Find Academic Supervisors

Daniel HuttenlocherView profile

Professor

• Computer Vision
• Social Networks
• Autonomous Vehicles
• +4 more

Daniel Huttenlocher is the John P. and Rilla Neafsey Professor of Computing, Information Science, and Business at Cornell University, where he serves as Dean of Computing and Information Science. Concurrently, he holds the position of Dean at MIT's Stephen A. Schwarzman College of Computing and is Vice Provost of Cornell Tech. His academic roles span Cornell's Computer Science Department and Johnson Graduate School of Management. Huttenlocher's research encompasses diverse domains including computer vision (image matching, object recognition, video analysis), social networks (governance models, link prediction, community dynamics), autonomous systems (DARPA Urban Challenge), geometric algorithms , and interactive document systems . His work bridges theoretical algorithms and practical applications, with recent advancements in structured models for visual recognition and large-scale network analysis. His publications (2010-2006) demonstrate a consistent focus on algorithmic innovation in computer vision and social computing. Recent works emphasize real-world applications like location recognition, social media governance, and image restoration, employing machine learning, probabilistic modeling, and large-scale data mining techniques across diverse visual and network datasets. While no awards are listed, Huttenlocher co-chaired major conferences (CVPR 2006/2009) and serves on the MacArthur Foundation Board. He has developed educational tools like the CoNote annotation system to foster collaborative learning and chaired Cornell's computing initiatives leading to the Faculty of Computing and Information Science.

Kayhan BatmanghelichView profile

Assistant Professor

• Medical Vision
• Machine Learning
• Bioinformatics
• +11 more

Kayhan Batmanghelich is an Assistant Professor in the Department of Electrical and Computer Engineering at Boston University , where he also holds affiliations as a Hariri Institute Junior Faculty Fellow and AIR Affiliate . Previously, he served as an Assistant Professor at the University of Pittsburgh's Department of Biomedical Informatics with a secondary appointment in their School of Computing and Information. Education : Ph.D. in Computer Science from the University of Pennsylvania (2012) Research Interests : Focus on overcoming AI challenges in healthcare, including Explainability, Data Efficiency, Multimodal Data Fusion, and Causality. His research develops AI systems for medical imaging, genetics, and electronic health records, particularly targeting diseases like Chronic Obstructive Pulmonary Disease (COPD) , Alzheimer’s Disease , and Non-Alcoholic Fatty Liver Disease (NAFLD) . Key innovations include biomechanically-informed neural networks for lung tissue stiffness estimation and graph-based AI for multi-scale pulmonary disease analysis. Recent publications highlight multimodal frameworks for COPD subtyping, vision-language models in mammography, and diffusion models for 3D CT synthesis. His work has been recognized with the NSF CAREER Award and Google Academic Research Award (GARA) , alongside Hariri Institute grants. Awards : NSF CAREER Award (2025) Google Academic Research Award (2024) Hariri Focused Research Program Awards (2024) His BatmanLab trains students in medical AI and collaborates with institutions like the University of Pittsburgh School of Medicine and Brigham and Women’s Hospital. Funding sources include NIH, NSF, and industry partnerships.

Heike HofmannView profile

Professor

• Data Visualization
• Statistical Computing
• Machine Learning
• +4 more

Heike Hofmann is a Professor at the University of Nebraska-Lincoln. Her work focuses on data visualization, statistical computing, and forensic statistics, with a strong emphasis on developing open-source software tools for data exploration in R. She actively contributes to academic research and software development. Current Institution: University of Nebraska-Lincoln Email: hofmann@iastate.edu Her research spans topics such as forensic toolmark analysis , machine learning explainability , and interactive visualization frameworks . She designs algorithms for bullet comparison and error rate assessment in forensic science, leveraging 3D imaging and statistical diagnostics . Recent publications highlight her work on human factors in visual inference , automated forensic matching , and reproducible pipelines for functional data. Notably, she creates tools like the cmcR package for bullet land scan comparison and nullabor for statistical significance calculations. Heike Hofmann contributes to software development with R packages including ggparallel (parallel coordinate plots), gglogo (sequence logos), and rotations (3D rotation analysis). Her GitHub activity shows consistent commits to statistical computing projects.

Petar StojanovView profile

Research Fellow

• Machine Learning
• Computational Biology
• Transfer Learning
• +3 more

Petar Stojanov is a Research Fellow at the Broad Institute of MIT and Harvard , working under the supervision of Prof. Gad Getz. His academic journey includes a PhD in Computer Science from Carnegie Mellon University , where he was advised by Jaime Carbonell and Kun Zhang. Prior to his doctoral studies, he served as an associate computational biologist in the Getz Lab. Research Focus : Machine learning, computational biology, causal inference, and domain adaptation. Key Contributions : Applying causal discovery to genomic analysis of cancer mutations and single-cell RNA sequencing data. His work bridges computational methodologies with biological applications, particularly in understanding cancer progression through causal relationships. Notable Collaborations : Rheinbay et al. (Nature 2017), Haradhvala et al. (Cell 2016), Crompton et al. (Cancer Discovery 2014)

• Environmental Psychology
• Social Psychology
• Pro-environmental Behavior
• +5 more

MARÍA DEL CARMEN AGUILAR LUZÓN is a researcher affiliated with the University of Granada, Department of Psychology. Her work bridges Environmental Psychology and Social Psychology, focusing on pro-environmental behavior, recycling practices, and collective action frameworks. University: University of Granada Department: Department of Psychology Academic Rank: Researcher Her research applies the Theory of Planned Behavior and Value-Belief-Norm models to predict recycling behavior, assess pandemic-related mental health impacts, and explore chatbot applications in education. Recent studies highlight gender differences in environmental concern, microlearning methodologies, and the role of threat perception in climate action. Articles demonstrate a multidisciplinary focus, including network restructuring via Markov Random Fields, psychometric validation of environmental scales, and emotional intelligence's influence on occupational stress in healthcare settings. No scientific awards are explicitly mentioned in the provided text.

Tamir HazanView profile

Associate Professor

• Machine Learning
• Theoretical Machine Learning
• Computer Vision
• +6 more

Associate Professor Tamir Hazan is a faculty member at Technion - Israel Institute of Technology, where he joined in 2015. His research focuses on theoretical and practical aspects of machine learning, with applications spanning computer vision, natural language processing, and computational biology. His work bridges mathematical foundations with real-world problem solving in complex systems. Professor Hazan received his Ph.D. from the Hebrew University in 2009. His academic trajectory has established him as a leading researcher in machine learning theory and its applications, with a particular emphasis on developing mathematically rigorous approaches to modern AI challenges. Professor Hazan's research centers on mathematically founded solutions to problems demonstrating non-traditional statistical behavior. His work encompasses perturbation models for efficient learning of high-dimensional statistics, deep learning of infinite networks, and primal-dual optimization for high-dimensional inference problems. His research program spans three major interconnected areas: attention models that improve prediction interpretability, perturbation frameworks that integrate optimization and sampling through extreme value statistics, and convex duality approaches to message-passing in graphical models. His work demonstrates both theoretical depth and practical relevance across multiple domains. Analysis of Professor Hazan's recent publications reveals an evolving research trajectory with increasing emphasis on interpretable machine learning, causal modeling, and applications in medical imaging and behavioral science. His work consistently bridges theoretical foundations with practical implementations, with recent publications showing strong connections between perturbation theory, attention mechanisms, and optimization frameworks. The interdisciplinary nature of his research is evident in applications ranging from pedestrian navigation using smartphone sensors to video-text matching systems and medical image analysis. Professor Hazan has mentored numerous students throughout his career, including: Alex Schwing, now Assistant Professor at UIUC Alon Cohen, now Associate Professor at Tel Aviv University Idan Schwartz, currently Postdoc at Tel Aviv University Current Ph.D. students: Guy Lorberbom, Itai Gat, and Hedda Cohen Multiple M.Sc. students including Adi Manos, Ram Yazdi, and others Professor Hazan's research group maintains an active program with several key focus areas: Attention models for interpretable and improved prediction processes in visual question answering and multimodal applications Perturbation models that enable efficient statistical reasoning in complex systems with exponential configuration spaces Markov random fields, convex duality, and message-passing algorithms for structured prediction and distributed computing

Vadim IndelmanView profile

Associate Professor

• Autonomous Systems
• Robotics
• Probabilistic Perception
• +5 more

Vadim Indelman serves as an Associate Professor in the Department of Aerospace Engineering at the Technion – Israel Institute of Technology, affiliated with the Technion Autonomous Systems Program (TASP) and Machine Learning and Intelligent Systems (MLIS) Center. He earned his B.A. (Cum Laude) in Computer Science and B.Sc. (Summa Cum Laude) in Aerospace Engineering from the Technion in 2002, completed his PhD in Aerospace Engineering there in 2011 under Pini Gurfil, Ehud Rivlin, and Hector Rotstein, and conducted postdoctoral research at Georgia Tech's Institute of Robotics and Intelligent Machines (2012-2014). His research focuses on probabilistic perception and state estimation for autonomous systems operating in uncertain dynamic environments, with core contributions in SLAM, vision-aided navigation, distributed information fusion, and belief-space planning for multi-agent systems. This work enables reliable real-time operation through probabilistic graphical models and active sensing methodologies. Indelman holds significant editorial roles including Associate Editor for IEEE Robotics & Automation Letters since 2017, Senior Editor for IROS (2021-2023), Area Chair for MRS 2021, and co-chair of IEEE RAS Technical Committee on Planning and Control Algorithms since 2019.

Jean-Baptiste MassonView profile

Researcher

• Computational Neuroscience
• Bayesian Decision Theory
• Biophysics
• +4 more

Jean-Baptiste Masson is a tenured Researcher at the Pasteur Institute since 2008. His work bridges Neuroscience , Biophysics , and Machine Learning , focusing on understanding decision-making processes in biological systems and developing innovative 3D visualization tools like DIVA and Genuage for medical imaging and single-molecule analysis.

Concha BielzaView profile

Professor

• Probabilistic Graphical Models
• Decision Analysis
• Metaheuristics for Optimization
• +7 more

Concha Bielza is a Full Professor of Statistics and Operations Research at the Department of Artificial Intelligence, Technical University of Madrid, since 2010. Her academic journey began with an M.S. in Mathematics from Universidad Complutense de Madrid (1989) and a Ph.D. in Computer Science from Technical University of Madrid (1996), where she received the extraordinary doctorate award. Her research focuses on Probabilistic graphical models Decision analysis Metaheuristics for optimization Data mining and classification models Applications in biomedicine, bioinformatics, neuroscience, industry, and sport analytics Recent publications emphasize Bayesian networks for dynamic microbial community simulation Advancements in Estimation of Distribution Algorithms Quantum computing integration with probabilistic models Feature selection in data streams Causal reinforcement learning in industrial contexts Semiparametric methods for optimization Explainability in Bayesian networks She has been recognized with 2014 UPM Research Prize 2020 Research Award in Machine Learning (India) 2024 National Award of Statistics (Spain) ELLIS Fellow (2023) Asia-Pacific Artificial Intelligence Association Fellow (2024) Bielza has supervised 23 PhD theses and contributes to scientific governance as a member of NorwAI's Scientific Advisory Board (2021). Her work spans theoretical advancements and real-world applications, with over 160 impact factor publications.

• Computer vision and multimedia computation
• Machine learning
• Graphics, augmented reality and games

Nicolas Heess is an Honorary Professor affiliated with the Department of Computer Science at University College London. His research focuses on Machine Learning and Computer Vision , with a particular emphasis on generative models and their applications in image analysis. His publication history reveals a commitment to advancing probabilistic modeling techniques, including Boltzmann Machines and Restricted Boltzmann Machines, for tasks like shape representation, texture generation, and motion perception. These works intersect with broader disciplines of Computer Science Neuroscience Artificial Intelligence While no specific awards or student advisement details are mentioned in the available records, his collaborations with prominent researchers (e.g., Geoffrey Hinton, John Winn) highlight his engagement with cutting-edge academic networks.

Pedro LarrañagaView profile

Professor

• Probabilistic Graphical Models
• Metaheuristics for Optimization
• Data Mining
• +6 more

Pedro Larrañaga is a Full Professor in Computer Science and Artificial Intelligence at the Technical University of Madrid (UPM) since 2007. Previously, he held academic positions at the University of the Basque Country as Assistant Professor (1985–1998), Associate Professor (1998–2004), and Full Professor (2004–2007). He earned his MSc in Mathematics (Statistics) from the University of Valladolid and a PhD in Computer Science from the University of the Basque Country, receiving an excellence award. His research spans probabilistic graphical models, optimization, data mining, and applications in biomedicine, bioinformatics, neuroscience, industry, and sports. Larrañaga leads extensive work on Bayesian networks, evolutionary algorithms, and high-dimensional data analysis. His recent publications emphasize scalable probabilistic modeling, quantum computing interfaces, and interpretable AI for biomedical and industrial contexts. Over 200 journal publications reflect sustained contributions to machine learning theory and computational intelligence. Awards & Fellowships: Fellow, European Association for Artificial Intelligence (2012) Fellow, Academia Europaea (2018) Fellow, Asia-Pacific Artificial Intelligence Association (2021) Fellow, Jakiunde—Academy for the Sciences, Arts, and Letters of the Basque Country (2022) Fellow, European Laboratory for Learning and Intelligent Systems (ELLIS) (2023) Fellow, IEEE (2023) Fellow, Industry Academy within the International Artificial Intelligence Industry Alliance (2024) Spanish National Prize in Computer Science (2013) Spanish Association for Artificial Intelligence Prize (2018) Amity Research Award in Machine Learning (2020) He has supervised 36 PhD theses and contributes to large-scale neuroscience collaborations, including brain morphology analysis and computational neuroanatomy. Research grants focus on Bayesian methodologies for industrial IoT, health informatics, and quantum algorithm development.

Nuria OliverView profile

Researcher

• Computational Models of Human Behavior
• Human-Computer Interaction
• Intelligent User Interfaces
• +12 more

Nuria Oliver, PhD is a pioneering computer scientist and ACM Fellow , recognized as the first female Spanish computer scientist to achieve both ACM Distinguished Scientist and IEEE Fellow status. Current affiliations include Microsoft Research and former roles at MIT Media Lab. First female Spanish ACM Fellow IEEE Fellow Academia Europaea member Her research spans human behavior modeling , intelligent user interfaces , and mobile computing , with notable work in: Wearable context-aware systems (DyPERS, HealthGear) Music-adaptive exercise platforms (MPTrain, TripleBeat) Social network analysis (information propagation, community link modeling) Computer vision interfaces (LAFTER facial expression recognition) Scientific achievements include: 41 patents 15+ years of continuous multimodal systems research Recognized for LAFTER system (2000 paper) She actively promotes technology accessibility through: Media collaborations Technical/non-technical keynotes STEM outreach for girls

Pilar Fuster ParraView profile

Associate Professor

• probabilistic graphical models
• decision analysis
• data mining
• +5 more

Pilar Fuster Parra is an Associate Professor in the Department of Mathematics and Computer Science at the University of the Balearic Islands. Her research focuses on probabilistic graphical models, decision analysis, data mining, and classification models with real-world applications. She is affiliated with the Soft Computing, Image Processing, and Aggregation (SCOPIA) R+D+I group. Education: M.Sc. in Mathematics from Universitat de València (1988) Ph.D. in Computer Science from Universitat de les Illes Balears (1996) Over the past five years, she has taught courses such as Mathematics II - Calculus for Informatics Engineering degrees, Mathematics II - Calculus for Automation and Industrial Electronic Engineering, and Statistical Learning and Decision-Making II for Master’s programs in Big Data Analysis. She also contributes to interdisciplinary programs like Data Analytics and Business Intelligence. Research Affiliations: Member of the Soft Computing, Image Processing, and Aggregation (SCOPIA) group Member of the Global Health (GH) R+D+I group

Mladen NikolićView profile

Associate Professor

• Machine Learning
• Automated Reasoning
• Earthquake Damage Assessment
• +2 more

Mladen Nikolić is an Associate Professor at the Faculty of Mathematics, University of Belgrade, specializing in machine learning and automated reasoning. He actively contributes to the Machine Learning and Applications Group (MLA@MATF) and the Automated Reasoning GrOup (ARGO) . His research spans earthquake damage assessment , deep learning object tracking , LLM integration with tabular data , and astronomical data analysis . Recent work focuses on the RELAR Project for rapid earthquake loss assessment, CNN architectures for structural damage classification, and novel approaches to incorporating large language model priors into machine learning systems. His publications demonstrate strong interdisciplinary applications connecting computer science with seismology, transportation engineering, and astrophysics. Developed frameworks for earthquake loss assessment and recovery Advanced deep learning filters for non-linear motion tracking Integrated LLMs with tabular data processing systems Contributed to LSST astronomical data challenges Nikolić maintains active research collaborations through MLA@MATF and ARGO groups, with publications spanning machine learning theory, automated reasoning systems, and practical applications across multiple scientific domains. His work shows increasing focus on real-world deployment of AI systems for disaster response and scientific data analysis.

Tomer UllmanView profile

Associate Professor

• Computational Cognitive Science
• Child Development
• Intuitive Physics
• +5 more

Tomer Ullman is an Associate Professor in the Department of Psychology at Harvard University, where he leads the Computation, Cognition, and Development Lab. He is also a member of the Center for Brains, Minds, and Machines (CBMM) and an affiliate of the Kempner Institute for Natural and Artificial Intelligence. Education : B.Sc. in Cognitive Science and Physics (Hebrew University, 2008), Ph.D. in Brain and Cognitive Sciences (MIT, 2015) Postdoctoral Training : Center for Brains, Minds, and Machines (2015-2018) Ullman's research focuses on computational models of high-level human cognition, particularly intuitive theories about physics and psychology, mental simulation in reasoning, and child development of social cognition. His work bridges cognitive science, AI development, and neuroscience through experimental and computational approaches. Recent publications analyze language model limitations in Theory of Mind tasks, mental simulation constraints , and emergent hierarchical emotion representations . His lab explores how humans and machines learn through probabilistic inference, with applications in child education and AI alignment. Scientific Awards : ICDL Best Paper Award (2012, with Bonawitz et al.) Ullman teaches courses on Decision-Making (PSY1322) and Imagination (PSY1340). His research has been funded by the National Science Foundation and CBMM.