Find Academic Supervisors

• Geometric Data Processing
• Computational Geometry
• Optimal Transport
• +7 more

Justin Solomon is an Associate Professor in the Department of Electrical Engineering & Computer Science at Massachusetts Institute of Technology, where he serves as Principal Investigator of the Geometric Data Processing Group. He maintains dual affiliations with the Computer Science and Artificial Intelligence Laboratory (CSAIL) and the MIT Center for Computational Science and Engineering (CCSE), reflecting his interdisciplinary research bridging theoretical mathematics with practical applications in graphics and machine learning. His research interests center around geometric data processing, computational geometry, and optimal transport theory, with significant contributions to computer graphics, machine learning, and computer vision. Solomon's work spans fundamental mathematical theory to practical implementations, particularly in shape analysis, 3D reconstruction, and geometric deep learning. His research demonstrates consistent innovation in developing algorithms that bridge discrete and continuous geometry with applications in graphics, vision, and AI. The publication trends reveal Solomon's evolving research trajectory from foundational work in geometry processing toward increasing integration with modern machine learning techniques. His recent work shows strong emphasis on diffusion models, geometric deep learning, and applications of optimal transport in AI, with significant contributions to SIGGRAPH, NeurIPS, and ICML proceedings. The research demonstrates both mathematical rigor and practical impact, with applications spanning character animation, 3D reconstruction, and generative AI. Amazon Research Award (2017) for Large-Scale Geometrically-Structured Sampling Amazon Research Award (2023) for Lightweight Algorithms for Generative AI Ben Wegbreit Prize for Best Undergraduate Honors Thesis Firestone Medal for Excellence in Undergraduate Research Boothe Prize for Excellence in Writing 2nd place, SGP best paper awards (2010) Solomon has secured substantial research funding through awards like the Amazon Research Awards and maintains active collaborations across academia and industry. His group has produced numerous influential publications with students and collaborators, contributing significantly to both theoretical foundations and practical implementations in geometric data analysis. His textbook "Numerical Algorithms" demonstrates his commitment to education alongside research. As Principal Investigator of the Geometric Data Processing Group, Solomon leads a research team focused on developing mathematical foundations for analyzing and processing geometric data. The group maintains strong connections with both theoretical mathematics and practical applications, working at the intersection of computer graphics, machine learning, and computational geometry. Their work has significant implications for fields ranging from computer animation to medical imaging and scientific computing.

Roman FriggView profile

Professor

• General philosophy of science
• Philosophy of physics
• Statistical mechanics
• +11 more

Roman Frigg is Professor of Philosophy at the London School of Economics and Political Science (LSE) , affiliated with the Department of Philosophy, Logic and Scientific Method . He serves as Director of the Centre for Philosophy of Natural and Social Science (CPNSS) and Co-Director of the Centre for the Analysis of the Time Series (CATS) . Holding a PhD from the University of London and MScs in theoretical physics and philosophy from the University of Basel, his research spans philosophy of science , statistical mechanics , and climate modeling . Education : PhD (University of London), MSc (University of Basel: Theoretical Physics, Philosophy) His work addresses foundational questions in statistical mechanics and thermodynamics , including equilibrium theory, entropy interpretation, and relations between Boltzmannian and Gibbsian frameworks. Recent publications focus on probabilistic forecasts in chaotic climate models , culminating in the co-authored book The Fundamentals of Thermodynamics (Springer, 2025). Articles explore topics like the Ergodic Hierarchy , GRW quantum theory , and robustness analysis in climate science. Roman has made significant contributions to reconciling deterministic dynamics with objective probability via Humean interpretations. His collaborations with Charlotte Werndl, Carl Hoefer, and David Lavis have produced key insights into typicality , chaos-randomness relations , and non-equilibrium thermodynamics . Scientific Awards : Friedrich Wilhelm Bessel Research Award (Alexander von Humboldt Foundation) Roman supervises PhD students on topics ranging from scientific representation to quantum mechanics and climate policy uncertainty . His website provides access to his publications and teaching materials.

Josien PluimView profile

Professor

• Medical Image Analysis
• Image Registration
• Image Segmentation
• +12 more

Josien Pluim is a Full Professor of Medical Image Analysis at Eindhoven University of Technology (TU/e), where she leads the Medical Image Analysis group and serves as vice-dean of the Department of Biomedical Engineering. She also holds a part-time professorship at the University Medical Center Utrecht. Her research is centered at the intersection of artificial intelligence and clinical medicine, with strong affiliations to EAISI (Eindhoven Artificial Intelligence Systems Institute) and the EAISI Health initiative. Her academic background includes a Master's in Computer Science from the University of Groningen (1996), specializing in Scientific Computing and Imaging, followed by a PhD (2001) from the Image Sciences Institute at UMC Utrecht on multimodality image registration using mutual information. She advanced from assistant to associate professor at UMC Utrecht before joining TU/e as a Full Professor in 2014, with a concurrent part-time appointment at UMC Utrecht since 2015. Pluim’s research interests span medical image analysis, including image registration, segmentation, detection, and deep learning, with clinical applications in neurology and oncology. She investigates both methodological development and real-world clinical translation. Recent work emphasizes generative AI for synthetic data, robustness in deep learning models, and super-resolution techniques for brain MRI. Her publications reveal a strong trend toward addressing data scarcity, generalization, and evaluation in medical AI, particularly through simulation and diffusion models. She has co-authored over 250 peer-reviewed papers and is recognized with prestigious fellowships: Fellow of the MICCAI Society IEEE Fellow Pluim has served in leadership roles across the academic community, including Associate Editor for journals such as IEEE Transactions on Medical Imaging , IEEE TBME , and Medical Image Analysis . She has chaired major conferences like WBIR 2006 and MICCAI 2010, and served on the Executive Board of the MICCAI Society. She actively supervises research and educational projects, including team challenges and capstone courses in medical image analysis. Her group is involved in significant collaborative research, such as the EU-funded openGTN project, which supports PhD training in generative models for medical imaging. She also contributes to scientific advisory boards, including the Hanarth Fonds.

Dan SteinbergView profile

Academic

• Probabilistic Machine Learning
• Variational Inference
• Bayesian Deep Learning
• +7 more

Dan Steinberg is a senior research scientist and team leader of the Decisions & Statistical Learning team at CSIRO Data61 in Canberra, Australia. His expertise lies in probabilistic machine learning, variational inference, Bayesian deep learning, causal inference, and their application to domains spanning synthetic biology, geospatial analytics, and algorithmic fairness. Education PhD in Computer Vision / Machine Learning (2013) – University of Sydney, Australian Centre for Field Robotics Bachelor of Engineering (Mechatronics, First-Class Honours) – University of Sydney (2008) Bachelor of Commerce (Finance) – University of Sydney (2008) Research Interests Steinberg’s core research agenda revolves around building scalable probabilistic models that can learn efficiently from limited or noisy data and provide principled uncertainty estimates. Key themes include: Variational Inference & Bayesian Deep Learning: developing lightweight yet powerful algorithms for approximate posterior inference in complex models (e.g., Aboleth, Revrand). Active Learning & Experimental Design: creating methods that decide which experiments or measurements will maximise information gain, with recent focus on in-silico protein engineering via Variational Search Distributions (VSD). Causal Inference: leveraging machine-learning tools to perform robust observational causal studies for evidence-based policy, including work on youth well-being and academic outcomes. Algorithmic Fairness: translating normative notions of equity into quantifiable objectives for regression-based decision systems. Large-scale Spatial Analytics: Landshark—an open-source TensorFlow toolkit for supervised learning on massive geospatial raster datasets. Notable Software & Tools Aboleth: A minimal-overhead TensorFlow framework for Bayesian deep learning. Landshark: Command-line tools for large-scale spatial inference. Revrand: Scalable Bayesian generalised linear models with non-conjugate likelihoods. libcluster: Extensible C++ library for hierarchical Bayesian clustering. Scientific Awards Oral Presentation Award – ICML 2025 Workshop on Scaling up Intervention Models (SIMS) Oral Presentation Award – NeurIPS 2024 Workshop on Bayesian Decision-making and Uncertainty (BDU) Oral Presentation Award – NeurIPS 2023 Workshop on Adaptive Experimental Design and Active Learning Spotlight Paper Award – NeurIPS 2014 (Extended and Unscented Gaussian Processes) Research Team & Collaborations As Team Leader – Decisions & Statistical Learning at CSIRO Data61, Steinberg directs a multi-disciplinary group that partners with government agencies (e.g., Jobs and Skills Australia, Australian Institute of Health and Welfare) and industry to deploy machine-learning solutions at scale. He has previously held roles as Principal Researcher at Gradient Institute (2019-2023), Senior Research Engineer at CSIRO Data61 (2016-2019), Researcher at NICTA (2013-2016), and Research Associate at the University of Sydney (2012-2013).

• Reinforcement Learning
• Safe AI
• Artificial Intelligence
• +7 more

Javier García is a faculty member at the University Carlos III of Madrid, affiliated with the Department of Computer Science within the School of Engineering. His research lies at the intersection of artificial intelligence, reinforcement learning, and robotics, with a strong emphasis on safety, transferability, and real-world applications. His research interests include: Safe and robust reinforcement learning Transfer and multi-task learning in AI Adversarial attacks and defenses in deep RL Automated planning and decision-making under uncertainty Applications in social assistive robotics and financial systems The recent trend in his publications shows a focus on formalizing similarity between decision-making tasks, improving robustness against corrupted rewards and adversarial policies, and applying AI techniques to domains such as geriatric care and automated market making. His work often involves collaboration with Fernando Fernández and other researchers in Spain. He has contributed significantly to frameworks like CLARC for comprehensive geriatric assessment using robots, and has developed tools such as TIMIPLAN for transportation tasks. His research bridges theoretical AI with practical deployment in cognitive robotics and business simulators. Notable scientific contributions include a comprehensive survey on safe reinforcement learning (JMLR 2015), foundational work on policy reuse, and the development of taxonomies for MDP similarity. He has secured research funding through collaborative projects, though specific grants are not detailed in the provided text. He has advised several students and junior researchers, evident from co-authored publications, though specific names are not listed. He is part of research teams working on lifelong learning technologies and cognitive robot systems.

Simon ThomasView profile

Professor

• Set Theory
• Model Theory
• Infinite Group Theory

Simon Thomas is a Distinguished Professor of Mathematics at Rutgers, The State University of New Jersey, specializing in set theory, model theory, and infinite group theory. He is actively involved in teaching and research, with a focus on classification problems in group theory and descriptive set theory. Research Interests Thomas's research spans a broad range of topics within mathematical logic and group theory. His primary interests include: Set Theory: Focus on foundational aspects and applications to group theory. Model Theory: Investigations into the structure of models and their implications for group classification. Infinite Group Theory: Study of infinite groups, particularly in the context of descriptive set theory and ergodic theory. His work often involves the interplay between logic and group theory, leading to significant contributions to understanding the complexity of classification problems for various classes of groups. Scientific Contributions Thomas has authored numerous papers on the classification of groups using tools from descriptive set theory and ergodic theory. His research includes studies on torsion-free abelian groups, finitely generated groups, and Borel equivalence relations. Notable contributions include work on the automorphism tower problem, superrigidity phenomena, and the complexity of isomorphism relations for various algebraic structures. Teaching and Mentorship Thomas teaches courses in set theory and logic at Rutgers, including Math 361: Set Theory. He also organizes the Rutgers Logic Seminar, a weekly meeting that brings together researchers in logic and related fields from around the world. The seminar covers topics in set theory, model theory, and applications to algebra and computer science.

Sourav MedyaView profile

Assistant Professor

• Machine Learning
• Graph Mining
• Data Science
• +3 more

Sourav Medya is an Assistant Professor in the Department of Computer Science at the University of Illinois at Chicago (UIC). Prior to joining UIC, he was a research assistant professor at the Kellogg School of Management and the Northwestern Institute on Complex Systems (NICO) at Northwestern University. He earned his Ph.D. in Computer Science from the University of California, Santa Barbara (2019) and a Master of Engineering in Computer Science and Automation from the Indian Institute of Science, Bangalore, India. Ph.D., University of California, Santa Barbara, 2019 M.Eng., Indian Institute of Science, Bangalore, India His research lies at the intersection of graphs, machine learning, and data science, with a focus on bias, fairness, and interpretability in AI. He also develops machine learning techniques with applications in healthcare, infrastructure, and computational social science. His recent publications address topics such as global counterfactual explainers for GNNs, game-theoretic node selection, and interpretable logical rule extraction from graph models. Medya has advised students in areas like multimodal learning, graph neural networks, explainable AI, and efficient AI. His scientific awards include the UIC Outstanding MS Thesis Award (2024) and Best Paper Award at the Machine Learning on Graphs (MLoG) Workshop at WSDM'23. He has reviewed for top conferences including NeurIPS, ICML, KDD, and AAAI, and recently taught courses on Machine Learning on Graphs (CS 519) and Introduction to Data Science (CS 418) at UIC.

Roy E. WelschView profile

Professor

• Statistics
• Data Science
• Statistical Learning
• +10 more

Dr. Roy E. Welsch is the Eastman Kodak Leaders for Global Operations Professor of Management and a Professor of Statistics and Data Science at the MIT Sloan School of Management and the MIT Center for Statistics and Data Science . He currently serves as Director of the MIT Center for Computational Research in Economics and Management Science . Education : AB in Mathematics from Princeton University (1969), MS and PhD in Mathematics from Stanford University Dr. Welsch's research spans advanced statistical methodologies and their interdisciplinary applications. His work focuses on: Robust statistical methods for regression and covariance modeling Applications in financial markets, biomedical imaging, and drug repurposing Machine learning algorithms for high-dimensional data analysis Statistical computing and computational finance Uncertainty quantification in experimental design His recent publications emphasize cross-domain applications of machine learning, particularly in financial forecasting, biomedical diagnostics, and network behavior modeling. Notable trends include: Development of robust statistical frameworks Integration of NLP techniques in financial analysis Biomedical image processing algorithms for disease quantification Time-series and high-content analysis Portfolio optimization under uncertainty Scientific Recognition : Fellow of the Institute of Mathematical Statistics Fellow of the American Statistical Association Fellow of the American Association for the Advancement of Science Eastman Kodak Leaders for Global Operations Professorship As a dedicated educator, Dr. Welsch teaches Data Analysis and Applied Statistics courses focusing on regression modeling, experimental design, and quality control with applications in finance and marketing.

Jeffrey M. ChungView profile

Associate Professor

• Epilepsy
• Neural Signal Processing
• Neurosurgery
• +4 more

Jeffrey M. Chung, MD, FAAN is an Associate Professor of Neurology at Cedars-Sinai Medical Center. He serves as Director of both the Epilepsy Program and the Neurophysiology Laboratory since 2013. His clinical practice focuses on epilepsy and neurophysiology, with 26 years of experience in neurology and clinical neurophysiology. Northwestern University Medical School (1999) - MD Evanston Hospital (2000) - Internship UCLA (2003) - Residency UCLA (2005) - Fellowship Dr. Chung's research explores epilepsy mechanisms, neural signal processing , multi-modal probabilistic mapping for surgical outcomes, and device-oriented therapies . His recent publications focus on theta phase precession in memory , single-neuron correlates of seizures , and neuroimaging in post-stroke conditions . He leads clinical trials on novel anti-seizure agents and neuromodulation techniques. Circle of Friends – Grateful Patient Donation Honoree (Cedars-Sinai) Southern California Super Doctors (Los Angeles Magazine) Falks Medical Scholar (Arthritis Foundation) Golden Apple Teaching Award (Cedars-Sinai) Dr. Chung directs the Epilepsy Program and Neurophysiology Laboratory at Cedars-Sinai, while also mentoring through journal clubs and clinical teaching. He has received multiple industry grants from companies like Marinus Pharmaceuticals and Vertex Pharmaceuticals for trials on treatment-resistant epilepsy. His work bridges clinical care with neurophysiological research and advanced neural signal processing .

• Computational Algebra
• Finite Group Theory
• Combinatorics
• +7 more

Alice Niemeyer is a Professor at the Algebra Teaching and Research Area of RWTH Aachen University , specializing in computational algebra, finite group theory, and interdisciplinary applications in civil engineering. She has co-authored over 30 publications in journals like PNAS Nexus , Linear Algebra and Its Applications , and International Journal of Solids and Structures , with recent work bridging abstract algebra and topological interlocking concrete systems. Research Interests : Finite groups, matrix decomposition algorithms, combinatorial design, and applications to sustainable construction. Key Collaborators : Cheryl Praeger, Tomasz Popiel, Wilhelm Plesken, Daniel Robertz, Reymond Akpanya. Notable Grants : Funding from the Simons Foundation for computational algebra research. Awards : No explicit awards mentioned in the provided text.

• Geophysics
• Machine Learning
• Joint Inversion
• +7 more

Jiajia Sun is an Associate Professor of Geophysics in the Department of Earth and Atmospheric Sciences at the University of Houston. Her research focuses on advancing subsurface imaging, uncertainty quantification, and mineral exploration through interdisciplinary approaches combining geophysics, machine learning, and computer vision. Education : PhD in Geophysics (2015, Colorado School of Mines); BS in Geophysics (2008, China University of Geosciences, Wuhan). Research Interests : Jiajia specializes in deep learning for geophysical inversion, multi-physics data integration, and probabilistic geological modeling. Her work leverages computational resources like GPUs and clusters to solve inverse problems and tackle magnetic remanence challenges. Recent Publications : Her research includes applying Bayesian frameworks, deep generative models, and joint inversion algorithms to airborne geophysics for critical mineral mapping and hydrogen reservoir detection. She emphasizes open-source tools like SimPEG for reproducibility. Awards : J. Clarence Karcher Award (SEG) Advising & Collaborations : She mentors PhD students in geophysics and collaborates with institutions like Amazon’s Generative AI Innovation Center, Stanford University, and University College Dublin. Her team also tests drones and magnetometers at the UH Coastal Center.

• computational neuroscience
• theoretical neuroscience
• sensory systems
• +3 more

Wiktor Młynarski is a Professor and Research Group Leader at the Faculty of Biology, Ludwig-Maximilians-University Munich, where he directs computational neuroscience research focused on adaptive neural computations in sensory systems. His work bridges theoretical frameworks with experimental validation to uncover fundamental principles of biological information processing. His research centers on computational and theoretical neuroscience, leveraging information theory, statistics, and probabilistic machine learning to model how neural systems efficiently represent dynamic natural environments. Key interests include sensory coding adaptation across timescales, statistical structure of natural stimuli, and the development of normative frameworks applicable from synaptic to behavioral levels. This inherently collaborative approach integrates theoretical predictions with experimental neuroscience to identify universal processing rules. Analysis of Młynarski's publication trajectory (2014-2025) reveals consistent exploration of efficient coding principles across auditory and visual domains, with recent work emphasizing anticipatory processing during locomotion, time-constrained decision-making, and panoramic visual statistics. His research demonstrates how environmental dynamics shape neural representations, moving beyond static models to capture the brain's adaptive capabilities in real-world contexts. Professor Młynarski leads the Computational Neurobiology Research Group (https://compneurobio.org), fostering interdisciplinary collaborations to confront theoretical models with experimental data. The group's work aims to establish general rules for biological information processing by examining how sensory systems dynamically optimize computations in response to environmental regularities and changes.

Ayush BhartiView profile

Research Fellow

• Probabilistic Machine Learning
• Bayesian Statistics
• Simulation-based Inference
• +3 more

Ayush Bharti is an Academy Research Fellow (Research Fellow) in the Department of Computer Science at Aalto University, Finland. His primary affiliations include the Probabilistic Machine Learning research group and the Academy Professorship led by Samuel Kaski . His research centers on probabilistic machine learning with dual focus areas: advancing simulation-based inference methodologies and applying statistical techniques to stochastic radio channel modeling. Key interests include Bayesian statistics, robustness under model misspecification, handling missing data, and developing efficient approximate Bayesian computation frameworks. His work bridges theoretical machine learning with practical wireless communication challenges. Analysis of his 15 most recent publications (2021-2025) reveals a consistent trajectory in simulation-based inference innovation, featuring neural processes, diffusion models, and multilevel architectures. Approximately 60% of his work targets general statistical challenges (e.g., robustness, cost-awareness), while 40% addresses radio channel modeling applications. Notable trends include integration of domain expertise in inference loops and development of context-aware optimization techniques. No information is available regarding student supervision or research grants. Bharti operates within Aalto University's Probabilistic Machine Learning ecosystem, contributing to the Academy Professorship project under Samuel Kaski. This collaborative environment focuses on developing scalable inference algorithms for complex real-world systems, with particular emphasis on uncertainty quantification in simulation-driven scientific discovery.

• Artificial Intelligence
• Physics
• Computer Technology
• +4 more

Pedro Lind is a Professor at Oslo Metropolitan University's Faculty of Technology, Art and Design, where he serves in the Department of Information Technology with a focus on Artificial Intelligence. His academic appointments include active participation in research groups for Applied Artificial Intelligence and Mathematical Modeling. Dr. Lind's research spans interdisciplinary domains including: Biomedical AI applications (EEG classification, ECG analysis, eye tracking) Stochastic processes and complex systems modeling Trustworthy machine learning for security/privacy Physics-inspired computational methods Renewable energy statistics and modeling His recent publications demonstrate strong focus on developing novel AI methodologies for medical diagnostics (2024-2025), particularly using generative models and interpretable AI approaches for physiological data analysis. He leads significant research initiatives including the AI-Mind project developing diagnostic tools for dementia. Additional projects include international technology transfer collaborations with Czech Republic institutions. Dr. Lind maintains active research teams and labs focused on computational neuroscience and applied AI.

Stephanie WeirichView profile

Professor

• Programming Languages
• Dependent Types
• Type Systems
• +3 more

Stephanie Weirich is the ENIAC President's Distinguished Professor in the Department of Computer and Information Science at the University of Pennsylvania's School of Engineering and Applied Science. Her research focuses on programming languages, with emphasis on dependent types, formal verification, and functional programming semantics. She leads Penn's Programming Languages Group (PLClub) and teaches courses including Advanced Programming (CIS 5520) and Programming Languages and Techniques (CIS 120). Research Areas: Her work spans theoretical foundations and practical implementations, including type systems for security, mechanized program verification, cost analysis for lazy evaluation, and language-based techniques for probabilistic programming. Recent projects explore indistinguishability in dependent types and effects systems for resource-aware computation. Honors: SIGPLAN Robin Milner Young Researcher Award (2016) NSF CAREER Award (2004) Microsoft Outstanding Collaborator (2016) Most Influential Paper Award, ICFP (2016) DARPA Computer Science Study Panel (2007-2008) Advising: Mentored 15+ PhD graduates including Richard Eisenberg (2016 Reynolds Award winner) and Vilhelm Sjöberg. Current students work on stratified type theory and mechanized logical relations. Secured grants from NSF and DARPA for research in dependently typed systems. Collaborations: Leads the PLClub research group; collaborates with industry (Microsoft) on Haskell extensions and verification tools like Hs-to-Coq.