جستجوی استاد راهنمای دانشگاهی

Diyi Yangمشاهده پروفایل

استادیار

• Computational Social Science
• Natural Language Processing
• Socially Aware NLP
• +۴ مورد دیگر

Dr. Diyi Yang is an Assistant Professor in the Computer Science Department at Stanford University. She leads the Social and Language Technologies (SALT) Lab, affiliated with the Stanford NLP Group, Stanford HCI Group, Stanford AI Lab (SAIL), and Stanford Human-Centered Artificial Intelligence (HAI). Her research focuses on socially aware natural language processing, large language models (LLMs), and human-AI interaction, aiming to improve human-human and human-computer communication through socially grounded AI systems. Education: Ph.D. in Language Technologies Institute, Carnegie Mellon University (2013–2019) B.S. in ACM Honored Class, Shanghai Jiao Tong University (2009–2013) Research Interests: Dr. Yang’s work bridges computational social science and NLP. She explores how AI can understand social contexts in language use and develop systems that respect cultural norms, ethical standards, and human values. Her lab’s projects include AI companions for skill training (e.g., Rehearsal Dialects), norm-aware LLMs (NormBank), and frameworks for human-AI collaboration (Co-Gym). Recent efforts address bias in AI, societal impacts of LLMs, and ethical evaluation of human-AI systems. Awards & Honors: 2024: Sloan Research Fellowship, ONR Young Investigator Award 2023: Adamic-Glance Young Distinguished Award (ICWSM), Kavli Fellow (NAS) 2022: NSF CAREER Award, Microsoft Research Faculty Fellow 2020: IEEE AI’s 10 to Watch Advising & Grants: Dr. Yang advises over 10 PhD students and postdocs, co-leading projects on LLM evaluation (SWE-bench/SWE-smith), human-AI ethics, and culturally aware NLP. Her research is supported by NSF, Amazon, DARPA, Google, and Stanford’s HAI initiative. Labs & Teams: The SALT Lab collaborates across disciplines, with projects spanning computer science, linguistics, and social sciences. Current initiatives include developing AI tools for mental health support (AI Partner & Mentor) and auditing societal impacts of LLMs.

Nancy Margaret Reidمشاهده پروفایل

استاد

• Theoretical Statistics
• Likelihood Inference
• Design of Studies
• +۳ مورد دیگر

Nancy Margaret Reid is a University Professor of Statistical Sciences at the University of Toronto, holding the Canada Research Chair in Statistical Theory and Applications. She has served as Scientific Director of the Canadian Statistical Sciences Institute (2015–2019) and led the Department of Statistical Sciences as Chair (1997–2002). Her research focuses on theoretical statistics, particularly likelihood inference and foundational aspects of statistical methodology. Reid earned her PhD from Stanford University (1979) under Rupert G. Miller, with Brad Efron and Vernon Johns on her committee. Reid's accolades include Fellowships from the Royal Society, Royal Society of Canada, and National Academy of Sciences, as well as the Guy Medal in Gold (2022) and David R. Cox Award (2023). She has authored influential books like *Theory of the Design of Experiments* and contributed to courses on mathematical statistics and likelihood inference. Active in academic service, she teaches graduate-level courses and has advised numerous students and postdocs in theoretical and applied statistical research.

Celestine Mendler-Dünnerمشاهده پروفایل

استاد

• Machine Learning in Social Context
• Algorithmic Collective Action
• Performative Prediction
• +۶ مورد دیگر

Celestine Mendler-Dünner is a Principal Investigator at the ELLIS Institute in Tübingen, co-affiliated with the Max Planck Institute for Intelligent Systems and the Tübingen AI Center. She leads the Algorithms and Society research group, focusing on machine learning in social contexts and the role of prediction in digital economies. Her work bridges theoretical machine learning with practical societal impact, developing tools for safe, reliable, and equitable AI ecosystems. Her educational background includes a PhD from ETH Zurich in collaboration with IBM Research, followed by an SNSF postdoctoral fellowship at UC Berkeley hosted by Moritz Hardt. She was previously a group leader at the Max Planck Institute for Intelligent Systems before joining the ELLIS Institute. Mendler-Dünner's research spans several interconnected themes including performative prediction (where predictions change the behavior they aim to predict), algorithmic collective action (how participants can steer AI systems toward common goals), and the role of LLMs in social science research. Her work combines theoretical foundations with practical implementations, addressing challenges in interactive machine learning, optimization in dynamic environments, and context-specific evaluation of AI systems. She particularly examines how algorithmic predictions mediate services and platforms at societal scale, exploring concepts of economic power in digital markets. Her publication record shows a clear evolution from system-aware machine learning algorithms (including foundational work on IBM Snap ML) toward increasingly sociotechnical questions at the intersection of machine learning, economics, and policy. Recent work focuses on measuring performative power in digital economies, evaluating LLMs as risk scores, and developing frameworks for algorithmic collective action in recommender systems and labor markets. Among her notable recognitions are the ETH Medal for her dissertation, the IBM Research Division Award, the Fritz Kutter Award, and the IBM Eminence and Excellence Award. She is an ELLIS Scholar, a fellow of the Elisabeth-Schiemann-Kolleg, and affiliated with several prestigious research programs including the International Max Planck Research School for Intelligent Systems and the Max Planck ETH Center for Learning Systems. ETH Medal (dissertation award) IBM Research Division Award Fritz Kutter Award IBM Eminence and Excellence Award SNSF Early Postdoc Mobility Fellowship Mendler-Dünner actively mentors the next generation of researchers, advising PhD student Patrik Wolf and supervising research interns including Joachim Baumann, Haiqing Zhu, and Anna Badalyan, as well as Master's student Dorothee Sigg. She serves as core faculty for the International Max Planck Research School and associated faculty for the Max Planck ETH Center for Learning Systems. Her group has secured significant research funding through fellowships and institutional support, enabling work on projects like Powermeter (measuring search engine influence) and Snap ML (resource-efficient machine learning library with over 1 million PyPI downloads). She leads the Algorithms and Society research group, which examines machine learning as part of broader sociotechnical ecosystems. The group explores human-population interactions with algorithmic systems and incorporates these insights into learning system fundamentals. Current projects include investigating economic incentives in digital platforms, developing tools for systematic LLM evaluation in social science contexts, and creating frameworks for collective action in algorithmic systems. Mendler-Dünner also co-organizes the Algorithmic Collective Action workshop at NeurIPS 2025, demonstrating her leadership in emerging research directions at the AI-society interface.

Erniel Bayhon Barriosمشاهده پروفایل

استاد

• Computational Statistics
• Nonparametric Methods
• Data Science/Big Data Analytics
• +۵ مورد دیگر

Erniel Bayhon Barrios is a Professor at the Malaysia School of Business, Monash University. Formerly a professor at the University of the Philippines Diliman and a visiting scholar at Karlstad University (Sweden) and the Asian Development Bank Institute (Japan). He holds a PhD in Statistics (1990) from the University of the Philippines Diliman. His research focuses on computational statistics, nonparametric methods, data science, and computational econometrics, with applications in spatiotemporal modeling, time series, and financial markets. He has contributed to high-dimensional data analysis, volatility modeling, and robust statistical techniques. Key projects include the National Mental Health Survey and Well-Being (2019–2021) and building a data-driven organization for the Habib Group (2025–2026). He is an elected member of the International Statistical Institute (2012), associate editor of Communications in Statistical Applications and Methods , and served on the board of the International Association of Statistical Computing (2022–2025). He advises PhD students on topics like stochastic frontier models, data assignment in big data, and high-frequency time series analysis. His work aligns with UN Sustainable Development Goals related to education and economic growth.

Dai Zhongxiangمشاهده پروفایل

استادیار

• Large Language Models Inference
• Prompt Optimization
• In-context Learning
• +۵ مورد دیگر

Dai Zhongxiang is an Assistant Professor and Presidential Young Fellow at the School of Data Science, The Chinese University of Hong Kong, Shenzhen (CUHKSZ), where he joined in August 2024. Previously, he was a Postdoctoral Associate at MIT's Laboratory for Information and Decision Systems (January-June 2024) and a Postdoctoral Fellow at the National University of Singapore's Department of Computer Science (April 2021-December 2023). He completed his Ph.D. in Artificial Intelligence at NUS under the supervision of Bryan Kian Hsiang Low and Patrick Jaillet. Dr. Dai's research focuses on the intersection of theoretical and practical AI, with particular emphasis on large language models (LLMs) and optimization techniques. His work spans both theoretical foundations of multi-armed bandits and Bayesian optimization, as well as practical applications in LLM inference, including prompt optimization, in-context learning, personalization of LLMs, LLM-based agents, and scaling up test-time computation of LLMs. His research approach often bridges theoretical principles with real-world applications, particularly in AI4Science problems. His recent publications demonstrate a clear trend toward advancing LLM capabilities through optimization techniques, with increasing focus on practical deployment challenges. The research spans both theoretical contributions to optimization theory and applied work on enhancing LLM performance in real-world scenarios. His work on dueling bandits, neural bandits, and zeroth-order optimization has been consistently published in top-tier venues including NeurIPS, ICML, ICLR, and ACL. Presidential Young Fellow, CUHKSZ (2024) Dean's Graduate Research Excellence Award, NUS (2021) Research Achievement Award × 2, NUS (2019 & 2020) Singapore-MIT Alliance Graduate Fellowship (2017) Dr. Dai actively mentors multiple Ph.D. students and research assistants, with several of his students' papers accepted to top conferences. His research has received significant attention, with invitations to serve as Area Chair for NeurIPS 2025 and ICLR 2025, reflecting his growing influence in the machine learning community. His work bridges theoretical machine learning with practical applications in large-scale AI systems.

Peter Hendersonمشاهده پروفایل

استادیار

• AI safety
• Foundation models
• Law and AI
• +۷ مورد دیگر

Peter Henderson is an Assistant Professor at Princeton University with joint appointments in the Department of Computer Science and the School of Public and International Affairs. He is affiliated with the Center for Information Technology Policy (CITP), Princeton Language and Intelligence Initiative (PLI), Center for Statistics and Machine Learning (CSML), and Program in Law & Public Policy (PLAW). J.D./Ph.D., Stanford University, 2023 M.Sc., McGill University and Montréal Institute for Learning Algorithms Henderson's research focuses on the critical intersection of artificial intelligence and law, with particular emphasis on AI safety, methods to improve reasoning in foundation models, interdisciplinary approaches to law and AI, and AI governance. His work spans language-grounded reinforcement learning, alignment techniques, strategic decision-making in legal contexts, and public interest artificial intelligence. He investigates how legal frameworks can guide the development of AI systems that benefit society while addressing potential harms. Henderson's recent publications reveal a strong trajectory toward addressing the safety, governance, and legal implications of foundation models. His work combines rigorous technical AI research with deep legal analysis, particularly examining how intellectual property law interacts with AI development, regulatory pathways that balance innovation with harm prevention, and the role of law in shaping responsible AI deployment. A significant thread throughout his research is the development of better evaluation methodologies for AI systems, especially in critical domains like law. SEAS Excellence in Teaching Award (2025) Henderson leads the Princeton Law+Language, AI, & Society (POLARIS) Lab, where he advises students and researchers working at the intersection of AI and legal studies. His research has been supported through collaborations with government agencies, including work with the IRS on audit selection algorithms. His findings have informed practical applications in government efficiency and equity, as well as legal system improvements. Henderson runs the POLARIS Lab at Princeton, which focuses on developing AI systems that work for the public interest, particularly in legal contexts. His team develops sequential decision-making systems for government efficiency, foundation models capable of reasoning about law, and improved safety evaluation frameworks for AI systems. The lab maintains strong connections with legal practitioners and policymakers to ensure research has real-world impact.

Sezgin Ayabakanمشاهده پروفایل

دانشیار

• Healthcare Analytics
• Business Value of Information Systems
• Data Analytics Techniques
• +۵ مورد دیگر

Dr. Sezgin Ayabakan is an Associate Professor in the Department of Management Information Systems at the Fox School of Business and Management, Temple University . He holds a PhD in Management Science from the University of Texas at Dallas, with a focus on Management Information Systems (MIS), an MS in Industrial and Systems Engineering from the University of Florida, and a BS in Industrial Engineering from Bilkent University in Turkey. His research focuses on healthcare analytics , particularly exploring how health information technology (HIT) impacts healthcare costs, quality, and utilization. Key research interests include data analytics applications in healthcare and social media, the business value of information systems, and open-source production strategies. He has examined topics such as telehealth efficacy, electronic health record (EHR) adoption challenges, hospital readmission reduction programs, and the interplay between healthcare IT and clinician well-being. Dr. Ayabakan teaches undergraduate and graduate courses in information systems, including MIS 2101: Digital Systems and SBM 3586: Diamond Peer Teachers - Internship II . His work has been published in top-tier journals like MIS Quarterly and Health Services Research and Management Epidemiology , with recent studies addressing telehealth utilization, EHR strategy impacts, and the role of organizational routines in clinician satisfaction. While no formal awards are listed, his contributions to healthcare IT research demonstrate significant academic impact. His advisory and grant activities are not detailed in the provided text, but his research often intersects with practical healthcare system challenges, suggesting potential collaborations with healthcare institutions and policy organizations.

Martin T. Wellsمشاهده پروفایل

استاد

• Applied Statistics
• Theoretical Statistics
• Bayesian Statistics
• +۷ مورد دیگر

Martin T. Wells is the Charles A. Alexander Professor of Statistical Sciences at Cornell University, with joint appointments in the Department of Statistical Science, Department of Biological Statistics and Computational Biology, Department of Social Statistics, and as Professor of Clinical Epidemiology and Health Services Research at Weill Medical School. He serves as Editor-in-Chief of the ASA-SIAM Book Series and Co-Editor of the Journal of Empirical Legal Studies. Cornell University, Ithaca, NY Weill Cornell Medical College Research Interests span applied and theoretical statistics, Bayesian methods, biostatistics, clinical epidemiology, and computational biology. His work bridges disciplines like finance, legal studies, and health services research. Article Trends highlight advancements in Bayesian modeling, quantum cognition machine learning, tensor analysis, and misclassification correction, with applications in genomics, finance, and public health. Fellow of the American Statistical Association Fellow of the Royal Statistical Society Contributions include developing statistical software (e.g., rTensor), methodological innovations in clinical trials, and empirical legal studies on civil rights and the death penalty.

Sheila McIlraithمشاهده پروفایل

استاد

• Artificial Intelligence
• AI Safety
• AI Alignment
• +۹ مورد دیگر

Sheila McIlraith is a Professor in the Department of Computer Science at the University of Toronto, holding the Canada CIFAR AI Chair at the Vector Institute and serving as Associate Director and Research Lead at the Schwartz Reisman Institute for Technology and Society. Her research spans AI safety, ethics, reinforcement learning, and knowledge representation, with a focus on human-compatible AI and long-term societal impacts. Her career includes six years as a Research Scientist at Stanford University and a year at Xerox PARC. McIlraith’s work has been recognized through ACM and AAAI fellowships, as well as prestigious awards like the SWSA 10-Year Award (2011) and the CAIAC Lifetime Achievement Award (2024). Research Interests: AI Safety and Alignment Human-Compatible AI Reinforcement Learning with Ethical Constraints Semantic Web Services Cognitive Robotics and Diagnostic Systems Probabilistic and Logical Reasoning Recent Contributions: Her work emphasizes ethical AI integration, such as the Embedded Ethics Education Initiative (E3I), and addresses challenges in long-term AI risks, multi-agent systems, and interpretable decision-making frameworks. Awards and Honors: ACM Fellow AAAI Fellow 2023 IJCAI-JAIR Best Paper Prize 2024 CAIAC Lifetime Achievement Award Labs and Teams: McIlraith leads initiatives at the Schwartz Reisman Institute and contributes to the Vector Institute, focusing on societal and ethical dimensions of AI technology.

• Behavioral Economics
• Decision Theory
• Game Theory
• +۸ مورد دیگر

Jose Apesteguia is an ICREA Research Professor at the Department of Economics and Business, Universitat Pompeu Fabra, Barcelona, Spain. His research focuses on behavioral economics, decision theory, experimental economics, and game theory, with particular emphasis on topics such as behavioral heterogeneity, rationality measures, stochastic choice models, and social preferences. Apesteguia has collaborated extensively with scholars like Miguel A. Ballester and Jörg Oechssler, producing influential work on topics ranging from imitation dynamics to the impact of language on moral decisions. His research integrates theoretical frameworks with experimental methods, exploring how individuals make decisions under uncertainty, time preferences, and social contexts. Key contributions include the development of measures of rationality and welfare, as well as analyses of behavioral adaptation and the role of information in competitive environments. His work frequently bridges economic theory with empirical validation, addressing real-world issues such as rule compliance in public institutions and team performance in organizations. Apesteguia’s publications span top journals including the American Economic Journal: Microeconomics , Journal of Economic Theory , and Econometrica . He teaches advanced courses on bounded rationality and behavioral decision theory at the undergraduate and graduate levels. His research has been applied to fields like finance (e.g., copy trading behavior) and public policy (e.g., promoting compliance in libraries).

Albert Prestoمشاهده پروفایل

استاد پژوهش

• air quality
• atmospheric particle studies
• climate resilience
• +۷ مورد دیگر

Albert Presto is a Research Professor in the Department of Mechanical Engineering at Carnegie Mellon University with a courtesy appointment in Civil and Environmental Engineering. He serves as Director of the Center for Atmospheric Particle Studies (CAPS) and is a Faculty Affiliate at the Steinbrenner Institute for Environmental Education and Research. His work spans multiple interdisciplinary collaborations across environmental science, engineering, and public health domains. Department of Mechanical Engineering, College of Engineering Center for Atmospheric Particle Studies (CAPS) Steinbrenner Institute for Environmental Education and Research Africa Clean Air Network Advisory Council Health Effect Institute's Energy Review Committee Presto's research focuses on pollutant emissions from energy extraction and consumption and their subsequent atmospheric transformations. His work examines how gas and oil wells emit methane, how vehicles emit carbon monoxide and particulate matter, and how these pollutants undergo oxidation in the atmosphere to create secondary pollutants like ozone and secondary particulate matter. His approach combines ambient measurements, laboratory experiments, source testing of pollution sources, and atmospheric models to provide a holistic view of pollutant emissions and transformations. His recent publications reveal a strong emphasis on air quality monitoring technologies, particularly low-cost sensor networks deployed internationally. There's a clear trend toward studying air pollution in African cities (especially Nairobi, Kampala, and Addis Ababa), examining spatial and temporal variability of particulate matter, and investigating the health impacts of air pollution exposure. His work increasingly integrates machine learning approaches for pollution forecasting and leverages international collaborations to address global air quality challenges. Member of Africa Clean Air Network Advisory Council Collaborator with US embassies worldwide on air quality data collection Quoted in The Guardian on inequality of air pollution exposure, specifically noting how people of color are more likely to be exposed to harmful air pollution Collaborated on research regarding chemicals released during the Ohio train derailment Presto actively collaborates with medical professionals to develop neighborhood-by-neighborhood studies of pollutant exposure and to understand relationships between emissions and health effects like childhood asthma. His work includes investigating racial-ethnic exposure disparities to airborne pollutants and examining how proximity to pollution sources like fast-food restaurants affects pediatric asthma outcomes. He has received seed funding from the Scott Institute for Energy Innovation for research in clean hydrogen production, AI hardware efficiency, and decarbonizing transportation. As Director of CAPS, Presto leads a research team focused on atmospheric particle studies with applications ranging from local Pittsburgh air quality to global monitoring initiatives. His lab develops and deploys advanced air quality monitoring technologies, including low-cost sensor networks that have been implemented in multiple cities across Africa. The CAPS research group works at the intersection of environmental engineering, atmospheric science, and public health to address critical air quality challenges worldwide.

Jason Rentfrowمشاهده پروفایل

استاد

• Personality Psychology
• Geographical Psychology
• Music Preferences and Psychology
• +۶ مورد دیگر

Professor Jason Rentfrow is a Professor of Personality & Individual Differences and Director of Graduate Education at the University of Cambridge's Department of Psychology. His research focuses on geographical psychology, exploring how regional environments shape personality, cultural differences, and societal outcomes. He also serves as Director of Studies in Psychology at Fitzwilliam College, Cambridge. Key research interests include the psychological effects of geography on traits like openness, conscientiousness, and neuroticism; the role of music preferences in personality expression; and the impact of socioeconomic status on self-concept. He has pioneered studies linking personality to political behavior, urban planning, and public health responses to crises like the 2020 pandemic. His work combines large-scale data analysis with spatial methodology, examining datasets from over 133 nations. Notable contributions include frameworks for measuring regional personality differences and their implications for innovation, entrepreneurship, and electoral outcomes. He has also developed psychometric tools like the Test of Attribute Preferences (TAP) for studying musical preferences. Recent research highlights include analyzing how Roman historical legacies influence modern German well-being disparities, the psychological roots of Brexit and Trump voter behavior, and the link between childhood trauma and adult empathy levels. His interdisciplinary approach spans psychology, sociology, economics, and geography. Professional activities include supervising graduate students, reviewing for top journals, and advising on cultural policy. He maintains an active lab focused on personality-environment interactions and their societal consequences.

• Sports Medicine
• Concussion Management
• Pediatric Sports Injuries
• +۵ مورد دیگر

Holly J. Benjamin, MD is a distinguished sports medicine specialist at the University of Chicago, serving as the inaugural Director of the Primary Care Sports Medicine Program at UChicago Medicine Student Wellness Center. She holds leadership positions including Chair of the Committee on Sports Medicine & Fitness for the Illinois Chapter of the American Academy of Pediatrics, and serves as Head Medical Team Physician for the WNBA Chicago Sky and medical officer for major events including the Bank of America Chicago Marathon, US Soccer, and USA Triathlon. Her educational background includes: B.S. in Integrated Life Sciences, Kent State University (1994) Combined BS/MD, Northeastern Ohio Universities College of Medicine (1994) MD, Northeastern Ohio Universities College of Medicine (1994) Residency in Pediatrics, The University of Chicago Children's Hospital (1997) Fellowship in Primary Care Sports Medicine, Lutheran General Hospital (1998) Dr. Benjamin specializes in sports medicine with particular expertise in youth, adolescent, and collegiate athletes. Her research spans acute and overuse sports injuries, concussion management, sport biomechanics, and musculoskeletal ultrasound. She has made significant contributions to understanding injury prevention strategies, safe return-to-play protocols, and the psychological aspects of sports injuries. Her work frequently addresses female athletes and elite/professional athletes, with emphasis on evidence-based approaches to optimize performance while minimizing injury risk. Recent publications demonstrate a strong focus on concussion research, with multiple studies examining diagnostic approaches, recovery trajectories, and factors influencing outcomes. Her work spans biomechanics, mental health considerations in sports injuries, and innovative approaches to injury prevention across various athletic populations. Her scientific recognition includes: Election as Fellow of the American College of Sports Medicine (2003), becoming the second female pediatrician ever elected Election as Fellow of the American Medical Society of Sports Medicine Distinguished Alumni Award from The New Aurora High School Alumni (1999) Invitation to serve as volunteer physician for the Olympic Training Center (2008) Multiple expert contributions to national media including Runner's World and the Naperville Sun Dr. Benjamin actively mentors through her coaching roles at high school and collegiate levels, emphasizing safe return-to-play post-injury and injury prevention. She has developed educational content through video productions for the American Academy of Pediatrics and serves as head team physician for the University of Chicago Lab School and the NCAA Division III UChicago Maroons. Notably, she is one of only a few sports medicine specialists in the country offering diagnostic and procedural musculoskeletal ultrasound in her office, demonstrating her commitment to advanced diagnostic capabilities for patient care.

Alexandra Boldyrevaمشاهده پروفایل

استاد

• Cryptography
• Information Security
• Searchable Encryption
• +۳ مورد دیگر

Alexandra Boldyreva is a Professor at the Georgia Institute of Technology, holding joint appointments in the School of Cybersecurity and Privacy and the School of Computer Science. She serves as Associate Chair for Graduate Studies in the School of Cybersecurity and Privacy and coordinates the Information Security Master’s program in the College of Computing. Her affiliations include the Institute for Information Security & Privacy (IISP), the Algorithms, Combinatorics and Optimization (ACO) program, and the Algorithms and Randomness Center (ARC). She earned her Ph.D. in Computer Science from the University of California, San Diego, and holds bachelor’s and master’s degrees in applied mathematics from St. Petersburg State Technical University, Russia. Her research focuses on cryptography and information security, with notable contributions to encryption methods, authentication protocols, and privacy-preserving systems. Boldyreva’s work emphasizes provable security analysis of protocols like FIDO2 and TLS 1.3, as well as searchable encryption and data privacy techniques. Her recent studies include secure communication channel establishment, leakage quantification in encryption systems, and applications of fuzzy search in encrypted databases. She has received Test of Time Awards for foundational contributions to cryptography. Boldyreva leads initiatives in cybersecurity education and has secured grants for research in secure communication protocols and human-computing approaches to key exchange. Her interdisciplinary collaborations span computer science, mathematics, and privacy engineering.

Richard Jochelsonمشاهده پروفایل

استاد

• Criminal Law
• Constitutional Law
• Police Powers
• +۷ مورد دیگر

Richard Jochelson serves as Dean of Law at the Faculty of Law, University of Manitoba (Robson Hall), a position he has held since July 1, 2016. Previously, he taught criminal law for 10 years at the University of Winnipeg. Dr. Jochelson holds a Ph.D. in Law from Osgoode Hall Law School at York University, an LLM from the University of Toronto Faculty of Law, an LL.B. with Gold Medal from the University of Calgary Faculty of Law, and a B.Sc. in Zoology with Distinction from the same institution. He completed his legal training as a clerk at the Alberta Court of Appeal and Court of Queen's Bench before working at a major Canadian law firm. Dr. Jochelson's research program centers on critical issues in criminal justice and constitutional law, with primary interests in police powers jurisprudence, sexual regulation (particularly obscenity and indecency law), jury systems and representativeness, and legal pedagogy. His interdisciplinary approach bridges law with sociology and psychology, bringing empirical and theoretical rigor to legal scholarship. His work consistently addresses social justice concerns, examining how vulnerable populations interact with the justice system and how concepts like risk, precaution, and visibility shape legal decision-making. He has developed a distinctive methodology combining doctrinal analysis with empirical research on judicial behavior and jury decision-making. His recent publications reveal evolving scholarly priorities that respond to contemporary legal challenges. The 2025 article "Morbid Matters" addresses medical assistance in dying within federal corrections, while his 2024 works on youth terrorism cases and agnotology demonstrate expansion into new interdisciplinary territories. His longstanding commitment to police powers scholarship continues with "Ancillary Police Powers in Canada" (2024). The pandemic period saw significant contributions to understanding digital justice, mental health in legal education, and remote court proceedings. His criminal law scholarship maintains focus on jury representativeness, particularly regarding Indigenous inclusion following Bill C-75's elimination of peremptory challenges. Students' Teacher Recognition Award (CATL) - 2018 Dr. Jochelson has secured multiple SSHRC Insight Grants as both Principal and Co-investigator, demonstrating his ability to lead interdisciplinary research teams. His mentorship extends to junior academics and students, with several publications co-authored with emerging scholars. He has built significant research infrastructure, notably assembling nearly 40 interdisciplinary scholars across Canada for Robsoncrim.com, which has elevated the Criminal Law Edition of the Manitoba Law Journal to prominence as one of Canada's preeminent criminal law publications. As Dean of Law, Dr. Jochelson provides leadership in academic governance and legal education innovation. His service includes extensive committee work on academic affairs, workload policy, and hiring/tenure committees. His commitment to universal design principles informs both his teaching approach and administrative leadership, emphasizing accessibility and student-centered pedagogy that allows flexible evaluation methods tailored to individual learning preferences.