructure for AI algorithms. He has received 3 school-wide awards for excellence in teaching, most recently in 2025, as well as recent 

 Zoom Link

Meeting ID: 977 5500 1285  | Passcode: 247306 

For more information, or for CODA guest access, please contact shatcher8@gatech.edu at least 2 business days prior to the event.

IDEaS-affiliated students and staff are also welcome.

If you do not have access to Coda, near the beginning of the event we will have a staff member or student waiting by the reception desk near the elevators to escort people up.

For questions please contact Ashley.edwards@gatech.edu

IDEaS-affiliated students and staff are also welcome.

If you do not have access to Coda, near the beginning of the event we will have a staff member or student waiting by the reception desk near the elevators to escort people up.

For questions please contact Ashley.edwards@gatech.edu

Coffee and snacks will be served.

COURSE IS FULL

What Participants Will Learn:                                                                                   

•     Deploy secure, scalable AI agents using AgentCore Runtime
•   Implement robust authentication and authorization with AgentCore Identity
•   Integrate external services through AgentCore Gateway
•   Extend agent capabilities using AgentCore Tools
•   Manage agent memory and context with AgentCore Memory
•   Monitor agent performance with AgentCore Observability

Schedule:

•     1 hour of presentation
•   3 hours of hands-on activities
•   1 hour lunch + networking + QA
•   The session will remain active for 72 hours afterward so students can continue practicing

REGISTER HERE

Abstract: Large Language Models are powerful models of language even for long stretches of generation. This has shifted the research frontier in natural language processing to where global coherence and consistency, strategic content planning and selection, and high-fidelity evaluation play increasingly important roles. These belong to discourse-level capabilities. I will first present a model of discourse in the framework of Questions Under Discussion (QUD), which provides the necessary data structure to move beyond a local view of text generation and informs us how to strategically decide which questions are worth answering in a given context. Next, I will share how this model can be used to measure and evaluate novelty, revealing the structural homogeneity in LLM outputs even when they differ in content. Finally, I will tackle the evaluation challenge for open-ended generation, focusing on fine-grained evaluation systems that capture implicit aspects absent from user specification. In all, this talk aims to address the need for discourse-level understanding and control in today’s generative models.

Bio: Jessy Li is an associate professor in the Linguistics Department and (by courtesy) the Computer Science Department at the University of Texas at Austin. She earned her Ph.D. from the Department of Computer and Information Science at the University of Pennsylvania. Her research interests are in computational linguistics and natural language processing, specifically discourse and pragmatics, natural language generation, evaluation, and language & code. She is a recipient of an NSF CAREER Award, ACL and EMNLP Outstanding Paper Awards, an ACM SIGSOFT Distinguished Paper Award, among others. Jessy is an AI research lead at the NSF-Simons CosmicAI Institute, and an affiliated faculty member at the Oden Institute for Computational Engineering & Sciences. She is the current Secretary of NAACL and a program chair of EMNLP 2026.

Zoom Link: https://gatech.zoom.us/j/95262711498?pwd=OoN50EsqjRdi5zAJtgWiZH7BLFYwOg.1

Meeting ID: 952 6271 1498 
Passcode: 144273

For CODA guest access, please contact shatcher8@gatech.edu at least 2 business days prior to the event.

Abstract: The ability to manufacture microscale sensors and other components for robotic systems has intrigued the robotics community for almost 40 years. There have been huge success stories; MEMS inertial sensors have enabled an entire market of low-cost, small-scale UAVs. However, the promise of ant-scale robots has largely failed. Ants and other small insects like mites can move at high speeds on surfaces from picnic tables to front lawns, but few small-scale robots have moved outside the lab. This talk will present themes across our work in scaling down robotics. A common thread across our work is the use of geometry, materials, and mechanisms to do part of the robot control and computation in hardware. For example, choosing the right foot geometry and size scale can result in a 3.6 cm high power autonomous biped capable of high speed locomotion at 25 cm/s. Novel fabrication processes can be designed to tightly couple high-bandwidth actuators, sensors, and mechanisms for locomotion and manipulation tasks. Finally, even sensors can be designed to quickly and easily detect flight-relevant events. 

Bio: Sarah Bergbreiter joined the Department of Mechanical Engineering at Carnegie Mellon University as a Professor in the fall of 2018 after spending ten years at the University of Maryland, College Park. She started her academic career with a B.S.E. degree in electrical engineering from Princeton University in 1999. After a short introduction to the challenges of sensor networks at a small startup company, she received her M.S. and Ph.D. degrees from the University of California, Berkeley in 2004 and 2007 with a focus on small-scale robots. Prof. Bergbreiter received the DARPA Young Faculty Award in 2008, the NSF CAREER Award in 2011, and the Presidential Early Career Award for Scientists and Engineers (PECASE) in 2013 for her research on engineering robotic systems down to millimeter size scales. She has received several Best Paper awards at conferences like ICRA, IROS, and Hilton Head Workshop with her fabulous group of students and former students, and is a Fellow of the ASME. She also served as Vice Chair of DARPA’s Microsystems Exploratory Council from 2020 through 2022. Outside of academia, she enjoys spending time with her husband and two daughters, running, biking, or skiing outside rather slowly, and the rare game of water polo.

Abstract: Abstract: Large pre-trained models trained on internet-scale data are often not ready for deployment out-of-the-box. They are heavily fine-tuned and aligned using large quantities of human preference data, usually elicited using pairwise comparisons. While aligning an AI/ML model to human preferences or values, it is important to ask whose preference and values we are aligning it to? The prominent approaches of preference alignment are severely limited due to inherent assumption of uniformity by the preference models. We aim to overcome this limitation by building mathematical foundations for learning heterogeneous human preferences. In this talk, I will present PAL, a personalize-able reward modelling framework for pluralistic alignment. PAL has modular design that leverages commonalities across users while catering to individual personalization, enabling efficient few-shot generalization. PAL is versatile to be applied to various domains and matches or outperforms state-of-the-art methods on both text-to-text and text-to-image tasks with 100x fewer parameters in practice. I will also present theoretical results on per user sample complexity for generalization and fundamental limitations when there are limited pairwise comparisons.

References:

1. PAL: Sample-Efficient Personalized Reward Modeling for Pluralistic Alignment (ICLR 2025)
2. One-for-all: Simultaneous metric and preference learning (Neurips 2022)
3. Metric learning via limited pairwise comparisons (UAI 2024)
4. Bridging Lifelong and Multi-Task Representation Learning via Algorithm and Complexity Measure (ALT 2026)

Bio: Ramya Korlakai Vinayak is the Dugald C. Jackson assistant professor in the Dept. of ECE and affiliated faculty in the Dept. of Computer Science and the Dept. of Statistics at the UW-Madison. Her research interests span the areas of machine learning, statistical inference, and crowdsourcing, with a focus on preference learning and alignment under heterogeneity, reliable and efficient dataset creation, and human-in-the-loop systems. Her works aim to address theoretical and practical challenges that arise when learning from heterogeneous societal data. Prior to joining UW-Madison, Ramya was a postdoctoral researcher in the Paul G. Allen School of Computer Science and Engineering at the University of Washington. She received her Ph.D. in Electrical Engineering from Caltech. She obtained her Masters from Caltech and Bachelors from IIT Madras. She is a recipient of the Schlumberger Foundation Faculty of the Future fellowship from 2013-15, and an invited participant at the Rising Stars in EECS workshop in 2019. She is the recipient of NSF CAREER Award in 2023.

ZOOM ACCESS
https://gatech.zoom.us/j/99656296661?pwd=W2wfPaoxiYZt3seJhLcK8I0eTr02YC.1

Meeting ID: 996 5629 6661 
Passcode: 797387 

This talk will be broken into two parts. First, we will talk about the hardware design of humanoid robots, in particular the design of IHMC's custom humanoids, Nadia and Alex. These systems are highly articulated and thus highly complex, with many degrees of freedom and many subsystems. Beyond complexity, however, enabling them to move through environments dynamically and efficiently requires careful consideration of not only their actuator design but also the passive dynamics. Then, we will talk about IHMC's work in the design of autonomy and human interfaces to enable humans to team with humanoid robots to explore and work in novel environments. This will overview the approach for one of the earliest uses of virtual reality to control humanoid robots.  We will also highlight IHMC's design of a flexible autonomy architecture built on behavior trees that enables on-the-fly drafting of novel behaviors and reliable reactivity, including the inclusion of human intervention when encountered with uncertainty.

Bio: Dr. Robert Griffin is a Senior Research Scientist at IHMC and Director of Research Professor at the University of West Florida. His research focuses on improving mobility and autonomy for legged robotics and powered exoskeletons. He is interested in system level approaches for improving the mobility and capability of these robotic platforms, including platform design, motion design and control, autonomy and manipulation, and perception. 

Dr. Griffin leads the robotics group at IHMC, directing a number of projects focusing on advancing robotics. This has included the Office of Naval Research's SquadBot program, which has led to the development of the Alex and Nadia humanoid robots. He has also led the algorithm development for NASA Johnson Space Center's Valkyrie project, including using Valkyrie as an Explosive Ordnance Disposal Technician. Dr. Griffin has also guided the development of a number of exoskeleton systems, including devices used for regaining mobility as well as worker assistance for the Department of Energy. The research in these projects has included the development of novel simplified models for locomotion, contact planning, environmental modeling, the development of autonomy algorithms, and hardware design. His work has resulted in a number of recognitions, including several best paper awards, qualifying as a finalist in the Toyota Mobility Foundation's Mobility Unlimited Challenge, a 2nd and 4th place finish in the Cybathlon Powered Exoskeleton race, and being named as the Outstanding Research Organization in the 2025 Humanoid Robotics Industry Awards. 

Dr. Griffin earned his bachelor’s degree from Tennessee Tech University, and his Ph.D. from Virginia Tech in 2017 with a primary research focus on the control of bipedal robotic mobility for humanoids and exoskeletons. This includes the Shipboard Autonomous Firefighting Robot with ONR and Virginia Tech’s entry into the 2015 DARPA Robotics Challenge.

Abstract: Highly performant AI models such as Large Language Models (LLMs) have achieved remarkable performance across various domains. It is widely acknowledged that their large model sizes lead to high computational costs (storage, inference latency, memory, etc) making serving and deployment expensive especially in low-resource environments. LLM pruning or more generally model compression is a line of work where LLM model parameters are compressed to reduce model footprint. For example, post-training model pruning aims to achieve model compression by removing less-important parameters while retaining model utility as much as possible. Depending upon available hardware, different types of model compression methods can be useful (eg, sparsity, sparse plus low-rank, quantization, etc). These problems can be formulated as large-scale discrete optimization problems posing interesting algorithmic research questions. In this talk, I will discuss our recent experience in using tools and insights from high-dimensional optimization and statistics in the context of LLM compression.  

Bio: Rahul Mazumder is the NTU Associate Professor of Operations Research and Statistics at MIT Sloan School of Management. He is affiliated with MIT OR Center, MIT Center for Statistics and Data Science, LIDS and IDSS.  His research interests are at the intersection of statistics, machine learning and mathematical programming, and their applications to industry, the government, and the sciences. He is a recipient of the Leo Breiman Junior Award from the American Statistical Association, International Indian Statistical Association Early Career Award in Statistics and Data Science, INFORMS Donald P. Gaver, Jr. Early Career Award for Excellence in Operations Research, INFORMS Optimization Society Young Researchers Prize, Office of Naval Research Young Investigator Award, INFORMS ICS Prize (Honorable Mention). He is currently serving as an AE of the Annals of Statistics, Operations Research and Journal of Machine Learning Research, and was an AE at Bernoulli.

Zoom info: https://gatech.zoom.us/j/98742217559?pwd=x3d62aS5ZXU1EI72lJm3vZBQvG0ZEW.1
Meeting ID: 987 4221 7559 
Passcode: 531419

IDEaS-affiliated students and staff are also welcome.

If you do not have access to Coda, near the beginning of the event we will have a staff member or student waiting by the reception desk near the elevators to escort people up.

What AI tools have you tried for writing, finding literature citations, making presentations, coding, or automating research tasks?  Come share your experiences and learn from others over coffee with the IDEaS community.

IDEaS-affiliated students and staff are also welcome.

If you do not have access to Coda, near the beginning of the event we will have a staff member or student waiting by the reception desk near the elevators to escort people up.

For questions please contact Ashley.edwards@gatech.edu

Abstract: Modern machine learning systems achieve impressive performance, but are largely built under a closed-world assumption: that the data distribution does not change from the distribution of the training set.  Real environments are open, dynamic, and filled with unknown unknowns. In such settings, knowing when a model’s output is reliable is critical.

This talk focuses on out-of-distribution (OOD) detection, a key capability for safe and reliable AI. The first part presents a mathematical theory of OOD detection that places state-of-the-art methods, largely heuristically derived, within a unified variational information-theoretic framework [1].  The theory provides plausible assumptions behind existing approaches and predicts new OOD detectors that are simple to implement and outperform state-of-the-art methods.

The second part of the talk addresses an often overlooked problem in practical deployment of OOD detectors, that is, OOD detectors depend on parameters that must be tuned, and a “given OOD” dataset is required [2]. In practice, such given OOD data may be difficult to obtain.  We formalize this problem and introduce a new tuning strategy that uses only the model’s training data and achieves similar or better performance compared to tuning on given OOD data, enabling robust and practical deployment.

The ideas will be illustrated across applications including automatic target recognition, cyber-security, large language models, and radio-frequency (RF) fingerprinting.

[1] https://arxiv.org/pdf/2506.14194

[2] https://arxiv.org/pdf/2602.05935

Bio: Dr. Ganesh Sundaramoorthi is Senior Research Fellow/Director of Research at RTX Technology Research Center, which is the research center for RTX (encompassing Raytheon, Collins Aerospace, and Pratt & Whitney) and also Adjunct Professor of ECE at Georgia Tech.  His research is in machine learning, computer vision, and artificial intelligence (AI), e.g., robustness, explainability, acceleration, and low size, weight & power.  Prior to his current position, he was on the faculty of KAUST, where he led a research group in computer vision.  His PhD was from Georgia Tech and he did postdoctoral work at UCLA in computer vision. He has led a number of internal and external research programs in AI including IARPA, NGA, ARPA-E and AFRL.  He was area chair for leading AI conferences (IEEE/CVF CVPR & ICCV). He has more than 60 publications in AI and nearly 50 patents and/or applications.

http://ganeshsun.com/

For more information, or for CODA guest access, please contact shatcher8@gatech.edu at least 2 business days prior to the event.

ZOOM ACCESS 

https://gatech.zoom.us/j/92002341992?pwd=ZLAiI8WdAu8arEo23SEArIhGU2smxm.1

Meeting ID: 920 0234 1992 
Passcode: 253459 

In this talk, I will describe how we achieve this in a material-like robotic collective by modulating the interunit tangential forces to control topological rearrangements of units within a tightly packed structure. This allows local control of rigidity transitions between solid and fluid-like states in the collective and enables spatiotemporal control of shape and strength. I will show examples of structure-forming and healing and how the collective can support 700 newtons (500 times the weight of a robot) before“melting” under its own weight.

Bio:  Elliot W. Hawkes is an Associate Professor of Mechanical Engineering at UCSB. He completed a postdoctoral fellowship at Stanford University, where he also earned his MS and PhD in Mechanical Engineering. Previously, he worked at the Harvard Microrobotics Lab and the ETH Multi-scale Robotics Lab. The Hawkes Lab's research is at the intersection of design, mechanics, and materials, and develops novel mechanisms and applies non-traditional materials to solve challenging problems in robotics, medicine, and biomechanics. He has spun two companies out of the Hawkes Lab, to bring new medical and rehabilitation devices into the world.

Bio: Robert Stuart-Smith is Founding Director of the Master of Science in Design: Robotics and Autonomous Systems (MSD-RAS) program, Assistant Professor of Architecture, and an affiliate faculty member in the Engineering School’s GRASP Laboratory at the University of Pennsylvania (Penn). He is also a Principal Research Associate in University College London’s (UCL) Department of Computer Science. Stuart-Smith leads the Autonomous Manufacturing Lab across Penn and UCL, where he has secured over $6.9M in competitive research funding and collaborates with industry partners including Cemex, Skanska, Mace, Buro Happold, Arup, and the Manufacturing Technology Centre (MTC). Stuart-Smith’s research focuses on robotics-enabled fabrication and construction systems, adaptive multi-robot processes, and integrated AI-driven generative design workflows. His Aerial Additive Manufacturing research —published in Nature—introduced in-flight additive manufacturing using coordinated aerial robotic agents. Stuart-Smith’s work has also been published in Science Robotics, AD Architectural Design, and L’Architecture d’Aujourd’hui, and featured by outlets including BBC Click, New Scientist, Smithsonian, and Architizer.

Abstract: Gradient-based data attribution methods, such as influence functions, are critical for understanding the impact of individual training samples without repeated model retraining. However, their scalability is often limited by the high computational and memory costs associated with per-sample gradient computation, especially for large-scale models and datasets. In this talk, I will present our recent work on scalable influence function computation through sparse gradient compression and projection techniques with provable guarantees. I will also discuss how these methods can be applied to real-world scenarios, such as online reinforcement learning where data filtering interacts with policy learning.

Bio: Dr. Han Zhao is an Assistant Professor of Computer Science at the University of Illinois Urbana-Champaign (UIUC). He is also an Amazon Scholar at Amazon AI. Dr. Zhao earned his Ph.D. degree in machine learning from Carnegie Mellon University. His research interest is centered around trustworthy machine learning, with a focus on algorithmic fairness, robust generalization and data interpretability. He has been named a Kavli Fellow of the National Academy of Sciences. His research has been recognized through an NSF CAREER Award, a Google Research Scholar Award, an Amazon Research Award, and a Meta Research Award.

For more information, or for CODA guest access, please contact shatcher8@gatech.edu at least 2 business days prior to the event.

Join Via Zoom: https://gatech.zoom.us/j/98188267850?pwd=SOTPAZaZm0qkaiGxezfwMFaIbP1eeI.1
Meeting ID: 981 8826 7850 
Passcode: 520805

Beginning with underwater vehicles and distributed control for multi-agent coordination, the talk will trace the evolution of my work through ground and aerial robotics, where nonlinear and robust control methods were developed to enable agility, adaptability, and resilience. The discussion will then move into human-centered robotics, including humanoid platforms, rehabilitation robotics, and powered exoskeletons designed to restore or augment human movement. These efforts have increasingly blurred the boundaries between machine and human, culminating in recent work on physical human-machine interaction and assistive technologies that embody both engineering rigor and social impact.

Drawing from experiences in academia, government, and interdisciplinary collaboration, the presentation will offer an integrated perspective on how robotic systems can enhance human capability, extend our reach into extreme environments, and inspire the next generation of engineers and explorers.

Bio: Dr. Alexander Leonessa is the Chair of the Department of Mechanical Engineering at Clemson University, where he also holds the D.W. Reynolds Distinguished Professorship. His career spans more than twenty-five years of research, teaching, and leadership in robotics, control systems, and human–machine interaction.

Before joining Clemson, Dr. Leonessa served as a Program Director at the National Science Foundation, where he led the Mind, Machine, Motor Nexus (M3X) program and oversaw the Foundational Research in Robotics (FRR) and the Dynamic, Control and Systems Diagnostics (DCSD) programs. In that role, he managed a research portfolio of approximately $10 million, advancing national efforts in robotics, assistive technologies, and neuroengineering.

Dr. Leonessa’s research has explored robotic systems across domains — underwater, ground, aerial, and humanoid — integrating robust and nonlinear control theory with mechanical design and human-centered applications. His work in rehabilitation robotics, exoskeletons, and brain–computer interfaces has aimed to enhance mobility and restore function for individuals with physical impairments, while his contributions to autonomous systems have influenced applications ranging from marine exploration to precision agriculture and planetary mobility.

He has published more than one hundred peer-reviewed papers, authored a book on nonlinear switching control, and secured over twenty-six million dollars in research funding from agencies including NSF, ONR, DARPA, and the Department of Agriculture. He has advised more than fifty graduate students and received multiple honors, including the NSF CAREER Award, Virginia Tech’s Alumni Award for Outreach Excellence, and the Dean’s Award for Outstanding New Assistant Professor.

Dr. Leonessa earned his B.S. in Aeronautical Engineering from the University of Rome “La Sapienza” and his M.S. and Ph.D. degrees in Aerospace Engineering from the Georgia Institute of Technology.

Bio: Kostas’ research interests are in computer vision and robotic perception. His research addresses challenges in the perception of motion and space, such as the geometric design of cameras, and the interplay of geometry and appearance in perception tasks. Kostas’s research gives solutions to perceptual tasks such as panoramic vision, localization, perception of self-motion, large-scale mapping, visual location recognition, 3-D object recognition, and vision-based flocking. Applications of his research involve robot navigation, tele-immersion, and image and shape retrieval.

Abstract: Digital twins are virtual counterparts of complex physical systems that adaptively refine their predictive models through sensor-driven data assimilation. Once calibrated, these twins can support real-time forecasting and decision-making, including the design of data acquisition strategies via sequential optimal experimental design. However, deploying digital twins for large-scale systems remains computationally challenging due to the high cost of high-fidelity simulations, the curse of dimensionality in uncertainty quantification, and sparse, noisy observations. In this talk, I will present fast, scalable methods to address these bottlenecks, including derivative-informed operator learning, latent neural dynamics, and latent diffusion for posterior sampling. I will illustrate how these approaches enable robust, uncertainty-aware digital twins in applications such as flood prediction, tumor growth monitoring, and contaminant source tracking.

Bio: Dr. Chen is an Assistant Professor in the School of Computational Science and Engineering (CSE) at Georgia Tech. Before joining Georgia Tech in 2022, he was a Research Scientist at The University of Texas at Austin and a Postdoctoral Researcher and Lecturer at ETH Zurich. He earned his PhD in Mathematics from EPFL. His research spans computational mathematics and scientific machine learning, with an emphasis on fast and scalable methods for integrating data with physical models under high-dimensional uncertainty. He develops algorithms for Bayesian inference and data assimilation, optimal experimental design, and PDE-constrained stochastic optimization, with applications in materials, energy, health, and natural hazards. He has published in machine learning venues including NeurIPS and ICLR and computational math/physics journals such as SISC, SIOPT, CMAME, and JCP. He serves on the editorial board of Numerical Methods for Partial Differential Equations.

For more information, or for CODA guest access, please contact shatcher8@gatech.edu at least 2 business days prior to the event.

Agenda

• Research overview
• Commercialization updates
• Q&A session

Held in the Clary Theater in the Bill More Student Success Center or virtually via Zoom.

Bio:  Prof. Veeravalli received the Ph.D. degree in Electrical Engineering from the University of Illinois at Urbana-Champaign in 1992, the M.S. degree from Carnegie-Mellon University in 1987, and the B.Tech degree from Indian Institute of Technology, Bombay (Silver Medal Honors) in 1985. He is currently the Henry Magnuski Professor in the Department of Electrical and Computer Engineering (ECE) at the University of Illinois at Urbana-Champaign, where he also holds appointments with the Coordinated Science Laboratory (CSL), the Department of Statistics, and the Discovery Partners Institute. He was on the faculty of the School of ECE at Cornell University before he joined Illinois in 2000. He served as a program director for communications research at the U.S. National Science Foundation during 2003-2005. His research interests span the theoretical areas of statistical inference, machine learning, and information theory, with applications to AI, data science, wireless communications, and sensor networks.  He was the Editor-in-Chief of the IEEE Transactions on Information Theory from 2023-2025. He is a Fellow of the IEEE and a Fellow of the Institute of Mathematical Statistics (IMS). Among the awards he has received for research and teaching are the IEEE Browder J. Thompson Best Paper Award, the U.S. Presidential Early Career Award for Scientists and Engineers (PECASE), the Abraham Wald Prize in Sequential Analysis (twice), the Fulbright-Nokia Chair in Information and Communication Technologies, and the Distinguished Alumnus Award (DAA) from IIT Bombay.

For more information, or for CODA guest access, please contact shatcher8@gatech.edu at least 2 business days prior to the event.

Join Zoom Meeting 
https://gatech.zoom.us/j/91055797517?pwd=GJToLOZ45q23cXQXe90sz78uiEAXmH.1

Meeting ID: 910 5579 7517 
Passcode: 056133

Bio: Pulkit Agrawal is an Associate Professor in the Department of Electrical Engineering and Computer Science at MIT. He earned his Ph.D. from UC Berkeley and co-founded Eka Robotics Inc., and SafelyYou Inc. Pulkit completed his bachelor’s from IIT Kanpur and was awarded the Director’s Gold Medal. His work has received multiple Best Paper Awards, the IEEE Early Career Award in Robotics and Automation, the IIT Kanpur Young Alumnus Award, the Sony Faculty Research Award, the Salesforce Research Award, the Amazon Research Award, the Signatures Fellow Award, the Fulbright Science and Technology Award, and others.

In this talk, I will present my lab’s research at the intersection of computer vision, graphics, and machine learning that takes an inverse graphics perspective: rather than forward-simulating the physics of light, materials, and deformation as in decades of computer graphics, we aim to invert this process to infer and control these properties directly from images. The first part of the talk will focus on explicitly estimating physics, with examples in recovering lighting, reflectance, and object deformation. The second part will show how controlled generative models allow us to manipulate physics, with applications in relighting and simulating facial aging. Together, these efforts highlight a path toward AI systems with a deeper and more actionable understanding of the physical world.

Bio: Roni Sengupta is an Assistant Professor of Computer Science at the University of North Carolina at Chapel Hill and directs the Spatial & Physical Intelligence (SPIN) Lab. Her research lies at the intersection of Computer Vision and Computer Graphics, with a focus on solving Inverse Graphics problems to enable applications in visual content creation and editing, telepresence, AR/VR, robotic perception, and medical imaging. Previously, she was a Postdoctoral Research Associate at the University of Washington, Seattle (2019–2022), following her PhD from the University of Maryland, College Park. She is a recipient of the New & Early Career Trailblazer Award from the National Institute of Biomedical Imaging and Bioengineering (NIH). Her work on Background Matting received a Best Student Paper Honorable Mention at CVPR 2021 (Top 7 out of 1600 accepted papers) and has been adopted by several companies, including Microsoft and Inter-State Studio.

Join Zoom Meeting 
https://gatech.zoom.us/j/94322039610?pwd=ZK7BSj8zuM0U3lylheI9YJalJE3Z3u.1

Meeting ID: 943 2203 9610 
Passcode: 547103

Bio: Doug Downey is Sr. Director of Semantic Scholar Research at the Allen Institute for AI (Ai2) and a Professor of Computer Science at Northwestern University. His research focuses on Natural Language Processing, with an emphasis on AI systems that support scientific discovery. At Ai2, he helps lead the Asta project, and his publications have received paper awards at ACL, EMNLP, IJCAI, and AKBC.

• Highlight promising new research directions
• Identify opportunities for synergy and collaboration across disciplines
• Develop strategies to position Georgia Tech for industrial and federal funding in these areas

• Opening Session & Talk: Vijay Ganesh and David Sherrill, who bring decades of expertise in neuro-symbolic AI and quantum chemistry.
• Talks: Alex Gagliano on AI in physics and astronomy, and Jake Rudolph on automated discovery of neutrino physics models using reinforcement learning.
• Hands-on Tutorials:
  • Inference & Agentic Systems for Science
  • Fine-Tuning (supervised and reinforcement learning) for Science

Preparation
Please bring your own laptop and complete a few basic installations and student verifications in advance. Full instructions and setup links will be provided on the website and in follow-up emails (for registered participants). This focused one-day workshop will help you confidently start working with LLMs for science, from inference to fine-tuning, without the distraction of rapidly changing trends. Seats are limited, so please register early to secure your place.

More details will be posted on the website as we get closer to the date.

In partnership with Sphere Inc., Warner Bros. Magnopus, teams from GDM and Google Cloud have reimagined "The Wizard of Oz" as an immersive experience for the Sphere, an enormous (yes) sphere-shaped venue that seats more than 17,000 people. A traditional movie experience it is not.  

In this talk, I will present details of this project, first announced at Google Cloud Next '25, The goal was to use AI to augment the work of VFX artists, elevating the visual content of the 1939 classic for Sphere's one-of-a-kind display all while honoring the original story. To tackle the unique challenges of enhancing and scaling this classic film for the world's largest screen, our teams developed new AI capabilities, including:

• Veo driven fine-tuning for character performance to ensure that beloved characters' movements remained authentic.
• AI-powered super-resolution to upscale classic footage to an unprecedented scale.
• Outpainting to extend characters and foreground elements beyond the boundary of the original film, which is limited in scale and aspect ratio.

Google teams developed novel AI techniques, to bring a classic film from a cinematic to an experiential medium and provided technologies to artists to create a truly special 75 minute movie + experience through care and commitment to craft, storytelling, and authenticity. This project involved working with over a thousand artists, researchers and engineers to create a new form of entertainment and a landmark effort in the history of entertainment that will be running in Las Vegas for a long time.

More details

• Google Cloud Blog | Google Keyword Blog
• Behind the scenes (curtain): The preview from Cloud Next '25.
• Can I see it? Yes! Get your tickets here.

Join Virtually: https://gatech.zoom.us/j/99776563764?pwd=lqx6UuqGbG6TrIlGSbq6WPshZc0ibT.1

Meeting ID: 997 7656 3764 
Passcode: 798205

Bio: Greg Durrett is an associate professor in the Department of Computer Science and the Center for Data Science at New York University. His research is broadly in the areas of natural language processing and machine learning. Currently, his group’s focus is on reasoning about knowledge in text, verifying correctness of generation methods, and studying how to make progress on problems that defy LLM scaling. He is a 2023 Sloan Research Fellow and a recipient of a 2022 NSF CAREER award. He has served in numerous roles for ACL conferences, recently as a member of the NAACL Board since 2024 and as Senior Area Chair for ACL 2025 and EMNLP 2025. He received his BS in Computer Science and Mathematics from MIT and his PhD in Computer Science from UC Berkeley, where he was advised by Dan Klein.

Join Via Zoom 

Meeting ID: 951 8905 8606 
Passcode: 907746 

Abstract: Lower limb prostheses have traditionally been energetically passive, and therefore have been functionally deficient relative to the healthy limb, which can both generate and dissipate power. Over the past 15 years or so, researchers have been exploring the development of powered prostheses, which employ a robotics approach to the design and control of lower limb prostheses. Adding power in this manner, particularly in a knee prosthesis, expands the range of knee behaviors into powered regions of the power plane, but also compromises the range of behaviors in passive regions of the power plane which are essential to human walking. This talk makes a case for the importance of retaining the very low-output-impedance behaviors in the passive regions of the power plane, and suggests approaches for supplementing passive functionality with power, without compromising or supplanting the low-impedance passive behaviors that are an essential aspect of human movement. 

Bio: Dr. Goldfarb is the H. Fort Flowers Professor of Mechanical Engineering at Vanderbilt University, with secondary appointments as a professor in Electrical Engineering and Physical Medicine and Rehabilitation. Dr. Goldfarb has authored over 250 publications and been awarded over 50 US patents on topics related to wearable robotics. Among his papers are ones awarded best-paper awards in 1997, 1998, 2003, 2007, 2009, 2013, 2020, and 2022 and others that were finalists for best paper awards in 2015, 2017, 2020, and 2024. Research interests include the development of robotic limbs for upper and lower extremity amputees, and the development of exoskeletons for individuals with spinal cord injury and stroke, including the development of a lower limb exoskeleton now sold as the Indego exoskeleton. Dr. Goldfarb was inducted into the US National Academy of Inventors in 2020. 

Bio: Professor Ann Fitz-Gerald is the Director of the Balsillie School of International Affairs since August 2019 and has led the School’s “Technology Governance Initiative” since 2023. She has degrees in both commerce and political science from Queen’s University and was the first civilian female to graduate from the Royal Military College of Canada. Before completing a PhD in the UK, she worked at the Pearson Peacekeeping Centre, NATO headquarters, and the North Atlantic Assembly. She has worked as an academic at King’s College, London University and Cranfield University, where, before her move back to Canada, she was Director, Defence and Security Leadership at Cranfield’s Defence Academy of the United Kingdom campus.

Ann is a Senior Research Fellow at the Royal United Services Institute, a Senior Fellow at the Institute for Peace and Diplomacy, a Fellow at McLaughlin College, York University and has served/still serves on a number of non-executive boards and in advisory roles for the British Government, the United Nations and the African Union. Ann is widely published on issues concerning the governance of national security and has helped facilitate national security policies and strategies in a number of conflict-affected countries including Afghanistan, Ethiopia, Sudan, Ukraine, Sierra Leone, Nepal, Serbia, Nigeria and others. She provides regular media commentary for both national and international broadcast media. She has also been appointed by organizations such as the United Nations, the African Union and the British Government to support internationally-sponsored peace talks, including the Sudan-South Sudan peace talks led by former South African President Thabo Mbeki – efforts for which the Government of Canada awarded Ann with the Queen’s Diamond Jubilee Medal. In December 2024, Ann was recognized for her ongoing research on defence and national security and for her leadership of the Balsillie School of International Affairs and awarded the King Charles III Coronation Medal. Foundations.

Join Via Zoom

Abstract: Lower-limb wearable robots—such as exoskeletons and robotic prostheses—have struggled to have the societal impact expected from these exciting technologies. In part, these challenges stem from fundamental gaps in our understanding of how and why these systems should assist their wearer during use. Wearable robots are typically designed to meet a single, specific objective (e.g. reduction of metabolic rate), however, in reality, assistive technologies impact many aspects of gait and user experience. In this talk, I will discuss our recent work leveraging user preference as a ‘meta-criterion’ in design and control, through which the wearer is able to internally balance the quantitative and qualitative tradeoffs that accompany wearing these technologies, including stability, comfort, exertion, or speed. I will highlight our work understanding user-preferred assistance settings in a variable-stiffness prosthesis and bilateral ankle exoskeletons, demonstrating user-preferred assistance settings are reliable yet diverse, and can be obtained in less than two minutes. In addition, I will discuss how user-preferred assistance can be optimized automatically with human-in-the-loop methods, which are able to converge on user-preferred settings with an accuracy of ~90%. Finally, I will introduce a new approach for understanding the success of assistive technologies using tools from behavioral economics. I will describe and quantify the economic value provided by ankle exoskeletons, including the cost incurred from wearing the added mass, as well as the value added by the assistance alone. Together, this talk will underscore the role of the user in the development of wearable robots, and advocate for a shift away from the conventional, single-objective assessment of these technologies.      

Bio: Elliott J. Rouse is an Associate Professor in the Departments of Robotics and Mechanical Engineering at the University of Michigan. He directs the Neurobionics Lab, who studies the dynamics of how and why people use wearable robotic systems. His group uses this understanding to develop novel hardware designs, including the Open-Source Leg. In addition, Dr. Rouse is a Senior Research Scientist at the Institute for Human and Machine Cognition (IHMC). He is a recipient of the NSF CAREER Award and the Henry Russel Award at UM. He is on the Editorial Board of Wearable Technologies and is a member of the IEEE EMBS Technical Committee on BioRobotics. He received the BS degree in mechanical engineering from the Ohio State University and the PhD degree in biomedical engineering from Northwestern University. Subsequently, he joined MIT as a Postdoctoral Fellow in the MIT Media Lab. Prior to joining U-M, Dr. Rouse was a principal investigator at the Shirley Ryan AbilityLab / Northwestern University, and worked as a mechanic in professional Le Mans autoracing. In 2019 – 2020, he was visiting faculty at (Google) X where he co-founded their exoskeleton development team. In 2024-2025, he was on sabbatical at the Robotics and AI (RAI) Institute, where he worked on advanced machine design workflows.

Abstract: Designing and assembling complex products traditionally demands extensive manual effort and expert knowledge. In this talk, I will introduce Prompt-to-Product, an automated pipeline that transforms natural language prompts into real-world assembly creations. Using LEGO bricks as a versatile platform, we automate both the design and physical construction of intricate assemblies.

We first present BrickGPT, a fine-tuned large language model with enhanced physical reasoning capability, enabling it to reliably generate assembly designs that are both creative and physically buildable. Next, we introduce BrickMatic, a dexterous bimanual manipulation platform that employs a skill-graph-based neural-symbolic approach to generate precise robot trajectories for long-horizon assembly tasks. BrickMatic achieves sub-millimeter accuracy and scales to complex builds with over 100 components.

Together, BrickGPT and BrickMatic bridge the gap between imagination and realization, making it dramatically easier for users to turn creative ideas into physical products. Through comprehensive user studies, we demonstrate that the Prompt-to-Product framework significantly lowers barriers, reduces manual effort, and empowers anyone to design and assemble sophisticated creations.

Bio: Changliu Liu is an associate professor in the Robotics Institute, School of Computer Science, Carnegie Mellon University (CMU), where she leads the Intelligent Control Lab. Prior to joining CMU in 2019, Dr. Liu was a postdoc at Stanford. She received her Ph.D. degree from University of California, Berkeley and her bachelor degree from Tsinghua University. Her research interests lie in the design and verification of human-centered intelligent systems with applications to manufacturing and transportation and on various robot embodiments. Dr. Liu co-founded Instinct Robotics, a robotics company for intelligent manufacturing. Her work has been recognized by NSF Career Award, Amazon Research Award, Ford URP Award, Advanced Robotics for Manufacturing Champion Award, Young Investigator Award at International Symposium of Flexible Automation, IEEE RAS Early Academic Career Award in Robotics and Automation, IFAC Robotics Outstanding Young Researcher Award, and many best/outstanding paper awards. 

Abstract: Within the general excitement about artificial intelligence, there has been special interest in the technology's application to discovery of scientific knowledge. Like AI itself, this subfield has a long history and many successes, but also outstanding challenges. In this talk, I focus on two problems that have received considerable attention: discovery of numeric equations and construction of qualitative process models. In each case, I define the computational task, review basic approaches, and report successes that led to scientific insights. After this, I turn to a third paradigm - inductive process modeling - that moves beyond earlier efforts to generate quantitative explanations of scientific data in terms of domain knowledge. I present multiple approaches to this problem, present encouraging results, and discuss open issues that merit further attention from the AI community.

Bio: Dr. Pat Langley is a Principal Research Scientist at Georgia Tech Research Institute and Director of the Institute for the Study of Learning and Expertise. He has contributed to AI and cognitive science for more than 40 years, publishing over 300 papers and five books on these topics. Dr. Langley developed some of the first computational approaches to scientific knowledge discovery, and he was an early champion of experimental studies of machine learning and its application to real-world problems. He is the founding editor of two journals, Machine Learning in 1986 and Advances in Cognitive Systems in 2012, and he is a Fellow of both AAAI and the Cognitive Science Society. Dr. Langley's current research focuses on architectures for embodied agents, explainable normative, and justified agency, and induction of dynamic process models from time series and background knowledge.

Zoom Access to Seminar

Abstract: Robots must behave safely and reliably if we are to confidently deploy them in the real world around humans. To complete tasks, robots must manage a complex, interconnected autonomy stack of perception, planning, and control software. While machine learning has unlocked the potential for full-stack end-to-end control in the real world, these methods can be catastrophically unreliable. In contrast, model-based safety-critical control provides rigorous guarantees, but struggles to scale to real systems, where common assumptions, e.g., perfect task specification and perception, break down.

However, we need not choose between real-world utility and safety. By taking an end-to-end approach to safety-critical control that builds and leverages knowledge of where learned components can be trusted, we can build practical yet rigorous algorithms that can make real robots more reliable. I will first discuss how to make task specification easier and safer by learning hard constraints from human task demonstrations, and how we can plan safely with these learned specifications despite uncertainty. Then, given a task specification, I will discuss how we can reliably leverage learned dynamics and perception for planning and control by estimating where these learned models are accurate, enabling probabilistic guarantees for end-to-end vision-based control. Finally, I will provide perspectives on ongoing work and future opportunities in reliable and secure data-driven autonomy, including adversarially-robust perception-based control and integration with pre-trained models.
 

Bio: Glen Chou is an assistant professor at Georgia Tech in the College of Computing, within the School of Cybersecurity & Privacy (SCP), and in the College of Engineering, within the School of Aerospace Engineering (AE). He joined Georgia Tech in November 2024. Glen directs the Trustworthy Robotics Lab, which focuses on the design of algorithms that can enable general-purpose robots and autonomous systems to operate capably, safely, and securely, while remaining resilient to real-world failures and uncertainty. To achieve this, the lab leverages control theory and machine learning, while connecting to optimization, perception, formal methods, motion planning, human-robot interaction, and statistics. Glen received dual B.S. degrees in EECS and ME from UC Berkeley in 2017 and M.S. and Ph.D. degrees in ECE from the University of Michigan in 2019 and 2022, respectively. Prior to joining Georgia Tech in 2024, Glen spent two years as a postdoc at MIT CSAIL.

Abstract: The 1966 film Fantastic Voyage inspired the concept of miniature machines traversing the human body to diagnose and treat disease. This once-futuristic vision is now being realized through the development of synthetic micro/nanorobots engineered for in vivo biomedical applications. In this talk, I will highlight recent advances in the design of microrobots powered by bioavailable fuels or externally applied fields such as ultrasound and magnetism. These systems enable a range of sophisticated capabilities, including biosensing, targeted drug delivery, bioimaging, and cellular isolation. I will discuss our recent work on ultrasound-propelled microrobots that achieve deep-tissue navigation and controllable drug release guided by real-time ultrasound imaging. In parallel, I will present biofuel-powered micro/nanomotors designed for effective tissue penetration and programmable therapeutic delivery, particularly in oncology settings. These platforms integrate responsive materials, wireless control, and clinical imaging interfaces, transforming synthetic motors into intelligent, adaptable therapeutic agents. Our results reveal the vast potential of medical microrobots in addressing unmet clinical needs across gastrointestinal disorders, inflammation, cancer, and precision medicine.

Bio: Wei Gao is a Professor of Medical Engineering and Heritage Medical Research Institute Investigator at the California Institute of Technology. He earned his Ph.D. from the University of California, San Diego in 2014, followed by a postdoctoral fellowship at the University of California, Berkeley from 2014 to 2017. He is an Associate Editor of Science Advances, npj Flexible Electronics, Biosensors and Bioelectronics, and Sensors & Diagnosis. He is a recipient of NSF Career Award, ONR Young Investigator Award, IAMBE Early Career Award, Sloan Research Fellowship, Pittcon Achievement Award, IEEE EMBS Early Career Achievement Award, IEEE EMBS Technical Achievement Award, IEEE Sensor Council Technical Achievement Award, MIT Technology Review 35 Innovators Under 35, and Falling Walls Breakthrough of the Year in Engineering and Technology. He is a World Economic Forum Young Scientist, a Highly Cited Researcher (Web of Science). He is an elected Fellow for AIMBE and RSC. His research interests include wearable biosensors, digital medicine, bioelectronics, additive manufacturing, and micro/nanorobotics.

Maani Ghaffari | U. Michigan Robotics

Abstract: Forthcoming mobile robots require efficient generalizable algorithms to operate in challenging and unknown environments without human intervention while collaborating with humans. Today, despite the rapid progress in robotics and autonomy, no robot can deliver human-level performance in everyday tasks and missions such as search and rescue, exploration, and environmental monitoring and conservation. In this talk, I will put forward a vision for enabling efficiency and generalization requirements of real-world robotics via computational symmetry and learning. I will walk you through structures that arise from combining symmetry, geometry, and learning in various foundational problems in robotics and showcase their performance in experiments ranging from perception to control. In the end, I will share my thoughts on promising future directions and opportunities based on lessons learned on the field and campus.

Bio: Maani Ghaffari received the Ph.D. degree from the Centre for Autonomous Systems (CAS), University of Technology Sydney, NSW, Australia, in 2017. He is currently an Assistant Professor at the Department of Naval Architecture and Marine Engineering and the Department of Robotics, University of Michigan, Ann Arbor, MI, USA, where he directs the Computational Autonomy and Robotics Laboratory (CURLY). His work on sparse, globally optimal kinodynamic motion planning on Lie groups received the best paper award finalist title at the 2023 Robotics: Science and Systems conference. He is the recipient of the 2021 Amazon Research Awards. His research interests lie in the theory and applications of robotics and autonomous systems. He has expertise in developing efficient generalizable algorithms for robot autonomy operating in unstructured, dynamic, and uncertain environments. He has developed geometric state estimation, geometric control methods, geometric deep neural network architectures (LieNeuron), information-theoretic motion planning, and mapping algorithms for mobile robots.

Bio: Joonho Lee is the Head of the AI Group at Neuromeka Inc., where he leads research and development of intelligent industrial robots. His research focuses on enabling robust autonomy for robots operating in dynamic and unstructured settings. He received his Ph.D. from the Robotic Systems Lab at ETH Zurich, where he specialized in robust locomotion and autonomous navigation for legged robots in outdoor settings. His work has been published in top venues such as Science Robotics and has been recognized with several honors, including the Willi Studer Prize, ETH Medal, and IEEE Best Paper Awards.

Lunch Provided - First Come, First Served Basis
Host: Sehoon Ha | Assistant Professor, School of Interactive Computing

Register Here!

• Sudhakar Pamidighantam | Associate Director - Center for Artificial Intelligence in Science and Engineering(ARTISAN), Principal Research Scientist - Institute of Data Engineering and Science (IDEaS)
• Suresh Marru | Director - Georgia Tech Center for Artificial Intelligence in Science and Engineering (ARTISAN), Research Professor - Institute for Data Engineering and Science (IDEaS)
• Giri Krishnan | Associate Director- Center for Artificial Intelligence in Science and Engineering (ARTISAN) & Principal Research Scientist
• Shava Smallen | University of California San Diego
• Jim Basney |  Principal Research Scientist - National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign
• David Sherill | Executive Director (Interim) - Institute for Data Engineering and Science, Regents' Professor - School Chemistry and Biochemistry, Georgia Institute of Technology
• Spencer Bryngelson | Assistant Professor - School of Computational Science and Engineering, Georgia Institute of Technology
• Victor Fung Assistant Professor - School of Computational Science and Engineering, Georgia Institute of Technology
• Aditya Kumar | Assistant Professor - School of Civil & Environmental Engineering, Georgia Institute of Technology

Register Here!

The event will conclude with a poster session from students researching robotics in their programs across the Schools of Mechanical, Electrical & Computer, Aerospace, and Biomedical Engineering, as well as students enrolled in Computer Science. The presenting group is a mixture of graduate and undergraduate students nearing graduation, mid-career students looking for internship opportunities, and new students looking to find promising research directions. Each student will present a research poster, offering you an opportunity to see their work and their scientific process.

Agenda

2:00 p.m. | Welcome
Gregory Sawicki | Interim Director; Institute for Robotics and Intelligent Machines

2:15 p.m. | Keynote: RoboGeorgia
Ward Broom | GT Enterprise Innovation Institute

2:45 p.m. | Q&A

3:00 p.m. | Panel Discussion: Robotics Startup Community in ATL
Moderator: Ward Broom  

3:45 p.m. | Q&A

4:00pm - 5:00pm | Poster Session

REGISTER HERE

Gaurav Sukhatme | University of Southern California

Abstract: We have recently demonstrated the possibility of learning controllers that are zero-shot transferable to groups of real quadrotors via large-scale, multi-agent, end-to-end reinforcement learning. We train policies parameterized by neural networks that can control individual drones in a group in a fully decentralized manner. Our policies, trained in simulated environments with realistic quadrotor physics, demonstrate advanced flocking behaviors, perform aggressive maneuvers in tight formations while avoiding collisions with each other, break and re-establish formations to avoid collisions with moving obstacles, and efficiently coordinate in pursuit-evasion tasks. The model learned in simulation transfers to highly resource-constrained physical quadrotors. Motivated by these results and the observation that neural control of memory-constrained, agile robots requires small yet highly performant models, the talk will conclude with some thoughts on coaxing learned models onto devices with modest computational capabilities.

Bio: Gaurav S. Sukhatme is Professor of Computer Science and Electrical and Computer Engineering at the University of Southern California (USC) and an Amazon Scholar. He is the Director of the USC School of Advanced Computing and the Executive Vice Dean of the USC Viterbi School of Engineering. He holds the Donald M. Aldstadt Chair in Advanced Computing and was the Chairman of the USC Computer Science Department from 2012-17. He received his undergraduate education in computer science and engineering at IIT Bombay and his M.S. and Ph.D. degrees in computer science from USC. Sukhatme is the co-director of the USC Robotics Research Laboratory and the USC Robotic Embedded Systems Laboratory director. His research interests are in networked robots, learning robots, and field robotics. He has published extensively in these and related areas. He is a Fellow of the AAAI, AAAS, and the IEEE, a recipient of the NSF CAREER award, the Okawa Foundation research award, and an Amazon research award. He is one of the founders of the Robotics: Science and Systems conference and was the program chair of the 2008 ICRA and 2011 IROS. He is the Editor-in-Chief of Autonomous Robots (Springer Nature).

Agenda includes a discussion led by Tim Lieuwen and Tanta Myles on how to conduct research collaboratively and securely given the continual shifts in the regulatory landscape for research.

There will be time for Q&A as well. 

Zoom link: https://gatech.zoom.us/j/97247122372

*If you do not have building access, gather in the Coda lobby near the reception desk by the elevators, and a student or staff member can escort you up.

 Sebastian Scherer | The Robotics Institute at Carnegie Mellon

Abstract: Robustness in AI and robotics shows great promise if systems can transition from the lab to real-world environments, moving beyond the single-operator per robot paradigm. However, the unstructured nature of the real-world demands nuanced decision-making from robots. In this talk, Prof. Scherer will outline approaches, progress, and results in multi-modal sensing, nuanced perception inputs, navigation in difficult terrain, and extensions to multi-robot teams, as well as future research directions.

Bio: Sebastian Scherer is an Associate Research Professor at the Robotics Institute (RI) at Carnegie Mellon University (CMU). His research focuses on enabling autonomy in challenging environments and previously led CMU’s entry to the SubT challenge. He and his team have shown several firsts for autonomy for flying robots and off-road driving . Dr. Scherer received his B.S. in Computer Science, M.S. and Ph.D. in Robotics from CMU in 2004, 2007, and 2010.  

Featuring Ufuk Topcu | University of Texas Austin

Abstract: Autonomous systems are emerging as a driving technology for countlessly many applications. Numerous disciplines tackle the challenges toward making these systems trustworthy, adaptable, user-friendly, and economical. On the other hand, the existing disciplinary boundaries delay and possibly even obstruct progress. I argue that the nonconventional problems that arise in designing and verifying autonomous systems require hybrid solutions at the intersection of control, learning, and formal methods (among other disciplines). I will present examples of such hybrid solutions in the context of learning in sequential decision-making processes. These results offer novel means for effectively integrating physics-based, contextual, or structural prior knowledge into data-driven learning algorithms. They improve data efficiency by several orders of magnitude and generalizability to environments and tasks the system had not previously experienced. I will conclude with remarks on a few promising future research directions.

Bio: Ufuk Topcu is a Professor in the Department of Aerospace Engineering and Engineering Mechanics at The University of Texas at Austin, where he holds W.A. “Tex” Moncrief, Jr. Chair in Computational Engineering and Sciences VI. He is a core faculty member at Texas Robotics and the Oden Institute for Computational Engineering and Sciences and the director of the Center for Autonomy. His research focuses on the theoretical and algorithmic aspects of the design and verification of autonomous systems.

Hardware Design and Control Algorithms for Agile and Versatile Legged Robots 

Haewon Park  | The Korea Advanced Institute of Science and Technology

Abstract: Biological animals excel at navigating complex and challenging environments, leveraging their hardware to perform dynamic motions and athletic skills that overcome diverse obstacles. Despite recent advancements, robotic systems still lack comparable dynamic locomotion capabilities. In this talk, I will present our lab's efforts to bridge this gap by developing robust hardware and effective control algorithms that enable both agility and robustness in legged robots. I will begin by introducing our quadruped robot platforms: HOUND, designed for high-speed locomotion on complex terrains, and MARVEL, designed for agile and versatile climbing. HOUND incorporates custom electric actuators, while MARVEL uses magnetic feet to generate climbing force. I will then discuss the control algorithms that drive these robots, leveraging model predictive control and reinforcement learning techniques. Finally, I will present our latest learning-based locomotion control framework, capable of synthesizing and executing diverse dynamic motions across various terrains. This framework combines a low-level skill policy, pre-trained using a large offline dataset generated via trajectory optimization, with a reinforcement learning policy trained on diverse terrains. With this integrated approach, HOUND achieves speeds of up to 9.5 m/s, making it the fastest legged robot, while MARVEL can traverse ceilings and vertical walls at speeds of up to 0.5 m/s and 0.7 m/s, respectively.

Robert Gregg | U. Michigan Robotics

Abstract: Even with the help of modern prosthetic and orthotic devices, individuals with lower-limb amputation, age-related motor deficits, or orthopedic disorders often struggle to navigate the home and community. Emerging powered prosthetic and orthotic devices could actively assist individuals to enable greater mobility, but these devices are typically designed to produce a small set of pre-defined motions. Although the field is beginning to embrace controllers that unify phases of the gait cycle, these devices still switch between distinct controllers for different tasks, e.g., uphill vs. downhill. This discrete control paradigm cannot continuously synchronize the robot’s motion to the variable activities of the human user. This talk will first present a new paradigm for controlling powered prosthetic legs over continuous variations of walking and stairs (i.e., different speeds and inclines), as well as continuous transitions between sitting and standing. These adaptable mid-level controllers facilitate a small activity space for intent classification, enabling amputee users to control activity transitions through intuitive, heuristic rules with over 99% accuracy. While these methods reproduce missing joint function, a different control philosophy is needed for exoskeletons that assist existing joint function. The last part of this talk will introduce an energetic control paradigm for backdrivable exoskeletons to reduce muscular effort by providing a faction of the human torque, without requiring explicit knowledge of the activity. This task-agnostic control method enabled a bilateral knee exoskeleton to mitigate the effects of quadriceps fatigue in able-bodied individuals during repetitive lifting-lowering and carrying over 5 terrains, thus reducing their risk for injuries due to fatigue-induced compensations. The talk will conclude with preliminary results from studies using hip and knee exoskeletons to enhance the mobility of elderly individuals in real-world scenarios.

Bio:  Robert D. Gregg IV received the B.S. degree in electrical engineering and computer sciences from the University of California, Berkeley in 2006 and the M.S. and Ph.D. degrees in electrical and computer engineering from the University of Illinois at Urbana-Champaign in 2007 and 2010, respectively. He joined the University of Michigan as an Associate Professor in the Department of Electrical Engineering and Computer Science and the Robotics Institute in Fall 2019, and he became the Associate Director of Robotics in Fall 2020. He joined the Department of Robotics upon its establishment in July 2022. Prior to joining U-M, he was an Assistant Professor in the Departments of Bioengineering and Mechanical Engineering at the University of Texas at Dallas. Dr. Gregg directs the Locomotor Control Systems Laboratory, which conducts research on the control mechanisms of bipedal locomotion with applications to wearable and autonomous robots. He is a recipient of the Eugene McDermott Endowed Professorship, NSF CAREER Award, NIH Director’s New Innovator Award, and Burroughs Wellcome Fund Career Award at the Scientific Interface. Dr. Gregg is a Senior Member of the IEEE.

Bio: Kijung Shin is an Ewon Endowed Assistant Professor (jointly affiliated) in the Kim Jaechul Graduate School of AI and the School of Electrical Engineering at KAIST. He received his Ph.D. from the Computer Science Department at Carnegie Mellon University in 2019. He has published more than 50 referred articles in major data mining venues, including KDD, WWW, and ICDM, and he won the best research paper award at KDD 2016. His research interests span a wide range of topics on graph mining, with a focus on scalable algorithm design and empirical analysis of real-world hypergraphs.

Hosted by: B. Aditya Prakash, Associate Professor, College of Computing, Georgia Tech

In this talk, we provide a technical survey our team’s efforts in researching, developing, operating, and continuously innovating open-source science gateway cyberinfrastructure software and point toward new opportunities. Key research areas include developing pervasive middleware for seamlessly integrating scientists’ local resources for computing and data with remote cloud and high-end supercomputing systems; designing and implementing flexibly managed, high-performance data transfer; creating “data lakes” that enable diverse, distributed data sets to be integrated seamlessly into research platforms; developing user interfaces and environments that are both flexible and usable; and developing end-to-end security approaches for managing users, groups, and access permissions across federated resources.

We discuss the integration of the above research areas into Apache Airavata, a comprehensive software system for science gateways that we illustrate with examples from collaborations with partners from chemistry, biophysics, digital humanities, and other research areas.

We review the “virtuous cycle” benefits of complementing systems research with open source engineering and “developer-operator” operations practices. We conclude with a discussion of new directions for science gateway research that can more comprehensively support scientists’ entire research processes by more seamlessly integrating all resources needed for research.

Marlon Pierce is the director of the Cyberinfrastructure Integration Research Center at Indiana University (CIRC), which develops and operates the open-source Apache Airavata software system for science gateways. Pierce’s research interests are in the application of distributed systems concepts, open source engineering practices, and science user-centered design to develop cyberinfrastructure to support scientific research. Pierce received his Ph.D. in computational condensed matter physics from Florida State University in 1999.

Suresh Marru is the Principal Investigator of the NSF-funded Cybershuttle project and Deputy Director and Chief Architect of the Cyberinfrastructure Integration Research Center at Indiana University. His background is in computational science, and he has worked extensively with Atmospheric Science and Geoscience projects, helping design and develop service-oriented architectures to orchestrate data processing and large-scale simulations. He is an elected member of the Apache Software Foundation and is the foundation’s vice-president for the Apache Airavata project.

Sudhakar Pamidighantam is a senior scientist at the Cyberinfrastructure Integration Research Center at Indiana University (CIRC). He has a Ph.D. in computational physical organic chemistry and experience in protein sequence, structure, and function; protein-small molecule interaction modeling; and deployed cyberinfrastructure for the chemistry, materials, and computational biology communities. His research involves ab initio and molecular dynamics modeling, reaction mechanism, and cyberinfrastructure for complex workflows. He works collaboratively with teams to design and deploy cyberinfrastructure solutions for diverse communities. He has been an affiliate of the IEEE and a member of the American Chemical Society since 1990.

Bio: Nima Fazeli is an Assistant Professor of Robotics at the University of Michigan (2020-Present) and holds courtesy appointments with Computer Science and Engineering (CSE) in EECS and Mechanical Engineering at UM. Nima is also the director of the Manipulation and Machine Intelligence (MMint) Lab. Nima’s primary research interest is enabling intelligent and dexterous robotic manipulation with emphasis on the tight integration of mechanics, perception, controls, learning, and planning. Nima received his PhD from MIT (2019) and completed his postdoctoral training (2020) working with Prof. Alberto Rodriguez. He received his MSc from the University of Maryland at College Park (2014) where he spent most of his time developing models of the human (and, on occasion, swine) arterial tree for cardiovascular disease, diabetes, and cancer diagnoses. His research has been supported by the NSF CAREER, National Robotics Initiative, and Advanced Manufacturing, the Rohsenow Fellowship and featured in outlets such as The New York Times, CBS, CNN, and the BBC.

Abstract: To transform our lives, autonomous systems need to interact with other agents in complex shared environments. For example, autonomous cars need to interact with pedestrians, human-driven cars, and other autonomous cars. Autonomous delivery drones need to navigate in the aerial space shared by other drones, or mobile robots in a warehouse must navigate in the factory space shared by robots. The multi-agent nature of such application domains requires us to develop a systematic methodology for enabling efficient interactions of autonomous systems across various applications. In this talk, I will first focus on game-theoretic planning and control for robots. To reach intelligent robotic interactions, robots must account for the dependence of agents' decisions upon one another. I will discuss how game-theoretic planning and control enables robots to be cognizant of their influence on other agents. I will present our recent results on leveraging the structure that is inherent in interactions to develop efficient motion planning algorithms which are suitable for real-time operation on robot hardware. In the second part of the talk, I will focus on how robots can learn and infer the intentions of their surrounding agents to account for agents' preferences and objectives. Currently, robots can infer the objectives of isolated agents within the formalism of inverse reinforcement learning; however, in multi-agent domains, agents are not isolated, and the decisions of all agents are mutually coupled. I will discuss a mathematical theory and numerical algorithms for inferring these interrelated preferences from observations of agents’ interactions.

Bio: Negar Mehr is an assistant professor in the Department of Mechanical Engineering at the University of California, Berkeley. Before that, she was an assistant professor of Aerospace Engineering at the University of Illinois Urbana-Champaign. She was a postdoctoral scholar at Stanford Aeronautics and Astronautics department from 2019 to 2020. She received her Ph.D. in Mechanical Engineering from UC Berkeley in 2019 and her B.Sc. in Mechanical Engineering from Sharif University of Technology, Tehran, Iran, in 2013. She is a recipient of the NSF CAREER Award. She was awarded the IEEE Intelligent Transportation Systems best Ph.D. dissertation award in 2020.

Bio: Katia Sycara holds the Edward Fredkin Research Chair in Robotics at the School of Computer Science at Carnegie Mellon University and is Associate Director for Faculty at the Robotics Institute. She is affiliated faculty of the departments of Computer Science, Machine Learning, Human Computer Interaction and Language Technologies at Carnegie Mellon. For the past 5 years she was the Director of the AFOSR Center of Excellence for Trustworthy Autonomy. She holds a B.S in Applied Mathematics from Brown University, M.S. in Electrical Engineering from the University of Wisconsin and PhD in Computer Science from Georgia Institute of Technology. She held the Sixth Century Chair in Computing at the University of Aberdeen UK. She was awarded a Doctorate Honoris Causa from the University of the Aegean. Her research interests are in AI/ML, multi-agent and multi-robot systems, human robot teaming.. She is a Fellow of the Institute of Electronic and Electrical Engineers (IEEE), Fellow of the AAAI, the recipient of the ACM/SIGART Agents Research Award and the recipient of the Lifetime Research Award of the Institute of Operations Research and the Management Sciences (INFORMS), Group Decision and Negotiation division. She has received 2 Influential 10-year paper awards and multiple best paper awards. She has given numerous invited talks, chaired and participated on the program committees of a large number of conferences.

Event Agenda Overview

9:00am  | Registration
9:15 | Introductory Remarks - Gary McMurray 

9:30 | Speaker Session 1 

• Sehoon Ha, "Motion-based Control of Quadrupedal and Bipedal Robots"
• Danfei Xu, "Robot Learning from Humans: Scaling up and Niching Down"
• Animesh Garg
• Zsolt Kira, "Vision-Language-Action Models for Generalizable Robotics"
• Evagelos Theodorou

10:45am | Break 

11:00am | Speaker Session 2

• Stephen Balakirsky
• Harish Ravichandar, "Frugal Robot Learning"
• Ye Zhao, "Enhancing Agility, Safety, and Social Intelligence in Legged Navigation"
• Tony Chen, "Mechanical Intelligent Design for Manipulation and Environment Interaction"
• Aaron Young, "Data-driven Deep Learning of Human Biology Enables Personalized Generalization of Control for Wearable Robotics"

12:15pm - 1:15pm | Lunch (Lunch reserved for registered attendees)

1:15pm | Speaker Session 3

• Jun Ueda, "Automated Magnetic Resonance Elastography Quality Assessment incorporating Mechanical Failure Detection for Liver Stiffness Evaluation"
• Jaydev Desai, "Medical Robotics Research at the RoboMed Lab"
• Yue Chen, "Bio-inspired Continuum Robots"
• Aaron Abramson, "Tissue Interfacing Robotic Therapeutics for Drug Delivery and Biosensing"
• Ai-Ping Hu, "Robotic Phenotyping for Small-Scale Urban Farms"
• Jonathan Rogers, "Control and Estimation Algorithms for Challenging Small UAS Missions"
   

2:45pm | Break

3:00pm | Speaker Session 4

• Anirban Mazumdar, "Enhancing Robot Mobility Using Physical Partnership"
• Matthew Gombolay, "Assistive Robots Learning from Human Interaction: Lessons from Working with Older Adults"
• Panos Tsiotris, "Research Adventures at the Intersection of Control and Robotics"
• Samuel Coogan, "Safety for Learning-Enabled Autonomous Systems"
• Shreyas Kousik, "Pushing Towards Formal Safety for Tricky Dynamics"
• Matthew Hale, "Multi-Agent Systems: The Roles of Information, Optimality, and Dynamics"

 Register Here!

• Jian Ma | Ray and Stephanie Lane Professor of Computational Biology, School of Computer Science, Carnegie Mellon University
• Jasmine Plummer | Director, Center for Spatial OMICs, St. Jude Children’s Research Hospital
• Ioannis Vlachos | Assistant Professor of Pathology, Harvard Medical School
• Xiuwei Zhang | J.Z. Liang Early-Career Assistant Professor, School of Computational Science and Engineering, Georgia Institute of Technology

We will reschedule this speaker for a later date.
Please forgive the late notice.

Abstract: Advances in generative modeling have opened up exciting new possibilities for music making. How can we leverage these models to support human creative processes? First, I’ll illustrate how we can design generative models to better support music composition and performance synthesis: Coconet, the ML model behind the Bach Doodle, supports a nonlinear compositional process through an iterative block-Gibbs like generative procedure, while MIDI-DDSP supports intuitive user control in performance synthesis through hierarchical modeling. Second, I’ll propose a common framework, Expressive Communication, for evaluating how developments in generative models and steering interfaces are both important for empowering human-ai co-creation, where the goal is to create music that communicates an imagery or mood. Third, I’ll introduce the AI Song Contest and discuss some of the technical, creative, and sociocultural challenges musicians face when adapting ML-powered tools into their creative workflows. Looking ahead, I’m excited to co-design with musicians to discover new modes of human-ai collaboration. I’m interested in designing visualizations and interactions that can help musicians understand and steer system behavior, and algorithms that can learn from their feedback in more organic ways. I aim to build systems that musicians can shape, negotiate with, and jam with in their creative practice.

Bio: Anna Huang is a research lead on the Magenta project at Google DeepMind. She also holds a Canada CIFAR AI Chair at Mila—Québec AI Institute. Her research is at the intersection of machine learning and human-computer interaction, with the goal of supporting music making, and more generally the human creative process. She is the creator of Music Transformer and Coconet. Coconet was the ML model that powered Google’s first AI Doodle, the Bach Doodle, in two days enabling tens of millions of users around the world to co-compose with ML in their web browser. She was an organizer for the international AI Song Contest, and a guest editor for TISMIR’s special issue on AI and Musical Creativity. This Fall, she will be joining Massachusetts Institute of Technology (MIT) as faculty, with a shared position between Electrical Engineering and Computer Science (EECS) and Music and Theater Arts (MTA).

SEMINAR CANCELLED!!

We will reschedule this speaker for a later date.
Please forgive the late notice.

The volume of data generated by scientific applications has continued to outpace the growth of computing power and storage capacities. Unlike video and image compression, scientists require downstream quantities of interest to be conserved along with error bounds on primary data. We have developed novel constrained auto encoders based algorithmic and software pipelines that satisfy both these requirements. For data generated using Tokomak simulation codes that produce petabytes of data using thousands of GPUS, we have shown that our approach can compress the data by two orders of magnitude while requiring less than one percent of computational resources.

Mitigating traffic congestion and improving safety are the cornerstones of transportation. We have developed an ML based edge-cloud based system to analyze and fuse data streams from LIDAR, cameras and ground sensor data for quantifying and impacting pedestrian and vehicle safety. Using the data collected, we are also developing digital twins that model behavior at traffic intersections. The underlying models are useful for detecting interruptions as well as optimizing signal timing plans. We will present experimental results on data collected from real-traffic intersections in the City of Gainesville and City of Orlando.

Bio: Sanjay Ranka is a Distinguished Professor in the Department of Computer Information Science and Engineering at the University of Florida. From 1999-2002, as the Chief Technology Officer at Paramark (Sunnyvale, CA), he developed a real-time optimization service called PILOT for marketing campaigns. PILOT served more than 10+ million optimized decisions a day in 2002 with a 99.99% uptime. Paramark was recognized by VentureWire/Technologic Partners as a Top 100 Internet technology company in 2001 and 2002 and was acquired in 2002. Sanjay has also held positions as a tenured faculty member at Syracuse University, academic visitor at IBM and summer researcher at Hitachi America Limited.

He has coauthored one book, four monographs, 340+ journal and refereed conference articles. His recent coauthored work has received a best student paper runner-up award at IGARSS 2015, best paper award at BICOB 2014, best student paper award at ACM-BCB 2010, best paper runner-up award at KDD-2009, a nomination for the Robbins Prize for the best paper in the Journal of Physics in Medicine and Biology in 2008, and a best paper award at ICN 2007. His work has received 15,900+ citations with an h-index of 63 (based on Google Scholar).

He is a fellow of the IEEE, AAAS and AAIA (Asia-Pacific Artificial Intelligence Association) and a past member of IFIP Committee on System Modeling and Optimization. He received the 2020 Research Impact Award from IEEE Technical Committee on Cloud Computing and the 2022 Distinguished Alumnus Award from Indian Institute of Technology, Kanpur. He is a board member of American Society on Artificial Intelligence.

He is currently an associate editor for ACM Computing Surveys, IEEE/ACM Transactions on Computational Biology and Bioinformatics, Sustainable Computing: Systems and Informatics, Knowledge and Information Systems, and International Journal of Computing. Additionally, he is a book series editor for CRC Press for Bigdata. In the past, he has been an associate editor for IEEE Transactions on Parallel and Distributed Systems and IEEE Transactions on Computers, and an associate editor-in-chief of the Journal of Parallel and Distributed Computing. He was a general co-chair for ICDM in 2009, International Green Computing Conference in 2010, International Green Computing Conference in 2011, ACM Conference on Bioinformatics Computational Biology in 2012, and IEEE DataCOM in 2017. He was a program chair for 2013 International Parallel and Distributed Processing Symposium, 2015 High Performance Computing Conference, and a program co-chair for ICDM 2022.

He has consulted for several startups and Fortune 500 companies.

Bio: Dr. Oliver Brdiczka is currently serving as the Director of Applied Science at Adobe Inc., where he spearheads the integration of Generative AI technologies into Adobe’s suite of products, including Adobe Firefly. Prior to his tenure at Adobe, Dr. Brdiczka ran his own AI startup, and also made significant contributions to groundbreaking DARPA programs such as PAL and ADAMS during his time at Xerox PARC. Dr. Brdiczka’s research focus lies at the intersection of artificial intelligence and human-centered computing. Over the years, he has shared his findings and insights through more than 100 peer-reviewed research papers and has been granted over 40 patents.

The symposium is a chance for faculty to meet new robotics students on campus, as well as a chance to get a better idea of what IRIM colleagues are up to these days. The goal of the symposium is to spark new ideas, new collaborations, and even new friends!

9:00am  | Registration

9:30am | Seth Hutchinson - Welcome and Introductory Remarks
9:45am | Sehoon Ha - Learning Interactive Skills of Legged Robots
10:00am | Stephen Balakirsky - Knowledge Driven Robotics
10:15am | Matthew Gombolay - RoboFederer: Towards Human-Robot Team Athletics
10:30am | Sonia Chernova - Autonomy in Human Spaces

10:45am | Break

11:00am | Danfei Xu - Towards a Data Flywheel for Long-horizon Manipulation
11:15am | Harish Ravichandar - New Wine in an Old Bottle: Classical Methods as Scaffolding for Robot Learning
11:30am | Zsolt Kira - Vision and Language Foundation Models for Open-World Robotics
11:45am | James Hayes - Understanding Grasping, Contact, and Pressure
12:00pm | Animesh Garg - Science and Systems for Foundation Models Manipulation

12:15pm - 1:15pm | Lunch [Box lunches provided to registered attendees]

1:15pm | Ai-Ping Hu - Agricultural Robotics at Georgia Tech
1:30pm | W. Hong Yeo - Smart and Connected Bioelectronics for Integration with Robots
1:45pm | Ellen Yi Chen Mazumdar - Soft and Additively Manufacturable Electromagnetic Actuators
2:00pm | Yue Chen - Tentacle-like Continuum Robots
2:15pm | Greg Sawicki - Beyond reducing cost of transport: What can lower-limb exoskeletons do on the shortest and longest timescales?
2:30pm | Ye Zhao- Can humanoid robots move and manipulate like humans?

2:45pm | Break

3:00pm | Spyros Reveliotis - Traffic Control of Multi-Agent Systems Circulating on Graphs
3:15pm | Shreyas Kousik - Towards Safe Full-Stack Design for Autonomous Robots
3:30pm | Charlie Kemp - TBA
3:45pm | Panos Tsiotras – Large-scale Stochastic Team Games

4:15pm | Closing Remarks

4:30pm - 6:30pm | Poster Session

6:00pm - 9:00pm | Robotics Meet-Up

Register Here

Co-Hosted by the Brook Byers Institute for Sustainable Systems & the The Institute for Data Engineering and Science at Georgia Tech.

Speaker Webpage: http://lk.zuckermaninstitute.columbia.edu/

Host: Hannah Choi

Speaker Webpage: http://lk.zuckermaninstitute.columbia.edu/

Host: Hannah Choi

Bio: : Dr. Andreas Malikopoulos is the Terri Connor Kelly and John Kelly Career Development Associate Professor in the Department of Mechanical Engineering and the Director of the Sociotechnical Systems Center at the University of Delaware (UD). Prior to these appointments, he was the Deputy Director and the Lead of the Sustainable Mobility Theme of the Urban Dynamics Institute at Oak Ridge National Laboratory, and a Senior Researcher with General Motors Global Research & Development. He received a Diploma from the National Technical University of Athens, Greece, and his M.S. and Ph.D. degrees from the University of Michigan, Ann Arbor, in 2004 and 2008, respectively, all in Mechanical Engineering. His research interests span several fields, including analysis, optimization, and control of cyber-physical systems; decentralized stochastic systems; stochastic scheduling and resource allocation; and learning in complex systems. Dr. Malikopoulos is the recipient of several prizes and awards, including the 2007 Dare to Dream Opportunity Grant from the University of Michigan Ross School of Business, the 2007 University of Michigan Teaching Fellow, the 2010 Alvin M. Weinberg Fellowship, the 2019 IEEE Intelligent Transportation Systems Young Researcher Award, and the 2020 UD’s College of Engineering Outstanding Junior Faculty Award. He has been selected by the National Academy of Engineering to participate at the 2010 German-American Frontiers of Engineering (FOE) Symposium and organize a session in transportation at the 2016 European-American FOE Symposium. He has also been selected as a 2012 Kavli Frontiers of Science Scholar by the National Academy of Sciences. Dr. Malikopoulos has been an Associate Editor of the IEEE Transactions on Intelligent Vehicles and IEEE Transactions on Intelligent Transportation Systems from 2017 through 2020. He is currently an Associate Editor of Automatica and IEEE Transactions on Automatic Control. He is a Senior Member of the IEEE, a Fellow of the ASME, and a member of the Board of Governors in the IEEE Intelligent Transportation Systems Society.

Kijung Shin | Ewon Endowed Assistant Professor; Kim Jaechul Graduate School of AI & the School of Electrical Engineering, KAIST

Kay Bailey Hutchison Convention Center Dallas

PROGRAM

November 13–18, 2022

EXHIBITS

November 14–17, 2022

Come see your friends and colleagues, explore incredible learning experiences in HPC, and walk the exhibit floor! There are several registration options available. Choose the one that’s right for you. We look forward to seeing you in Dallas or via the Digital Experience.

For Georgia Tech specific information, visit Georgia Tech@SC22

Two mini-symposia will highlight short talks in the areas of bioinformatics/single cell analytics, and in experimental applications of single cell technologies including scRNAseq, snATACseq, Multi-omics, and others, for biological, clinical, and translational research. 

We look forward greatly to hosting you at the Institute for Bioengineering and Biosciences at Georgia Tech! 

Visit workshop website to register.

Where: GT Hotel Club Room
800 Spring St NW, Atlanta, GA 30308
the Club Room is located on the first floor of the Hotel
When: Thursday, March 31, 5 - 8 p.m.

There are two pool tables in the club room and we have reserved a photo booth so you can have a souvenir of the evening. Refreshments and drink tickets provided. So take a break from your studies, and come relax and have fun!

March 11 
11 a.m. - 2 p.m. 
In-person and virtual participation welcome. Register here to save your spot! https://tinyurl.com/WomenInDataScience22 

The College of Computing and the Machine Learning Center at Georgia Tech are hosting Dean, who is speaking on March 8, in the Clough Auditorium (144). His presentation, Five Exciting Trends in Machine Learning, begins at 11 a.m.

Power of Two Schedule

9 -10:30 a.m.| Breakfast for everyone in Computing!... bacon - Klaus Atrium 

11 a.m. -12 p.m. | Computing Student Conversations w/ the Dean - Klaus 1443 

1200pm-130pm | GT Computing Faculty and Staff Lunch - Klaus Atrium

2 -4 p.m. | Afternoon snacks and games for everyone in Computing - Klaus 1116 

2:22 p.m. | Giveaways - Klaus 1116  

5:00 p.m. | In-person Fireside Chat with Dean Isbell - Clough 144
(Join virtually https://primetime.bluejeans.com/a2m/live-event/fcqeauxz)

Live stream for Computing, Alumni and OMSCS  

6:30 – 9 p.m. | Reception and Monte Carlo Night - Alumni, Students, Faculty, and Staff - Klaus Atrium and Room 1116

To join the meeting: 
BlueJeans Link:  https://bluejeans.com/706539276/0761   Passcode 0761

Opening remarks and welcome:
Srinivas Aluru, IDEaS Executive Director 

Symposium speakers:
Laura Cadonati, Associate Dean for Research, College of Sciences
“Exploring the Cosmic Graveyard with LIGO and Advanced Research Computing”

Rich Vuduc, Director, CRNCH
“Life after Moore’s Law: HPC is Dead, Long Live HPC!”
 
Pam Buffington, Associate Director of Research Cyberinfrastructure and Director of Faculty & External Engagement, Center for 21st Century University
“PACE Update on Advanced Research Computing at Georgia Tech”
 
We’ll also be following along with the 72-hour supercomputing student cluster competition! Learn more here, including details on Team Phoenix. 
 
For more details, check out our website, and to submit a poster to ARC Symposium, see the call for participation page.  

If there are any questions, please contact us.

Abstract: It is obviously important and even crucial that the founders of a new start-up company thoroughly understand the technology and the market for their products, as well as how their products will be manufactured, and the supply chain associated with manufacturing. It may not be immediately obvious, however, that the founders also need to thoroughly understand what their potential investors are looking for. Clearly, investors are looking for a return on their investment, but in order to improve their chances of getting a return, there are many features of the new company and the team which investors closely and carefully scrutinize. This talk will focus on what those features are. What aspects of your company do investors consider crucial and which aspects will increase your potential for securing funding? Also, there are several types of investors, individual angels, angel groups, strategic corporate investors, and venture capitalists. Each of these groups have different motivations and agendas which drive their decisions. When founders genuinely comprehend what investors like to see in their portfolio companies, they will not only find it easier to obtain funding, but their chances of being commercially successful will also be enhanced.

Bio: Kurt Petersen received his BS degree cum laude in EE from UC Berkeley in 1970 and a PhD in EE from the Massachusetts Institute of Technology in 1975.  Dr. Petersen established a micromachining research group at IBM from 1975 to 1982, publishing the most frequently referenced work in the field of micromachining and micro-electro-mechanical systems (MEMS). Since 1982, Dr. Petersen has co-founded six companies in MEMS technology. In 2011, he joined the Silicon Valley Band of Angels, where he now co-chairs the HardTech group. The Band is an angel investment group which mentors and invests in early stage, high-tech, start-up companies. Dr. Petersen has published over 100 papers and has been granted over 35 patents in the field of MEMS.  He was awarded the prestigious IEEE Medal of Honor in 2019 as well as the IEEE Simon Ramo Medal in 2001 for his contributions to MEMS.  Dr. Petersen is a member of the National Academy of Engineering and is a Life Fellow of the IEEE in recognition of his contributions to “the commercialization of MEMS technology”.

Access the Lecture at: https://tinyurl.com/NNCIinnovateNANO