Find Academic Supervisors

Ryan StidhamView profile

Associate Professor

• Medical image analysis
• Artificial Intelligence in Healthcare
• Inflammatory bowel diseases (IBD)
• +8 more

Ryan Stidham, M.D., M.Sc., is an Associate Professor at the University of Michigan Medical School with joint appointments in Computational Medicine & Bioinformatics, Internal Medicine (Gastroenterology & Hepatology), Radiology, and Biostatistics. His research integrates AI/ML, medical image analysis, and NLP to advance inflammatory bowel disease (IBD) diagnostics and management. Key focuses include automating endoscopic/imaging quantification, developing clinical decision tools, and enhancing clinical trials through data-driven approaches. His work targets Crohn's disease and ulcerative colitis, emphasizing: Automated extraction of disease metrics from colonoscopy, CT, MRI, and ultrasound Novel biomarkers for inflammation/fibrosis using segmentation and transfer learning NLP pipelines for symptom quantification from clinical notes AI-enabled clinical trial optimization Research is funded by NIH, DOD, NSF, and industry partnerships, with collaborations spanning the Department of Learning Health Sciences. Recent publications (2024–2025) center on AI-driven endoscopic scoring, biomarker validation (e.g., CRP, MRI-based fibrosis markers), surgical risk prediction, and large language model applications for symptom analysis. Trends highlight rigorous validation of AI tools against clinical outcomes and emphasis on translational impact in IBD care.

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.

• 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.

• Artificial Intelligence
• Machine Learning
• Biomedical Informatics
• +5 more

Professor Annie Qu is a faculty member in the Department of Statistics at the University of California, Irvine (UCI), affiliated with the Donald Bren School of Information and Computer Sciences. Her research focuses on advancing statistical methodologies for analyzing complex, heterogeneous data, particularly in biomedical and healthcare domains. Notable areas include machine learning applications for medical imaging, epigenetic biomarker discovery, and personalized medicine. Education: Ph.D. in Statistics from Pennsylvania State University. Her work integrates optical imaging techniques with statistical models to enhance cancer detection and explores socio-biologic mechanisms underlying health disparities in populations exposed to trauma. Key projects include identifying epigenetic predictors of PTSD and Alzheimer’s disease in African American cohorts. Research Interests: AI/ML, biomedical informatics, statistical theory, genomics, and health disparities. Labs/Teams: Leads interdisciplinary teams collaborating with clinicians and engineers on precision medicine and wearable health technologies. Awards include being elected as a Member of the International Statistical Institute (2022) and named 2024 IMS Medallion Lecturer. Her work has been recognized for advancing causal AI in precision medicine and reducing cancer risks through smart health research. Her recent publications emphasize dynamic mediation analysis, heterogeneous data integration, and mobile health applications. Current efforts focus on culturally tailored interventions for dementia caregivers and developing robust algorithms for healthcare data.

Joel A. TroppView profile

Professor

• Mathematics of Data Science
• Machine Learning
• Numerical Linear Algebra
• +3 more

Joel A. Tropp is the Steele Family Professor of Applied & Computational Mathematics at the California Institute of Technology (Caltech), within the Division of Engineering and Applied Science. His academic career includes roles as Assistant Professor (2007–2012), Professor (2012–2017), and Steele Family Professor (2017–present). He holds a Ph.D. in Computational Applied Mathematics from the University of Texas at Austin (2004). Tropp's research focuses on applied mathematics, machine learning, data science, numerical algorithms, and random matrix theory, with notable contributions to matching pursuit algorithms, randomized SVD methods, and matrix concentration inequalities. Education: Ph.D. in Computational & Applied Mathematics, University of Texas at Austin (2004) M.S. in Computational & Applied Mathematics, University of Texas at Austin (2001) B.S. in Mathematics and B.A. in Plan II Liberal Arts, University of Texas at Austin (1999) Research Interests: Tropp’s work bridges applied mathematics, computer science, and engineering, emphasizing rigorous, practical algorithms for linear algebra, numerical analysis, and optimization. He develops user-friendly tools for high-dimensional probability and matrix analysis, with applications in machine learning, signal processing, and data science. His recent focus includes randomized algorithms for large-scale matrix computations, kernel methods, and quantum computing. Articles Trends: His recent publications address scalable randomized algorithms for kernel matrices, eigenvalue problems, and matrix approximation. Themes include computational efficiency, theoretical guarantees, and applications in machine learning, quantum computing, and dynamical systems. Awards: 2025 Richard P. Feynman Prize for Excellence in Teaching 2024 IMS Fellow 2020 IEEE Fellow 2019 SIAM Fellow 2008 PECASE Award Advising & Grants: Tropp has advised numerous Ph.D. students and postdoctoral researchers in areas like randomized algorithms, optimization, and quantum computing. He leads grants from ONR, NSF, and Caltech’s Carver Mead Fund, focusing on large-scale kernel computations and matrix solvers. His mentorship extends to interdisciplinary collaborations in turbulence modeling and signal processing. Labs & Teams: He contributes to Caltech’s Center for Mathematics of Information (CMI) and Computational Mathematics + X (CMX) initiatives, fostering research in data science, optimization, and computational methods.

Erik HoyView profile

Associate Professor

• Quantum Chemistry
• Electron Transport
• Quantum Sensors
• +3 more

Erik Hoy is an Associate Professor in the Department of Chemistry & Biochemistry at Rowan University, part of the College of Science & Mathematics. He holds a Ph.D. in Physical Chemistry from the University of Chicago and postdoctoral training at Johns Hopkins University and Northwestern University. His research focuses on quantum chemistry methods for studying electron transport, molecular devices, and organic materials. Key areas include developing novel Green’s function approaches combined with high-accuracy quantum chemistry techniques to design quantum sensors, electronic devices, and renewable energy materials. Education: Postdoctoral Associate at Johns Hopkins University and Northwestern University; Ph.D. in Physical Chemistry (University of Chicago); B.S. in Chemistry (Tennessee Tech University). Research Interests: Quantum Chemistry, Electron Transport, Quantum Sensors, Molecular Electronics, Computational Organic Reactions. His group emphasizes high-accuracy electronic structure methods and their application to nanoscale systems, with recent work on molecular junctions and electrocatalysis. Awards: NSF CAREER Award (2024), NSF ExpandQISE Award (2024), Eppley Foundation Grant (2023). His group has secured grants for projects like 'Strongly Correlated Molecular Qubits' and 'Quantum Sensors.' Advising & Grants: Supervises a diverse group of undergraduate and graduate students, including notable advisees such as Grace McGowan and Kale Kroenke. Collaborates with industry and academic partners, such as the Lofland group at Rowan and the Sand group at Butler University. Lab Activities: The Hoy Research Group develops computational tools like pyRUQT and contributes to software like MoleMod. Their work is highlighted in journals like Advanced Functional Materials and Physical Chemistry Chemical Physics .

Ali JadbabaieView profile

Professor

• Network Science
• Control Theory
• Optimization
• +7 more

Ali Jadbabaie is the JR East Professor of Engineering and Department Head of Civil and Environmental Engineering at MIT. He also serves as Associate Director of the Institute for Data, Systems, and Society (IDSS) and Director of the Sociotechnical Systems Research Center (SSRC). His research focuses on network science, control theory, optimization, and their applications in social networks, robotics, and complex systems. He holds a B.S. from Sharif University of Technology (1995), M.S. from the University of New Mexico (1997), and Ph.D. from Caltech (2000). Key research interests include consensus dynamics in social networks, cooperative control of multi-agent systems, and applications of algebraic topology in sensor networks. His work bridges theoretical foundations with real-world challenges in engineering, economics, and biology. Recent publications emphasize network science, AI ethics (e.g., bias in LLMs), optimization algorithms, and control systems. He has contributed to understanding misinformation spread, liquid democracy mechanisms, and federated learning frameworks. His articles frequently explore intersections between data science and societal systems. Notably, he co-authored studies on opinion dynamics under social pressure, transformer model biases, and robust control strategies for distributed systems. His lab’s work integrates systems theory with emerging technologies like autonomous vehicles and smart infrastructure.

Tamara PolajnarView profile

Researcher

• Natural Language Processing
• Computational Linguistics
• Distributional Semantics
• +2 more

Dr. Tamara Polajnar is a Visiting Researcher at the Department of Computer Science and Technology, University of Cambridge. Her research focuses on computational linguistics and natural language processing, with particular emphasis on distributional semantics and semantic compositionality. Her research interests span multiple areas including computational semantics, distributional models, and semantic composition. She develops tensor-based methods for representing meaning and investigates techniques for semantic analysis of text at different levels from phrases to documents. Her work combines theoretical linguistics with practical machine learning approaches. Her publications primarily focus on compositional distributional semantics, exploring how to effectively combine word vectors to represent larger linguistic units. Recent work examines discourse-level representations and evaluation methods for semantic models. She has contributed datasets and frameworks that advance the understanding of how computational models can capture linguistic phenomena. She actively contributes to the research community through the development of specialized datasets and evaluation resources. Her work on RELPRON provides valuable resources for evaluating compositional distributional models, while her research on short scientific summaries addresses challenges in processing academic text.

Laura GrigoriView profile

Professor

• Numerical Linear Algebra
• High Performance Computing
• Deterministic and Randomized Algorithms
• +4 more

Laura Grigori is a Full Professor and Chair of High Performance Numerical Algorithms and Simulations at EPFL's School of Basic Sciences (SB) Department of Mathematics (MATH). Her research focuses on numerical linear algebra, high performance computing, and tensor computations, with applications in astrophysics and molecular simulations. She leads the HPNalgs lab and teaches courses in numerical analysis and HPC. Her awards include the SIAM Supercomputing Career Prize (2024) and SIAM Fellow distinction (2020). She advises four PhD students and has authored numerous papers on communication-avoiding algorithms, randomized methods, and parallel linear algebra techniques. Her work addresses scalability challenges in scientific computing and large-scale data analysis. Labs/Teams: HPNalgs Lab (https://www.epfl.ch/labs/hpnalgs/) Grants: ERC Synergy Grant (2019) for Extreme-scale Computational Chemistry

Lexing YingView profile

Professor

• Applied Mathematics
• Computational Science
• Quantum Computing
• +7 more

Lexing Ying is a Professor of Mathematics at Stanford University. His research focuses on applied mathematics, computational science, and interdisciplinary areas such as quantum computing, machine learning, and numerical analysis. He has made significant contributions to high-dimensional problems, inverse problems, and the development of efficient algorithms for complex systems. His work spans theoretical advancements and practical applications, including quantum signal processing, tensor-based numerical methods, and probabilistic modeling. Key themes in his research include the analysis of diffusion models, stochastic systems, and optimization algorithms. Recent publications highlight innovations in solving high-dimensional PDEs, improving sampling techniques, and advancing quantum algorithms. Notably, Ying has developed methods for operator learning, functional hierarchical tensors, and robust phase estimation. His research often bridges mathematical theory and computational practice, addressing challenges in areas like statistical inference and numerical simulation.

Jamie HaddockView profile

Assistant Professor

• Mathematical Data Science
• Optimization
• Applied Convex Geometry
• +6 more

Dr. Jamie Haddock is the Iris & Howard Critchell Assistant Professor of Mathematics at Harvey Mudd College. Her research focuses on mathematical data science, optimization, and applied convex geometry, with emphases on randomized numerical linear algebra, tensor decomposition, and community detection. She holds a Ph.D. from UC Davis and a postdoc at UCLA. Her work is supported by NSF grants and collaborations across academia and medicine. Notable awards include the NSF CAREER Award (2024) and leadership in curriculum development for data science. Education: Ph.D. in Applied Mathematics (UC Davis, 2019), CAM Postdoc (UCLA Mathematics, 2019–2021). Research interests span networks, optimization methods, and medical applications. Recent projects include robust algorithms for corrupted data and tensor models in healthcare. She mentors undergraduates and develops courses like 'Mathematical Methods in Data Science' and 'Scientific Computing.' Teaching includes courses at HMC and UCLA, with a focus on numerical analysis and machine learning. She emphasizes Federico Ardila’s axioms to promote inclusivity in mathematics.

Laurent LE BRUSQUETView profile

Associate Professor

• Multivariate Data Analysis
• Tensor Decomposition
• Statistical Learning
• +6 more

Laurent LE BRUSQUET is an Associate Professor at CentraleSupélec's Laboratoire des Signaux et Systèmes (L2S), specializing in multivariate data analysis, tensor decomposition, and statistical learning. His research focuses on applications in health informatics, energy systems, and Industry 4.0, with notable contributions to the RGCCA package and exposome research. He collaborates on the AP-HP/INRIA/CentraleSupélec chair addressing frailty in aging. Active in teaching, he leads courses on machine learning, optimization, and multivariate data analysis for engineering students. His team includes PhD students Abdelhak Boudehane, Fabien Girka, and Lucas Sort. Research Interests: Multiway data analysis, tensor factorization, joint analysis of heterogeneous data, health biomarkers, and stochastic modeling. Key Projects: ATHLETE (exposome research), 3D stochastic dosimetry for electromagnetic fields, and UAV path optimization. Publications span journals like Biometrics and IEEE Journal of Electromagnetics , emphasizing methodological advancements and interdisciplinary applications. Teaching: Courses on statistics, machine learning, optimization, and probability across CentraleSupélec's engineering programs. Labs/Teams: Modelling and Estimation team, SYCOMORE, Inverse Problems Group.

Charles NicholasView profile

Professor

• Cybersecurity
• Machine Learning
• Data Science
• +3 more

Charles Nicholas is a Professor of Computer Science and Electrical Engineering at the University of Maryland, Baltimore County (UMBC). He holds a Ph.D. in Computer and Information Science from Ohio State University (1988), an M.S. from the same institution (1982), and a B.S. in Computer Science from the University of Michigan–Flint (1979). His research focuses on applying data science and machine learning to cybersecurity challenges, particularly in ransomware analysis, malware detection, and threat intelligence. He previously served as Chair of UMBC’s Department of Computer Science and Electrical Engineering (2004–2010) and has chaired the Conference on Information and Knowledge Management five times. His work spans cutting-edge topics such as quantum algorithms for malware classification, knowledge graph generation, and efficient feature extraction methods for malware detection. Notable contributions include frameworks for evaluating malware datasets, improving classifier robustness, and leveraging antivirus scan data for large-scale analysis. Dr. Nicholas is a Senior Member of both ACM and IEEE, reflecting his longstanding contributions to the field. His research emphasizes practical applications of machine learning to cybersecurity, with a focus on scalable solutions for real-world threats.

Lixin ShenView profile

Professor

• Applied and Computational Harmonic Analysis
• Optimization
• Imaging Science
• +4 more

Lixin Shen is a Professor in the Department of Mathematics at Syracuse University, part of the College of Arts and Sciences. His research focuses on applied and computational harmonic analysis, optimization, imaging science, and information processing. Shen holds a Ph.D. in Mathematics from Sun Yat-Sen University (1996), an M.Sc. from Peking University (1990), and a B.Sc. from Peking University (1987). Education: Ph.D., Mathematics, Sun Yat-Sen University, 1996 M.Sc., Mathematics, Peking University, 1990 B.Sc., Mathematics, Peking University, 1987 Research Interests: Shen’s work emphasizes sparse optimization, image and signal processing, computational mathematics, and their applications. Notable areas include wavelet analysis, tensor decomposition, and robust algorithms for noise removal and high-resolution imaging. Grants & Awards: NSF Grant: Collaborative Research: Sparse Machine Learning and Sparse Optimization (2022–2025) Excellence in Graduate Education Faculty Recognition Award, Syracuse University (2024) Air Force Summer Faculty Fellowship (2024, 2023, 2021, 2020) Service & Leadership: Chair of Graduate Committee, Mathematics Department (2024–2025) Editor, Frontiers in Applied Mathematics and Statistics Organizer, SIAM Conferences on Optimization and Imaging (2023–2020) Teaching: Recent courses include Numerical Linear Algebra, Numerical Methods with Programming, Sparse Optimization, and Partial Differential Equations.

Matteo BarigozziView profile

Professor

• Political Economy
• Econometrics
• Time Series Analysis
• +4 more

Matteo Barigozzi is a Full Professor in the Department of Economic Sciences at the University of Bologna. He holds a PhD in Economics and Management from Scuola Superiore Sant'Anna and a Physics degree from the University of Milan. Previously, he was an Associate Professor at the London School of Economics (LSE) and held post-doctoral positions at Université libre de Bruxelles and the Max Planck Institute. His research focuses on econometrics, dynamic factor models, high-dimensional data analysis, and macroeconomic policy. He has developed influential methodologies for analyzing large datasets and their applications in economics and finance. Education : PhD in Economics and Management, Scuola Superiore Sant'Anna (2008) Master's in Physics, University of Milan (2002) Advanced Studies in Mathematical Modelling at ICTP Trieste (2003) Research Interests : Dr. Barigozzi specializes in dynamic factor models, high-dimensional time series analysis, and their applications to macroeconomic and financial systems. His work bridges econometric theory and practical policy analysis, with contributions to output gap measurement, monetary policy effects, and energy demand forecasting. Recent projects emphasize robustness in factor models and network-based approaches. Articles Trends : His recent work emphasizes methodological advancements in factor models, including tail-robust estimation, tensor factor models, and dynamic network autoregressions. Applications span euro area economic policy, energy systems, and financial volatility analysis. Awards : No specific awards listed, but recognized for contributions to econometric methodology. Advising/Grants : Active in academic advising and research grants, though specific details are not provided in the text. Labs/Teams : Affiliated with CREST (ENSAE) and ECARES (ULB). Collaborates with institutions like the Federal Reserve Board and Sciences Po.