ABOUT THE ACADEMY:
Eight standalone sessions will orient researchers to NIH and its most frequently used award mechanisms – the R01. This program will review the core components of R01 applications, provide suggestions for structuring your proposal, and demystify the review process.

WHO SHOULD PARTICIPATE? 
This academy is best suited for early-career faculty members and others who have not yet secured an R01 or equivalent award. Applicants at any stage of readiness are welcome, and those committed to applying within the next six to 12 months are particularly encouraged to join.

WHEN & WHERE?
Courses will take place via Zoom on Mondays from 12 - 1 p.m., Sept. 28 - Nov. 16.

ABOUT THE ACADEMY:
Eight standalone sessions will orient researchers to NIH and its most frequently used award mechanisms – the R01. This program will review the core components of R01 applications, provide suggestions for structuring your proposal, and demystify the review process.

WHO SHOULD PARTICIPATE? 
This academy is best suited for early-career faculty members and others who have not yet secured an R01 or equivalent award. Applicants at any stage of readiness are welcome, and those committed to applying within the next six to 12 months are particularly encouraged to join.

WHEN & WHERE?
Courses will take place via Zoom on Mondays from 12 - 1 p.m., Sept. 28 - Nov. 16.

ABOUT THE ACADEMY:
Eight standalone sessions will orient researchers to NIH and its most frequently used award mechanisms – the R01. This program will review the core components of R01 applications, provide suggestions for structuring your proposal, and demystify the review process.

WHO SHOULD PARTICIPATE? 
This academy is best suited for early-career faculty members and others who have not yet secured an R01 or equivalent award. Applicants at any stage of readiness are welcome, and those committed to applying within the next six to 12 months are particularly encouraged to join.

WHEN & WHERE?
Courses will take place via Zoom on Mondays from 12 - 1 p.m., Sept. 28 - Nov. 16.

Abstract: Time series learning has become a central problem in modern machine learning, with applications spanning science, engineering, healthcare, and finance. While much of the existing research has focused on improving forecasting accuracy, accurate prediction is only the starting point. This talk explores a broader view of time series learning, moving from forecasting toward reasoning and scientific discovery. I will first discuss our work on efficient time series forecasting, introducing cross-architecture knowledge distillation to develop lightweight models that achieve strong predictive performance with reduced computational costs. I will then explore spatio-temporal reasoning, presenting methods that integrate time series, spatial structures, and large language models to understand how events evolve and propagate across connected systems. Finally, I will discuss whether AI can move beyond prediction to discover the underlying mathematical and causal relationships governing temporal dynamics. These directions highlight the potential of time series learning to advance from accurate prediction toward deeper understanding and scientific discovery.

Bio: Wei Jin is an Assistant Professor in the Department of Computer Science and the Department of Biostatistics and Bioinformatics at Emory University. His research focuses on time series analysis and AI for health. His honors and achievements include two NIH R01 awards as principal investigator, IJCAI-ECAI Early Career Spotlights, AAAI New Faculty Highlights, KAUST Rising Stars, the INNS Doctoral Dissertation Runner-up Award, recognition for Most Influential Papers in KDD and WWW by Paper Digest, and top finishes in three NeurIPS competitions. His research has been published in Nature Portfolio journals, PNAS, and leading conferences including ICLR, KDD, ICML, and NeurIPS. He has also organized multiple tutorials and workshops at major AI conferences.

Seminar Title

Evaluating Lightweight Contact Feedback in Dual-Operator Collaborative Humanoid Teleoperation

Affiliation

Ph.D. student in Robotics, George W. Woodruff School of Mechanical Engineering, College of Engineering

XR Bytes is a graduate-student-led initiative dedicated to showcasing the trajectory of Extended Reality (XR) research across the Institute. Our goal is to highlight the versatile ways XR technologies are applied across various disciplines and majors, fostering an interdisciplinary community of innovation at Georgia Tech.

Participation & Visibility

We actively encourage cross-departmental participation and welcome engagement from both internal academic units and external industry partners. Our goal is to foster a robust community of practice around XR at Georgia Tech.

Lunch will be served for those who RSVP.

Subscribe & Sign Up

To join our mailing list and RSVP for these seminar series, please sign up here: RSVP HERE.

Seminar Title

How Does Active Exploration in Virtual Reality Shape Spatial Memory?

Affiliation

Ph.D. student in Adult Development and Aging, College of Sciences

XR Bytes is a graduate-student-led initiative dedicated to showcasing the trajectory of Extended Reality (XR) research across the Institute. Our goal is to highlight the versatile ways XR technologies are applied across various disciplines and majors, fostering an interdisciplinary community of innovation at Georgia Tech.

Participation & Visibility

We actively encourage cross-departmental participation and welcome engagement from both internal academic units and external industry partners. Our goal is to foster a robust community of practice around XR at Georgia Tech.

Lunch will be served for those who RSVP.

Subscribe & Sign Up

To join our mailing list and RSVP for these seminar series, please sign up here: RSVP HERE.

Seminar Title

Beyond Screens: Designing Human Interfaces for Spatial Computing

Affiliation

Ph.D. Student in Human-Centered Computing, College of Computing

XR Bytes is a graduate-student-led initiative dedicated to showcasing the trajectory of Extended Reality (XR) research across the Institute. Our goal is to highlight the versatile ways XR technologies are applied across various disciplines and majors, fostering an interdisciplinary community of innovation at Georgia Tech.

Participation & Visibility

We actively encourage cross-departmental participation and welcome engagement from both internal academic units and external industry partners. Our goal is to foster a robust community of practice around XR at Georgia Tech.

Lunch will be served for those who RSVP.

Subscribe & Sign Up

To join our mailing list and RSVP for these seminar series, please sign up here: RSVP HERE.

Abstract: Quantum magnetism studies how large numbers of interacting quantum particles organize and evolve collectively. Despite simple microscopic ingredients, quantum magnetic materials exhibit rich emergent behavior, novel phases of matter, and dynamics that often challenge our ability to predict, model, or even measure them. In this talk, I will introduce the basic concepts of quantum magnetism and discuss a variety of open problems at the intersection of optimization, statistical inference, inverse problems, uncertainty quantification, complexity, and information. Along the way, I will show how modern experiments probe quantum matter and how physicists seek simplified, predictive descriptions of systems whose underlying complexity is often enormous. I will also discuss how emerging quantum computing tools are used to study such systems, complementarily to experiments on real materials.
 

Bio: Martin Mourigal is a Professor in the School of Physics at Georgia Tech, holds the Dunn Family Professorship, and serves as Co-Director of the Quantum Applications Research Center. He received his undergraduate degree in materials science from École des Mines de Nancy in 2007 and his M.S. and Ph.D. in physics from École Polytechnique Fédérale de Lausanne in 2008 and 2011, respectively. He was a postdoctoral fellow at Johns Hopkins University from 2011 to 2014 and joined Georgia Tech in 2015. His honors include a 2018 National Science Foundation CAREER Award, a 2019 Kavli Fellowship by the U.S. National Academy of Sciences, and the 2022 Science Prize from the Neutron Scattering Society of America.

Every smartphone, laptop, gaming console, and AI accelerator begins as a digital chip design. Yet one of the most important
steps in bringing that design to life remains largely invisible to most engineers and consumers. At Synopsys, engineers develop the software, algorithms, models, and simulation tools that enable both the design and manufacturing of advanced semiconductor devices. This work spans computer science, electrical engineering, physics, mathematics, high-performance computing, and advanced manufacturing, creating exciting career opportunities for students interested in solving some of the world’s most challenging technical problems.

A critical stage in a chip’s journey from concept to product is tapeout, the process between design and manufacturing. During tapeout, engineers transform a completed integrated circuit design into a set of photomasks used to print billions of transistor features onto silicon wafers. Because these features are now measured in nanometers, manufacturing introduces complex optical and physical effects that can distort the intended circuit patterns. To overcome these challenges, engineers use advanced computational lithography techniques to analyze, modify, and optimize mask patterns so the manufactured chip matches the original design intent.
This presentation will explore the fascinating technologies behind leading-edge photomask development, including pattern correction, verification, mask writing, and inspection. Attendees will learn how sophisticated software, physics-based modeling, and advanced manufacturing technologies work together to make modern semiconductor devices possible. Along the way, real-world engineering challenges that require expertise across multiple disciplines will be examined and demonstrate how innovation in computational engineering continues to drive the semiconductor industry forward.

By the end of this session, students will have a behind-the-scenes view of how a digital chip design becomes a manufacturable product and discover a hidden but highly impactful career
path at the intersection of software, physics, chip design, and semiconductor manufacturing. Whether your interests lie in computer science, electrical engineering, data science, or applied physics, tapeout engineering offers a unique opportunity to
help create the technologies that power the modern world. Manufacturing and chip design careers will also be discussed.

Bio: Larry Melvin is a Senior Director in the Product Management Group at Synopsys, where he leads development of software and models used in semiconductor manufacturing. His work focuses on predicting how microchips will be fabricated and ensuring that chip designs can be manufactured accurately and efficiently.

Throughout his career, he has worked on a variety of advanced lithography and semiconductor technologies, including extreme ultraviolet (EUV) lithography, optical proximity correction (OPC), and computational lithography. Recent projects include developing specifications for EUV photomasks, designing optical devices that account for manufacturing effects, and creating semiconductor digital twins to model the chip design and manufacturing process to an electrical analysis. Dr. Melvin received his Ph.D. from The University of Texas at Austin and his B.S. from the University of Illinois Urbana- Champaign. He also completed postdoctoral research in X-ray lithography and microelectromechanical systems (MEMS) at the University of Wisconsin-Madison. His career spans the intersection of physics, engineering, manufacturing, and software, with a focus on helping the semiconductor industry design and build increasingly complex microchips.

Abstract: Abstract: Decision-making under uncertainties has been a subject of study for many years. In particular, the decision making (optimization) for complex systems such as engineering and economic systems involves human-decision phase. This triggers the occurrences of uncertainties in the decision making phase in addition to the widely-existed inherent uncertainties in the actual systems. In this talk, such challenges will be addressed by summarizing the year’s efforts of the presenter when he was with the University of Manchester before 2016. At first, a generic framework on the decision making for uncertainty systems has been formulated into a “closed loop control design problem”, where the uncertainties in both decision making phase and the actual systems have been represented by the uncertainty injections to the “controller” and to the “plant to be controlled”. The uncertainties in human decision making phase is characterized by their probability density functions (PDF) of the brain-cell’s signal collected via brain-computer-interface (BCI). Using such a generic framework, decision making under uncertainties are formulated as a PDF control problem of objective functions and constraints- leading to a total solution for the stochastic optimization for complex systems. Two case studies will be presented together with the rationale and claims that most of the existing stochastic optimizations are special cases of the presented theory. Note that the presentation is based upon the published materials by the author.
 

Bio: Professor Hong Wang (Member of European Academy of Sciences and Arts, FIEEE, FIET, FInstMC, SMIEEE) received the BSc form Anhui University of Science and Technology in 1982, the master’s and Ph.D. degrees from the Huazhong University of Science and Technology, Wuhan, China, in 1984 and 1987, respectively. He was a Research Fellow with Salford University, Salford, U.K., Brunel University, Uxbridge, U.K., and Southampton University, Southampton, U.K., before joining the University of Manchester Institute of Science and Technology (UMIST), Manchester, U.K., in 1992. He was a Chair Professor in process control of complex industrial systems with the University of Manchester (UoM), U.K., from 2002 to 2016, where he originated probability density function control theory in 1996 and was the Deputy Head of the Paper Science Department, the Director of the UMIST Control Systems Centre from 2004 to 2007, which is the birthplace of Modern Control Theory established in 1966. He was a University Senate member and a member of general assembly during his time in Manchester. Whilst he has been an Emeritus Professor of UoM, from 2016 to 2018, he was with the Pacific Northwest National Laboratory (PNNL), Richland, WA, USA, as a Laboratory Fellow and Chief Scientist,
and was the Co-Leader and the Chief Scientist for the Control of Complex Systems Initiative. He joined the Oak Ridge National Laboratory in January 2019.

His research focuses on stochastic distribution control, fault diagnosis and tolerant control, and intelligent controls with applications to complex system area. He has published 6 books and 200+ journal papers in these areas together with 10+ awards/prizes at international conferences.

He is an associate editor for IEEE Transactions on Neural Networks and Learning Systems and was an Associate Editor of IEEE Transactions on Automatic Control (2002 – 2004), IEEE Transactions on Control Systems Technology (2013 – 2018), and the IEEE Transactions on Automation Science and Engineering. He is also a member for three IFAC Committees for the three areas of his research.

Abstract: We study a class of nonconvex–nonconcave minimax problems in which the inner maximization problem satisfies a local Kurdyka–Łojasiewicz (KL) condition that may vary with the outer minimization variable. In contrast to the global KL or PL conditions commonly assumed in the literature—which are significantly stronger and often too restrictive in practice—this local KL condition accommodates a broader range of practical scenarios. However, it also introduces new analytical challenges. In particular, as an optimization algorithm approaches a stationary point, the region over which the KL condition holds may shrink, leading to a more intricate and potentially ill-conditioned landscape. To address this challenge, we show that the associated maximal function is locally generalized Hölder smooth. Leveraging this key property, we develop an inexact proximal gradient method for solving the minimax problem, where the inexact gradient of the maximal function is computed by applying a proximal gradient or sequential convex programming method to a KL-structured subproblem. Under mild assumptions, we establish complexity guarantees for computing an approximate stationary point of the minimax problem.

This is joint work with Xiangyuan Wang (University of Minnesota).
 

Bio: Zhaosong Lu is a Full Professor in the Department of Industrial and Systems Engineering at the University of Minnesota. He received his Ph.D. in Operations Research from Georgia Tech. His research focuses on the theory and algorithms of continuous optimization, with applications in data science and machine learning. Dr. Lu has published extensively in leading journals, and his research has been supported by major funding agencies, including AFOSR, NSF, and ONR. He has served on several prize committees, such as the INFORMS George Nicholson Prize Committee and the ICCOPT Best Paper Award Committee. He has also served as an Associate Editor for leading journals, including Mathematics of Operations Research, SIAM Journal on Optimization, Computational Optimization and Applications, and Journal of Global Optimization.

ABSTRACT
The plasma membrane serves as the critical interface where cells endure and respond to their physical environment. We recently revealed that the PIEZO1 mechanosensing axis actively safeguards membrane integrity through a hierarchical, two-layer mechanoprotective mechanism. This involves rapid architectural buffering followed by a calcium-dependent positive feedback loop engaging Calmodulin and Filamin A to reinforce cortical stability. This fundamental discovery in mechanoprotection serves as the blueprint for our translational innovations in "Thrombo-protective Oncology." By engineering a microfluidic "CAT-on-chip" to recapitulate venous shear stress, we discovered that metastatic cancer cells exploit a strikingly similar mechanobiological network to trigger pathological clotting. Specifically, tumor-induced hypercoagulation is driven by a Focal Adhesion Kinase (FAK)-Calmodulin (CaM) axis, which structurally "decrypts" Tissue Factor (TF) by disengaging it from cytoskeletal Filamin A. To translate these microfluidic discoveries into clinical practice, we developed "SmartClot", an AI-driven Point-of-Care Testing (POCT) platform that rapidly stratifies cancer-driven thrombotic risk using blood diffusion kinetics. This seminar will highlight how decoding fundamental nanoscale mechanosensing paves the way for next-generation diagnostic and therapeutic technologies.

BIO
Professor Lining Arnold Ju (PhD, GAICD, FHEA) is a Snow Fellow, Australian Heart Foundation Future Leader Fellow, and recipient of the Australian Academy of Science John Booker Medal. He received his PhD in Biomedical Engineering from Georgia Tech and Emory University in 2013 and now directs the Mechanobiology and Biomechanics Laboratory (MBL) at the University of Sydney's School of Biomedical Engineering.

Ju’s research operates at the intersection of fundamental mechanobiology and biomedical engineering. His team recently decoded the PIEZO1 mechanosensing axis and has pioneered multiple biomechanical nanotools, including multi-parametric thrombus profiling microfluidics ("CAT-on-chip") and patient-specific vessel-on-a-chip platforms. His novel understanding of the mechanics behind blood clot formation has directly driven the development of point-of-care testing (POCT) technologies, such as the machine learning-assisted SmartClot diagnostic tool. His overarching vision integrates advanced biomanufacturing, high-throughput microfluidic phenotyping, and generative AI to create intelligent biosensing technologies to predict and prevent cardiovascular and cancer-associated thrombotic risks.

Andrés García, faculty host

ABOUT THE ACADEMY:
This eight-week proposal development academy will orient researchers to NIH and its most frequently used award mechanisms – the R01. This program will review the core components of R01 applications, provide suggestions for structuring your proposal, and demystify the review process.

WHO SHOULD PARTICIPATE? 
This academy is best suited for early-career faculty members and others who have not yet secured an R01 or equivalent award. Applicants at any stage of readiness are welcome, and those committed to applying within the next six to 12 months are particularly encouraged to join.

WHEN & WHERE?
Courses will take place via Zoom on Mondays from 12 - 1 p.m., Sept. 28 - Nov. 16.

ABOUT THE ACADEMY:
This eight-week proposal development academy will orient researchers to NIH and its most frequently used award mechanisms – the R01. This program will review the core components of R01 applications, provide suggestions for structuring your proposal, and demystify the review process.

WHO SHOULD PARTICIPATE? 
This academy is best suited for early-career faculty members and others who have not yet secured an R01 or equivalent award. Applicants at any stage of readiness are welcome, and those committed to applying within the next six to 12 months are particularly encouraged to join.

WHEN & WHERE?
Courses will take place via Zoom on Mondays from 12 - 1 p.m., Sept. 28 - Nov. 16.

Abstract: This talk describes the progress in analysis and optimization of Markov Decision Processes (MDPs) and Partially Observable MDPs (POMDPs) with infinite state spaces and possibly noncompact action sets. We shall also discuss applications to inventory control and to controlled linear Gaussian systems. 
 

Bio: Eugene A. Feinberg received MS in Applied Mathematics and Computer Engineering from Moscow University of Transportation, Russia, in 1976 and Ph.D. in Probability and Statistics from Vilnius University, Lithuania, in 1979. Currently he is Distinguished Professor at the Department of Applied Mathematics and Statistics of Stony Brook University.

His research interests include stochastic models of operations research, probability theory, real analysis, Markov Decision Processes, and applications of operations research and statistics to engineering, biology, and medicine. He has published more than 100 papers and edited the Handbook on Markov Decision Processes. His research has been partially supported by the National Science Foundation, Office of Naval Research, National Institute of Health, New York Office of Science, Technology and Academic Research, and private industry. He has served as a Council Member of the INFORMS Applied Probability Society and on several editorial boards. He is a fellow of INFORMS.

Grab your team and put your creativity to the test as you compete to build the ultimate edible haunted house. Expect surprise twists, mystery ingredients, fun challenges, and plenty of Halloween chaos along the way.

Prizes and bragging rights are on the line!

Bring your own decorating materials and/or use materials provided by GT Arts.

Sign up your team today! Sign up link: https://gatech.co1.qualtrics.com/jfe/form/SV_cNreuRHaI9X2cse)

Abstract: Modern manufacturing generates increasingly rich in-situ sensing data, creating new opportunities for AI to enable automated and intelligent quality control decisions. However, conventional quality control using supervised learning relies on abundant labeled data and assumes that future product defects or process faults resemble those known during training. In practice, new defect types emerge, while abnormal conditions may be rarely observed or completely unknown. These challenges are particularly important for in-situ quality control, where defects must be detected or correctly classified for real-time decision-making, including newly emerging defects with limited or unavailable labels. Moreover, for latent defects that cannot be directly inspected online, defects must instead be predicted from indirect process-sensing signals. This requires mapping process-signal changes to possible defects despite scarce or unavailable defect training samples. This talk explores how advances in AI can address these challenges and enable more adaptive and intelligent in-situ quality control and decision-making for smart manufacturing.
 

Bio: Dr. Judy Jin is the A. Galip Ulsoy Collegiate Professor of Engineering and Professor of Industrial and Operations Engineering at the University of Michigan. Her research lies at the intersection of data science and quality engineering, with a focus on synergistically integrating engineering models, AI, and advanced quality control methods to improve system design and operational performance. She has served as PI/Co-PI on more than $20 million in federally and industry-funded research. Her work has received numerous honors, including 18 Best Paper Awards, the S.M. Wu Research Implementation Award from SME, the Forging Achievement Award from FIERF, the NSF CAREER Award, and the NSF PECASE Award.

Dr. Jin currently serves as Editor-in-Chief of IISE Transactions. She has also served as Vice President of INFORMS, Chair of the INFORMS Quality, Statistics and Reliability Section, and President of the IISE Quality Control and Reliability Engineering Division. She is a Fellow of ASME, IISE, and INFORMS.

Abstract:

The development of controlled polymerization methods has broadly expanded the chemical space accessible for polymerization and enabled the production of well-defined synthetic materials targeting numerous applications, including polyolefin thermoplastics and styrenebutadiene- styrene (SBS) rubber. These achievements represent decades of advancements in polymer technology; however, our ability to prepare materials with backbones comprised of functional groups beyond alkyl or ester chains remains limited. Well-defined materials featuring unique molecular structures, and by extension exhibiting unique properties, will be required to meet the demand for high-performance materials that address contemporary challenges. This presentation will discuss our recent eForts in post-polymerization modification, monomer design, and methods development towards achieving this goal.

Bio: 

Aaron earned a B.S. degree in Chemistry in 2012 from the University of Nevada, Reno prior to attending the University of Texas at Austin, where he completed a PhD in Chemistry in 2016. His dissertation work with Prof. Christopher Bielawski focused on the design and development of photoswitchable N-heterocyclic carbenes for enhanced control over organocatalyzed and transition metal catalyzed transformations. Aaron subsequently joined the lab of Prof. Frank Leibfarth at the University of North Carolina at Chapel Hill as a postdoctoral fellow. His work at UNC focused on the development of new stereoselective ionic polymerizations and subsequent exploration of the resulting polar thermoplastics. Aaron joined the faculty at the University of Kansas as an Assistant Professor in 2021, where his group is focused on the design and synthesis of new, functional synthetic macromolecules.

Radiation Worker Registration and X-Ray Safety Training Sign-Up

1. Complete a Form B, Radiation Worker registration before attending class (you may need to be connected to the GT VPN). After the training, the completed Form B will be sent to your Authorized User (AU - this is whoever manages the x-ray machine you'll be using) for signature via DocuSign.
2. Go to Genius LMS and use your GT Login
3. Click "View Course Catalog"
4. Search for X-Ray
5. Click "Register" for your preferred date
6. Click "Proceed to Checkout" and follow the instructions

IF YOU HAVE TROUBLE REGISTERING, or IF YOU DON'T GET AN EMAIL IMMEDIATELY AFTER REGISTERING, please contact ors@ors.gatech.edu.

Radiation Worker Registration and X-Ray Safety Training Sign-Up

1. Complete a Form B, Radiation Worker registration before attending class (you may need to be connected to the GT VPN). After the training, the completed Form B will be sent to your Authorized User (AU - this is whoever manages the x-ray machine you'll be using) for signature via DocuSign.
2. Go to Genius LMS and use your GT Login
3. Click "View Course Catalog"
4. Search for X-Ray
5. Click "Register" for your preferred date
6. Click "Proceed to Checkout" and follow the instructions

IF YOU HAVE TROUBLE REGISTERING, or IF YOU DON'T GET AN EMAIL IMMEDIATELY AFTER REGISTERING, please contact ors@ors.gatech.edu.

Radiation Worker Registration and X-Ray Safety Training Sign-Up

1. Complete a Form B, Radiation Worker registration before attending class (you may need to be connected to the GT VPN). After the training, the completed Form B will be sent to your Authorized User (AU - this is whoever manages the x-ray machine you'll be using) for signature via DocuSign.
2. Go to Genius LMS and use your GT Login
3. Click "View Course Catalog"
4. Search for X-Ray
5. Click "Register" for your preferred date
6. Click "Proceed to Checkout" and follow the instructions

IF YOU HAVE TROUBLE REGISTERING, or IF YOU DON'T GET AN EMAIL IMMEDIATELY AFTER REGISTERING, please contact ors@ors.gatech.edu.