Find Academic Supervisors

Raymond J. MooneyView profile

Professor

• Artificial Intelligence
• Bioinformatics and Computational Biology
• Machine Learning
• +14 more

Raymond J. Mooney is a Professor in the Department of Computer Science at the University of Texas at Austin, where he has been a faculty member since 1987. He is the Director of the UT Artificial Intelligence Laboratory and affiliated with multiple research groups including the Machine Learning Research Group, UT Computational Linguistics Lab, and the UT Center for Computational Biology and Bioinformatics. He holds a B.S., M.S., and Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign, where his thesis was supervised by Gerald DeJong. His research spans diverse areas in artificial intelligence, machine learning, and natural language processing: Natural Language Learning Connecting Language and Perception Statistical Relational Learning Information Extraction Transfer and Active Learning Abductive Reasoning Text Mining and Clustering Recommender Systems Knowledge-Base Refinement Recent publications highlight trends in grounded language processing, human-robot interaction, and multimodal reasoning. He has been recognized with prestigious fellowships including ACL (2014), ACM (2010), and AAAI (2005). Scientific awards: Fellow of the Association for Computational Linguistics (2014) Fellow of the Association for Computing Machinery (2010) Fellow of the American Association for Artificial Intelligence (2005) Classic Paper Award (2019) Best Paper Awards (2007, 2004, 1996) He teaches graduate courses like CS 371R: Information Retrieval and Web Search (Fall 2025) and CS 395T: Grounded Natural Language Processing (Spring 2025). His research labs include: UT Artificial Intelligence Laboratory Machine Learning Research Group UT Computational Linguistics Lab UT Center for Computational Biology and Bioinformatics

Hassan Z. AshtianiView profile

Associate Professor

• Machine Learning Theory
• Privacy-Preserving Algorithms
• Gaussian Mixture Models
• +5 more

Hassan Z. Ashtiani is an Associate Professor in the Department of Computing and Software within the Faculty of Engineering at McMaster University. His academic profile shows consistent engagement in both teaching and research activities, with evidence of active participation in major machine learning conferences and journals through 2025. Dr. Ashtiani's research focuses on the theoretical foundations of machine learning, with particular expertise in privacy-preserving algorithms, Gaussian mixture models, and adversarial robustness. His work bridges statistical learning theory with practical algorithm design, often addressing fundamental questions about sample complexity and computational efficiency in learning systems. A significant portion of his recent work explores the intersection of differential privacy with statistical learning, developing methods for private density estimation and distribution learning. Analysis of his publication record reveals a strong trend toward increasingly sophisticated theoretical frameworks for private and robust learning. His work consistently appears in top-tier venues including NeurIPS, ICML, COLT, and ALT, with recent contributions extending into agnostic private density estimation and robust learning with tolerance. The research demonstrates progression from foundational work on nearest neighbor search and clustering algorithms toward more complex problems in private learning of high-dimensional distributions. Dr. Ashtiani teaches across multiple levels of computer science education, including undergraduate courses in Automata and Computability (COMPSCI 2AC3) and Principles of Programming (COMPSCI 2S03), as well as graduate-level courses such as Fundamentals of Machine Learning (COMPSCI 4ML3) and Theoretical Foundations of Unsupervised Learning (CAS 775). His teaching portfolio shows consistent involvement in machine learning education since at least 2019, with evidence of teaching multiple sections each academic year. His scholarly impact is reflected in mentions across 3 news outlets, reference in 1 policy source, engagement from 7 X users, and 90 readers on Mendeley, suggesting growing recognition of his contributions to theoretical machine learning.

Sainyam GalhotraView profile

Assistant Professor

• Databases
• Data Management
• Responsible Data Science
• +7 more

Sainyam Galhotra is an Assistant Professor in the Department of Computer Science at Cornell University. His research focuses on developing data science tools for effective and responsible analytics, leveraging techniques from causal inference, data management, theoretical computer science, machine learning, and human-computer interaction to address challenges in trustworthy system design including robustness, explainability, and fairness. Education: Postdoc: University of Chicago PhD: University of Massachusetts Amherst (supervised by Barna Saha) BTech: Indian Institute of Technology Delhi (IIT Delhi) (supervised by Prof. Amitabha Bagchi) Research Interests: Dr. Galhotra's research spans several interconnected areas in data science and artificial intelligence. His work primarily focuses on Responsible Data Science , where he develops methods to ensure that data-driven systems operate fairly and transparently. Within this broad area, his specific interests include: Causal Inference techniques for understanding cause-effect relationships in complex data Algorithmic Fairness approaches to mitigate bias in machine learning systems Explainable AI methods that make black-box models more interpretable Data Management systems for efficient and reliable data processing Entity Resolution techniques for integrating data from multiple sources Trustworthy System Design that addresses robustness, explainability, and fairness His recent publications demonstrate a clear trend toward developing frameworks that combine causal reasoning with practical data management systems, particularly focusing on how to make data-driven decisions more transparent and equitable. The intersection of database systems with fairness considerations appears to be a particularly active area of his research. Scientific Awards: Rising Star in Data Science at the Data Science Institute, UChicago (Oct 2021) Computing Innovation Fellowship Award Recipient (by CRA, CCC and NSF) (Apr 2021) DAAD AInet Fellow (Feb 2021) ACM SIGMOD Entity Resolution Programming Contest – Top 5 finalist (May 2020) Most reproducible paper award in SIGMOD 2018 and 2019 (Jun 2019) First recipient of Krithi Ramamritham Computer Science Scholarship (Jun 2019) Best paper award in SIGSOFT FSE 2017 (May 2017) Dr. Galhotra is actively seeking students to collaborate with on his research projects. His work has been supported by various fellowships and awards, including the prestigious Computing Innovation Fellowship. He has mentored several students through his research projects, with a focus on developing the next generation of data scientists who can build responsible and trustworthy systems. His research group appears to focus on the intersection of database systems and responsible AI, developing tools like HypeR for causal reasoning, Ver for view discovery, and Nexus for correlation discovery in spatio-temporal data. This work suggests a cohesive research agenda centered around making data systems more transparent, fair, and user-friendly.

Byron BootsView profile

Professor

• Machine Learning
• Artificial Intelligence
• Robotics
• +5 more

Byron Boots is the Amazon Professor of Machine Learning in the Paul G. Allen School of Computer Science and Engineering at the University of Washington, where he directs the UW Robot Learning Laboratory. He also serves as a Principal Research Scientist in the Seattle Robotics Lab at NVIDIA Research and co-chairs the IEEE Robotics and Automation Society Technical Committee on Robot Learning. Dr. Boots received his Ph.D. from the Machine Learning Department in the School of Computer Science at Carnegie Mellon University, where he was a member of the Sense, Learn, Act (SELECT) Lab co-directed by Carlos Guestrin and his advisor Geoff Gordon. Prior to joining the University of Washington faculty, he was an Assistant Professor in the School of Interactive Computing within the College of Computing at Georgia Tech, and before that, he completed a post-doc in the Robotics and State Estimation Lab directed by Dieter Fox at the University of Washington. Professor Boots' research focuses on the intersection of machine learning, artificial intelligence, and robotics, with particular emphasis on developing theory and systems that tightly integrate perception, learning, and control. His work spans computer vision, state estimation, localization and mapping, high-speed navigation, motion planning, and robotic manipulation. His group develops algorithms drawing from deep learning and neural networks, nonparametric statistics, graphical models, nonconvex optimization, quantum physics, online learning, reinforcement learning, and optimal control. The research demonstrates a strong theoretical foundation while maintaining practical relevance to real-world robotic systems. His recent publications reveal a clear trend toward integrating advanced machine learning techniques with robotics, particularly in model predictive control, motion planning, and learning-based approaches to robot control. His work shows increasing focus on developing theoretically grounded methods that can handle the complex, nonlinear dynamics of real-world robotic systems while maintaining computational efficiency. The publications span top venues including ICRA, CoRL, IROS, and NeurIPS, demonstrating broad impact across multiple subfields of robotics and AI. Finalist for Best Systems Paper at Conference on Robot Learning (CoRL-2021) Multiple papers selected for oral presentations at top robotics conferences Work recognized for theoretical contributions and practical applications in robot learning As director of the UW Robot Learning Laboratory, Boots leads a vibrant research group focused on fundamental and applied research in robot learning. The lab maintains strong collaborations with NVIDIA Research and has produced numerous high-impact publications that bridge theory and practice. Professor Boots teaches courses in autonomous robotics, machine learning, and reinforcement learning, contributing to both undergraduate and graduate education at the University of Washington.

• Artificial Intelligence
• Machine Learning
• Deep Learning
• +3 more

Maciej Zięba is an academic researcher affiliated with the Faculty of Information and Communication Technology at Wrocław University of Science and Technology, specifically within the Department of Artificial Intelligence . His work spans machine learning, deep learning, and computer vision, with a focus on hyperspectral imaging, autonomous systems, and 3D modeling. Recent research includes uncertainty-aware sensor deployment for autonomous vehicles, low-light image enhancement algorithms, and probabilistic regression frameworks for tabular data. He has co-authored publications on flow-based models, hypernetworks, and neural radiance fields (NeRF) applied to 3D face rendering. Contact: maciej.zieba@pwr.edu.pl

Irina RishView profile

Professor

• Machine Learning
• Neural Scaling Laws
• Emergent Behaviors in AI
• +13 more

Irina Rish is a Full Professor at the Université de Montréal and a core academic member of Mila – Quebec Artificial Intelligence Institute, where she leads the Autonomous AI Lab. She holds a Canada Excellence Research Chair (CERC) and a CIFAR AI Chair, reflecting her leadership in foundational AI research. Her work is supported by major initiatives, including the U.S. Department of Energy’s INCITE project on Summit and Frontier supercomputers. PhD in AI, University of California, Irvine MSc in AI, University of California, Irvine MSc in Applied Mathematics, Moscow Gubkin Institute Her research focuses on machine learning, neural scaling laws, emergent behaviors in foundation models, continual learning, robustness, and neuroscience-inspired AI . She explores how AI systems can become more general, flexible, and aligned with human cognition. Her recent work investigates training dynamics in large language models, efficient pruning techniques, and the development of time-series foundation models. The analysis of her recent publications reveals a strong focus on scaling behaviors, continual adaptation, and robustness in AI systems . Her work spans theoretical understanding of training dynamics (e.g., zero-sum learning), practical optimization methods, and applications in climate modeling and mental health. She emphasizes open science, leading open-source projects and co-founding Nolano.ai to build efficient, compressed foundation models. Canada Excellence Research Chair (CERC) CIFAR AI Chair IBM Eminence & Excellence Award (2018) IBM Outstanding Innovation Award (2018) IBM Outstanding Technical Achievement Award (2017) IBM Research Accomplishment Award (2009) Irina Rish advises a large group of PhD and Master’s students across Université de Montréal, McGill, and Concordia. She leads major research grants and collaborates internationally on HPC-based AI research. She is also the co-founder and CSO of Nolano.ai, driving innovation in efficient AI systems. She leads the Autonomous AI Lab, which focuses on building large-scale foundation models, understanding neural scaling laws, and developing bio-inspired learning systems. She actively organizes reading groups on scaling, continual learning, and out-of-distribution generalization, fostering a collaborative research environment.

Mustafa BilgicView profile

Professor

• Artificial Intelligence
• Machine Learning
• Active Learning
• +5 more

Mustafa Bilgic is a Professor and Chair of the Computer Science Department at Illinois Institute of Technology, where he also directs the Master of Artificial Intelligence program and the Machine Learning Laboratory. His research focuses on machine learning, active learning, explainable AI, and probabilistic graphical models, with applications in healthcare, social media analysis, and biomedical engineering. He has received funding from NSF, NIH, and Samsung, among others. Education: PhD in Computer Science, University of Maryland at College Park (2010) M.S. in Computer Science, University of Maryland at College Park (2006) B.S. in Computer Science, University of Texas at Austin (2004, with High Honors and Special Honors) Research Highlights: Dr. Bilgic's work emphasizes AI ethics, algorithm transparency, and interactive machine learning systems. Notable projects include analyzing political news engagement dynamics and developing frameworks for eliminating explanation noise in AI models. His lab explores tools like OrganoID for tracking organoid growth and IDGI for improving model interpretability. Awards: NSF CAREER Award (2014) ACM SIGKDD Best Student Paper Award (2008) Illinois Tech College of Computing Teaching Excellence Award (2021) Teaching and Leadership: Bilgic teaches advanced courses in AI, machine learning, and data mining. He leads initiatives to bridge AI theory and practical applications, emphasizing interdisciplinary collaboration. His administrative roles include overseeing the AI master’s program and fostering innovation in computing education.

Robert PatroView profile

Associate Professor

• Computational Biology
• Genomics
• Algorithms
• +8 more

Rob Patro is an Associate Professor in the Department of Computer Science at the University of Maryland, with an appointment at the University of Maryland Institute for Advanced Computer Studies. His work bridges computational biology and computer science, focusing on algorithm design and data structures for genomics applications. Education: Ph.D. in Computer Science, University of Maryland, College Park (2012) B.S. in Computer Science, University of Maryland, College Park (2006) with academic and departmental honors Patro’s research centers on developing computational methods for analyzing high-throughput genomics data, combining algorithmic innovation with statistical inference. His work extends to programming languages, parallel computing, and machine learning applications in biology. His publications at ISMB, RECOMB, and in journals like Nature Methods and Cell Systems highlight advancements in RNA-seq quantification, metagenomic analysis, and compressed genomic representations. These contributions emphasize efficiency and scalability in genomic data processing.

Qi LongView profile

Professor

• Genomics
• Computational Biology
• Biostatistics
• +10 more

Qi Long is a Professor at the University of Pennsylvania, holding joint appointments in the Department of Biostatistics, Epidemiology and Informatics (Perelman School of Medicine), Department of Computer and Information Science (School of Engineering and Applied Science), and Department of Statistics and Data Science (The Wharton School). He serves as Founding Director of the Center for Cancer Data Science, Associate Director of the Penn Institute for Biomedical Informatics, and Associate Director for Quantitative Data Science at the Abramson Cancer Center. His research bridges statistical and machine learning (ML/AI) method development with biomedical applications, focusing on precision medicine and population health. Education : Ph.D. (2005) and M.S. (2003) in Biostatistics from University of Michigan; B.S. (1998) in Computer Science from University of Science and Technology of China. Research interests include: Robust statistical and ML/AI methods for big health data (-omics, EHRs, imaging, mHealth) Multimodal data integration and subgroup heterogeneity analysis Missing data, causal inference, Bayesian methods, and clinical trials Data privacy, algorithmic fairness, and responsible AI in healthcare Foundation models and agentic AI for biomedicine His publications focus on privacy-preserving AI, fairness-aware ML, and integrative models for multi-omics and EHRs. Recent work explores LLMs and watermark detection in hybrid human-AI settings. Scientific Awards : Elected fellow: AAAS, ASA, IMS, ISI, AMIA He leads large NIH- and ARPA-H-funded initiatives, directing statistical coordinating centers for national clinical trials. His lab trains numerous PhD/Master’s students and postdocs, many of whom hold prestigious academic or industry positions.

• Machine Learning
• Artificial Intelligence
• Neural Networks
• +2 more

Geoffrey Hinton is a Professor in the Department of Computer Science at the University of Toronto , where he has been a pivotal figure in advancing artificial intelligence research. His work focuses on neural networks, deep learning, and machine learning, revolutionizing how machines process information and learn from data. With collaborations spanning institutions like NYU and IIT Mumbai, Hinton’s influence extends beyond academia into public discourse through lectures like the Romanes Lecture (2024) . His research explores Deep Belief Networks , Gradient Methods , Neural Network Architectures , and Probabilistic Models , with recent publications addressing novel algorithms like the Forward-Forward Algorithm and frameworks for Panoptic Segmentation . Though he no longer accepts students, current advisees include Jimmy Lei Ba and Cem Anil. Hinton’s contributions to AI are complemented by media engagements, including CBS 60 Minutes (2023) and CNN Amanpour (2023) , reflecting his role as a thought leader. His technical outputs, such as Nature Deep Learning Review (2015) with Y. LeCun and Y. Bengio, remain foundational texts in the field.

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

Niels Richard Hansen is a Professor at the Department of Mathematical Sciences , University of Copenhagen, leading research at the intersection of Artificial Intelligence and Statistics . He co-founded the Copenhagen Causality Lab and focuses on automating causal explanation discovery from data using Bayesian networks, stochastic processes, predictive models, and machine learning. His work emphasizes creating interpretable and robust AI systems capable of generalizing across domains. His research has produced over 56 publications spanning causal inference , graphical modeling , stochastic processes , and machine learning . Recent work includes: Predictive and causal learning (2018 keynote) High-dimensional regression solutions (2016 lecture) Interdisciplinary applications in actuarial science , environmental statistics , and neuroscience He actively contributes to scientific communication through media appearances and public explanations of statistical concepts, including analyses of: Gaussian correlation inequality proofs Daylight saving time and blood clots Mathematical approaches to lotteries Climate change vs lunar effects

Sriraam NatarajanView profile

Professor

• Artificial Intelligence
• Machine Learning
• Healthcare Applications
• +5 more

Sriraam Natarajan is a Professor and Director of the Center for Machine Learning and StaRLing Lab at The University of Texas at Dallas (UTD), part of the Erik Jonsson School of Engineering & Computer Science. He holds additional roles as a hessian.AI Fellow at TU Darmstadt and an RBCDSAI Distinguished Faculty Fellow at IIT Madras. His expertise spans Artificial Intelligence, Machine Learning, and their applications in healthcare, with a focus on Relational Learning, Reinforcement Learning, and Graphical Models. He has been honored as an AAAI Fellow (2025), elected to the AAAI Executive Council, and recognized with the UTD Outstanding Graduate Teaching Award. Education: Completed his PhD in Computer Science at Oregon State University in 2007 under Prof. Prasad Tadepalli. Postdoctoral work at the University of Wisconsin-Madison with Professors Jude Shavlik and David Page. Previously served as faculty at Indiana University and Wake Forest School of Medicine. Research: Active in developing AI systems for healthcare, including predictive models for gestational diabetes and cardiac arrest in children. His work emphasizes integrating human knowledge into machine learning (e.g., Human-in-the-Loop systems) and advancing statistical relational AI frameworks like Markov Logic Networks and Probabilistic Circuits. Publications: Over 100 peer-reviewed articles, including notable works on causal learning, relational reinforcement learning, and knowledge graph construction. Recent focuses include explainable AI and scalable probabilistic models. Awards: AAAI Fellow, RBCDSAI Distinguished Fellowship, UTD Teaching Excellence Award. Advising and Collaboration: Mentored over 30 students, many now in academia and top institutions like IBM Research, Facebook, and Microsoft. Collaborates globally on projects like GLAD (Glocalized Anomaly Detection) and StaRLing Lab initiatives. Labs/Teams: Leads the StaRLing Lab, focusing on statistical relational AI, and directs UTD's Center for Machine Learning. Engaged in interdisciplinary projects with healthcare, robotics, and data science communities.

Paul Neville BalisterView profile

Professor

• Combinatorics
• Graph Theory
• Discrete Mathematics
• +4 more

Professor Paul Neville Balister is a faculty member and Fellow in the Mathematical Institute at the University of Oxford, where he serves as a University Lecturer and Professor of Mathematics. His research is centered in combinatorics and discrete mathematics, with strong ties to probabilistic methods and theoretical computer science. His research interests include combinatorics, graph theory, random graphs, probabilistic combinatorics, cellular automata, and discrete probability. These areas are evident from his extensive publication record in top-tier mathematical journals such as the Journal of the European Mathematical Society and Random Structures and Algorithms . The recent publications demonstrate a consistent focus on structural and probabilistic aspects of discrete systems, particularly in monotone cellular automata, graph saturation games, and random graph orderings. His work often explores threshold phenomena, extremal configurations, and asymptotic behavior in combinatorial models. While no formal awards are listed in the provided text, his active collaboration with leading mathematicians and publication in premier venues indicate significant recognition in the field. Professor Balister advises students and contributes to research grants, though specific names and projects are not detailed. He is part of the Combinatorics research group at Oxford, which fosters collaborative work in discrete mathematics and its applications.

Aryeh KontorovichView profile

Professor

• Theoretical Machine Learning
• Probability
• Statistics
• +4 more

Aryeh Kontorovich is a Professor in the Computer Science Department at Ben-Gurion University. His research primarily focuses on theoretical machine learning, with expertise in probability, statistics, Markov chains, and metric spaces. His research interests span theoretical machine learning, with particular emphasis on: Probability theory and concentration inequalities Statistical learning theory Markov chains and mixing time estimation Metric space learning Kernel methods Sample compression schemes Professor Kontorovich's recent publications (2021-2025) demonstrate a continued focus on theoretical foundations of machine learning. His work shows strong trends in statistical estimation for Markov processes, distribution learning, metric space analysis, and sample compression. Many papers explore the intersection of probability theory and machine learning, particularly examining concentration inequalities, minimax optimality, and theoretical guarantees for learning algorithms. His research consistently bridges abstract mathematical theory with practical machine learning applications. Scientific awards and recognitions: Distinguished contribution award at MLG 2007 for "A Universal Kernel for Learning Regular Languages" Professor Kontorovich has advised numerous students and collaborated extensively with researchers in theoretical machine learning. His work spans both theoretical foundations and practical applications, with significant contributions to understanding the mathematical limits of learning algorithms. While specific grant information isn't provided in the source material, his extensive publication record in top venues suggests successful funding for his research programs. He maintains active collaborations with researchers worldwide, including prominent names like L. Gottlieb, D. Berend, and S. Hanneke.

Tao ZouView profile

Associate Professor

• Covariance regression modeling
• Network data modeling
• Financial statistics
• +3 more

Tao Zou is an Associate Professor at the Research School of Finance, Actuarial Studies and Statistics, Australian National University. His research spans covariance regression modeling, network data analysis, and applications in financial and environmental statistics. He earned a Ph.D. in Statistics in 2016. Ph.D. in Statistics, 2016 Dr. Zou’s work pioneers covariance regression, where covariances are modeled as functions of covariiates. Key contributions include robust estimation techniques, spatio-temporal modeling, missing data imputation via semi-supervised learning, and distributed data aggregation. His methods address challenges in high-dimensional and non-Euclidean data analysis. Recent publications (2025–2023) explore quasi-score matching for spatial autoregressive models, regularization in network regression, functional principal component analysis for complex data, and environmental applications like PM2.5 pollution studies. These works emphasize robustness, scalability, and interdisciplinary relevance in economics, finance, and environmental science. Dr. Zou collaborates on projects like the 2023 Data Analysis App to Empower Assessment of Immunogenicity of Biologics (Co-Investigator). While his student supervision list isn’t explicitly provided, his methodological advancements influence big data and spatial statistics. He contributes to open-access software and continues expanding covariance regression for non-normal and functional data.