Find Academic Supervisors

Nilam RamView profile

Professor

• Longitudinal Research Methodology
• Digital Media and Mental Health
• Data Science Applications in Psychology
• +3 more

Nilam Ram is a Professor of Communication and Psychology at Stanford University, with affiliations in the Wu Tsai Human Performance Alliance and the Symbolic Systems Program. His research focuses on the dynamic interplay of psychological processes and media use, leveraging longitudinal methodologies and intensive data streams from digital devices. He holds dual appointments in Communication and Psychology, emphasizing interdisciplinary approaches to studying change across lifespan development. Education: B.A. in Economics (not specified), followed by transitions into kinesiology and psychology. Current research explores media effects, digital phenotyping via the Human Screenome Project, and applications of AI to longitudinal data. He teaches courses on temporal data analysis, statistical methods, and media psychology. Research Interests: Longitudinal study designs, intensive longitudinal data (e.g., smartphone screen captures), affective aging, and the impact of digital media on mental health. His work bridges computational methods with psychological theory, emphasizing person-specific analyses over aggregated trends. Recent Article Themes: Smartphone use and suicide risk prediction, digital nature vs. physical nature impacts, transformer models for mortality prediction, and mindfulness interventions. These reflect a focus on real-time behavioral tracking and AI-driven insights. Labs/Teams: The Change Lab @ Stanford Grants/Advising: Mentors doctoral and postdoctoral researchers in media psychology and data science. Courses include advanced statistical methods and interdisciplinary projects.

Alex TownsendView profile

Associate Professor

• Applied Mathematics
• Numerical Analysis
• Scientific Computing
• +6 more

Alex Townsend is an Associate Professor of Mathematics at Cornell University, affiliated with the College of Arts and Sciences. He holds the Stephen H. Weiss Junior Fellowship and has been recognized for both research and teaching excellence. His research focuses on numerical analysis, scientific computing, and theoretical aspects of deep learning, with contributions to spectral methods, low-rank techniques, and computational algebraic geometry. Education: Townsend earned a DPhil (PhD) in Mathematics from the University of Oxford in 2014. Research Interests: Townsend's work spans several areas: novel spectral methods for differential equations, low-rank matrix and tensor techniques, theoretical foundations of deep learning, and computational algebraic geometry. His research emphasizes developing fast, accurate, and robust numerical algorithms with applications in science and engineering. Teaching & Mentoring: Townsend is a dedicated educator, having taught courses at MIT and Cornell on topics ranging from linear algebra and numerical analysis to advanced graduate-level subjects like kernel-based learning and top-ten algorithms of the 20th century. He has mentored numerous PhD students and postdocs, many of whom now hold academic and industry positions. Awards & Honors: 2022 Stephen H. Weiss Teaching Award 2022 Simons Fellowship in Mathematics 2018 SIAG/LA Early Career Prize 2015 Leslie Fox Prize in Numerical Analysis Grants & Funding: Townsend has secured significant funding, including an NSF CAREER grant (2021), to support his work on operator learning and spectral methods. Labs & Collaborations: While not tied to a specific lab, his research frequently intersects with computational mathematics and machine learning communities. He collaborates widely, contributing to open-source tools like Chebfun and Diskfun.

Alan EdelmanView profile

Professor

• Julia Programming
• High Performance Computing
• Linear Algebra
• +3 more

Alan Edelman is a Professor at the Massachusetts Institute of Technology (MIT), renowned for his work in high-performance computing, linear algebra, and random matrix theory. He is a co-creator of the Julia programming language, which emphasizes efficiency and versatility for scientific computing. His research integrates mathematics and computer science, focusing on numerical methods, parallel computing, and applications in fields like quantum control and climate modeling. Edelman leads projects such as Oceananigans.jl for geophysical fluid dynamics and Circuitscape for connectivity analysis in conservation biology. His academic contributions span theoretical advancements in matrix theory and practical implementations of computational tools. Edelman collaborates across disciplines, bridging scientific computing with machine learning and quantum physics. His work on differentiable programming frameworks and GPU acceleration has impacted both academic research and industry applications. Key Projects: Julia programming language, Oceananigans.jl, Circuitscape Research Themes: High-performance computing, numerical linear algebra, random matrices, scientific machine learning Affiliations: MIT’s Theory of Computation Community of Research, PI of multiple NSF-funded projects Edelman’s recent work emphasizes interdisciplinary applications, including climate policy modeling and automated materials discovery. His publications reflect a blend of foundational mathematics and cutting-edge computational techniques.

Shuangning LiView profile

Assistant Professor

• Econometrics
• Statistics
• Machine Learning
• +5 more

Shuangning Li is an Assistant Professor of Econometrics and Statistics at the University of Chicago's Booth School of Business. He holds a Ph.D. from Stanford University's Department of Statistics, advised by Professors Emmanuel Candès and Stefan Wager, and a Bachelor of Science from the University of Hong Kong. Prior to his current role, he was a postdoctoral fellow in Statistics at Harvard University. His research focuses on causal inference, machine learning, and statistical methodology with applications in econometrics, networks, and genomics. **Education:** Ph.D. in Statistics, Stanford University (Advisors: Emmanuel Candès, Stefan Wager) Bachelor of Science, University of Hong Kong **Research Interests:** Causal inference in complex systems (e.g., networks, high-dimensional data) Statistical methods for experimental design and robustness Machine learning applications in genomics and reinforcement learning Randomization-based testing and knockoff filters **Recent Work Trends:** His articles emphasize methodological innovations in causal effect estimation, network interference modeling, and transfer learning. Recent work addresses challenges in stochastic congestion, multi-environment analysis, and cooperative learning frameworks. His 2024 paper advances covariate shift correction for conditional randomization tests, while his 2023 studies explore robustness in model-X inference and dyadic reinforcement learning dynamics. **Advising & Academic Background:** His doctoral training under Candès and Wager shaped his focus on rigorous statistical foundations. He has not yet listed advising relationships in available materials, but his research collaborations span academia and industry.

Josie ClowneyView profile

Associate Professor

• Neural Circuits
• Developmental Neuroscience
• Chemosensory Systems
• +3 more

Josie Clowney is an Associate Professor in the Department of Molecular, Cellular, and Developmental Biology at the University of Michigan, where she has held faculty position since 2017. Her research investigates the genomic algorithms that construct neural circuits during development, using Drosophila as a model to study how chemosensory systems drive both instinctual behaviors and learning. She teaches Bio 172 and an upper-level seminar on cellular diversity and scientific writing, and mentors graduate students through MCDB, CMB, NGP, and BIOINF PhD programs. Her educational background includes: Ph.D. in Biomedical Sciences (2012) from the University of California, San Francisco B.S. in Cellular and Molecular Biology (2005) from the University of Michigan, where she conducted research with Cunming Duan Clowney's research centers on understanding how definitive neuronal parameters are encoded in genomic information and translated into cellular architectures. Her lab hypothesizes that developmental algorithms for learning circuits versus instinctual circuits differ fundamentally in their genomic requirements, with chemosensory circuits serving as key models. Using fruit flies for their tractable brain organization, her work bridges computational principles and biological implementation to uncover universal brain organization rules. Analysis of her 15 most recent publications (2016-2025) reveals consistent focus on Drosophila mushroom body development, neural sexual differentiation, and spatial constraints in circuit formation. Key themes include non-deterministic mechanisms diversifying cell surface expression, chromatin dynamics in circadian regulation, and how input density tunes sensory responses. Her work integrates genomics, neuroanatomy, and behavior to model how compact genomic information generates complex neural architectures. No scientific awards were mentioned in the provided text. Dr. Clowney advises graduate students through multiple PhD programs at the University of Michigan, though specific student names and grant details are not provided in the source material. Her teaching includes foundational undergraduate coursework and advanced seminars emphasizing scientific writing. The active publication record spanning 2016-2025 indicates sustained research funding supporting her lab's investigations into neural circuit development. The Clowney Lab, housed in the Biological Sciences Building (4218 BSB), employs Drosophila genetics and neuroanatomical techniques to dissect developmental algorithms of brain wiring. Current projects explore how spatial constraints structure learning circuits, mechanisms of neural sexual differentiation, and the genomic encoding of circuit diversity. The lab collaborates within Michigan's neuroscience community through the Program in Biology and participates in interdisciplinary initiatives studying brain evolution and function.

Trent GauglerView profile

Associate Professor

• Applied Statistics
• Computational Statistics
• Interdisciplinary Research
• +3 more

Trent Gaugler is an Associate Professor of Mathematics at Lafayette College, specializing in applied and computational statistics. He emphasizes collaborative research across disciplines and integrates simulation-based methods into his teaching. His work focuses on leveraging computational tools to address complex data analysis challenges. Gaugler holds a Ph.D. from Pennsylvania State University and a B.S. from Bucknell University. Education: Ph.D., Mathematics, Pennsylvania State University B.S., Mathematics, Bucknell University Research Interests: Gaugler’s work bridges statistical theory and real-world applications, particularly in interdisciplinary collaborations. He develops computational methods to analyze complex data sets, emphasizing practical solutions for fields like health sciences, environmental studies, and social sciences. His teaching philosophy emphasizes hands-on learning with real-world data, fostering student confidence and analytical skills. Recent Research Trends: His articles reflect a focus on interdisciplinary applications of statistics, including health outcomes, community response to environmental factors (e.g., noise pollution), and generational workforce dynamics. He frequently employs randomized controlled trials and longitudinal studies to assess interventions and social phenomena. Community Engagement: Gaugler collaborates with local nonprofits like the United Way and Easton Area Neighborhood Center, applying statistical methods to address community challenges such as education equity and health disparities. He mentors students in applied projects, enhancing their practical research skills. Labs/Teams: His collaborations span Lafayette College’s departments and external organizations, highlighting his commitment to bridging academic expertise with real-world impact.

Ant ShepherdView profile

Associate Professor

• Clinical Exercise Physiology
• Metabolic Health
• Cardiovascular Function
• +5 more

Dr. Ant Shepherd is an Associate Professor in Clinical Exercise Physiology at the University of Portsmouth, affiliated with the School of Psychology, Sport and Health Sciences within the Faculty of Science & Health. He also contributes to the Institute of Life Sciences and Healthcare and the Centre for Integrated Health and Wellbeing. Dr. Shepherd holds an ORCID identifier (0000-0001-6392-7944) and is actively involved in PhD supervision and academic leadership. Education: BSc (Hons) Sports Science (Bangor University), MSc Exercise Rehabilitation (Bangor University), PhD (University of Exeter, 2015) Postdoctoral Experience: University of Exeter Medical School Research Interests: Dr. Shepherd focuses on using novel interventions—such as nutritional strategies and exposure to extreme environments—to improve metabolic and cardiovascular health. His work emphasizes patient-centered outcomes, particularly in chronic conditions like type 2 diabetes, cystic fibrosis, and chronic obstructive pulmonary disease. He explores disease pathophysiology to identify therapeutic strategies. Publications: His recent work includes studies on cardiovascular function in cystic fibrosis patients, the effects of ketone esters in diabetes, and the role of nitrate in vascular health. Articles often employ randomized controlled trials and observational designs. Awards: Runner-up, Inaugural Annual Review Prize of the Physiological Society (2021) Advising & Grants: Supervises multiple PhD students and postdocs. Serves as a grant reviewer for the Medical Research Council and reviews for journals including Journal of Applied Physiology and Thorax . Active in collaborative projects, such as investigating climate adaptation strategies in diabetes and the effects of passive heating on older adults. Labs & Teams: Leads research teams in translational exercise physiology and collaborates internationally on projects related to cystic fibrosis, diabetes, and vascular health.

Frédéric PASCALView profile

Professor

• Statistical Signal Processing
• Machine Learning
• Adaptive Radar Detection
• +5 more

Frédéric Pascal is a Full Professor at CentraleSupélec, part of the University of Paris-Saclay, and a member of the Laboratoire des Signaux et Systèmes (L2S). His research focuses on statistical signal processing, machine learning, and robust estimation techniques, with applications in radar detection, covariance matrix estimation, and information geometry. He has held roles including Coordinator of AI activities at CentraleSupélec and Head of the "Signals and Statistics" group at L2S. His academic journey includes a PhD from University Paris X – Nanterre (2006) and an HDR (2012) from University Paris-Sud. His work emphasizes adaptive signal processing in non-Gaussian environments, with contributions to robust covariance estimation, M-estimators, and applications in radar systems and biomedical signal processing. He has authored over 100 journal/conference papers and serves as an Associate Editor for IEEE Transactions on Signal Processing and Elsevier Signal Processing. Current research interests include AI transparency, data-driven methods for industry 4.0, and health-related signal analysis.

Helen NobleView profile

Professor

• Chronic Kidney Disease
• Palliative Care
• Qualitative Research Methods
• +3 more

Helen Noble is a Professor of Nursing and Director of Postgraduate Research at the School of Nursing and Midwifery, Queen’s University Belfast. She leads international research in nephrology, palliative care, and arts-based interventions for kidney patients. Her roles include supervising PhD candidates, designing postgraduate training programs, and advocating for patient-centered care. She is a Fellow of the Higher Education Authority and has received accolades such as the Walls Bursary and Donna Lamping Award. Her research focuses on improving care for patients with chronic kidney disease, end-stage renal disease, and palliative needs. Notable initiatives include the UK Renal Arts Group, which uses creative arts to enhance patient well-being, and the Compassionate Mindful Resilience (CMR) program for kidney patients. She has secured funding from NIHR, Marie Curie, and other bodies. Teaching responsibilities include modules like Renal Nursing and Renal Replacement Therapies. She serves as an external examiner for nursing programs at City University London and University of Hertfordshire. Her grants and projects span caregiver support, palliative care partnerships, and mindfulness interventions in healthcare. Awards include the RCN Impact Award 2022 for her mindfulness program for PhD students. Her work is cited in international guidelines and disseminated through conferences, media, and collaborations with global organizations like the EDTNA/ERCA Research Board.

Alex KontorovichView profile

Professor

• Automorphic Forms and Representations
• Homogeneous Dynamics
• Harmonic Analysis
• +5 more

Alex Kontorovich is a Distinguished Professor of Mathematics at Rutgers University, where he holds the academic rank of Professor. His primary affiliation is with the Department of Mathematics, School of Arts and Sciences. He currently serves as Managing Editor of the Journal of the Association for Mathematical Research and is Executive Director of Rutgers MathCorps . During the 2024-2025 academic year, he is on leave, visiting Princeton University and the Institute for Advanced Study (IAS). Kontorovich's research focuses on automorphic forms, homogeneous dynamics, harmonic analysis, and number theory, with interdisciplinary connections to data science and machine learning. His work bridges pure mathematics with computational aspects, including sphere packing geometry and arithmetic dynamics. He has held distinguished visiting roles, such as the 2020-21 Distinguished Visiting Professor for the Public Dissemination of Mathematics at the National Museum of Mathematics (MoMath), where he contributed to academic content and exhibits. His academic career includes teaching advanced courses like Graduate Complex Analysis, Automorphic Representations, and History of Mathematics. Notable awards include the Simons Foundation Fellowship, von Neumann Fellowship at IAS, and Alfred P. Sloan Research Fellowship. Grants include multiple NSF awards (regular, CAREER, FRG) and a Binational Science Foundation grant. His research explores topics like spectral gaps, thin groups, and applications of modular forms to equidistribution problems. Kontorovich’s scholarly contributions extend beyond academia: he serves on the Scientific Board of Quanta Magazine , advises the Lean Focused Research Organization , and participates in editorial and strategic roles across institutions. His work emphasizes the interplay between theoretical mathematics and computational methods, shaping modern research in number theory and dynamics.

Fabio NobileView profile

Professor

• Scientific Computing
• Uncertainty Quantification
• Numerical Analysis
• +5 more

Fabio Nobile is a Full Professor at the École Polytechnique Fédérale de Lausanne (EPFL) in the School of Basic Sciences (SB), Department of Mathematics (MATH), holding the CADMOS Chair in Scientific Computing and Uncertainty Quantification. He leads the CSQI (Chair of Scientific Computing and Uncertainty Quantification) group. His work focuses on numerical methods for partial differential equations (PDEs), uncertainty quantification, stochastic modeling, and computational fluid dynamics. He is involved in collaborative projects involving fluid-structure interaction, cardiac electro-mechanics, and energy systems. Professor Nobile has extensive teaching experience, including courses on advanced analysis, stochastic simulation, and numerical integration of stochastic differential equations. He supervises numerous PhD students and has contributed to over 200 peer-reviewed publications, covering topics such as low-rank approximation methods, multilevel Monte Carlo techniques, and optimal control under uncertainty. His research emphasizes interdisciplinary applications, including biomedical engineering (e.g., personalized cardiac simulations) and renewable energy (e.g., probabilistic load forecasting). He collaborates with industries and academic institutions globally, advancing computational methodologies for engineering and scientific challenges.

Jennifer TangView profile

Research Fellow

• information theory
• prediction and estimation
• quantization and data compression
• +4 more

Jennifer Tang is a Postdoctoral Associate at the Massachusetts Institute of Technology (MIT), holding dual appointments in the Institute for Data, Systems, and Society (IDSS) and the Laboratory for Information and Decision Systems (LIDS). She conducts her research under Professor Ali Jadbabaie, focusing on interdisciplinary problems at the intersection of information theory, network science, and social dynamics. Her position is temporary as she actively seeks a permanent academic role through the 2025 job market. Her academic credentials include: Ph.D. in Electrical Engineering and Computer Science from MIT, advised by Professor Yury Polyanskiy Bachelor of Science in Engineering (B.S.E.) in Electrical Engineering from Princeton University, with independent work supervised by Paul Cuff Dr. Tang's research program centers on theoretical and applied aspects of information theory, including channel capacity, quantization, and data compression. She investigates prediction and estimation in high-dimensional settings, data analytics for complex systems, and mathematical modeling of social dynamics and inference in multi-agent networks. Her work employs tools from statistics, optimization, and network theory to address challenges in communication, decision-making, and societal systems, with particular emphasis on opinion dynamics under social pressure and efficient representation of probability distributions. Analysis of her publication record reveals consistent contributions to information-theoretic limits, social network modeling, and compression techniques. Her works frequently appear in top venues like IEEE Transactions on Information Theory and major conferences (ISIT, CDC, ACC), demonstrating expertise in bridging theoretical foundations with real-world applications in networked systems and societal challenges. Her scientific achievements have been recognized with: Best Student Paper Award at IEEE International Symposium on Information Theory (ISIT) 2022 Best Student Paper Award at IEEE Machine Learning for Signal Processing (MLSP) 2022 Student Competition Winner at the Shannon Centennial Celebration Dr. Tang maintains an active teaching portfolio, having served as instructor for MIT 1.022: Introduction to Network Models (Spring 2025) and teaching assistant for multiple core courses including 6.008 (Introduction to Inference), 6.041/6.431 (Probabilistic Systems Analysis), 6.437 (Inference and Information), and 6.439 (Statistics, Computation and Applications). She also contributed to the MIT Women's Technology Program as a Mathematics Instructor during summer 2017. Her research is embedded within MIT's Laboratory for Information and Decision Systems (LIDS) and Institute for Data, Systems, and Society (IDSS), two premier interdisciplinary laboratories fostering collaboration on data-driven decision-making, societal challenges, and foundational theory in information and systems.

• Mathematical Physics
• Partial Differential Equations
• Probability
• +3 more

Felipe Hernández is an Assistant Professor in the Department of Mathematics at Pennsylvania State University. He is affiliated with the Department of Mathematics, located in McAllister Building, and can be contacted at felipeh@psu.edu. His research interests span Mathematical Physics, Partial Differential Equations, and Probability, with a focus on quantum systems, stochastic processes, and theoretical analysis of complex systems. His work explores topics such as quantum diffusion in disordered systems, decoherence dynamics, and the interplay between classical and quantum mechanics. He also investigates computational methods like neural networks to enhance accuracy in quantum chemistry calculations and explores foundational questions in quantum computing, such as BosonSampling complexity. Recent contributions include studies on Hodge systems, divergence-free vector fields, and endpoint estimates in functional analysis. While no specific awards or grants are listed, his research demonstrates a commitment to advancing mathematical physics and computational methods through rigorous theoretical analysis and interdisciplinary approaches.

• Algorithms
• Theoretical Computer Science
• Networks
• +4 more

David Kempe is a Professor in the Thomas Lord Department of Computer Science at the University of Southern California (USC), part of the Viterbi School of Engineering. His research focuses on algorithms, theoretical computer science, and their applications to networks, auctions, mechanism design, and information flow. He has advised numerous Ph.D., M.S., and undergraduate students, many of whom now hold prominent roles in academia and industry. His research has been supported by grants including NSF CAREER, Sloan Fellowship, and ONR Young Investigator awards. Kempe organizes conferences such as STOC 2018 and co-founded the USC Theory Group, fostering collaboration through regular meetings and seminars. His work bridges algorithmic foundations with real-world applications, addressing challenges in fairness, network dynamics, and machine learning. Key achievements include developing voting rules with optimal metric distortion and algorithms for fair matching under uncertainty. His publications span influential topics in social networks, game theory, and data science. Collaborations with industry (e.g., Facebook, Snapchat) highlight applied contributions to recommendation systems and media matching.

• Malaria Research
• Infectious Disease Epidemiology
• Vector Biology
• +3 more

Morgan Goheen is a Clinical Fellow and Instructor in the Section of Infectious Diseases at Yale School of Medicine. His research focuses on malaria transmission dynamics, particularly the impact of antimalarials on mosquito vectors and drug resistance in sub-Saharan Africa. He is affiliated with the EMD (Epidemiology of Microbial Diseases) program at Yale School of Public Health, collaborating with mentors Prof. Sunil Parikh and Dr. Amy Bei. His work combines field research in Burkina Faso with lab-based studies to explore vector biology, transmission-blocking interventions, and the effects of long-acting antimalarials on parasite development. Educated at Princeton University (AB, Molecular Biology), University of North Carolina at Chapel Hill (MD/PhD, Microbiology and Immunology), he has clinical training through Yale’s Internal Medicine residency and Physician-Scientist Training Program. His research has addressed host iron metabolism’s role in malaria susceptibility, with findings published in high-impact journals. Key research interests include: mosquito salivary antigens as transmission biomarkers, vector-parasite-drug interactions, and global health strategies to combat antimicrobial resistance. His recent Centennial Travel Award supports fieldwork in Burkina Faso, studying antimalarial exposure in mosquitoes and its implications for resistance spread. Mentored by EMD faculty in translational malaria research Recipient of Centennial Travel Award for tropical disease research Published over 20 peer-reviewed articles on malaria pathogenesis and clinical interventions His clinical and research activities reflect a commitment to addressing global health disparities through interdisciplinary approaches, bridging basic science, field epidemiology, and clinical practice.