Find Academic Supervisors

• Statistics
• Deep Learning
• Probability
• +7 more

Hailin Sang is an Associate Professor in the Department of Mathematics at the University of Mississippi , affiliated with the College of Liberal Arts . He holds a Ph.D. in Mathematics from the University of Connecticut (2008) and has previously held visiting assistant professor and postdoctoral research fellow positions at institutions including the University of Cincinnati, National Institute of Statistical Sciences/Duke University, and Indiana University. Dr. Sang’s research spans theoretical and applied statistics, with emphasis on deep learning , probability theory , empirical processes , time series analysis , random fields , nonparametric and robust statistics , and self-normalized statistics . His work also extends to survey sampling design and analysis . Recent publications highlight applications in generative adversarial networks , modified ReLU networks , and entropy estimation , with methodological contributions to limit theorems and deviation bounds for complex stochastic structures. His research has received partial support from the Simons Foundation . He has not been explicitly recognized for scientific awards in the provided text. Dr. Sang teaches a range of courses from elementary statistics to advanced statistics seminars , reflecting his broad educational contributions.

• Differential Geometry
• Geometric Analysis
• Minimal Surfaces
• +4 more

Antoine Song is an Assistant Professor of Mathematics at the California Institute of Technology (Caltech). He earned his B.S. (2014) and M.S. (2015) from Pierre and Marie Curie University, followed by a Ph.D. from Princeton University in 2019. Before joining Caltech in 2022, he was a postdoctoral researcher at UC Berkeley and held a Clay Research Fellowship (2019-2024). His office is located in 177 Linde Hall and can be reached at 626-395-4323 or via email at aysong@caltech.edu. Song's research focuses on Differential Geometry and Geometric Analysis , with specific interests in minimal surfaces, harmonic maps, and their connections to representation theory, geometric group theory, and random matrices. His work explores geometric optimization problems, stability phenomena in manifold theory, and probabilistic methods in geometry. His recent publications predominantly investigate minimal hypersurfaces, hyperbolic manifolds, and geometric flows. Common themes include stability analysis in Riemannian geometry, entropy quantification in topological spaces, and variational methods in geometric PDEs. The works frequently bridge geometric analysis with topology and probability theory. Awards and Fellowships: Sloan Research Fellowship Clay Research Fellowship (2019-2024) Song maintains an active research group exploring connections between geometric analysis and other mathematical fields, though no specific students or grants are detailed in available sources.

Hao ZhouView profile

Associate Professor

• Generative AI
• Molecular Modeling
• Protein Engineering
• +3 more

Hao Zhou is a Research Associate Professor at the Institute for AI Industry Research (AIR), Tsinghua University. His research focuses on developing Generative AI models for discrete symbols such as text, small molecules, and proteins. Previously, he worked as a Research Scientist/Manager at Bytedance AI Lab. Currently, he leads the Generative Symbolic Intelligence group at Tsinghua AIR and co-leads the SIA Lab, a joint laboratory with ByteDance for LLM research. His research interests span molecular representation learning, generative models for scientific discovery, and applications in drug design and protein engineering. Recent work includes advancements in 3D molecular modeling, protein language models, and non-autoregressive sequence generation. These efforts aim to bridge AI techniques with scientific domains like chemistry and biology. Recent publications highlight contributions to areas such as auto-encoder-based molecular representation (Mol-AE), multi-scale protein modeling (ESM All-Atom), and structure-based drug design (MolCRAFT). These papers reflect a focus on improving generative models' accuracy and applicability to real-world scientific challenges. Best Short Paper of INLG (2022) Ranked #1 on WMT EN-DE translation (2021) CCF NLPCC Distinguished Young Scientist (2021) Best Paper Award of ACL (2021) CAAI Doctoral Dissertation Award (2019) He advises several students, including Qiying Yu, Yuxuan Song, and Danqing Wang, and oversees grants related to AI in drug discovery and molecular modeling. His labs are actively hiring postdocs and research assistants, and he encourages student interns to join collaborative projects. Key initiatives include the SIA Lab’s work on LLM research and the Generative Symbolic Intelligence group’s exploration of AI-driven scientific breakthroughs. These efforts emphasize interdisciplinary approaches to tackle complex challenges in AI and life sciences.

Dario Alejandro Garcia RicoView profile

Professor

• Model Theory
• Mathematical Logic
• Combinatorics
• +5 more

Dario Alejandro Garcia Rico is a full-time faculty member (Docente de Planta) in the Department of Mathematics at the University of the Andes, Colombia. His research focuses on model theory with particular interest in ultraproducts of finite structures and their applications to combinatorics. His educational background includes: PhD in Mathematics from the University of Los Andes (2015) Master in Mathematics from the University of Los Andes (2010) Bachelor's degree in Mathematics from the National University of Colombia (2008) Dr. Garcia's research primarily centers on model theory, specifically examining the model-theoretic properties of ultraproducts of finite structures with potential applications to combinatorics. His work explores pseudofinite structures, asymptotic classes, and O-minimality. He has made significant contributions to understanding pseudofiniteness, measurability, and the connections between model theory and combinatorial structures through his investigations of random graphs (RG), r-regular infinite forests (Tr), and infinite-regular forests (Tinf). Throughout his publications, Garcia has established important results in the model theory of pseudofinite structures, particularly in the areas of ultraproducts of finite graphs, asymptotic classes, and the relationship between pseudofinite dimension and forking independence. His work demonstrates how polynomial expressions can characterize definable sets in pseudofinite structures and how combinatorial cell decomposition techniques can be adapted to ordered asymptotic classes. His notable scientific achievement includes: Marie Curie Research Fellowship at the University of Leeds (2016-2018) under grant agreement MODFIN 656422 on model theory of pseudofinite structures Dr. Garcia has supervised graduate students working on topics related to model theory of finite structures. His research has been supported by prestigious grants including the Marie Curie Fellowship. He has collaborated with prominent researchers in the field including Alf Onshuus (University of the Andes) and Thomas Scanlon (UC Berkeley). His work is centered in the Department of Mathematics at the University of the Andes, where he continues to advance research in model theory and its applications to combinatorial problems through the study of pseudofinite structures.

Hong Kun ZhangView profile

Professor

• Dynamical Systems
• Ergodic Theory
• Chaotic Billiards
• +5 more

Hong Kun Zhang is a Full Professor in the Department of Mathematics & Statistics at the University of Massachusetts Amherst. His research focuses on dynamical systems, ergodic theory, chaotic billiards, and financial mathematics. He holds a PhD from the University of Alabama at Birmingham (2005) under Nikolai Chernov's supervision. His work includes studies on statistical properties of hyperbolic systems, random billiards, and quantum billiards. Education: PhD in Mathematics (2005), University of Alabama at Birmingham M.S. in Mathematics (2001), University of Alabama at Birmingham M.S. in Mathematics (1998), University of Inner Mongolia, China B.S. in Mathematics (1993), University of Inner Mongolia, China Research Interests: His primary interests span billiard dynamics, statistical properties of chaotic systems, financial time series analysis, and applications of deep learning to dynamical systems. He has contributed to understanding mixing rates, entropy production, and spectral gaps in billiard systems. Awards and Grants: NSF CAREER Award (DMS-1151762) Simons Fellowship (2015–2016) NSF Grant (DMS-0901448) Dean’s Award (UAB, 2005) Key Contributions: Zhang has organized international conferences on statistical properties of dynamical systems and co-authored influential papers on Lorentz gases, spectral analysis of billiards, and applications of GARCH models in financial markets. His work bridges pure mathematics with applied fields like financial engineering and data science.

Andreas Basse-O'ConnorView profile

Professor

• Stochastic Processes
• Fractional Calculus
• Probability Theory
• +3 more

Andreas Basse-O'Connor is a Professor at the Department of Mathematics, Aarhus University. His research focuses on stochastic processes, fractional calculus, and probability theory, particularly in areas like harmonizable fractional stable processes, infinite divisibility, and Ornstein–Uhlenbeck dynamics. His recent work explores power variations for random fields, asymptotic behavior of fractional Lévy motions, and statistical properties of heavy-tailed distributions. He collaborates on topics including stochastic delay differential equations and synchronization of chaotic fractional-order systems.

• Signal Processing
• Audio Signal Processing
• Machine Learning
• +7 more

Roland Badeau is a Full Professor in the Signal, Statistics and Learning (S2A) team within the Image, Data, Signal (IDS) Department at Télécom Paris, Institut Polytechnique de Paris. His primary affiliation is with the Information Processing and Communication Laboratory (LTCI). Research Interests: Badeau specializes in statistical modeling of non-stationary signals, with core expertise in adaptive high-resolution spectral analysis and Bayesian extensions to Non-negative Matrix Factorization (NMF). His work spans room acoustics (stochastic reverberation models), data representation (dimensionality reduction, time-frequency analysis), probabilistic latent variable modeling, and algorithm development (Bayesian estimation, optimization methods, fast adaptive algorithms). Applications focus on audio/music processing including source separation, denoising, dereverberation, multipitch estimation, and automatic music transcription, with extensions to biomedical data analysis and digital communications. Key Trends in Publications: Recent work centers on Statistical Wave Field Theory, establishing mathematical frameworks for reverberation modeling using energy-stress tensor formalism and Riemannian geometry. His publications demonstrate a progression from foundational signal processing algorithms (e.g., YAST, ESPRIT) to physics-informed approaches for polyhedral rooms and frequency-dependent attenuation, with strong emphasis on Bayesian and alpha-stable distribution methods for robust audio separation. Academic Leadership: Badeau supervises doctoral and master’s theses while leading teaching units at Télécom Paris. He serves as the TSIA study track supervisor (Signal Processing for Artificial Intelligence) and Master ATIAM correspondent. His team (S2A) develops tools like DESAM for joint source separation and multi-track coding.

Hugo Duminil-CopinView profile

Professor

• Probability Theory
• Mathematical Physics
• Statistical Physics
• +4 more

Hugo Duminil-Copin is a full professor at the University of Geneva (since 2013) and a permanent professor at the Institut des Hautes Études Scientifiques (IHES) in Bures-Sur-Yvette (since 2016). A 2022 Fields Medalist, his research lies at the intersection of probability theory, mathematical physics and combinatorics, with a focus on critical phenomena in lattice models. Education: École Normale Supérieure (ENS), Paris PhD, Université Paris-Saclay Research Interests: Duminil-Copin's work centers on probability theory and statistical physics , particularly the rigorous study of critical phenomena. He uses probabilistic methods to analyze classical lattice models—Ising, Potts, percolation, self-avoiding walks—shedding light on phase transitions and universality. A major breakthrough is his development of dependent percolation theory , which has yielded precise results on critical exponents and scaling limits. His research spans several subfields: Critical percolation and random-cluster models Conformal invariance and Schramm–Loewner evolution Mathematical theory of phase transitions Discrete geometry and combinatorial enumeration Random walks and polymers Scientific Awards & Honors: Fields Medal (2022) Dobrushin Prize (2019) European Mathematical Society Prize (2016) New Horizons in Mathematics Prize (2017) Loève Prize (2017) Jacques Herbrand Grand Prize (2017) ERC Starting Grant “CriBLaM” (2017–2022) Member, French Academy of Sciences Member, Academia Europaea Supervision & Grants: He leads an active research group comprising postdoctoral fellows and doctoral students in Geneva and at IHES. Since 2013 he has supervised 15+ PhD students and mentored numerous postdocs. The ERC Starting Grant “Critical Behavior of Lattice Models” (€1.5 M) supports his team’s work on scaling limits and critical phenomena. Research Group: At the University of Geneva he heads the “Analysis, Mathematical Physics and Probability” group, collaborating closely with colleagues such as Antti Knowles, Stanislav Smirnov, Vincent Vargas and Yvan Velenik.

Sarasij MaitraView profile

Visiting Assistant Professor

• Commutative Algebra
• Machine Learning
• Data Science
• +3 more

Sarasij Maitra is a Visiting Assistant Professor of Mathematics and Statistics at Haverford College. Previously, he was a Postdoctoral Researcher/Lecturer in Mathematics at the University of Utah. He earned his PhD from the Department of Mathematics at the University of Virginia under the guidance of Professor Craig Huneke. Dr. Maitra completed his bachelor's and master's degrees at Indian Statistical Institute Bangalore and Indian Statistical Institute Kolkata, respectively. His research primarily focuses on Commutative Algebra, specifically studying Noetherian commutative local rings and the structure theory of modules over such rings. He investigates concepts such as torsion, reflexivity, and Ulrichness of modules using tools like local cohomology, Hilbert-Samuel Multiplicity, numerical semi-group rings, and Frobenius numbers (related to the Chicken McNugget Problem!). Dr. Maitra has developed a strong research portfolio examining Berger's Conjecture, trace ideals, and the module of differentials. His work shows a consistent pattern of collaboration with researchers like Vivek Mukundan, Craig Huneke, and others, demonstrating expertise in both theoretical aspects of commutative algebra and computational approaches using Macaulay2. His recent publications indicate a growing interest in applying algebraic methods to data science and machine learning. All University Graduate Teaching Award (University of Virginia) Postdoc Teaching Award (University of Utah) Dr. Maitra actively mentors undergraduate students, currently supervising senior theses at Haverford College in Commutative Algebra, Cryptography, and Deep Learning. He has taught a wide range of undergraduate mathematics courses including Multivariable Calculus, Real Analysis, Linear Algebra, and specialized topics like Commutative Algebra. He has also contributed to the mathematical community through software development for Macaulay2, including the RandomRationalPoints and SwitchingFields packages.

Daniel AhlbergView profile

Associate Professor

• Probability Theory
• Spatial Stochastic Processes
• Percolation Theory
• +3 more

Daniel Ahlberg is an Associate Professor in the Department of Mathematics at Stockholm University. His research utilizes probability theory to study spatial random processes including percolation models, random growth systems, branching processes, and combinatorial optimization problems. Current investigations focus on phase transitions in percolation, noise sensitivity of random systems, and geometric properties of random metrics. Research has established fundamental results on sharpness of phase transitions in continuum percolation, scaling limits of Boolean functions, and asymptotic behavior of first-passage percolation models. Recent work examines chaos phenomena in random growth models and statistical properties of geodesics in disordered media. Funded by multiple Swedish Research Council grants including a current research project grant (2022-2025). Recipient of the Ruth and Nils-Erik Stenbäck Foundation stipend (2022). Supervises doctoral students in probability theory and stochastic processes. Developed educational materials on probability fundamentals and Markov chain mixing. Teaches courses in probability theory, stochastic processes, and discrete mathematics.

• Cardiology
• Internal Medicine
• Coronary Artery Disease
• +3 more

Merete Heitmann serves as a Clinical Associate Professor at the Department of Clinical Medicine within the Faculty of Health and Medical Sciences at the University of Copenhagen. Her clinical specialization focuses on Internal Medicine with emphasis on Cardiology. She maintains dual institutional affiliations with both the University of Copenhagen (website: ikm.ku.dk) and the Capital Region of Denmark healthcare system, as evidenced by her regionh.dk email address. Her physical location is at Blegdamsvej 3, 2200 København N, which corresponds to the medical campus housing both Rigshospitalet and the university's medical facilities. Dr. Heitmann's research program centers on cardiovascular diagnostics and management with particular expertise in coronary artery disease assessment and familial hypercholesterolaemia. She has been instrumental in developing non-invasive diagnostic approaches, most notably through her leadership in the FILTER-SCAD trial investigating acoustic-based rule-out methods for stable coronary artery disease. Her work bridges clinical practice and research, addressing practical questions about diagnostic pathways, treatment timing, and patient management in cardiology. Analysis of her publication trends from 2021-2025 reveals a consistent focus on translating research into clinical practice, with increasing emphasis on cost-effectiveness analysis and implementation science. Her recent work shows progression from observational studies toward prospective randomized controlled trials, indicating growing research maturity and influence in her field. The interdisciplinary nature of her collaborations spans cardiology, radiology, genetics, and primary care. Her research has garnered significant attention in both academic and clinical communities, with several publications highlighted by news outlets, referenced in clinical guideline sources, and widely shared on social media platforms. This demonstrates the practical relevance and impact of her work on cardiovascular medicine practice. Dr. Heitmann maintains active clinical responsibilities alongside her research, as evidenced by her work in lipid clinics and general practice settings. Her ongoing involvement in multiple clinical trials suggests she continues to be at the forefront of cardiovascular research innovation in Denmark.

Timothy BrownView profile

Assistant Professor

• Brain-inspired analog hardware for AI
• Neuromorphic computing
• Materials selection for emerging computer architectures
• +4 more

Timothy Brown is an Assistant Professor in the School of Electrical and Computer Engineering at Oklahoma State University, focusing on brain-inspired analog hardware for AI and neuromorphic computing using materials like VO 2 , NbO 2 , and LaCoO 3 . He holds a PhD in Materials Science & Engineering from Texas A&M University (2019) and completed postdoctoral work at Texas A&M and Sandia National Laboratories. PhD, Materials Science & Engineering, Texas A&M University (2019) BSc, Electronics Engineering Physics, University of Tulsa (2014) His research bridges physics, materials science, and electrical engineering to design devices with internal state variables for applications in neuromorphic computing, active signal transmission, and low-energy computational systems. Key techniques include electro-thermal modeling, memristor characterization, and predictive algorithms for material selection in shape memory alloys. Recent work includes developing CMOS-compatible artificial neurons, demonstrating axon-like signal amplification without discrete components, and engineering spin crossover materials for true random number generation. Publications in Nature , Nature Electronics , and Advanced Materials highlight his contributions to mixed-physics computing primitives and predictive design frameworks. The BRAIN-C3 lab under Brown investigates materials like VOx and NbOx for neuromorphic applications, emphasizing thermal dynamics, reconfigurability, and device scaling. Collaborative projects span flexible substrates for spatiotemporal communication and inverse design methodologies for material property optimization.

Johan SegersView profile

Professor

• Extreme Value Theory
• Dependence Modelling via Copulas
• Statistical Learning
• +5 more

Johan Segers is a Full Professor at the Department of Mathematics, KU Leuven , and a Visiting Professor at the Institut de statistique, biostatistique et sciences actuarielles (UCLouvain). His research bridges extreme value theory , copulas , statistical learning , and optimal transport , with applications in finance, environmental risk analysis, and multivariate statistics. Research Trends : Focus on extremal dependence, vine copulas, and Monte Carlo methods. Recent Article Trends : Combines extreme value theory with graphical models and optimal transport for high-dimensional data analysis. Awards & Honors : Prix Adolphe Wetrems (2012–2013) from the Académie Royale des Sciences, des Lettres et des Beaux-arts de Belgique Fellow of the Institute for Mathematical Statistics (IMS) Elected Member of the International Statistical Institute (ISI) Editorial Roles : Associate Editor for Advances in Applied Probability , Bernoulli , Electronic Journal of Statistics , and others. He also contributes to software development, including the R-package spatialTailDep .

Mark RudelsonView profile

Professor

• Banach Spaces
• Convex Bodies
• Probability Theory
• +3 more

Mark Rudelson is a Professor of Mathematics at the University of Michigan, part of the College of Literature, Science, and the Arts (LSA). He holds a Ph.D. from the Hebrew University of Jerusalem (1997) and an M.Sc. from Leningrad Polytechnical Institute (1988). His research focuses on geometric functional analysis, probability theory, and random matrix theory, with contributions to the study of high-dimensional normed spaces, convex bodies, and spectral properties of random graphs and matrices. Rudelson's work bridges pure mathematics and applications in data science and signal processing. His research interests include the geometric and spectral properties of random matrices, convex geometry, and the interplay between probability and high-dimensional structures. Notable contributions include studies on the invertibility of random matrices, delocalization of eigenvectors, and the circular law for sparse matrices. Rudelson has collaborated widely, with impactful papers in top journals such as Annals of Mathematics , Probability Theory and Related Fields , and Advances in Mathematics . Rudelson's recent work explores the volume estimation of convex bodies, sparse matrix theory, and applications of random matrix techniques to problems in statistics and machine learning. His research often addresses foundational questions in mathematics with implications for computational and theoretical data analysis.

Runyu YangView profile

Associate Professor

• Particle Science
• Powder Technology
• Numerical Modeling
• +7 more

Associate Professor Runyu Yang is a faculty member at the School of Materials Science & Engineering, University of New South Wales (UNSW), where he has served since 2003. His academic progression at UNSW includes positions as ARC-CSIRO Postdoctoral Fellow (2003-2006), Lecturer (2006-2008), Senior Lecturer (2008-2011), and Associate Professor (2012-present). His research focuses on particle/powder science and technology, with particular expertise in numerical modeling of particulate systems. Dr. Yang's research interests center on discrete element method (DEM) modeling, granular flow dynamics, particle packing, and multi-phase flow systems. His work has significantly advanced understanding in powder dispersion mechanisms, particularly in pharmaceutical applications such as dry powder inhalers. He has developed improved DEM models by incorporating rolling friction and van der Waals forces, and demonstrated that the loosest state of mechanically stable random packings does not exist—a finding published in Physical Review Letters. His research spans both fundamental particle science and practical applications in pharmaceuticals, mineral processing, and energy systems. Analysis of Dr. Yang's recent publications (2023-2025) reveals a strong focus on computational modeling of particulate systems across diverse applications. His work demonstrates a clear trajectory toward increasingly sophisticated multi-physics approaches, combining DEM with CFD, VOF, and GPU acceleration techniques. The research spans pharmaceutical aerosols (particularly dry powder inhalers), mineral processing (HPGR and SAG mills), metallurgical applications (blast furnace slag), and fundamental particle science. This interdisciplinary approach connects fundamental particle physics with practical engineering applications across multiple industries. Dr. Yang has published over 60 high-quality, peer-reviewed journal and conference papers in prestigious journals including Physical Review Letters/E, Journal of Applied Physics, Journal of Colloid and Interface Science, and AIChE Journal. His work has been cited over 460 times with an h-index of 12, reflecting significant impact in his field. Dr. Yang has attracted more than $1 million in research funding from the Australian Research Council (ARC), CSIRO, and industry partners. He is currently supervising 1 postdoctoral fellow and 6 postgraduate students. His teaching contributions include courses on Material Balance and Heat Transfer, Heat, Fluid and Mass Flow, Modelling in Material Engineering, and Pollution Control in Materials Processing.