AI is an essential tool to maximize the discovery potential of the upcoming flood of data from the next generation of high energy particle physics experiments. The field is moving beyond existing paradigms to explore density estimation techniques that offer exciting new capabilities. Unsupervised learning enables anomaly detection to search for unexpected signs of new physics. Generative learning provides faster, higher quality simulations to match increasing data volumes and precision requirements. These and other AI algorithms, when deployed on coprocessors such as GPUs and neuromorphic chips, accelerate data processing far beyond conventional computing. Current results and future prospects will be discussed.

Kevin Pedro, Scientist, Fermilab

Host: Mayda Velasco

Winter exams begin

Title: Computational Molecular Pharmacology.

Abstract: Computer modeling of structural, thermodynamic, and pharmacodynamic properties can accelerate drug design by inspiring new designs and prioritizing those that are most likely to be successful. My research group has recently made significant progress in binding pose, affinity, and signaling efficacy prediction. Based on a statistical mechanics framework that I derived, we have developed a method that exploits multiple rigid protein conformations to quickly compute protein-ligand binding poses and free energies. For a series of 58 ligands from a drug lead optimization campaign against the SARS-CoV-2 main protease, the method achieves a Pearson R of 0.52 and RMSE of 1.24 kcal/mol in comparison to experiment. In what could be a significant breakthrough in GPCR signaling, we have also developed a computational method that combines molecular simulation and machine learning to model structural mechanisms of activation and calculate the signaling efficacy along multiple pathways. It computes the signaling efficacy of G protein signaling along multiple subtypes and of arrestin recruitment for a diverse set of mu opioid receptor and cannabinoid receptor 2 ligands with a mean absolute error of less than 20%. I am excited about opportunities to further validate these technologies and apply them to accelerating the discovery of safe and effective therapeutics.

Speaker: David Minh, PhD; Robert E. Frey, Jr. Endowed Chair in Chemistry, Illinois Tech; Co-founder and Chief Scientific Officer, Biagon Inc.

AI is rapidly reshaping higher education, offering new opportunities for teaching and learning. But what does AI integration look like in practice?
Join this session to hear firsthand success stories on leveraging AI for learning. Gain practical insights, explore challenges, and walk away with strategies to integrate AI into your own courses.

Presenters:
Leigh Cohen: Assistant Clinical Professor and Speech Language Pathologist (SLP) at Northwestern University
Fransiska Lys: Professor of German and Director of Undergraduate Studies in the department of German as well as the Director of the MENA Languages program.
Mindy Thorpe: Assistant Professor, Physical Medicine and Rehabilitation.

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Spring Break Begins

The NU-MRSEC will partner with M3S for this event.

Breakfast & Lunch will be provided.
* Registration is required to receive complimentary lunch and breakfast.

Spring Break Ends

Registration is now open for Northwestern’s second annual Computation and Data Exchange (CoDEx) symposium on Tuesday, April 8. This campus-wide event will showcase research with computational and data-intensive aspects at Northwestern and provide cross-discipline collaboration and networking opportunities for University faculty, postdoctoral researchers, staff, and students.

Symposium Highlights
Attendees will discover solutions and discuss challenges the Northwestern research community faces in computation, data science, AI, machine learning, and other data-enabled research. See the agenda for a list of the day’s activities, including:

Keynote Speaker Adam Miller, assistant professor, Department of Physics and Astronomy

Parallel Research Talks from professors and staff from Pritzker School of Law, Weinberg College of Arts and Sciences, Feinberg School of Medicine, Northwestern Libraries, CIERA, School of Communication, and McCormick School of Engineering.

Lightning Talks, which are five-minute, highly focused talks designed to showcase interesting projects, spark discussions, and ignite collaborations across a wide range of research fields.

Visualization Challenge presentations from Northwestern graduate students, postdocs, and undergraduate students.

Poster Session featuring graduate and undergraduate student researchers.

Demonstrations from our corporate sponsors, Lenovo, Nvidia, and Wolfram Research.

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Title: Nonlinear tomographic reconstruction via nonsmooth optimization

Members of the NITMB community are invited to join us for Research-In-Progress meetings, an informal venue for members of the NITMB to discuss ongoing and/or planned research.

Vasilis Charisopoulos is a postdoctoral scholar in the Willett Group at the University of Chicago. He is broadly interested in developing numerical optimization methods for machine learning, signal processing and scientific computing. He holds a PhD in Operations Research & Information Engineering from Cornell University. Vasilis was recognized as a Rising Star in Computational and Data Sciences by the UT Austin Oden Institute in 2023.

Learn more about Vasilis Charisopoulos' research and engage in discussion with the NITMB community. Research-In-Progress talks take place on Wednesdays at 3pm at the NITMB office (875 N Michigan Ave., Suite 4010). Snacks and coffee will follow.

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Xuesong (Simon) Zhou is a Professor of Transportation Systems at the School of Sustainable Engineering and the Built Environment, Arizona State University (ASU), Tempe, Arizona. Dr. Zhou's research focuses on developing methodological advancements in multimodal transportation planning applications, including dynamic traffic assignment, traffic estimation and prediction, large-scale routing, and rail scheduling. Dr. Zhou has served as an Associate Editor of Transportation Research Part C, is currently the Executive Editor-in-Chief of Urban Rail Transit, and an Editorial Board Member of Transportation Research Part B. He has also chaired the INFORMS Rail Application Section (2016 and 2025) and currently serves as a subcommittee chair of the TRB Committee on Transportation Network Modeling (AEP40).

Dr. Zhou is the Director of the ASU Transportation+AI Lab, where he is the principal architect and programmer for several open-source packages, including DTALite, NEXTA, and OSM2GMNS, which have collectively received over 100,000 downloads and many system deployments at various metropolitan planning agencies and state DOTs. He has published over 100 papers in Transportation Research Part B, Transportation Research Part C, and other leading transportation journals, with an H-index of 60 and a total of 11,000 citations in Google Scholar.

In addition to his academic achievements, Dr. Zhou is passionate about connecting practitioners, researchers, academics, students, and others involved in transportation planning and travel modeling. He serves as the conference chair for the TRB Innovations in Travel Analysis and Planning Conference in 2023, and a board member of Zephyr Foundation, a non-profit organization dedicated to advancing transportation research and education.

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