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Yiping LuView profile

Assistant Professor

• Machine Learning
• Scientific Machine Learning (AI4Science)
• Numerical Algorithms
• +7 more

Yiping Lu is an Assistant Professor in the Department of Industrial Engineering and Management Sciences at Northwestern University's McCormick School of Engineering. His research focuses on developing interdisciplinary approaches combining domain knowledge (differential equations, stochastic processes), machine learning, and experiments. Key interests include scientific machine learning (AI4Science), stochastic simulation, and robust machine learning. Education: Ph.D. in Applied and Computational Mathematics, Stanford University (2023) B.S. in Computational Mathematics, Peking University (2019) Research Highlights: Hybrid research integrating ML with scientific domains like PDEs and inverse problems Development of Physics-Informed Learning frameworks Contributions to deep learning theory (ResNets, neural collapse) Advances in kernel operator learning and adversarial robustness Awards: CPAL Rising Star Award (2024) University of Chicago Data Science Rising Star (2022) Stanford Interdisciplinary Graduate Fellowship (2021) Labs/Teams: SCALE Lab (Scientific Computation and Learning at Northwestern) Collaborations with NYU's Courant Institute and Stanford

• Language Models
• Diffusion Models
• Theory of Deep Learning
• +6 more

Song Mei is an Assistant Professor in the Department of Statistics and Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley. She received her Ph.D. from Stanford University in 2020 under Andrea Montanari and maintains active research collaborations with institutions including Amazon (as a 2023 Amazon Research Award recipient) and OpenAI (where she is currently on leave). Her research spans the intersection of statistics, machine learning, information theory, and computer science, with particular emphasis on foundational theories for modern AI systems. Key interests include language models, diffusion models, quantum algorithms, and high-dimensional statistics, often leveraging insights from statistical physics literature. Analysis of her recent publications reveals a strong focus on theoretical underpinnings of generative AI, with significant contributions to understanding contrastive pre-training (CLIP), attention mechanisms in LLMs, and mathematical foundations of diffusion models. Her work demonstrates consistent interdisciplinary connections between statistical theory and practical AI development. Sloan Research Fellowship (2025) Noether Early Career Scholar Award (2025) Google Research Scholar Award (2024) Amazon Research Award (2024) She actively advises graduate students through MA programs and has secured significant research grants including Amazon Research Awards. Her work with AGI Labs at Amazon demonstrates applied impact of theoretical research. Current projects focus on mechanistic interpretability of large language models and mathematical frameworks for generative AI. Professor Mei leads research on the statistical principles behind frontier AI models, with particular focus on developing rigorous theoretical frameworks for understanding emergent behaviors in large-scale systems.

Norbert LutkenhausView profile

Professor

• Quantum Communication
• Quantum Key Distribution (QKD)
• Quantum Information Processing
• +4 more

Prof. Norbert Lütkenhaus is a Professor and Executive Director of the Institute for Quantum Computing (IQC) at the University of Waterloo, cross-appointed to the Department of Applied Mathematics. He holds affiliations with Perimeter Institute and the Centre for Applied Cryptographic Research. His research focuses on quantum communication theory, particularly quantum key distribution (QKD) and quantum repeaters. He has pioneered methods to bridge abstract quantum protocols with practical optical implementations, emphasizing secure key rate calculations and overcoming quantum channel limitations. Education: PhD (2003) in Physics from University Erlangen-Nürnberg, MSc (1993) and BSc (1990) from Ludwig-Maximilians-Universität München and RWTH Aachen, respectively. Awards include the 2015 American Physical Society Outstanding Referee Award and a 2009 University of Waterloo Excellence Award. Research interests span QKD protocols (e.g., decoy-state BB84, phase-error mitigation), quantum repeater architectures, and entanglement verification. He develops numerical tools for key rate analysis and addresses implementation security loopholes. His work includes theoretical frameworks for long-distance quantum communication and practical QKD system optimizations. Teaching includes courses on quantum information processing (PHYS 768/QIC 890) and mechanics (PHYS 115). He contributes to international standards via ETSI’s QKD-ISG and the QCrypt steering committee. His patents cover QKD system designs and phase-randomization techniques.

John MacLaren WalshView profile

Professor

• Information Theory
• Network Coding
• Distributed Computing
• +4 more

John MacLaren Walsh is a Professor in the Department of Electrical and Computer Engineering at Drexel University, where he leads the Adaptive Signal Processing and Information Theory Research Group. He holds BS, MS, and PhD degrees from Cornell University, all completed under Dr. C. Richard Johnson, Jr. His research spans information theory, network coding, distributed computing, and machine learning applications in patent analysis. His work focuses on: Bounding entropic vectors and their impact on communication networks Rate region computation for network coding and distributed storage Information theory for distributed function computation Machine learning-enhanced patent processing systems Publications emphasize entropy geometry, network coding complexity, distributed algorithms, and patent analysis, with consistent themes of optimization and combinatorial methods. Recent work (2016-2019) shows increased focus on probabilistic supports and computational efficiency in network coding. Awards: 2011 NSF CAREER Award for 'Entropy Geometry in Variational Inference Signal Processing' He has advised PhD students on topics like entropy region mapping, network coding, and distributed control. Key grants include NSF CAREER and AFOSR funding for wireless network overhead control. He directs the Adaptive Signal Processing and Information Theory Research Group, which develops algorithms for network coding, distributed storage, and patent analysis systems.

Li ChenView profile

Associate Professor

• Deep Learning Systems
• Federated Learning
• Cloud Computing
• +3 more

Dr. Li Chen is an Alfred and Helen Lamson/BORSF Endowed Associate Professor in the School of Computing and Informatics at the University of Louisiana at Lafayette. She leads the CELESTIAL research lab, focusing on distributed systems and networking for machine learning and AI. Her research interests include federated learning, cloud computing, and resource optimization. Dr. Chen holds a Ph.D. from the University of Toronto and has received awards such as the NSF EPSCoR RII Track-4 grant and the BoRSF Endowed Professorship. Education: Ph.D. (2018), M.A.Sc. (2015) in Electrical and Computer Engineering from University of Toronto; B.Eng. (2012) in Computer Science from Huazhong University of Science and Technology. She also visited Hong Kong Polytechnic University (2013-2014). Research spans federated learning frameworks (e.g., SEAFL, FedClust), cloud resource scheduling (e.g., Hadar, HarmonyBatch), and applications in weather forecasting (e.g., MMST-ViT). Her work is supported by NSF, Louisiana BoRSF, and industry partners like XRMedix. Awards include the Alfred and Helen Lamson/BORSF Endowed Professorship (2024-2027), NSF EPSCoR grant (2024-2026), and best paper recognitions at IEEE conferences. She advises a diverse group of graduate students and has supervised alumni now in academia and industry. Teaching includes courses on computer networks, operating systems, and distributed systems. She organizes workshops and tutorials (e.g., 2023 Summer Tutorial on ML & Meteorology) and serves on conference committees such as INFOCOM and IWQoS.

Christopher M. WolvertonView profile

Professor

• Computational Materials Science
• Energy Storage Materials
• First-Principles Quantum Mechanics
• +7 more

Dr. Christopher M. Wolverton is a Professor of Materials Science and Engineering at Northwestern University , where he leads the Wolverton Research Group . His work focuses on computational materials science with applications in energy sustainability , particularly in batteries , hydrogen storage , and thermoelectrics . PhD in Physics from University of California, Berkeley BS in Physics (summa cum laude) from University of Texas, Austin His research leverages first-principles quantum mechanical simulations and machine learning to enable virtual materials synthesis before laboratory testing. The group specializes in hybrid computational methods integrating Density Functional Theory (DFT) , Monte Carlo simulations , and phase-field microstructural models . The article portfolio shows leadership in energy storage materials , with recent work on data-driven nanoparticle facet control , mixed-anion semiconductors , and machine learning-accelerated discovery . Publications span top journals including Nature Energy , Nature Materials , and Science . 2006 Ford Motor Company Technical Achievement Award 2005 Ford Patent & Publication Awards 2003 Ford Environmental/Physical Sciences Recognition As advisor to PhD candidates Zhenpeng Yao , Shiqiang Hao , and Shane Patel , he fosters interdisciplinary research connecting materials informatics with experimental validation . The group maintains active collaborations with Argonne National Lab and MIT/Harvard teams.

• Materials Science
• Corrosion Engineering
• Alloy Design
• +3 more

Dr. KN Sasidhar is a Researcher in the Department of Microstructure Physics and Alloy Design at Heinrich Heine University Düsseldorf. His work focuses on advanced materials science, particularly corrosion mechanisms, alloy design, and nanoscale structural analysis. He employs cutting-edge techniques like in situ synchrotron investigations and deep learning frameworks to study material behavior under extreme conditions. Current research emphasizes corrosion resistance in stainless steels, phase transformations during nitriding, and radiation effects on coatings. Key achievements include pioneering studies on nanoscale amorphization in metallic systems, data-centric approaches for materials discovery, and the development of predictive models for alloy performance. His work bridges experimental materials characterization with computational methods, addressing challenges in energy and aerospace applications. Publications span corrosion analysis, microstructural evolution under irradiation, and phase separation phenomena. Collaborative projects involve synchrotron facilities and interdisciplinary teams focusing on materials informatics. No formal awards or grants are explicitly listed in the provided texts, though his prolific publication record indicates active academic engagement.

Surya GanguliView profile

Associate Professor

• Theoretical Neuroscience
• Computational Neuroscience
• Machine Learning
• +4 more

Surya Ganguli is an Associate Professor in the Department of Applied Physics at Stanford University, with courtesy appointments in Neurobiology and Electrical Engineering. He serves as Senior Fellow at the Stanford Institute for Human-Centered AI and is affiliated with the Stanford Neuroscience Institute , Bio-X , and Wu Tsai Neurosciences Institute . His research spans theoretical neuroscience, machine learning, and statistical mechanics. Ph.D. , UC Berkeley, Theoretical Physics (2004) M.A. , UC Berkeley, Physics (2000) M.A. , UC Berkeley, Mathematics (2004) M.Eng. , MIT, Electrical Engineering and Computer Science (1998) B.S. , MIT, Physics (1998) B.S. , MIT, Mathematics (1998) B.S. , MIT, Electrical Engineering and Computer Science (1998) His lab explores how higher-level cognitive phenomena emerge from neural network dynamics, focusing on perception, memory, attention, and decision-making . Research themes include statistical mechanics of learning , neural representational geometry , and biologically plausible learning rules . Current work examines nonlinear interactions in neural networks through the Schmidt Science Polymath Award (2023). Key article trends reveal expertise in neural coding limits (2022 Nature), synaptic plasticity models (2022 Neural Computation), and deep learning theory (2022 NeurIPS publications). His 2019 Annual Review chapter on statistical mechanics of deep learning established foundational insights into network criticality. Scientific Awards : NSF Career Award (2019) Simons Foundation Investigator (2016) McKnight Scholar Award (2015) James S. McDonnell Foundation Scholar (2014) Sloan Research Fellow (2013) As advisor, he mentors 10+ doctoral students across Applied Physics, Neurosciences, and Computer Science, including Vamshi Balanaga and Mason Kamb. His lab collaborates with experimental teams at Stanford and beyond, supported by grants from NSF , Simons Foundation , and Swartz Foundation . The Neural Dynamics & Computation Lab unites physicists, mathematicians, and neuroscientists to decode cognition through interdisciplinary methods.

Paata IvanisviliView profile

Associate Professor

• Analysis
• Partial Differential Equations
• Probability
• +5 more

Paata Ivanisvili is an Associate Professor at the University of California, Irvine (UCI), Department of Mathematics, School of Physical Sciences. His research focuses on Analysis, Probability, Harmonic Analysis, and Functional Analysis, with a particular emphasis on isoperimetric inequalities, functional inequalities, and discrete structures such as the Hamming cube. He has held visiting positions at institutions including the Hausdorff Research Institute for Mathematics and Princeton University. Ivanisvili has organized conferences such as the Dual Trimester Program at the Hausdorff Institute on Boolean Analysis in Computer Science (2024) and annual Summer/Fall Schools since 2021. He earned his PhD in Mathematics from Michigan State University (2015) and a BS from Saint Petersburg State University (2011). His research interests include sharp inequalities in analysis (e.g., Poincaré, Beckner, Ehrhard), hypercontractivity, and applications to discrete mathematics and probability. He has collaborated with prominent mathematicians such as Fedor Nazarov, Alexander Volberg, and Roman Vershynin. Notable awards include the NSF CAREER Award (2021–2025) and Simons Fellowship in Mathematics (2025–2026). Ivanisvili’s recent work explores the interface between harmonic analysis and discrete mathematics, including studies on additive energies, convex hulls of space curves, and learning theory. His articles frequently address foundational questions in geometric functional analysis, often using tools like Bellman functions and optimal control theory. He actively advises PhD students and has mentored visiting researchers at UCI.

Mohammad ModarresView profile

Professor

• Probabilistic risk assessment
• Uncertainty analysis
• Probabilistic Physics of Failure
• +5 more

Mohammad Modarres is the Nicole J. Kim Eminent Professor at the University of Maryland within the A.J. Clark School of Engineering. He serves as Director of the Center for Risk and Reliability (CRR) and is a Professor of Nuclear Engineering in the Department of Mechanical Engineering. Dr. Modarres co-founded the world's first degree-granting graduate curriculum in reliability engineering at the University of Maryland and has established himself as an international expert in reliability and risk analysis. Dr. Modarres received his educational credentials from prestigious institutions: B.S. in Mechanical Engineering from Tehran Polytechnic M.S. in Mechanical Engineering from MIT M.S. and Ph.D. in Nuclear Engineering from MIT Dr. Modarres' research spans multiple critical areas in engineering risk and reliability. His primary interests include probabilistic risk assessment, uncertainty analysis, probabilistic physics of failure, and probabilistic fracture mechanics. His work encompasses both experimental investigations and sophisticated probabilistic model development. He has made significant contributions to materials degradation science, prognosis and health management systems, and nuclear safety analysis. His research bridges theoretical developments with practical applications in complex engineering systems, particularly in nuclear power and aerospace sectors. Analysis of Dr. Modarres' recent publications reveals a strong trend toward integrating advanced data science techniques with traditional reliability engineering. His work increasingly incorporates machine learning, deep learning, and entropy-based approaches to solve complex problems in prognostics and health management. There's a clear focus on multi-unit systems, particularly in nuclear power applications, and a growing emphasis on data-driven methodologies for remaining useful life estimation and failure prediction. His research maintains a strong foundation in probabilistic methods while embracing cutting-edge computational approaches. Dr. Modarres has received numerous prestigious honors and awards throughout his distinguished career: Nicole Y. Kim Eminent Professorship in Engineering Minta Martin Professorship in A.J. Clark School of Engineering University of Maryland Distinguished Scholar-Teacher (2019) Tommy Thompson Award for outstanding lifetime contributions to nuclear safety (American Nuclear Society) Fellow, American Nuclear Society Fellow, Institute of Electrical and Electronics Engineers (IEEE) Life Fellow of IEEE 1996 Maryland Inventor of the Year Award (in Information Sciences) FDA Commissioner Special Citation for Contributions to Risk Assessment Methods (2004) 2008 International Research Leadership Award (Society for Reliability Engineering, Quality and Operations Management) Honorary Doctorate from Universidad Da Vinci de Guatemala As the founding director of the Center for Risk and Reliability, Dr. Modarres has built a world-renowned program that has awarded over 500 Ph.D. and master's degrees. His research has been supported by significant grants from government agencies and industry partners, particularly in nuclear safety, aerospace reliability, and critical infrastructure protection. He has mentored numerous students who have gone on to become leaders in reliability engineering across various industries. His center collaborates extensively with the International Atomic Energy Agency (IAEA) and other international organizations on risk assessment methodologies. The Center for Risk and Reliability (CRR), which Dr. Modarres directs, serves as a hub for multidisciplinary research in risk and reliability engineering. The center has recently renovated its facilities to enhance collaboration among researchers from different engineering disciplines. CRR maintains strong partnerships with industry leaders including Amazon Lab126 (as evidenced by their collaboration on device durability research) and has been instrumental in advancing probabilistic risk assessment tools used in nuclear power plant safety analysis. The center hosts regular seminars, workshops, and international conferences, positioning itself at the forefront of risk and reliability research globally.

Gabriele FarinaView profile

Assistant Professor

• Game Theory
• Machine Learning
• Optimization
• +7 more

Gabriele Farina is an Assistant Professor at MIT in the Department of Electrical Engineering and Computer Science (EECS) and the Laboratory for Information and Decision Systems (LIDS), with additional affiliations at the Operations Research Center (ORC). Holding the X-Window Consortium Career Development Chair, his research focuses on theoretical and algorithmic foundations for learning and computational decision-making under imperfect information, integrating game theory, machine learning, optimization, and statistics. He previously served as a Research Scientist at Meta's Fundamental AI Research (FAIR) group, where he contributed to Cicero, a human-level AI agent combining strategic reasoning and natural language. Ph.D. in Computer Science from Carnegie Mellon University (advisor: Tuomas Sandholm) Facebook Fellowship (2019-2020) in Economics and Computation Recipient of multiple awards including ACM SIGecom dissertation award, NSF CAREER, and AI2050 Early Career Fellow His research spans four key areas: (1) No-Regret Learning Dynamics in extensive-form games; (2) Correlation and Mediated Equilibria in sequential decision-making; (3) Team Games and Team Max-Min Equilibria; and (4) Human Modeling and Equilibrium Perfection. His work addresses challenges in scalable equilibrium computation, stability of learning algorithms, and robustness to mistakes in multi-agent systems. Recent publications highlight advancements in polynomial-time equilibrium computation, cautious optimism algorithms, and connections between regret minimization and mirror descent. These contributions appear in top venues like COLT, NeurIPS, ICML, and AAAI, with keywords spanning game theory, optimization, and machine learning. NSF CAREER award AI2050 Early Career Fellow Facebook Fellowship ACM SIGecom dissertation award GameSec 2024 best paper award ICLR 2023 outstanding paper honorable mention His research group at MIT collaborates on projects involving strategic reasoning, human-level AI agents, and equilibrium refinements, with applications to games like Diplomacy and poker. Current efforts include developing faster algorithms for correlated equilibria and exploring connections between machine learning and economic theory.

• Information Theory
• Statistics
• Machine Learning
• +1 more

Varun Jog is Professor of Information Theory and Statistics in the Department of Pure Mathematics and Mathematical Statistics (DPMMS) at the University of Cambridge, Faculty of Mathematics. Previously, he served as Assistant Professor at the University of Wisconsin-Madison (2016-2020) and at the University of Cambridge (2021-2024). His academic background includes a B.Tech. in Electrical Engineering from IIT Bombay (2010) and a Ph.D. in Electrical Engineering and Computer Sciences from UC Berkeley (2015). Professor Jog's research centers on fundamental questions at the intersection of information theory, statistics, and machine learning. He develops theoretical frameworks for statistical inference under constraints such as limited communication and privacy requirements, with significant contributions to hypothesis testing, differential privacy, adversarial risk analysis, and information-theoretic inequalities. His work bridges abstract mathematical principles with practical applications in data science and robust machine learning. Recent publications demonstrate a concentrated focus on distributed inference systems, particularly examining sample complexity limits in hypothesis testing under information constraints and privacy-preserving mechanisms. His research consistently reveals deep connections between information theory and statistical learning, with increasing emphasis on adversarial robustness and foundational inequalities. His scientific contributions have earned recognition through prestigious awards: NSF-CAREER Award (2020) R. Narasimhan Memorial Lecture Award (2020) Eli Jury Award from UC Berkeley EECS Department (2015) Jack Keil Wolf student paper award at ISIT (2015) Professor Jog maintains an active research group, currently supervising one PhD student while having graduated four PhD students and four Master's students. His mentorship extends to postdoctoral researchers including Amir Asadi, Deepanshu Vasal, and Andre Wibisono. Research funding includes the competitive NSF-CAREER grant. He co-organizes the Cambridge Information Theory Seminar, fostering academic exchange and collaboration within the theoretical research community.

Maarten de BoerView profile

Professor

• Mechanics of Materials
• MEMS (Microelectromechanical Systems)
• Micro/Nano Manufacturing
• +7 more

Maarten de Boer is a Professor in the Department of Mechanical Engineering at Carnegie Mellon University (CMU), with a courtesy appointment in Materials Science and Engineering. He joined CMU in 2007 after roles as a process engineer at Hewlett-Packard (1983–1991) and principal member of technical staff at Sandia National Labs (1996–2010). He holds a Ph.D. in Materials Science (University of Minnesota, 1996), an MS in Electrical Engineering (University of Colorado, 1982), and a BS in Electrical Engineering (Cornell University, 1981). His research focuses on nanomechanical behavior of materials, MEMS, and additive manufacturing. Key projects include tantalum-based thermal actuators, high-entropy alloys, and micromachine reliability. His work is funded by the DOE, NSF, NASA, and the Army Research Lab. He has authored over 90 peer-reviewed articles, holds seven US patents, and advises students in the de Boer Group. Research Themes: Micro/Nano Manufacturing, Thin Film Mechanics, Friction & Wear, MEMS Reliability Funding Sources: NSF, DOE, NASA, ARL Courses Taught: Mechanics of Materials, Material Selection, Electronics for Sensing, Thermodynamics Notable collaborations include Gianluca Piazza (NSF LEAP-HI grant), Jack Beuth, and Bryan Webler (high-entropy alloys). Media highlights include breakthroughs in tantalum MEMS and ultra-strong polymer nanofibers. His group operates advanced test facilities for in-situ environmental studies of materials.

Erik WaingartenView profile

Assistant Professor

• Algorithms for Massive Datasets
• High-dimensional Geometry
• Property Testing
• +5 more

Erik Waingarten is an assistant professor at the University of Pennsylvania in the Computer and Information Science department. His research focuses on algorithms for massive datasets, including similarity search, streaming/sketching, property testing, and distribution testing. Former postdoctoral researcher at Stanford's CS Department under Moses Charikar PhD from Columbia University advised by Xi Chen and Rocco Servedio Key research areas: High-dimensional geometry Streaming algorithms Property testing Sketching techniques Clustering and metric optimization Recent article trends show expertise in: 2025 publications on monotonicity testing and metric property analysis 2024 work on Earth Mover's Distance and kernel evaluations 2023 papers on clustering, optimal transport, and MST algorithms 2022-2020 foundations in sublinear algorithms and entropy estimation Scientific recognition: NSF CAREER Award (2023) CCC Best Paper Award (2017) Invited to Journal of the ACM (2017) Academic advising includes PhD students: Ashwin Padaki Tian Zhang Nicolas Menand Krish Singal Junkai Song

Diego GarlaschelliView profile

Professor

• Network Theory
• Statistical Physics
• Econophysics
• +7 more

Diego Garlaschelli is Professor of Theoretical Physics at the IMT School for Advanced Studies in Lucca, Italy, and at the Lorentz Institute for Theoretical Physics, University of Leiden, the Netherlands. He leads the NETWORKS research unit at IMT and the Econophysics and Network Theory group at Leiden. He is also an external faculty member at the Complexity Science Hub in Vienna and an associate member of the Enrico Fermi Research Center in Rome. His affiliations reflect a strong international and interdisciplinary research profile in network science and statistical physics. He holds a master's degree in theoretical physics from the University of Rome III (2001) and a PhD in Physics from the University of Siena (2005). His postdoctoral experience includes positions at the Australian National University, the University of Siena, the University of Oxford, and the Sant’Anna School of Advanced Studies in Pisa. Garlaschelli’s research spans network theory, statistical physics, econophysics, financial complexity, ecological networks, and social dynamics. He applies maximum entropy models, information theory, and random graph frameworks to understand complex real-world systems. His teaching includes courses in Network Theory, Econophysics, and Complex Systems at both PhD and MSc levels. The 15 most recent publications highlight a consistent focus on network reconstruction, ensemble inequivalence, renormalization, and applications to financial and socio-economic systems. Key themes include statistical inference in networks, resilience, and multi-scale modeling, with publications in top journals such as Nature Reviews Physics , Physics Reports , Science , and Physical Review Letters . His scientific awards include the Best Paper Award at the 6th International Workshop on Self-Organizing Systems (2012) and the Jan Kijne Prize (2013) as supervisor. He has secured multiple grants from NWO, the European Union, and the Royal Society, and has supervised over 40 students at PhD, master’s, and bachelor’s levels. He also mentors postdocs and visiting scientists. Garlaschelli leads and organizes major international workshops and schools in network science and complex systems. He serves on scientific committees and is an active referee for journals like Nature and Physical Review Letters , as well as funding agencies including the ERC and NWO.