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Xuhui HuangView profile

Professor

• Theoretical Chemistry
• Molecular Biophysics
• Protein Folding
• +6 more

Dr. Xuhui Huang is the Hirschfelder Chair in Theoretical Chemistry and Professor of Chemistry at the University of Wisconsin-Madison. He also serves as Director of the Theoretical Chemistry Institute and leads interdisciplinary research bridging computational chemistry, biophysics, and machine learning. Education: B.S. (2001) - University of Science and Technology of China Ph.D. (2006) - Columbia University Postdoctoral Fellow (2006-2009) - Stanford University Research Focus encompasses theoretical chemistry and molecular biophysics, particularly: Development of advanced Markov State Models and Generalized Master Equations for biomolecular dynamics Elucidating RNA polymerase conformational changes and molecular recognition mechanisms Creating novel integral equation theories for solvation and deep-learning-based drug discovery frameworks Extracting structural ensembles from heterogeneous cryo-EM datasets Publication Trends reveal his group's emphasis on merging statistical mechanics with machine learning for studying: Protein folding pathways and allosteric regulation RNA polymerase dynamics and error-prone transcription Self-assembly processes and solvation modeling Development of open-source tools like EPIPY and FeatureDock Applications in drug-resistant pathogen targeting and unnatural base pair transcription Scientific Leadership includes: NSF grant recipient for polarizable 3DRISM model development Organizer of CECAM workshops on biomolecular dynamics Educational innovator - developed UW-Madison's 'Machine Learning in Chemistry' course Collaborative Network spans institutions like UCSF, Stanford, MIT, and HKUST, with group alumni transitioning to Bay Area biotech and top graduate programs.

Yen Huan LiView profile

Associate Professor

• Machine Learning
• Optimization
• High-dimensional Statistics
• +6 more

Yen Huan Li is an Associate Professor in the Department of Computer Science and Information Engineering at National Taiwan University's College of Engineering. He leads the Learning Theory and Optimization Methods Laboratory and maintains an active research program in theoretical machine learning and optimization. His research expertise spans machine learning, convex optimization, high-dimensional statistics, and quantum information theory . Professor Li specializes in decision making under uncertainty with weak or no assumptions about reality, developing theoretical frameworks that bridge game theory and machine learning. His recent work focuses on Blackwell approachability, probability forecasting calibration, online prediction algorithms, and optimization methods for strongly convex problems. These theoretical contributions have implications for designing robust machine learning algorithms that perform well under minimal assumptions. Professor Li is actively recruiting students and research assistants interested in theoretical aspects of machine learning and optimization. Prospective students must demonstrate knowledge of relevant theory papers and be prepared to discuss them in depth. His laboratory work emphasizes rigorous mathematical analysis of learning algorithms, with applications spanning from basic theoretical questions to practical algorithm design considerations. The research environment combines deep theoretical investigation with attention to practical implications for machine learning systems.

Aaron ScheinView profile

Assistant Professor

• Bayesian statistics
• Causal inference
• Machine learning
• +9 more

Aaron Schein is an Assistant Professor in the Department of Statistics and the Data Science Institute at the University of Chicago. His interdisciplinary work bridges statistics, computer science, and social sciences, developing innovative methodologies for analyzing complex data across multiple domains. His educational background includes: PhD in Computer Science from University of Massachusetts Amherst (2019), advised by Hanna Wallach in the Machine Learning for Data Science lab MA in Linguistics (Computational Linguistics) from University of Massachusetts Amherst BA in Political Science from University of Massachusetts Amherst Schein's research focuses on developing statistical models and computational methods for analyzing modern large-scale data in the social and biomedical sciences. His work spans several interconnected areas: Bayesian statistics - developing novel hierarchical Bayesian models for complex data structures Causal inference - designing methodologies for estimating causal effects in observational and experimental settings Machine learning - creating tensor decomposition methods and dynamical systems for high-dimensional data Computational social science - applying data science methods to political science, economics, and international relations Biomedical applications - developing statistical methods for genomics and cancer research His recent publications demonstrate a clear trajectory of methodological innovation applied to substantive problems. Early work focused on Bayesian tensor decomposition for analyzing country-to-country event data in international relations. This evolved into Poisson-randomized gamma dynamical systems for modeling sequential count data. More recently, Schein has conducted large-scale digital field experiments to assess the causal effects of friend-to-friend mobilization on voter turnout in US elections, bridging computational methods with real-world political applications. His work increasingly intersects with large language models and their applications in social science measurement. Aaron Schein has published in top-tier venues including NeurIPS, ICML, ACL, KDD, and PNAS, demonstrating the interdisciplinary impact of his research across computer science, statistics, political science, and biomedical fields. Before joining the University of Chicago, Schein was a postdoctoral fellow in the Data Science Institute at Columbia University, working with David Blei and Donald Green. He has industry experience from internships at Google, Microsoft Research, and policy work at the MITRE Corporation. He also serves as a senior technical advisor at Ocurate and a research affiliate at PredictWise.

Artur IzmaylovView profile

Professor

• Quantum Dynamics
• Theoretical Chemistry
• Quantum Computing
• +5 more

Artur Izmaylov is a Professor of Theoretical Chemistry at the University of Toronto with dual departmental appointments: the Department of Chemistry at the St. George campus and the Department of Physical and Environmental Sciences at the University of Toronto Scarborough (UTSC). He leads the Izmaylov Research Group and is affiliated with the Center for Quantum Information and Quantum Control. His offices are located at EV356 (UTSC) and LM420C (St. George), and he can be contacted at artur.izmaylov@utoronto.ca or via phone at 416-208-2951 (UTSC) / 416-946-8405 (St. George). Professor Izmaylov's research develops novel theoretical and computational approaches to quantum dynamics in complex systems. Key focus areas include: Quantum processes in organic photovoltaics, biomolecules, and catalytic surfaces Hybrid quantum-classical methodologies for subsystem-environment interactions Renormalization techniques for efficient quantum dynamics simulations Quantum computing applications for chemical problems and electronic structure Nonadiabatic dynamics near conical intersections and spin-charge transfer His recent publications (2023-2025) demonstrate strong emphasis on quantum algorithm development for chemical applications, particularly: Advancements in variational quantum eigensolver (VQE) methodologies Quantum resource optimization and error mitigation strategies Novel Hamiltonian decomposition techniques for efficient simulation Applications in molecular vibrations, electronic structure, and materials science Hybrid quantum-classical approaches for scalable computations The Izmaylov Research Group actively recruits graduate students and postdoctoral researchers, with opportunities through NSERC USRA, CQIQC, CHMD90/91, CHM499Y/PHY479Y courses, and Mitacs Globalink programs. Current research directions emphasize quantum computing implementations for chemical dynamics and surface interactions.

Thomas GuyetView profile

Researcher

• Artificial Intelligence
• Temporal Pattern Mining
• Healthcare Analytics
• +3 more

Thomas Guyet is a full researcher at the Inria Center of Lyon since 2021, part of the AIstroSight team focused on digital tools for drug design and healthcare analytics. He holds a co-chair position in the Inria/APHP AI-Raclès initiative. Previously, he was an assistant professor at Institut Agro (2007–2021), leading the Health and Biology theme in IRISA's LACODAM team. He earned his PhD in Computer Science from Grenoble INP (2007) and a habilitation in 2020. His research spans artificial intelligence, temporal/spatial pattern discovery, and interactive systems for data exploration. Applications include healthcare (care pathways, cell dynamics) and agronomy (remote sensing, landscape analysis). Current projects include QuickRare (diagnosis tools for rare diseases) and SafePaw (care pathway analysis). He supervises multiple PhD candidates and has led initiatives like the EGC 2025 conference. Key contributions include VCNet (counterfactual generation) and SWoTTeD (temporal phenotyping). His work bridges AI with ethics, law, and cognitive sciences, reflected in projects like ED-AIM (ethical AI in medicine). He is Vice-President of the French Association for Artificial Intelligence (AFIA) and contributes to international conferences and journals.

Edward ValeevView profile

Professor

• Electronic Structure Theory
• Density Functional Theory
• Wave Function Methods
• +7 more

Edward Valeev is a Professor in the Department of Chemistry at Virginia Polytechnic Institute and State University (Virginia Tech) , part of the College of Science . His research focuses on developing advanced electronic structure methods and high-performance computing frameworks for chemistry and materials science. Education: M.S., Higher Chemistry College of Russian Academy of Sciences, Moscow, Russia (1996) Ph.D., University of Georgia (2000) Research Interests: Dr. Valeev pioneers methods like reduced-scaling wave function approaches, real-space orbital representations, and quantum computing applications. His work emphasizes tensor compression (tensor networks) and software development (e.g., MPQC, Libint, TiledArray). Key areas include density functional theory corrections, periodic systems, and exascale computing. Key Contributions: His group develops open-source software like MPQC , enabling distributed-memory parallel computing. Their methods address challenges in predicting molecular properties and materials behavior at scale. Awards: Blavatnik National Award Finalist (2016) Dirac Medal (2015) Kavli Fellow (2013) Advising & Grants: Funded by NSF, DOE, and others, his research supports next-generation computational tools. Collaborations include developing quantum algorithms and exascale-ready software frameworks. Labs & Teams: Leads the Valeev Research Group , advancing theoretical chemistry and computational infrastructure.

• Statistics
• Machine Learning
• Tensor Methods
• +3 more

Yuchen Zhou is an Assistant Professor in the Department of Statistics at the University of Illinois. His research focuses on high-dimensional statistics, tensor methods, and optimization, with applications in data analysis and machine learning. He has contributed to advancements in areas such as heteroskedastic PCA, low-rank tensor inference, and sparse group lasso regularization. His work emphasizes statistical theory and methodology, addressing challenges in high-dimensional data structures and computational efficiency. Collaborations and research outputs highlight interests in matrix and tensor decomposition, regularization techniques, and non-asymptotic analysis. Zhou's publications span reputable journals like the Annals of Statistics and IEEE Transactions on Information Theory, reflecting his expertise in both theoretical and applied statistical problems. He has no listed academic awards or current advisees.

Bing OuyangView profile

Research Professor

• Ocean Engineering
• Machine Learning Applications
• Underwater Robotics
• +4 more

Bing Ouyang is an Associate Research Professor at Florida Atlantic University's Department of Ocean and Mechanical Engineering, with an affiliation as Affiliate Professor in the Oceanographic Center (OME). His research focuses on advanced technologies for marine and environmental systems, including underwater robotics, sensor development, and machine learning applications in aquaculture and oceanography. He leads projects integrating physics-constrained models with AI for biomass prediction, energy-efficient underwater systems, and LiDAR-based marine life monitoring. Key research areas include: Underwater Inflatable Co-prime Sonar Arrays (UICSA) for marine surveillance Compressive sensing and hyperspectral imaging systems Autonomous robotics for aquaculture maintenance Machine learning for environmental data analysis Optical sensor systems for marine environments Recent work emphasizes AI-driven solutions for IMTA (Integrated Multi-Trophic Aquaculture) systems, developing advanced sensing technologies like the Pseudorandom Encoded Light for Evaluating Biomass (PEEB) sensor. His lab also explores energy-efficient materials for underwater structures and turbulence-resistant imaging systems. Notable collaborations involve the Harbor Branch Oceanographic Institute (HBOI) and the development of the Hybrid Aerial Underwater Robotic System (HAUCS). Projects often combine mechanical engineering innovations with advanced data analytics to address challenges in marine resource management and environmental monitoring.

Masoud AtaeiView profile

Assistant Professor

• Statistical Modeling
• Financial Mathematics
• Machine Learning
• +5 more

Masoud Ataei is an Assistant Professor, Teaching Stream in the Department of Applied Statistics at the University of Toronto's Mathematical and Computational Sciences school. His research spans statistical geometry, financial chaos indices, neural network optimization, and spatio-temporal systems analysis. He holds a position focused on teaching excellence within the applied statistics discipline. Research interests include developing mathematical frameworks for complex systems analysis, with applications in finance, materials science, and biomedical signal processing. His work emphasizes interpretable machine learning models and optimization algorithms for high-dimensional data. Key contributions involve the Financial Chaos Index for market volatility modeling and the GEOM-BP algorithm for bin packing problems. Publications demonstrate interdisciplinary impact across mathematics, computer science, and finance. No scientific awards are listed, but his active publication record reflects ongoing research productivity. Advising and grant activities are not detailed in available information.

• Machine Learning
• Medical Image Analysis
• Multimodal Learning
• +3 more

Mohammod Naimul Islam Suvon serves as a Research Assistant in Machine Learning for Medical Image Analysis at the University of Sheffield's School of Computer Science within the Department of Computer Science. He is an active member of the Machine Learning research group focusing on healthcare applications. His research spans Medical Image Analysis , Cardiac Hemodynamics , and Multimodal Learning with emphasis on cost-effective diagnostic solutions. Key projects include pulmonary hypertension detection using cardiac MRI and ECG analysis, leveraging tensor-based multimodal fusion and variational autoencoders. His work bridges clinical cardiology needs with advanced AI techniques. Analysis of his 15 most recent publications reveals strong specialization in medical multimodal AI (80% of works), particularly for cardiovascular diagnostics. Secondary interests include pandemic response systems (CT-scan analysis for COVID-19), agricultural AI, and educational technology applications. His methodology consistently employs deep learning architectures adapted for specific clinical constraints. Suvon maintains active collaborations with clinical researchers at Sheffield, evidenced by co-authorship with medical professionals like Swift AJ and Alabed S. His publication pattern shows increasing focus on low-cost diagnostic solutions since 2022, suggesting strategic alignment with healthcare accessibility goals.

Bo ShenView profile

Assistant Professor

• Machine Learning
• Deep Learning
• Additive Manufacturing
• +4 more

Bo Shen is an Assistant Professor in the Department of Mechanical and Industrial Engineering at New Jersey Institute of Technology (NJIT). His research focuses on machine learning, deep learning, additive manufacturing, and data analytics with applications in solar physics, healthcare, and materials science. He holds a Ph.D. in Industrial and Systems Engineering from Virginia Tech (2022) and a B.S. in Statistics from the University of Science and Technology of China (2017). His work bridges theoretical advancements in AI with practical industrial and scientific challenges. Education: Ph.D., Industrial and Systems Engineering, Virginia Polytechnic Institute and State University, 2022 B.S., Statistics, University of Science and Technology of China, 2017 Research Interests: Prof. Shen’s research spans deep learning , optimization algorithms , additive manufacturing process analytics , and solar physics data modeling . He develops novel methods for model explainability, anomaly detection in manufacturing, and high-dimensional data analysis. His work often integrates physics-based principles with machine learning frameworks. Publications: His recent articles address topics like neural operator learning for fatigue prediction, Bayesian optimization in additive manufacturing, and deep learning-driven solar image reconstruction. These reflect a trend toward applying advanced AI techniques to solve interdisciplinary problems in engineering and science. Awards/Grants: No awards explicitly listed, but his prolific publication record suggests active research engagement. Details on grants or lab affiliations are not provided in the text. Labs/Teams: While specific lab affiliations are not mentioned, his work aligns with NJIT’s strategic research focus areas in data analytics and advanced manufacturing.

• Human-Robot Interaction
• Reinforcement Learning
• Artificial Intelligence
• +6 more

Jamison Heard is an Assistant Professor in the Department of Electrical and Microelectronic Engineering at the Kate Gleason College of Engineering, Rochester Institute of Technology (RIT). His research focuses on adaptive human-robot teams, human-aware reinforcement learning, and real-time human state assessment. He teaches courses in artificial intelligence, biorobotics/cybernetics, and machine learning methodologies. He holds a B.S. in Electrical Engineering from the University of Evansville (2013), and M.S. and Ph.D. degrees in Electrical Engineering from Vanderbilt University (2016 and 2019). His work spans interdisciplinary areas including human-robot interaction dynamics, task recognition, and the integration of biomedical applications with robotic systems. His research publications emphasize advancements in human-robot collaboration, reinforcement learning techniques, and multimodal data analysis. He has explored topics such as stress impact on teleoperation performance, neural architecture search for GANs, and emotion estimation in human-robot interactions. Dr. Heard’s teaching responsibilities include courses such as EEEE-447 (Introduction to AI), EEEE-536/636 (Biorobotics/Cybernetics), and IDAI-710 (Fundamentals of Machine Learning). His work often bridges theoretical AI advancements with practical applications in healthcare and industrial systems.

Devin MatthewsView profile

Assistant Professor

• Computational Chemistry
• Quantum Mechanics
• Computational Spectroscopy
• +3 more

Devin Matthews is an Assistant Professor of Chemistry at Southern Methodist University (SMU), leading the Matthews Research Group. His research focuses on developing and applying ab initio quantum mechanical methods, with an emphasis on computational spectroscopy, tensor contraction algorithms, and coupled cluster theory. He holds a Ph.D. from The University of Texas at Austin (2014), following a B.S. in Chemistry (2010, also from UT Austin). Prior to SMU, he was a Postdoctoral Fellow at UT Austin's Institute for Computational Engineering and Sciences (2014-2018). Key research interests include Development of high-performance computational methods Quantum mechanical modeling of molecular systems X-ray spectroscopy simulations Algorithm optimization for parallel computing Matthews has been recognized with prestigious awards, including the Arnold O. Beckman Postdoctoral Fellowship (2015-2018) and the DOE Computational Science Graduate Fellowship (2010-2014). His work bridges chemistry, physics, mathematics, and computer science, with applications in understanding molecular structure and reactivity. Publications highlight advancements in tensor contraction techniques, coupled cluster theory scalability, and computational spectroscopy. The Matthews Group collaborates on projects involving electronic structure calculations and algorithm design for large-scale systems.

• Gravitational Wave Physics
• Black Hole Physics
• General Relativity
• +4 more

Nicolas Yunes is a Professor of Physics at the University of Illinois and a member of the National Center for Supercomputing Applications (NCSA). His research focuses on gravitational wave physics, black hole dynamics, and testing general relativity in extreme environments. Yunes leads efforts in modeling neutron star structure, analyzing gravitational wave data from LIGO/Virgo/KAGRA, and exploring modified gravity theories using astrophysical observations. He is a recipient of the APS Fellow award (2022) for contributions to theoretical and computational astrophysics. His work includes developing the MUSES framework for neutron star calculations, advancing parametrized post-Einsteinian tests of GR, and investigating dark matter effects on compact objects. Yunes collaborates extensively with international teams on projects like pulsar-timing arrays and next-generation gravitational wave detectors. Recent research highlights include studies on gravitational parity violation probes, systematic biases in waveform models, and constraints on dark-sector interactions via compact binary inspirals. His interdisciplinary approach bridges fundamental physics, computational methods, and observational astronomy.

Joshua AgterbergView profile

Assistant Professor

• High-Dimensional Statistics
• Matrix and Tensor Analysis
• Nonconvex Optimization
• +4 more

Joshua Agterberg is an Assistant Professor in the Department of Statistics at the University of Illinois, College of Liberal Arts & Sciences. His research focuses on high-dimensional statistics, matrix and tensor analysis, nonconvex optimization, and statistical network analysis. He holds a B.S. in Actuarial Science and Mathematics from the University of Wisconsin-Madison, followed by M.S. and Ph.D. degrees in Statistics from Johns Hopkins University. After a postdoctoral fellowship at the University of Pennsylvania, he returned to the Midwest to join UIUC. Education: Bachelor of Science in Actuarial Science and Mathematics, University of Wisconsin-Madison Master of Science in Statistics, Johns Hopkins University Doctor of Philosophy in Statistics, Johns Hopkins University Research Interests: His work bridges theoretical foundations and practical applications, addressing problems in nonconvex matrix sensing, multilayer network analysis, and statistical-computational gaps. He aims to develop methodologies for analyzing complex data structures, particularly in neuroscience, sociology, and engineering. Recent projects include advancing spectral methods, nonconvex optimization, and hypothesis testing frameworks for networks. Teaching Philosophy: Agterberg emphasizes statistical intuition over formulaic approaches, encouraging students to tackle problems from first principles. He teaches courses on theoretical statistics and hopes to develop a high-dimensional statistics curriculum for PhD students. Future Goals: He seeks to create a principled pipeline for network data analysis and to enhance understanding of machine learning algorithms' theoretical underpinnings. Collaborations include work on dynamic networks and statistical methodologies for connectomics.