Find Academic Supervisors

• Robotics
• Human-Robot Interaction (HRI)
• Artificial Intelligence
• +3 more

Marc Hanheide is a Professor of Intelligent Robotics and Interactive Systems at the University of Lincoln 's School of Computer Science. With a career spanning EU projects like VAMPIRE, COGNIRON, CogX, and STRANDS, his work focuses on long-term robotic behavior, human-robot spatial interaction, and cognitive system architectures. He has secured over 12 major grants from organizations including EPSRC, BBSRC, and the European Commission. Key Research Areas : Autonomous robotics, HRI, AI, cognitive systems, agricultural robotics Current Projects : STRANDS (long-term behavior), AgriFoRwArdS (robotics training), NCNR (nuclear robotics) Major Contributions : Human-aware navigation modules, topology optimization for robot fleets, causal analysis frameworks Scientific Awards: While no specific awards are listed, his numerous EPSRC grants and leadership in multi-institutional projects highlight his impact. He has over 172 publications and collaborates with institutions like CoR-Lab and CITEC.

• Operations Management
• Algorithmic Decision-Making
• Revenue Management
• +3 more

Ningyuan Chen is a faculty member at the University of Toronto with affiliations at the Rotman School of Management and University of Toronto at Mississauga's Department of Management. His research spans operations management with a focus on algorithmic decision-making, revenue management, and data analytics. Chen's research interests center on the intersection of algorithms and human decision-making processes, with particular emphasis on how human knowledge can safeguard and improve algorithmic recommendations. His work addresses critical challenges in commercial AI solutions where human analysts have domain-specific insights that may conflict with algorithmic outputs. He investigates conditions under which human knowledge augmentation benefits algorithmic decision-making, particularly when facing algorithmic pitfalls like lack of domain knowledge, model misspecification, and data contamination. Chen's publication trends reveal a strong focus on practical business applications of operations research, with recent work examining assortment pricing with transaction data, vaccine allocation under limited supply, and simultaneous versus sequential product release strategies. His research combines theoretical modeling with practical business implications, often collaborating with Ming Hu and other researchers at the Rotman School. His work demonstrates how data-driven approaches can be enhanced through human expertise, particularly in contexts where pure algorithmic recommendations might fail due to real-world complexities that data alone cannot capture. This research has important implications for business intelligence systems across various industries where human judgment remains critical alongside algorithmic recommendations.

Xingye QiaoView profile

Professor

• Statistics
• Machine Learning
• Causal Inference
• +3 more

Xingye Qiao is a Professor and Chair in the Department of Mathematics and Statistics at Binghamton University, State University of New York . He serves as Chair of the Data Science Transdisciplinary Area of Excellence steering committee and has been affiliated with Binghamton since 2010. Education : Ph.D. in Statistics (2010), University of North Carolina at Chapel Hill M.S. in Statistics (2007), University of North Carolina at Chapel Hill B.S. in Mathematics and Applied Mathematics (2005), Fudan University His research focuses on Statistics, Machine Learning, and Causal Inference , with recent work exploring conformal prediction methods, treatment effect estimation, and set-valued classification techniques. His publications span journals like Transactions on Machine Learning Research , NeurIPS , and AAAI . His 15 most recent articles (2025-2020) demonstrate expertise in areas including: bandit feedback systems, treatment effect heterogeneity analysis, spectral clustering for neuroscience, and conformal prediction under distribution shifts. These works blend statistical theory with real-world applications in healthcare, ecology, and data science. He mentors Ph.D. students in mathematical sciences and has supervised research topics such as: machine learning in precision medicine, goodness-of-fit tests for spatial processes, and high-dimensional data analysis. Courses he teaches include Math 605: Theory of Machine Learning and Data 501: Predictive & Inferential Analytics .

Wolfgang StammerView profile

Researcher

• Explainable AI
• Neuro-Symbolic Architectures
• Concept Learning
• +3 more

Wolfgang Stammer is a PostDoc researcher in the Machine Learning Group at TU Darmstadt's Computer Science Department. His work focuses on making AI models more interpretable and interactive, particularly in explainable AI (XAI), neuro-symbolic architectures, and systematic compositionality challenges in neural networks. He completed his Ph.D. in Machine Learning at TU Darmstadt (2019–2025), an M.Sc. in Computer Science at Goethe University Frankfurt (2016–2018), and a B.Sc. in Cognitive Science at the University of Osnabrück (2011–2015). Research Interests : Stammer's research bridges gaps between human understanding and AI capabilities. Key areas include: Explainable AI (XAI) and interactive machine learning (XIL) Neuro-symbolic integration for logical reasoning and visual concepts Mitigating shortcut learning and confounding factors in datasets Concept discovery and program synthesis for interpretable models Publications : His work spans foundational contributions to AI benchmarks (e.g., V-LoL, SLR-Bench) and frameworks (Neural Concept Binder, Revision Transformers). Recent studies highlight AI's limitations in systematic generalization and propose solutions for aligning reinforcement learning agents with human values. Grants & Labs : He contributes to the Machine Learning Lab at TU Darmstadt and co-organized workshops like the Interactive Machine Learning Workshop @ AAAI 2022. His research bridges theoretical advances with practical applications in healthcare and ethical AI systems.

Pierre-Yves LajoieView profile

Assistant Professor

• Robotics
• Computer Vision
• Artificial Intelligence
• +5 more

Pierre-Yves Lajoie is an Assistant Professor in the Department of Computer Engineering and Software Engineering at Polytechnique Montréal, a leading engineering school affiliated with Université de Montréal. His research focuses on robotics and artificial intelligence, with specialization in robotic perception for single and multi-agent systems. He has held research positions at prestigious institutions including the Massachusetts Institute of Technology (2019), Samsung AI Center (2023), and University of Oxford (2024). Education: Ph.D. in Computer Engineering, Polytechnique Montréal Dr. Lajoie's research interests span across robotics, computer vision, and distributed systems. He specializes in developing algorithms for robotic perception in challenging environments, with applications in aerial, underground, indoor, and space robotics. His work focuses on enabling robots to understand their surroundings through visual and sensor data, particularly in collaborative multi-robot scenarios where communication may be limited or unreliable. His primary research center of excellence is the Industry of the Future and Digital Society, with secondary centers in Modeling and Artificial Intelligence and New Frontiers in Information and Communication Technologies. His publication record demonstrates a strong focus on collaborative SLAM (Simultaneous Localization and Mapping) systems, with recent work addressing challenges in planetary exploration, swarm robotics, and pedestrian positioning. His research combines computer vision, machine learning, and distributed systems to create robust solutions for real-world robotic applications, particularly in environments with communication constraints. Scientific Awards: Vanier Canada Scholarship Best Paper Award at IEEE ICC 2024 Dr. Lajoie is actively recruiting graduate students for PhD and Master's programs, with openings for Fall 2025 and Spring 2026. He encourages students to apply for various scholarship opportunities including NSERC, FRQ, and IVADO scholarships at multiple academic levels. His research is supported by collaborations with academic and industrial partners, focusing on applications in space robotics, automated manufacturing, and service robotics. He has supervised research projects in areas such as search and rescue with sparsely connected swarms and distributed risk-aware exploration systems.

Leonard J. SchulmanView profile

Professor

• Algorithms
• Communication Protocols
• Combinatorics
• +6 more

Leonard J. Schulman is a Professor of Computer Science at the California Institute of Technology (Caltech), where he has been on the faculty since 2000. He is affiliated with the Caltech Center for the Mathematics of Information (which he directed from 2003 to 2017) and the Institute for Quantum Information and Matter. His academic appointments have included positions at UC Berkeley, the Weizmann Institute of Science, the Georgia Institute of Technology, and the Mathematical Sciences Research Institute. Schulman received his BSc in Mathematics in 1988 and his PhD in Applied Mathematics in 1992, both from the Massachusetts Institute of Technology (MIT). Schulman's research spans several overlapping areas in theoretical computer science and applied mathematics. His work focuses on algorithms and communication protocols , combinatorics and probability , coding and information theory , and quantum computation . More recently, his research has expanded into causal inference and machine learning , particularly in the areas of mixture models, causal discovery, and structure learning. His approach combines deep theoretical insights with practical applications across multiple domains. An analysis of Schulman's recent publications (2019-2025) reveals a strong focus on causal inference and machine learning, particularly in the areas of mixture models, causal discovery, and structure learning. His work bridges theoretical computer science with statistical learning, often developing novel algorithms with provable guarantees. He has also maintained his foundational work in coding theory, algorithms, and quantum computation, demonstrating remarkable breadth across theoretical computer science. IEEE Schelkunoff Prize (2004) ACM Notable Paper (2012) UAI Best Paper Award (2016) FOCS Test of Time Award (2022) S. A. Schelkunoff Transactions Prize Paper Award (2004) SIAM Fellow NSF CAREER award NSF mathematical sciences postdoctoral fellowship MIT Bucsela prize in mathematics Schulman has advised numerous PhD students and postdoctoral researchers who have gone on to successful careers in academia and industry. His former students and postdocs include notable researchers such as Ashwin Nayak, Yaoyun Shi, Sean Hallgren, Jie Gao, and Michael Langberg. He served as Editor-in-Chief of the SIAM Journal on Computing from 2013 through 2018 and has been on the editorial boards of several prestigious journals including the Journal of the ACM, ACM Transactions on Algorithms, and SIAM Journal on Discrete Mathematics. Schulman directs the Caltech Center for the Mathematics of Information, a research center focused on the mathematical foundations of information processing, communication, and computation. His work often involves interdisciplinary collaborations across computer science, mathematics, physics, and economics.

Dokyun LeeView profile

Associate Professor

• Generative AI
• Economics of Unstructured Data
• Human-AI Collaboration
• +5 more

Dokyun Lee is a Kelli Questrom Chair Associate Professor of Information Systems and Computing & Data Science at Boston University , where he founded the Business Insights through Text (BIT) Lab and co-leads the BU Digital Business Institute Generative AI Lab . His research focuses on the responsible application, development, and impact of AI in business, particularly analyzing unstructured data (e.g., text, images) across digital consumer management, platform design, and human-AI collaboration. Education : PhD in Operations, Information & Decisions (Wharton School), MS in Statistics (Yale University), BA in Computer Science (Columbia University) His work addresses Generative AI (unintended consequences, human-integration frictions), Economics of Unstructured Data (content extraction, marketing), and AI Regulation in contexts like advertising, market competition, and creativity. Recent articles explore LLMs in corporate risk nowcasting, generative AI's effects on online communities, and interpretable deep learning for churn management. His research trends span NLP, computer vision, and causal inference, with applications in digital marketing, e-commerce, and innovation analytics. Scientific Awards : INFORMS ISS Gordon B David Young Scholar, INFORMS ISS Sandy Slaughter Early Career, CDO Magazine Leading Academic Data Leader, AMA Don Lehmann Award 2024, MSBA Teaching Award He has secured grants from Adobe, Google, Microsoft, MassMutual, and Prudential Foundation. His labs (BIT Lab, Generative AI Lab) mentor students and collaborate on AI-driven solutions for business challenges.

Jeremy M G TaylorView profile

Professor

• Biostatistics
• Survival Analysis
• Longitudinal Data Analysis
• +5 more

Jeremy M G Taylor is the Pharmacia Research Professor of Biostatistics and holds professorships in the Department of Radiation Oncology in the School of Medicine and the Department of Computational Medicine and Bioinformatics at the University of Michigan. He serves as the Associate Director for Biostatistics at the Comprehensive Cancer Center and directs both the University of Michigan Cancer Center Biostatistics Unit and the Cancer/Biostatistics training program. His educational background includes: B.A. in Honours Mathematics from Cambridge University (1978) Dip. Stat. in Statistics from Cambridge University (1979) PhD in Statistics from University of California, Berkeley (1983) Professor Taylor's research focuses on the theory and application of statistics to biomedical problems, with an emphasis on data-driven, robust, and flexible statistical methods that incorporate scientific knowledge from specific research domains. His theoretical interests span Box-Cox power transformations, robust methods, causal inference, nonparametrics, and smoothing techniques. His applied work centers on cancer and AIDS research, particularly in radiation oncology, where he has developed methods involving mixture models, stochastic processes, multistate models, and multiple imputation. His extensive publication record demonstrates a continued focus on survival analysis, longitudinal data modeling, biomarker evaluation, and clinical trial design. Recent work explores time-varying effects, joint modeling approaches, multiple imputation techniques, and the integration of external information into statistical models, with applications across various cancer types including prostate, head and neck, and liver cancers. Awarded the Mortimer Spiegelman Award from the American Public Health Association, the Michael Fry Award from the Radiation Research Society, and the Jerome Sacks Award from the National Institute of Statistical Sciences, Professor Taylor has established himself as a leading biostatistician in cancer research methodology. With over 35 years of academic experience (UCLA 1983-1998, University of Michigan 1998-present), Professor Taylor has maintained an active research program while mentoring numerous students and collaborating extensively with clinical researchers. His work bridges sophisticated statistical methodology with practical applications in cancer treatment and outcomes research. As director of the Cancer Center Biostatistics Unit and the Cancer/Biostatistics training program, he leads a significant research team focused on advancing statistical methods for cancer research and training the next generation of biostatisticians.

Hongzhe LiView profile

Professor

• bioinformatics
• biostatistics
• computational biology
• +3 more

Hongzhe Li is a Professor of Biostatistics and Director of the Laboratory of Statistical Genetics and Genomics. His research spans methodological and collaborative work in bioinformatics, biostatistics, computational biology, statistical genetics, genomics, and metagenomics, with applications to complex human diseases. His primary research focuses on developing statistical methodologies for multi-omics data integration, particularly in microbiome-host interactions. Key areas include robust high-dimensional modeling, transfer learning, mediation analysis with complex mediators, and phylogenetic methods. His work addresses critical challenges in analyzing compositional data, longitudinal microbiome dynamics, and multi-tissue genetic studies, with significant applications in inflammatory bowel disease, kidney disease progression, and rare disorders like Castleman disease. Recent publications (2023-2025) demonstrate a cohesive trajectory in advancing statistical frameworks for microbiome and multi-omics research. Dominant themes include transfer learning for high-dimensional models, mediation analysis with distributional mediators, and robust covariance estimation. These methods are consistently applied to translational problems in gastroenterology, nephrology, and rare hematologic diseases, emphasizing clinical utility through prospective cohort studies and multi-omics integration. Scientific Awards: No awards mentioned in provided text. Advising and Grants: Student advising details and grant information absent from source material. Research appears driven by methodological innovation with strong clinical collaborations, particularly in IBD and kidney disease consortia. Laboratory: Leads the Laboratory of Statistical Genetics and Genomics, which develops computational frameworks for genetic and microbiome data analysis. The lab specializes in creating open-source statistical tools for high-dimensional biological data, with emphasis on reproducibility and clinical translation through partnerships with medical researchers.

• Natural Language Processing
• Semantic Parsing
• Large Language Models
• +3 more

Rui Zhang is an Assistant Professor in Computer Science and Engineering , with research expertise spanning Natural Language Processing , Large Language Models , and Semantic Parsing . His recent work focuses on enhancing multimodal consistency , fairness in summarization , and mathematical reasoning capabilities of LLMs. Key Research Themes: Text-to-SQL and cross-domain semantic parsing Multimodal learning (vision-language models) Fairness and bias mitigation in NLP tasks Efficient model training and prompt optimization Scientific Awards: National Science Foundation CAREER Award (2024) Grants & Projects: CAREER: Trustworthy Human-Centered Summarization (NSF, 2024-2029) addressing LLM trustworthiness through user-centric summarization frameworks. Article Trends: Recent publications emphasize LLM collaboration , compressed reasoning models , and cross-domain knowledge alignment . He explores multi-agent systems , mathematical reasoning , and vision-language limitations , particularly in geometric perception. Applications span bioinformatics (Alzheimer's biomarker discovery) and democratic AI frameworks.

Srijith P.K.View profile

Associate Professor

• Machine Learning
• Artificial Intelligence
• Computer Science
• +16 more

Dr. Srijith P.K. is an Associate Professor in the Department of Computer Science & Engineering at Indian Institute of Technology Hyderabad , where he leads research in probabilistic machine learning, Bayesian learning, and deep learning. He is affiliated with the BRAIN (Bayesian Reasoning And INtelligence) Lab , focusing on bridging human and machine learning through interdisciplinary applications in social networks, astrophysics, autonomous navigation, and sustainable cities. Research Interests : Probabilistic machine learning Bayesian learning Continual learning and causal learning Multi-modal learning and physics-informed neural networks Uncertainty quantification and generative modeling Scientific Awards : Sony Research Award 2021 Major Research Grants : Funding from AGC Inc, Japan, on Scientific Large Language Modeling Ministry of Education (MoE) funding for the Centre of Excellence in AI for Sustainable Cities Hyundai funding for Physics-Informed Neural Networks Sony Research India funding for Multimodal Learning JICA funding for Causal Learning in collaboration with RIKEN SERB and DST grants for projects on continual learning and astrophysical data analysis Advising : Supervises a team of students and researchers including Manisha Dubey, Rohan Tondulkar, Uddipta Bhattacharjee, Ayush Jain, Shamik Kundu, Posham Ramesh, Shikhar Sanjay, and Priyambada

Katharine SudingView profile

Professor

• Plant Community Ecology
• Restoration Ecology
• Conservation Biology
• +4 more

Katharine Suding serves as a Professor in the Department of Ecology and Evolutionary Biology at the University of Colorado Boulder, leading the Suding Lab focused on plant community ecology at the intersection of ecosystem, landscape, and population biology. Her work integrates long-term monitoring, modeling, and experimental approaches across diverse ecosystems—from alpine tundra to grasslands—with conservation groups, government agencies, and land managers to develop evidence-based solutions for restoration, species invasion, and environmental change. Her educational background includes a PhD from the University of Michigan (1999) and a BS from Williams College (1994). Key research interests center on plant-soil feedbacks, functional traits, species effects on ecosystem processes, and non-linear/threshold dynamics in ecological systems. Her work emphasizes usable science that addresses biodiversity conservation, human well-being, and management opportunities through frameworks like cross-scale facilitation and adaptive capacity for restoration. Recent publications reveal strong trends in integrating remote sensing with causal inference for biodiversity monitoring, redefining restoration intervention continua beyond active-passive dichotomies, and exploring climate change impacts on alpine phenology and soil communities. Her research consistently bridges theoretical ecology with practical applications, particularly in grassland and tundra restoration under global change. Scientific recognition includes: Bower Award and Prize for Achievement in Science (Franklin Institute, 2025) Eminent Ecologist (Journal of Ecology, 2023) Highly Cited Researcher (Web of Science, 2016-2019) MacArthur Award (Ecological Society of America, 2018) Fellow of AAAS and ESA Dr. Suding actively mentors 26 degree-seeking students (current and alumni), including PhD candidates in grassland restoration and MS students in conservation biology. Her lab secures significant grants for projects like alpine climate resilience and invasive species management, emphasizing collaborative frameworks with stakeholders. The Suding Lab maintains a strong commitment to inclusive science through field safety protocols, peer mentorship, and equitable opportunity strategies, operating from CU Boulder's Sustainability, Energy and Environment Community building. The lab fosters interdisciplinary teams studying critical transitions in ecological systems, with current projects spanning from Colorado grasslands to tropical restoration. Future work focuses on scaling restoration science through remote sensing integration and developing adaptive capacity metrics for climate-vulnerable ecosystems.

• Causal Inference
• Machine Learning
• High Dimensional Data Analysis
• +3 more

Jincheng Shen is an Assistant Professor in the Department of Population Health Sciences at the University of Utah, with adjunct roles in the Departments of Family & Preventive Medicine and Internal Medicine. He completed his PhD in Biostatistics at the University of Michigan, Ann Arbor, postdoctoral training at Harvard T.H. Chan School of Public Health, and holds a BS and MS in Computer Science from Tsinghua University. His methodological research focuses on causal inference , machine learning , high-dimensional data analysis , and statistical genetics . Applications span cancer studies, cardiovascular health, opioid use disorder, and chronic disease management. He actively collaborates with clinicians, biologists, and epidemiologists, particularly in analyzing longitudinal data from trials like SPRINT and AASK. Recent publications (2024–2025) highlight his work in optimizing dynamic treatment regimes for diabetes, cardiovascular disease, and cancer, using machine learning and epigenetic biomarkers . He also contributes to school-based interventions for skin cancer prevention and community pharmacy protocols for opioid misuse. Collaborations include large-scale studies on DNA methylation , aging , and environmental exposures , with applications in COPD, cardiovascular outcomes, and cancer risk prediction. His interdisciplinary approach bridges statistical methodology with real-world clinical and public health challenges.

• Biostatistics
• Neuroscience
• Cancer Research
• +3 more

Shengxian Ding is a Postdoctoral Associate at the Yale School of Public Health , focusing on Biostatistics, Neuroscience, and Public Health . Her research develops advanced statistical models for biomedical data analysis. Her work includes 2025: Subgroup Mediation Analysis , 2024: Shape Mediation in Alzheimer’s Disease , and 2023: Tumor Growth Quantification via MRI , reflecting expertise in Regression Models, Neuroimaging, and Computational Biology . Contact: naomi.ding@yale.edu

Filipe RodriguesView profile

Associate Professor

• Machine Learning
• Intelligent Transportation Systems
• Transportation Science
• +7 more

Filipe Rodrigues is an Associate Professor in the Department of Technology, Management and Economics at the Technical University of Denmark (DTU), where he conducts research in intelligent transportation systems and transportation science. His work integrates machine learning, artificial intelligence, and behavioral modeling to improve urban mobility and public transport systems. His research interests lie at the intersection of machine learning , transportation science , and behavioral modeling . He specializes in discrete choice modeling , reinforcement learning , graph neural networks , and smart card data analytics . His work contributes to sustainable urban mobility, leveraging big data and AI for proactive traffic control and public transport optimization. The recent publications highlight a strong trend toward integrating AI and behavioral science in transportation. Key themes include ride-sourcing driver behavior , public transport trip validation , autonomous fleet control , and causal machine learning . These works predominantly employ deep learning , Bayesian modeling , and offline reinforcement learning techniques, often applied to real-world datasets from Denmark and beyond. Scientific Contributions: Active contributor to journals like Transportation Research Part C and Journal of Choice Modelling . Supervises multiple PhD projects on AI in transportation and causal modeling. Regular presenter at major transportation and AI conferences. Advising and Grants: Filipe Rodrigues is the main or co-supervisor of several PhD students including O. B. Lassen, F. M. F. Santos, A. Nguyen, and X. Wu. He leads and participates in funded research projects such as 'Proactive traffic control through AI and Big Data' and 'Causal Graph Neural Networks for machine learning meta-modelling', indicating sustained grant support. His collaborative network spans institutions in Europe and beyond. Labs and Teams: He is part of the Intelligent Transportation Systems research group at DTU, collaborating closely with researchers like F. C. Pereira and C. M. L. Azevedo. The team focuses on data-driven mobility solutions, combining simulation, machine learning, and behavioral insights.