Find Academic Supervisors

• Health Services Research
• Narrative Interventions
• Hypertension Control
• +3 more

Dr. Jeroan Allison is Chair and Professor at UMass Chan Medical School's Department of Population and Quantitative Health Sciences. Trained as a primary care physician and epidemiologist (Harvard SPH), he leads research in narrative interventions for health behavior change, with continuous funding from NIH, AHRQ, and RWJF. His work spans hypertension control in Vietnam, AI-driven health communication, and implementation science for non-communicable diseases. Harvard School of Public Health: Master of Science in Epidemiology University of Alabama at Birmingham: MD Samford University: BS in Chemistry Research interests include narrative medicine , health disparities , mHealth , and health services research . He has published extensively on hypertension interventions in Vietnam, digital health tools, and tobacco cessation strategies using peer engagement. Recent publications focus on AI applications , global implementation science , and social determinants of health . His team has developed culturally adapted interventions for HPV vaccination and hypertension management using community storytelling methods. Scientific contributions include: Co-Editor-in-Chief of Medical Care Developing simulation-based training for research assistants Designing peer recruitment models for smoking cessation Dr. Allison holds multiple grants and leads the UMMS-Vietnam research collaboration , focusing on late-stage translation research in low-resource settings.

Théo WinterhalterView profile

Researcher

• Formal Methods
• Type Theory
• Proof Assistants
• +3 more

Dr. Théo Winterhalter is a researcher at the Deducteam within INRIA Saclay and the Laboratoire de Méthodes Formelles (LMF) at École Normale Supérieure Paris-Saclay. Previously, he held a postdoctoral position at the Max Planck Institute for Security and Privacy (MPI-SP) in Bochum, Germany. He completed his PhD in computer science at the University of Nantes under the supervision of Nicolas Tabareau and Matthieu Sozeau, focusing on the formalization and meta-theory of type theory. His research centers on proof assistants, particularly Coq, and involves advancing formal methods for security, type systems, and dependent type theory. Winterhalter's work includes contributions to the MetaCoq project, enabling meta-programming and formal reasoning about Coq itself. He is also involved in developing verified cryptographic frameworks like SSProve and SecRef*, ensuring secure integration of verified and unverified code. His educational background includes a PhD in type theory and prior work in cryptographic algorithms, such as randomized scalar multiplication. He actively participates in academic communities, serving on program committees for ICFP, TYPES, and POPL. His teaching includes co-teaching the MPRI course on proof assistants with Yannick Forster. Supervision activities include PhD student Yann Leray and multiple interns focusing on Coq extensions and formal verification.

Saumen MandalView profile

Professor

• Optimal experimental design
• Biostatistics
• Data science
• +7 more

Dr. Saumen Mandal is a Professor in the Department of Statistics at the University of Manitoba, Faculty of Science. He holds a PhD from the University of Glasgow, UK, and MSc/BSc (Gold Medal) from the University of Calcutta, India. His research focuses on optimal experimental design, biostatistics, data science, shrinkage estimation, and constrained optimization. He has received numerous teaching awards including the Dr. and Mrs. H.H. Saunderson Award for Excellence in Teaching, Students Choice Best Professor Award, and multiple Merit Awards. He is also a P.Stat. designee from the Statistical Society of Canada. Education: PhD (Statistics), University of Glasgow, UK MSc (Statistics), University of Calcutta, India (First Class First, Gold Medal) BSc Honours (Statistics), University of Calcutta, India Research Interests: Optimal design theory and applications Biostatistical methods for clinical trials and healthcare data Data science and machine learning techniques Shrinkage estimation and model selection Linear models and goodness-of-fit testing Publications span topics like optimal regression designs, response-adaptive clinical trial methods, and statistical models for healthcare data. His work emphasizes practical applications in medicine and data-driven decision making. Awards include: Teaching Excellence Awards (2005-2007) Merit Awards for Teaching and Research (2010-2019) Faculty of Science Innovation in Teaching Award (2020) He advises graduate students in statistics and contributes to research teams in biostatistics and data science. His office is temporarily located at 256 Parker Building during construction.

Lenka ZdeborováView profile

Associate Professor

• Statistical Physics
• Machine Learning
• Computational Biology
• +3 more

Lenka Zdeborová is an Associate Professor at EPFL, jointly affiliated with the School of Basic Sciences and School of Computer and Communication Sciences. She leads the Laboratory of Statistical Physics of Computational Systems, where her research bridges statistical physics, machine learning, and computational biology. Education: PhD in Physics, Université Paris-Cité (2012) MSc in Fundamental Physics, École Normale Supérieure (2009) BSc in Physics, École Normale Supérieure de Lyon (2007) Her work focuses on phase transitions in learning algorithms, high-dimensional statistics, and neural network theory. Current projects investigate fundamental limits of machine learning, dynamics of graph neural networks, and applications to biological systems. Recent publications explore attention mechanisms in transformers, neural network depth advantages, and Bayes-optimal learning. Methodological innovations include cavity methods for hypergraphs and analysis of high-dimensional inference problems. Supervises doctoral students researching statistical physics approaches to machine learning and optimization. Teaches graduate courses in data science and machine learning for physicists.

Sisi ZhouView profile

Researcher

• Quantum Metrology
• Quantum Error Correction
• Quantum Learning
• +2 more

Sisi Zhou is a Research Faculty member at the Perimeter Institute for Theoretical Physics, specializing in quantum information science. Her work focuses on quantum metrology, quantum error correction, and quantum learning, with a particular emphasis on advancing methodologies for noisy quantum systems. Her research explores theoretical frameworks to enhance precision in quantum measurements, optimize error-correcting codes, and apply machine learning techniques to photonic platforms. Recent contributions include studies on non-Markovian metrology, Gaussian measurement limitations, and entanglement-enabled protocols for bosonic channels. Zhou has delivered seminars at institutions such as the Banff International Research Station and the University of Calgary, addressing topics like quantum metrological limits in noisy environments and error mitigation strategies for near-term quantum processors.

• Autism Spectrum Disorder (ASD)
• Gut Microbiome Research
• Computational Materials Science
• +3 more

James Adams is the President's Professor in the School for Engineering of Matter, Transport and Energy at Arizona State University (ASU), serving as the undergraduate program chair for materials science and engineering within the Ira A. Fulton Schools of Engineering. His research spans computational materials modeling and biomedical investigations into autism-related issues such as nutritional deficiencies, heavy metal toxicity, and gut microbiome dysregulation. He directs the Autism/Asperger's Research Program at ASU, focusing on translational research to improve clinical outcomes. Education: Ph.D. Materials Science and Engineering, University of Wisconsin-Madison (1987) M.S. Materials Science and Engineering, University of Wisconsin-Madison (1986) B.S. Physics, Duke University (1984) Research Focus: Adams bridges materials science and biomedical engineering, with seminal work on microbiota transfer therapy for ASD, metabolomics in autism, and heavy metal toxicity. His studies emphasize clinical applications, including nutritional interventions and dietary therapies to address gastrointestinal and neurobehavioral symptoms in children with ASD. Grants & Awards: Presidential Young Investigator Award (NSF, 1996–1998) Over 30 research grants from NSF, DOE, and industry partners (e.g., Motorola, CFD Research Corp) Labs/Initiatives: Biodesign Center for Health Through Microbiomes Autism/Asperger's Research Program Teaching: Courses include undergraduate/graduate research supervision (MSE 792, 799), honors thesis指导, and engineering foundations (FSE 100). He mentors students in materials science, biomedical engineering, and interdisciplinary projects.

Vernon ChinchilliView profile

Professor

• Biostatistics
• Clinical Trials
• Multivariate Analysis
• +4 more

Vernon M. Chinchilli is a Distinguished Professor of Public Health Sciences and Professor of Statistics at The Pennsylvania State University. He holds appointments in the Department of Public Health Sciences (Division of Biostatistics and Bioinformatics) and the Department of Statistics. His primary affiliation is with the College of Medicine. Dr. Chinchilli received his PhD in Statistics from the University of North Carolina at Chapel Hill (1979) and a BS in Mathematics from Penn State (1974). His research focuses on biostatistical methodology for multi-center clinical trials, multivariate analysis of longitudinal data, and statistical methods for crossover designs. He has led NIH-funded Data Coordinating Centers for studies on lung disease, kidney disease, and acute kidney injury (AKI). Notable roles include Principal Investigator for the Natural History of AKI Consortium and the Biostatistics Research Center for Impaired Awareness of Hypoglycemia. Dr. Chinchilli teaches doctoral courses in multivariate analysis (PHS 523) and statistical methods in public health (PHS 554/555). He has advised 35 PhD and 40 MS students. His honors include Fellow of the American Statistical Association (1997) and extensive contributions to methodological advancements in clinical trial design and analysis. Key areas of investigation include: 1) statistical methods for agreement assessment and equivalence testing in clinical trials, 2) biomarker development for kidney function decline, and 3) application of machine learning in predicting post-hospitalization outcomes. He collaborates internationally on projects like the ENICTO cancer treatment outcomes initiative and the S-AKI sepsis biomarker study. Recent work explores: cognitive change measurement in aging populations, opioid alternative therapies for chronic pain, and vaccine efficacy in immunocompromised patients. His research spans 541 peer-reviewed publications and 15 active NIH grants, with particular emphasis on translating statistical innovations into clinical practice.

• Machine Learning
• Theoretical Computer Science
• Data Science
• +3 more

Lev Reyzin is a Professor of Mathematics, Statistics, and Computer Science at the University of Illinois Chicago (UIC) and Director of the IDEAL Institute. He specializes in the theory of machine learning, data science, and artificial intelligence, with affiliations to theoretical computer science and mathematical foundations. Prior roles include a Simons Postdoctoral Fellowship at Georgia Tech and an NSF Computing Innovation Fellowship at Yahoo! Research. He earned his Ph.D. from Yale University (NSF doctoral fellowship) and a bachelor’s degree from Princeton University. Research interests focus on algorithmic learning theory, computational complexity, and applications of machine learning to real-world problems. Notable work includes contributions to statistical learning algorithms, adversarial bandits, and graph theory. His research has been funded by NSF grants (e.g., ECCS-2217023, CCF-2307106), DOD programs, and others since 2015. He has received awards at leading conferences (ICML, COLT, AISTATS). Reyzin holds editorial roles, including Editor-in-Chief of Mathematics of Data, Learning, and Intelligence and Chair positions in major conferences like FOCS 2024 and ALT 2017. His leadership in academic organizations highlights his influence in shaping theoretical computer science and machine learning research.

• Bayesian statistics
• High-dimensional inference
• Statistical machine learning
• +3 more

Mai Dao is an Assistant Professor in the Department of Mathematics, Statistics, and Physics at Wichita State University's Fairmount College of Liberal Arts and Sciences. She earned her Ph.D. from Texas Tech University under the mentorship of Professors Min Wang and Souparno Ghosh. Research Focus: Bayesian statistics, high-dimensional inference, and statistical machine learning Contact: mai.dao@wichita.edu | Jabara Hall 319 | Office hours: Tue & Thu 3:30-4:30 p.m. Research Interests include: Bayesian quantile regression High-dimensional data analysis Statistical machine learning algorithms Variable selection techniques Computational statistics Econometric modeling Recent Article Trends : Mai Dao's publications (2021-2025) emphasize Bayesian quantile regression methods, focusing on variable selection, high-dimensional inference, and computational approaches. Key themes include handling non-ignorable missing data, macroeconomic stress testing, and bridge-randomized regression techniques. Academic Expertise spans: Bayesian statistical modeling High-dimensional inference Machine learning applications Quantile regression methodologies Statistical computing

Thomas Le BarbanchonView profile

Professor

• Labor Economics
• Public Finance
• Econometrics
• +5 more

Thomas Le Barbanchon is a Full Professor of Economics at Bocconi University since 2025 and holds the Rodolfo Debenedetti Chair in Labor Economics. He received his PhD from Ecole Polytechnique (CREST-ENSAE) in 2012 and has been affiliated with institutions like CEPR, CREST, J-PAL, LEAP, BIDSA, IGIER, IZA, and IFS. His research focuses on labor economics, particularly job search mechanisms, unemployment insurance effects, gender disparities, and labor market policy evaluation. Educational background: PhD in Economics (Ecole Polytechnique), MSc in Economics (Universitat Pompeu Fabra), Diplôme d'ingénieur statisticien-économiste (ENSAE), and Diplôme d'ingénieur (Ecole Polytechnique) Research highlights include: Quantifying the impact of unemployment insurance reforms in France Analyzing gender differences in job search behavior Studying the effectiveness of hiring credits during economic crises Investigating how traditional AI improves job matching Examining migrant-native job search segregation patterns Scientific contributions appear in top journals like American Economic Journal: Applied Economics , Quarterly Journal of Economics , and Review of Economic Studies . Awards include two ERC grants (2017 & 2024) and the Bocconi Impact Award (2022). He mentors PhD students in economics and labor policy, currently serves as Director of IGIER research center, and maintains active editorial roles at Review of Economic Studies and Journal of the European Economic Association .

Nils-Christian DeteringView profile

Associate Professor

• Mathematical Finance
• Probability Theory
• Energy Markets
• +4 more

Nils-Christian Detering is an Associate Professor in the Department of Statistics & Applied Probability at the University of California, Santa Barbara (UCSB). He also serves as the Undergraduate Diversity, Equity, and Inclusion Officer. His research focuses on financial mathematics, probability theory, and their applications in systemic risk analysis, energy markets, and machine learning. Detering has contributed to understanding default contagion in financial systems, stochastic processes in energy derivatives, and neural network applications in functional data analysis. Research Interests : Financial systemic risk: Analyzing default contagion using random graphs and systemic stability metrics Infinite-dimensional stochastic analysis: Modeling energy markets via stochastic PDEs and forward curves Machine learning: Developing neural network frameworks for functional spaces and financial applications Teaching includes courses on stochastic processes, mathematical finance, and probability theory at both undergraduate and graduate levels. His work has been recognized with awards such as the Best Paper Award at the ACM International Conference on AI in Finance (2023). Key Publications address topics like reinforcement learning in banking networks, neural network calibration of energy curves, and integrated fire sales models. His research bridges theoretical probability with applied financial engineering challenges.

• Rock Mechanics
• Civil Engineering
• Mining Geotechnics
• +4 more

Professor Anna Giacomini is a leading academic in Rock Mechanics and Civil Engineering at the University of Newcastle. She holds a PhD from the University of Parma, Italy, and has been at the University of Newcastle since 2005. Her roles include Director of the Priority Research Centre for Geotechnical Science and Engineering and Deputy President of the Academic Senate (Research). She specializes in rockfall hazard analysis, mine geotechnics, and numerical modeling of geomechanical systems. Her research focuses on improving safety in mining and civil environments, with over $7.5M in funding and 140+ publications. Key areas include rockfall trajectory analysis, energy absorption in safety barriers, and drapery systems. She has led 20 major projects through ACARP and pioneered low-cost photogrammetric monitoring systems for rock slopes. Professor Giacomini is also a co-founder of HunterWiSE, promoting women in STEM. She has received prestigious awards such as the 2022 NSW Premier’s Engineering Prize and the 2019 John Booker Medal. Her administrative roles include membership in the ARC College of Experts and leadership in gender equity initiatives. Her technical contributions span experimental and numerical rock mechanics, including advancements in discrete element modeling (DEM) and stochastic approaches for discontinuity shear strength prediction. She collaborates internationally with institutions like the Colorado School of Mines and the University of Bologna.

Efthymios N. KaratzasView profile

Assistant Professor

• Numerical Analysis
• Computational Mathematics
• Optimal Control
• +5 more

Efthymios N. Karatzas serves as an Assistant Professor in the Department of Mathematics at Aristotle University of Thessaloniki, Faculty of Sciences, within the Computer Science and Numerical Analysis Section. He maintains an active research profile in computational mathematics with strong institutional affiliations including collaborations with SISSA mathLab and FORTH Institute of Applied and Computational Mathematics. His academic credentials include: PhD in Mathematics, National Technical University of Athens (2015) Master's in Applied Mathematical Sciences – Computational Mathematics, NTUA (2009) Master's in Applied Mathematics, University of Patras (2001) Bachelor's in Mathematics (Computational Mathematics), University of Patras (1999) Dr. Karatzas' research program centers on advanced numerical techniques for partial differential equations , with pioneering work in reduced order modeling , embedded boundary methods , and optimal control systems . His expertise spans computational fluid dynamics, uncertainty quantification, and biomechanical applications, characterized by methodological innovation in handling geometrically complex domains through cut finite element approaches and shifted boundary formulations. Analysis of his 15 most recent publications reveals a cohesive research trajectory focused on developing efficient numerical frameworks for parametrized PDE systems. His work consistently bridges theoretical rigor with practical implementation, particularly in advancing reduced basis methods for fluid-structure interaction and biological modeling, demonstrating significant contributions to computational mathematics through high-impact journal publications. No major scientific awards are documented in the available sources. Dr. Karatzas demonstrates research leadership through project management roles including Scientific Manager for the ELIDEK project at NTUA (2019-2021) and Project Manager for the European Social Fund HEaD initiative at SISSA (2017-2019). His grant administration experience encompasses coordinating interdisciplinary teams and securing external funding for computational mathematics research. He maintains active collaborations with the SISSA mathLab in Trieste (particularly with Prof. Gianluigi Rozza's group) and the FORTH Institute in Crete, participating in international workshops including the Reduced Order Methods in CFD Summer School (2019) and SIAM UQ conferences. His research network spans computational mathematics groups across Europe with emphasis on advancing numerical methodologies for real-world engineering and biological applications.

• Psychology
• Public Health
• Exercise Science
• +5 more

James A. Dimmock is a Professor affiliated with James Cook University, specializing in psychology, public health, and exercise science. His research focuses on mental health interventions, maternal well-being, climate change impacts on health, and behavioral strategies in healthcare settings. He collaborates extensively with researchers in areas such as cardiovascular health, youth mental health, and parenting support programs. Recent work includes analyzing climate change's effects on maternal mental health and developing peer support systems for new mothers in Australia. His studies often integrate psychological theory with practical applications, such as improving engagement in lifestyle interventions for obesity and enhancing mental health outcomes through physical activity. Education: No formal education details provided in the text, but his academic publications suggest advanced degrees in psychology or related fields. Research Interests: Psychological interventions for mental health Exercise and nutrition's role in cardiovascular disease prevention Climate change and public health challenges Maternal and child well-being Behavioral health interventions Peer support and community programs Key Contributions: Over 120 publications spanning 20 years, with a focus on randomized controlled trials, systematic reviews, and innovative interventions. Collaborations include institutions like the Perinatal Society of Australia and New Zealand, and international journals in psychology and medicine. Grants and Funding: Not explicitly listed, but his research scope implies involvement in health-related grants for climate change, maternal health, and exercise science. Labs/Teams: Works within multi-disciplinary teams at James Cook University, collaborating with departments in psychology, public health, and exercise science.

Suresh VenkatasubramanianView profile

Professor

• Algorithmic Fairness
• Automated Decision Making
• Computational Geometry
• +4 more

Suresh Venkatasubramanian is a Professor at Brown University, previously at the University of Utah's School of Computing. His research focuses on algorithmic fairness, automated decision systems, computational geometry, and the societal impacts of AI. He co-founded the FAT* conference and sits on the ACLU of Utah board. He holds a B.Tech from IIT Kanpur and a Ph.D. from Stanford University. Education : B.Tech (Computer Science, IIT Kanpur), Ph.D. (Stanford University). Key Roles : Member of Computing Community Consortium Council, Research Advisory Council for NYC's FTA Tool, and First Judicial District of Pennsylvania. Research interests emphasize fairness, accountability, and transparency in algorithms. Notable contributions include work on predictive policing biases, Shapley-value critiques, and information access gaps. Awards include an NSF CAREER Award and an ICDE Test-of-Time Award. Grants : Mozilla Foundation, NSF BIGDATA, DARPA A4V. Teaching : Advanced Algorithms, Ethics of Data Science, and courses on algorithmic fairness. Service roles include organizing conferences like FAT* and ALENEX, and advising on algorithmic governance in criminal justice systems.