جستجوی استاد راهنمای دانشگاهی

Jamal Atifمشاهده پروفایل

استاد

• Artificial Intelligence
• Responsible AI
• Privacy Preservation in Machine Learning
• +۴ مورد دیگر

Jamal Atif is a Professor at Paris-Dauphine University and holds multiple significant leadership positions including Project Manager for 'Data Science and Artificial Intelligence' at the Institute of Information Sciences and their Interactions (INS2I) of the CNRS, Deputy Scientific Director of 3IA PRAIRIE, Head of the MILES team/project at LAMSADE (UMR CNRS-Université Paris-Dauphine), Co-leader of the Transverse Artificial Intelligence Program at PSL University, and Director of the Dauphine Numérique program. Professor Atif's primary research focuses on the foundations of responsible artificial intelligence, with specific expertise in privacy preservation in machine learning, robustness of deep learning algorithms to malicious attacks, causality, and explainability. His work bridges theoretical foundations with practical applications in security and reliability of AI systems. He has developed innovative approaches to address adversarial vulnerabilities in machine learning models and has made significant contributions to privacy-preserving techniques in data analysis. His publication record demonstrates a consistent focus on robust and trustworthy AI systems, with recent work exploring differential privacy in clustering, adversarial robustness, and explainable AI. The research spans theoretical foundations in logic and knowledge representation to practical applications in finance, healthcare, and computer vision. His publications appear in top-tier venues including Machine Learning journal, Neural Information Processing Systems, and International Joint Conferences on Artificial Intelligence. Scientific Awards: Recipient of two awards from the North American Society of Radiology for his thesis work Professor Atif has co-supervised or is currently supervising around fifteen doctoral students, demonstrating his commitment to mentoring the next generation of AI researchers. His leadership extends to directing major institutional programs including Dauphine Numérique and the Transverse Artificial Intelligence Program at PSL University, where he shapes strategic research directions in AI. He leads the MILES team/project at LAMSADE, which focuses on foundational aspects of machine learning and artificial intelligence. The team's research spans theoretical aspects of learning algorithms to practical applications requiring robust and reliable AI systems, with particular emphasis on security and privacy considerations in modern machine learning deployments.

• Bioinformatics
• Computational Biology
• Pharmaceutical Drug Discovery & Development
• +۱۳ مورد دیگر

Leonard Wesley is an Associate Professor in the Department of Computer Science at the College Of Science, San Jose State University. His research spans interdisciplinary domains at the intersection of Bioinformatics , Computational Biology , and Machine Learning , with specific applications in Pharmaceutical Drug Discovery , Genomic Data Analysis , and Autonomous Robotics . Education: Ph.D. in Computer Science, University of Massachusetts M.S. in Computer Science, University of Massachusetts B.A. in Physics and Math, Northeastern University Research Interests include Approximate Reasoning (probabilistic, evidential, and fuzzy logic), Agent-Oriented Systems , and Sensor Fusion . His work applies these methodologies to Drug Portfolio Management , Protein Structure Scoring , and Medical Diagnostics . Publication Trends show a consistent focus on Computational Biology , Robotics , and Uncertainty Quantification over four decades. Early work in Computer Vision evolved into modern applications in Pharmaceutical Analytics and AI in Aerospace . Key Projects include SVM-based drug affinity prediction, evidence-driven decision support systems for biopharma, and real-time agent development frameworks like ROADS. He has contributed to CFD code control and Mobile Network Congestion solutions. Collaborations with institutions like NASA, Los Alamos National Laboratory, and international conferences (WMSCI, ICINCO, AIAA) highlight his cross-disciplinary impact. His teaching includes Artificial Intelligence and Bioinformatics courses.

Ayesha Aliمشاهده پروفایل

استاد

• Graphical Markov models
• Pollination networks
• Multivariate statistics
• +۷ مورد دیگر

Ayesha Ali is a Professor of Statistics and Director of the Master of Data Science program at the University of Guelph. She holds a PhD in Statistics from the University of Washington (2002) and has expertise in statistical methods for complex high-dimensional systems, including ecological networks, causal inference, and bioinformatics. Her research integrates graphical Markov models, machine learning, and statistical computing to address challenges in plant-pollinator networks, livestock genetics, and disease risk modeling. Education: B.Sc. Honours in Statistics and Actuarial Science, University of Western Ontario (1996) M.Sc. in Statistics, University of Toronto (1998) Ph.D. in Statistics, University of Washington (2002) Research Interests: Graphical Markov models and ecological networks Causal inference and longitudinal data analysis Machine learning and high-dimensional predictive modeling Statistical methods for livestock genetics and animal health Computational statistics and bioinformatics Articles Trends: Her recent work spans interdisciplinary applications, including veterinary oncology biomarker discovery, remote sensing for agricultural suitability, and pipeline development for cross-species transcriptomics. She emphasizes graphical structure exploitation in regression and predictive modeling, with contributions to both theoretical and applied statistical methodologies. Awards: Canadian Journal of Statistics Award (2020) for groundbreaking work on doubly sparse regression NSERC Discovery Grant (2018) NSERC Collaborative Research and Development Grant (2015) Advising & Grants: She has supervised numerous graduate and undergraduate students on projects ranging from plant-pollinator network analysis to bioinformatics. Her grants include NSERC-funded research on milk fatty acid genetics and statistical methods for clustered data. Labs/Teams: Involved in the Bioinformatics program at the University of Guelph, contributing to interdisciplinary research collaborations in ecology and animal science.

• Bayesian nonparametrics
• causal inference
• variational inference
• +۵ مورد دیگر

Kolyan Ray is a Senior Lecturer (equivalent to Associate Professor) in Statistics at the Department of Mathematics, Imperial College London. He holds affiliations within the Faculty of Natural Sciences and contributes to the Statistics research group. His research focuses on Bayesian nonparametrics, causal inference, variational inference, inverse problems, and asymptotic statistics. Academic Background: PhD in Statistics from the University of Cambridge (supervised by Richard Nickl) Postdoc at Leiden University under Aad van der Vaart Lecturer (Assistant Professor) at King’s College London before joining Imperial Professional Roles: Associate Editor at the Electronic Journal of Statistics His work bridges theoretical and applied statistics, with emphasis on foundational methods and interdisciplinary applications. He maintains an active research program in statistical methodology and collaborates across academic institutions.

• Artificial Intelligence and Law
• Computational Argumentation
• Legal Reasoning
• +۵ مورد دیگر

Professor Henry Prakken is a leading researcher in Artificial Intelligence and Law at Utrecht University's Department of Computer Science. He holds a prominent position in the Responsible AI research group and has been instrumental in advancing computational models of legal argumentation. With an extensive publication record spanning over three decades, Prakken has established himself as a central figure in the intersection of law and artificial intelligence. Prakken's research interests focus on computational models of argumentation, legal reasoning systems, and the application of AI to legal domains. His work bridges theoretical foundations with practical applications, particularly in case-based reasoning, Bayesian networks for legal evidence, and formal argumentation frameworks. He has made significant contributions to understanding how computational models can represent and analyze legal reasoning processes, with particular attention to precedent-based reasoning and argument strength. His publication record demonstrates consistent research productivity with numerous high-impact papers in venues like Artificial Intelligence, Artificial Intelligence and Law, and proceedings of major conferences including ICAIL and COMMA. Recent work has expanded into evaluating generative AI's legal reasoning capabilities and developing more sophisticated models of argument strength and acceptability. Past president of the International Association for AI & Law (IAAIL) Past president of the JURIX Foundation for Legal Knowledge-Based Systems Past president of the steering committee of the COMMA conferences on Computational Models of Argument Editorial board member of Artificial Intelligence and Law Associate editor of Artificial Intelligence (2017-2022) Prakken has played a significant role in mentoring and shaping the field through his leadership positions and editorial work. His research has influenced both theoretical developments in computational argumentation and practical applications in legal technology, contributing to the growing field of responsible AI in legal contexts.

• Developmental Psychopathology
• Antisocial Behavior
• Behavioral Genetics
• +۳ مورد دیگر

Terrie Moffitt is a Professor in the field of Social Behavior & Development at the Social Genetic & Developmental Psychiatry department in the UK. Her work focuses on developmental psychopathology, antisocial behavior trajectories, and longitudinal research methodologies. She has contributed to over 400 research outputs and leads major studies like the Dunedin Longitudinal Study and E-Risk Twin Study. Research Interests: Natural history of antisocial behavior across lifespans Behavioral genetics and neuropsychological assessment Longitudinal methodologies for mental health studies Childhood adversity and developmental outcomes Recent Projects: Midlife Aging in the Dunedin Study Phase 52 (2023-2028) E-Risk @30: Longitudinal Twin Study (2023-2027) Childhood Maltreatment Resilience Study (2017-2019) Grant funding includes Medical Research Council (MRC) and Economic and Social Research Council (ESRC) awards. Her research contributes to UN Sustainable Development Goals related to good health and well-being (SDG 3).

Nikil Duttمشاهده پروفایل

استاد

• Embedded Systems
• Electronic Design Automation
• Computer Architecture
• +۶ مورد دیگر

Nikil Dutt is a Chancellor’s Professor at the University of California, Irvine (UCI), with academic appointments in Computer Science, Electrical Engineering and Computer Science (EECS), and Cognitive Sciences. He is affiliated with UCI's Center for Embedded Computer Systems (CECS), Center for Cognitive Neuroscience and Engineering (CENCE), Calit2, CPCC, and LUCI. His research focuses on embedded systems, electronic design automation, computer architecture, and healthcare IoT. Dutt has authored/co-authored seven books and holds IEEE Fellow and ACM Distinguished Scientist titles. Education: B.E. (Mechanical Engineering) from Birla Institute of Technology and Science (Pilani, India), 1980; M.S. (Computer Science) from Pennsylvania State University, 1983; Ph.D. (Computer Science) from University of Illinois at Urbana-Champaign, 1989. Research Interests: Embedded systems, brain-inspired architectures, neuromorphic computing, and healthcare IoT. Current projects include the Information Processing Factory (IPF) for autonomous systems and CareDex for disaster resilience in aging communities. Awards: Multiple Best Paper Awards, NSF grants, and fellowships. Serves as editor for ACM TECS, IEEE TVLSI, and former Editor-in-Chief of ACM TODAES. Active in academic service, including ESWEEK Steering Committee roles. Grants: NSF IPF, UNITE, CareDex, and industry partnerships (e.g., Facebook). Research addresses energy-efficient data centers, autonomous driving systems, and wearable health technologies. Labs/Teams: Leads the Dutt Research Group (DRG), focusing on self-aware systems, edge computing, and neuromorphic architectures.

Leong Shu Minمشاهده پروفایل

مدرس

• Face Recognition
• Emotion Recognition
• Face Analysis
• +۳ مورد دیگر

Leong Shu Min is a Lecturer in the School of Information Technology at Monash University Malaysia. She holds a Ph.D. in IT from Monash University Malaysia (2023), focusing on privacy-preserving and emotional understanding of human faces using machine learning. She earned her Master of Engineering Science (2020) and B.Eng. (Hons) in Electronics with Computer specialization (2018) from Multimedia University. Her research emphasizes face analysis, emotion recognition, and security-related image processing. Education Ph.D., IT, Monash University Malaysia (2019–2023) M.Eng.Sc., Multimedia University (2018–2020) B.Eng., Multimedia University (2014–2018) Research Interests Her work centers on facial recognition systems, emotion analysis, and privacy-preserving techniques. She explores Local Binary Pattern algorithms and micro-expression recognition, aiming to enhance security and ethical AI applications. Recent projects include detecting synthetic music and uncovering biases in video-based emotion recognition systems. Projects Chief Investigator in the Æinstein: Adversarial AI amongst Materials Discovery Domains project (2024–2026), focusing on AI-driven material discovery and ethical AI challenges. Advising She has been accepting PhD students since 2020, mentoring research in facial analysis and machine learning applications.

Patryk Perkowskiمشاهده پروفایل

استادیار

• Information Technology
• Human Resource Management
• Causal Inference
• +۳ مورد دیگر

Patryk Perkowski is an Assistant Professor of Strategy and Entrepreneurship at the Sy Syms School of Business, Yeshiva University. He holds a PhD from Columbia Business School and completed a postdoctoral fellowship in Business, AI, and Democracy at Columbia. His research focuses on the intersection of information technology, human resource management, and causal inference, examining how technological advancements like AI reshape talent competition and organizational strategies. He also develops methodological tools for empirical business research. Education: PhD and Postdoctoral Scholar at Columbia Business School. Research interests include AI's impact on workplaces, internal talent markets, labor discrimination trends, and algorithmic hiring. He publishes in top journals such as the Strategic Management Journal and Management Science. His work analyzes modern workforce dynamics, including automation effects, team coordination, and central banking AI applications. Notable studies address gender representation in hiring algorithms and long-term discrimination trajectories through meta-analyses. Advising: No advisee list provided. Grants and funding details not specified. His research bridges theory and practice, offering insights for firms to leverage human capital strategically in tech-driven environments.

Carlos Castilloمشاهده پروفایل

استاد پژوهش

• Algorithmic Fairness
• Social Computing
• Artificial Intelligence Ethics
• +۳ مورد دیگر

Carlos Castillo is an ICREA Research Professor at Universitat Pompeu Fabra, leading the Social and Responsible Computing Research Group. Their work focuses on algorithmic fairness, social computing, and mitigating bias in AI systems across criminal justice, education, healthcare, and hiring. Key projects include the Horizon Europe FINDHR initiative to detect discrimination in algorithmic hiring, causal inference studies on recidivism risk prediction, and analyzing gender biases in student evaluations. Research Interests: Algorithmic fairness, bias mitigation in AI systems, social data ethics, criminal justice analytics, education technology, and health informatics. Notable contributions include the FA*IR fair ranking algorithm and foundational work on auditing algorithms in digital health. Recent Trends in Articles: Recent work emphasizes interdisciplinary applications of algorithmic fairness—e.g., improving representativeness in hiring datasets, analyzing disparities in medical AI models, and addressing bias in student satisfaction surveys. Publications also explore radicalization pathways via recommendation systems and the societal impact of algorithmic decision-making. Awards: Best Paper Awards in CIKM 2022 (Francesco Fabbri), ICAIL 2019 (Marius Miron), and ISCRAM 2013 (Muhammad Imran). Grants: Leads the Horizon Europe FINDHR project and collaborates with the European Commission on algorithmic discrimination research. Labs/Teams: Directs the Social and Responsible Computing Group, focusing on interdisciplinary projects with PhD students and industry partnerships.

Jan van den Brakelمشاهده پروفایل

استاد

• Survey Methodology
• Statistical Analysis
• Time Series Modeling
• +۷ مورد دیگر

Jan van den Brakel is an Extraordinary Professor of Survey Methodology at Maastricht University and a Senior Statistician at Statistics Netherlands. He holds a position in the Department of Quantitative Economics within the School of Business and Economics. His primary affiliation is with the methodology department at Statistics Netherlands, where he focuses on advancing statistical methods for official statistics. **Research Interests:** Professor van den Brakel specializes in survey methodology, with a focus on mixed-mode surveys, time series modeling, small area estimation, and discontinuity analysis. He explores topics such as sampling techniques, variance estimation, and the integration of non-probability data sources (e.g., social media) into official statistics. His work addresses challenges in labor force surveys, health surveys, and the impact of technological changes like tablet-based data collection. **Key Projects:** Current projects include mixed-frequency time series modeling for short-term statistics, accuracy assessments of non-probability web panels, and modeling discontinuities in official surveys. He has contributed to high-impact studies, such as analyzing mobility patterns during the pandemic and improving unemployment rate estimation using Bayesian models. **Awards:** His 2013 paper on factorial designs in probability samples was honored as the best journal paper by Survey Methodology in 2014. He is a member of the Advisory Committee on Statistical Methods at Statistics Canada, reflecting his international leadership in statistical methodology. **Professional Roles:** In addition to his academic role, he collaborates extensively with national statistical offices, advising on methodological challenges. His work bridges theory and practice, ensuring statistical methods are robust and applicable to real-world data collection and analysis.

Zhijing Jinمشاهده پروفایل

استادیار

• Large Language Models (LLMs)
• Natural Language Processing (NLP)
• Causal Inference
• +۷ مورد دیگر

Zhijing Jin is an Assistant Professor at the University of Toronto and a postdoc at the Max Planck Institute for Intelligent Systems, working with Bernhard Schölkopf. She is also a faculty member at the Vector Institute and an ELLIS advisor. Her research focuses on Causal Reasoning with LLMs , Moral Reasoning in LLMs , and AI Safety , with contributions to AI for Science and NLP for Social Good. She leads the Jinesis AI Lab , which explores causal LLMs, multi-agent systems, and ethical AI applications. Education: PhD in Computer Science from Max Planck Institute (Germany) and ETH Zurich (Switzerland) Bachelor’s degree from University of Hong Kong, with visiting semesters at MIT and National Taiwan University Research Interests: Her work bridges causal inference and NLP, addressing robustness, interpretability, and ethical alignment of LLMs. Key projects include Corr2Cause (causal reasoning), GovSim / MoralSim (multi-agent LLMs), and frameworks like NLP4SG for social impact. She advocates for causal mechanisms to tackle AI safety and societal challenges. Recent Articles: Recent work explores political bias in LLMs, ethical dilemmas in multi-agent systems, and causal foundations for trustworthy AI. These studies emphasize real-world applications, such as healthcare NLP and policy analysis. Awards & Recognition: 3 Rising Star Awards 2 Best Paper Awards at NeurIPS 2024 Workshops Fellowships from Open Philanthropy and Future of Life Institute Grants & Mentorship: Funded by NSERC, Schmidt Sciences, and the Cooperative AI Foundation. She mentors ~20 students globally, including PhD candidates in multi-agent LLMs, causal LLMs, and AI safety. The Jinesis Lab offers remote mentorship across institutions like UofT, ETH Zurich, and University of Michigan. Labs & Collaborations: Active collaborations include MPI-IS, Vector Institute, and ETH Zurich’s AI Center. Her lab emphasizes open science, with tools like MoralLens and RouterAttack released publicly.

Max Schaubمشاهده پروفایل

استادیار

• Violence Studies
• Migration Studies
• Political Science
• +۳ مورد دیگر

Prof. Max Schaub is an Assistant Professor of Political Science at the University of Hamburg and a WZB Fellow at the Berlin Social Science Research Center. His research focuses on violence, migration, health, and their societal impacts, employing experimental and mixed-methods approaches. He holds a Ph.D. from the European University Institute (2016), an M.Sc. from Oxford, and a B.A. from Dresden. Prior roles include postdoctoral positions at WZB and Bocconi University. Education: Ph.D. in Political and Social Sciences, European University Institute (2016) M.Sc. in African Studies, University of Oxford B.A. in International Relations, University of Dresden Research Interests: Violence dynamics and conflict consequences Migration determinants and societal integration Health outcomes in conflict zones and policy responses Political behavior under economic stress Current Projects: WarGen (DFG-funded): Examines electoral turnovers and health costs during crises ViVac (GLOHRA-funded): Analyzes civilian targeting in interstate wars Cultural roots of prejudice via antisemitism studies Teaching Roles: University of Hamburg: Courses on peacebuilding, political violence, global health Hertie School: Causal inference Free University of Berlin: Historical political economy Labs/Teams: Leads WarGen and ViVac projects, collaborating with institutions in Germany, Italy, and Senegal.

Sriraam Natarajanمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Statistical Relational Learning
• +۵ مورد دیگر

Sriraam Natarajan is a Professor and Director of the Center for Machine Learning at the Erik Jonsson School of Engineering & Computer Science, University of Texas at Dallas. He previously served as an Associate Professor at Indiana University (on leave since 2017) and Wake Forest School of Medicine. His research focuses on artificial intelligence, machine learning, and their biomedical applications, particularly in relational learning, reinforcement learning, and graphical models. He leads the StaRLing Lab and holds fellowships from hessian.AI and RBCDSAI. Education: PhD in Computer Science from Oregon State University (2007), advised by Prasad Tadepalli. Postdoctoral research at University of Wisconsin-Madison under Jude Shavlik and David Page. Research interests span statistical relational AI, causal inference, and healthcare applications. Notable awards include AAAI Fellow (2025), UTD Outstanding Graduate Teaching Award, and roles as AAAI Program Co-Chair and CODS-COMAD 2024 co-chair. Students supervised include over 20 PhD/MS graduates and current advisees in AI and machine learning. Active in editorial roles for JAIR, Machine Learning Journal, and conference PCs (ICML, AAAI, NIPS).

Xu Shiمشاهده پروفایل

دانشیار

• Biostatistics
• Causal Inference
• Healthcare Data Analysis
• +۵ مورد دیگر

Xu Shi is an Associate Professor in the Department of Biostatistics at the University of Michigan. Previously, they held a postdoctoral fellowship at Harvard’s Data Science Initiative (2017) under Tianxi Cai and Eric Tchetgen Tchetgen. Their research focuses on statistical methods for administrative healthcare data, electronic health records (EHR), and causal inference. Key projects include developing scalable pipelines for EHR curation, causal inference methods for comparative effectiveness studies, and co-leading the FDA’s Sentinel Initiative Innovation Center’s Causal Inference Core. Education: Ph.D. in Biostatistics from University of Washington (201?), B.S. in Mathematics & Applied Mathematics from Zhejiang University, China. Research emphasizes harmonizing distributed EHR data, addressing unmeasured confounding in air pollution studies, and rare adverse event analysis through flexible propensity score methods. They also explore medical knowledge extraction from ICD code patterns and natural indirect effect estimation robust to measurement errors. Awards: None explicitly listed. Grants & Advising: No specific grants or students listed, but their work involves collaborative initiatives like the FDA Sentinel Program. Labs/Teams: Active in the Causal Inference Core and distributed healthcare data partnerships with institutions like Henry Ford Health System and Kaiser Permanente.