2026-2027 NNL Distinguished Lecture - Prof. Benoit Forget, KEPCO, Professor, Nuclear Engineering MIT

Topic - Embedding Nuclear Data Uncertainties in Monte Carlo Analysis: Toward predictive simulation and improved nuclear data

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Dr. Leong will present two lectures.

Thursday, September 17 at 4PM in Benedum Hall 157: Reimagining Cationic Biomaterials for Therapeutic Applications

Friday, September 18 at Noon in Benedum Hall 102: A Scientific Career from Slide Rule to AI Tools

Dr. Leong is one of the pioneers in developing multifunctional nanocarriers for delivery of drugs, antigens, proteins, siRNA, pDNA, and mRNA. Dr. Leong's contributions have been recognized by his election to the USA National Academy of Engineering, National Academy of Medicine, National Academy of Inventors, and the Asian American Academy of Science and Engineering. Recent awards include the Society for Biomaterials' Founders Award (2022), the IEEE-EMBS Career Achievement Award (2023), and the IEEE Biomedical Engineering Award (2024). Dr. Leong has been serving as the Editor-in-Chief of Biomaterials for the past decade.

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Abstract: : Artificial intelligence is rapidly evolving from a passive analytical tool into an active partner in scientific discovery. In the material world, this transition requires systems that do more than recognize patterns: they must reason across scales, formulate hypotheses, design and execute investigations, learn from failure, and revise their own representations in response to evidence. This talk presents emerging approaches toward “superintelligent” discovery engines that integrate reinforcement learning, graph-based reasoning, physics-informed neural architectures, generative models, and autonomous tools for simulation, experimentation, design, and manufacturing. Particular emphasis will be placed on multi-agent systems inspired by collective intelligence in nature, in which specialized agents collaborate, critique one another, and continuously improve their strategies as they solve complex scientific problems. Case studies from materials science, engineering, and biology will illustrate how these systems can uncover hidden structure-property relationships, generate and evaluate novel material and molecular designs, connect knowledge across disciplinary boundaries, and transform scientific failures into new hypotheses and executable knowledge. These advances point toward a new form of artificial intelligence that actively expands the boundaries of human knowledge.

Short Bio Sketch: Markus J. Buehler is the McAfee Professor of Engineering at MIT, a pioneer in AI-driven knowledge discovery and materials science. He created graph-aware, multi-agent AI platforms that turn heterogeneous data into science-grounded, actionable insight, powering breakthroughs in materials science, biology, and healthcare. Buehler is among the world's most-cited materials scientists and the recipient of numerous honors, including the Feynman Prize, ASME Drucker Medal, J. R. Rice Medal, and the Washington Award. He is a member of the U.S. National Academy of Engineering, a Fellow of the Royal Society of Chemistry, and a Fellow of the American Institute for Medical and Biological Engineering (AIMBE). For more than a decade he has also taught executive and technical professionals at MIT, shaping the next generation of leaders in engineering, knowledge discovery, and artificial intelligence.

Host: Dr. John Keith

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Dr. Leong will present two lectures.

Thursday, September 17 at 4PM in Benedum Hall 157: Reimagining Cationic Biomaterials for Therapeutic Applications

Friday, September 18 at Noon in Benedum Hall 102: A Scientific Career from Slide Rule to AI Tools

Dr. Leong is one of the pioneers in developing multifunctional nanocarriers for delivery of drugs, antigens, proteins, siRNA, pDNA, and mRNA. Dr. Leong's contributions have been recognized by his election to the USA National Academy of Engineering, National Academy of Medicine, National Academy of Inventors, and the Asian American Academy of Science and Engineering. Recent awards include the Society for Biomaterials' Founders Award (2022), the IEEE-EMBS Career Achievement Award (2023), and the IEEE Biomedical Engineering Award (2024). Dr. Leong has been serving as the Editor-in-Chief of Biomaterials for the past decade.

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Speaker Sheila Vélez Martínez - Jack and Lovell Olender Professor of Asylum Refugee and Immigration Law, Director Center for Civil Rights and Racial Justice, Director Immigration Law Clinic, University of Pittsburgh - School of Law

RSVP to: http://pi.tt/LatinxHispanicMonth2026

Lunch will be provided

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Bio: Pramod K. Varshney was born in Allahabad, India, in 1952. He received the B.S. degree in electrical engineering and computer science (with highest honors), and the M.S. and Ph.D. degrees in electrical engineering from the University of Illinois at Urbana-Champaign in 1972, 1974, and 1976 respectively. Since 1976 he has been with Syracuse University, Syracuse, NY where he is currently a Distinguished Professor of Electrical Engineering and Computer Science. His current research interests are in distributed sensor networks and data fusion, detection and estimation theory, wireless communications, machine learning, AI and radar.

Dr. Varshney was the recipient of the 1981 ASEE Dow Outstanding Young Faculty Award. He was elected to the grade of Fellow of the IEEE in 1997 for his contributions in the area of distributed detection and data fusion. In 2000, he received the Third Millennium Medal from the IEEE and Chancellor’s Citation for exceptional academic achievement at Syracuse University. He is the recipient of the IEEE 2012 Judith A. Resnik Award. He received an honorary Doctor of Engineering degree from Drexel University in 2014, ECE Distinguished Alumni Award from UIUC in 2015, the Yaakov Bar-Shalom Award for Lifetime Excellence in Information Fusion as well as ISIF Fellow designation in 2018, the Claude Shannon-Harry Nyquist Technical Achievement Award from the IEEE Signal Processing Society, the Pioneer Award from the IEEE Aerospace and Electronic Society in 2021, and Syracuse University Chancellor’s Lifetime Achievement Award in 2023.

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In this talk, we will discuss causal representation learning (CRL) as a principled foundation for building world models of physical systems. CRL aims to uncover latent variables that correspond to the system’s underlying generative factors and to organize them in a way that reflects causal relationships, invariances, and intervention effects. Such representations can enable models that extrapolate more reliably beyond the training regime, support counterfactual and interventional reasoning, and provide a pathway toward more interpretable and transferable decision-making. We will present our perspective on why causality is essential for world modeling, outline key challenges in learning causal latent structure from high-dimensional observations, and highlight emerging directions for integrating CRL with dynamics, control, and scientific machine learning.

Bio: Ali Tajer received a B.Sc. and an M.Sc. degree in Electrical Engineering from Sharif University of Technology, an M.A. in Statistics, and a Ph.D. in Electrical Engineering from Columbia University. During 2010-2012, he was a Postdoctoral Research Associate at Princeton University. He is currently a Professor of Electrical, Computer, and Systems Engineering and the Associate Dean for Academic Affairs in the School of Engineering at RPI. His research interests include mathematical statistics, machine learning, and information theory. He is currently an Associate Editor for the IEEE Transactions on Information Theory and a Senior Area Editor for the IEEE Transactions on Signal Processing. In the past, he has served as an Associate Editor for the IEEE Transactions on Signal Processing, an Editor for the IEEE Transactions on Communications, and a Guest Editor for the IEEE Signal Processing Magazine. He received the Jury Award (Columbia University), a few research and teaching awards at RPI, and a CAREER award from the U.S. National Science Foundation. He is a member of the 2025-2026 class of Distinguished Lecturers of the IEEE Information Theory Society.

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Abstract

The purpose of this talk is twofold. First, general purpose robots, including humanoids - since they look like us, occupy our spaces, and are able to perform tasks in a manner similar to us - have the ability, at least in theory, to completely replace the majority of human-performed tasks. What are the ethical, legal, and social implications of this sort of technology? It’s tempting to think that humanity has always faced these sorts of labor pressures, but is that actually true? Secondly, humans are not passive observers of our technical decisions and deployments. Most of us welcome some level of automation in our lives (we’re not washing our clothes in boiling cauldrons these days), but also push back against non-human content in the form of “AI slop” on YouTube. We have the opportunity to ask a more general question: “What do we want from our machines?”. The hard part is answering it, but this is something that it won’t be possible to be on the sidelines for. While I’ll provide my own answer as part of this presentation, the main takeaway will be this: form your own opinion, and hold politicians, industry, and yourselves accountable.

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Dr. Danielle Maddix Robinson (virtual), Principal Research Scientist, Siemens Physical AI

Topic - Science in the Age of Foundation Models

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We first consider how hidden beliefs can be reconstructed from observed actions using inverse filtering, and how information is fused across networks through word-of-mouth learning. Extending this viewpoint to networks of interacting large language models reveals a striking phenomenon: endogenous information exchange can create a persistent “glass ceiling” in influence. We then discuss how revealed preference theory and inverse reinforcement learning serve as principled tools for inferring utility from sensing actions. Finally, we reverse the perspective: how can a sensing system conceal its objectives from an adversarial observer while preserving operational effectiveness? We discuss utility masking and information-theoretic approaches that degrade identifiability while maintaining performance.

Bio:
Vikram is a professor in Electrical and Computer Engineering at Cornell University. His research interests are in statistical signal processing, inverse reinforcement learning, stochastic optimization and partially observed Markov decision processes. He is a Fellow of IEEE, served as distinguished lecturer for the IEEE Signal Processing Society and Editor in Chief of IEEE Journal Selected Topics in Signal. He was awarded an honorary doctorate from KTH, Sweden in 2013

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Abstract

Our decisions about whether to act depend not only on the costs and rewards of actions in our external environment, but also on our internal state. Physical fatigue provides a powerful example: the same action can feel easy when rested but prohibitively costly when fatigued, even though its objective requirements are unchanged. In this talk, I will present a framework for understanding fatigue as an internal state that reshapes the subjective valuation of physical effort and, consequently, decisions about whether to act. Using behavioral experiments, computational modeling, and neuroimaging, I will show that changes in neuromuscular state systematically increase the subjective cost of effort, biasing individuals away from exertion. I will present evidence that a distributed neural network, including the prefrontal and insular cortices, integrates information about the body's state into the valuation of effortful actions. I will then describe how we are extending this framework to long COVID, a condition in which persistent fatigue is among the most debilitating symptoms. Individuals with long COVID exhibit heightened sensitivity to effort costs, providing a potential mechanism linking persistent fatigue to reduced engagement in everyday activities. Together, these findings position fatigue not simply as a limitation on what the body can do, but as an internal state that shapes what the brain decides is worth doing.

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Prof. Sunčica Čanić, Professor Mathematics, University of California, Berkeley

Topic - Advances in Mathematical Analysis and Numerical Simulations Motivated by Bioartificial Organ Design

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Abstract

Human physiology unfolds in dynamic rhythms, yet conventional diagnostics largely rely on isolated measurements that can miss critical hormonal and inflammatory transitions. This seminar will present wearable electrochemical biosensing technologies designed to continuously and non-invasively measure biomarkers in passive sweat. The talk will highlight two applications. The first examines continuous monitoring of estradiol and progesterone across the menstrual cycle, to resolve individualized hormonal trajectories that discrete blood, saliva, and urine measurements may not capture. The second explores the concurrent monitoring of cortisol, melatonin, interleukin-6, and tumor necrosis factor-alpha to characterize circadian regulation, stress responses, inflammation, and age-associated changes. In a 48-hour study, sweat-based cortisol and melatonin measurements showed strong agreement with salivary reference measurements. Together, these studies illustrate how wearable biosensors can move health assessment from episodic testing toward continuous, multidimensional phenotyping. The seminar will discuss sensor design, electrochemical detection, analytical validation, temporal modeling, and the translational challenges of converting complex biosignals into meaningful physiological insights. Ultimately, this work establishes a framework for personalized monitoring across reproductive health, stress, aging, chronic disease, and preventive care.

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Dr. Vaibhav Yadav, Distinguished Staff Scientist, Idaho National Laboratory

Topic - Can Modern AI Architectures Achieve Nuclear-Domain Expertise in Reasoning and Analysis?

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Bio: Nadir Kaplan earned his B.S. degree in Engineering Physics from Istanbul Technical University (Turkey), M.S. degree in Physics from Koç University (Turkey), and Ph.D. in Physics from Brandeis University. He then held postdoctoral positions in Applied Math and Materials Science at Harvard School of Engineering and Applied Sciences. Currently, he is an Assistant Professor of Physics at Virginia Tech. His research at the interface of fluid mechanics, elasticity, and geometry is aimed at developing novel theoretical and computational approaches to state-of-the-art materials design.

Host: Dr. Sachin Velankar

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Prof. Craig Arnold, Susan Dod Brown Professor, Mechanical and Aerospace Engineering, Princeton University

Topic - Non-Traditional Methods of Controlling the Material Structure in Laser-Based Additive Manufacturing: Reactive Laser Powder Bed Fusion and Electric Field Mediated Processing

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Abstract: The emergence of electrically conductive metal-organic frameworks (c-MOFs) has been one of the most paradoxical developments in the field in the last few years. Indeed, how can one transport charges through a material that is mostly “empty” space? In this sense, MOFs made from layers of organic ligands connected by (typically) square-planar metal ions have shown particularly good electrical conductivity. However, a precise mechanism for charge transport is still the subject of debate, with various experimental and computational reports describing these materials as metals, semiconductors, or semimetals. This lecture will describe the latest efforts from our group to understand the intrinsic properties of 2D c-MOFs, especially as related to single-crystal electrical measurement studies and will discuss in particular the unexpectedly large influence of out-of-plane transport. The seminar will also discuss how these fundamental studies have led directly to the discovery of a sustainable, organic cathode material that is competitive with incumbent oxides in Li-ion and Na-ion rechargeable batteries.

Short Bio Sketch: Mircea Dincă is the Alexander Stewart 1886 Professor of Chemistry at Princeton University. He grew up in Romania and moved to the United States to pursue his Bachelor’s degree in Chemistry at Princeton University. Graduate studies in Inorganic Chemistry at UC Berkeley were followed by a postdoctoral appointment at MIT. He started his independent career in 2010 at MIT and moved his group to Princeton in 2025. His research focuses on the synthesis of new multifunctional materials for applications in electrical and electronic devices, heterogeneous catalysis, and various uses in clean and renewable energy. In recognition of his group's research, Dincă has been awarded the Alan T. Waterman Award from the NSF in 2016, the ACS Award in Pure Chemistry in 2018, and the Blavatnik National Award in Chemistry in 2021, among several others. He has been named to the Thomson-Reuters/Clarivate Analytics Highly Cited Chemists List yearly since 2014.

Google Scholar: https://scholar.google.com/citations?user=RWC2ERYAAAAJ&hl=en

ORCID: https://orcid.org/0000-0002-1262-1264

Hist: Dr. Christopher Wilmer

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Assoc. Prof. Xiang Yang, Kenneth K. & Olivia J. Kuo Early Career Professor, Penn State

Topic - AI for Reliable Turbulence Modeling: From Physical Constraints to Scientific Discovery

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