جست‌وجوی استاد و پژوهشگر

Shahin Jabbariمشاهده پروفایل

استادیار

• Machine Learning
• Algorithmic Fairness
• Game Theory
• +۷ مورد دیگر

Shahin Jabbari is an Assistant Professor in the Computer Science Department at the College of Computing & Informatics, Drexel University, where he is a member of the EconCS research group. His research lies at the intersection of machine learning, game theory, and algorithmic fairness, with a focus on ethical AI and its societal implications. Prior to Drexel, he was a CRCS postdoctoral fellow at Harvard University's School of Engineering and Applied Sciences, hosted by Milind Tambe, and affiliated with the EconCS group. Education: PhD in Computer and Information Science, University of Pennsylvania (2013–2019), advised by Michael Kearns Master's in Computing Science, University of Alberta, advised by Robert Holte and Sandra Zilles Bachelor's in Computer Engineering, Sharif University of Technology His research interests center on machine learning, algorithmic fairness, and game theory, particularly focusing on how AI systems can be designed to be more equitable, interpretable, and robust. He investigates ethical aspects of algorithmic decision-making, aiming to ensure AI technologies contribute positively to society. His work often integrates human behavior modeling and experimental validation, especially in cybersecurity and public health domains. His recent publications span top venues including ICML, NeurIPS, AAAI, AAMAS, PNAS, and TMLR. The research trends show a consistent focus on fairness in AI, explainability, robustness, and strategic interactions in complex systems. Topics include fair influence maximization, adaptive phishing training, cyber deception games, and ethical machine learning frameworks. These works reflect a multidisciplinary approach combining theoretical rigor with real-world applicability. Scientific Awards and Recognitions: Best Paper Finalist, AAMAS 2021 Best Paper, GameSec 2020 Spotlight Presentation, ICML 2021 Best Paper, KI 2012 Shahin Jabbari actively contributes to the academic community through advising, teaching, and service. He teaches graduate courses such as CS 589: Responsible Machine Learning and CS 590: Privacy. He has served on the senior program committees of ICML and NeurIPS, is an Action Editor for TMLR, and has reviewed for numerous top-tier conferences and journals. He mentors students through research projects and invites prospective PhD candidates to apply through Drexel’s formal channels. He is involved in the Drexel Computer Science Theory Reading Group and contributes to advancing responsible AI practices. He is affiliated with the EconCS group at Drexel, which focuses on economic and computational aspects of AI, including game theory, mechanism design, and multi-agent systems. His lab integrates tools from machine learning, behavioral modeling, and optimization to develop AI systems that are not only intelligent but also fair and trustworthy. Future work is expected to further explore human-AI collaboration, ethical AI deployment, and policy-aware algorithm design.

Suzanne Chiodoمشاهده پروفایل

استادیار

• Civil Procedure
• Class Actions
• Access to Justice
• +۴ مورد دیگر

Suzanne Chiodo is an Assistant Professor at Osgoode Hall Law School, York University, specializing in civil procedure, class actions, and access to justice. She joined Osgoode Hall Law School in July 2022 after serving as a visiting professor at Western University Faculty of Law from 2020 to 2022. Prior to her academic career, Professor Chiodo practiced as a class actions lawyer and insurance defense lawyer, and served as a judicial clerk to Justice James A. O'Reilly with the Federal Court of Canada. Professor Chiodo's educational background includes: BA (Oxford) MA (Oxford) JD (Western), with distinction LLM (Osgoode) DPhil (Oxford) Professor Chiodo's research focuses on civil justice reform, with particular emphasis on class actions, access to justice, and courts modernization. Her work explores the historical development of class action mechanisms, comparative civil procedure across jurisdictions, and the impact of the COVID-19 pandemic on court processes. She approaches these topics through both theoretical and normative lenses, examining how procedural mechanisms affect substantive justice outcomes. Her scholarly output demonstrates a consistent focus on class action mechanisms across multiple jurisdictions, with particular attention to Canadian and English developments. Professor Chiodo's work shows how class action frameworks have evolved through judicial interpretation and legislative reform, and how these mechanisms impact access to justice for ordinary citizens. Her research also highlights the transnational influences in class action development, particularly between Canada and the United Kingdom. Professor Chiodo's notable scientific achievements include: Peter Oliver Prize in Canadian Legal History for her book "The Class Actions Controversy: The Origins and Development of the Ontario Class Proceedings Act" Shortlisted for the 2019 Legislative Assembly of Ontario Speaker's Book Award Professor Chiodo actively supervises graduate students, currently working with Daniel Escott and Adaeze Udeze on research related to civil justice. Her service to the academic community is extensive, including serving on the Civil Rules Review Working Group and numerous journal editorial boards such as the Western Journal of Legal Studies, Osgoode Hall Law Journal, and Canadian Bar Review. She has also organized significant conferences, including the International Access to Justice Forum and "Access to Justice and Class Actions in England and Wales," demonstrating her leadership in advancing discourse on civil justice reform.

• Artificial Intelligence
• Machine Learning
• Multi-Agent Systems
• +۸ مورد دیگر

Thanh H. Nguyen is an active Assistant Professor in the Department of Computer Science within the College of Arts and Sciences at the University of Oregon. She teaches courses including Introduction to Artificial Intelligence (CS 471/571) and Multi-Agent Systems (CS 410/510), with office hours held in Deschutes Hall. Her educational background includes a Ph.D. from the University of Southern California (2016), a postdoctoral position at the University of Michigan (2016-2018), and a B.Sc. from Hanoi University of Science and Technology. Her research bridges theoretical AI with practical applications addressing real-world societal challenges. Dr. Nguyen's work focuses on Artificial Intelligence applications for societal benefit, particularly in Public Safety and Security (urban crime prevention, counterterrorism), Cybersecurity (protection from stealthy botnets), Sustainability (wildlife and fish protection), and Public Health (diabetes prevention). Her research integrates techniques from Multi-Agent Systems, Game Theory, Machine Learning, and Optimization with insights from Psychology and Conservation Biology. Her recent publications demonstrate strong trends in security games, adversarial learning, and health applications, with significant focus on strategic deception, robust decision-making under uncertainty, and real-world deployment of AI systems. The work shows increasing interdisciplinary collaboration and practical implementation of theoretical models. Deployed Application Award (IAAI 2016) for PAWS wildlife protection system Runner-up Best Innovative Application Paper Award (AAMAS 2016) WiSE Merit Fellowship from University of Southern California (2015) Runner-Up Best Paper Award at Recourse-21 Workshop (ICML 2021) Army Research Office Grant (~$343K) for Adversarial Reasoning (2020-2023) Dr. Nguyen actively advises PhD students including Sarah Kinsey and Michael Dushkoff (co-advised with Prof. Allen D. Malony), along with numerous master's and undergraduate students. Her research has received significant grant funding, including an Army Research Office grant for developing methods to tackle sequential and coordinated attacks in security domains with real-time information. Her work on the PAWS (Protection Assistant for Wildlife Security) system has been deployed by NGOs like Panthera and the Wildlife Conversation Society in conservation areas in Malaysia and Uganda. She leads the AI Lab at the University of Oregon, which focuses on multiple research projects including AI for Public Health, Deception in Security Games, Security in Data-based Decision Making, Information Leakage and Exploration, and Game Theory for Cybersecurity. The lab maintains strong industry and NGO partnerships for real-world application of research findings.

Thomas TY Winمشاهده پروفایل

مدرس ارشد

• Cybersecurity
• Cloud Security
• Cyber Threat Intelligence (CTI)
• +۵ مورد دیگر

Thomas TY Win is an Associate Head of School in Computer Science at the University of Sunderland's Faculty of Technology. He completed his PhD in Computer Science at Glasgow Caledonian University in 2016, focusing on virtualisation security and security analytics. Research Interests Cloud and operating systems security Security analytics using Cyber Threat Intelligence (CTI) Cyber-Physical Systems security in Industry 4.0 contexts AI-based cybersecurity, including MLSecOps (Machine Learning Security Operations) Explainable AI (XAI) and actionable recourse frameworks Malware analysis and detection methodologies Teaching Lectures on CETM44 (Cybersecurity and User Experience Design) Delivers CET324 (Advanced Cyber Security) module Collaborations and Recognition Established international research collaborations across Europe and Asia Holds FHEA and MBCS credentials