جست‌وجوی استاد و پژوهشگر

Guang Chengمشاهده پروفایل

استاد

• Generative Data Science
• Trustworthy AI
• Machine Learning Theory
• +۵ مورد دیگر

Guang Cheng is a Full Professor of Statistics and Data Science at the University of California, Los Angeles (UCLA), and serves as the Graduate Vice Chair. He leads the Trustworthy AI Lab, focusing on generative data science, privacy-preserving synthetic data, and the theoretical foundations of machine learning. His research explores generative AI, trustworthy synthetic data, and high-dimensional statistics. Education : Ph.D. in Statistics, University of Wisconsin-Madison (2003-2006) B.A. in Economics, Tsinghua University (1998-2002) Research Interests : Generative Data Science, trustworthy AI, machine/deep learning theory, privacy-preserving techniques, high-dimensional statistics, and business intelligence applications in finance, healthcare, and marketing. His lab develops tools like artificially generated tables for privacy-preserving data sharing. Publications : Recent work includes advancements in generative models (e.g., TimeAutoDiff, MissDiff), fair classification algorithms, and theoretical analyses of deep learning (e.g., attention mechanisms, minimax analysis). Key themes include synthetic data utility/privacy trade-offs and adversarial robustness. Awards : IMS Fellow (2020), Adobe Data Science Award (2020), NSF CAREER Award (2012). Advising & Grants : Supervises PhD/Master’s/postdoc researchers in trustworthy AI and generative data science. Alumni hold roles at Meta, Amazon, and academia. Active in editorial roles for JASA - Theory & Methods and Canadian Journal of Statistics . Labs & Teams : Leads the Trustworthy AI Lab at UCLA, organizing workshops on synthetic data in finance and healthcare. Collaborates with Amazon as an Amazon Scholar.

Feras Saadمشاهده پروفایل

استادیار

• Probabilistic Programming
• Artificial Intelligence
• Programming Languages
• +۴ مورد دیگر

Feras Saad is an Assistant Professor in the Computer Science Department at Carnegie Mellon University, affiliated with the Principles of Programming and Artificial Intelligence groups. He received his Ph.D. in Computer Science from the Massachusetts Institute of Technology (MIT) in 2022, where his dissertations on probabilistic programming systems earned him the George M. Sprowls PhD Thesis Award and Charles & Jennifer Johnson MEng Thesis Award. His research focuses on developing scalable computing systems for probabilistic modeling and inference, integrating ideas from programming languages and probabilistic AI. Key research themes include probabilistic programming languages, automated probabilistic model discovery, statistical estimation and testing, random sampling algorithms, and applications in science and engineering. His lab explores new techniques to improve reasoning systems through automation, accuracy, and scale. Dr. Saad has published extensively in top venues including PLDI, POPL, ICML, and Nature Communications. His work spans foundational computational questions to practical software systems for probabilistic inference. Research trends show consistent focus on bridging theoretical computer science with practical applications in probabilistic modeling, with recent advances in random sampling algorithms and probabilistic programming systems. Awards and honors include: George M. Sprowls PhD Thesis Award in Artificial Intelligence and Decision Making (2023) Charles & Jennifer Johnson MEng Thesis Award in Computer Science (2017) Editor's Highlight for Nature Communications paper (2024) He currently advises graduate students Gaurav Arya and Thomas Draper in the Probabilistic Computing Systems Lab. His research is supported by software libraries including GenSQL, BayesNF, and SPPL that enable practical applications across scientific domains.

Mateo Espinosa Zarlengaمشاهده پروفایل

مدرس

• Machine Learning
• Artificial Intelligence
• Explainable AI
• +۳ مورد دیگر

Mateo Espinosa Zarlenga is a Lecturer in the Department of Computer Science and Technology at the University of Cambridge. His research focuses on Explainable Artificial Intelligence (XAI), interpretable deep learning architectures, and the application of these techniques in healthcare. He explores concepts such as concept-based explainability, representation learning, and the mitigation of bias in AI systems. His work emphasizes the design of learning algorithms that produce human-understandable explanations and their practical deployment in critical domains. He has supervised multiple MPhil/Part III projects, including studies on concept intervention policies, iterative concept discovery, and the ethical implications of AI systems. His research has been recognized through prestigious awards, including a Best Paper Award nomination at ECCV 2024 and spotlight presentations at NeurIPS workshops and AAAI conferences. Mateo collaborates with experts in healthcare and computational biology to advance interpretable AI models for real-world applications. Key achievements include the development of Concept Embedding Models (CEMs) and TabCBM, which bridge the gap between model accuracy and explainability, particularly in tabular data. His work often involves designing interventions to improve model performance through expert feedback, as seen in projects addressing concept leakage and bias mitigation without privileged information.

Glenn Shaferمشاهده پروفایل

استاد

• Probability Theory
• Game-Theoretic Probability
• Statistics
• +۴ مورد دیگر

Glenn Shafer is a University Professor and Board of Governors Professor at Rutgers University, teaching in the Department of Accounting and Information Systems at the Rutgers Business School. He has been a university educator for 50 years, renowned for pioneering work on the Dempster-Shafer theory of evidence and co-developing the game-theoretic framework for probability. His research spans probability, statistics, causality, and finance, with notable contributions to foundational theories and applications in artificial intelligence and engineering. Education: A.B. in Mathematics (Princeton University, 1968), Ph.D. in Statistics (Princeton University, 1973). Roles: Former Dean of Rutgers Business School (2011–2014), Guggenheim Fellow, Fulbright Scholar, and recipient of the Gorenstein Award for Research and Service. Research Interests: Glenn's work focuses on foundational probability theories, including the Dempster-Shafer theory and game-theoretic probability. He has contributed to the history of probability and statistics, publishing extensively and translating historical works. His recent focus includes the game-theoretic foundations for finance and the historical analysis of martingales. His research bridges philosophical, mathematical, and applied domains, influencing fields like artificial intelligence, finance, and engineering. Awards and Recognition: Honorary doctorate from the University of Economics, Prague (2009); Kampé de Feriet Award (2018); Sarton Medal (2018); Special issue of International Journal of Approximate Reasoning (2022). Grants and Service: Principal investigator on multiple NSF grants, editorial roles at journals like Statistical Science , and service on academic committees at Rutgers. His work has been supported by organizations including the National Science Foundation and the National Endowment for the Humanities.

Alfred Heroمشاهده پروفایل

استاد

• Data Science
• Statistical Machine Learning
• Distributed Optimization
• +۱۰ مورد دیگر

Alfred Hero is the John H. Holland Distinguished University Professor of Electrical Engineering and Computer Science and the R. Jamison and Betty Williams Professor of Engineering at the University of Michigan. He is currently on leave from the University of Michigan as a Program Director in the CISE Directorate at the National Science Foundation. His primary appointment is in the Department of Electrical Engineering and Computer Science (EECS) with secondary appointments in the Department of Biomedical Engineering and the Department of Statistics. He is affiliated with multiple research centers including the UM Center for Computational Medicine and Bioinformatics (CCMB), the UM Graduate Program in Applied and Interdisciplinary Mathematics (AIM), the UM Applied Physics Program, and the Michigan Institute for Data Science (MIDAS). Hero's research focuses on data science, developing theory and algorithms for multimodality data collection, fusion, analysis and visualization using statistical machine learning and distributed optimization. His work has applications in wearable technologies for personalized health and predictive medicine, spatio-temporal networks in biology, climate, and social discourse, anomaly detection, and data analysis for international security. His research group has produced numerous PhD students who have gone on to prominent academic and industry positions. His recent publications show a strong focus on high-dimensional statistical methods, machine learning theory, network analysis, and applications in biomedical domains. The research trends indicate increasing emphasis on multimodal data fusion, robust learning algorithms, and applications to complex systems in biology and security domains. His work bridges theoretical foundations with practical implementations across diverse application areas. Fellow of the Institute of Electrical and Electronics Engineers (IEEE) Fellow of the Society for Industrial and Applied Mathematics (SIAM) Fourier Award in Signal Processing from the IEEE Hero has advised numerous PhD, MS, and undergraduate students who have gone on to successful careers in academia and industry. His research has been supported by various grants, though specific grant details are not provided in the source material. His lab collaborates extensively across disciplines with researchers in statistics, biomedical engineering, and computational medicine. The Hero Research Group maintains active collaborations with institutions worldwide and participates in major conferences in machine learning, signal processing, and data science.

• Recommender Systems
• Context-aware Recommendation
• Explainable AI
• +۵ مورد دیگر

Markus Zanker is a Professor in the Department of Knowledge Engineering within the Faculty of Computer Science at Free University of Bozen-Bolzano, Italy. With an extensive publication record spanning over two decades, he has established himself as a leading expert in recommender systems research, with particular expertise in context-aware, group, and knowledge-based recommendation approaches. His work bridges theoretical foundations with practical applications across diverse domains including tourism, healthcare, and e-commerce. Zanker's research interests center on advancing the theoretical underpinnings of recommender systems while addressing practical challenges in real-world deployments. His recent work has focused on causal decision-making frameworks for recommendation, intent-aware systems, and the integration of generative AI in group recommendation scenarios. He has made significant contributions to explainable recommendation systems, medical recommendation applications, and tourism recommendation systems, demonstrating both theoretical rigor and practical impact. His recent publication portfolio reveals a strong trend toward addressing fundamental challenges in recommendation science, including the growing emphasis on causal reasoning to move beyond correlation-based approaches, the integration of generative AI capabilities for more sophisticated group decision support, and the development of frameworks for ethical and socially beneficial recommendation systems. His work increasingly bridges the gap between traditional recommendation algorithms and emerging AI paradigms while maintaining focus on real-world applicability. Zanker is actively involved in the academic community as a workshop organizer, having co-chaired the Knowledge-aware and Conversational Recommender Systems (KaRS) workshop and the Recommenders in Tourism (RecTour) workshop for multiple consecutive years at major conferences including RecSys. His research has been supported through collaborations with numerous international researchers and institutions, focusing on both theoretical advances and practical implementations of recommendation technology across multiple application domains.

Yona Rubinsteinمشاهده پروفایل

استاد

• Managerial Economics
• Strategy
• Business Strategy
• +۳ مورد دیگر

Yona Rubinstein is a Professor of Managerial Economics and Strategy at the London School of Economics and Political Science (LSE). Her academic affiliation is with the Department of Managerial Economics and Strategy within LSE. Research interests can be inferred from her departmental affiliation, focusing on areas such as strategic management, organizational economics, and business decision-making frameworks. No specific publications or awards are explicitly mentioned in the provided text. She holds a full-time faculty position at LSE.

Wynne Chinمشاهده پروفایل

استاد

• Information Systems
• Structural Equation Modeling
• Technology Acceptance
• +۵ مورد دیگر

Wynne Chin is a Professor of Decision & Information Sciences at the C.T. Bauer College of Business, University of Houston. He holds a Ph.D. from the University of Michigan, MBA from the University of Michigan, and degrees from Northwestern University and UC Berkeley. His research focuses on IT adoption, structural equation modeling (SEM), and predictive analytics. He developed the PLS-Graph software, widely used in PLS-based SEM, and has contributed to cross-cultural analysis of IT sourcing and workplace assessment. Education: Ph.D. (Computers & Information Systems, U. Michigan), M.S. (Chemical Engineering, Northwestern), MBA (U. Michigan), B.S. (Biophysics, UC Berkeley). Research interests include IT decision-making, end-user satisfaction, group process measures, and methodological innovations in SEM. He has been recognized globally, ranking 3rd in first-authored articles in MISQ/ISR. Awards include the AIS Fellow, Esther Farfel Award, and AIS Technology Legacy Award. His work bridges IS and statistics, influencing both academic and applied fields. His articles emphasize technology acceptance models, causal modeling, and PLS techniques. Grants and advising contributions are significant but not explicitly detailed here. He has held visiting roles at institutions like the Australian School of Business and is a leader in the PLS research community.

Andreas Krauseمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• Reinforcement Learning
• +۵ مورد دیگر

Andreas Krause is a Professor of Computer Science at ETH Zurich, leading the Learning & Adaptive Systems Group and serving as Chair of the ETH AI Center and Academic Co-Director of the Swiss Data Science Center. He previously held an Assistant Professorship at Caltech. His research focuses on machine learning, Bayesian optimization, reinforcement learning, and AI safety. Key contributions include advancements in active sensing, learning-based control, and probabilistic AI. Affiliations: ETH AI Center, Swiss Data Science Center, Max Planck Institute for Intelligent Systems (Fellow) Education: PhD in Computer Science (Carnegie Mellon University, 2008); Diplom in Computer Science and Mathematics (Technical University of Munich, 2004) His work bridges theory and practice, with applications in robotics, healthcare, and climate science. Notable awards include the IEEE Fellow distinction, Rössler Prize, and multiple ERC grants. Krause actively chairs ICML conferences and contributes to global AI policy through the UN’s High-level Advisory Body on AI. Research Interests: Machine learning fundamentals, adaptive systems, AI safety, and optimization. Recent work explores safe exploration in reinforcement learning, causal modeling, and large-scale generative AI.

Ruth Leeمشاهده پروفایل

مدرس ارشد

• Developmental Psychology
• Cognitive Psychology
• Child Development
• +۴ مورد دیگر

Dr. Ruth Lee is a Senior Lecturer at the School of Education, Language and Psychology at York St John University. She holds a PhD from the University of Toronto (2018) and has held postdoctoral positions at Queen's University Belfast and the University of York. Her research focuses on developmental psychology, cognitive processes, and social relationships in childhood. Key areas include temporal cognition, children's comprehension of fictional narratives, and language processing. She is also an external examiner for Leeds Beckett University's MSc Psychology conversion program. Education: BA in Russian Studies (Sheffield, 1995), MEd in Psychology of Education (Manchester, 2008), PhD in Psychology (Toronto, 2018). Research Interests: Temporal asymmetries in memory and experience valuation Grandparent-grandchild closeness measurement Language processing in children and adults Publications: Over 15 peer-reviewed articles since 2001, including work on time perception, fictional discourse, and corruption in Russian law enforcement. Recent collaborations involve institutions like the University of Warwick, University of Edinburgh, and University of South Dakota. Labs/Teams: Collaborates with interdisciplinary teams on projects involving cognitive development, social psychology, and educational psychology.

Ivana Malenicaمشاهده پروفایل

استادیار

• Causal Inference
• Machine Learning
• Nonparametric Statistics
• +۳ مورد دیگر

Ivana Malenica is an Assistant Professor of Biostatistics at the University of North Carolina at Chapel Hill's Gillings School of Global Public Health. Previously, she was a HDSI Fellow at Harvard Data Science Initiative and Postdoctoral Fellow in Statistics at Harvard University. She holds a Ph.D. in Biostatistics from UC Berkeley and a B.S. in Mathematics from Arizona State University. Her research focuses on causal inference, machine learning, nonparametric statistics, efficiency theory, and precision health. She specializes in longitudinal and structured dependent settings including adaptive sequential experiments, online learning, and reinforcement learning applications in personalized health. Her recent publications demonstrate strong methodological contributions to causal inference and machine learning, with applications spanning clinical trials, public health, genomics, and reinforcement learning. Her work consistently develops novel statistical approaches for complex data structures. Awards include: Harvard Data Science Initiative Fellowship (2022) Berkeley Wellness Letter Fellowship (2020) Wellness Scholarship in Honor of Chin Long Chiang (2019) Berkeley Institute for Data Science Moore-Sloan Fellowship (2018) She teaches graduate courses including Advanced Probability and Statistical Inference I (BIOS 760). Her computational work includes contributions to the tlverse ecosystem for causal inference in R.

Sijie Sunمشاهده پروفایل

استادیار

• Cross-cultural consumer psychology
• Creativity
• Social media consumption
• +۴ مورد دیگر

Dr. Sijie Sun is an Assistant Professor of Marketing at the Asper School of Business, University of Manitoba. Previously, he taught at the University of Hawaii at Hilo. His research focuses on cross-cultural consumer psychology, creativity, social media consumption, and prosocial behavior. He holds a PhD in Marketing from the University of Texas at Arlington and serves on the editorial review board of the Journal of Business Research . Education: PhD in Marketing, College of Business, University of Texas at Arlington Research interests span cultural influences on consumer behavior, including creativity, social media engagement, and how identity salience impacts purchasing decisions. His work bridges cultural psychology with marketing strategy, particularly in service recovery, crisis management, and brand equity. Articles often explore cross-cultural dynamics in price-quality judgments, impulse consumption, and the moderating role of cultural dimensions like tightness-looseness. His publications appear in top-tier journals such as Journal of Marketing and Journal of Hospitality Marketing & Management . Recent work investigates service robots' impact on consumer evaluations and the interplay between managerial trust and employee performance. Despite no explicitly listed awards, his editorial role and prolific publication record highlight scholarly influence. No advising or grant details are provided, but his editorial contributions and conference engagements (ACR, SCP, AMA) suggest active involvement in academic discourse. His research often intersects with practical applications in hospitality, retail environments, and digital marketing strategies.

Rrita Zejnullahiمشاهده پروفایل

استادیار بالینی

• Biostatistics
• Meta-Analysis
• Epidemiology
• +۳ مورد دیگر

Rrita Zejnullahi is a Clinical Assistant Professor of Biostatistics at the University of Illinois Chicago, with primary affiliation in the School of Public Health's Division of Epidemiology and Biostatistics, and a joint appointment in the College of Applied Health Sciences. She also serves as a Statistician in the Office of Research within the College of Applied Health Sciences. Education: Ph.D. in Statistics from Northwestern University (2021). Research focuses on statistical methodologies to support policy-making, including uncertainty quantification in predictive models, effect size estimation in experimental designs, and meta-analysis for small samples. Substantive applications include refugee population analyses, air quality data integration, sport epidemiology, aging mobility, mental health interventions, and physiological mechanisms in muscle blood flow. Her recent publications address robust meta-analysis techniques and effect size calculations in ANCOVA and difference-in-differences designs. Active in open-source contributions, she maintains repositories on meta-analysis and statistical methods (e.g., RVE-Meta-analysis-SMD and Effect-sizes-ANCOVA-DiD-designs).

• Artificial Intelligence
• Data Science
• Machine Learning
• +۷ مورد دیگر

Hady W. LAUW is an Associate Professor in the School of Computing and Information Systems (SCIS) at Singapore Management University (SMU), serving as Director of the BSc (Computer Science) Programme and a Lee Kong Chian Fellow. He holds a PhD from Nanyang Technological University (2008). His research focuses on artificial intelligence, data science, machine learning, and recommender systems, with notable contributions to multimodal recommendation frameworks like Cornac and collaborative filtering techniques. He teaches advanced courses including IS712 Machine Learning for postgraduate students and CS608 Recommender Systems for MITB programme participants. His work emphasizes practical applications, such as designing explainable recommendation systems and integrating A/B testing into frameworks. Key research interests include web mining, preference learning, representation learning, and decision support systems. He has advised multiple students on topics like neural networks, collaborative filtering, and comparative analysis of reviews. Scientific achievements include the Lee Kong Chian Fellowship and over 100 publications in top venues like ACM WWW and IEEE TKDE. He actively contributes to conferences as a PC member and chairs events like PAKDD. His Cornac framework supports reproducible research in multimodal recommendations. He leads the Preferred.AI research group, fostering undergraduate and postgraduate research in computing. His work bridges theoretical advancements with industry applications, particularly in data-driven decision making and automated evaluation metrics.

Marcel Weberمشاهده پروفایل

استاد

• Philosophy of Biology
• Philosophy of Evolutionary Linguistics
• General Philosophy of Science
• +۳ مورد دیگر

Marcel Weber is a Full Professor of Philosophy of Science at the Department of Philosophy, University of Geneva, with a focus on the philosophy of life sciences and general philosophy of science. He also teaches general epistemology and modern philosophy, particularly Kant. PhD in Philosophy (1996, University of Konstanz) Habilitation (2002, Leibniz University Hannover) Previous roles: SNSF Professor at University of Basel (2004–2009), Full Professor at University of Konstanz (2009–2011), and visiting appointments at institutions like University of California, Irvine, and Max Planck-Institute for the History of Science. His research spans the intersection of biology and philosophy, emphasizing evolvability, causality in biological systems, and the ontology of biological functions. Key projects include collaborations with the Institute of Genetics and Genomics of Geneva (iGE3), NCCR Evolving Language, and the DACH project on Inferentialism and Bayesianism. Marcel Weber's recent publications explore evolutionary developmental biology, causal specificity, and the metaphysics of biological modalities. His work often integrates experimental practices across levels of biological organization and critiques deterministic versus probabilistic interpretations in evolutionary theory. 2012: Elected to the German National Academy of Sciences Leopoldina 1996: Science Prize of the District of Constance He has edited volumes on probabilities in science, philosophical confirmation, and collective epistemology. His laboratory affiliations include interdisciplinary groups like 'What if? On Counterfactual Claims' and lgBig (Lake Geneva Biological Interest Group).