Find Academic Supervisors

• Machine Learning
• Probability
• Mathematical Physics
• +6 more

Boris Hanin is an Associate Professor at Princeton University's Department of Operations Research and Financial Engineering (ORFE) and Associated Faculty at the Program in Applied and Computational Mathematics (PACM). Prior to Princeton, he held academic positions at Texas A&M University (Assistant Professor of Mathematics), MIT (NSF Postdoctoral Fellow), and Northwestern University (PhD in Mathematics under Steve Zelditch). He works part-time at Foundry, an AI/computing startup, leading the Foundry Institute. PhD in Mathematics, Northwestern University NSF Postdoctoral Fellowship, MIT Mathematics Associate Professor, Princeton ORFE Part-Time Leader, Foundry Institute His research spans machine learning, probability theory, and mathematical physics, focusing on neural network theory (approximation power, optimization guarantees), random matrix theory, and spectral asymptotics. He has made foundational contributions to understanding gradient behavior, initialization, and infinite-width limits in deep learning. Boris has supervised numerous PhD students and postdocs, with former members securing prestigious positions at Harvard, MIT, and Huawei. He serves as Associate Editor for journals like Pure and Applied Analysis and Mathematics of Operations Research , and has taught short courses at Oxford, Luxembourg, and Tor Vergata on statistical physics of neural networks and deep learning theory. 2024 Sloan Fellowship in Mathematics NSF CAREER grant DMS-2143754 NSF grant DMS-2133806 His research group collaborates on topics including hyperparameter transfer, architecture-aware scaling, and spectral analysis of random waves and neural networks. He actively contributes to theoretical machine learning through foundational publications in venues like Probability Theory and Related Fields , Journal of Machine Learning Research , and Communications in Mathematical Physics .

Brent DoironView profile

Professor

• Computational Neuroscience
• Neural Networks
• Theoretical Neuroscience
• +2 more

Brent Doiron is a Professor at the University of Chicago, holding appointments in the Departments of Neurobiology and Statistics, and serving on the Committee on Computational and Applied Mathematics (CCAM). His research integrates nonlinear dynamics and statistical mechanics to study neural circuit variability, focusing on mechanisms underlying neural coding and network learning through collaborations with experimentalists in sensory systems. Education: PhD in Physics (University of Ottawa, 2004) Postdoc: Center for Neural Science at New York University (2017) Previous Roles: Mathematics Professor at University of Pittsburgh (2007-2020), Co-Director of Neural Computation Program at Carnegie Mellon Neuroscience Institute Research interests center on neuronal population dynamics, recurrent circuit mechanisms, and computational neuroscience. Current work investigates correlated variability in cortical networks, inter-areal communication, and stochastic spiking models. Recent publications emphasize cortical stability/gain modulation, asynchronous/synchronous activity balance, and Bayesian inference frameworks. Key themes include sensory processing, network plasticity, and dimensionality reduction in neural coding. Scientific Awards Alfred P. Sloan Research Fellowship in Neuroscience Vannevar Bush Faculty Fellowship Chancellor’s Distinguished Research Award (University of Pittsburgh) Active grants include NIH R01 and R90/T90 awards for neuronal dynamics research and computational neuroscience training programs.

Tommi JaakkolaView profile

Professor

• Machine Learning
• Artificial Intelligence
• Computational Biology
• +7 more

Tommi Jaakkola is the Thomas Siebel Professor of Electrical Engineering and Computer Science and the Institute for Data, Systems, and Society at the Massachusetts Institute of Technology. He received his MSc in theoretical physics from Helsinki University of Technology in 1992 and his PhD from MIT in computational neuroscience in 1997. After completing a postdoctoral position in computational molecular biology as a DOE/Sloan fellow at UCSC, he joined the MIT EECS faculty in 1998. His research advances how machines can learn, predict or control, and do so at scale in an efficient, principled, and interpretable manner. His work in machine learning extends from foundational theory to modern applications, focusing especially on statistical inference and estimation tasks that lie at the heart of complex learning problems. He designs new methods, theory and algorithms to automate the use and generation of semi-structured data such as natural language text, images, molecules, or strategies. Jaakkola applies and develops algorithms to solve multi-faceted recommender, retrieval, or inferential tasks (particularly in biomedical contexts), design and optimize molecules or reactions for drug design, and model strategic, game theoretic interactions. His recent work heavily focuses on diffusion models, protein structure prediction, molecular design, and generative AI, with significant publications in top conferences including ICML, NeurIPS, and ICLR. His scientific contributions span multiple disciplines with significant impact in both theoretical machine learning and practical applications in computational biology and chemistry, including notable work on antibiotic discovery published in Cell. Current advisees: Julia Balla, Bowen Jing, Hannes Stärk, Peter Holderrieth, Chenyu Wang Recent graduates: Gabriele Corso (Boltz PBC), Ezra Erives (DE Shaw), Jason Yim (Xaira) Jaakkola maintains an active research program through MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) and the Institute for Data, Systems, and Society (IDSS), with his office located in the Stata Center (32-G470). His work bridges theoretical machine learning with practical applications, making significant contributions to both the academic field and potential real-world impact in healthcare and drug discovery.

Insup LeeView profile

Professor

• Cyber-physical Systems
• Real-time and Embedded Systems
• Safe Autonomy
• +3 more

Insup Lee is the Cecilia Fitler Moore Professor in the Department of Computer and Information Science and Director of the PRECISE Center at the University of Pennsylvania's School of Engineering and Applied Science. He holds a secondary appointment in the Department of Electrical and Systems Engineering and the Perelman School of Medicine’s Department of Biostatistics, Epidemiology, and Informatics. IEEE TCCPS Distinguished Leadership Award (2023) Fellow of the AAAS (2022) Test of Time Award, Runtime Verification (2019) Fellow of the ACM (2017) Best Paper Awards at IEEE ICPS, ACM/IEEE ICCPS, and MEMOCODE His research focuses on cyber-physical systems , real-time and embedded systems , safe autonomy , and internet of medical things , with applications in healthcare and connected systems. He advises PhD students including Eric Lu, Kaustubh Sridhar, Sooyong Jang, and Jean Park (co-advised with Kevin Johnson). Recent publications address safety monitoring for learning-enabled systems, model-free control synthesis using reinforcement learning, and multilingual toxicity guardrails for large language models. His team collaborates with institutions like Hillrom and Penn Nursing to optimize medical device usage in clinical settings.

Michael A OsborneView profile

Professor

• Machine Learning
• Bayesian Optimization
• Gaussian Processes
• +3 more

Michael A Osborne is Professor of Machine Learning at the University of Oxford and leads the Bayesian Exploration Lab . He serves as Director of the EPSRC Centre for Doctoral Training in Autonomous Intelligent Machines and Systems and co-directs the Oxford Martin AI Governance Initiative. His research focuses on Bayesian optimization, Gaussian processes, and probabilistic numerics with applications in quantum devices, battery modeling, and AI governance. Key Positions: Professor of Machine Learning, University of Oxford Official Fellow, Exeter College Co-founder of Mind Foundry Lead Researcher, Oxford Martin Programme on Technology and Employment Research Themes: Probabilistic modeling for quantum systems Uncertainty quantification in energy storage AI safety and societal impact analysis Automated experimental design Quantum device calibration Probabilistic numerical methods Technical Contributions: Bridging reality gap in quantum devices Efficient Bayesian quadrature techniques Personalized neurostimulation algorithms Automated measurement protocols Quantum-classical hybrid ML

Tülay AdaliView profile

Professor

• Statistical Signal Processing
• Machine Learning
• Medical Image Analysis
• +3 more

Tülay Adali is a Professor in the Department of Computer Science and Electrical Engineering at the University of Maryland, Baltimore County (UMBC). She has held this position since 1992 and was named a Distinguished University Professor in 2015 for her contributions to statistical signal processing and machine learning. Currently serving as Editor-in-Chief of the IEEE Signal Processing Magazine, she has also held leadership roles in IEEE committees and conferences. Her research focuses on statistical signal processing, machine learning, and their applications in medical imaging and data fusion. Dr. Adali earned her Ph.D. in Electrical Engineering from North Carolina State University in 1992. Her work integrates foundational signal processing techniques with biomedical applications, addressing challenges in neuroimaging analysis. She leads the Machine Learning for Signal Processing laboratory, supported by grants from NSF and NIH. Her lab develops algorithms for analyzing complex signals in medical contexts, emphasizing reproducibility and interdisciplinary collaboration. Recognition includes IEEE Fellow, AIMBE Fellow, AAIA Fellow, Humboldt Research Award, and NSF CAREER Award. She has authored numerous papers on fMRI analysis, independent component analysis, and multimodal data fusion. Her editorial leadership and service to technical communities reflect her commitment to advancing signal processing and education. Education: Ph.D. in Electrical Engineering, North Carolina State University (1992) Grants: NSF, NIH-funded projects on medical imaging and signal processing Awards: SPS Meritorious Service Award, SPIE Pioneer Award

Katie BylView profile

Associate Professor

• Robotics
• Control Systems
• Robot Dynamics and Control
• +7 more

Katie Byl is an Associate Professor in the Departments of Electrical and Computer Engineering and Mechanical Engineering at the University of California, Santa Barbara (UCSB). Her research focuses on robot dynamics and control, particularly in locomotion and manipulation, with applications in rough terrain legged locomotion, supervised autonomy, and flapping-wing flight. She holds B.S., M.S., and Ph.D. degrees in Mechanical Engineering from MIT. Affiliations: Center for Control, Dynamical Systems and Computation (CCDC) Mechanical Engineering Department Research Interests: Byl’s work emphasizes modeling and control of underactuated systems, stochasticity in real-world environments, and the development of robust control principles for dynamic systems. Her applied projects include exoskeletons, autonomous robots like RoboSimian (part of the DARPA Robotics Challenge), and flapping-wing microrobotics. Scientific Awards: NSF Early Career Development Award Hellman Faculty Fellowship Alfred P. Sloan Foundation Fellowship in Neuroscience Regents’ Junior Faculty Fellowship Teaching & Advising: Byl teaches courses in control systems (e.g., ECE 147B, ECE 179D) and robotics. She advises students in UCSB’s Robotics Lab, emphasizing a mix of control theory, mechanical design, and algorithm development. Undergraduate researchers are also recruited annually for summer projects. Labs & Teams: Her Robotics Lab focuses on hardware implementation of control ideas, maintaining a high robot-to-student ratio. Collaborations include the Army’s Institute for Collaborative Biotechnologies and the DARPA Robotics Challenge with JPL.

• Data Science
• Machine Learning
• Computational Harmonic Analysis
• +6 more

Pierre Vandergheynst is a Full Professor at the Swiss Federal Institute of Technology Lausanne (EPFL) in the Department of Electrical Engineering, with a courtesy appointment in Computer and Communication Sciences. He serves as EPFL’s Vice-Provost for Education since 2015 and leads the Signal Processing Laboratory 2 (LTS2). His research spans harmonic analysis, sparse approximations, mathematical data processing, and applications in signal/image processing, computer vision, machine learning, and graph-based data analysis. PhD in Mathematical Physics (1998), Université catholique de Louvain Postdoctoral Researcher at EPFL (1998-2001) Assistant Professor at EPFL (2002-2007) His research explores geometry/symmetry in high-dimensional data, redundant dictionaries for dimensionality reduction, and computational harmonic analysis on manifolds. Recent work focuses on protein structure modeling, geometric deep learning, and graph-based signal processing. Key article trends include graph neural networks for protein analysis, geometric deep learning in neuroscience, and structured knowledge priors in neural models. His 2023-2025 publications emphasize interpretable AI, long-range dependencies in graphs, and molecular representation learning. Scientific Awards: IEEE Signal Processing Magazine Best Paper Award (2023) Signal Processing Society Best Paper Award (2022) Apple ARTS Award (2007) De Boelpaepe Prize, Royal Academy of Sciences of Belgium (2009-2010) He has supervised over 30 PhD theses and contributed to foundational work in graph signal processing, compressive sensing, and geometric deep learning. His lab develops tools for data science on non-Euclidean structures, with applications in medicine, astronomy, and wireless systems.

Jean-Philippe ThiranView profile

Professor

• Computational Imaging
• Medical Image Analysis
• Diffusion MRI
• +5 more

Prof. Jean-Philippe Thiran is a Full Professor at École Polytechnique Fédérale de Lausanne (EPFL), where he serves as Director of the Signal Processing Laboratory (LTS5) and Director of the Institute of Electrical and Micro Engineering. He also maintains a part-time Associate Professor position with the Department of Radiology of the University Hospital Center (CHUV) and University of Lausanne (UNIL). Born in Namur, Belgium in 1970, he received his Electrical Engineering degree and PhD from the Université catholique de Louvain (UCL), Belgium, in 1993 and 1997 respectively. He joined EPFL in 1998 and has established himself as a leading researcher in computational imaging. His research focuses on computational imaging , with significant contributions to medical image analysis (particularly diffusion MRI, ultrasound imaging, and digital pathology) and computer vision . His recent work integrates advanced modeling, simulation, and machine learning techniques to extract microscopic tissue information from macroscopic MRI signals. This approach combines hyper-realistic synthetic tissue models, advanced Monte-Carlo simulations, and ML-based estimation techniques for brain microstructure analysis with potential applications to other tissues. Senior Member of IEEE Fellow of the European Association for Signal Processing (EURASIP) Prof. Thiran has authored or co-authored 1 book, 9 book chapters, 250 journal papers and over 270 peer-reviewed conference papers, and holds 12 international patents. He previously served as Co-Editor-in-Chief of the Signal Processing journal (2001-2005) and associate editor of IEEE Transactions on Image Processing. He has chaired major conferences including EUSIPCO 2008 and IEEE ICIP 2015. His laboratory at EPFL brings together interdisciplinary researchers to develop innovative imaging techniques that bridge macroscopic measurements and microscopic tissue properties, with significant potential for medical diagnostics and treatment planning applications.

Benjamin MachtaView profile

Assistant Professor

• Biophysics
• Condensed Matter Physics
• Statistical Physics
• +3 more

Benjamin Machta is an Assistant Professor of Physics at Yale University, affiliated with the Department of Physics and the QBio Institute. He holds a BS from Brown University and a PhD from Cornell University, followed by a postdoctoral fellowship at Princeton University. His research focuses on applying theoretical physics to understand biological systems, particularly leveraging statistical physics and information theory to study biological membranes near critical points and the energetic constraints of biological signaling. Education: BS in Physics (Brown University), PhD in Physics (Cornell University), Postdoc at Princeton University (Lewis-Sigler Theory Fellow). Research Interests include: membrane criticality, phase transitions in biological systems, information-theoretic limits in organism function, and energy dissipation in biological processes. His work often bridges theoretical models with experimental data, such as collaborations with Sarah Veatch’s lab on membrane phase behavior. Publications highlight themes like membrane criticality, protein phase separation, and energy constraints in signaling. His group’s current projects explore cochlear mechanics, thermodynamic control in biological systems, and the role of criticality in sensory systems. Awards: 2019 Simons Investigator Award. Lab Affiliations: QBio Institute and Department of Physics at Yale, located in YSB-C164. Group members include postdocs Isabella Graf and Michael Abbott, and graduate students Asheesh Momi, Mason Rouches, and others.

Erik WillcuttView profile

Professor

• Behavioral Genetics
• Psychiatric Genetics
• ADHD Etiology
• +3 more

Erik Willcutt is a Professor in the Department of Psychology and Neuroscience at the University of Colorado Boulder. His research focuses on the genetic and neurobehavioral underpinnings of ADHD, learning disabilities, and developmental psychopathologies. He holds positions at the Institute for Behavioral Genetics and the Center for Neuroscience. Education: PhD in Psychology from the University of Denver (1998). Research Interests: Etiology and assessment of ADHD, reading disabilities, and developmental psychopathologies. His work integrates behavioral genetics, neuroimaging, and longitudinal twin studies to understand cognitive and psychiatric disorders. Key topics include neuroanatomical correlates of ADHD, genetic influences on dyslexia, and comorbidity between learning disabilities and psychiatric conditions. Publications highlight advanced methods like genome-wide association studies (GWAS) and phenotype harmonization (e.g., Rosetta method). His work bridges molecular genetics with clinical psychology, emphasizing translational research. Lab/Affiliations: Active in the Institute for Behavioral Genetics and collaborates with interdisciplinary teams studying neurodevelopmental disorders. Office located at Muenzinger D451B.

Huiyan SangView profile

Professor

• Spatial Statistics
• Bayesian Nonparametrics
• Machine Learning
• +9 more

Huiyan Sang is a Professor and Director of the Undergraduate Program in the Department of Statistics at Texas A&M University (College of Arts & Sciences). She earned her Ph.D. in Statistics from Duke University and a B.Sc. in Mathematics and Applied Mathematics from Peking University. Her research focuses on spatial statistics, Bayesian nonparametric methods, machine learning, computational statistics, and applications in environmental sciences, geosciences, urban planning, and biomedical research. Her interdisciplinary work integrates statistical methodologies with real-world challenges, such as analyzing extreme environmental events, optimizing urban infrastructure, and modeling complex systems like human mobility during pandemics. She has contributed to advancing spatio-temporal modeling, Gaussian processes, and Bayesian hierarchical frameworks for large datasets. Recent publications highlight innovations in nonparametric regression, spatial functional data analysis, and stochastic frontier analysis, often leveraging computational efficiency and scalability. Her work addresses critical societal issues, including the impact of community design on public health and environmental monitoring through remote-sensing data. No scientific awards are explicitly listed in the provided texts. She advises no students or grants in the current dataset but collaborates widely on interdisciplinary projects. Her research lab focuses on developing cutting-edge statistical tools with applications in engineering, public health, and environmental science.

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.

Andrea Lecchini VisintiniView profile

Associate Professor

• Systems Modelling and Control
• Aerospace Engineering
• Biomedical Applications
• +1 more

Dr. Andrea Lecchini Visintini is an Associate Professor at the School of Electronics and Computer Science , University of Southampton. He specializes in systems modelling and control with applications in aerospace engineering and biomedical domains, utilizing Monte Carlo methods for stochastic optimization. Cyber-Physical Systems Research Group Institute for Life Sciences Research Focus: His work bridges computational methods with practical applications in: Neurovascular coupling and brain tissue pulsation analysis Advanced control strategies for aerospace systems Stochastic optimization in machine learning and fault detection Medical imaging and diagnostic protocol development Publication Trends: Recent work emphasizes interdisciplinary approaches combining computational neuroscience with engineering, focusing on brain hemodynamics, MIMO system control, and data augmentation techniques for imbalanced datasets. Supervision: Currently supervising PhD student Xuankun Cai in Computer Science.

• Self-organized criticality
• Neuronal network dynamics
• Excitation/Inhibition balance
• +5 more

Anna Levina is an Assistant Professor for Computational Neuroscience at the University of Tübingen , affiliated with the Department of Computer Science under the Faculty of Science. Her research focuses on the self-organization of neuronal activity, critical dynamics in neural networks, and the excitation/inhibition balance in cortical circuits. Current positions: Assistant Professor (since 2018), Group Leader (2017-2018), Equality Officer (Computer Science) Previous roles: IST Fellow (2015-2017), Associated Researcher (2011-2015), Postdoc/PI (2011-2015), Postdoc (2008-2011) Her research integrates mathematical modeling , statistical physics , and computational neuroscience to study criticality phenomena, neural avalanches, and adaptive network dynamics. Key interests include: Self-organized criticality in neural systems Excitation/Inhibition balance mechanisms Network topology and dynamics Timescale analysis in neural processing Stochastic modeling of neural activity Recent publications reveal trends in understanding critical dynamics across biological and artificial networks, with applications to memory systems, sensorimotor integration, and disease modeling. She has received recognition as an IST Fellow .