جست‌وجوی استاد و پژوهشگر

Raed Al Kontarمشاهده پروفایل

دانشیار

• Federated Learning
• Personalized Data Science
• Uncertainty Quantification
• +۵ مورد دیگر

Raed Al Kontar serves as an Associate Professor with tenure in the Industrial & Operations Engineering (IOE) department at the University of Michigan's College of Engineering. He leads the Data Science Lab and holds affiliate appointments with the Michigan Institute for Data Science and Computational Discovery and Engineering. His research bridges probabilistic modeling with engineering applications, focusing on personalized, collaborative, and distributed data analytics where knowledge from diverse sources is integrated while preserving privacy. His research program addresses three core questions across descriptive, predictive, and prescriptive analytics: extracting shared/unique patterns across datasets, enabling collaborative model improvement while maintaining data privacy, and optimizing distributed trial-and-error processes. Key research areas include Federated Learning, Uncertainty Quantification, Bayesian Optimization, and Digital Twins, with applications spanning healthcare, manufacturing, and materials science. Current funding includes NSF (including a 2022 CAREER award), NIH, NLM, and industry partnerships. Research Trends: His recent publications demonstrate a strong focus on heterogeneous data integration through novel matrix/tensor factorization techniques, personalized PCA frameworks, and consensus-based distributed optimization. The work consistently bridges theoretical guarantees (e.g., identifiability conditions) with practical applications in hydrogel development, 3D printing, and pharmaceutical safety. Award Highlights: His group has won 12 best paper awards since 2022 across INFORMS, ASA, and IISE, including the 2024 Wilson Prize and INFORMS Data Mining Section's Best General Track Paper. Dr. Al Kontar has successfully placed multiple PhD students in tenure-track positions including Naichen Shi (Northwestern), Xubo Yue (Northeastern), and Seokhyun Chung (University of Virginia). His lab maintains active collaborations with medical researchers (NIH/NLM projects), materials scientists (autonomous experimentation), and pharmaceutical safety experts. The lab combines theoretical innovation with real-world impact, exemplified by their NSF CAREER-funded work on the Internet of Federated Things.

Markus Ewertمشاهده پروفایل

پژوهشگر

• Game Theory
• Auction Theory
• Market Design
• +۴ مورد دیگر

Markus Ewert is a PhD researcher at the Decision Sciences & Systems group within the Department of Computer Science at the Technical University of Munich (TUM), supervised by Prof. Martin Bichler since May 2021. His work bridges computer science, economics, and operations research to advance computational methods for game-theoretic market analysis, with a focus on behavioral equilibria in incomplete information settings. His educational background includes: Master in Information Systems (M.Sc.) from Technical University Munich (2018-2020) Bachelor in Statistics (B.Sc.) from Ludwig-Maximilians-University Munich (2016-2021) Bachelor in Information Systems (B.Sc.) from Technical University Munich (2014-2017) Ewert's research centers on game theory and market design, specializing in auction theory, equilibrium computation, and behavioral modeling. He develops novel computational frameworks integrating machine learning with economic theory to analyze strategic interactions in complex markets. His work particularly addresses human behavior anomalies in auctions through equilibrium learning and Bayesian optimization techniques, contributing to more robust market mechanism design. His publication record reveals a strong trajectory in merging computational methods with economic theory, with recent work emphasizing structural estimation via optimal transport and equilibrium learning for asymmetric market environments. Key themes include resolving the overbidding puzzle in all-pay auctions, designing robust mechanisms for utility asymmetries, and computational approaches to Bayes-Nash equilibria in crowdsourcing contests. Ewert actively mentors students through bachelor and master thesis supervision, guiding projects spanning derivative-free optimization, market mechanism design, and behavioral game theory applications. His advisees frequently collaborate with industry partners including SWM, Mercedes-Benz AG, SAP, and NavVis on energy systems, investment strategies, and sales analytics. As a core member of TUM's Decision Sciences & Systems research group, he contributes to teaching courses in Business Analytics and Learning in Games while advancing the group's mission in optimization, market design, and computational social choice through ongoing research projects.

• Cognitive Modelling
• Artificial Intelligence
• Decision-Making
• +۳ مورد دیگر

Dr. Leendert van Maanen is an Associate Professor at the Department of Experimental Psychology, Utrecht University, where he studies cognitive mechanisms underlying information processing and decision-making. His research focuses on developing and validating computational cognitive models, with applications in human-machine interaction and cognitive neuroscience data analysis. Research Themes: Cognitive Modeling, Decision-Making, Human-Centered Artificial Intelligence, Applied Data Science Labs: Leads the COBRA (Cognitive and Behavioral Research in Artificial Intelligence) lab His work spans three levels of cognitive integration in AI: model development, human-machine deployment, and neural data analysis. Recent projects include cognitive strategy detection, digital twin user modeling, and neural event detection using multivariate time series. For collaborations and publications, see leendertvanmaanen.com or Utrecht University Research Portal.

Anup Malaniمشاهده پروفایل

استاد

• Law and Economics
• Health Economics
• Development Economics
• +۹ مورد دیگر

Anup Malani is the Lee and Brena Freeman Professor at the University of Chicago Law School and a Professor at the Pritzker School of Medicine. He also holds significant affiliations as a University Scholar at Resources for the Future in Washington, a Research Associate at the National Bureau of Economic Research in Boston, and a Senior Fellow at the Schaeffer Center at the University of Southern California. Malani co-founded and serves as Faculty Director of the International Innovation Corps, a social service program sending University of Chicago and foreign university graduates to work on development projects in India and Brazil. Malani earned his PhD in economics and JD, both from the University of Chicago. He clerked for Judge Stephen Williams on the US Court of Appeals for the District of Columbia Circuit and Justice Sandra Day O'Connor on the US Supreme Court. His academic journey includes previous appointments at Harvard Law School as Roscoe Pound Visiting Professor and at the University of Virginia Law School as Associate Professor. Malani's research spans law and economics, health economics, and development economics. His law and economics work focuses on judicial behavior, empirical analysis of legal reforms, and blockchain. His health economics research examines infectious disease control, medical innovation value, health insurance, placebo effects, and medical conflicts of interest. His development economics work concentrates on healthcare supply and financing in India and urban slum quality of life. He leads major research initiatives including the Indian Health Insurance Experiment (an 11,000 household RCT) and impact evaluations of Mission Kakatiya in Telangana, India. Malani's research has been published in leading journals across multiple disciplines including Proceedings of the National Academy of Sciences, Journal of Political Economy, Harvard Law Review, and Nature Medicine. His recent work has focused significantly on COVID-19 pandemic analysis, particularly in Indian contexts, examining infection rates, mortality, and policy responses. Member, American Academy of Arts and Sciences (2021) Emergent Ventures Prize (twice in 2020 for serological work and epidemiological modeling) Order of the Coif (2000) John M. Olin Fellowship in Law and Economics (1998-2000) Bradley Foundation Fellowship (1997-2000) Malani advises governments in India and Indonesia on COVID policy with support from the Asian Development Bank and has advised the World Bank on COVID in South Asia. He serves on University of Virginia's working group planning a National COVID Commission. His teaching spans corporate law, health law, food and drug law, law and economics, and development in the Law School, plus economics and public policy courses across the university. He has supervised numerous research projects and has been principal investigator on multiple large-scale studies examining healthcare and development in India. Malani directs the International Innovation Corps, which partners with government officials in India and Brazil on innovative development projects. He previously served as Faculty Director of the Tata Centre for Development at the University of Chicago. His work bridges academic research with practical policy implementation through collaborations with government agencies, international organizations, and development institutions.

Leonard Ng Wei Tatمشاهده پروفایل

استادیار

• Additive and Advanced Manufacturing
• Nanomaterials and Nanotechnology
• Smart Cities
• +۴ مورد دیگر

Leonard Ng Wei Tat serves as an Assistant Professor at Nanyang Technological University's School of Materials Science & Engineering, where he directs the NGenuity Lab focused on data-driven roll-to-roll printed flexible electronics and self-driving laboratories for renewable energy systems. His work bridges advanced manufacturing, nanomaterials, and artificial intelligence to develop sustainable energy solutions. Ng's academic foundation includes a PhD in Engineering from the University of Cambridge (2019), where he pioneered low-cost printed electronics using graphene and 2D materials. This was followed by a prestigious NTU International Postdoctoral Scholarship with CSIRO and a Royal Academy of Engineering Enterprise Fellowship for renewable energy commercialization. His research spans seven core areas: Additive and Advanced Manufacturing, Nanomaterials and Nanotechnology, Smart Cities infrastructure, Surfaces and Coatings engineering, Artificial and Augmented Intelligence, Biomedical Informatics, and 2D Materials applications. Current projects emphasize machine-learning enabled inverse design for high-throughput manufacturing of photovoltaics, with particular focus on stability enhancement and efficiency optimization of printed solar cells. Analysis of Ng's recent publications reveals a clear trajectory toward integrating AI with laboratory automation, with dominant themes in self-driving laboratories (35% of output), printed photovoltaics (40%), and stability enhancement techniques (25%). His work demonstrates exceptional translational impact, with multiple industry features including PV Magazine coverage of his perovskite solar cell breakthroughs. Scientific recognition includes: NTU International Postdoctoral Scholar Royal Academy of Engineering Enterprise Fellowship Ng actively mentors eight PhD students and multiple postdoctoral researchers in organic and perovskite photovoltaics, self-driving lab development, and AI-driven materials design. His group secures substantial funding through competitive mechanisms including Nanyang PhD Graduate Scholarships, SINGA Scholarships, and industry partnerships, supporting both fundamental research and commercial translation efforts. The NGenuity Lab operates as a dynamic hub for innovation, featuring specialized facilities for roll-to-roll printing, photovoltaic characterization, and AI-driven experimental automation. Current initiatives focus on democratizing laboratory robotics through low-cost 3D printing and developing closed-loop optimization systems for industrial-scale renewable energy manufacturing.

• Computational Electromagnetics
• Inverse Problems in Electromagnetism
• Bioimpedance and Medical Diagnostics
• +۳ مورد دیگر

Alice Reinbacher-Köstinger is an Associate Professor at the Institute for Fundamentals and Theory of Electrical Engineering, Graz University of Technology, Faculty of Electrical Engineering. Her research focuses on computational electromagnetics, inverse problems, and bioimpedance analysis. Role: Research and Teaching Research Areas: Transformer modeling, NFC systems, aortic dissection detection, and surrogate-based optimization Email: alice.koestinger@tugraz.at Her work combines advanced modeling techniques with machine learning, particularly in magnetic material characterization and bioimpedance signal analysis. Recent publications emphasize physics-informed neural networks, harmonic balance methods, and Bayesian experimental design. Current projects include optimizing sensor placement for magnetic property measurements and developing simulation tools for medical diagnostics. Her research bridges electrical engineering with biomedical applications through multiphysics simulations and data-driven methods.

Marjo Saastamoinenمشاهده پروفایل

استاد

• Ecology
• Evolutionary Biology
• Climate Change Adaptation
• +۴ مورد دیگر

Marjo Saastamoinen is a Professor at the University of Helsinki, affiliated with the Faculty of Biological and Environmental Sciences and the Organismal and Evolutionary Biology Research Programme. Her research focuses on understanding how species cope with environmental variation through genetic, experimental, and statistical modeling approaches, particularly using the Glanville fritillary butterfly as a model system. University of Helsinki Faculty of Biological and Environmental Sciences Organismal and Evolutionary Biology Research Programme Research Interests include ecological and evolutionary responses to climate change, metapopulation dynamics, phenotypic plasticity, and genetic diversity. She integrates long-term monitoring data with molecular tools to study adaptive mechanisms in insects. Notable Scientific Awards include the Finnish Academy PhD-thesis award (2008). Her recent publications address biodiversity stability, pesticide impacts on butterflies, and thermal adaptation mechanisms. Advising includes PhD and postdoctoral supervision in evolutionary biology. She is involved in projects like WildAP (Wild Animal Phenotyping Infrastructure) and biodiversity metrics initiatives.

Yingni Guoمشاهده پروفایل

استادیار

• Mechanism Design
• Information Economics
• Game Theory
• +۲ مورد دیگر

Yingni Guo is an Assistant Professor in the Department of Managerial Economics & Decision Sciences at Northwestern University's Kellogg School of Management. She is currently a visiting scholar at Duke University during the 2024-2025 academic year and teaches advanced courses including Econ 885 on mechanism design without transfers. Her research bridges theoretical economics with real-world applications in market design and organizational behavior. Guo's research centers on mechanism design without monetary transfers , exploring optimal information disclosure, dynamic delegation, and strategic persuasion. Her work examines how agents communicate and make decisions under uncertainty across contexts like voting systems, labor markets, and organ allocation. She employs rigorous game-theoretic models to derive policy-relevant insights into market failures and institutional design, with significant contributions to understanding discriminatory practices and monopoly regulation. Analysis of her publication record reveals a cohesive trajectory in information economics , with increasing focus on dynamic settings and real-world applications since her 2016 AER lead article. Recent work spans top journals including Econometrica (3 papers), AER (2 papers), and QJE, demonstrating consistent impact in microeconomic theory. Her research group actively contributes to conferences like the Women in Economic Theory Workshop and ASSA meetings. Guo secured NSF Grant SES-1530608 (2015-2019) for Dynamic Mechanisms without Transfer jointly with Johannes Hörner, supporting her foundational work in non-monetary allocation. While current advisees aren't listed, her Spring 2025 reading group and course instruction indicate active mentorship of graduate students in economic theory.

Wencong Suمشاهده پروفایل

استاد

• Power and Energy Systems
• Transportation Electrification
• Cyber-Physical Systems
• +۵ مورد دیگر

Dr. Wencong Su is a Professor and Chair of the Department of Electrical and Computer Engineering at the University of Michigan-Dearborn , where he leads research in power systems, transportation electrification, and cyber-physical systems. He earned his B.S. (2008) from Clarkson University, M.S. (2009) from Virginia Tech, and Ph.D. (2013) from North Carolina State University. Research Interests: Power and energy systems, renewable integration, electric vehicles, machine learning, and smart grid technologies. Editorial Roles: Associate Editor for IEEE Transactions on Smart Grid , IEEE Access , and IEEE DataPort . His research focuses on optimizing power electronics, enhancing grid stability with distributed energy resources, and applying AI to energy systems. Recent work includes surrogate modeling for converter design, safe reinforcement learning in power grids, and cyber-physical solutions for digital substations. Publications span topics like second-life battery applications, high-frequency AC microgrids, and AI-driven energy management. His articles emphasize machine learning, optimization, and robust control in renewable integration and transportation electrification. Awards include IEEE Fellowships, Top 2% Scientist recognition (Stanford), and multiple IEEE best paper awards. He has secured grants from NSF, DoE, Ford, Toyota, and DTE Energy. Labs operate in the Institute for Advanced Vehicle Systems (IAVS-2060, IAVS-1060, ELB-1026, ELB-1042), focusing on power electronics, smart grid validation, and electrified transportation.

• Natural Language Generation
• Computational Linguistics
• Referring Expression Generation
• +۴ مورد دیگر

Kees van Deemter is a Professor in the Department of Computing Science at the University of Aberdeen's School of Natural and Computing Sciences. With a research career spanning over three decades, he has established himself as a leading figure in Natural Language Generation and Computational Linguistics. His work bridges theoretical linguistics with practical NLP applications, with particular focus on referring expression generation, multilingual processing, and evaluation methodologies. Van Deemter's research interests center on Natural Language Generation, with special emphasis on Referring Expression Generation (REG), computational modeling of linguistic phenomena across languages, and the development of robust evaluation frameworks for NLP systems. His work spans theoretical foundations to practical applications, including logic-to-text generation, cross-linguistic studies comparing East Asian and European languages, and addressing challenges in hallucination and reproducibility in NLP. His recent publications demonstrate a consistent focus on improving evaluation methodologies in NLP, with particular attention to task-based assessments rather than purely ratings-based approaches. He has made significant contributions to understanding how linguistic features influence reference production across languages, with extensive work on Chinese and Mandarin processing. His research group has developed several important corpora including QTUNA for quantifier studies and Mtuna for Mandarin referring expressions. Co-authored the influential survey 'Computational Generation of Referring Expressions: A Survey' (2012) Developed the Mtuna corpus for Mandarin referring expressions Contributed foundational work on the incremental algorithm for referring expression generation Pioneered research on vague language generation from a game-theoretic perspective Investigated cross-linguistic differences in reference production between English and East Asian languages Van Deemter has supervised numerous PhD students who have become active researchers in NLP, including Guanyi Chen, Fahime Same, and Albert Gatt. His collaborative work extends across multiple institutions and has significantly shaped current approaches to referring expression generation and evaluation in NLP. His recent work critically examines the limitations of current evaluation practices in NLP and proposes more scientifically grounded assessment frameworks.

Mukund Raghothamanمشاهده پروفایل

پژوهشگر

• Programming Languages
• Software Engineering
• Automated Reasoning
• +۷ مورد دیگر

Mukund Raghothaman is a researcher at the University of Southern California , focusing on the intersection of programming languages, software engineering, and automated reasoning. He leverages techniques from machine learning and formal methods to develop tools for program synthesis, verification, and static analysis that improve software quality and developer productivity. Key research areas: Semantic Regular Expressions , Subspecifications , Bayesian Program Reasoning , Datalog Synthesis Contributed to foundational frameworks like SyGuS and Bingo/Drake for probabilistic static analysis Active in program committee roles for conferences like POPL , PLDI , and SPLASH Scientific contributions include: Distinguished Artifact Award ( ICSE 2022 ) Distinguished Paper Award ( PLDI 2019 ) His recent work explores LLM integration for invariant generation, network configuration explanations, and function name synthesis to enhance program understanding.

David Shulmanمشاهده پروفایل

مدرس

• Pediatric Oncology
• Bone Tumors
• Brain Tumors
• +۶ مورد دیگر

David Shulman, MD, is an Attending Physician at Dana-Farber/Boston Children's Cancer and Blood Disorders Center and a Lecturer in Pediatrics at Harvard Medical School. His clinical expertise encompasses pediatric bone tumors, brain tumors, leukemia, neuroblastoma, and soft tissue sarcomas. He actively contributes to the Dana-Farber Experimental Therapeutics Program, focusing on early-phase clinical trials and biomarker research. Dr. Shulman received his medical degree from Harvard Medical School (2013), completed pediatric residency at Boston Combined Residency Program, and a fellowship in Pediatric Hematology/Oncology at Boston Children's Hospital/Dana-Farber Cancer Institute. His research integrates novel therapies and biomarkers for pediatric solid tumors, with emphasis on circulating tumor DNA (ctDNA) as a tool for treatment optimization. Recent publications highlight sarcoma biomarker validation, clinical trial innovations, and socioepidemiological factors in cancer outcomes. His work frequently involves collaborations with the Children's Oncology Group. Dr. Shulman's recent articles (2022-2025) predominantly address sarcoma therapeutics, liquid biopsy applications, and statistical methodologies in oncology trials. A consistent theme is optimizing diagnostic and therapeutic strategies for high-risk pediatric cancers through molecular profiling and innovative trial designs.

Mark Edwardsمشاهده پروفایل

دانشیار

• Visual Cognition
• Attentional Control
• Cognitive Empathy
• +۷ مورد دیگر

Mark Edwards is an Associate Professor in Psychology at the Australian National University's School of Medicine and Psychology. His academic career spans over two decades, with a focus on visual cognition, attentional control, and cognitive empathy. Edwards holds a PhD in Psychology from the University of Melbourne, a Psychology degree from ANU, and a Mechanical Engineering degree from the University of Queensland. Edwards' educational background is uniquely interdisciplinary, beginning with a Mechanical Engineering degree from the University of Queensland (1982-1985), followed by a Psychology degree at ANU (1987-1990), and culminating in a PhD in visual psychophysics from the University of Melbourne (1991-1995). After seven years of research at NTT Basic Research Laboratories in Japan and the School of Optometry at the University of California at Berkeley, he returned to ANU in 2001 to take up a faculty position. Edwards' research primarily focuses on visual cognition and attentional control. His work investigates how the human visual system processes information, with particular emphasis on motion processing, face recognition, and stereopsis. He explores how various visual pathways interact at different levels in the brain, including situations where the visual system misinterprets combinations of features, such as in synesthesia. More recently, his research has expanded to examine the implications of attentional control in everyday functioning, particularly in driving contexts, and how it varies across different populations including those with psychopathic traits. Analysis of Edwards' recent publications (2023-2024) reveals a strong focus on individual differences in cognitive processes. His work examines how attentional breadth, working memory, and emotional processing vary across individuals and impact real-world performance. Key themes include the relationship between psychopathic traits and visual attention, cognitive failures in trait anxiety, emotion-induced blindness, and the development of tools like the Cognitive Failures Questionnaire 2.0 to measure subjective cognitive experiences. 2019-2021: Australian Research Council Discovery Grant ($390,000) 2011-2015: Australian Research Council Discovery Grant ($582,074) 2009-2014: Australian Research Council Discovery Grant ($475,000) 2006-2011: Australian Research Council Centre of Excellence in Vision ($10,000,000) Edwards has served as Principal Investigator or Co-Investigator on numerous research projects, including "Investigating cognitive predictors of crash risk in provisional drivers" and "Using a scientific understanding of human cognition to reduce crash risk in older adult drivers." His research has practical applications in understanding and improving driver safety, particularly for vulnerable populations.

Timo Roettgerمشاهده پروفایل

استاد

• Cognitive Science
• Speech Production
• Speech Perception
• +۹ مورد دیگر

Timo Roettger is a Professor of General Linguistics at the Department of Linguistics and Scandinavian Studies, University of Oslo, since 2022. Previously, he served as an Associate Professor (2020-2021), Postdoctoral Researcher at the University of Osnabrück (2020), Northwestern University (2018-2019), and the University of Cologne (2016-2017). His academic journey includes a PhD in Phonetics from the University of Cologne in 2016. Core research areas: Cognitive Science, Phonetics, Psycholinguistics Methodological focus: Quantitative methods, statistical modeling, open science practices Leadership role: Chair of the ManyLanguages consortium Roettger's research investigates the relationship between multi-dimensional speech signals and cognitive representations, with emphasis on predictive processing of intonation, analytic flexibility in speech research, and reproducibility. His work bridges phonetics, phonology, and cognitive science through rigorous empirical approaches. Recent publications highlight his methodological contributions to experimental linguistics and his advocacy for transparent research practices. Key projects include the ICONIC study on iconicity in spoken language. Roettger actively promotes diversification of languages, participants, and researchers in the language sciences. He employs interdisciplinary approaches combining phonetic analysis with cognitive theory to address foundational questions about speech production and perception.

• Behavioral Economics
• Decision Making under Uncertainty
• Ambiguity Aversion
• +۳ مورد دیگر

Chen Li serves as a Full Professor in Behavioural Economics at Erasmus School of Economics, Erasmus University Rotterdam. Her research centers on decision-making under uncertainty and over time, examining how ambiguity attitudes affect economic choices across social contexts and demographic groups. Her core research domains include Behavioral Economics, Decision Making under Uncertainty, Ambiguity Aversion, Social Interactions, and Bayesian Updating. She investigates psychological mechanisms behind economic decisions, particularly focusing on how poverty influences risk perception and how learning shapes belief formation in uncertain environments. Her work bridges theoretical models with empirical validation through controlled experiments. Analysis of her 2022-2025 publications reveals a cohesive trajectory in ambiguity and uncertainty research. Key themes include experimental methods for measuring deviations from Bayesian updating, theoretical frameworks for tractable ambiguity modeling, and the observability of preference structures in social interactions. Her work consistently integrates mathematical rigor with behavioral insights, advancing methodologies for studying decision processes in complex economic scenarios.