Find Academic Supervisors

• Biomedical Informatics
• Genomics
• Neurodevelopmental Disorders
• +4 more

Isaac Kohane is a Research Professor at Boston Children's Hospital , leading the Computational Health Informatics Program. His work bridges genomics, biomedical informatics, and machine learning to diagnose rare diseases and advance precision medicine. He co-leads large-scale collaborations like the Undiagnosed Diseases Network and 4CE Consortium , focusing on electronic health record analysis and neurodevelopmental disorders . Research Interests include: Genomic discovery of rare neurodevelopmental syndromes (e.g., variants in PPFIA3 , SDHA , ZBTB47 ) Machine learning for clinical risk prediction (e.g., suicide attempt modeling, type 2 diabetes subtyping) Ethical challenges in genomic research and AI implementation Health disparities in genetic variant interpretation International multi-institutional data harmonization Recent Publications emphasize genotype-phenotype mapping , AI-driven biomarker discovery , and repurposing real-world data for long COVID and autoimmune disease studies. His work highlights cross-modal data integration and multi-omics approaches in rare disease diagnostics.

• Artificial Intelligence
• Machine Learning
• Business Analytics
• +6 more

Yifan Yu is an Assistant Professor at the Department of Information, Risk, and Operations Management, McCombs School of Business, The University of Texas at Austin. He holds a Ph.D. in Information Systems from the University of Washington and bachelor's/master's degrees in Management Science and Engineering from Tsinghua University. Research Focus: Economics of AI/ML, unstructured data analytics (text/images/video), and data-driven sustainable operations. Methodologies: Machine/deep learning, network analysis, econometric/game-theoretical modeling, and experiments. Publications: Appeared in top journals like Management Science , MIS Quarterly , and Information Systems Research . Awards: Best paper recognitions at INFORMS, CIST, WITS, and teaching awards from University of Washington.

Daniel ChenView profile

Assistant Professor

• Operations Strategy
• Online Platforms
• Gig Economy
• +5 more

Daniel Chen is an Assistant Professor of Business Analytics at Boston College's Carroll School of Management. His research focuses on online platform strategies, gig economy dynamics, and human-AI interaction. He holds a Ph.D. in Operations Management from the Wharton School (University of Pennsylvania) and degrees from the University of Southern California (BS Economics/Mathematics, MS Mathematical Finance). Education: BS in Economics/Mathematics, University of Southern California MS in Mathematical Finance, University of Southern California Ph.D. in Operations Management, Wharton School, University of Pennsylvania Research Interests: Chen analyzes strategic behavior in gig economies, algorithmic pricing transparency, and network-based solutions for online ratings. His work combines machine learning, structural estimation, and economic modeling to address platform efficiency, labor market dynamics, and regulatory challenges. Key Findings: His studies reveal that enabling gig workers to multihome (switch platforms) boosts earnings and service levels, while inefficient routing reduces worker utilization. He also highlights risks of prediction errors in causal inference using machine learning. Awards: Research Fellowship, Coalition for Equity and Opportunity (2023-2025) Finalist, INFORMS Behavioral Operations Management Best Working Paper Award (2023) Advising & Grants: Collaborates with driver analytics firms on empirical studies. Advises on gig economy policy and algorithmic fairness. Active in securing research fellowships through the Mack Institute and Coalition for Equity and Opportunity.

• Information Retrieval
• Recommender Systems
• Music Information Retrieval
• +4 more

Associate Professor Peter Knees holds a position at the Technische Universität Wien's Faculty of Informatics, specifically within the Department of Data Science. He serves as a Curriculum Coordinator for the Bachelor's Specialization in Artificial Intelligence and Machine Learning. His research focuses on Information Retrieval, Music Information Retrieval, Recommender Systems, and Digital Humanism. Knees has coordinated major projects such as the Vienna Doctoral College on Digital Humanism (2024–2029) and contributes to initiatives like FAIR-AI and HumRec. He teaches courses including Digital Humanism and Introduction to Information Retrieval. His work bridges technical innovation with ethical considerations, emphasizing transparency and fairness in AI systems. Education: Holds Dipl.-Ing. (TU Wien) and Dr.techn. (Technical Doctorate). His interdisciplinary projects span music technology, ethical AI, and smart grid systems. He has supervised numerous theses on topics like music recommendation algorithms and audio processing techniques. Research Interests: Explores intersections of music technology and AI, including content-driven recommendation systems, reproducibility in machine learning, and the societal impact of digital technologies. Recent work emphasizes ethical frameworks for recommender systems and human-centered design principles. Grants & Projects: Lead roles in WWTF-funded Digital Humanism initiatives, FFG projects on AI innovation, and EU-backed MIR research. Active in organizing conferences like RecSys and ISMIR workshops. Labs/Teams: Involved with the Vienna Doctoral College, TU Wien's Data Science group, and collaborations with institutions like ASEA-UNINET for ethnomusicology data integration. Leads efforts in neural-symbolic systems for smart grids and generative music interfaces.

Devavrat ShahView profile

Professor

• Machine Learning
• Artificial Intelligence
• Data Science
• +7 more

Devavrat Shah is a Professor at the Massachusetts Institute of Technology (MIT), affiliated with the Computer Science and Artificial Intelligence Laboratory (CSAIL) and the Laboratory for Information and Decision Systems (LIDS). His research focuses on machine learning, causal inference, and algorithmic fairness, with applications in healthcare outcomes, criminal justice reform, and cryptocurrency analysis. He has contributed to groundbreaking work, such as developing a machine-learning algorithm that nearly doubled Bitcoin investments over 50 days using real-time price data analysis. Shah's academic work integrates statistical methods with real-world challenges, including personalized medicine, policy evaluation, and bias detection in algorithms. His recent projects include global clinical studies on lymphoma treatments and frameworks for auditing racial bias in law enforcement systems. His interdisciplinary approach bridges computer science, statistics, and domain-specific applications. Key areas of his research include causal inference frameworks for data-rich environments, algorithmic fairness in decision-making systems, and predictive modeling for healthcare and public policy. His work often combines theoretical rigor with practical impact, addressing societal challenges through data-driven solutions. Shah's contributions to synthetic interventions and causal simulators have advanced trace-driven simulation methodologies, while his collaboration with global consortia like PETAL highlights his commitment to translational research. His students, including Kang Zhang, have contributed to impactful projects in algorithm design and data science.

Erica FleishmanView profile

Professor

• Climate Change
• Ecological Conservation
• Wildfire Management
• +3 more

Erica Fleishman is a Professor and Director of the Oregon Climate Change Research Institute (OCCRI) at Oregon State University. Her research focuses on ecological responses to climate and land-use change, conservation strategies, and the application of remote sensing in environmental sciences. She holds a B.S. and M.S. in Biological Sciences from Stanford University (1991–1992) and a Ph.D. in Ecology, Evolution, and Conservation Biology from the University of Nevada, Reno (1997). Dr. Fleishman’s work emphasizes interdisciplinary approaches to address environmental challenges, particularly in the western U.S. Her research spans wildfire dynamics, climate adaptation, biodiversity conservation, and human-environment interactions. She leads projects analyzing wildfire causes, socio-ecological systems, and climate projections at regional scales. She has contributed to horizon scans of global conservation issues and developed datasets (e.g., FPA-FOD-Attributes) to improve wildfire prediction and management. Her recent articles highlight trends in wildfire mitigation strategies, climate-driven shifts in species distributions, and the impacts of socio-economic factors on ecological systems. She collaborates with policymakers and stakeholders to bridge scientific knowledge and actionable solutions for climate resilience.

Kai ZhouView profile

Research Fellow

• Machine Learning
• Deep Learning
• Heavy Ion Physics
• +4 more

Dr. Kai Zhou is a Research Fellow at the Frankfurt Institute for Advanced Studies (FIAS) in the Theoretical Sciences division, where he leads the 'Deepthinkers' research group. Born in China in 1987, he completed his B.Sc. in Physics from Xi'an Jiaotong University in 2009 and earned his PhD with 'Wu You Xun' Honors from Tsinghua University in 2014. After postdoctoral research at Goethe University Frankfurt's Institute for Theoretical Physics, he joined FIAS in August 2017 as a Research Fellow focusing on Deep Learning applications in physics. Frankfurt Institute for Advanced Studies (FIAS), Theoretical Sciences (2017-present) Goethe University Frankfurt, Institute for Theoretical Physics (Postdoc) Tsinghua University, Physics (PhD, 2014) Xi'an Jiaotong University, Physics (B.Sc., 2009) Dr. Zhou's research bridges artificial intelligence and theoretical physics, with a focus on applying machine learning techniques to complex physical systems. His work spans heavy-ion collisions, lattice quantum field theory, seismology, and renewable energy systems. He has developed innovative deep learning approaches to extract physical insights from complex data, including constructing an Equation-Of-State meter for heavy ion collisions. His research demonstrates how physics can inform AI development while AI enhances our understanding of physical phenomena. Analysis of Dr. Zhou's recent publications reveals a strong trend toward integrating physics principles with machine learning architectures. His work increasingly focuses on Bayesian inference methods applied to QCD phase transitions, physics-informed neural networks for solving inverse problems in nuclear physics, and developing specialized architectures that preserve physical symmetries. The interdisciplinary nature of his research is evident in applications spanning from heavy-ion collisions to neutron star physics and industrial process optimization. Wu You Xun Honors (PhD) Third party funding through Samson AG: AI for science BMBF funding within ErUM data program: Deep Learning for CBM computing DAAD exchange program Xidian-FIAS International Joint Research Center: AI for science BMWI: AI for energy Nvidia: GPU Grant Dr. Zhou actively mentors doctoral and master's students, currently advising seven graduate students across multiple institutions. His research group 'Deepthinkers' has secured significant third-party funding from diverse sources including industrial partners (Samson AG), government agencies (BMBF, BMWI), and international collaborations (DAAD, Xidian University). His approach combines theoretical physics with cutting-edge AI techniques to address complex problems in both fundamental science and industrial applications. The 'Deepthinkers' research group operates at the intersection of AI and physics, developing novel methodologies that leverage physical principles to enhance machine learning and vice versa. Their work on applying deep learning to heavy-ion collisions represents a significant advancement in extracting meaningful physical insights from complex collision data. The group maintains strong international collaborations, particularly with Chinese institutions through the Xidian-FIAS International Joint Research Center.

Minjee SunView profile

Assistant Professor

• Digital Marketing
• Machine Learning
• Behavioral Economics
• +3 more

Minjee Sun is an Assistant Professor in the Department of Marketing at Tippie College of Business, University of Iowa. Her research focuses on marketing analytics, digital marketing, and behavioral economics, with a particular emphasis on marketing-sales interfaces, customer feedback dynamics, and digital content optimization. She received her PhD in Marketing from the University of Toronto and holds an MSc and BBA from Korea University. Education: PhD in Marketing, University of Toronto MSc in Marketing, Korea University BBA, Korea University Research Interests: Minjee's work explores how digitization transforms marketing value through three key contexts: (1) marketing analytics enhancing sales performance, (2) satisfaction surveys as strategic targeting tools, and (3) feedback dynamics in digital content creation. Her studies integrate machine learning and causal inference methodologies. Article Trends: Her publications and presentations (2018–2023) reflect expertise in digital marketing analytics, customer feedback systems, and salesforce management. Key themes include data-driven sales strategies, behavioral economic models for feedback analysis, and structural modeling of consumer interactions. Scientific Awards: Fellow - ISMS Early-Career Scholars Camp (2023) Wendell Smith Award (2023) Old Gold Summer Faculty Fellowship (2022) Doctoral Completion Award (2021) AMA-Sheth Doctoral Consortium Fellow (2019) First Place, Poster Presentation (2018) Research Initiative Competition Award (2018) Rose Patten Fellowship (2017–2018) Advising & Grants: While formal advisee lists are unavailable, she leads funded research initiatives like The Impact of Business Engagement in Refugee Hiring on Consumer Attitudes (Tippie College of Business, 2023–2025) and Don’t Judge a Book by its Cover (University of Iowa, 2022–2023). She also collaborates on projects like Interactive Customer Feedback (IMI, 2022–2023).

Yongyi GuoView profile

Assistant Professor

• Statistical Learning
• Decision-Making Methods
• Digital Health Interventions
• +3 more

Yongyi Guo serves as an Assistant Professor in the Department of Statistics at the University of Wisconsin-Madison, following a postdoctoral fellowship at Harvard University under Susan A. Murphy. Her academic foundation includes a Ph.D. in Statistics from Princeton University (2022) advised by Jianqing Fan and a B.S. in Mathematical Sciences from Peking University (2016). Dr. Guo's research integrates statistical learning with digital health applications, specializing in reinforcement learning for personalized mobile interventions and causal inference methodologies. Her work demonstrates particular expertise in micro-randomized trials for cannabis use reduction, where she developed the MiWaves and reBandit algorithms to optimize just-in-time adaptive interventions through online decision-making frameworks. Analysis of her publication trends reveals consistent focus on bridging theoretical statistics with health implementation challenges, featuring innovations in anytime-valid inference for N-of-1 trials, communication-efficient distributed estimation, and robust regression techniques for dependent data structures.

Matthias GrossglauserView profile

Professor

• Machine Learning
• Stochastic Networks
• Discrete Choice Models
• +4 more

Matthias Grossglauser is a Full Professor at the School of Computer and Communication Sciences at École Polytechnique Fédérale de Lausanne (EPFL), where he co-directs the Information and Network Dynamics lab. He serves on the Federal Communications Commission (ComCom), Switzerland's telecommunications regulatory authority, and previously directed EPFL's Doctoral School in Computer and Communication Sciences (2016-2019). His career includes positions at Nokia Research Center (Internet Laboratory lead), AT&T Research, and EPFL (Assistant Professor). Education Ph.D. in Computer Science from Sorbonne Universités M.Sc. in Electrical Engineering from Georgia Institute of Technology Engineering degree in Communication Systems from EPFL Research Focus Grossglauser's research integrates machine learning, stochastic networks, and discrete choice models to address challenges in artificial intelligence, network science, computational social sciences, and recommender systems. His work emphasizes both theoretical foundations and practical applications, including political forecasting, climate communication, and network dynamics. Publication Trends Recent articles demonstrate strong focus on causal inference, optimal learning algorithms, and social network analysis. Dominant themes include reinforcement learning optimization, graph-based modeling, and NLP applications in political science. Methodological innovations in matrix factorization, Bayesian modeling, and stochastic processes recur throughout. Awards & Honors Fellow of IEEE and ELLIS Cor Baayen Award (1998) CoNEXT/SIGCOMM Rising Star Award (2006) Best Paper Awards: ACM COSN (2014), IEEE INFOCOM (2001) Nokia Mobile Data Challenge Winner (2012) Academic Leadership Has advised 16+ PhD students to completion and currently supervises 4 doctoral candidates. Secured research funding for projects including dynamic recommender systems, network alignment algorithms, and computational social science tools (e.g., Predikon.ch vote prediction platform). Leads the Information and Network Dynamics lab, focusing on AI-driven network analysis.

• Explainable Artificial Intelligence (XAI)
• Counterfactual Reasoning
• Machine Learning
• +4 more

Prof. Mark Keane has served as Chair of Computer Science at University College Dublin since 1998. A cognitive psychology expert with a PhD from Trinity College Dublin, his career spans multiple institutions including the University of London, Open University, Cardiff University, and TCD. He has held leadership roles such as Director of ICT at Science Foundation Ireland (2004-2006) and Director General at SFI (2006-2007), overseeing €700M+ research investments. His work focuses on explainable AI, counterfactual reasoning, and sustainable agriculture applications. BA (UCD), PhD (TCD) in Cognitive Psychology Over 200 publications, H-index 45, 10,800+ citations Key advisor for Ireland's €3.7B Science, Technology & Innovation Strategy His research explores counterfactual explanations , semi-factual reasoning , and XAI in domains like dairy farming and climate resilience. Recent work combines machine learning with cognitive models to improve grass growth prediction and mastitis detection . He also investigates surprise theory through computational models. As Vice-President of Innovation & Partnerships at UCD (2007-2009), he drove academic-industry collaborations. Current affiliations include the Insight Centre for Data Analytics, where he leads AI research teams advancing case-based reasoning and deep learning integrations for explainable systems.

• Cardiovascular Genetics
• Genomics
• Precision Medicine
• +5 more

Vincent Mooser, MD, is a Professor in the Department of Human Genetics at McGill University's Faculty of Medicine and Health Sciences and an Associate Investigator at the Research Institute of the McGill University Health Centre (RI-MUHC). He is a key member of the Cardiovascular Health Across the Lifespan Program at RI-MUHC, focusing on translational genomic research in cardiovascular and metabolic disorders. Dr. Mooser's research centers on cardiovascular genetics, genomics, and precision medicine, with emphasis on pharmacogenetics, obesity-related diseases, and population-specific genetic variations. His work leverages multi-ancestry cohorts and integrative 'omics' approaches to identify causal pathways and therapeutic targets, particularly in coronary artery disease and metabolic complications. He has pioneered research on genetic determinants of drug responses and disease susceptibility across diverse populations. Analysis of his recent publications (2024-2025) reveals a dominant focus on multi-ancestry Mendelian randomization, proteogenomics, and biobank-driven studies. Key trends include the identification of actionable mediators like endotrophin in obesity-related cardiovascular disease, development of population-specific genomic tools for French-Canadian cohorts, and investigations into long COVID genetics. His work consistently bridges fundamental genomics with clinical applications through initiatives like the Biobanque québécoise de la COVID-19 (BQC19). Dr. Mooser co-leads the BQC19 biobank initiative and contributes to the Cardiovascular Health Across the Lifespan Program at RI-MUHC, utilizing large-scale genomic datasets and collaborative frameworks to advance precision medicine in cardiovascular health.

Xia ChenView profile

Research Fellow

• Human-AI Alignment
• Knowledge Representation and Reasoning
• Machine Learning
• +5 more

Dr. Xia Chen is a Postdoctoral Fellow at the Technical University of Munich (TUM), working at the Georg Nemetschek Institute (GNI) within the Chair of Computing in Civil and Building Engineering. His research focuses on human-AI alignment, knowledge-integrated machine learning, and bio-inspired adaptive intelligence, with applications in engineering and scientific contexts. Dr. Chen earned his Ph.D. with summa cum laude distinction from Leibniz University Hannover and Technical University Berlin. His dissertation, "Beyond Predictions: Alignment between Prior Knowledge and Machine Learning for Human-Centered Augmented Intelligence," established foundational work in aligning AI systems with human cognition and decision-making processes. Prior to his current position, he was a Visiting Scholar at UC Berkeley's Center for the Built Environment (CBE). Dr. Chen's research interests center on developing AI systems that enhance rather than replace human capabilities in engineering contexts. His work spans several interconnected areas: human-AI alignment, where he explores how artificial systems can dynamically align with human cognition and values; knowledge representation and reasoning, focusing on integrating domain knowledge with data-driven approaches; machine learning methodologies that incorporate physics-based constraints; and applications of AI in the built environment for energy-efficient design and sustainable infrastructure. His publication record demonstrates a consistent focus on bridging the gap between theoretical AI advancements and practical engineering applications. Dr. Chen has pioneered approaches like component-based machine learning (CBML), which transforms system-level extrapolation problems into component-level interpolation challenges, making AI more robust with limited data. His recent work on symbolic neural networks for building physics and causal inference in design processes represents cutting-edge integration of domain knowledge with machine learning. summa cum laude PhD distinction from Leibniz University Hannover and TU Berlin Dr. Chen has contributed to several national research initiatives (DFG, BMBF), with projects ranging from component-based machine assistance to forecasting frameworks for energy systems. His work at the E.ON Energy Research Center (RWTH Aachen) involved meta-neural network ensembles for renewable prediction and economic evaluations of the German Energy Transition. He actively serves as a reviewer for venues including Advanced Engineering Informatics, Energy and Buildings, EG-ICE, and IBPSA. Dr. Chen's research is conducted within TUM's vibrant ecosystem for computational engineering, with connections to the Georg Nemetschek Institute's focus on digital transformation in the built environment. His work intersects with several research groups including those focused on Information Management, Digital Twinning, and Knowledge Representation and Reasoning within the Department of Civil and Environmental Engineering.

• Veterinary Epidemiology
• Antimicrobial Resistance Monitoring
• Animal Welfare Assessment
• +4 more

Amanda Brinch Kruse serves as Assistant Professor at the University of Copenhagen's Department of Veterinary and Animal Sciences within the Faculty of Health and Medical Sciences. She maintains dual research affiliations in both the Production, Nutrition and Health section and the Animal Welfare and Disease Control section, reflecting her interdisciplinary focus on livestock health systems. Her primary research addresses antimicrobial use monitoring, meat inspection modernization, and disease control frameworks within Danish animal production systems. Her research interests center on veterinary epidemiology with specific emphasis on antimicrobial resistance surveillance , biosecurity implementation , and animal welfare assessment in livestock production. Through extensive analysis of Danish national databases including VetStat, she investigates antimicrobial consumption patterns across pig, veal, and companion animal sectors. Her work employs advanced statistical methodologies including multivariate analysis and Bayesian network modeling to identify risk factors and develop evidence-based disease control strategies. Kruse's publication portfolio demonstrates consistent focus on practical applications for veterinary public health, particularly in meat inspection modernization (evidenced by her 2021 Food Control review) and antimicrobial stewardship frameworks. Her research leverages Denmark's comprehensive agricultural registries to establish correlations between farm management practices, antimicrobial usage, and health outcomes across multiple livestock species. Her collaborative network spans veterinary epidemiology, public health, and food safety disciplines, with significant contributions to Danish regulatory frameworks for antimicrobial use monitoring. Current research trajectories indicate deepening focus on data-driven approaches to optimize biosecurity protocols and antimicrobial stewardship within European livestock production systems.

Guido ImbensView profile

Professor

• Econometrics
• Causal Inference
• Program Evaluation
• +5 more

Guido Imbens is a Professor of Economics at Stanford University's Graduate School of Business and a Senior Fellow at the Stanford Institute for Economic Policy Research (SIEPR). He is also a Faculty Affiliate at the Institute for Human-Centered Artificial Intelligence (HAI) and Faculty Director of the Stanford Causal Science Center. His research focuses on causal inference in observational studies, program evaluation, and econometric methods, with a particular emphasis on matching, instrumental variables, and regression discontinuity designs. Education: Ph.D. in Economics, Brown University; Academic appointments at Harvard, UCLA, UC Berkeley prior to Stanford (2012). Key Research Areas: Econometrics, Causal Inference, Bayesian Methods, Semi-Parametric Analysis, Instrumental Variables, Machine Learning applications. Scientific Awards: Honorary Doctorate, Brown University (2022) Fellow, National Academy of Sciences (2022) Nobel Memorial Prize in Economic Sciences (2021) Amman Mineral Faculty Fellow (2021–2022) Recent Publications: His work spans causal inference in panel data, synthetic control methods, experimental design, and statistical significance interpretation. Articles in journals like Journal of Econometrics , Econometrica , and Management Science highlight his contributions to statistical methodology and policy analysis. Advising: Guido actively mentors doctoral students and postdoctoral researchers, serving as advisor for Lea Bottmer, Janelle Nelson, and others. He also advises undergraduate majors and contributes to curriculum development in econometrics education.