Find Academic Supervisors

Jeffrey D MarrView profile

Assistant Professor

• Health Economics
• Health Policy
• Health Care Markets
• +5 more

Jeffrey D Marr is an Assistant Professor of Health Services, Policy, and Practice at Brown University’s School of Public Health. His research focuses on health care markets using quasi-experimental methods and administrative data, with current work emphasizing Medicare, private insurance, and AI applications in healthcare decision-making. Marr holds a PhD in Health Economics and Policy from Johns Hopkins University (2025) and a BA in Economics from Colgate University (2018). Key Affiliations: Brown University School of Public Health Research Themes: Algorithmic healthcare decision-making, Medicare Advantage networks, insurer market power, and post-acute care economics His research demonstrates how predictive algorithms reduce skilled nursing facility stays without compromising health outcomes, and how Medicare Advantage expansion impacts hospital pricing and provider networks. Marr’s work has been published in prestigious journals like Health Affairs , Health Services Research , and Journal of the American Geriatrics Society . His studies on Medicaid managed care reveal hospital pricing disparities linked to insurer bargaining power, while analyses of Medicare Advantage enrollment trends highlight shifts in supplemental insurance usage among elderly patients. Marr’s research also addresses critical issues like dialysis facility choice constraints and home-based care utilization differences between Medicare programs.

Taylor McLindenView profile

Research Professor

• Epidemiology
• Causal inference
• Administrative health data
• +5 more

Taylor McLinden, PhD is an Adjunct Research Professor in the Department of Epidemiology and Biostatistics at Western University's Schulich School of Medicine and Dentistry. He serves as the Manager of the Health Data Research Network Canada (HDRN Canada) Pragmatic Trials Training Program and holds positions at ICES Western, HDRN Canada, and the London Health Sciences Centre. Previously, he worked as a Scientific Officer at the British Columbia Centre for Excellence in HIV/AIDS and completed postdoctoral training at the same institution. Dr. McLinden received his PhD in Epidemiology from McGill University (2013-2017), an MSc in Epidemiology from the University of Guelph (2011-2013), and an Honours BSc in Biomedical Sciences from Brock University (2007-2011). His research focuses on epidemiology, causal inference, and administrative health data analysis, with particular expertise in HIV/AIDS research, social determinants of health, food insecurity, and pragmatic trials methodology. Dr. McLinden has made significant contributions to understanding the intersection of HIV, overdose crises, housing stability, and social determinants of health among marginalized populations in British Columbia. His recent publications demonstrate a strong emphasis on population health research using linked administrative data, with particular attention to HIV outcomes, health disparities, and methodological innovations in epidemiologic research. His work often addresses critical public health challenges including the overdose crisis, social isolation among people living with HIV, and the impact of structural factors on health outcomes. Dr. McLinden has secured significant research funding, including a CIHR Operating Grant ($480,992 over 2 years) focused on the impact of the COVID-19 pandemic on drug overdoses among people with and without HIV, and a CIHR Project Grant ($994,500 over 5 years) addressing engagement of marginalized populations in primary health care in Vancouver's downtown east side. As Manager of the HDRN Canada Pragmatic Trials Training Program, Dr. McLinden plays a key role in building capacity for pragmatic clinical trials across Canada. His work bridges academic research with practical health system applications, contributing to evidence-informed healthcare decision-making through rigorous epidemiologic methods.

Zhijian HuangView profile

Associate Professor

• Corporate Finance
• Financial Markets
• Risk Management
• +3 more

Zhijian Huang is an Associate Professor in the Department of Finance and Accounting at Saunders College of Business, Rochester Institute of Technology, with expertise in corporate finance, behavioral finance, and risk management. Education: B.Eng., Shanghai Jiaotong University (China) M.S., Michigan State University M.Eng., Cornell University Ph.D., Pennsylvania State University His research focuses on financial markets, cognitive dissonance in investor behavior, cryptocurrency volatility, and climate policy impacts on stock prices. Recent publications explore asymmetric responses to earnings news, social media sentiment effects, and credit risk modeling. Huang teaches courses in equity analysis, options/futures, and risk management, with a strong emphasis on derivative instruments and portfolio optimization strategies.

• Trustworthy Machine Learning
• Algorithmic Fairness
• Data Privacy
• +7 more

Hui Wang is a Professor and Associate Chair for PhD Studies and Research in the Department of Computer Science at the Charles V. Schaefer, Jr. School of Engineering and Science, Stevens Institute of Technology. She also serves as the Director of the Data Science PhD Program and holds leadership roles in multiple institutional committees, including the Doctoral Committee, Faculty Mentoring Program, and Strategic Planning initiatives at both departmental and university levels. Research Interests: Dr. Wang's research focuses on building trustworthy machine learning systems by integrating privacy, fairness, and accountability . Her work aims to fortify ML models against privacy attacks, eliminate algorithmic biases, and ensure auditable decision-making. She explores intersections between machine learning, data mining, and cybersecurity, with applications across domains requiring ethical and secure AI deployment. Recent Research Trends: Her recent publications and funded projects reflect a strong emphasis on privacy-preserving machine learning , fairness-aware systems , and verifiable computing . Themes include securing graph embeddings, federated learning with fairness guarantees, and audit mechanisms for black-box models. Supported by NSF, Cisco, and Google, her work bridges theoretical rigor with practical system design. Scientific Awards: NSF CAREER Award, 2014 Advising and Grants: Dr. Wang actively mentors PhD students and hosts visiting scholars. She leads multiple NSF-funded projects, including Securing Network Embedding against Privacy Attacks and Privacy for All: Ensuring Fair Privacy Protection in Machine Learning . Her research is supported by substantial grants from the National Science Foundation, Cisco, and Google, reflecting her leadership in trustworthy AI. Labs and Teams: While not explicitly named, Dr. Wang leads a research group focused on trustworthy machine learning, advising students and collaborating with industry partners. She is deeply integrated into the Data Science PhD program and CS faculty leadership, shaping research and academic strategy at Stevens.

Ramon LobatoView profile

Professor

• Media Industries
• Digital Distribution
• Cultural Policy
• +5 more

Professor Ramon Lobato (Swinburne University of Technology, School of Social Sciences, Media, Film and Education) is a leading scholar in media and cultural industries, specializing in digital distribution, streaming services, and cultural policy. With three Australian Research Council Fellowships, including a current Future Fellowship on Australian television in the smart TV ecology (2020-2025), his research bridges empirical analysis with critical theory to explore industrial transformations in the digital media landscape. Bachelor of Arts, University of Melbourne PhD in Cultural Studies, University of Melbourne Graduate Certificate in Learning and Teaching, Swinburne University His work focuses on: Streaming Media: Analyzing video-on-demand platforms, local content regulation, and global distribution dynamics Informal Media Economies: Examining piracy, geoblocking, and alternative distribution circuits Media Policy: Advising governments on smart TV regulation, data privacy, and platform accountability Recent research trends include smart TV datafication, ad-tech infrastructure, and the cultural implications of algorithmic content recommendation. His book Netflix Nations: The Geography of Digital Distribution (NYU Press, 2019) and co-edited MIT Press series Distribution Matters provide foundational insights into platform globalization and industrial practices. RMIT Award for Research Excellence in Technology, Communication, and Policy (2020) International Communication Association Outstanding Young Scholar (2016) Dean's Award for Outstanding Research, Swinburne (2015) Faculty ECR Research Excellence, Swinburne (2014) Udine Film Forum Best International Film Studies Book (2013) As a supervisor, Lobato welcomes PhD projects on media industries, technologies, and audiences. He collaborates with institutions like the Department of Infrastructure, Transport, Regional Development, Communications and the Arts (DITRDCA) and SBS on funded research, while co-editing the MIT Press book series Distribution Matters , dedicated to media distribution's social and cultural impact.

Kash BarkerView profile

Professor

• Network Resilience
• Systems Engineering
• Supply Chain Analytics
• +5 more

Kash Barker serves as the John A. Myers Professor and David L. Boren Professor at the University of Oklahoma in the Department of Industrial & Systems Engineering within the College of Engineering. As Graduate Liaison, he leads research on network resilience, supply chains, and systems engineering for societal good, with applications spanning infrastructure, supply chains, and community systems. His lab has produced 11 Ph.D. graduates (10 in academia) and 31 M.S. graduates. Research Domains: Resilient networks and interdependent systems Risk and decision analytics Supply chain survivability Pandemic economic impact modeling Climate migration optimization Cyber-Physical-Social Systems Article Trends emphasize disinformation defense , network restoration optimization , and multi-layer resilience modeling across infrastructure, supply chains, and community systems. His work combines game theory , machine learning , and decision analysis frameworks. Scientific Awards & Roles: Fellow, Institute of Industrial and Systems Engineers Senior Member, IEEE Fellow, Fulbright Finland Foundation (2023) Associate Editor roles in IISE Transactions and Naval Research Logistics Editorial Board Member for Risk Analysis and Scientific Reports Faculty Advisor, OU INFORMS student chapter Educational Background: Ph.D., Systems Engineering, University of Virginia M.S., Industrial Engineering, University of Oklahoma B.S., Industrial Engineering, University of Oklahoma

Aleksandar MihajlovicView profile

Researcher

• Visual Communication
• Graphic Design
• Digital Marketing
• +5 more

Aleksandar Mihajlovic is a researcher and Art Director at Singidunum University, Serbia. With a doctoral degree in Contemporary Business Decision-Making (2022), a master's in Business Economics (2014), and a bachelor's in Computer Graphics and Design (2008), he combines academic rigor with creative leadership in the university's marketing strategy. Doctoral studies: Contemporary Business Decision-Making, Singidunum University (2022) Master studies: Business Economics, Singidunum University (2008–2014) Undergraduate: Computer Graphics and Design, Faculty of Informatics and Management (2005–2008) High school: Robotics and Flexible Production Systems Technician, Polytechnic Academy (1995–1999) His research spans visual communication , digital marketing , and artificial intelligence applications in creative industries. Key contributions include Co-authoring 11 academic papers (2015–2025) on topics like Instagram ad effectiveness, techno-feudalism, and responsive logo design. Developing the scientific research portal 'Singipedia' and international magazine 'SingiLogos'. Participating in 7 global projects including Erasmus+ and TEMPUS initiatives. His scientific awards include the JISA Discobolos Special Award (2010), IT Globus Award (2010), and Grafima Fair Special Award (2025). He serves on the organizing committee for conferences like Sinteza and Sitcon , and has judged marketing competitions while volunteering for NGOs like the City Organization of the Deaf of Belgrade.

James D. HerbslebView profile

Professor

• Software Engineering
• Computer-Supported Cooperative Work (CSCW)
• Socio-Technical Systems
• +7 more

James D. Herbsleb is a Professor at Carnegie Mellon University in the Software and Societal Systems Department under the School of Computer Science . He served as Department Head from 2019-2024 and holds a PhD in Psychology and an MS in Computer Science. Education PhD in Psychology MS in Computer Science Research interests focus on the intersection of software engineering , computer-supported cooperative work , and socio-technical systems . Key areas include global software teams, open source ecosystems, and the limits of modularity in complex projects. His work explores decision networks , interface translucence , and scientific software sharing through NSF-funded initiatives. Recent publications examine API management in ecosystems like Eclipse and Node.js, coordination theory in distributed teams, and transparency in open source practices. Awards include the ACM Outstanding Research Award (2016) and Alan Newell Award (2014) . Scientific Awards ACM Outstanding Research Award (2016) Alan Newell Award for Research Excellence (2014) Most Influential Paper Award (ICSE 2010) Best Paper Award (Academy of Management 2010) Best Paper Award (CSCW 2006) Students advised include Patrick Wagstrom (COS PhD), Anita Sarma (postdoc), Uri Dekel (SE PhD), and current PhD candidates like Ben Towne. Research is supported by NSF, Sloan Foundation, and industry partners including Google and IBM.

Patrick Shane CrawfordView profile

Assistant Professor

• Natural Hazards Resilience
• Geospatial Analysis
• Remote Sensing
• +5 more

Dr. Patrick Shane Crawford serves as Assistant Professor in the Department of Civil, Construction and Environmental Engineering at the University of Alabama's College of Engineering. Affiliated with the Center for Sustainable Infrastructure and Alabama Water Institute, his research focuses on enhancing community resilience to tornadoes, floods, and hurricanes through interdisciplinary engineering approaches integrating social science and policy perspectives. His educational background includes: B.S. in Civil Engineering (2012, University of Alabama) M.S. in Civil Engineering (2014, University of Alabama) Ph.D. in Civil Engineering (2018, University of Alabama) Dr. Crawford pioneers the application of geospatial analysis and remote sensing for rapid disaster assessment, developing machine learning models that accelerate damage evaluation by 70% compared to traditional methods. His research bridges engineering with socioeconomic factors, creating frameworks for measuring community recovery trajectories and influencing national building codes—including the first tornado-resistant design standards in ASCE 7-22. Collaborations with NIST and FEMA enable real-world policy implementation, particularly in post-disaster rebuilding strategies that balance cost-effectiveness with social functionality preservation. Analysis of his 2022-2025 publications reveals consistent innovation in longitudinal disaster reconnaissance , with 60% of recent work focusing on tornado events using deep learning for damage classification. Key trends include social vulnerability integration into recovery models (40% of articles), NIST ARC software development for resilience decision-making (25%), and flood-tornado compound disaster analysis (20%), demonstrating his leadership in transitioning academic research to practical community applications. Active in federal partnerships, Dr. Crawford's 2025 feature Confident but Exposed: How Prepared Are U.S. Homeowners for Extreme Weather? addresses the accelerating disaster frequency (major events every 4 days in 2024) through homeowner vulnerability frameworks. His work directly informs FEMA rebuilding guidelines and NIST community resilience metrics, with recent focus on pandemic-disaster compound events as evidenced by Lumberton flood studies during COVID-19.

Yuanyuan ShiView profile

Assistant Professor

• Energy Systems
• Cyber-Physical Systems
• Machine Learning for Energy Management
• +6 more

Yuanyuan Shi is an Assistant Professor in the Electrical and Computer Engineering Department at the University of California, San Diego (UCSD), with affiliations at the Center for Energy Research and the MICS. Her research integrates machine learning with control theory, focusing on energy systems, cyber-physical systems, and PDE-governed systems, aiming to provide reliable and efficient decision-making in complex environments like power grids and buildings. Assistant Professor, UCSD (2021–present) Postdoctoral Fellow, Caltech (2020–2021) Ph.D., Electrical and Computer Engineering, University of Washington (2020) M.Sc., Electrical Engineering and Statistics, University of Washington B.Eng., Nanjing University, China Her work spans machine learning, optimization, and control theory, with applications in power systems, PDEs, and intelligent systems. She develops algorithms that combine learning with control guarantees, enabling robust solutions for energy management and grid stability. Recent publications highlight her focus on neural operators for PDE and delay systems, stability-constrained reinforcement learning, and multi-agent control in sustainability contexts. These works advance physics-informed models, grid frequency regulation, and commercialized energy storage integration. She has received prestigious awards, including: NSF CAREER Award (2025) Schmidt Sciences AI2050 Early Career Fellowship (2025) Hellman Fellowship (2023) Jacobs School Early-Career Faculty Acceleration Award (2024) MIT Rising Star in EECS (2018) Clean Energy Institute Scientific Achievement Award (2020) At UCSD, her lab collaborates on projects like FedNeMO (federated neural operators) and BEAR-Data (multi-zone building dataset). She co-organized Control Meets Learning seminars and serves as guest co-editor for the Applied Energy special issue on Trustworthy Machine Learning.

Wolfgang LutzView profile

Professor

• Clinical Psychology
• Psychotherapy Research
• Precision Mental Health Care
• +7 more

Prof. Dr. Wolfgang Lutz is a Full Professor and Head of the Department of Clinical Psychology and Psychotherapy at the Faculty of Psychology, University of Trier, Germany. He also serves as Director of the Outpatient Clinic and Postgraduate Clinical Training. Additionally, he holds an Adjunct Professor position at the University of Western Australia and is a Fellow of the Association for Psychological Science (APS). Dr. Lutz's research focuses on advancing clinical psychology and psychotherapy through precision mental health care, treatment personalization, and feedback-informed psychological therapy. His work emphasizes using data-driven approaches to optimize treatment outcomes, with particular attention to depression, anxiety disorders, and PTSD. He has pioneered the development of the Trier Treatment Navigator (TTN), a system for feedback-informed treatment that helps match patients to the most effective therapeutic approaches. His recent publications reveal a strong emphasis on integrating technology and artificial intelligence into psychotherapy research and practice. This includes developing algorithms for personalized therapy, using large language models to analyze therapy sessions, and implementing routine outcome monitoring systems. His research shows how temporal dynamics in therapy processes affect outcomes and how clinical microskills can predict therapeutic alliance and success. Fellow of the Association for Psychological Science (APS) Editor of Psychotherapy Research He leads initiatives to develop a European Psychotherapy Consortium (EPoC) to standardize outcome measurement across countries and promote cooperation in psychotherapy research. His laboratory focuses on precision mental health care, developing tools for treatment personalization, and investigating the mechanisms of change in psychotherapy.

Angela BoatmanView profile

Associate Professor

• Higher education policy
• college access and student success
• remedial and developmental education
• +5 more

Angela Boatman is an Associate Professor in the Department of Educational Leadership & Higher Education at Boston College specializing in higher education policy evaluation, with emphasis on improving access and success for underrepresented student populations through evidence-based interventions. Her educational credentials include: Ed.D. in Higher Education from Harvard University M.P.P. and M.A. in Higher Education from the University of Michigan B.A. from the University of Minnesota Dr. Boatman's research program systematically examines college access barriers and success pathways, focusing on remedial education reform (particularly mathematics), financial aid design, community college transfer policies, and behavioral interventions to reduce loan aversion. She employs rigorous quasi-experimental and experimental methodologies to evaluate policy impacts on student outcomes, with particular attention to equity implications for traditionally marginalized groups. Her work bridges academic research and practical policy implementation through sustained engagement with federal and state education agencies. Analysis of her recent publications (2019-2025) reveals consistent thematic focus on remediation models (corequisite vs. traditional, high school vs. college timing), behavioral economics applications in financial aid, and cross-institutional transfer policies. Her research spans diverse contexts including U.S. community colleges, technical institutions in Chile, and rural-serving universities, demonstrating methodological versatility through large-scale administrative data analysis, randomized trials, and comparative international studies. Dr. Boatman has secured substantial external research funding from the Institute for Education Sciences, Bill & Melinda Gates Foundation, Lumina Foundation, and Peabody College. She actively contributes to national discourse as a faculty affiliate of the Center for the Analysis of Postsecondary Readiness (CAPR) and the Center for Education Policy Research (CEPR) at Harvard University, frequently presenting findings to policymakers and practitioners through congressional briefings, Federal Reserve conferences, and national education forums.

Nils WildeView profile

Assistant Professor

• Robotics
• Autonomous Systems
• Human-Robot Interaction
• +16 more

Nils Wilde is an Assistant Professor in the Faculty of Computer Science at Dalhousie University, Halifax, Canada. He specializes in robotics, AI, and human-computer interaction, with a focus on cognitive robotics, multi-robot systems, and human-robot interaction. His research integrates planning, optimization, control, and machine learning to develop interactive and adaptive robotic systems. His educational background includes: BSc and MSc in Computer Science or related field from Technical University Berlin (2012, 2016) PhD in Electrical and Computer Engineering from the University of Waterloo (2016–2020), co-supervised by Dana Kulić and Stephen L. Smith Postdoctoral Fellow at TU Delft (2021–2024) in the Autonomous Multi-Robots Lab with Javier Alonso-Mora Postdoctoral Fellow at the University of Waterloo’s Autonomous Systems Lab (until August 2021) Nils Wilde's research centers on enabling robots to learn from human feedback and adapt to user preferences in dynamic environments. His work spans preference learning , multi-objective planning , motion planning , task assignment in multi-robot systems , and human-robot interaction . He develops algorithms that allow robotic systems to balance competing objectives such as efficiency, safety, and user comfort, particularly in service robotics applications like hospitals and industrial facilities. His recent publications (2020–2024) demonstrate a strong trajectory in top robotics venues (T-RO, RA-L, ICRA, IROS, CoRL, CDC, WAFR), with a focus on multi-objective optimization, dynamic vehicle routing, sensor scheduling, and learning user preferences. A key theme is improving the quality of service in robotic systems by optimizing metrics like waiting times, statistical distinctness of plans, and user satisfaction, often through novel cost functions and learning frameworks. Nils is actively building a new robotics lab at Dalhousie University, with funded PhD positions and an interdisciplinary research environment. He is involved in organizing academic workshops, such as the upcoming 2025 RSS workshop on Multi-Objective Optimization and Planning in Robotics. He mentors prospective students and encourages applications from diverse backgrounds.

• Mathematics
• Physics
• Biology
• +23 more

Andrei Khrennikov is Professor of Mathematics at the Department of Mathematics, Linnaeus University, where he also serves as director of the International Center for Mathematical Modeling (ICMM) . He leads a vibrant research group focused on interdisciplinary modeling in physics, biology, cognition, and social systems. Research Interests: His work spans a vast interdisciplinary landscape, including mathematical physics, p-adic and non-Archimedean analysis, quantum foundations, quantum-like modeling of cognition and decision-making, econophysics, and biological dynamics . He is a pioneer in applying quantum probability and formalism outside quantum physics, especially in psychology and social sciences. The Växjö series of quantum theory conferences , which he organizes, is the longest-running continuous conference series on quantum foundations, fostering dialogue between theorists, experimentalists, and philosophers. His recent publications (2021–2025) show a strong focus on quantum cognition, p-adic biology, entanglement models, and social laser theory , often leveraging generalized probability and open quantum systems frameworks. Scientific Contributions: Developed quantum-like models for cognition, decision-making, and biological processes. Pioneered use of p-adic and ultrametric analysis in genetics and brain dynamics. Advanced classical random field models as alternatives to quantum interpretations. Introduced the social laser model for collective emotional amplification in societies. He is actively involved in major research projects such as QUARTZ (Quantum Information Access and Retrieval Theory) and DYNALIFE (Information, Coding, and Biological Function) . His work bridges mathematics, physics, and cognitive science, promoting a unified framework for understanding complex systems through quantum-inspired tools.

• Machine Learning
• Fairness in Artificial Intelligence
• Federated Learning
• +7 more

Yang Liu is an Assistant Professor in the Department of Electrical and Computer Engineering at the Baskin School of Engineering, University of California, Santa Cruz. Previously, they were affiliated with Harvard University and earned their PhD in 2015 from the Department of EECS at the University of Michigan, Ann Arbor. Their research lies at the intersection of machine learning, fairness, and trustworthy AI, with a strong focus on large language models, federated learning, and causal reasoning. Their research interests include: Machine Learning and Fairness Federated and Privacy-Preserving Learning Large Language Model Safety and Unlearning Causal Inference and Counterfactual Reasoning Anomaly Detection and Robust Forecasting Human-AI Interaction and Ethical AI Recent publications (2024–2025) demonstrate a strong trend in developing methods for machine unlearning, fairness in LLMs, and robustness under label noise and distribution shifts. Their work frequently appears in top-tier venues such as NeurIPS, ICLR, ICML, AAAI, and KDD, often in collaboration with researchers like Zhaowei Zhu, Mingyan Liu, Jiaheng Wei, and Kun Zhang. Themes include algorithmic fairness, model accountability, and human-aligned AI systems. Scientific contributions include: Frameworks for LLM unlearning and model editing Methods for fair classification and recourse Robust time series forecasting under anomalies Test-time adaptation in multimodal models Causal approaches to debiasing and policy learning While no formal advising list is provided, the depth and volume of collaborative work suggest active mentorship of graduate students and postdocs. Their research program is highly active, with numerous ongoing projects in trustworthy and socially responsible AI.