Find Academic Supervisors

Serena BoothView profile

Assistant Professor

• Human-AI Interaction
• Reinforcement Learning
• AI Policy
• +3 more

Serena Booth is an incoming Assistant Professor in Computer Science at Brown University. Previously, she served as an AAAS AI Policy Fellow in the U.S. Senate, advising the Senate Banking Committee on AI policy. She holds a PhD from MIT CSAIL (2023) and a BA from Harvard College (2016). Her research focuses on human-AI interaction, specification design for AI systems, and ethical AI practices. She also worked as an Associate Product Manager at Google, scaling ARCore to 100 million devices. Her research explores how humans specify AI behaviors, assess system success, and mitigate misalignment risks. Key contributions include Bayes-TrEx (model transparency via Bayesian sampling) and RoCUS (robot controller understanding). Her work has been supported by NSF GRFP and MIT Presidential Fellowships. She advocates for science policy equity through MIT's Science Policy Initiative and co-founded initiatives to support women in computing (e.g., GW6 at MIT). Education: PhD MIT CSAIL (2023), BA Harvard College (2016) Awards: Rising Star in EECS, HRI Pioneer, NSF GRFP Key Areas: Reward design pitfalls, human-robot trust, ethical AI curriculum development Her recent publications analyze reward function misdesign (AAAI 2023), human-AI teaching frameworks (HRI 2022), and feature attribution reliability (AAAI 2022). She currently seeks PhD students/postdocs focusing on human-AI alignment, reinforcement learning, and policy implications.

James MitchellView profile

Professor

• Public Policy
• Devolution Studies
• Scottish Politics
• +5 more

James Mitchell is a Professor of Public Policy at the University of Edinburgh’s School of Social and Political Science . He holds a MA (Political Studies) from the University of Aberdeen and a D.Phil. (Oxford). His research focuses on British politics, devolution, public policy, territorial politics, and Scottish nationalism. He has supervised over 30 years of PhDs and Masters in public policy, devolution, and constitutional politics. Key research themes include fiscal accountability in Scotland, party membership surges post-referendum, and multi-level governance. Research Projects: ESRC-funded study on SNP and Scottish Greens’ post-referendum membership growth Investigation of fiscal devolution’s accountability gaps Analysis of Scotland’s constitutional questions and local governance Research Interests: Mitchell explores territorial politics, public service reform, and political behavior in sub-state governments. He has contributed to debates on Scottish independence, EU referendum impacts, and intergovernmental relations. Recent work includes studies on the SNP leadership contest (2023) and Holyrood’s fiscal structures. Grants & Awards: He has secured grants from the Economic and Social Research Council (ESRC) for projects on party membership and constitutional dynamics. No specific scientific awards are listed, but his work is widely cited in political science and public policy. Labs & Teams: He is part of the Territorial Politics Research Group and has collaborated on regional economic forecasting and governance studies. His work often bridges academia and policy, influencing debates on devolution and public finance.

Michael PawlovichView profile

Assistant Professor

• Traffic Safety
• Traffic Operations
• Statistical Analysis
• +2 more

Michael Pawlovich is Assistant Professor of Civil and Environmental Engineering at South Dakota State University's Jerome J. Lohr College of Engineering. His research focuses on traffic safety analytics, statistical modeling of crash data, and transportation infrastructure evaluation. Research expertise includes: Statistical methods for safety evaluation (Empirical Bayes, Bayesian modeling) Geometric design safety impacts Rural intersection safety Infrastructure treatment effectiveness Honors include multiple National Roadway Safety Awards and AASHTO recognitions. Publications demonstrate consistent application of advanced statistics to traffic safety challenges.

Michael A. NewtonView profile

Professor

• Biostatistics
• Genomics
• Computational Statistics
• +5 more

Michael A. Newton is a Professor and Chair of the Department of Biostatistics and Medical Informatics at the University of Wisconsin–Madison, School of Medicine and Public Health. His research focuses on statistical methodologies for high-dimensional biomedical data, including cancer biology, immunology, and genomics. He is renowned for developing empirical Bayesian methods, stochastic models, and computational tools for analyzing molecular data. His work integrates statistical theory with interdisciplinary collaborations, contributing to advancements in translational biomedicine. Newton has held prestigious awards, including the Mortimer Spiegelman Award (2003) and the COPSS Presidents' Award (2004). He is an elected Fellow of the American Statistical Association and an elected Member of the International Statistical Institute. He leads the Biostatistics and Epidemiology Research and Design (BERD) core at the Institute for Clinical and Translational Research and is affiliated with the Carbone Comprehensive Cancer Center and the Center for Genome Science and Innovation. His teaching includes advanced courses in computational statistics, Bayesian analysis, and statistical methods in molecular biology. Newton directs graduate programs in Statistics and Biomedical Data Science, emphasizing interdisciplinary training.

Catherine PetersView profile

Professor

• Environmental Engineering
• Geochemistry
• Carbon Sequestration
• +3 more

Catherine Peters is the George J. Magee Professor of Geosciences and Geological Engineering, and Professor of Civil and Environmental Engineering at Princeton University. She serves as Director of the Program in Geological Engineering and holds associated faculty roles in the High Meadows Environmental Institute (HMEI), Princeton Institute for the Science and Technology of Materials (PRISM), and the Andlinger Center for Energy and the Environment. Her work focuses on environmental chemistry, geochemical reactions, and subsurface energy technologies, including carbon sequestration, geothermal energy, and shale gas extraction. Education: PhD (Joint degree in Civil Engineering and Engineering & Public Policy), Carnegie Mellon University, 1992 MS in Civil Engineering, Carnegie Mellon University, 1987 BSE in Chemical Engineering, University of Michigan, 1985 Research Interests: Dr. Peters explores environmental challenges of subsurface energy systems through experimental, imaging, and modeling approaches. Key areas include CO2 storage in saline aquifers, geochemical reaction kinetics, and remediation of organic pollutants. Her lab uses synchrotron-based techniques and reactive transport simulations to study processes at nanometer to basin scales. She emphasizes collaborative research with national labs and universities. Recent Article Trends: Her publications (2023-2025) highlight advancements in carbon mineralization, hydrogen storage security, microbial geochemical interactions, and multi-scale material characterization. These works bridge environmental engineering, geochemistry, and data science to address decarbonization challenges. Awards & Leadership: Fellow, Association of Environmental Engineering and Science Professors (AEESP) since 2016 2012 EPA P3 Award for the 'Power-in-a-Box TM' sustainable design project Princeton Commendation List for Outstanding Teaching Grants & Collaborations: Funded by NSF, DOE, and EPA, her projects address subsurface energy systems and climate mitigation. She collaborates with national labs via synchrotron facilities and leads initiatives like the Grand Challenges Program. Courses taught include environmental engineering, statistical methods, and geochemical kinetics modeling. Labs & Teams: Affiliated with HMEI, PRISM, and the Andlinger Center, she integrates interdisciplinary teams to advance sustainable energy and environmental solutions. Her group’s work often intersects with global sustainability and equity goals.

Liqiang WangView profile

Professor

• Big Data Analytics
• High-Performance Computing (HPC)
• Parallel Computing
• +3 more

Liqiang Wang is a Professor in the Department of Computer Science at the University of Central Florida (UCF), where he directs the Big Data Lab. Previously, he served as faculty at the University of Wyoming (2006-2015). He holds a Ph.D. in Computer Science from Stony Brook University (2006) and spent a visiting research period at IBM T.J. Watson Research Center (2012-2013). His research focuses on big data analytics, high-performance computing, parallel systems optimization, and applying deep learning to detect programming errors and enhance model robustness. Education: Ph.D., Computer Science, Stony Brook University (2006); Visiting Researcher, IBM Watson (2012-2013). Research Interests: Improving accuracy and security of big data models, optimizing parallel computing systems (HPC, Cloud, GPUs), program analysis for concurrency errors, and deep learning applications in anomaly detection and adversarial robustness. Notable projects include scalable LSQR algorithms for seismic tomography and the OpenMP Analysis Toolkit (OAT) for concurrency error detection. Key Awards: NSF CAREER Award (2011), Castagne Faculty Fellowship (2013-2015), UCF Mid-Career Refresh Award (2020), and grants including a $50K NSF CIVIC-PG grant (2022) and Google/Meta donations. Advising and Grants: Supervises over 20 Ph.D./M.S. students and has secured grants totaling over $100K. Notable collaborations include seismic tomography with NCAR and cloud computing optimization. Labs/Teams: Director of UCF’s Big Data Lab, collaborating on projects like Parallel LSQR and Anti-Neuron Watermarking.

Brian LeventhalView profile

Associate Professor

• Item Response Theory
• Bayesian Applications in Psychometrics
• Survey Methodology
• +3 more

Brian Leventhal is an Associate Professor of Graduate Psychology and Director of the Assessment & Measurement PhD program at James Madison University. He holds a PhD in Research Methodology, Measurement, and Statistics from the University of Pittsburgh (2017), an MA in Applied Statistics (2013), and a BS in Mathematics from Clarkson University (2011). His work focuses on advancing psychometric methodologies, particularly in item response theory (IRT), Bayesian applications, and survey development. Leventhal’s research emphasizes multidimensional IRT, response style analysis, and the application of IRTree models to assess disengagement and validation processes. He leads the ITEMS (Interactive Tools for Measurement Education) initiative, developing online modules to enhance educational measurement practices. His recent work explores interprofessional collaboration between behavior analysts and school psychologists, as well as alignment between general education and program-specific learning outcomes. He has contributed to the NCME’s digital modules on data visualization and Monte Carlo simulations, and his articles frequently address methodological innovations in psychometrics. Leventhal is actively involved in James Madison University’s CARS (Center for Assessment and Research Studies), driving institutional assessment strategies and program evaluation initiatives.

Swati ChandnaView profile

Senior Lecturer

• Statistics
• Data Science
• Network Theory
• +3 more

Dr. Swati Chandna is a Senior Lecturer at the School of Computing and Mathematical Sciences, Birkbeck, University of London. She holds an honorary position as an Honorary Lecturer in Statistics at University College London (UCL) from January 2023 to January 2026. She earned her PhD in Statistics from Imperial College London in 2013. Her research focuses on statistical modeling, network analysis, and bioinformatics, with notable contributions to stochastic networks, single-cell genomic data analysis, and complex-valued signal processing. Teaching responsibilities include modules such as Bayesian Methods, Analysing Data, Statistical Analysis, and Project Applied Statistics. She serves as Admissions Tutor for Graduate Certificate and Diploma in Statistics for Data Science and as School Ethics Lead at Birkbeck. Her work bridges theoretical statistics with practical applications in genomics, environmental modeling, and biomedical research. Dr. Chandna’s recent research explores topics like covariate-driven network estimation, stochastic modeling of genomic data, and bootstrap techniques in source separation. Her publications reflect interdisciplinary collaboration across statistics, computer science, and life sciences.

• Biostatistics
• Clinical Trials
• Public Health
• +8 more

Professor Ian Marschner is a leading academic in biostatistics, currently holding the position of Professor of Biostatistics and Co-Director of Biostatistics at the NHMRC Clinical Trials Centre, University of Sydney. He has extensive experience spanning over 30 years, including roles as Professor and Head of the Department of Statistics at Macquarie University, Director of Biometrics at Pfizer, and Associate Professor at Harvard University. His research focuses on biostatistical applications in clinical trials, epidemiology, and public health, with a particular emphasis on adaptive trial designs, meta-analysis, and disease surveillance. Professor Marschner has contributed to major clinical trials in cardiovascular medicine, oncology, HIV/AIDS, neonatal/perinatal care, and COVID-19. He co-authored the book Inference Principles for Biostatisticians and is involved with the Biostatistics Collaboration of Australia (BCA) in developing and teaching the Masters of Biostatistics program. His grants include the NHMRC Centre of Research Excellence (AusTriM) and a National Critical Research Infrastructure Initiative grant totaling over $20 million. Research students under his supervision include Aydin HIBBERT, focusing on generalized joint regression models for longitudinal data. His work addresses methodological challenges such as bias in early-stopped trials, surrogate endpoints, and statistical frameworks for adaptive experiments. Recent contributions include risk modeling for diabetes, cardiovascular mortality prediction, and biomarker analysis in cancer therapies.

Giuliano CasaleView profile

Professor

• Performance Engineering
• Cloud Computing
• Resource Management
• +6 more

Giuliano Casale is a Professor in the Department of Computing at Imperial College London, leading the Quality of Service Research Lab (QORE). His research focuses on performance assurance, resource management, and fault-tolerance in distributed systems. He teaches courses on Probability and Statistics and Scheduling and Resource Allocation at undergraduate and Master’s levels. Casale’s work spans cloud computing, edge AI, and machine learning applications in system modeling. Key contributions include methodologies for performance engineering, anomaly detection, and automated resource management in large-scale systems. He actively participates in international conferences, delivering keynote speeches on topics such as performance evaluation and AI-driven systems. His research integrates queueing theory, machine learning, and generative models to address challenges in distributed software systems. Casale also engages in service activities like PhD admissions tutoring and collaborates on projects involving resilience planning and cloud service optimization. His lab, QORE, emphasizes practical solutions for real-world distributed systems, including edge federations and serverless architectures. Casale’s work bridges theoretical performance analysis with industrial applications, contributing to advancements in both academia and industry.

Rüdiger BachmannView profile

Professor

• Macroeconomics
• Heterogeneous Agents
• Expectation Formation
• +2 more

Rüdiger Bachmann is the Stepan Family College Professor of Economics at the University of Notre Dame, with affiliations at the Centre for Economic Policy Research, CESifo, and ifo Institute. His research focuses on macroeconomic effects of uncertainty and expectation formation. Research examines: Impacts of economic uncertainty on business investment and hiring Survey-based measurement of firm expectations Labor market dynamics and productivity gaps Distributional consequences of energy policy shifts Behavioral responses to fiscal interventions Recent publications analyze German energy independence scenarios, pandemic consumption patterns, and firms' decision-making under ambiguity. He has advised German government institutions on economic policy matters and contributed to public debates through influential policy briefs. Professional experience includes positions at Goethe University, RWTH Aachen University, University of Michigan, and visiting appointments at leading institutions including Harvard and Yale. Teaches advanced macroeconomics courses at both undergraduate and graduate levels.

Donat FähView profile

Professor

• Seismology
• Geophysics
• Earthquake Engineering
• +7 more

Prof. Donat Fäh is a faculty member at ETH Zurich's Institute of Geophysics, part of the Swiss Seismological Service (SED). His work focuses on advancing seismic hazard assessment and site response modeling in Switzerland and beyond. He leads interdisciplinary projects integrating geophysical surveys, machine learning, and empirical data to refine risk models for urban areas like Basel and Lucerne. Key contributions include developing the ERM-CH23 national earthquake risk framework and improving methodologies for nonlinear soil behavior analysis using KiK-net data from Japan. His research emphasizes high-resolution amplification mapping, subsurface characterization via ambient vibrations, and understanding glacial and subaqueous slope dynamics. Research interests span seismic site effects, soil mechanics, landslide stability monitoring, and the application of advanced geophysical inversion techniques. He collaborates internationally to enhance earthquake risk communication and building code compliance, particularly in low seismicity regions. Current efforts include refining 3D geophysical models for urban settings and exploring Bayesian methods for subsurface structure identification. His work bridges fundamental geophysical research with practical engineering solutions for infrastructure resilience. Advising and grants: No formal advisees or grant details are explicitly listed in the provided texts. His collaborative projects, however, suggest involvement in large-scale initiatives such as URBASIS and the Swiss strong-motion network modernization. Labs and teams: Prof. Fäh is affiliated with the Swiss Seismological Service (SED) and actively contributes to ETH Zurich’s seismic monitoring infrastructure. His team collaborates with institutions in Japan (KiK-net network) and applies cutting-edge geophysical techniques to study subglacial environments and lakebed geotechnics.

Jeffrey DoserView profile

Assistant Professor

• Ecological Modeling
• Species Distribution Analysis
• Spatial Statistics
• +3 more

Jeffrey Doser is an Assistant Professor in the Department of Forestry and Environmental Resources at North Carolina State University (NC State), within the College of Natural Resources. His research focuses on ecological modeling, species distribution analysis, spatial statistics, and biodiversity monitoring. He specializes in developing R software tools for ecological data analysis, such as the spAbundance and spOccupancy packages. His work emphasizes improving statistical methods for occupancy models, integrating acoustic and survey data, and addressing environmental challenges like invasive species and climate change impacts on ecosystems. Key research themes include species-environment interactions, spatially varying coefficients in ecological models, and functional reproducibility in scientific coding. His recent studies address topics such as American chestnut restoration, wild bee community dynamics, and early detection of invasive aquatic species. Doser collaborates on interdisciplinary projects involving forest inventory, avian soundscapes, and long-term ecological monitoring. His publications (2020–2025) highlight methodological advancements in species distribution modeling, occupancy frameworks for multi-season data, and applications of Bayesian approaches. He advocates for rigorous statistical practices and reproducible research in ecology.

Yongwan ChunView profile

Professor

• Geographic Information Science (GIS)
• Spatial Statistics
• Urban Geography
• +5 more

Professor Yongwan Chun is a faculty member in the Geospatial Information Sciences program at the University of Texas at Dallas , affiliated with the School of Economic, Political and Policy Sciences. His academic journey includes a PhD in Geography (2007) and MS in Applied Statistics (2006) from the Ohio State University. Research Interests: Methodological advancements in GIScience, spatial statistics, environmental justice, urban crime analysis, population migration, and commodity flow modeling. Awards: Emerging Scholar Award (2017) from the Spatial Analysis and Modeling Specialty Group of the Association of American Geographers. Grants: Multiple awards from National Science Foundation and National Institute of Health totaling $1.4 million. Article Trends reflect expertise in spatial autocorrelation (Moran's I, Geary's C), location-allocation modeling (p-median, p-dispersion), and applications to healthcare accessibility, urban crime patterns, housing market segmentation, and climate impacts. His work integrates GIScience with Bayesian methods, eigenvector filtering, and big data uncertainty. Collaborators: Daniel A. Griffith, Hyun Kim, and students like Dongeun Kim and Changho Lee. Recent Topics: Trauma center accessibility, snowpack variation modeling, and spatiotemporal crime analysis. Contact: ywchun@utdallas.edu | Office: GR 3.208, UT Dallas

Elena A. EroshevaView profile

Professor

• Hierarchical Modeling
• Longitudinal Data Analysis
• Latent Variable Modeling
• +4 more

Elena A. Erosheva is a Professor in the Department of Statistics and Social Work at the University of Washington , where she also serves as the Associate Director of the Center for Statistics and the Social Sciences (CSSS). Her research focuses on hierarchical modeling , longitudinal data analysis , and latent variable models , with applications in social sciences, disability research, and health statistics. She has taught courses like Multivariate Data Analysis, Sample Survey Techniques, and Statistical Modeling with Latent Variables. Education : PhD in Statistics, University of Washington (2002) Current Research : Measuring gender-based homophily in collaborations, modeling life course transitions, partial mastery cognitive diagnosis models, and bias detection in grant review Scientific Awards : 2019 NIH Center for Scientific Review First Prize for Most Creative Idea for Detection of Bias in Peer Review 2013 Mitchell Prize, International Society for Bayesian Analysis 2015 ASA Student Paper Award (with Maryclare Griffin) Her students and postdoctoral researchers include Sheridan Grant, Y. Samuel Wang, and Jonathan Gruhl. She uses R software extensively in her teaching and research. Contact: erosheva@uw.edu | Personal Website