Find Academic Supervisors

• Clinical Pharmacology
• Pharmacogenomics
• Pharmacometrics
• +7 more

Associate Professor Sophie Stocker is affiliated with the Sydney Pharmacy School (University of Sydney), St Vincent’s Hospital (Honorary Senior Hospital Scientist in Clinical Pharmacology and Toxicology), and the Garvan Medical Research Institute . She also serves as an Adjunct Senior Lecturer at the St Vincent’s Clinical School, University of New South Wales. Research Focus: Her work spans clinical pharmacology , pharmacogenomics , pharmacometrics , and therapeutic drug monitoring (TDM) . Key areas include optimizing diabetes therapies through metformin pharmacogenomics, managing gout via urate-lowering strategies, and improving antimicrobial dosing in critically ill patients. Recent Trends: 2025 publications highlight her leadership in Bayesian model selection for antibiotic precision dosing Saliva-based TDM innovations Pharmacogenomic tools for allopurinol and clopidogrel Evaluating SGLT2 inhibitors in chronic kidney disease Global standards for antifungal TDM Scientific Awards: Victor Armstrong Young Investigator Award (2022) Emerging Leader Award (2020) CERTARA Young Investigator Award (2020) St Vincent's Rising Star (2018) ASCEPT Presidential Trainee Award (2013) Supervision: She mentors research students working on projects such as voriconazole TDM strategies , saliva-based tuberculosis monitoring , and CYP2C19-guided antiplatelet therapy . International Links: Collaborates with institutions in Germany (University of Hamburg) and New Zealand (University of Otago) on projects like model-informed precision dosing and gout management.

Martin PetersonView profile

Professor

• Ethics of Technology
• Normative Ethics
• Decision Theory
• +3 more

Martin Peterson is the Sue and Harry Bovay Professor of History and Ethics of Professional Engineering at Texas A&M University's Department of Philosophy. He previously taught at Eindhoven University of Technology and served as a Research Fellow at Cambridge University. He earned his Ph.D. in Philosophy from KTH Royal Institute of Technology, Sweden (2003). His work integrates normative ethics, decision theory, and the ethics of technology, focusing on principles like the precautionary and sustainability principles in technological contexts. Education: Ph.D. in Philosophy, KTH Royal Institute of Technology, Sweden (2003) Research Interests: Peterson emphasizes ethical challenges in engineering and technology, exploring how moral principles can be systematically applied. His normative ethics work includes defending gradualism—the idea that moral rightness exists on a spectrum—and consequentialist frameworks. In decision theory, he examines rational choice under uncertainty and the implications of value theory for technological decisions. He also critiques simplistic ethical binaries in fields like nuclear power and abortion debates. Advising & Grants: Though no formal advisees are listed, his teaching contributions include co-authoring Ethics for Engineers and developing pedagogical modules on diversity and equity in engineering education. His grants likely fund interdisciplinary research on ethics and technology, but specific grants are not mentioned in the text. Labs/Teams: Not explicitly detailed in the text. His research is theoretical and book-focused, suggesting collaboration with academic networks rather than formal lab structures.

Fang LiuView profile

Professor

• Differential Privacy
• Statistical Machine Learning
• Bayesian Statistics
• +7 more

Fang Liu serves as Notre Dame Collegiate Professor and Associate Chair in the Department of Applied and Computational Mathematics and Statistics (ACMS) at the University of Notre Dame's College of Science. She holds a Ph.D. from the University of Michigan (2003), M.S. from Iowa State University (1999), and B.S. from Peking University (1997). Ph.D., University of Michigan, 2003 M.S., Iowa State University, 1999 B.S., Peking University, 1997 Her research spans five interconnected domains: 1) Development of modern data privacy protection approaches including differential privacy; 2) Statistical and machine learning methodologies with complex model regularization; 3) Bayesian frameworks for medical, biological and social science data analysis; 4) Advanced missing data analysis techniques; and 5) Biostatistical and epidemiological applications. She pioneered the integration of differential privacy concepts from computer science into statistical methodology, identifying numerous research opportunities at this interdisciplinary intersection. Dr. Liu leads the Trustworthy and Ethical Statistical Learning Lab (TESLLa), which focuses on differential privacy, trustworthy AI, synthetic data generation, and probabilistic machine learning. Her lab has produced significant output in privacy-preserving statistical inference and applications to pandemic response, vector-borne disease control, and social media emotional analysis. Key honors include: Elected Fellow of the American Statistical Association Elected Member of the International Statistical Institute (2024) As an advisor, she has mentored over 40 graduate students and researchers since joining Notre Dame in 2011, with alumni securing positions at institutions including Sandia National Laboratories, Los Alamos National Lab, JP Morgan Chase, TikTok, and academic faculty roles. Her research has received substantial funding from NSF, NIH, private foundations, and Notre Dame internal grants, including critical work on campus COVID-19 surveillance testing strategy and privacy-preserving pandemic data collection.

Min ChenView profile

Professor

• Statistical Genomics
• Bioinformatics
• Bayesian Methods
• +5 more

Dr. Min Chen is a Professor in the Department of Mathematical Sciences at the University of Texas at Dallas (UTD), affiliated with the School of Natural Sciences and Mathematics. He holds an adjunct professorship at the University of Texas Southwestern Medical Center. His expertise spans statistical genomics, bioinformatics, Bayesian methods, and sampling techniques. He completed his Ph.D. in Statistics and Decision Science at the University of Texas at Austin and a postdoctoral fellowship in statistical genomics at Yale University. Education: B.S. in Computer Science (University of Science & Technology of China, 1994); M.A. in Statistics (University of Pittsburgh, 1999); Ph.D. in Statistics (UT Austin, 2006); Postdoc in Statistical Genomics (Yale University, 2008–2010). Research focuses on statistical methodologies in genomics, including genome-wide association studies, network-based modeling, and Bayesian integrative analysis. He also explores spatial modeling and ranked set sampling. His work addresses challenges in cancer genetics, epigenetics, and single-cell gene regulation. Notable awards include the NIH Career Development Award (2013), David Bruton Fellowship (2006), and R.L. Anderson Student Paper Award (2006). He is a member of the American Statistical Association and International Chinese Statistical Association. He advises graduate students in Data Science, Statistics, and Bioinformatics & Computational Biology (BCBM) programs. His teaching includes courses on advanced statistical methods and data science. Research contributions span over 40 peer-reviewed articles, with recent work in tumor pathology imaging, antibiotic resistance, and Alzheimer’s disease mechanisms.

• Applied probability theory
• Markov chain Monte Carlo methods
• Spatial statistics
• +4 more

Jesper Møller is a Professor in the Department of Mathematical Sciences at Aalborg University's Faculty of Engineering and Science, specializing in Statistics and Mathematical Economics. His research focuses on advanced statistical methodologies with applications across various scientific domains. His educational background includes extensive training in mathematical sciences, though specific degree details aren't provided in the current materials. His research interests span: Applied probability theory Markov chain Monte Carlo methods (MCMC) Spatial statistics Stochastic geometry Stochastic simulation Point process modeling Professor Møller's recent publication record shows consistent productivity with 239 research outputs including journal articles, reports, and book chapters. His work demonstrates strong focus on spatial point processes, Bayesian inference methods, and applications of stochastic geometry. The research trends indicate increasing sophistication in modeling complex spatial patterns and developing computational methods for statistical inference. His scientific contributions have been supported by numerous research projects, with 28 projects documented including the current "Peculiar Distribution Functions and Interesting Stochastic Processes" (2022-2026). His work has generated significant scholarly impact with citations across multiple disciplines. Professor Møller has supervised 8 PhD students and maintains active collaborations across international research networks. His current projects suggest continued research activity in developing novel statistical methodologies for complex spatial data analysis with applications in materials science, neuroscience, and environmental statistics.

Zeynep Işıl KalaylıoğluView profile

Professor

• Bayesian Statistics
• Environmental Statistics
• Health Statistics
• +3 more

Prof. Zeynep Işıl Kalaylıoğlu is a Professor of Statistics at the Department of Statistics, Middle East Technical University (METU), Ankara, within the Faculty of Arts and Sciences. She holds a Ph.D. in Statistics from North Carolina State University (2002) and has held roles including Associate Professor (2014–2022), Assistant Professor (2009–2014), and researcher at the National Cancer Institute (2002–2007). She has served in administrative roles such as Assistant to the Department Head and member of METU’s Wind Energy Research Center and Faculty Board. Ph.D.: Statistics, North Carolina State University, 2002 M.S.: Statistics with Computational Engineering minor, North Carolina State University, 1999 B.S.: Statistics with Computer Engineering minor, Middle East Technical University, 1995 Her research focuses on Bayesian statistical methodologies for environmental and health phenomena. Key areas include spatial/temporal modeling of atmospheric and ecological variables, prediction of migratory patterns (e.g., wind direction, bird movements), and risk modeling for breast cancer using mammogram data. She develops flexible models for circular and extremal data, emphasizing applications in ecology, epidemiology, and environmental science. Her recent publications address topics like goodness-of-fit tests for statistical distributions, predictive model selection for circular data, and Bayesian approaches for missing covariates in generalized linear models. Current projects include mobile apps for olive harvest optimization and frost预警 systems, reflecting her commitment to applied statistical solutions. Labs/Teams: She leads the BayeZian Research Group, collaborating with METU’s Ecosystem Research Center on interdisciplinary projects. Her teaching expertise spans mathematical statistics, Bayesian theory, and statistical inference at undergraduate and graduate levels.

Elizabeth SchifanoView profile

Associate Professor

• Biostatistics
• Statistical Genomics
• High-Dimensional Data Analysis
• +3 more

Elizabeth Schifano is an Associate Professor in the Department of Statistics at the University of Connecticut. She focuses on biostatistics, statistical genomics, and high-dimensional data analysis with applications in medical research and genetic studies. Her work addresses complex challenges in missing data imputation, longitudinal studies, and variable selection in high-dimensional genomic datasets. Research Interests: Development of Bayesian methods for nonignorable missing data in clinical trials Statistical methodologies for analyzing SNP sets in genetic association studies Online updating algorithms for streaming big data Survival analysis with monotone partial likelihood Publications reflect her expertise in statistical genomics and advanced computational methods. She collaborates with researchers in public health and biomedical sciences to advance methodological innovations in data analysis. Office: AUST 317 | Contact: (860) 486-6143

• Computational Mechanics
• Granular Materials
• Machine Learning in Physics-Based Simulations
• +7 more

H. Cheng is a researcher at the University of Twente, affiliated with the Faculty of Engineering Technology and the Department of Mechanics of Solids, Surfaces and Systems. He plays a central role in several interdisciplinary research projects focused on computational modeling of granular materials, geohazards, and machine learning integration in physics-based simulations. His research centers on advancing numerical methods such as the Discrete Element Method (DEM) and developing machine learning surrogates for efficient uncertainty quantification in complex systems. Key project areas include offshore infrastructure resilience under climate change (POSEIDON), dynamic fault slip in induced seismicity (FastSlip), upscaling particulate systems for industrial applications (TUSAIL), and automated segmentation of soil-root systems using micro-CT imaging (UNSAT). H. Cheng leads and supervises multiple early-career researchers across EU-funded initiatives, including MSCA Doctoral Networks and COST Actions. He is the main applicant and supervisor in the GrainLearning project, which integrates Bayesian inference with physics-based models to improve simulation accuracy and efficiency. His scientific contributions span collaborative research across academia and industry, with a strong emphasis on open science, reproducibility, and cross-sectoral training. He contributes to community-building through initiatives like ON-DEM, promoting best practices in particle-based simulations. Supervisor of multiple PhD students and postdoctoral researchers Daily supervisor in POSEIDON, FastSlip, TUSAIL, UNSAT Vice-lead of Working Group 1 in ON-DEM COST Action Main applicant and project lead for GrainLearning H. Cheng is actively involved in training the next generation of computational scientists and engineers, with a focus on interdisciplinary methodologies that bridge mechanics, data science, and industrial applications.

• Stochastic Simulation
• Optimization
• Machine Learning
• +5 more

Giulia Pedrielli is an Associate Professor at Arizona State University's School of Computing and Augmented Intelligence, with affiliations as a Senior Global Futures Scientist. She holds a Ph.D., M.Sc., and B.S. in Mechanical and Industrial Engineering from Politecnico di Milano, Italy. Her research focuses on stochastic simulation, optimization, and machine learning applications in biomanufacturing, digital twins, and cyber-physical systems. She has contributed to frameworks for pandemic modeling (PySIRTEM), RNA design, and high-dimensional optimization. Pedrielli has collaborated with institutions like NUS and UC Berkeley, and her work spans biomedical engineering, supply chain resilience, and security of critical infrastructure. Recent articles emphasize co-simulation tools, AI-driven logic translation, and falsification methods for CPS security. Her teaching includes advanced stochastic simulation courses and thesis supervision across multiple disciplines. Education: Ph.D. Mechanical Engineering, Politecnico di Milano, Italy M.Sc. Industrial Engineering, Politecnico di Milano, Italy B.S. Industrial Engineering, Politecnico di Milano, Italy Research interests integrate simulation-based optimization with machine learning, particularly in safety-critical systems like healthcare and smart infrastructure. She develops digital twin methodologies for real-time control under uncertainty, leveraging Gaussian processes and Bayesian approaches. Pedrielli's work bridges theoretical advancements (e.g., BASSO optimization algorithm) with practical applications in biomanufacturing and pandemic response.

Partha LahiriView profile

Professor

• Survey Sampling
• Small-Area Estimation
• Bayesian Inference
• +3 more

Partha Lahiri is a Professor at the University of Maryland, College Park, holding dual appointments in the Joint Program in Survey Methodology (JPSM) and the Department of Mathematics . He also serves as an Adjunct Research Professor at the Institute of Social Research, University of Michigan, Ann Arbor. Previously, he was the Milton Mohr Distinguished Professor of Statistics at the University of Nebraska-Lincoln. His expertise spans Survey Sampling , Small-Area Estimation , Bayesian and Empirical Bayes Inference , and Resampling Methods . He has advised organizations such as the United Nations Development Program, World Bank, and Gallup, and contributed to panels like the U.S. Census Advisory Committee. He is a Fellow of the American Statistical Association and the Institute of Mathematical Statistics, and an elected member of the International Statistical Institute. Research interests include methodological advancements in small area estimation, survey data integration, and statistical linkage. His work emphasizes practical applications in policy and public health, including poverty measurement and pandemic-related studies (e.g., mask effectiveness perception during the pandemic). He has authored influential papers on topics like multi-goal prior selection, probabilistic record linkage, and resampling techniques for uncertainty quantification. His leadership roles, including directing JPSM, highlight his commitment to training future survey methodologists. Awards: ASA and IMS Fellowships, ISI Elected Membership Advisory Roles: U.S. Census Advisory Committee, National Academy of Sciences panels Publications Trends: Focus on small area estimation, Bayesian methods, and policy-relevant applications Grants/Projects: Collaborations with international organizations and academic institutions on statistical methodologies Lahiri’s research bridges theoretical statistics and practical survey challenges, with a strong emphasis on data integration and uncertainty estimation in complex systems.

Jurij KarlovsekView profile

Senior Lecturer

• Geotechnical Engineering
• Underground Engineering
• Sustainable Infrastructure
• +4 more

Dr. Jurij Karlovsek is a Senior Lecturer in the School of Civil Engineering at the University of Queensland (UQ), specializing in Geotechnical and Underground Engineering. His research focuses on sustainable infrastructure development, BIM integration in tunnelling, and non-destructive testing (NDT) using technologies like GPR and LiDAR. He leads initiatives at the Sustainable Infrastructure Research Hub and co-founded the Infrastructure CoLab. He teaches courses including Soil Mechanics, Sustainable Infrastructure Design, and Underground Structures. Active in professional organizations, he serves as Vice-President of the Australian Tunnelling Society (ATS) and Past Chair of the ITA Working Group 22 on BIM in Tunnelling. His awards include the ITA Young Tunneller of the Year (2015) and the David Sudgen Award (2012). He supervises PhD students in areas like tunnel safety, observational methods, and geotechnical information value. His recent research includes Bayesian network models for tunnel fire safety and digital twin frameworks for urban sustainability. Education: PhD in Civil Engineering from UQ. Grants: Includes funding from Lloyd's Register Foundation and Natural Hazards Research Australia. Labs/Teams: Sustainable Infrastructure Research Hub, Infrastructure CoLab.

Jonas KloecknerView profile

Researcher

• Geostatistics
• Petroleum Engineering
• Mining Engineering
• +7 more

Jonas Kloeckner is a PhD Candidate at the Federal University of Rio Grande do Sul (UFRGS) in the Mineral & Environmental Technology and Extractive Metallurgy Program, focusing on Geostatistics, and concurrently serves as a Visiting Research Scholar at Stanford University under Professor Jef Caers. His academic journey includes a MSc in Engineering and Mining Engineering from UFRGS, a Bac +5 degree from Ecole des Mines d'Alès (France), and a Visiting Student stint at Columbia University (USA). His research interests span geostatistical modeling of carbonate facies, Bayesian approaches for uncertainty quantification, and applications of machine learning in geological and medical domains. Key technical areas include Markov Chain Random Fields, seismic data integration, and spatial continuity analysis, with a focus on improving sustainability in mining operations and reservoir characterization. Notable contributions include advancements in covariance table methodologies for automated spatial analysis and risk assessment frameworks for bauxite mining. His work bridges geological data interpretation, stochastic simulation techniques, and computational tools for resource optimization. Currently affiliated with Stanford's energy geoscience initiatives, his research emphasizes interdisciplinary applications of geostatistical models.

John ConlonView profile

Assistant Professor

• Behavioral Economics
• Experimental Economics
• Cognitive Science
• +5 more

John Conlon is an Assistant Professor at Carnegie Mellon University's Dietrich College of Humanities and Social Sciences within the Department of Social and Decision Sciences . As a behavioral economist, his work bridges economics and psychology through experimental methods to analyze how attention, memory, and cognitive biases influence economic decision-making. Ph.D. in Business Economics from Harvard University B.A. in Economics and Philosophy from The Ohio State University Postdoctoral researcher at Stanford University His research investigates belief formation under cognitive constraints, focusing on how selective memory recall, attention allocation, and social learning distort economic choices. Key applications include education decisions (major selection, salary expectations) and labor markets (teacher recruitment, job search behavior). Article trends reveal systematic exploration of non-Bayesian belief updating , social learning asymmetries , and cue-driven economic biases . These studies employ dual-task experiments , nationally representative surveys , and household fieldwork to challenge rational choice frameworks. Dr. Conlon's interdisciplinary work aligns with Dietrich College's focus on cross-disciplinary collaboration between economists and psychologists. His methodological approach integrates structural modeling with behavioral interventions to address policy-relevant questions.

Joseph GlazView profile

Professor

• Applied Probability
• Geometrical Probability
• Probability Approximations
• +7 more

Joseph Glaz is a Professor in the Department of Statistics at the University of Connecticut. His work focuses on applied probability and statistical methodology, particularly in scan statistics and related inference techniques. Contact: joseph.glaz@uconn.edu Phone: (860) 486-4193 (Storrs Campus) Research highlights: Develops scan statistics for discrete , continuous , and conditional data frameworks Specializes in change-point detection for normal data mean/variance Innovates robust methods for outlier-prone datasets Extends scan statistics to network and graph structures Contributed to foundational texts like the Handbook of Scan Statistics Publication trends include: Scan statistics for genomic data (Hi-C translocation detection) Nonparametric and Bayesian extensions of scan methods Approximations and inequalities for sequential testing Applications in quality control , medical imaging , and sensor networks

Jason PoulosView profile

Research Fellow

• Machine Learning
• Causal Inference
• Health Policy
• +5 more

Jason Poulos is a data scientist and researcher currently working at the intersection of machine learning, causal inference, and health policy. He holds a Ph.D. in Political Science from the University of California, Berkeley, with a designated emphasis in Computational Science and Engineering. His academic journey includes postdoctoral positions at Harvard Medical School's Department of Health Care Policy and Brigham and Women's Hospital. Dr. Poulos's research focuses on developing and applying machine learning methods to address challenges in complex, high-dimensional longitudinal health data. His work spans several interconnected areas including extending causal inference methods for multi-valued treatments, developing frameworks for adversarial machine learning, applying neural networks for health outcome prediction, and evaluating deep learning approaches for missing data imputation in survey data. His recent publications demonstrate expertise in targeted learning methodologies, particularly in observational studies with multi-level treatments, with applications in pharmacoepidemiology. His research output shows a consistent pattern of interdisciplinary work bridging political science, statistics, and health policy. The majority of his recent publications (2021-2024) appear in high-impact journals across biostatistics, political methodology, and machine learning domains, reflecting his ability to contribute to multiple academic communities. His work often employs advanced statistical techniques to address real-world policy questions, particularly in healthcare settings. As evidenced by his publications and research trajectory, Dr. Poulos has established himself as a methodological innovator in causal inference with multi-valued treatments, with particular expertise in applying these methods to evaluate the safety and effectiveness of antipsychotic medications. His work combines deep methodological rigor with practical policy applications.