Find Academic Supervisors

• Computer Vision
• Artificial Intelligence
• Extended Reality
• +4 more

Prof. Dr. Helge Rhodin is a faculty member at the University of Bielefeld, serving as Head of the Visual AI for Extended Reality Group within the Faculty of Engineering. His research activities are centered at the Center for Cognitive Interaction Technology (CITEC), a central academic institute at the university focused on interdisciplinary research in cognitive systems, robotics, and human-computer interaction. His office is located in CITEC building room 3-225, with secretariat contact via susanne.strunk@uni-bielefeld.de. Professor Rhodin's research focuses span multiple cutting-edge areas in computer vision and artificial intelligence. His primary interests include Computer Vision, Artificial Intelligence, Extended Reality, Human-Computer Interaction, 3D Reconstruction, and Motion Capture. His work demonstrates strong interdisciplinary connections between the university's 'Socio-Technical World' strategic research area, which examines how humans, robots, and AI interact in complex environments. Analysis of Professor Rhodin's recent publications reveals a consistent focus on advancing techniques for human and object representation in virtual and augmented environments. His work spans from fundamental computer vision techniques like keypoint detection and motion capture to advanced applications in digital twin generation, neural rendering, and animal behavior analysis. Notably, his research shows practical applications in sports science (particularly skiing analysis) and biological motion tracking. Within the university governance structure, Professor Rhodin serves as a member of both the Habilitation Committee and the Faculty Conference of University Professors within the Faculty of Engineering, indicating his active participation in academic leadership and quality assurance processes.

• Semantics
• Linguistics
• Computational Linguistics
• +4 more

Yoad Vinter Seggev is a Professor of Computational Semantics and AI at Utrecht University , affiliated with the Institute for Language Sciences and the Artificial Intelligence program . His work bridges theoretical linguistics, computational linguistics, and cognitive science, focusing on formal semantics and human-centered AI. Research Interests include semantic theory, computational linguistics, and the cognitive foundations of natural language. He explores topics like mass/count noun distinctions, reciprocal constructions, event semantics, and the formalization of nonverbal communication systems such as drum languages. Scientific Awards include the ERC Advanced Grant (2016) for the project Forests and Trees: the Formal Semantics of Collective Categorization (ROCKY) and leadership of the NWO VICI program Between language and common sense (2010-2014). Advising and Grants include leading major projects like the NWO Free Competition project When language has a beat (since 2016) and serving on editorial boards of top journals such as Linguistics and Philosophy and Journal of Semantics .

Sitan ChenView profile

Assistant Professor

• Machine Learning
• Quantum Engineering
• Theory of Computation
• +7 more

Sitan Chen is an Assistant Professor of Computer Science at Harvard University's John A. Paulson School of Engineering and Applied Sciences. His research focuses on foundational aspects of machine learning, quantum information, and algorithm design, with a particular emphasis on provable guarantees for generative modeling, deep learning, and quantum learning. He is affiliated with the Theory of Computation group, the ML Foundations group, and the Harvard Quantum Initiative. Education: PhD in EECS from MIT (advised by Ankur Moitra) Bachelor's in Mathematics and Computer Science from Harvard (advised by Salil Vadhan and Leslie Valiant) Grants & Awards: NSF CAREER Award (CCF-2441635) NSF Small (CCF-2430375) Harvard Dean's Competitive Fund for Promising Scholarship Research Interests: Generative models and diffusion processes Quantum tomography and quantum learning Algorithmic foundations for inverse problems Provably efficient learning algorithms Advising & Teaching: Advises PhD/Masters students in quantum computing and machine learning Teaches courses like Quantum Learning Theory and Algorithms for Data Science Key Contributions: First polynomial-time algorithms for learning narrow neural networks Advances in quantum state estimation with minimal resources Provably efficient sampling methods for diffusion models

Matthew M. ConroyView profile

Academic

• Number Theory
• Mathematics Education
• Mathematical Visualization
• +3 more

Matthew M. Conroy is an Associate Teaching Professor and Director of the Math Study Center at the University of Washington's Department of Mathematics. He holds a PhD from the University of Colorado (1997) and degrees from the University of Lowell (M.S., 1990; B.S., 1988). His research focuses on Number Theory, Mathematics Education, and Mathematical Visualization. Education: PhD, University of Colorado, 1997; M.S., University of Lowell, 1990; B.S., University of Lowell, 1988 He leads the WXML Number Theory and Noise project since 2016, exploring auditory representations of integer sequences (e.g., primes, Ulam numbers). Collaborations with students have produced sound libraries and geometric animations (e.g., octagon theorems). His work bridges education and research, emphasizing interdisciplinary approaches. Labs/Teams: Directs the Math Study Center, supporting student learning. Active in visualizing mathematical concepts through animations and sound.

• Knowledge Representation
• Ontologies
• Description Logics
• +4 more

Professor Ian Horrocks FRS is a distinguished academic in Computer Science at the University of Oxford, holding the position of Professor of Computer Science and a Fellowship at Oriel College. His research focuses on knowledge representation, ontologies, description logics, automated reasoning, and their applications in the Semantic Web and e-Science. He leads the Data and Knowledge Group, developing tools like HermiT, ELK, and RDFox, and has pioneered ontology languages and reasoning systems. Education: Not explicitly detailed in text, but his academic contributions suggest a strong background in Computer Science and Logic. Research interests include foundational work on description logics, ontology languages, and scalable reasoning systems. He is Editor-in-Chief of the Transactions on Graph Data and Knowledge, and founder of Oxford Semantic Technologies. His work emphasizes practical applications in industry and academia, with contributions to semantic technologies for big data and AI. Key publications span foundational theory (e.g., description logics) to applied systems like ontology alignment and knowledge graph completion. He advises numerous doctoral students, many of whom have gone on to prominent roles in industry and academia. Awards: Fellow of the Royal Society (FRS), highlighting his significant contributions to computer science and AI. Labs/Teams: Leads the Data and Knowledge Group at Oxford, collaborating on projects like Optique and Semantic Technologies. Maintains a repository of ontologies and datasets for research.

• Image Processing
• Signal Processing
• 3D Environments
• +4 more

Stuart Perry is a Professor at the University of Technology Sydney (UTS), serving as Head of Discipline for Signal Processing and Analytics in the School of Electrical and Data Engineering. He holds affiliations with UTS' Faculty of Engineering and Information Technology, the Global Big Data Technologies Centre, and the Visualization Institute. With over 20 years of experience, his career spans roles at DSTO and Canon Information Systems Research Australia (CiSRA), focusing on image processing, signal processing, and perceptual quality measurement. Perry co-directs the Perceptual Imaging Laboratory (PILab), researching 3D environments, light field technologies, and human perception in immersive realities. He actively contributes to international standards committees like ISO/TC42 and ISO/SC29/WG7, leading JPEG's point cloud coding efforts. His research emphasizes adaptive image processing, machine learning-driven object detection, and medical imaging applications. Perry has authored 60+ publications, two books, and 20 patents. Current projects include VR empathy case studies, point cloud compression, and disaster management digital transformation. Education: PhD in Engineering, University of Sydney (1999) Research Interests: His work bridges computational imaging and human perception, addressing challenges in augmented/virtual reality (AR/VR), 3D scanning, and immersive media. Key areas include light field and point cloud coding, psychophysics of visual perception, and medical diagnostics via machine learning. He explores how perceptual principles can optimize interactive technologies for education, healthcare, and entertainment. Recent Research Trends: Recent articles focus on 3D Gaussian splatting, glaucoma detection via deep learning, and JPEG Pleno standards for plenoptic imaging. His work balances technical innovation (e.g., efficient point cloud compression) with human-centric design (e.g., reducing VR motion sickness through display lag analysis). Awards & Recognition: Member of IEEE and founding SPINet participant. Over 60 refereed publications and 20 patents highlight his industry-academic impact. Grants & Leadership: Leads SmartSat CRC projects on yield estimation and Aus4innovation-funded disaster response tech. Manages grants totaling millions AUD. Editorial roles include Associate Editor of SPIE/IS&T Journal of Electronic Imaging. Labs & Collaborations: PILab collaborates internationally on perceptual imaging standards. Active in ISO committees shaping future media technologies.

Stefanie JegelkaView profile

Associate Professor

• Algorithmic Machine Learning
• Optimization Algorithms
• Discrete and Combinatorial Machine Learning
• +7 more

Stefanie Jegelka is an Associate Professor (on leave) at MIT's Department of Electrical Engineering and Computer Science, and a Humboldt Professor at Technical University of Munich (TU Munich). She is a member of CSAIL (Computer Science and Artificial Intelligence Laboratory), IDSS (Institute for Data, Systems, and Society), and Machine Learning at MIT. Her research spans algorithmic machine learning with focus on modeling, optimization algorithms, theory, and applications. Dr. Jegelka completed her PhD at the Max Planck Institutes in Tuebingen and ETH Zurich, followed by a postdoc at UC Berkeley's AMPlab and computer vision group. Her academic journey reflects a strong foundation in both theoretical and applied aspects of machine learning. Her research focuses on exploiting mathematical structure for discrete and combinatorial machine learning problems, robustness, and scaling machine learning algorithms. Key areas include submodular optimization, graph neural networks, invariant and equivariant learning, and representation theory in machine learning. Her work bridges theoretical foundations with practical applications across various domains, with particular emphasis on how mathematical structure can enhance algorithmic performance. Dr. Jegelka's recent publications demonstrate significant contributions to understanding the theoretical properties of graph neural networks, developing methods for invariant learning, and advancing representation learning techniques. Her work consistently shows strong connections between mathematical structure and machine learning performance, with applications spanning natural language processing, computer vision, and scientific domains. Sloan Research Fellowship NSF CAREER Award DARPA Young Faculty Award Dr. Jegelka has advised numerous graduate students and postdocs, many of whom have gone on to successful careers in academia and industry. Her research has been supported by prestigious grants from NSF, DARPA, ONR, and industry partners including Google, Two Sigma, and Adobe. She serves as Program Chair for ICML 2022 and has held numerous editorial and organizational roles in the machine learning community. She leads a research group investigating fundamental questions in machine learning, particularly how mathematical structure can be leveraged to develop more efficient, robust, and scalable learning algorithms. Her group collaborates across disciplines, connecting theoretical machine learning with applications in science and engineering, and has produced influential work on submodularity, graph representation learning, and invariant learning methods.

Michael CochezView profile

Assistant Professor

• Machine Learning
• Knowledge Graphs
• Graph Neural Networks
• +7 more

Dr. Michael Cochez is an Assistant Professor in the Department of Computer Science at Vrije Universiteit Amsterdam, with a secondary appointment in Artificial Intelligence. His research focuses on knowledge graph embeddings, graph neural networks, and neuro-symbolic systems. He has published over 70 works and contributed to datasets like KGloVe and Inductive WN18RR. Research Interests: Machine Learning, Knowledge Representation, Graph Theory Applications, Explainable AI, and Bioinformatics Integration. His work bridges theoretical advancements with practical applications in industry and healthcare. Recent Trends in Publications: Emphasis on scalable knowledge graph systems, causal reasoning in economic forecasting, and neuro-symbolic frameworks for complex queries. Active in organizing workshops on DL4KG and industry knowledge graph scaling. Awards: None listed. Grants: Not specified. Advising: No students listed but contributes to courses like Deep Learning and Machine Learning for Graphs. Labs/Teams: Involved in projects like Graph-Massivizer (sustainable data center modeling) and Graph-Scrutinizer (massive analytics tools). Ancillary Activity: Consultancy in Abcoude since 2022.

• Machine Learning
• Algorithm Engineering
• Combinatorial Optimization
• +3 more

Dr. Deepak Ajwani is an Assistant Professor in the School of Computer Science at University College Dublin . His research focuses on leveraging machine learning techniques for solving combinatorial optimization problems, with expertise in algorithm design, algorithm engineering, and graph algorithms. He holds a PhD from the University of Saarland, Germany, and has held postdoctoral positions at Aarhus University (Denmark) and University College Cork (Ireland). He also worked at Nokia Bell Labs (2012–2018) on learning systems for unstructured content analysis. Dr. Ajwani has received over 50 peer-reviewed publications in top-tier conferences and journals. He serves on the editorial board of the Machine Learning journal and is a frequent senior program committee member for conferences like WWW, IJCAI, AAAI, and ALENEX. He is a funded investigator at the SFI Centre for Research Training in Machine Learning (ML-Labs) . His teaching excellence has been recognized through multiple nominations for the UCD Teaching and Learning Award . His students have won the Franz Geiselbrechtinger Medal (2023, 2024) for best final-year projects in theoretical computer science. Key grants include the Irish Research Council and IBM Research Grant (2010–2012) for graph partitioning techniques in exascale computing. His research explores integrating machine learning with optimization algorithms, graph neural networks, and algorithm engineering for real-world applications.

Rajiv SatsangiView profile

Associate Professor

• Special Education
• Mathematics Education
• Assistive Technology
• +3 more

Dr. Rajiv Satsangi is an Associate Professor at George Mason University's Helen A. Kellar Institute for Human disAbilities, within the College of Education and Human Development. He holds a PhD from Purdue University and focuses on special education with an emphasis on mathematics instruction for students with disabilities. His work emphasizes the use of technology-based interventions, such as virtual manipulatives and video modeling, to enhance learning outcomes for students with learning disabilities, intellectual disabilities, and other exceptionalities. Dr. Satsangi's research interests center on inclusive education strategies, assistive technology integration, and evidence-based practices in mathematics education. He has extensively studied the efficacy of virtual tools like number lines, graphic organizers, and algebraic manipulatives in improving mathematical proficiency among students with disabilities. His work also addresses policy implementation (e.g., NCTM/CEC guidelines) and teacher training in inclusive pedagogy. His recent publications highlight trends toward technology-driven solutions for teaching algebra, fractions, and geometry to students with learning challenges. He has explored comparative studies between virtual and concrete manipulatives, as well as video modeling versus traditional instruction methods. Dr. Satsangi collaborates closely with the Helen A. Kellar Institute to develop adaptive curricula and interventions that promote educational equity in STEM fields. No awards or grants are explicitly listed in the provided texts. His teaching focuses on special education coursework related to general curriculum adaptations. While no advising records are mentioned, his research consistently involves collaborative efforts to improve educational access through innovative methodologies.

J. Andreas BærentzenView profile

Associate Professor

• Computer Graphics
• Shape Modelling
• Real-Time Rendering
• +5 more

Professor J. Andreas Bærentzen is an Associate Professor at the Department of Applied Mathematics and Computer Science, Technical University of Denmark (DTU), where he has held academic positions since 2001. His research focuses on computer graphics, shape modeling, real-time rendering, and geometry processing. He holds a MSc Eng (1998) and PhD (2003) from DTU. His work emphasizes topological adaptability in shape representation and efficient geometry processing, with notable contributions to structural topology optimization, 3D reconstruction, and virtual reality applications. Education: MSc Eng, Technical University of Denmark, 1991–1998 PhD, Technical University of Denmark, 1998–2003 Research Interests: Interactive 3D modeling and real-time graphics Topology optimization and structural infill design Shape modeling using distance fields and deformable hypersurfaces Applications in biomedical imaging (e.g., white matter dynamics) and environmental engineering (e.g., tree geometry simulation) His recent publications highlight advancements in neural network-based shape representation, skeletonization for tree reconstruction, and inverse-designed structural infill for engineering. He supervises multiple PhD students and leads projects on robotic manufacturing and virtual reality visualization. Bærentzen collaborates internationally, including visits to Stony Brook University (USA) and Padova University (Italy). Advisees & Projects: PhD Students: Rui Cui, Thomas D. V. Christiansen, Elias Theil Gæde Key Projects: 'Generative Methods for Brain Tissue Phantoms', 'Neural Form Representation', 'Graph Algorithms with Geometric Applications' His work bridges theoretical geometry processing with practical applications in architecture, biomedical engineering, and environmental science, leveraging DTU's interdisciplinary research ecosystem.

Serkan HostenView profile

Professor

• Commutative Algebra
• Combinatorics
• Algebraic Statistics

Serkan Hosten is a Professor in the Department of Mathematics at San Francisco State University's College of Science and Engineering. His research focuses on Commutative Algebra, Combinatorics, and Algebraic Statistics. He serves as an adviser for the Applied Math BS program and maintains active seminar engagements at UC Berkeley. His research explores the intersection of algebraic geometry with statistical optimization, leveraging combinatorial structures to solve problems in information theory and machine learning. Recent publications demonstrate consistent work in toric geometry, matroid theory, and computational algebra. Publications over the past decade show a strong emphasis on: (1) geometric methods in statistical inference, (2) combinatorial optimization frameworks, (3) algebraic approaches to machine learning, and (4) computational aspects of commutative algebra. This interdisciplinary work bridges pure mathematics with data science applications.

• Computational Social Science
• Natural Language Processing
• Social Network Analysis
• +3 more

Roshni Chakraborty is an Assistant Professor at the Institute of Computer Science, University of Tartu, Estonia, where she leads research in the Computational Social Science Group. Her work bridges natural language processing, social network analysis, and machine learning to address challenges in social media analytics, disaster response, and misinformation detection. Research Interests: Her expertise spans: Computational Social Science : Analyzing social dynamics via digital footprints. Disaster Informatics : Developing NLP tools for crisis-related tweet summarization (e.g., ATSumm, IKDSumm). Political & Media Analysis : Detecting biases in community-driven fact-checking and news stance. Network Science : Advancing signed network modeling (SigGAN) and cross-network alignment (HCNA). Publication Trends: Recent articles (2022-2025) focus on generative AI for fact-checking, disaster tweet summarization benchmarks, and political bias analysis. Her work emphasizes practical NLP solutions for social good, leveraging transformer models, adversarial learning, and ontology-driven approaches. Academic Activities: She actively recruits PhD students and research interns for projects in social computing. Her group collaborates on datasets like BD2TSumm and Adsumm, fostering open research in crisis informatics.

Mubarak ShahView profile

Professor

• Computer Vision
• Video Surveillance
• Visual Tracking
• +7 more

Dr. Mubarak Shah is the Trustee Chair Professor of Computer Science and Founding Director of the Center for Research in Computer Vision (CRCV) at the University of Central Florida. His research focuses on computer vision, including video surveillance, visual tracking, human activity recognition, and UAV video analysis. He has held prestigious fellowships from ACM, NAI, AAAS, IAPR, and IEEE. Roles: Trustee Chair Professor, Director of CRCV, Graduate Faculty Member Affiliations: University of Central Florida, College of Engineering and Computer Science Research interests span visual analysis of crowded scenes, video registration, and privacy-aware diffusion models. He has pioneered geolocalization techniques and multimodal learning frameworks. His work impacts security, autonomous systems, and medical imaging. Recent publications emphasize diffusion models, 3D object detection, and adversarial learning. His work has been presented at CVPR, ECCV, and NeurIPS. Awards: Pegasus Professor (2006) ACM SIGMM Technical Award (2019) Multiple fellowships and recognitions from IEEE, AAAS, and others Advising and mentorship include over 50 graduate students and NSF REU programs. He leads CRCV, a hub for interdisciplinary vision research, and contributes to high-impact datasets like MAVREC. Labs/Teams: CRCV hosts cutting-edge projects in geolocalization, action recognition, and medical vision. Collaborative efforts include global partnerships in AI and robotics.

• Artificial Intelligence
• Optimization Techniques
• Reinforcement Learning
• +2 more

Michele Ciavotta is an Associate Professor at the University of Milano-Bicocca's Department of Computer Science, Systems, and Communication, specializing in AI-driven optimization for complex systems. His research integrates reinforcement learning, graph neural networks, and metaheuristics applied to distributed computing and physical systems like smart mobility and production lines. Research spans cloud/edge computing optimization, industrial production systems, smart city applications, and graph-based learning methods. Recent publications demonstrate focus on decentralized AI systems, geospatial data processing, and hypergraph neural networks for chemical and urban applications. Extensive involvement in European R&D projects addresses challenges in cloud computing infrastructure, Industry 4.0 implementations, and distributed AI solutions.