Find Academic Supervisors

Wei-Lun ChaoView profile

Associate Professor

• Machine Learning
• Computer Vision
• Autonomous Driving
• +3 more

Wei-Lun (Harry) Chao is an Associate Professor in the Department of Computer Science and Engineering at the Ohio State University (OSU), College of Engineering. Promoted to this role in May 2025, he is also an Innovation Scholar and Distinguished Assistant Professor of Engineering Inclusive Excellence. His work spans machine learning, computer vision, and their applications in autonomous driving, healthcare, biology, and natural language processing. Research Focus: Machine learning with imperfect data, interpretable and personalized learning, robust perception for autonomous systems, and visual recognition in real-world scenarios. Awards: 2025 OSU Early Career Distinguished Scholar Award, CVPR Best Student Paper Award (2024), CSE Faculty Teaching Award (2024), Lumley Research Award (2023). Grants: Funded by NSF, NIH, ONR, Cisco, AWS, and Google. Notable Research Trends: The 15 most recent articles highlight his work on vision foundation models, federated learning, diffusion models for biological species generation, interpretable vision transformers, and robust perception systems for autonomous driving. Key subfields include sparse autoencoders, 3D object detection, semi-supervised learning, and anomaly detection in scientific domains. Scientific Awards: 2025 Early Career Distinguished Scholar Award (OSU) CVPR Best Student Paper Award (2024) CSE Faculty Teaching Award (2024) Lumley Research Award (2023) Mentoring & Grants: As an advisor for the OSU Buckeye AutoDrive Team and AI Club, he mentors graduate and undergraduate students. His research is supported by major grants from NSF, NIH, ONR, and industry partners like Cisco and Google.

Brett WelchView profile

Assistant Professor

• Psychology
• Communication Disorders
• Voice Disorders
• +4 more

Brett Welch is an Assistant Professor and Biostatistician at the University of Houston with dual appointments in the Department of Communication Sciences and Disorders and the Department of Psychological, Health, and Learning Sciences within the College of Liberal Arts and Social Sciences. Dr. Welch's educational background includes: Ph.D. in Communication Science and Disorders from the University of Pittsburgh M.S. in Communication Sciences and Disorders from the University of Texas Rio Grande Valley B.S. in Linguistics from the University of Texas at Austin B.A. in Communication Sciences and Disorders – Speech-Language Pathology from the University of Texas at Austin Dr. Welch's research sits at the intersection of psychology and communication. He seeks to understand how psychological processes like personality and psychopathology influence communication behaviors, and conversely, how communication behaviors impact psycho-social well-being. His work leverages quantitative methods, cross-sectional studies, and intensive longitudinal designs to interrogate the dynamic interplay between psychological processes and voice and communication behaviors. This research aims to advance understanding of functional voice disorders, improve assessment of psychopathology, and promote well-being for people with and without communication disorders. Dr. Welch has received several notable awards: Honors Council of Academic Programs in Communication Sciences and Disorders PhD Scholarship (2024) New Century Scholars Doctoral Scholarship from the American Speech-Language-Hearing Foundation (2023) New Investigator Forum from The Voice Foundation (2021) As an academic, Dr. Welch serves as an ad hoc peer reviewer for various scientific journals in speech-language pathology and psychology. He is also a mentor in the Students to Empowered Professional Mentoring Program and a member of the American Speech-Language-Hearing Association. His teaching includes Special Problems: Introduction to Quantitative Modeling (COMD 8398). Dr. Welch maintains an active research program focused on the intersection of psychology and communication, with particular emphasis on voice disorders and their psychological correlates. His work combines biostatistical approaches with clinical insights to advance understanding in this specialized field.

Qianwen WangView profile

Assistant Professor

• Interactive Visualization
• Interpretable Machine Learning
• Human-AI Collaboration
• +5 more

Qianwen Wang is a tenure-track Assistant Professor in the Computer Science department at the University of Minnesota, Twin Cities. Her research combines interactive visualization with interpretable machine learning to foster intuitive, efficient, and reliable Human-AI collaboration. She actively seeks motivated students, research assistants, and interns to join her dynamic research team at UMN CS. Dr. Wang's research focuses on three primary themes: Human-AI Collaboration, where she designs tools to facilitate Human-AI interaction; Automatic & Intelligent Visualization, where she develops techniques to make visualizations accurately interpreted and easily used; and VIS+(X)AI in Biomed/Healthcare, where she studies how visualization and explainable AI can promote scientific discoveries in biomedicine and healthcare. Her work has made significant contributions to visualization, human-computer interaction, and bioinformatics, with particular applications in biomedical knowledge graphs and single-cell omics analysis. Her recent publications demonstrate a strong trend toward integrating visualization with large language models and graph neural networks for biomedical applications. She has published extensively at top venues including IEEE VIS, ACM CHI, and Nature Medicine, with a focus on making AI systems more interpretable and useful for domain experts, particularly in healthcare contexts. Her work often bridges theoretical advances in visualization with practical applications in genomics and healthcare. Two IEEE VIS Honorable Mention Awards (2022, 2024) Best Paper Award from IMLH@ICML 2021 Two Best Abstract Awards from BioVis ISMB (2021, 2022) HDSI Postdoctoral Research Fund Award Research covered by MIT News and Nature Technology Features Dr. Wang actively contributes to the academic community through service roles including General Chair for ISMB BioVis, VisNotes and Poster Chair for IEEE PacificVis, and Program Committee member for IEEE VIS, ACM CHI, and ACM IUI. Her research has been supported by various grants that enable her team to develop innovative visualization techniques and explore their practical applications in biomedical domains. Her research group maintains an active presence in the visualization and HCI communities, with members participating in major conferences and workshops. The lab focuses on creating tools that bridge the gap between complex AI models and human understanding, with particular emphasis on making these technologies accessible and useful for domain experts in biomedical research.

Colleen ConleyView profile

Clinical Professor

• Developmental Psychology
• Clinical Psychology
• Mental Health Interventions
• +5 more

Colleen Conley is a Clinical Professor in the Department of Psychology at Loyola University Chicago, specializing in clinical psychology with a focus on adolescent and emerging adult mental health. She directs the IMPACT (Improving Mental-health and Promoting Adjustment through Critical Transitions) Lab, which conducts research on psychological well-being during developmental transition periods. Dr. Conley earned her Ph.D. from the University of Illinois at Urbana-Champaign and her B.A. from Lawrence University. Her research program blends observational studies, meta-analyses, and intervention development to improve well-being, particularly during critical life transitions like the college years. Her work examines psychological trajectories in adolescence and emerging adulthood, with special attention to gender issues and LGBTQIA+ well-being. Her research interests center on developmental psychopathology, examining the interplay between individuals and their contexts over time. She investigates how biological, psychological, cognitive, and social factors interact to influence psychological distress and well-being. Much of her work focuses on college student mental health, including trajectories of well-being during the transition to college and interventions to address mental health challenges. Analysis of her recent publications reveals a strong focus on college student mental health, with emphasis on developmental trajectories, intervention effectiveness (particularly peer-led and technology-delivered approaches), and the impact of transition periods on psychological functioning. Her work increasingly incorporates digital interventions and examines moderators of intervention effectiveness across diverse student populations. Research Mentoring Award Program recognition for mentoring award-winning students Heckler Summer Research Fellowship recognition for mentoring fellowship recipients Dr. Conley mentors numerous graduate students in the clinical psychology program, with many completing dissertations on topics related to mental health interventions, student well-being, and developmental transitions. Her IMPACT Lab collaborates with university staff, administrators, and other researchers to develop practical interventions that address student mental health needs. Current projects include Honest, Open, Proud (HOP) for stigma reduction, Wellness Advising with Motivational Interviewing (WAMI), Students Taking on Effective Post-Graduate Skills (STEPS), and Supportive Accountability Methods for Mentoring Young Adults (SAMMY-A). The lab also partners with commercial entities like Headspace for mindfulness research.

Melvyn R.W. HamstraView profile

Professor

• Organizational Behavior
• Leadership
• Human Resource Management
• +6 more

Melvyn R.W. Hamstra is a Full Professor at IÉSEG School of Management in France since 2023. His academic career includes roles as Associate Professor (2021–2023) and Assistant Professor (2020–2021) at IÉSEG, as well as Assistant Professor positions at the University of Maastricht (2015–2020) and the University of Amsterdam (2013–2015). He holds a Ph.D. in Behavioural and Social Sciences from the University of Groningen (2013), preceded by a Master’s (2009) and Bachelor’s (2007) in Psychology from the same institution. Research Interests : His work centers on organizational behavior, leadership, and employee motivation, with a strong emphasis on regulatory focus theory, employee voice dynamics, and knowledge management. Key themes include how leadership styles (e.g., promotion vs. prevention focus) influence team innovation, conflict resolution, and workplace morale. Publication Trends : Recent publications (2025–2024) explore topics such as gender bias in leadership evaluation, financial stress and counterproductive behavior, ethical conflict in teams, and the dual-edged role of humor in workplace negotiations. His longitudinal studies on religious engagement and life satisfaction, as well as theoretical models on knowledge withholding, highlight interdisciplinary breadth. Teaching : He contributes to bachelor and master’s programs at IÉSEG, focusing on leadership development, positive leadership, and organizational learning.

Marina AgranovView profile

Professor

• Experimental Economics
• Behavioral Economics
• Political Economy
• +3 more

Marina Agranov is Professor of Economics at the California Institute of Technology (Caltech), affiliated with the Division of Humanities and Social Sciences. She directs research through the Ronald and Maxine Linde Institute of Economic and Management Sciences, Center for Social Information Sciences (CSIS), and Center for Theoretical and Experimental Social Sciences (CTESS), and serves as Research Associate at the National Bureau of Economic Research (NBER). Her academic credentials include a B.A. from St. Petersburg State Technical University (1999), M.A. from Tel Aviv University (2004), and Ph.D. from New York University (2010). She joined Caltech as Assistant Professor in 2010 and was promoted to full Professor in 2017. Agranov's research pioneers experimental and behavioral economics, focusing on strategic decision-making in bargaining games, social learning environments, network interactions, and information dynamics. Her work examines how individuals form beliefs and navigate tensions between personal goals and collective outcomes, often using controlled laboratory experiments to test theoretical predictions about human behavior under uncertainty. Her recent publications reveal a consistent methodological approach: blending game-theoretic models with experimental validation to investigate communication effects, randomization preferences, and institutional design. Key trends include analyzing how uncertainty impacts committee negotiations, how complexity influences egalitarian outcomes in legislative bargaining, and how information structures shape social learning on networks. Her scientific recognition includes: Associated Students of Caltech (ASCIT) Teaching Award (2017-18) Professor Agranov's research has secured significant institutional support through Caltech centers and NBER affiliation, with findings featured in major economics journals and Caltech news coverage including "Decision by Committee: How Uncertainty Shapes Negotiations" (December 2024) and "Experimental Economics in Theory and Practice" (July 2023). Her work on committee decision-making under uncertainty has direct implications for institutional design in political and corporate governance. She actively contributes to Caltech's research ecosystem through CSIS and CTESS, which facilitate interdisciplinary collaborations in social sciences and experimental methodology development.

Alison LedgerwoodView profile

Professor

• Social Psychology
• Open Science
• Methodology
• +4 more

Alison Ledgerwood is a Professor in the Department of Psychology at the University of California, Davis, and Principal Investigator of the Attitudes and Group Identity Lab. Her research examines how social context shapes attitudes and preferences, with a focus on open and inclusive scientific practices. Ph.D., Social Psychology, New York University (2008) M.A., Psychology, New York University (2006) B.A., Psychology, Amherst College (2003) Her research explores psychological distance , framing effects , and system justification theory , while methodologically advancing preregistration , collaborative science , and equity in publishing . She also investigates group identity dynamics and implicit/explicit bias measurement . Recent publications highlight her work on racial bias methodology , open science reform , and contextual framing . Awards include the 2024 Distinguished Service to the Society award, 2021 UC Davis Advising and Mentoring Award, and 2017 SPSP Service to the Field award. 2024 Distinguished Service to the Society, SPSP 2021 UC Davis Graduate Advising and Mentoring Award 2017 Service to the Field, SPSP Hellman Fellowship (2010-2011), UC Davis UC Davis Chancellor's Fellow APS Fellow SESP Fellow Ledgerwood advises on scientific integrity through roles like Chair of the Transparency and Openness Promotion (TOP) II Guidelines Task Force and Anti Colorism and Eurocentrism in Methods and Practices (ACEMAP) Task Force. Her lab fosters contextual evaluation studies and psychological distance frameworks .

Wei PengView profile

Professor

• Health Communication
• Human-Computer Interaction
• Media Psychology
• +7 more

Dr. Wei Peng is a Professor in the Department of Media and Information at Michigan State University, specializing in health communication technology design and human-computer interaction. Her research explores behavior change mechanisms through interactive media. Ph.D. in Communication from University of Southern California (2006) Key research areas include: Psychological and social effects of interactive media AI-driven health promotion systems Conversational agents for wellness Digital games for physical activity Mobile health applications Combating health misinformation through technology Her 2024-2021 publications demonstrate expertise in AI ethics, health gamification, and technology-based behavior change. Grant collaborations include: National Science Foundation (NSF) - Sensor-enabled family routines for child obesity prevention Robert Wood Johnson Foundation (RWJF) - Exergame development Scientific recognition includes multiple ICA Top Paper Awards and MSU Teacher-Scholar honors. She actively serves on editorial boards of: Journal of Communication Health Communication Games for Health Journal BMC Public Health Currently advising in AI applications for health misinformation and persuasive technology research.

Jennifer WhittyView profile

Professor

• Health Economics
• Preferences for Health and Health Care
• Valuing Health Outcomes
• +7 more

Professor Jennifer Whitty is an applied health economist at the University of East Anglia , affiliated with the Norwich Medical School and the Centre of Research Excellence in Telehealth . Her research bridges health economics, patient preferences, and health policy, with a focus on quality of life valuation and economic evaluation. Education: Doctor of Philosophy (Griffith University, 2008), Graduate Diploma in Clinical Pharmacy (University of Queensland, 2002), Bachelor of Pharmacy (University of Wales, 1992) Her work emphasizes person-centered methodologies , particularly discrete choice experiments, to evaluate patient and public preferences for healthcare delivery and outcomes. Key research areas include: Treatment burden and quality of life in chronic diseases like cystic fibrosis Economic evaluation of pharmaceuticals and health technologies Asset-based approaches to community health Priority-setting frameworks in health policy Prof. Whitty has secured over AU$27 million in research funding from organizations including the NHMRC and National Institute for Health and Care Research , and serves on editorial boards for Medical Decision Making and Applied Health Economics and Health Policy .

Jacky BourgeoisView profile

Assistant Professor

• Data-Centric Design
• Human-Computer Interaction
• Data Science
• +5 more

Jacky Bourgeois is an Assistant Professor of Data-Centric Design at Delft University of Technology's Faculty of Industrial Design Engineering (IDE), leading the Data-Centric Delft Design Lab. His work bridges Human-Computer Interaction, Data Science, and Participatory Design, focusing on methods like data donation to empower collaborative data exploration. He holds a computer science background, previously researching ubiquitous technologies for domestic energy practices of 'energy farmers' (solar households). Key projects include the PROMISE study on patient-led oncological care and the Data Donation initiative promoting ethical data practices. Education: Background in Computer Science; academic focus on sustainable design engineering and IoT. Teaching includes courses like 'Data-Centric Design for...' (2024) and 'Digital Product Development' (2023). Research Interests: Data as subjective inquiry material, data intimacy, data commons, and human-data interaction in home contexts. Notable contributions include PAIRcolator for collaborative data reflection and Tangi for 360° video insights. Awards include Best Paper at CHI 2025 and DIS '24 Best Paper. Grants & Collaborations: NWO-funded research on hospital sounds (2024), IoT Rapid Proto Labs for industry-academia partnerships, and ancillary role as a scientific employee at The Open University (2025–2027). Labs & Teams: Director of the Data-Centric Delft Design Lab; collaborates with the HCI Data & Design SIG. Current projects address ethical frameworks for data donation and patient-centered healthcare systems.

Emma BrunskillView profile

Associate Professor

• Reinforcement Learning
• Educational Technology
• Artificial Intelligence
• +3 more

Emma Brunskill is an Associate Professor of Computer Science at Stanford University, with a courtesy appointment in Education. She holds a PhD in Computer Science from MIT (2009). Her research focuses on reinforcement learning, educational technology, and healthcare applications, aiming to develop AI systems that support human learning and decision-making. Notable projects include AI tutoring systems, policy evaluation methods, and behavior change interventions using large language models. Her work bridges theory and practice, addressing challenges in off-policy evaluation, fairness-aware decision making, and scalable educational tools. Brunskill has contributed to foundational research in reinforcement learning algorithms and their applications in real-world scenarios such as healthcare, education, and human-AI collaboration. She also leads initiatives to improve equity and efficiency in educational technologies through data-driven approaches. Brunskill's research has been supported by grants such as the NSF RI: Small grant for data-efficient reinforcement learning. She actively explores the ethical implications of AI systems, particularly in healthcare and education settings. Her recent work emphasizes leveraging large language models (LLMs) for personalized feedback and simulated training environments, as seen in studies like GPTCoach and LLM-based counselor upskilling.

• Statistical Reinforcement Learning
• Mobile Health (mHealth)
• Clinical Trials
• +3 more

Susan A. Murphy is the Mallinckrodt Professor of Statistics and of Computer Science at Harvard University, with affiliations to the Kempner Institute. She leads the Statistical Reinforcement Learning Lab, focusing on developing algorithms to inform sequential decision-making in health, particularly for Just-in-Time Adaptive Interventions (JITAIs) and micro-randomized trials (MRTs). Her work is funded by NIH institutes, including NIDA, NHLBI, and NIBIB. Dr. Murphy has been awarded a MacArthur Fellowship (2013) and is a member of the National Academy of Medicine (2014) and the National Academy of Sciences (2016). Her research integrates statistical methods with computer science techniques to optimize mobile health interventions. She collaborates with d3Lab and mDOT on projects like HeartSteps and Sense2Stop, evaluating real-time treatment policies. Notable contributions include advancing MRT designs, sample size calculations, and reinforcement learning algorithms for personalized healthcare. Dr. Murphy advises a large team of postdocs, graduate students, and undergraduates, many of whom hold academic and industry roles globally. She emphasizes engagement in digital interventions, balancing personalization with ethical considerations. Her lab’s work spans algorithm development, clinical trial design, and causal inference, aiming to improve health outcomes through adaptive interventions.

• Machine Learning
• Genomics
• Gene Regulatory Networks
• +3 more

Sushmita Roy is a Professor at the University of Wisconsin–Madison, affiliated with the Department of Computer Sciences and the College of Letters and Science. Her research focuses on developing computational methods in statistical machine learning to understand gene regulatory networks in living cells, particularly under environmental, developmental, disease, and evolutionary contexts. She explores bulk and single-cell genomic data integration to study processes like cell fate specification, host-microbe interactions, and diseases such as cancer and neurodevelopmental disorders. Her work emphasizes three key areas: inference of genome-scale transcriptional networks, evolutionary analysis of regulatory networks, and 3D genome organization dynamics. Roy’s lab collaborates across disciplines, leveraging genomic data from plant and mammalian systems. She has contributed to methodologies for analyzing chromatin accessibility, single-cell profiling, and network-based models of pathogen systems. Her affiliations include Wisconsin Institutes for Discovery, and she is a leader in computational biology and systems genomics research.

Abigail ScholerView profile

Professor

• Self-Regulation
• Motivation
• Decision-Making
• +3 more

Abigail Scholer is a Professor specializing in self-regulation and motivation. Her research explores how motivational orientations influence decision-making, self-control conflicts, and adaptive change. She holds a BA from Gettysburg College and a PhD from Columbia University. Her work bridges educational, social, and cognitive psychology, with a focus on understanding the mechanisms behind human triumphs and failures in facing life's demands. Key research themes include metamotivational processes, goal pursuit dynamics, and the interplay between motivation and emotional regulation. Her lab, the Self-Regulation and Motivation Lab , investigates practical applications of these theories in academic and organizational settings. Publications span topics like motivational affordance, risk preferences, and the impact of threat on stereotyping. While no awards are listed, her contributions to motivational science are reflected in high-impact journals such as Journal of Personality and Social Psychology and Psychological Science . No advising or grant details are provided, though her lab's activities suggest active research participation.

Jason BrownleeView profile

Professor

• Comparative Politics
• Democracy
• Development
• +4 more

Jason Brownlee is a Professor in the College of Liberal Arts at the University of Texas at Austin. His research focuses on comparative politics, democracy, and authoritarianism, with a regional emphasis on the Middle East and Southeast Asia. Ph.D., Princeton University Contact: brownlee@austin.utexas.edu Research Interests : Brownlee investigates the persistence of authoritarian regimes, the political economy of repression, and the impact of foreign intervention on domestic politics. His work often analyzes Egypt, Iran, Malaysia, and the Philippines. Key Publications : Authoritarianism in an Age of Democratization (Cambridge, 2007) Democracy Prevention: The Politics of the U.S.-Egyptian Alliance (Cambridge, 2012) The Arab Spring: Pathways of Repression and Reform (Oxford, 2012, co-authored with Tarek Masoud and Andrew Reynolds) Teaching : Brownlee offers courses such as "War/Freedom in South Asia/Middle East" and "Issues & Policies in American Government," delivered both in-person and online.