Find Academic Supervisors

Kylie L. AnglinView profile

Assistant Professor

• Educational Policy
• Data Science
• Natural Language Processing
• +3 more

Kylie L. Anglin is an Assistant Professor in the Department of Educational Psychology at the University of Connecticut, specializing in Research Methods, Measurement, and Evaluation (RMME). She holds a Ph.D. in Education Policy from the University of Virginia (2021), an MPP in Leadership and Public Policy (2018), and a BA in Political Science (2013) from Southwestern University. Her research focuses on developing natural language processing (NLP) techniques to monitor program implementation in impact evaluations, enhancing causal validity and replicability in education research. Dr. Anglin teaches graduate courses in research methods, data science, and text analytics. Her work has been published in journals such as the Journal of Research on Educational Effectiveness , Prevention Science , and AERA Open . She has received the NAEd/Spencer Dissertation Fellowship and participated in the IES Pre-doctoral Training Program. Her research emphasizes scalable methods for analyzing policy variation, improving intervention adherence, and leveraging large language models for education evaluation. She actively mentors students interested in education policy or data science, and her contact details are available at kylie.anglin@uconn.edu .

Osama MahmoudView profile

Lecturer

• Predictive Modelling
• Health Data Science
• Machine Learning
• +6 more

Dr. Osama Mahmoud is a Lecturer (Assistant Professor) in Data Science and Statistics at the University of Essex, affiliated with the School of Mathematics, Statistics and Actuarial Science (SMSAS) and the Department of Mathematical Sciences. He holds dual roles as the Director of the BSc Data Science and Analytics programme and Deputy Director of Research at SMSAS. His academic journey includes a PhD in Statistics from the University of Essex and an Honorary Senior Researcher position at the University of Bristol Medical School (2020–2024). Dr. Mahmoud's research focuses on Predictive Modelling, Health Data Science, Machine Learning, Bio and Medical Statistics, and Explainable AI. He pioneered the Slope-Hunter method (Nature Communications, 2022) for bias correction in genome-wide studies and developed tools like the Proportion Overlapping Score (POS) and open-source packages on CRAN/GitHub. His work bridges statistical methodology with applications in healthcare, environmental epidemiology, and industrial collaboration. Key contributions include studies on sleep-breast cancer mortality linkages (2025), lung function-cardiovascular risk relationships (2024), and smoking's role in depression recovery (2022). He has secured grants supporting collaborations with UNDP, Rolls Royce, and Recruitment Smart. Teaching innovations include pioneering programming and text analytics modules. Education: PhD in Statistics, University of Essex Affiliations: UK Reproducibility Network (UKRN) Institutional Lead, British Data Science Society Member Grants: Projects in Data Science, theoretical/applied Statistics, and industrial partnerships Tools: ESKNN, OTE, propOverlap packages on CRAN His work emphasizes reproducibility, with over 50 peer-reviewed publications and international teaching engagements at ICTP (Italy), EuADS (Luxembourg), and UK institutions.

James C. Reeder IIIView profile

Assistant Professor

• Sales Promotions
• Sales Force Management
• Digital Promotions
• +6 more

James C. Reeder III is an Assistant Professor of Marketing at the School of Business , University of Kansas. His academic work focuses on causal inference, machine learning applications in marketing, and sales strategy optimization. Ph.D. in Business Administration (Marketing), University of Rochester (2014) M.S. in Applied Economics and Statistics, University of Rochester (2007) MBA in Marketing and Strategy, University of Rochester (2007) B.A. in Management, Hood College (2003) A.A. in Business Administration, Frederick Community College (2001) Reeder's research examines: Strategic utilization of sales representatives Promotion deployment optimization Machine learning-enhanced empirical estimation Generative AI applications in marketing Publications appear in Marketing Science and Management Science . His methodological work spans causal inference, structural modeling, and digital promotion analysis.

Jennie E. BrandView profile

Professor

• Social Stratification
• Inequality and Mobility
• Social Demography
• +3 more

Jennie E. Brand is a Professor of Sociology and Professor (by courtesy) of Statistics and Data Science at the University of California, Los Angeles (UCLA). She co-directs the Center for Social Statistics (CSS) and serves as President of the Association of Population Centers (APC). Her affiliations include the Population Association of America (PAA), International Sociological Association (ISA), and editorial boards of Social Forces , Sociological Methodology , and Science Advances . Research Interests include social stratification, inequality, causal inference, and the socioeconomic impact of higher education. She explores Access to and returns from higher education Consequences of job displacement and disruptive events Machine learning applications in social science Intergenerational mobility and family effects Scientific Awards include the ASA Mathematical Sociology Outstanding Publication Award (2024), Sociological Research Association membership (2019), and the ASA Methodology Leo Goodman Mid-Career Award (2016). Grants awarded by NICHD, NIH, NSF, and Robert Wood Johnson Foundation support her work on population research, causal inference, and health disparities. She has mentored numerous graduate students, including Ph.D. recipients at NYU, Stanford, and Oxford.

Michelle TorresView profile

Assistant Professor

• Political Methodology
• Political Behavior
• Computer Vision
• +5 more

Margaret Michelle Torres is an Assistant Professor in the Department of Political Science at the University of California, Los Angeles (UCLA). Her research focuses on political methodology, computer vision, causal inference, and survey methodology, with substantive interests in political media communication, participation, and attitude formation. She holds a Ph.D. in Political Science and an A.M. in Statistics from Washington University in St. Louis, alongside a B.A. in Political Science and International Relations from CIDE (Mexico City). Her work bridges computational methods and social science inquiry, emphasizing innovative tools for analyzing visual and textual data. Recent projects explore how ideology influences perceptions of political groups, the role of visual frames in protests, and methodological issues in causal inference. Torres has authored influential papers on topics ranging from election dynamics to the application of machine learning in political analysis. Notable contributions include frameworks for unsupervised visual analysis and critiques of posttreatment variable pitfalls in experiments. Her research frequently intersects with questions of media representation, public opinion, and institutional trust. Torres advises students in quantitative methods and political behavior, though specific advisee names are not listed here.

Gennady SamorodnitskyView profile

Professor

• Stochastic Modeling
• Heavy Tailed Distributions
• Long-Range Dependence
• +6 more

Gennady Samorodnitsky is a Professor in the School of Operations Research and Information Engineering (ORIE) at Cornell University. He holds a B.S. from the Moscow Steel and Alloys Institute (1978), M.S. from Technion – Israel Institute of Technology (1983), and a D.Sc. from Technion (1986). He joined Cornell in 1988 and has held visiting positions at the University of North Carolina at Chapel Hill and Boston University. His research focuses on stochastic processes, particularly heavy-tailed distributions, long-range dependence, and extreme value theory, with applications in finance, teletraffic, and climate modeling. Education: B.S., Moscow Steel and Alloys Institute, USSR, 1978 M.S., Technion – Israel Institute of Technology, 1983 D.Sc., Technion – Israel Institute of Technology, 1986 Samorodnitsky’s research interests span stochastic modeling, including heavy-tailed processes, self-similar processes, and extreme value analysis. He examines the behavior of financial and telecommunication systems under long memory and non-Gaussian conditions. Key areas include the statistical analysis of extremes in climate data and the theoretical foundations of stable and infinitely divisible processes. His work bridges probability theory with applications in risk management, network traffic analysis, and climate science. His publications explore topics such as high-level excursion sets in random fields, tail inference, and the interplay between ergodic theory and stochastic processes. He has contributed to books like Stochastic Processes and Long Range Dependence and authored numerous technical reports on topics like ruin probabilities and multivariate extremes. Samorodnitsky teaches advanced courses, including ORIE 7590: Martingales in Discrete and Continuous Time , and maintains an active role in academic conferences and collaborations. His research group investigates cutting-edge problems in high-dimensional extremes, privacy-aware learning, and topological data analysis.

Natesh PillaiView profile

Professor

• Applied Probability
• Computational Methods
• Markov Chain Monte Carlo (MCMC)
• +5 more

Natesh Pillai is a Professor in the Department of Statistics at Harvard University and a Distinguished Engineer at LinkedIn, focusing on Responsible AI. He holds a Bachelors from IIT Madras, a PhD from Duke University's Department of Statistical Science (2008), and completed a postdoc at the University of Warwick's CRiSM (2008-2010). His research spans applied probability, computational methods, MCMC theory, algorithmic fairness, and climate science. He serves on editorial boards for journals like SIAM Journal on Mathematics of Data Science and Harvard Data Science Review . Key awards include the 2018 Young Statistical Scientist Award and 2021 Fellowship in the Institute of Mathematical Statistics. His work emphasizes bridging theory and practice, with contributions to statistical methodology, causal inference, and scalable computational techniques. Recent collaborations include industry roles at Amazon (2021-2023) and interdisciplinary climate science projects analyzing agricultural yield predictability. Education: Bachelor's: Indian Institute of Technology (IIT) Madras PhD: Duke University, Department of Statistical Science Research Focus: MCMC mixing times, Bayesian methodology, reinforcement learning, climate data modeling Publications emphasize algorithmic efficiency, fairness in AI, and probabilistic frameworks for complex systems. His lab integrates theoretical rigor with real-world applications, including climate modeling and healthcare analytics.

Hangfeng HeView profile

Assistant Professor

• Machine Learning
• Natural Language Processing
• Reasoning
• +4 more

Hangfeng He is an Assistant Professor of Computer Science and Data Science at the University of Rochester , affiliated with the Hajim School of Engineering & Applied Sciences. He holds a PhD from the University of Pennsylvania and focuses on machine learning and natural language processing. His research emphasizes incidental supervision for natural language understanding, interpretability of deep neural networks, and reasoning in natural language. Education : PhD in Computer Science from University of Pennsylvania Research Interests : Machine Learning (especially deep learning theory, generalization, and optimization) Natural Language Processing (incidental supervision, language understanding, and multimodal reasoning) Reasoning (inductive biases, temporal data analysis, and model interpretability) Research Trends : His work spans foundational AI research and applied NLP, with recent focus on large language models, multimodal systems, and constraint-aware methods. He explores how models generalize from limited supervision and mitigate biases in real-world data. Key themes include improving model robustness, understanding inductive mechanisms, and applying AI to financial and social text analysis. Labs/Teams : While specific lab affiliations aren’t listed, his work aligns with the Department of Computer Science’s research clusters in AI, machine learning, and data science.

Yan WangView profile

Professor

• Trust Management
• Recommender Systems
• Social Networks
• +7 more

Yan Wang is a Full Professor in the School of Computing at Macquarie University, Australia. He obtained his PhD from Harbin Institute of Technology (HIT), China, and has been at Macquarie since 2003 following a postdoctoral fellowship at the National University of Singapore. His research focuses on trust management, recommender systems, social networks, and services computing, with a strong emphasis on applications in cyber security and data analytics. Wang has published extensively in top-tier venues like AAAI, IJCAI, and IEEE Transactions, and has led over 25 research projects, including current initiatives like Trust-Oriented Data Analytics in Online Social Networks (DP230100676) and Combating Fake News on Social Media (2020–2023). He has received prestigious awards such as the IEEE TCSVC Outstanding Service Award (2017) and the Vice-Chancellor’s Excellence in Research Supervision Award (2014). He serves on editorial boards of journals like IEEE Transactions on Services Computing and has organized major conferences including IEEE ATC2013 and IEEE CLOUD2017. His work bridges theoretical advancements in machine learning and practical challenges in social computing, edge computing, and federated learning. Affiliations: School of Computing, Data Horizons Research Centre, Frontier AI Research Centre Key Projects: Trust-Oriented Data Analytics in Online Social Networks (2023–2026) Combating Fake News on Social Media (2020–2023) Research Themes: Recommender systems, graph learning, fake news detection, and trust management in distributed systems. Wang’s recent work explores causal representation learning, cross-domain recommendations, and privacy-preserving techniques in edge computing environments.

• Disease Ecology
• Spatial Epidemiology
• GIS in Public Health
• +4 more

Michael E. Emch is a W.R. Kenan, Jr. Distinguished Professor of Geography and Epidemiology at the University of North Carolina at Chapel Hill (UNC-Chapel Hill). He holds a PhD in Medical Geography from Michigan State University (1998) and has been a Fellow at the Carolina Population Center. His research integrates spatial analysis with epidemiological and ecological theories to study infectious diseases in developing regions, focusing on disease ecology, spatial epidemiology, and GIS applications in public health. He directs the Spatial Health Research Group and is affiliated with the UNC Infectious Diseases, Epidemiology, and Ecology Lab (IDEEL). His research explores spatio-temporal patterns of diseases like malaria, hepatitis B, and influenza, emphasizing how social, environmental, and built factors influence disease spread. Notable projects include studies on swine influenza evolution, malaria vaccination efficacy (RTS,S/AS01), and hepatitis B elimination strategies in the Democratic Republic of Congo. He also investigates environmental health issues, such as groundwater contamination and cholera transmission in Bangladesh. Education: PhD in Medical Geography (1998, Michigan State University), MA in Geography (1992, Miami University of Ohio), BA in Biology (1987, Alfred University). Service: Editorial roles in journals like Health & Place and SSM - Population Health . Awards: W.R. Kenan, Jr. Distinguished Professorship, Fellow of the Carolina Population Center. Teaching focuses on spatial components of health, including courses like GIS in Public Health and Infectious Disease Ecology . Recent articles highlight cutting-edge work on machine learning in environmental health, vaccine efficacy analysis, and spatial epidemiology of emerging pathogens. His advocacy for interdisciplinary approaches bridges geography, epidemiology, and ecology to address global health challenges.

Didong LiView profile

Assistant Professor

• Biostatistics
• Machine Learning
• Geometric Data Analysis
• +7 more

Dr. Didong Li is an Assistant Professor in the Department of Biostatistics at the Gillings School of Global Public Health , University of North Carolina at Chapel Hill. His research focuses on statistical methods for complex data, including manifold learning, nonparametric Bayesian inference, and spatial statistics, with applications in healthcare, genomics, and environmental health. He holds a PhD in Mathematics from Duke University (2020), and degrees from Beijing Institute of Technology (BS 2012, MS 2015). His research interests include dimension reduction, Gaussian processes, geometric data analysis, and network analysis. He has developed novel methods for analyzing electronic health records, single-cell RNA sequencing data, and spatial omics datasets. Notable contributions include the spherelet manifold approximation framework and the STimage-1K4M dataset. Dr. Li has received the Inaugural IMS Lawrence D. Brown PhD Student Award (2019) . He serves on the editorial board of the Journal of Machine Learning Research and organized sessions at JSM 2021-2022 on geometric Bayesian methods. His lab supports interdisciplinary collaborations, with ongoing projects in spatial transcriptomics, healthcare access analysis, and AI-driven medical informatics. Advising highlights include guiding students through successful PhD defenses (e.g., Dr. Sam Hawke, Dr. Jiawen Chen) and postdoctoral placements. His team secured a NVIDIA Academic Grant (2024) for multi-modal AI in spatial transcriptomics. The Li Lab maintains active partnerships with institutions worldwide, advancing both statistical theory and real-world applications.

• Distributed Systems
• Cloud Computing
• Fault Tolerance
• +3 more

Rodrigo Miragaia Rodrigues is a full professor at the Instituto Superior Técnico (ULisboa) and a researcher at INESC-ID since 2015. He previously held roles as an associate professor at Universidade Nova de Lisboa, tenure-track faculty at MPI-SWS, and completed his PhD at MIT in 2005 under Barbara Liskov. Education: PhD in Computer Science, MIT, 2005 Research Interests: Focuses on distributed systems, fault-tolerant computing, cloud infrastructure, and consistency models. His work bridges theoretical foundations and practical implementations, addressing challenges in geo-replication, secure analytics, and resource allocation in serverless environments. He emphasizes scalable systems and resilient data management. Awards: Best Paper Award at SOSP ERC Starting Grant Google Faculty Research Award Advising & Grants: Has advised 7 PhD students as main advisor, with graduates in top institutions like Purdue, TU Munich, and USTC. Secured funding from the European Research Council (ERC) and Google, focusing on projects like DependableCloud (ERC Grant 307732). Labs & Teams: Leads research at INESC-ID and previously directed the Dependable Systems Group at MPI-SWS. Active in academic leadership roles, including President of the Scientific Council at IST.

• Wearable Computing
• Human-Computer Interaction
• Machine Learning
• +3 more

Silvia Santini is an Associate Professor at the Faculty of Informatics of USI since 2016, leading the People-Centered Computing Lab. Previously, she held roles at TU Dresden (2014–2016) and TU Darmstadt (2011–2014), focusing on embedded systems and wireless sensor networks. She completed her PhD at ETH Zurich in 2009 and earned a Telecommunication Engineering degree from Sapienza University of Rome (2004). Her research centers on wearable computing, human-computer interaction, and sensor networks applied to health monitoring, activity recognition, and physiological signal analysis. Notable projects include BiHeartS (bilateral heart rate monitoring) and studies on sleep quality using wearable devices. Key achievements include being ranked among the World’s top 2% of Scientists (2022) and securing grants for projects like XAI-FinCrime. She advises on PhD and postdoc positions, such as the 2025 Innosuisse-funded project in explainable AI for financial crime detection. Labs/Teams: People-Centered Computing Group (USI), collaborating on cross-disciplinary projects in pervasive computing and healthcare technology.

Ruitao LinView profile

Assistant Professor

• Bayesian adaptive design
• Robust Bayesian methods
• Meta-analytic trial design
• +6 more

Dr. Ruitao Lin is an Assistant Professor in the Department of Biostatistics at The University of Texas MD Anderson Cancer Center and an Adjunct Assistant Professor at Rice University. He holds a PhD in Biostatistics from The University of Hong Kong (2012–2016) and completed postdoctoral training at the University of Washington and MD Anderson. His research focuses on Bayesian adaptive designs, robust statistical methods, and clinical trial methodologies, particularly in oncology and precision medicine. He leads the development of novel Bayesian approaches for dose-finding in phase I/II trials, meta-analytic trial designs, and high-dimensional data analysis. Dr. Lin is an Associate Editor for Contemporary Clinical Trials , Biometrical Journal , and Pharmaceutical Statistics , and has contributed to software tools like the BOIN design suite and TITE-BOIN12. His work bridges statistical innovation with practical clinical challenges, including late-onset toxicities in immunotherapy and heterogeneous treatment effects. He has secured NIH funding (e.g., R01CA261978) and collaborated with academic and industry partners to advance trial design and analysis. His software platforms, including R packages and Shiny apps, enable clinicians to implement cutting-edge statistical methods in real-world settings. Education : PhD, Biostatistics, The University of Hong Kong (2016); Postdocs at University of Washington (2016–2017) and MD Anderson (2017–2019). Grants : NIH/NCI R01CA261978 (2021–2025), focusing on Bayesian methods for precision oncology trials. Teaching : Teaches clinical trial design courses at Rice University and UTHealth. Labs/Software : Developed the BOIN design suite, TITE-BOIN12, and other tools for dose-finding and phase I/II trials.

Bertram F. MalleView profile

Professor

• Moral Psychology
• Social Cognition
• Human-Machine Interaction
• +3 more

Bertram F. Malle is a Professor of Cognitive and Psychological Sciences at Brown University, holding a joint appointment in the Department of Cognitive and Psychological Sciences. He is affiliated with the Humanity-Centered Robotics Initiative and has collaborated with institutions like the Army Research Laboratory and the Office of Naval Research. His academic journey includes a Master’s in Philosophy/Linguistics (1987) and Psychology (1989) from the University of Graz, Austria, followed by a Ph.D. in Psychology from Stanford University (1995). Before joining Brown in 2008, he was at the University of Oregon. His research focuses on moral psychology, social cognition, and human-robot interaction. Key interests include moral judgments, blame attribution, social norms, and the cognitive foundations of human-machine collaboration. He has been funded by NSF, DARPA, and the Department of Defense, among others. Malle’s work bridges psychology, philosophy, and AI, emphasizing moral competence in robots and the societal implications of human-robot interaction. His awards include the SESP Outstanding Dissertation Award (1995), NSF CAREER Award, and the 2019 SESP Scientific Impact Award. He is also a Fellow of multiple prestigious societies, including APS and SPSP. His teaching spans courses like Social Psychology, Moral Psychology, and Multivariate Statistics. Collaborators include experts in computer science and robotics, reflecting his interdisciplinary approach to advancing human-centric AI systems.