Abstract: Two-sided matching platforms, including labor markets, dating apps, accommodation services, and ridesharing systems, must make matching decisions in the presence of choice congestion and strategic platform design challenges. When agents have correlated preferences, popular options can attract too much demand, reduce overall efficiency, and lead to poor market outcomes. In this talk, I will present a framework for two-sided assortment optimization that studies how a platform should decide which options to display to agents and in what order, with the goal of improving matching performance. The main focus will be on maximizing the expected number of matches under general choice models. I will describe several natural classes of platform policies, ranging from static simultaneous displays to fully adaptive sequential policies, and compare their power through adaptivity gap results. I will also discuss polynomial-time approximation algorithms for computing near-optimal policies, and then briefly discuss the revenue-maximization version of the problem, where matches generate pair-dependent rewards. This talk is based on joint works with Alfredo Torrico, Ulysse Hennebelle, and Mohammadreza Ahmadnejadsaein.
 

Bio: Omar El Housni is an Assistant Professor in the School of Operations Research and Information Engineering at Cornell Tech and Cornell University. He is a Field Member of the Center of Applied Mathematics at Cornell. He is also an Amazon Scholar. His research focuses on decision-making under uncertainty where he aims to develop optimization models and design robust and efficient algorithms to address a wide range of operational problems, including revenue management problems such as assortment optimization and online matchings. Omar has spent time as a research scientist at Amazon and Uber where he contributed to the design and implementation of data-driven optimization models for matching and retailing platforms. Omar holds a PhD in Operations Research from Columbia University and an MS and BS in Applied Mathematics from Ecole Polytechnique (Paris). His work has been recognized by INFORMS George Nicholson award and his current research is supported by NSF. 

The application, details, and rubric can be found here: https://gatech.infoready4.com/#freeformCompetitionDetail/2030289

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.

Abstract: Recent advances in neuroimaging have produced massive and heterogeneous datasets, ranging from fMRI with high spatial resolution to EEG with high temporal resolution, characterized by complex spatiotemporal correlations and substantial inter-subject variability. Traditional regression models and Gaussian process (GP) approaches with fixed parametric kernels often fail to model such complex data effectively while maintaining scalability and interpretability. This talk introduces a family of modern Bayesian GP frameworks that integrate deep kernel learning, neural network priors, and geometric modeling for large-scale neuroimaging analysis. An example is the Deep Kernel Learning Process (DKLP), which embeds deep neural networks within GP priors to learn data-adaptive covariance structures directly from imaging data. DKLP provides a unified modeling foundation for image-on-scalar, scalar-on-image, and image-on-image regression, supported by theoretical guarantees and efficient posterior computation. Applications to fMRI data from the Adolescent Brain Cognitive Development (ABCD) study reveal reproducible cortical activation patterns associated with cognitive ability, while analyses of EEG-based brain–computer interface data demonstrate robust neural decoding under high noise. I will also discuss scalable heat-kernel GPs on manifolds and thresholded GP–based spatially varying neural network priors, which together expand the scope of Bayesian inference for complex neuroimaging data.
 

Bio: Dr. Jian Kang is Professor and Associate Chair for Research in the Department of Biostatistics at the University of Michigan. His research lies at the intersection of Bayesian statistics, machine learning, and artificial intelligence, with applications in neuroimaging, brain–computer interfaces, omics, and precision medicine. He has published more than 175 papers in leading statistics, machine learning, and biomedical journals. Dr. Kang has served as an Associate Editor for several premier statistical journals, including the Journal of the American Statistical Association (JASA), The Annals of Applied Statistics (AOAS) and Biometrics. He is a Fellow of both the Institute of Mathematical Statistics (IMS) and the American Statistical Association (ASA). He currently serves as Chair of the ASA Section on Statistics in Imaging.

Host: Dr. Meg Millhouse

Title: Growing the RIFT: an update on features and applications for the gravitational wave inference code RIFT

Abstract:
As the sensitivity of the gravitational wave (GW) detectors improve, the number of detections for future observing runs are expected to dramatically increase. Due to this, the need for fast, accurate, and increasingly automated parameter inference code becomes ever more relevant. The GW parameter inference code RIFT is a grid-based code that takes advantage of the parameter-dependencies of terms within the likelihood to efficiently marginalize the extrinsic parameters. This piecewise approach to the construction of the likelihood allows for highly parallelizable calculations and computational speed compared to standard Markov-Chain Monte Carlo methods. In this talk, I will present some recent improvements to RIFT as well as some recent applications where RIFT was used to produce novel results. This includes analyses with initially unbound, eccentric-precessing, and eccentric-matter waveforms. Fast and accurate inference of these more exotic sources will be essential as we approach design sensitivity of LIGO/Virgo and look forward to 3G detectors.

Abstract:

Fungal metabolites are well known for providing humanity with medical benefits, with penicillin and the antibiotics revolution that it stimulated probably the greatest example from the 20th century. Our team has been studying fungi for drug leads effective in the fight against cancer, with an emphasis on ovarian cancer, and this presentation will peer into two different chapters of that ongoing story. The verticillins are potent, cytotoxic leads, first discovered over 50 years ago. Our emphasis has been on amplifying their supply, generating semisynthetic analogues, understanding their mechanism of action, and optimizing their delivery. In contrast, wheldone is a molecule that we first described recently. Interestingly, it is only biosynthesized via the antagonism of two fungi fighting for limited resources in co-culture. We’ve spent considerable effort developing ways to supply this compound on a larger scale, as the scale up of fungal co-cultures is not discussed in the literature. We’ve also probed its mode of action and are starting to generate semisynthetic analogues. In sum total, this presentation will provide a glimpse into how natural products drug discovery from fungi is carried out in the modern research setting.

Bio: 

Nicholas Oberlies received his B.S. in Chemistry from Miami University (Oxford, Ohio) in 1992 and his Ph.D. in Medicinal Chemistry and Pharmacognosy from Purdue University (West Lafayette, Indiana) in 1997, where he studied under Professor Jerry L. McLaughlin. He then spent a year as a postdoctoral chemist at American Cyanamid (Princeton, New Jersey), where he investigated leads with insecticidal, herbicidal, and fungicidal properties from natural sources. In 1998, he joined the Natural Products Laboratory at Research Triangle Institute (RTI) in North Carolina, specifically to be mentored by the late Dr. Mansukh C. Wani and the late Dr. Monroe E. Wall, who are most well known as the co-discoverers of taxol and camptothecin. He rose progressively through the ranks of RTI and eventually directed the Natural Products Laboratory. In 2009, he moved his research group to the Department of Chemistry and Biochemistry at the University of North Carolina at Greensboro. 

He has received several awards that speak to his accomplishments, including the Matt Suffness Young Investigator Award from the American Society of Pharmacognosy, the 40 under 40 Leadership Award from the Triangle Business Journal, and the North Carolina Distinguished Speaker Award from the NC Section of the American Chemical Society. 

He is a Fellow and Past President of the American Society of Pharmacognosy.

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.

Host: Dr. Matthew Liska

Title: Multimessenger Modeling of Supermassive Binary Black Holes with Next-Generation GRMHD Simulations

 Abstract:                                                                                                                                       Supermassive binary black holes (SMBBHs) are expected to be excellent mHz gravitational wave (GW) sources that also emit across the electromagnetic (EM) spectrum. However, electromagnetically identifying SMBBHs to prepare for and complement future GW observations remains a fundamental challenge facing our field. In this talk, I will discuss our efforts to self-consistently model multimessenger emission from SMBBHs during their late inspiral through merger with state-of-the-art numerical simulations in full general relativity. I will describe smoking-gun synchrotron emission from eccentric SMBBHs, the importance of magnetic reconnection and eruptions in shaping the accretion flow near the binary, the potential for a magnetically arrested cavity, and the longest general-relativistic simulation of SMBBH accretion to date. Finally, I will outline our efforts to develop a next-generation, GPU-accelerated code to model SMBBH accretion with radiation transport at unprecedented resolutions and durations.

Register at Fall 2026 Digital Accessibility Training Series Registration.

Jennifer Glass, Jean “Chris” Purvis Professor, School of Earth and Atmospheric Sciences, Georgia Institute of Technology

Abstract: The ocean plays a major role in Earth’s climate and biogeochemical cycles. Microorganisms are key players in ocean carbon cycle. Archaea and bacteria oxidize most of the methane that diffuses upward from vast reservoirs beneath the seafloor before it can reach the atmosphere. In this seminar, I will present recent findings from our lab on the role of microbes and their proteins on methane and methanol cycling in three habitats: under the seafloor, in a deep brine pool, and in the surface ocean. First, I will highlight our discovery of methane hydrate binding proteins made by subseafloor bacteria, and how collaboration with GT biochemists enabled characterization of these novel proteins that are only found in bacteria that inhabit deep seafloor sediments rich in methane. Second, I will present our recent study of marine archaea that prevent leakage of methane out of a deep anoxic salt pond on the seafloor of the Gulf of Mexico. Third, I will highlight our discovery of Methylaequorales, an uncultivated order of alphaproteobacteria that are highly active in the surface ocean and use lanthanide-requiring enzymes to grow on methanol. Despite recent progress, we have barely scratched the surface of knowledge of the biochemical repertoire of microbial proteins, particularly those from habitats rarely visited by humans, and the need for fundamental science to uncover the chemical potential of environmental microbes is stronger than ever.

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.

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.