Details of the session are as follows:

• Date: Friday, November 20th
• Time: 10 - 11:30 am ET

You will also have the chance to ask any questions you may have about the program or the application process.

We look forward to meeting you and helping you take the next step in your academic journey.

Register For Event

Abstract: LLM watermarking has emerged as a promising tool for attributing AI-generated text, but its practical deployment
remains limited. In this talk, I will first present a unified theoretical framework that jointly optimizes watermark
design and detection, characterizing the fundamental trade-off between detectability, false positive control, and text
distortion. While this formulation yields sharp optimality insights, it typically requires the LLM provider to implement
the watermark during generation, which has limited its deployment in practice. I will then discuss the reasons
behind this gap, emphasizing stakeholder incentives and real-world deployment constraints. Motivated by these
challenges, I will present in-context watermarking, a model-agnostic approach that embeds detectable signals
through prompts rather than decoding-time access. This perspective suggests that effective watermarking requires
not only strong algorithms, but also incentive-aligned designs tailored to practical domains such as peer review
and education.

Bio: Yuheng Bu is an Assistant Professor in the Department of Computer Science at the University of California, Santa
Barbara. His research lies at the intersection of information theory, machine learning, and trustworthy AI. He
received the NSF CAREER Award in 2026. Before joining UC Santa Barbara, he was an Assistant Professor in the
Department of Electrical and Computer Engineering at the University of Florida. Prior to that, he was a Postdoctoral Research Associate at the Research Laboratory of Electronics and the Institute for Data, Systems, and Society at the Massachusetts Institute of Technology. He received his Ph.D. in 2019 from the Coordinated Science Laboratory and the Department of Electrical and Computer Engineering at the University of Illinois Urbana-Champaign. He earned his B.S. degree with honors in Electronic Engineering from Tsinghua University in 2014.

Abstract: Discrete diffusion models achieve strong performance in text and image generation, but inference remains slow
and requires balancing sampling efficiency with sample quality. We systematically study how the degree of
stochasticity in Markov transitions governs this tradeoff, finding that deterministic transitions converge quickly but
accumulate errors, while stochastic transitions converge more slowly but can achieve better final sample quality.
Through an information-theoretic analysis, we show that redundant transitions that symmetrically exchange
probability mass between states can contract sampling errors, providing an error-correcting effect. Motivated by
this insight, we introduce Discrete Churn and Restart Sampling (DCRS), which injects controlled stochasticity by
alternating between forward and reverse diffusion. Across synthetic, image, and language benchmarks, DCRS
improves the speed-quality tradeoff at low numbers of function evaluations, achieving up to a 10× reduction in
image sampling steps while revealing more nuanced behavior for language generation.

Bio: William Yuan is a Ph.D. student in Electrical and Computer Engineering at the Georgia Institute of Technology,
advised by Prof. Amirali Aghazadeh. His research focuses on developing methods to accelerate the inference in
deep generative models. He received his B.S. from Cornell University.

Abstract: We present a new computationally efficient method for multi-beamforming in the broadband setting. Our “fast
beamspace transformation” forms B beams from M sensor outputs using a number of operations per sample that
scales linearly (to within logarithmic factors) with M when B∼M . While the narrowband version of this
transformation can be performed efficiently with a spatial fast Fourier transform, the broadband setting requires
coherent processing of multiple array snapshots simultaneously. Our algorithm works by taking N samples off of
each of M sensors and encoding the sensor outputs into a set of coefficients using a special non-uniformly spaced
Fourier transform. From these coefficients, each beam is formed by solving a small system of equations that has
Toeplitz structure. The total runtime complexity is O(M log N + B log N ) operations per sample, exhibiting
essentially the same scaling as in the narrowband case. Our method offers significantly higher accuracy and lower
computational complexity than delay-and-sum filtering techniques and significantly higher accuracy and lower
latency than standard frequency- domain techniques based on sub-band processing. An additional benefit of the
proposed approach is that it allows for a careful error analysis; we give concrete formulas for the bias and variance
of the beamformer given the array parameters. We also provide a host of numerical experiments demonstrating the
algorithm’s favorable computational scaling and high accuracy. Finally, we demonstrate how tasks such as
interpolating to “off-grid” angles and nulling an interferer are more computationally efficient when performed directly
in beamspace.

Bio: Nakul Singh received a bachelor’s degree in electrical engineering from the Indian Institute of Technology, Kanpur,
India, in 2022. He is currently working toward the Ph.D. degree at the Georgia Institute of Technology, advised by
Prof. Justin Romberg and Prof. Mark Davenport. His research interests include machine learning, signal
processing, and massive MIMO.

Host: Itamar Kolvin

Title: Flexible computation, learning and generalization with chemical neuromodulators

Abstract: Neural computation is usually thought to be defined by the synaptic connectome. Beyond this wiring, nervous systems from invertebrates to mammals signal through neuromodulators, chemicals that are postulated to dynamically change how neurons respond. In C. elegans, this signaling has been mapped as a dense chemical network layered on the synaptic one. Here we ask: does this chemical layer just scramble the dynamics, or enable a network to compute beyond what its wiring allows? Our results point to neuromodulation as a general route to flexible computation.

Bio: Suri Vaikuntanathan co-authored research on MXenes that describes direct synthesis and chemical vapor deposition of 2D carbide and nitride materials. He is a Professor of Chemistry at the University of Chicago, where the study of statistical mechanics, information theory, and ML in chemical and biological systems and adaptable materials. His work includes Topological waves in fluids with odd viscosity and Fluctuating hydrodynamics of chiral active fluids. Suri earned a PhD in chemical physics from the University of Maryland, College Park, and completed a postdoctoral fellowship at UC Berkeley.

Johnny Jones is a career foreign service officer with the U.S. Department of State currently working in New York at the U.S. Mission to the United Nations at the rank of Minister Counsellor.       

Jones most recently served in Washington, D.C. after previous overseas assignments in Lebanon, Libya, France, Pakistan, Egypt, and Iraq. His diplomatic career has primarily focused on political developments and counterterrorism issues in the Middle East and North Africa.    

Jones is a 2002 graduate of the Sam Nunn School of International Affairs at Georgia Tech and a 2005 graduate of the Georgia State University College of Law in Atlanta, Georgia. He is a member of the State Bar of Georgia, a former term member at the Council on Foreign Relations, and is proficient in French.

Ambassador Kulminski has over two decades of experience in conflict resolution, foreign policy, strategic planning, and international development. He has served as Deputy Prime Minister for Reintegration, foreign policy and strategic planning Advisor to the Prime Minister, Secretary of State at the Ministry of Foreign Affairs, and worked with the UN, UNDP, World Bank, GIZ, and Humanitarian Dialogue on negotiation, mediation, and peacebuilding across Eurasia region. His work has included the Black Sea Grain Initiative and programs in conflict resolution, social cohesion and post-conflict recovery.   

He holds Master's degrees in Public Administration from the Maxwell School of Citizenship and Public Affairs (USA) and in International Relations and European Studies from the Central European University in Budapest. He is a Fulbright, John Smith, and Munich Security Conference Fellow.   

This event is part of the Nunn School's Diplomats-in-Residence Program and is generously supported by the Arthur M. Blank Family Foundation.

Georgia Tech Arts microgrants infuse campus with the spirit of creativity. These grants up to $1000 are available to Georgia Tech students, registered student organizations, and postdocs for creative projects across disciplines — including those with an arts research focus. All projects must be shared with the campus community. 

Bio: Abigale Stang is a researcher at Georgia Tech’s MultiSense.Studio in the School of Industrial Design. Her work focuses on accessibility, inclusive design, and the development of accessible visual and tactile media. Her research explores AI-generated image descriptions, tactile media design and production, and the collaborative processes involved in making information accessible. She also directs the Tactile Media Alliance, an initiative connecting researchers, practitioners, and organizations to advance tactile media accessibility.

---

IPaT: GVU Lunch Lecture Series

• 11 – 11:30 a.m.: Overview of All IAC M.S./Ph.D. Programs
• 12:30 – 1 p.m.: M.S./Ph.D. in History and Sociology of Technology and Society
• 1 – 1:30 p.m.: M.S. Applied Linguistics and Intercultural Studies
• 1:30 – 2 p.m.: M.S. Global Media and Cultures
• 1:30 – 2 p.m.: Carter School of Public Policy M.S./Ph.D. Programs
• 2 – 2:30 p.m.: Overview of All IAC M.S./Ph.D. Programs

Advisor: Dr. Dimitri Mavris

Milestone: MS Thesis Final Examination (Defense)

Degree Program: Aerospace Engineering

Title: A Continuous Engine Attachment Methodology for Gradient-Based Aerostructural Optimization, with Application to the Athena Wing

Abstract: Unconventional aircraft configurations, such as the Athena developed by the Aerospace Systems Design Laboratory for the NASA AACES 2050 initiative, have few or no previous designs to guide the engine position, which must therefore be searched together with the structural sizing. Gradient-based optimization with adjoint derivatives is suited to this search, but it requires the engine position to be a continuous design variable, while finite-element models of the wing usually attach the engine through rigid elements to a fixed set of nodes. This thesis develops a continuous engine attachment for gradient-based aerostructural optimization, and uses it to study the structural trade-offs of engine placement on the Athena wing. The proposed sliding kernel attachment is based on the linearized MELD transfer scheme. A smooth spanwise weighting function moves the engine load with the engine and shares it among the rib corners of the pylon strip, conserving force, moment and virtual work. Its derivatives with respect to the engine coordinates are combined with the adjoint sensitivities of the coupled analysis, assembled in OpenMDAO with TACS and a vortex lattice method, so that the engine coordinates become design variables together with the structural sizing. The attachment is verified on the Simple Transonic Wing benchmark. Force, moment and virtual work are conserved to machine precision, the outputs vary continuously with the engine position, and the total derivatives agree with finite differences within 1.6 × 10⁻⁶. For engine positions aligned with a rib station, the results match those of an RBE3 attachment within 0.2% for the global quantities and 1.4% for the local failure index. A structural optimization with the engine position as a design variable reaches the same wingbox mass within 0.004% from different starting points, but without other trade-offs it moves the engine close to the tip. The framework is then applied to the Athena wing, in a structural and in an aerostructural optimization. The pylon weight, the vertical tail required with one engine inoperative, and the interference drag between nacelle and wing are included as penalties in the objective.

Date and time: 2026-11-18, 10:00 AM - 12:00 PM

Location: Collaborative Visualization Environment (CoVE) Weber Space Science and Technology Building (SST II)

Committee:
Dr. Dimitri Mavris (advisor), School of Aerospace Engineering
Dr. Graeme J. Kennedy, School of Aerospace Engineering
Dr. Christian Perron, School of Aerospace Engineering
Dr. Heriberto David Solano Sarmiento, School of Aerospace Engineering
, 

 

Thursday, November 12 | 6:00–9:00 PM
Georgia Tech Hotel & Conference Center – Ballroom Salons 4, 5 & 6

College of Computing students can register through Handshake.

Fireside Chat with Candace V. Mitchell
Tuesday, November 10 | 6:00 PM
Klaus 1116 East/West

College of Computing students can register through Handshake.

Founders Friday featuring VinoBarrel
Friday, November 13 | 3:00–4:30 PM
Klaus Atrium

College of Computing students can register through Handshake.

Bio: Lyle Ungar is a Professor of Computer and Information Science at the University of Pennsylvania, where he holds secondary appointments in Psychology, Bioengineering, Genomics and Computational Biology, and Operations, Information and Decisions. His group builds chatbots and language analysis tools for psychological and medical research.  They study the drivers of physical and mental well-being and their cultural variation, and develop socially sensitive AI-based tutors and coaches.

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

Zoom link: https://gatech.zoom.us/j/91607375625?pwd=ybCsFPzknA8VqYZaSqvenqlWbrvyzx.1

Meeting ID: 916 0737 5625
Passcode: 038828

• Jeff Leaders, President & Co-Founder, Quantasonix Technologies, a deep‑tech startup developing a “Fluid Intelligence Platform” for real‑time, non‑destructive sensing and analytics of industrial fluids  
• Jonathan Rogers, Lockheed Martin Professor of Avionics Integration, School of Aerospace Engineering, Georgia Tech  
• Matthew McDowell,  Associate Chair for Research, Carter N. Paden, Jr. Distinguished Chair, Professor, and co-director of the Georgia Tech Advanced Battery Center

We eagerly look forward to your positive response and your valuable presence at this event. For more information including registration, please check out the ATL CleanTech Connect webpage.

Follow us on LinkedIn, X and subscribe to our mailing list to get the latest updates.

Build Your Plate: Healthy Choices Made Simple  | 10:30 – 11:30 am.

Presented by Mitzie Rojas, Registered Dietitian

In recognition of National Diabetes Awareness Month, this interactive session will explore practical ways to build balanced meals that support steady energy, support your well-being, and sustainable long-term health. Join us to learn about Diabetes, nutrition basics, and building a balanced plate and sustainable nutrition habits into busy schedules.

Pause & Recharge  | 11:30 a.m. – 1:30 p.m.

Employees can stop by at their convenience for stretching exercises (massage), relaxing activities, and other simple ways to pause and recharge.

Register

Virtual Event | Register

Date: Wednesday, November 4, 2026

Time: 11:00 am-noon

Location: Hybrid (Teams for virtual and in-person at Student Center - Peachtree Room)

Small nonprofits are often told that sustainability comes from better grant writing, stronger boards, and more diversified revenue. But many of their barriers are structural: limited public investment, funding requirements built for larger institutions, and little access to policymakers. In Metro Atlanta, half of the region’s arts nonprofits operate on budgets under $150,000, and public arts funding averages about $1.96 per person against a national average of $5.44.

Arts Capital | Atlanta, a coalition of arts nonprofits, funders, and public partners across 11 counties, has been testing what happens when small organizations stop working alone. Its experiments include public funding advocacy, a regional pooled fund, and a non-competitive pilot that sends unrestricted dollars to every eligible organization. This conversation will ask whether coalitions can actually shift the structures that govern funding, who holds power inside them, and what research and technology might contribute.

Speaker: Laura Hennighausen 
Executive Director Arts Capital | Atlanta and
Director of Strategic Philanthropy, Purpose Possible

Laura Hennighausen is a strategist dedicated to reshaping the nonprofit landscape by empowering organizations to operate with greater equity, transparency, and impact. As Director of Strategic Philanthropy, Laura sits at the intersection of donor intent and mission-driven reality, partnering with corporate, private, and individual funders to establish, evaluate, and modernize charitable practices.

Laura’s work is characterized by a sophisticated understanding of the third sector, which she tracks closely through her insightful monthly publication, Dispatches from the Philanthrosphere. Whether she is facilitating complex board retreats, navigating capital campaigns, or guiding organizations through the nuances of interim leadership, Laura brings a steady, research-backed approach that helps clients align their internal operations with their broader philanthropic goals.

Her client portfolio spans a wide spectrum of the philanthropic world, including Arts Capital | Atlanta, Mailchimp, the Intuit Financial Freedom Foundation, the T. Rowe Price Foundation, Borealis Philanthropy, and the Kataly Foundation. Her deep involvement in the field is bolstered by her leadership; she currently serves as the Board Chair for both MODA and Soul Food Cypher.

Laura’s commitment to the field is both practical and academic. Having built a strong career in public space, historic preservation, and visual arts, she is now deepening her expertise as a PhilD candidate at the Lilly Family School of Philanthropy—the premier institution for the study of the philanthropic sector. She holds a Master of Arts in Arts Administration and dual Bachelor of Arts degrees in Japanese Language and Literature and Art History.

---------------
Conversations@TechSquare is a dynamic space for new ideas, not an environment for established concepts. We gather creators and innovators from across Georgia Tech, the broader Tech Square community, and beyond, to engage in open dialogue around some of today’s biggest challenges and opportunities. These gatherings are organized and hosted by the Georgia Tech Institute for People and Technology (IPaT) and will occur several times during a semester at the Tech Square Clubhouse. The Clubhouse address is 848 Spring Street NW, Atlanta, Georgia, 30308 (street level, behind the Centergy One Bldg.).

Tech Square Clubhouse

PARKING:
Parking is available at the Centergy One parking deck, located right next door. You can also find additional street parking via Park Mobile on Spring and 5th Street. Centergy One parking deck daily rates: Half Hour: $3.00 | One Hour: $6.00 | Two Hours: $12.00 | Daily Max: $20.00

GT CAMPUS BUS: Take the Gold route bus which stops at Tech Square.

Date: Thursday, November 19, 2026

Time: 12:00 PM – 2:00 PM ET

Location: George Tower, Room 607, and virtual via Microsoft Teams

Meeting link: https://teams.microsoft.com/meet/251426656237229?p=Rtcf8xozrWXtD8QpJP

(Meeting ID: 251 426 656 237 229, Passcode: X7PR3MY3)

Moyi Guo

Ph.D. Candidate

H. Milton Stewart School of Industrial and Systems Engineering

Georgia Institute of Technology

Committee

1. Dr. Shijie Deng (Advisor), H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology
2. Dr. David Goldsman, H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology
3. Dr. Valerie Thomas, H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology
4. Dr. Shihao Yang, H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology
5. Dr. Daniel Molzahn, School of Electrical and Computer Engineering, Georgia Institute of Technology
6. Dr. Aram Sogomonian, COO and Partner, GenBright LLC, an ENGIE company

Abstract

Electricity load and price forecasts underpin unit commitment, market bidding, and risk management, yet the series they target are strongly seasonal, heteroskedastic, and prone to extreme events, and operational decisions increasingly require not a single number but a calibrated description of uncertainty. This dissertation develops probabilistic forecasting methods for deregulated electricity markets in three research threads, progressing from point accuracy to calibrated uncertainty to joint scenarios across locations. The first thread proposes GARCHFormer, a Transformer-style encoder–decoder that maintains conditional-mean, conditional-variance, and fused latent paths at every layer, linked by a GARCH-inspired attention mechanism and a FiLM-based variance-to-mean fusion. Evaluated on public load and price datasets under both standard and capacity-controlled protocols, it delivers consistently strong point accuracy while learning time-varying volatility directly. The second thread extends this architecture to probabilistic forecasting with quantile, sigma-scaled, and Gaussian output modes and benchmarks it against a roster of deep learning models. Because raw prediction intervals from different models reach different coverage levels, it introduces a calibrate-then-compare protocol built on conformalized quantile regression, refined with per-horizon corrections and temporal copulas, and ranks forecasters by minimax regret across markets; under this rule the proposed model is the most robust choice. The third thread turns to multi-location renewable generation, developing a copula-based framework that produces spatially coherent solar and wind scenarios, improves on an industrial baseline, and stitches together scenario sets for wind speed and wind generation that come from separate forecast providers. Together, the results indicate that explicitly modeling volatility improves both point and probabilistic forecasts, that probabilistic forecasters should be compared only after they are calibrated, and that copula-based scenario generation turns marginal forecasts into the joint scenarios that market operations require.

Advisor: Dr. Dimitri Mavris

Milestone: PhD Thesis Proposal

Degree Program: Aerospace Engineering

Title: An MBSE Framework for Certification Informed Design Decisions

Abstract: Aircraft continue to undergo design changes throughout their service life to accommodate new technologies, operational needs, and customer requirements. These modifications can affect systems beyond those directly changed and require previous compliance demonstrations to be reconsidered. The resulting certification work can influence whether a proposed modification is worthwhile and which alternative should be selected. However, the information needed to assess these consequences is often distributed across design documents, certification records, and engineering disciplines. Establishing the relationships between a modification, its effects on the aircraft, and the associated compliance work therefore requires substantial interpretation and coordination. Engineering change management methods provide ways to represent dependencies and identify how changes propagate through a system. Some methods also relate these effects to engineering effort through component costs, task dependencies, and repeated work. Model-based certification studies address another part of the problem by connecting regulations to aircraft characteristics and verification activities. Although these approaches support individual assessments, they do not consistently connect aircraft design changes with the certification requirements and demonstrations they affect. Classification also depends on interpreting quantitative and qualitative criteria using information about the change. A further need is to relate the identified certification consequences to the work required, so that modifications can be compared during early design. This proposal presents three investigations toward a model-based methodology for assessing aircraft design changes and their certification consequences. The first investigates how aircraft structure, functions, system interactions, and certification artifacts can be connected to identify and trace change effects. The proposed approach uses conditional relationships to represent the applicability of requirements and compliance activities across aircraft configurations. The second investigates how regulatory classification criteria can be expressed as evaluable conditions and connected to the design characteristics and affected areas represented in the model. The aim is to derive classification inputs from the modeled change rather than require users to answer each classification question. The third investigates how the type, scope, and dependencies of the associated compliance activities can be combined into a relative certification-effort measure. Recorded certification project durations provide an empirical reference for developing and evaluating this measure. The proposed methodology will be examined through representative aircraft modifications, drawing on regulatory guidance, aircraft-family configurations, and documented supplemental type certificate projects. These applications will assess whether a connected model can identify certification impacts, produce traceable classification decisions, and distinguish the relative effort of different modifications. Together, the investigations aim to provide a reusable approach for considering certification consequences alongside the technical benefits of a design change, supporting the comparison of alternatives before detailed certification planning is complete. 

Date and time: 2026-11-17, 10 am - 1 pm

Location: Collaborative Visualization Environment (CoVE) Weber SST II

Committee:
Dr. Dimitri Mavris (advisor), School of Aerospace Engineering
Dr. Daniel P. Schrage, School of Aerospace Engineering
Dr. Kai A. James, School of Aerospace Engineering
Dr. Elena Garcia, School of Aerospace Engineering
Dr. Angela Campbell, FAA

Talk Title: From GenAI to Agentic AI: The Transformative Impact on Academics

Date/Location: Friday, Nov. 20, 2026; 11 a.m., KACB 1447

Abstract: Agentic AI represents the shift from stateless inference and reactive generation to autonomous and goal-driven orchestration. By chaining generative models together with goal decomposition, multi-round planning, memory retention, and tool-routing, Agentic AI systems solve complex and multi-step reasoning problems end-to-end. Realizing this vision demands a tight equilibrium: efficiency through adaptive learning and scaling, and robustness through continuous governance. Drawing on empirical research and classroom experience, this talk explores how agentic AI is transforming learning and research.

Bio: Ling Liu is a Professor in the School of Computer Science at Georgia Institute of Technology. Her recent research is mainly centered on efficient AI systems, algorithms and optimizations, as well as responsible AI through developing failure-resilient and trustworthy guardrails. She has graduated 40 PhD students since joining Georgia Tech in 1999, all well placed in either academic institutions or industry. Her current research is primarily supported by USA National Science Foundation, CISCO and IBM. Prof. Liu is a recipient of the best paper award from numerous top venues, including IEEE ICDCS, WWW, ACM/IEEE CCGrid, IEEE Cloud, ACM ICMR. She served as the editor in chief of multiple IEEE/ACM Transactions, incl. IEEE Transactions on Services Computing (2013-2016), ACM Transactions on Internet Computing (2019-2024), and currently the editor in chief of IEEE Transactions on Big Data (since 2025). Prof. Liu is a frequent keynote speaker in top-tier venues in Big Data, AI and ML systems, Cloud Computing and Distributed Computing Systems.

Xuanang Li
School of Mathematics

Date: November 18, 2026
Time: 12:30 PM (EST)
Location: Skiles 114
Microsoft Teams Link: Join the meeting

Committee:
Prof. Tom Kelly (Advisor, School of Mathematics, Georgia Tech)
Prof. Xingxing Yu (School of Mathematics, Georgia Tech)
Prof. Will Perkins (School of Computer Science, Georgia Tech)
Prof. Xiaoyu He (School of Mathematics, Georgia Tech)
Prof. Rose McCarty (School of Mathematics, Georgia Tech)

Abstract:
Dirac's classical theorem states that every graph on n vertices with minimum degree at least n/2 contains a Hamilton cycle. This fundamental result has inspired extensive research on Hamiltonicity in directed and oriented graphs. In our work, we establish robust versions of Dirac-type theorems for Hamilton cycles in directed and oriented graphs using vertex-spread distributions.

We prove that there exists a constant C such that every sufficiently large oriented graph G on n vertices with minimum semidegree at least (3n-4)/8 admits a (C/n)-vertex-spread distribution on Hamilton cycles. We also establish a corresponding result for robust out-expanders with linear minimum semidegree. Building on the recent resolution of the Kahn–Kalai conjecture by Park and Pham, we further obtain the following robust Dirac result: if each edge is kept independently with probability at least Clog n/n, then the resulting random subgraph contains a Hamilton cycle for every sufficiently large oriented graph with minimum semidegree at least (3n-4)/8. Our work is the first robust Dirac-type results for oriented graphs.

Take a break and join us for Coffee + CAD ☕💻

Unwind with a fresh cup while you design your perfect coffee setup, get hands-on help with your CAD work, and turn a frustrating grind into a chill, productive session.

Less stress. More coffee. Better CAD.

See you there.

As our biggest event of Fall 2026, we invite all our peers to join us on a nostalgic journey through the rich culture, traditions, and heritage of Old Dhaka!

Dress Code: Traditional / Semi formal

About our organization: SKY

SKY Campus Happiness is a comprehensive well-being and resilience program designed to help students, faculty and staff achieve their fullest potential. In this 40-minute live interactive session, we will share:

• How to overcome mind wandering
• a powerful breathing technique
• a guided meditation

We would love to see you in this workshop and grow on the self care journey together! If you have any questions please write to us at yesplusgt@gmail.com.

Our inspiration

We are an open space to drop in and find calm. Knowing the benefits of breath-work and meditation, we love to share these tools with more people.

A 2020 study at Yale that explored mental wellness interventions with surprising results. Discover how SKY Breath Meditation/ SKY Happiness Retreat created significant improvements in stress, anxiety, depression, mental health, social connectedness, mindfulness, and positive emotions for Yale students.

Yale press release

We welcome submissions across all genres and styles: documentary, portrait, street, experimental, still life, film, digital, color, black and white - whatever reflects your point of view. Selected works will be printed and displayed in a dedicated on-campus exhibition space, with an opening reception to celebrate all contributors.

Panelists will introduce themselves, share how they got from Georgia Tech to where they are now, and take questions from the audience. Afterward, the group splits into tables by field, so you can sit down with alumni in the areas you’re most interested in and keep the conversation going. Dinner will be served during the table discussions.

Whether you’re exploring career paths, looking for advice on internships and recruiting, or just curious how alumni navigated their first years after graduation, this is a chance to hear directly from people who’ve been in your shoes.

This event is open to all GT students!

Location: Bill Moore Student Success Center President's Suite
Time: 6:30-8PM

From VRChat to multiplayer games, come share your experiences and discuss what works, what doesn’t, and how we connect in the virtual world. We’ll also discuss AI in VRChat and the debates surrounding its use. Afterwards, we’ll jump into a philosophy corner and then to project work!

Hear directly from Howard Hughes Medical Institute (HHMI) program leads about research support opportunities for biological and biomedical scientists at every career stage, from undergraduates to faculty. Program leads will answer your questions live. Open to faculty, postdocs, Ph.D. students, and undergrads. 

Panelists:

• Joshua Hall - Lead Senior Director, Scientific Program Officer
• José Faraldo-Gómez - Senior Director, Scientific Program Officer
• Michelle Reniere - Senior Director, Scientific Officer

Following the presentation, three current HHMI scientists representing the Freeman Hrabowski Scholars and Hanna H. Gray Fellows programs will join the discussion to share their experiences and answer audience questions:

• HaoSheng Sun - Freeman Hrabowski Scholar at The University of Alabama at Birmingham
• Christopher Medina - Hanna Gray Faculty Fellow at the Moffitt Cancer Center
• Shan Meltzer - Hanna Gray Faculty Fellow at Vanderbilt University School of Medicine

How is artificial intelligence changing the way knowledge is created? Examine AI's growing role in academic research, including how researchers use emerging tools to accelerate discovery, approach complex questions, and navigate the ethical and practical implications of AI-assisted scholarship.

Co-sponsored by the Michael E. Tennenbaum Family Institute for Technology and Civic Leadership.

Panelists

Sebastian Porsdam Mann, Assistant Professor, CeBIL, University of Copenhagen

Sebastian Porsdam Mann is an Assistant Professor at the University of Copenhagen’s Center for Advanced Studies in Bioscience Innovation Law. With training spanning philosophy, neuroscience, applied ethics, and international human rights law, his research examines how emerging technologies can advance scientific progress while navigating ethical and regulatory challenges. His work has directly explored generative AI in academic writing and idea generation, as well as AI's potential role in the future of academic peer review, positioning him to address both the opportunities and risks AI creates throughout the research process. 

Sarah H. Salter, Director of the Writing Program & Professor of Pedagogy, Emory University

Sarah H. Salter is a Teaching Professor and Director of the Writing Program at Emory University whose research focuses on American literature, periodical studies, collaboration, and digital humanities. Her scholarship has included interdisciplinary work using machine-learning methods to analyze the visual structures of historical newspapers, combining literary study, library science, and computational approaches. Her perspective brings an important humanities dimension to the conversation about how computational tools and AI are changing research methods, scholarly interpretation, writing, and knowledge production.

Speaker: Dr. Carlos A. Meriles - City College of New York

Host: Chunhui Du

Abstract: Color centers in diamond offer a versatile platform for quantum information processing, combining long-lived spin states with optical interfaces. While most efforts have focused on coherent spin manipulation and photon-mediated interactions, the ability to control and probe charge at the individual-defect level opens complementary opportunities for building scalable quantum architectures.

In this talk, I will discuss recent advances in the optical interrogation and charge-state manipulation of nitrogen-vacancy centers in diamond. I will first describe how resonant optical excitation enables reversible charge control and nondestructive readout with spatial resolution well below the diffraction limit. I will then show how correlated spectral fluctuations reveal the local electrostatic environment, allowing us to reconstruct the three-dimensional arrangement of neighboring defects. Finally, I will discuss experiments resolving individual carrier-capture events and their dependence on the surrounding charge environment.

Our results illustrate how optical spectroscopy can provide access to charge dynamics at the single-defect level, opening new possibilities for controlling interactions between solid-state quantum systems.

Short Bio: Carlos Meriles was born and raised in Córdoba , Argentina . He obtained his Ph.D in physics at FaMAF – the mathematics, physics and astronomy division of the Universidad Nacional de Córdoba – under the supervision of Prof. A.H. Brunetti. His doctoral studies focused on the use of Nuclear Quadrupole Resonance (NQR) to probe structural and dynamical disorder in organic crystals. After graduating in 2000, he joined the group of Prof. Alexander Pines at the University of California , Berkeley to work on Nuclear Magnetic Resonance (NMR), his present field of research. In 2004, he joined the Department of Physics at City College where he now teaches as a Professor.

Advisors: Josh Kacher and Aaron Stebner

will defend a doctoral thesis entitled,

Influence of Heterogeneous Particulate Reinforcements on Deformation and Damage in Aluminum Composites Formed by Reactive Additive Manufacturing

On

Thursday, November 12, 2026

12 pm – 2 pm

-------------

oGuggenheim Room 246

or virtually via Teams:

Join: https://teams.microsoft.com/meet/248266455555984?p=93WUJ1utqZm4Pd8eD9

Meeting ID: 248 266 455 555 984

                                                                                            Passcode: zZ9Kw7aL

                                                      Passcode: zZ9Kw7aL

Committee:

Prof. Josh Kacher - School of Materials Science and Engineering (advisor)

Prof. Aaron Stebner - School of Materials Science and Engineering (co-advisor)

Prof. Naresh Thadhani - School of Materials Science and Engineering

Prof. Richard Neu - Woodruff School of Mechanical Engineering

Prof. Ashley Spear - Department of Mechanical Engineering, University of Utah

Abstract

   Laser powder bed fusion (PBF-LB) enables the fabrication of complex parts, but aluminum alloys manufactured through this process remain susceptible to solidification cracking and anisotropic mechanical properties associated with columnar grain growth. Reactive additive manufacturing (RAM) is a method of promoting grain refinement and introducing reinforcement particles through in situ chemical reactions between reactive powder constituents during processing. However, incomplete reactions contribute to heterogeneous microstructures containing variations in particle size, shape, composition, and spatial distribution, which can influence deformation and fracture behavior. Understanding these effects is important for identifying potential damage initiation mechanisms and their effect on tensile properties. In this work, I investigated the relationships between heterogeneous reinforcement particles, deformation and fracture, and mechanical properties in Al-Ti-B₄C composites formed through RAM.

First, in situ scanning electron microscopy (SEM) tensile testing was coupled with digital image correlation (DIC) to analyze local strain distributions as they relate to the reinforcement particles over different volume fractions. Machine learning approaches were then employed to quantify the relative importance of particle geometry and composition in predicting particle crack initiation and propagation under uniaxial tension. Finally, the laser-material interaction time during PBF-LB was progressively increased to study the influence of increasing thermal exposure on the progression of the in situ chemical reactions, porosity, and subsequent tensile properties. These studies provide a foundation for the microstructural considerations necessary in developing PBF-LB processing strategies that balance the benefits of in situ reinforcement particle formation against the introduction of features which may compromise tensile performance.

Ph.D. Thesis Defense Announcement

ADVANCING GLOBAL ECO-HYDROLOGICAL MONITORING: AN INTEGRATED PHYSICAL MODELING FRAMEWORK FOR MULTI-SENSOR REMOTE SENSING

By: Kyeungwoo Cho

SEB 122
https://teams.microsoft.com/meet/29605540773959?p=jXeeOpEq9oF8aZVsVa

2026-11-19 2:00 PM EST

Rafael Bras, Aris Georgakakos, Yi Deng, Robinson Negron-Juarez

Passive microwave remote sensing provides extensive spatial coverage of terrestrial water dynamics, yet accurate retrieval of hydrological variables remains challenging because of the complex interactions among soil, vegetation, and snow within observed microwave signals. This dissertation investigates a unified physical modeling framework based on the maximum entropy production (MEP) principle to improve the interpretation of microwave observations across diverse terrestrial environments. Integrating radiative transfer theory with thermodynamically constrained energy partitioning, the MEP framework enables the simultaneous retrieval of soil moisture (SM) and vegetation water content (VWC). Evaluations across diverse ecosystems demonstrate strong agreement between retrieved SM and ground observations, with correlation coefficients ranging from 0.74 to 0.91 at three Soil Moisture Active Passive (SMAP) core validation sites. Further applications reveal seasonal relationships between satellite-derived VWC and live fuel moisture content in the western United States, while analyses of the 2023–2024 Amazon drought identify widespread soil moisture deficits accompanied by spatially heterogeneous vegetation responses. An extension of the framework to a coupled snow and soil system further demonstrates the potential for estimating snow water equivalent (SWE) from microwave observations at multiple frequencies, although retrieval performance depends on prescribed snow properties and calibrated model parameters. Collectively, these findings demonstrate the capabilities and limitations of MEP-based retrievals and establish a physically interpretable foundation for integrated monitoring of terrestrial water dynamics across multiple satellite sensors.

Advisors: Naresh Thadhani and Josh Kacher

will propose a doctoral thesis entitled,

Investigating the Effects of Phase Transformation and Loading Conditions on Shock Dissipation and Spall Failure in
 Titanium Alloys

On

Monday, November 23, 2026

12 pm – 2 pm

MRDC 4404

Teams Meeting Link: Georgia Hilburn's Thesis Proposal

Abstract

This work will establish a fundamental understanding of the relationships between initial microstructure, shock-loading history, and martensitic phase transformation, and their effects on shock dissipation and subsequent spall failure in titanium alloys. Plate-impact experiments coupled with time-resolved interferometry and post-mortem characterization will investigate additively manufactured (AM) and wrought metastable β-Ti5553 and dual-phase Ti64 alloys.

Heat treatments of Ti5553 will vary the α-phase fraction, β-phase stability, and phase morphology to assess the contribution of the β → α′′ transformation on shock-wave attenuation across different initial microstructures. The influence of varying microstructures and impact conditions above and below the phase-transformation threshold on wave propagation, tensile loading histories, and damage development will be investigated in Ti5553 alloy. Complementary multi-sample target configurations and wedged-impact experiments on Ti64 will investigate the individual and coupled effects of peak stress, strain rate, and tensile-pulse duration on spall kinetics.

The central hypothesis is that transformation-associated energy dissipation modifies the tensile loading path and can offset the damage-promoting effects of newly formed phase interfaces, producing measurable changes in spall strength and failure mode. Varied impact orientations relative to the AM build direction is expected to influence this competition through its effects on phase transformation and spall damage. Variations in loading history will further govern void nucleation, growth, and coalescence, providing a basis for developing and evaluating kinetic descriptions of damage evolution.

The expected outcome is a mechanistic understanding of how varying microstructures and phase transformation control shock propagation, mitigation, and spall failure. These relationships are necessary for informing physically motivated models of dynamic damage and guide the design of metastable titanium alloys with improved failure resistance.   

Committee:

Prof. Naresh Thadhani - School of Materials Science and Engineering (advisor)

Prof. Josh Kacher - School of Materials Science and Engineering (co-advisor)

Prof. Preet Singh - School of Materials Science and Engineering

Dr. Paul Specht - Sandia National Laboratories

Dr. Kaila Bertsch - Lawrence Livermore National Laboratories

will propose a doctoral thesis entitled,

Development of Novel Electrode and Electrolyte Materials for Proton-Conducting Solid Oxide Cells

On

Monday, November 30 at 2:30 p.m.
J. Erskine Love Room 210

Committee
            Prof. Meilin Liu – School of Materials Science and Engineering (advisor)
            Prof. Preet Singh – School of Materials Science and Engineering
            Prof. Hamid Garmestani – School of Materials Science and Engineering

            Prof. Faisal Alamgir – School of Materials Science and Engineering

            Prof. Nian Liu – School of Chemical and Biomolecular Engineering

Abstract

Proton-conducting solid oxide cells (P-SOCs) offer great potential for highly efficient electricity generation and fuel production. However, their widespread application is impeded by insufficient air-electrode activity and durability under practical operating conditions, particularly in H2O- and CO2-rich environments. This dissertation aims to address these challenges through the development of new electrolyte and air-electrode materials. rational design, synthesis, and mechanistic investigation of, and multi-doped proton-conducting electrolytes.

The first objective is to develop triple ionic-electronic conducting (TIEC) air electrode materials with high catalytic activity for oxygen reduction and evolution while maintaining adequate stability against high concentrations of steam and CO2. BaCoO3-δ (BCO)-based TIEC air electrodes will be optimized through B-site substitution with cations commonly used in proton-conducting electrolytes, such as Hf, Sn, Y, and Yb, to promote coupled electronic, oxygen-ion, and proton transport while improving structural stability under high-steam conditions. Electrochemical activity and durability will be evaluated in symmetrical cells and single cells. The oxygen reduction reaction (ORR) kinetic will be investigated using distribution of relaxation times (DRT) analysis and in situ Raman spectroscopy, while oxygen and proton transport will be characterized using electrical conductivity relaxation (ECR) measurements.

The second objective is to enhance chemical stability of electrolytes against H2O and CO2 while maintaining high proton conductivity. Multi-dopant strategies integrating donor, isovalent, and acceptor dopants will be explored to mitigate the conductivity-stability trade-off. The electrolyte will be characterized using electrochemical impedance spectroscopy (EIS), potentiometry, hydrogen-deuterium isotope exchange, thermogravimetric analysis (TGA), and steam/CO2 exposure tests to assess proton transport, hydration behavior, and chemical stability. Promising compositions will be evaluated for compatibility with Ni-based fuel electrodes and then incorporated into P-SOCs to assess cell performance and durability.

Abstract: We develop a test for equality of regression functions across two populations that allows unequal covariate distributions and uses flexible modern learners for regression estimation. We target the regression contrast through pseudo-residuals, which we construct by multiplying residuals from the pooled outcome regression by the difference between the population indicator and its propensity. Under overlap, the conditional mean of the proposed pseudo-residuals identifies the regression contrast. Furthermore, the pseudo-residuals satisfy Neyman orthogonality, which removes the first-order population bias from nuisance estimation and allows us to test the regression contrast under weaker nuisance estimation rate requirements. To construct our test statistic, we embed the conditional moment of the pseudo-residuals in a reproducing kernel Hilbert space (RKHS), leading to a simple closed form expression and calibrate its empirical norm using a Gaussian multiplier bootstrap. We establish a finite-sample calibration bound that separates oracle Gaussian approximation, nuisance estimation, and covariance estimation errors. Our main result gives conditions under which we can use the same observations for both nuisance fitting and testing, yielding valid and powerful inference without sample splitting. In a fixed Gaussian regression model, we establish matching lower and upper bounds for the minimax separation order (n^{-1/2}) in the RKHS discrepancy and show that the proposed test attains this order. We conduct extensive numerical studies to examine size and power across learners and demonstrate power gains over a single sample split and other competitors and illustrate the test on an exploratory application to negotiation dialogues for regression comparison with text covariates.
 

Bio: Paromita Dubey is an assistant professor in the DSO department at the USC Marshall School of Business. Her research centers around developing statistical methods and inference for non-Euclidean data, examples being distribution and network valued data. She is also working on addressing challenges in the analysis of dynamic time-evolving data, particularly in non-Euclidean and high dimensional settings. The goal of her research is to create easily implementable and theory-backed off the shelf methods for practitioners. She received the Zumberge Individual Research Award in 2022.

Entrance is strictly from 7:30-8:00 so plan accordingly! (If you don’t show up within this time, Netherworld will not grant you entrance.) 

Tickets out NOW!

Host: Prof. John Wise

Title: Spectral survey instrument reveals notes of discord with cosmic sound waves: DESI overview, BAO measurements and their cosmological implications

Abstract:

We have known that the Universe is vast and expands with acceleration for many years, yet the physical origin of this acceleration remains an open question. While the concordance cosmological model, ΛCDM, has been remarkably successful, recent cutting-edge observational results suggest that dark energy may evolve with time or that the standard model might not keep up with all the highest-quality data.

In this talk, I will introduce baryon acoustic oscillations (BAO), relic sound waves from the early Universe whose imprint in the large-scale distribution of galaxies provides a robust standard ruler for measuring the cosmic expansion history. I will then present an overview of the Dark Energy Spectroscopic Instrument (DESI), a 5000-object fiber spectrograph with robotic positioning highly optimized for conducting a spectroscopic galaxy survey at a record-breaking speed and scale.

One of DESI's main objectives is to measure BAO across cosmic time. I will discuss the methodology and results of the recent BAO analysis from DESI Data Release 2, based on the first three years of observations. Combining these measurements with cosmic microwave background and Type Ia supernova data yields intriguing indications of departures from the standard ΛCDM model, including possible evidence for evolving dark energy.

I will conclude with an outlook on upcoming DESI cosmological analyses, including full-shape clustering measurements from Data Release 2 and BAO results from the 5-year Data Release 3.

BioE PhD Proposal Presentation

3:30pm, Friday, November 13th, 2026

Location: Suddath Room, IBB 1128

Zoom Link: https://gatech.zoom.us/j/98553969898?pwd=U3vz1wmih9cpJJ6DUllWQdLeBrRwbu.1

Advisor: Dr. Melissa Kemp (Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology & Emory University)

Committee Members:

Dr. Facundo Fernandez (School of Chemistry & Biochemistry, Georgia Institute of Technology)

Dr. Randolph Ashton (Department of Biomedical Engineering, University of Wisconsin-Madison)

Dr. Saurabh Sinha (Wallace H. Coulter Department of Biomedical Engineering & H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology & Emory University)

Dr. Shuichi Takayama (Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology & Emory University)

INVESTIGATING EARLY NEURAL DEVELOPMENT THROUGH SPATIOTEMPORAL METABOLIC CHARACTERIZATION OF HUMAN NEURAL ROSETTES

Neural tube formation is a fundamental process during early embryonic development that establishes the brain and spinal cord. Disruption of this process can result in neural tube defects, including spina bifida and anencephaly. Although cellular metabolism is increasingly recognized as an important regulator of cell fate and tissue organization, the spatial and metabolic changes accompanying early neural development remain poorly understood. Neural rosettes, which recapitulate key structural features of the developing neural tube, provide an in vitro model for investigating the relationship between cellular organization and metabolic activity. While conventional fluorescence microscopy can characterize neural rosette structures, it does not provide direct information about their spatial molecular composition. Additionally, bulk metabolomic approaches lack the spatial resolution needed to distinguish molecular differences between lumen-associated regions and surrounding cells. This research seeks to address these limitations by integrating matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) with fluorescence microscopy to characterize spatial and temporal lipid-associated metabolic changes during neural rosette development. The central hypothesis is that neural rosette formation is accompanied by spatially organized lipid remodeling that changes throughout differentiation and responds to alterations in the cellular microenvironment. Aim 1 will develop and validate a computational framework using dimensionality reduction and unsupervised clustering to identify spatially distinct metabolic states and evaluate their correspondence with neural rosette structures. Aim 2 will investigate how exogenous lipid exposure influences the temporal metabolic and structural development of neural rosettes. Aim 3 will characterize lipid remodeling under constrained and unconstrained differentiation conditions using micropatterned cultures to determine how geometric confinement influences the spatial organization and progression of metabolic states. These studies will establish an integrated approach for linking spatial metabolic organization with neural rosette development, providing new insight into lipid-associated processes during early human neural differentiation.

Learn more and register at https://oit.gatech.edu/ai/forge. 

Advisor: Dr. Hang Lu (School of Chemical and Biomolecular Engineering)

Committee Members:
Dr. Patrick McGrath (School of Biological Sciences)
Dr. Gordon Berman (Department of Biology, Emory University)
Dr. Simon Sponberg (School of Physics)
Dr. Eva Dyer (School of Engineering and Applied Science, University of Pennsylvania)

To eat or not to eat: A Computational Framework for Modeling Feeding and Exploratory Behavior in Aging C. elegans
To eat, or not to eat? This seemingly simple question reflects the complex coordination of sensory, motor, and physiological processes that allow organisms to locate and consume food. Understanding how feeding-related behaviors change with food conditions and age requires methods capable of capturing subtle movements and their organization over time. In this work, I develop computational approaches for quantifying feeding and exploratory behaviors in freely moving Caenorhabditis elegans and investigate how feeding history, sensory information, and aging influence these behaviors.
First, I introduce EZ-FRCNN and PumpKin, which enable automated detection of biological structures and measurement of pharyngeal pumping without immobilization or genetic modification. Next, I present CastKin, a method for isolating exploratory head movements from broader body motion. Using these tools, I demonstrate that feeding-related behaviors vary with feeding history and pumping ability, while both olfactory and mechanosensory cues independently influence head casting.
Finally, I develop a head-centered Markov model of foraging behavior and apply it to over 1,400 videos of well-fed and starved worms across the first 14 days of adulthood. This analysis reveals previously unresolved behavioral subtypes and age- and feeding-dependent differences in how worms occupy and transition between behavioral states.
Together, these findings demonstrate the value of measuring fine-scale behaviors and their temporal dynamics, providing new tools and insights for understanding how feeding and exploration are regulated throughout adulthood.
 

Learn more and register at https://oit.gatech.edu/ai/forge. 

Learn more and register at https://oit.gatech.edu/ai/forge. 

Microsoft Teams meeting: https://teams.microsoft.com/meet/2281380832970516?p=FiJzIUWDYE7NffNMFY
Meeting ID: 228 138 083 297 051 6 
Passcode: XX2fj3k7

About the FORGE AI Summit
The FORGE AI Summit, Nov. 16-18, will offer three days of learning and exploration designed to build understanding, showcase what’s possible, and create opportunities to put new AI ideas into action. From Oct. 26 through Nov. 13, the Georgia Tech community can also participate in pre-summit education sessions.

You can register and learn more about the FORGE AI Summit and pre-summit education sessions by visiting b.gatech.edu/FORGESummit.

Microsoft Teams meeting: https://teams.microsoft.com/meet/2528060701418972?p=imLcMYZuih82bOnFl7
Meeting ID: 252 806 070 141 897 2 
Passcode: 3ZB6WV3o

About the FORGE AI Summit
The FORGE AI Summit, Nov. 16-18, will offer three days of learning and exploration designed to build understanding, showcase what’s possible, and create opportunities to put new AI ideas into action. From Oct. 26 through Nov. 13, the Georgia Tech community can also participate in pre-summit education sessions.

You can register and learn more about the FORGE AI Summit and pre-summit education sessions by visiting b.gatech.edu/FORGESummit.

Date: Monday, November 16, 2026
Time: 9:00 AM – 11:00 AM ET
Location: George Tower 602 (Physical Internet Center), and virtual via Microsoft Teams
Meeting link: https://teams.microsoft.com/meet/289903746942780?p=1oJY14wMv5EhJQQbBY (Meeting ID: 289 903 746 942 780, Passcode: 4DZ3JX6Q)

Sahrish Jaleel Shaikh
Industrial Engineering PhD Student
H. Milton Stewart School of Industrial and Systems Engineering
Georgia Institute of Technology

Committee

1. Dr. Benoit Montreuil (Advisor), H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology
2. Dr. Chelsea (Chip) White, H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology
3. Dr. Valerie Thomas, H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology
4. Dr. Walid Klibi, Centre of Excellence for Supply Chain Innovation and Transportation, KEDGE Business School
5. Dr. Rod Franklin, Professor Emeritus of Logistics, Kühne Logistics University (KLU)

Abstract
Freight transportation moves a large share of its capacity without freight, and the Physical Internet was proposed as a response: an open logistics system in which modular containers move through shared hubs under common protocols. Simulation studies show strong benefits, but implementation has been limited, because a full Physical Internet needs many organizations to change at once, because each hub must decide without a global view of the network, and because competing organizations will not share all of their operational information. This dissertation addresses these challenges in three research threads, progressing from a single organization to an ecosystem of organizations. The first thread defines the Physical Intranet, the application of Physical Internet principles within a single organization. It develops an operational framework of modular containerization, tiered mesh networks, continuous sorting and frequency-based transportation, and validates it on the network of a major North American parcel provider. The second thread focuses on the two decisions every hub in an open network must take on its own: where each container goes next and when the freight waiting on a lane should leave. It designs routing and dispatch protocols that make these decisions from what the hub can observe. The third thread introduces a framework of graduated data sovereignty, in which networks commit to verifiable outcomes at their boundaries while choosing how much operational data to disclose. It uses the framework to identify what information each routing and dispatch protocol needs from other organizations. Together, the results indicate that much of what the Physical Internet promises is available to a single provider, that hubs deciding locally come close to central coordination, and that networks can capture most of the benefit while disclosing little.

Microsoft Teams meeting: https://teams.microsoft.com/meet/2500181329963597?p=0OPpDWcca3wf3LwDrY
Meeting ID: 250 018 132 996 359 7
Passcode: He7dT3rs

About the FORGE AI Summit
The FORGE AI Summit, Nov. 16-18, will offer three days of learning and exploration designed to build understanding, showcase what’s possible, and create opportunities to put new AI ideas into action. From Oct. 26 through Nov. 13, the Georgia Tech community can also participate in pre-summit education sessions.

You can register and learn more about the FORGE AI Summit and pre-summit education sessions by visiting b.gatech.edu/FORGESummit.

Advisor: Dr. Dimitri Mavris

Milestone: PhD Thesis Final Examination (Defense)

Degree Program: Aerospace Engineering

Title: A Framework for Concurrent Design and Fleet Operations Optimization of Unmanned Aerial Vehicle-Based Last-Mile Delivery Networks

Abstract: Last-mile delivery is one of the most costly and operationally complex stages of parcel distribution. Unmanned Aerial Vehicles (UAVs) offer a promising complement to ground delivery by bypassing road congestion and providing rapid service for lightweight packages. However, their limited payload capacity, battery energy, and endurance constrain their operational capabilities. A delivery network also requires aircraft to perform a wide range of missions with different distances, payloads, and energy requirements. Conventional aircraft design typically sizes a vehicle for a representative mission, while logistics planners optimize delivery networks around an existing or assumed aircraft. Decoupling aircraft design from delivery network planning can lead to undersized or oversized aircraft, compromising network feasibility, increasing fleet requirements, and raising overall delivery costs. This dissertation develops Concurrent Optimization of Network, Design, and Operations (CONDOR), a framework that integrates Conceptual Vehicle Design (CVD) and Fleet-Level Operations (FLO) planning for UAV-based last-mile delivery. The framework is developed and evaluated through three research areas. The first formulates a Location-Fleet-Routing Problem (LFRP) that jointly optimizes depot placement, fleet sizing, and delivery routes and evaluates its advantages over sequential planning. The second extends fleet-level planning to uncertain demand and examines the trade-offs between cost, infrastructure investment, and service reliability. The third develops a multidisciplinary design optimization (MDO) architecture that couples aircraft design with delivery network planning and evaluates how aircraft characteristics and operational requirements influence system performance. The results demonstrate that coordinating depot, fleet, and routing decisions can reduce operating costs and improve resource utilization. Planning under demand uncertainty reveals the additional infrastructure required to achieve reliable service and the limits of feasible coverage. The overarching demonstration shows that concurrent aircraft and network optimization can achieve lower delivery costs than conventional design-first and network-first approaches while maintaining the same service coverage. The findings establish a methodology for evaluating aircraft designs through their effects on network feasibility, fleet requirements, and total system cost, providing a foundation for more efficient UAV-based delivery systems. 

Date and time: 2026-11-10, 12:00 p.m. EST

Location: Collaborative Visualization Environment (CoVE), Weber Space Science and Technology Building (SST II) And Teams Meeting: https://teams.microsoft.com/meet/246945013645082?p=9gLD2KJWMu2cAm9SR5

Committee:
Dr. Dimitri Mavris (advisor), School of Aerospace Engineering
Dr. Daniel P. Schrage, School of Aerospace Engineering
Dr. Kai A. James , School of Aerospace Engineering
Dr. Raphaël H. Gautier , School of Aerospace Engineering
Dr. Marco Ceze , Prime Air, Amazon

 

BioE PhD Proposal Presentation

1:30 PM, Monday November 16th, 2026

Location: EBB 5029

Zoom Link: https://gatech.zoom.us/j/97816529623

Advisor: Dr. Mark Styczynski (School of Chemical and Biomolecular Engineering)

Committee Members:

Dr. John Blazeck (School of Chemical and Biomolecular Engineering)

Dr. Julie Champion (School of Chemical and Biomolecular Engineering)

Dr. Brian Hammer (School of Biological Sciences)

Dr. Valeria Milam (School of Materials Science and Engineering)

Engineering Modular Target Recognition for Cell-Free Biosensing in One-Pot PURE

Point-of-care (POC) diagnostics benefit from low-cost biosensing platforms that offer simple readouts and can be readily adapted to detect diverse analytes. Cell-free protein synthesis (CFPS) offers a programmable environment for combining molecular recognition elements and genetic circuits for biosensing. One such approach, the T7 RNA polymerase-Linked ImmunoSensing Assay (TLISA), uses nanobodies to reassemble split polymerase fragments upon simultaneous binding to a target protein, driving reporter expression. A more sensitive version of TLISA has been implemented in PURE, a CFPS system reconstituted from purified components. However, commercial PURE costs $1,110 per mL of reaction, making biosensor optimization expensive and limits affordability for POC deployment. One-pot PURE simplifies preparation by jointly purifying the 36 PURE proteins from a co-culture, reducing costs to $90 per mL. I hypothesize that one-pot PURE will improve TLISA’s affordability, while interchangeable nucleic acid recognition elements will enable modular detection of protein and nucleic acid targets. These elements would address a separate limitation: adapting TLISA to new targets requires cloning new affinity domain–polymerase fusions, slowing sensor development. First, I will establish and optimize one-pot PURE in-house and validate its support for TLISA sensing. Second, I will engineer an RNA hairpin-aptamer TLISA architecture that tethers target-binding aptamers to split polymerase fragments through specific protein-RNA interactions. This design would allow target specificity to be changed by switching out aptamers while retaining the same polymerase fusion proteins, reducing the need for cloning. Third, I will adapt TLISA to detect microRNA, extending the platform to sense nucleic acid biomarkers. Together, this work aims to establish an affordable, modular cell-free biosensing platform that accelerates sensor development and supports future POC diagnostic applications.

Zoom: Link
Meeting ID: 242 927 8475
Passcode: 423370

Committee:
Judy Hoffman (Advisor) - School of Information and Computer Science, UC Irvine
Danfei Xu (Advisor) - School of Interactive Computing, Georgia Tech
Siddharth Karamcheti - School of Interactive Computing, Georgia Tech
Dhruv Batra - Chief Scientist, Yutori
Sergey Levine - Department of Electrical Engineering and Computer Sciences, UC Berkeley

Abstract: Robot foundation models have begun to solve dexterous manipulation tasks with sparks of broad open world generalization. The most promising results are driven by large scale teleoperation, but this data source cannot scale as fast as other sources of data, which has spawned considerable work to learn from human videos. Despite progress here, the human to robot gap makes it difficult to directly transfer capabilities in human video to robot policies, forcing most prior works to introduce some inductive bias. In this thesis, we argue that co-design of training algorithms and data collection systems enables us to directly transfer capabilities from human experience. We begin the thesis by proposing embodied human data, defined as egocentric video paired with 3D hand and head pose tracking, as well as a system to collect it, and an algorithm to train with it. Second, we collect the largest dataset of embodied human data, enabling both controlled scientific exploration as well as large scale training. Third, we find that human to robot transfer emerges in foundation models pretrained across many tasks and embodiments.

Date: Monday, November 16, 2026
Time: 2:00 PM – 4:00 PM ET
Location: Virtual via Microsoft Teams
Meeting link: https://teams.microsoft.com/meet/297219506494396?p=jBxsWTUiavcTjnCxMG (Meeting ID: 297 219 506 494 396, Passcode: cp9zJ6Ty)

Yujia Xu
Machine Learning PhD Student
H. Milton Stewart School of Industrial and Systems Engineering
Georgia Institute of Technology

Committee
1 Dr. Benoit Montreuil (Advisor), H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology
2 Dr. Walid Klibi, Centre of Excellence for Supply Chain Innovation and Transportation, Kedge Business School
3 Dr. Yao Xie, H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology
4 Dr. Valerie Thomas, H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology
5 Dr. Mathieu Dahan, H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology

Abstract
Hyperconnected logistics networks comprise interconnected hubs and organizations whose resources can be shared, so that capacity committed at one location can serve demand arising elsewhere in the network. Resource pooling therefore extends capacity planning under uncertainty beyond individual facilities and firms to include the coordination of shared resources and the discovery of outside capacity. This dissertation develops stochastic optimization, decomposition, and learning methods for resource-pooling decisions, from deploying known resources to expanding the pool beyond the network. We first formulate the dynamic deployment of pooled workers and autonomous mobile robots across urban parcel hubs as a rolling-horizon two-stage stochastic program that respects worker shift and location preferences, and solve large-scale instances with an accelerated Benders decomposition and clustering-based relax-and-fix heuristics. Then we design battery swapping and charging networks that support long-haul truck electrification by keeping swapping close to freight demand while allowing charging to shift to grid-suitable hubs. We model this design problem as a multi-period, two-stage stochastic location–inventory–fleet program and solve it with a Lovász-cut Benders decomposition that exploits the submodularity of the battery recourse function. Lastly, we introduce open capacity pooling, in which a disrupted network discovers capacity held by previously unengaged providers through a closed loop of coordination, search, and admission under a limited budget of requests for quotation. Each request targets a capacity gap remaining after incumbent firms coordinate through the alternating direction method of multipliers (ADMM) without sharing their operating models, and is directed to a provider ranked by a machine-learning layer built on directory records and large language model readings of provider self-descriptions. We evaluate the proposed methods in computational studies grounded in real-world data from industry partners and public sources. The findings show how resource pooling helps logistics networks respond to uncertain demand and disruptions by coordinating shared resources and incorporating capacity from outside providers.

Presenter:
Samantha Yeligar
Associate Professor of Medicine
Emory University School of Medicine / Atlanta VA Medical Center

Ask questions about the CoC's newest program, "Technology Design and Ethics in India," a 6-week program (May 17 - June 25, 2027), with 4 weeks of online instruction followed by 2 weeks of continued GT instruction in Mumbai, India. Drop in for a few minutes to ask your question or stay the whole hour and hear what other students are asking. 

Program Courses:

The 2 foundational Computer Science courses of the program will be taught by Georgia Tech CS faculty and Program Director, Naveena Karusala.

• CS 4001 – Computing & Society (Ethics Credit) - Students will critically examine how issues around technology, such as globalization, bias, privacy, and others, affect people worldwide.

• CS 3751 – Intro to User Interface Design - Students will explore culturally situated approaches to design and, through the course project, create an inventive design artifact.

No questions and ready to go? Apply HERE. Email contact: indiaprogram@cc.gatech.edu. To learn more about available virtual meeting opportunities, visit cc.gatech.edu/india.

Doctor of Philosophy in Physics 

School of Physics Dissertation Defense

Jiahao Chen
Dr. Dragomir Davidovic, School of Physics, Georgia Institute of Technology (Advisor)

Numerical Approach to the Time-Convolutionless (TCL) Master Equation

Date: Tuesday, November 10, 2026
Time: 8:30 a.m. EST
Location: Howey N201/202
Virtual: Join the Microsoft Teams meeting / Meeting ID: 273 524 787 663 512 4 / Passcode: ub6Fd2TB

Thesis Committee:

Dr. Brian Kennedy, School of Physics, Georgia Institute of Technology

Dr. Martin Mourigal, School of Physics, Georgia Institute of Technology

Dr. Xueda Wen, School of Physics, Georgia Institute of Technology

Dr. Qi Tang, School of Computational Science and Engineering, Georgia Institute of Technology

Abstract:

Open quantum systems are quantum systems that interact with their environment. The time-convolutionless (TCL) master equation provides a formally exact, time-local description of their dynamics. However, the computational cost of evaluating higher-order terms often limits practical applications to the second-order approximation. We develop systematic numerical methods that reduce the temporal complexity to O(N log N) for TCL4 and O(N log² N) for TCL6, where N is the number of time points. We extend the construction to higher orders and assess its accuracy using spin–boson benchmarks. We also investigate the convergence limits of the TCL expansion and explore resummation methods for extending accurate predictions beyond direct perturbative truncation.

Abstract: 

Rare earth elements (REEs) are essential to advanced and clean energy technologies. Sustainable REE resource development requires both a fundamental understanding of the geochemical processes governing resource formation and advances in environmentally compatible extraction and recovery strategies. Highly weathered, clay-rich deposits represent important natural REE resources. However, the molecular-scale mechanisms by which solution chemistry, mineral surfaces, and naturally occurring ligands regulate REE behavior remain incompletely understood. Meanwhile, conventional REE extraction and recovery often involve high chemical and energy consumption and substantial waste generation. Bio-assisted approaches, including fungal bioleaching, offer promising alternatives that operate under milder conditions and may reduce chemical consumption and environmental impacts. However, the mechanistic links among cultivation conditions, microbial metabolism, and REE mobilization remain poorly understood.

This dissertation investigates the key geochemical controls on REE speciation, mobility, secondary phase formation during chemical weathering, and explores fungal bioleaching as a promising approach to REE extraction. Controlled laboratory experiments integrate solid-state nuclear magnetic resonance spectroscopy, synchrotron X-ray absorption spectroscopy, microscopy, aqueous-phase analyses, and extracellular metabolomics to connect molecular-scale reaction mechanisms controlling REE behavior at mineral-water interfaces and extraction performance. Using yttrium (Y) as a probe for heavy REEs, phosphate and citrate as representative ligands, kaolinite as a mineral substrate, this work identifies complementary mechanisms governing REE retention and release during weathering. Phosphate promotes Y immobilization at kaolinite-water interfaces through ternary surface complex and secondary Y-phosphate precipitation. In contrast, citrate can promote either Y retention or remobilization, depending on ligand concentration, solution pH, and reaction sequence. Reactant concentrations and solution pH further regulate the crystallinity of newly formed REE-bearing secondary phase. Extending this investigation to biologically mediated mobilization, experiments with Aspergillus niger and natural REE-bearing mineral sands suggest that initial pH act as a signaling cue shaping subsequent metabolic response and leaching trajectories. These findings suggest that cultivation conditions could be used to guide microbial metabolism and regulate the balance between REE dissolution and precipitation.

Together, this work advances the mechanistic understanding of REE speciation, mobility, redistribution, and secondary-phase formation in weathering systems and provides a basis for tuning bioleaching systems to achieve desired extraction outcomes.

Committee members: Dr. Yuanzhi Tang (Advisor), Dr. Martial Taillefert, Dr. Jennifer B. Glass, Dr. Christopher T. Reinhard, and Dr. Brian L. Phillips

Date & Time: Friday November 13th, 2:00 pm 

Location: ES&T L1114

Abstract:

Reliable geological carbon storage requires estimates of subsurface CO₂ saturation and pressure despite incomplete observations and uncertain reservoir properties. This dissertation develops uncertainty-aware Digital Shadows and Digital Twins that combine physics-based simulation with amortized Bayesian inference for monitoring evolving reservoir states.

The proposed framework uses ensembles propagated through multiphase flow, rock physics, seismic modeling, and imaging to train conditional normalizing flows that generate posterior samples from time-lapse seismic and well observations. The dissertation examines three extensions of this framework: multimodal monitoring, robustness to uncertain rock physics, and context-aware inference. Rock-physics augmentation improves reconstruction under tested model mismatch, while a three-phase extension enables joint estimation of oil and CO₂ saturation. A sensitivity-aware Digital Twin treats rock-physics assumptions as explicit context, supporting assumption-specific inference and what-if analysis. Finally, a collaborative case study demonstrates how probabilistic Digital Shadow estimates can be supplied to a separately developed injection-control workflow. Together, these studies provide a simulation-based framework for uncertainty-aware subsurface monitoring and decision support.

Defense date: November 6, 2026

Defense time: 10:30 am - 01:30 pm EST

Defense location: ES&T L1114

Advisor (Chair):

Felix Herrmann, Professor, Georgia Institute of Technology

Committee members:

Andrew Newman, Professor, Georgia Institute of Technology

Zhigang Peng, Professor, Georgia Institute of Technology

Sheng Dai, Professor, Georgia Institute of Technology

Olav Moyner, Senior Research Scientist, SINTEF, Norway

Wenhao Qin

Advisor:  Dr. Shiladitya Banerjee, School of Physics, Georgia Institute of Technology

Boundary Manifestations of Higher Berry Phases
Friday, November 6, 2026

10:00 a.m.      

Howey W401

Virtual: Wenhao Qin Thesis Proposal 

Committee Members:

Dr. JC Gumbart, School of Physics, Georgia Institute of Technology

Dr. Zeb Rocklin, School of Physics, Georgia Institute of Technology

Dr. Minsu Kim, Department of Physics, Emory University

Weaving a Web is the fourth in a series of five talks featuring artists on view in The Craft of Paper: Contemporary Takes on Tradition exhibit at the Robert C. Williams Museum of Papermaking.

In this virtual talk, we will be hearing from exhibiting artists, rhiannon skye tafoya and Julie VonDerVellen, both working the tradition of weaving into their paper art.

About the Exhibit: 

Curated by paper artist Michael Velliquette, The Craft of Paper: Contemporary Takes on Tradition explores modern interpretations of paper crafts, including cutting, folding, rolling, weaving, and layering. This exhibition aims to establish a unified language of paper craft techniques, while highlighting how contemporary artists are adapting these methods in innovative ways. It underscores the tools and techniques that have contributed to the recent resurgence of paper crafts, such as the development of high-quality, commercially available paper stocks. Central themes include the evolution of paper as an artistic medium, the fusion of traditional and modern techniques, and the diverse applications of paper in both art and design.

How to Register for this event:

Register here for workshop. Please email anna.doll@rbi.gatech.edu for questions or more information.

Mathematics is being transformed by the combination of AI, formal methods, and computer algebra (AI+FM+CA). The Center aims to catalyze and reorient this work at Georgia Tech and tighten its connections to mathematics by securing public and private funding, arranging compute resources, supporting team building, and organizing seminars, workshops, and tutorials. We also hope to openly discuss the AI-driven transformation of mathematical research, including concerns about the diverging value systems of academia and the companies working on AI for math.

The town hall is held as part of the AI for the Working Mathematician lunch discussion group, which meets on select Thursdays, 11:30am to 12:30pm in Skiles 006.

Join Justin Burns, PhD, Chief Innovation Officer and Vice President of Innovation and Entrepreneurship, Georgia Research Alliance, from noon to 1:00 PM via Zoom. Drawing on tech transfer, venture investing, and running GRA’s commercialization funding, Burns will walk through how funders size technical, market, and regulatory risk, which milestones change a valuation, and what to stop spending on before your next raise or license.

The Bench2Market Talks series is created to provide commercialization guidance to the university research community. For more details on the series, visit our website.

Whether you're looking to make new friends, explore student organizations, learn about MSA, or simply take a break and have a conversation, everyone is welcome. Stop by, say hello, grab a treat, and connect with us! Students are also welcome to join our community chats to stay informed about upcoming events, service opportunities, and ways to get involved.

Our goal is to foster a more welcoming, inclusive, and connected campus where students from all backgrounds feel comfortable building relationships, finding community, and getting involved.

All Georgia Tech students are welcome. Come as you are, bring a friend, and leave with a new connection!

Help EMSaT keep our CPR-athon going!!! 

Free food will be provided (usually pizza).

Open to all students!

You will get to try 茶道 (Sadou) firsthand using real bamboo whisks and traditional bowls to make your own matcha. We will also have plenty of Japanese tea snacks for everyone to enjoy!

No prior experience with tea etiquette is needed. Just come by, make some tea, and meet new people!

Event Details

• When: November 13, 5:00 PM - 7:00 PM
  
   
• Where: George/Scheller Tower 232

Committee Members:

Dr. David Myers (Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology & Emory University)

Dr. Tara Deans (Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology & Emory University)

Dr. Todd Sulchek (George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology)

Dr. Ovijit Chaudhuri (Department of Mechanical Engineering, Stanford University)

Dynamics of Blood Clot Viscoelasticity Mediates Clot Maturation via Platelet Mechanotransduction

Clot formation is an essential step in hemostasis and needs to be precisely regulated. Dysfunctional clot growth can manifest in different cardiovascular conditions. Excessive clotting can lead to thrombosis, while insufficient clotting can cause hemorrhagic disorders. While clot initiation has been extensively studied and well-characterized, clot cessation represents a critical gap in our understanding of the hemostatic process.  Clots represent a highly dynamic environment, rapidly transitioning from a liquid-like, viscous material to a solid-like elastic material. Platelets have been known to sense their mechanical environments, specifically substrate stiffness. I extend this reasoning to substrate viscoelasticity and hypothesize that clot viscoelasticity is a mechanical regulator of clot growth arrest. To test this, I will use tunable interpenetrating networks (IPNs) to investigate how substrate viscoelasticity regulates platelet activation at the molecular level, and how physiological shear stresses interact with clot mechanics to govern clot cessation. Aim 1 will establish physiological viscoelastic relevance of the IPNs by comparing their viscoelastic properties of the IPNs to whole blood clots and contrived spatial clot models. Aim 2 will be to investigate platelet response to viscoelastic microenvironments in both 2D and 3D environments and elucidate the molecular mechanisms that govern platelet activation through integrin-driven signaling pathways. Aim 3 will extend these findings to physiological flow conditions using microfluidic platforms, determining the synergistic effect of hemodynamic shear and clot viscoelasticity on platelet mechanotransduction. These studies will uncover viscoelastic mechanosensing as a previously unrecognized regulator of clot cessation, generating a new mechanobiological perspective for understanding hemostasis and thrombosis with direct implications for the development of precision therapies for both thrombotic and hemorrhagic disorders.