جست‌وجوی استاد و پژوهشگر

Ian Horrocksمشاهده پروفایل

استاد

• Knowledge Representation
• Description Logics
• Automated Reasoning
• +۴ مورد دیگر

Ian Horrocks is a Professor of Computer Science at the University of Oxford and a Fellow of Oriel College. His research focuses on knowledge representation, description logics, automated reasoning, and semantic web technologies. He has held academic positions at the University of Manchester (2003–2007) and served as Chief Scientist at Cerebra Inc. (2001–2006). Horrocks earned his BSc (1st class), MSc, and PhD in Computer Science from the University of Manchester (1981–1997). His work includes foundational contributions to ontology languages (e.g., OWL) and reasoning systems such as HermiT and ELK. He has supervised over twenty doctoral students and postdoctoral researchers. His honors include Fellowships from the Royal Society (2011), ECCAI (2009), and the British Computer Society (2005). He serves as Editor-in-Chief of the Transactions on Graph Data and Knowledge and leads initiatives in semantic web standards and knowledge graph applications. Key Roles: Editor-in-Chief (Journal of Web Semantics), Co-Chair (W3C OWL Working Group) Grants: EPSRC Senior Research Fellowship (2005), numerous international collaborations Labs: Oxford Semantic Technologies, involvement in projects like RDFox and PAGOdA

Bruno Felisberto Martins Ribeiroمشاهده پروفایل

دانشیار

• Networking and Operating Systems
• Artificial Intelligence
• Machine Learning
• +۴ مورد دیگر

Bruno Felisberto Martins Ribeiro is an Associate Professor of Computer Science at Purdue University, joining the department in Fall 2015. His research focuses on endowing machine learning algorithms with robust invariant representations for relational and temporal data, emphasizing causal and associational tasks. Key research areas include Networking and Operating Systems, Artificial Intelligence, Machine Learning, and Natural Language Processing. He holds a Ph.D. in Computer Science from the University of Massachusetts Amherst (2010). Education: Ph.D., Computer Science, University of Massachusetts Amherst, 2010 Research Interests: Explores invariances in mathematics and machine learning to improve model robustness. Key topics include graph and tensor invariances, causal relationships, adversarial robustness, and applications in recommendation systems, robotics, and drug discovery. His lab’s work has advanced counterfactual task frameworks and causal reasoning in machine learning. Recent Contributions: Recent publications address zero-shot generalization in graph neural networks, causal discovery methods, and defenses against adversarial attacks. His work spans conferences like ICML, NeurIPS, and SIGCOMM. Awards: Best Paper Award at ACM CODASPY 2021 Best Paper Award at SIGMETRICS 2016 Best Paper Award at IEEE NetSciCom 2014 Advising & Students: Supervises current PhD students Beatrice Bevilacqua, Jincheng Zhou, and Yucheng Zhang, along with MSc student Ipsit Mantri. Notable former students include S Chandra Mouli (Meta), Yangze Zhou (Spotify), and Jianfei Gao (Vector Institute). Labs & Teams: Leads research in invariant representations and causal ML, collaborating with institutions like Stanford during his sabbatical. His work bridges theory and practice, impacting areas like network analysis and AI-driven healthcare.

Pekka Marttinenمشاهده پروفایل

دانشیار

• Machine Learning
• Large Language Models
• Reinforcement Learning
• +۸ مورد دیگر

Pekka Marttinen is a tenured Associate Professor of Machine Learning at Aalto University, Department of Computer Science, and leads the Machine Learning for Health (Aalto-ML4H) group within the Helsinki Institute for Information Technology HIIT. Education: M.Sc. in Applied Mathematics, University of Helsinki (2004) Ph.D. in Statistics, University of Helsinki (2008) Title of Docent in Information and Computer Science, Aalto University (2015) Research Focus: His methodological work spans large language models, reinforcement learning, deep learning, probabilistic machine learning, and causal inference . These techniques are applied to critical domains of healthcare, bioinformatics, statistical genetics, epidemiology, and personalized medicine . The group develops novel algorithms, theoretical guarantees, and open-source software that enable data-driven discovery and decision-making in medicine and biology. Publication Trends: Across 2022-2024 the lab has concentrated on (i) rigorous causal reasoning over temporal clinical data, (ii) principled uncertainty quantification in LLMs, (iii) representation learning for neural network comparison, and (iv) translational projects that turn raw EHRs into actionable clinical insights. Earlier work integrated high-dimensional genomics with metabolomics and mapped evolutionary forces in bacterial pathogens. Scientific Awards & Recognition: While no specific awards are listed, his sustained publication record in top-tier venues (NeurIPS, ICML, AISTATS, Nature Genetics, PLOS CB) and his role as responsible professor of the Machine-Learning, Data-Science and AI major signify significant peer recognition. Advising & Grants: Prof. Marttinen currently mentors 8 PhD students as primary supervisor and an additional 7 PhD students as co-supervisor. He has already graduated 8 PhDs since 2014. The group is supported through competitive funding including the Finnish Center for Artificial Intelligence (FCAI) doctoral program. Labs & Teams: He directs the Machine Learning for Health (Aalto-ML4H) research group, comprising postdocs Hans Moen, Ti John, Alexander Nikitin, Negar Safinianaini, Linli Zhang, Zhiyuan Li, and the above-mentioned PhD cohort.

• Computer Science
• Software Engineering
• Artificial Intelligence
• +۵ مورد دیگر

Dongwoo Kim is a researcher affiliated with Hanyang University, ERICA Campus (Department of Electronics and Communication Engineering) and has previously collaborated with institutions like POSTECH , Chungnam National University , and Microsoft . His work spans interdisciplinary domains in Computer Science and Engineering . Hanyang University, ERICA Campus - Department of Electronics and Communication Engineering POSTECH - Power Analog Electronics & Semiconductor Devices Lab Microsoft Chungnam National University Kim's research focuses on formal verification of automotive control software, deep learning applications in environmental monitoring, 3D modeling for indoor positioning, and machine learning for signal processing. His recent publications highlight advancements in graph neural networks (GNNs), including analyzing oversmoothing and gradient dynamics, as well as developing geometric vision-language models with domain-agnostic encoders. His 15 most recent articles (2023-2025) address topics like: Optimizing hybrid electric vehicle engine performance 3D modeling for indoor localization GNN training stability UAV-based environmental monitoring Algorithm difficulty prediction for programming problems Millimeter-wave antenna design Kim collaborates with researchers in software engineering , signal processing , and environmental science domains. His work intersects formal methods , applied machine learning , and embedded systems research.

Melanie Weberمشاهده پروفایل

استادیار

• Geometric Machine Learning
• Optimization on Manifolds
• Non-Euclidean Machine Learning
• +۳ مورد دیگر

Melanie Weber is an Assistant Professor of Applied Mathematics and Computer Science at Harvard University's John A. Paulson School of Engineering and Applied Sciences (SEAS), leading the Geometric Machine Learning Group. Her research focuses on leveraging geometric structures in data for designing efficient machine learning and optimization algorithms with theoretical guarantees. She holds a PhD from Princeton University (2021) and has held fellowships at the Mathematical Institute of Oxford, Brasenose College, and the Simons Institute. Her work bridges geometry, optimization, and machine learning, with funding from NSF, Sloan Foundation, and Harvard initiatives. Education : PhD in Applied Mathematics, Princeton University (2021) BSc/MSc in Mathematics and Physics, University of Leipzig (2016) Research Interests : Dr. Weber's research integrates geometric principles into machine learning and optimization, focusing on non-Euclidean spaces, graph structures, and manifold-based methods. Key areas include optimization on Riemannian manifolds, curvature-based analysis (e.g., Ricci curvature), and developing algorithms resilient to data geometry challenges like over-smoothing in graph neural networks. Her work emphasizes theoretical foundations while addressing practical scalability in high-dimensional data. Awards & Recognition : 2024 Sloan Research Fellowship 2023 Leslie Fox Prize in Numerical Analysis 2023 NSF Grant for Geometric Optimization Grants & Funding : Supported by National Science Foundation (NSF), Alfred P. Sloan Foundation, Aramont Foundation, Harvard Dean’s Fund, and Harvard Data Science Initiative. Labs & Collaborations : Leads the Geometric Machine Learning Group at SEAS, collaborating with institutions like MIT, Max Planck Institute, and industry labs (Facebook, Google, Microsoft). Active in organizing workshops on geometric methods and curvature analysis.

Julie Bergnerمشاهده پروفایل

استاد

• Homotopy Theory
• Algebraic Topology
• Algebraic K-Theory
• +۳ مورد دیگر

Julie Bergner is a Professor in the Department of Mathematics at the University of Virginia. She is currently on sabbatical during 2024-25 as a Visiting Fellow at Clare Hall, University of Cambridge. Her research focuses on Homotopy Theory, Algebraic Topology, Algebraic K-Theory, and Category Theory, with a particular emphasis on higher category theory and homotopical algebraic structures. University of Virginia (Professor, Department of Mathematics) University of Cambridge (Visiting Fellow at Clare Hall, 2024-25) Her work spans 2-Segal objects, model categories, Waldhausen constructions, and applications to topological field theories. She has received NSF CAREER awards for her research on equivariant topological field theories and higher cluster categories. Her recent publications explore combinatorial models, enriched categories, and homotopy limits in the context of functor calculus and higher structures. Julie Bergner contributes to mathematical exposition through articles on writing papers in the mathematical sciences and homotopy theory applications. She is actively involved in the department's topology seminar and supports collaborative learning initiatives. Her research has implications in algebraic topology, category theory, and their interdisciplinary applications.

Kostas Daniilidisمشاهده پروفایل

استاد

• Computer Vision
• Robotics
• Machine Learning
• +۵ مورد دیگر

Kostas Daniilidis is the Ruth Yalom Stone Professor at the University of Pennsylvania in the School of Engineering and Applied Science , specifically the Department of Computer and Information Science . He is also affiliated with the GRASP Laboratory and Archimedes, Athena Research Center, Greece . Education : PhD in Computer Science (1992) from the University of Karlsruhe with Hans-Hellmut Nagel Diploma in Electrical Engineering (1986) from the National Technical University of Athens Research Interests : Kostas Daniilidis is a leading researcher in Computer Vision and Robotics , with significant contributions to event-based vision , equivariant learning , 3D human pose estimation , and hand-eye calibration . His work spans neural rendering , dynamic scene modeling , and low-latency sensing systems . Article Trends : Daniilidis’s recent publications focus on event cameras for low-light and high-speed applications, Gaussian splatting for real-time 3D reconstruction, and equivariant neural architectures for robust motion estimation. His work bridges deep learning with geometric vision , emphasizing human mesh recovery and multi-agent coordination . Scientific Awards : Best Conference Paper Award at ICRA 2017 IEEE Fellow (2012) Teaching : He has taught courses such as CIS580: Machine Perception and CIS121: Data Structures , alongside advanced topics in robotics and computer vision. Lab & Collaborations : As director of the GRASP Laboratory (2008–2013), he fostered interdisciplinary research in robotics, and currently collaborates with institutions like the Athena Research Center in Greece.

Xiaofeng Qianمشاهده پروفایل

دانشیار

• Materials Theory
• Quantum Materials
• 2D Materials
• +۷ مورد دیگر

Dr. Xiaofeng Qian is an Associate Professor in the Department of Materials Science & Engineering at Texas A&M University, with joint appointments in Physics and Astronomy, and Electrical & Computer Engineering. His research focuses on materials theory , quantum materials design , and high-throughput computational discovery , particularly for 2D materials and energy applications . Educational Background: Ph.D., Nuclear Science and Engineering, Massachusetts Institute of Technology (2008) B.S., Engineering Physics, Tsinghua University (2001) Research spans first-principles electronic structure methods , nonlinear optical responses , and multiscale modeling of electronic, thermal, and ionic transport. Key areas include quantum spin Hall effect , ferroelectric switching , and machine learning for materials prediction . Notable Awards: Dean of Engineering Excellence Award (2024) Engineering Genesis Multidisciplinary Award (2024) AZZ Faculty Fellow (2021) NSF CAREER Award (2018) Manson Benedict Fellowship (2006) Actively recruiting PhD, MS, and UG researchers with backgrounds in physics, materials science, or computational methods. Collaborates extensively on hybrid AI-materials projects and topological device concepts .

Mathias Niepertمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Graph Neural Networks
• +۵ مورد دیگر

Mathias Niepert is a Professor at the Institute for Artificial Intelligence within the Faculty of Computer Science, Electrical Engineering and Information Technology at the University of Stuttgart. His research focuses on advancing machine learning techniques with applications in scientific computing, graph neural networks, and medical imaging. He is particularly known for contributions to physics-informed neural networks, equivariant models, and graph learning frameworks. Key research areas include: Scientific Machine Learning for PDEs and molecular modeling Graph neural networks and their theoretical limitations Medical vision-language models and multimodal learning Efficient neural network architectures (transformers, FNOs) Domain knowledge integration in deep learning His work often bridges theoretical foundations with practical applications, as evidenced by extensive publications (2018–2025) on topics like adaptive message passing, equivariant networks, and medical imaging systems. He has contributed to benchmark development through initiatives like PDEBench and pioneered methods for equivariant diffusion models and molecular representation learning. His current projects emphasize: Improving generalization in Fourier Neural Operators Addressing oversmoothing in graph networks Combining physics principles with neural architectures Medical AI applications through multimodal fusion

• Tropical Geometry
• Cluster Algebras
• Combinatorics
• +۵ مورد دیگر

David E Speyer is a Professor in the Department of Mathematics at the University of Michigan . His research focuses on algebraic problems with combinatorial flavors , particularly in tropical geometry , cluster algebras , and geometry of Lie groups . He has supervised multiple PhD students, including Shelby Cox, Will Dana, and John Wiltshire-Gordon, and collaborated on projects with undergraduates like Grant Barkley and Benjamin Branman. Education: PhD in Mathematics from UC Berkeley under Bernd Sturmfels; undergraduate at Harvard. Research: Key areas include tropical geometry , cluster algebras , and flag manifolds . His work often bridges combinatorics, algebraic geometry, and representation theory. Publications: Over 40 papers, including breakthroughs in cluster algebras , affine weak order , and braid variety cluster structures . Awards: Clay Research Fellow (2005-2010). Teaching: Coordinates courses like Math 593 (graduate algebra) and Math 214 , with a focus on inquiry-based learning .

Mikkel N. Schmidtمشاهده پروفایل

دانشیار

• Statistical Modeling
• Machine Learning
• Bayesian Methods
• +۳ مورد دیگر

Mikkel N. Schmidt is an Associate Professor in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU). His research focuses on statistical modeling, Bayesian methods, and their applications in science and industry. He has held visiting roles at Columbia University (2007) and Cambridge University (2008-2009). His work integrates probabilistic modeling with computational inference to address complex problems in diverse fields such as molecular discovery, optical communication, and brain connectivity analysis. Education highlights include visiting scholar and postdoctoral experiences at top-tier institutions. Research interests span statistical methodology development, machine learning applications, and interdisciplinary problem-solving. Current projects involve Bayesian neural networks for molecular discovery and federated learning optimization. Advising efforts include supervising multiple PhD students in areas like molecular discovery and denoising diffusion models. Notable collaborations involve work on materials science, quantum communication, and medical signal processing. His contributions bridge theoretical advancements with practical industrial applications, emphasizing interdisciplinary innovation.

Ben Bloem-Reddyمشاهده پروفایل

استادیار

• Machine Learning
• Causal Inference
• Bayesian Methods
• +۳ مورد دیگر

Ben Bloem-Reddy is an Assistant Professor in the Department of Statistics at the University of British Columbia (UBC), Vancouver Campus. His research focuses on statistical theory and applications in machine learning, particularly in causal inference, Bayesian methods, neural networks, and probabilistic models. He advises current students Quanhan (Johnny) Xi, Kenny Chiu, and Gian Carlo Diluvi. His work bridges foundational statistical theory with practical machine learning challenges, including causal discovery, model identifiability, and uncertainty quantification. Recent research explores topics such as latent variable models, generative processes, and symmetry in data and algorithms. His contributions span interdisciplinary areas like particle physics applications and information theory-based compression techniques. Ben’s research trends emphasize advancing theoretical guarantees for modern machine learning systems while addressing real-world problems. His publications frequently intersect with algebraic topology (e.g., cocycles in causal inference) and nonparametric methods. He maintains an active lab within the Department of Statistics, fostering collaborations across UBC’s academic ecosystem. No scientific awards are explicitly listed in the provided information. His advising and grant activities focus on statistical methodology development, as evidenced by his student supervision and published work. His office is located in ESB 3168, and he can be reached at benbr@stat.ubc.ca.

Qin Liمشاهده پروفایل

دانشیار

• Numerical Analysis
• Scientific Computing
• Inverse Problems
• +۸ مورد دیگر

Qin Li is an Associate Professor in the Mathematics Department at the University of Wisconsin-Madison. She holds affiliations with the Wisconsin Institutes for Discovery and serves as a senior PI at the Institute for Foundations of Data Science. Her research focuses on numerical analysis, scientific computing, and inverse problems, with a strong emphasis on kinetic theory and multiscale PDEs. Her work spans computational methods for inverse transport and radiative transfer equations, Bayesian approaches in optical tomography, and optimization techniques for solving stochastic and deterministic PDEs. Recent publications highlight applications of diffusion models, Wasserstein gradient flow, and random sampling in inverse problems, as well as control theory for Vlasov-Poisson systems and reconstruction of chemotaxis kernels. She leads a research group within the Mathematics Department and has received funding from the National Science Foundation (NSF), the Office of Naval Research (ONR), and the Wisconsin Alumni Research Foundation (WARF). Her lab, Kinetic At Madison, explores nonlinear hyperbolic PDEs and their applications. She also contributes to teaching as a TA Supervisor.

Xieyuanli Chenمشاهده پروفایل

دانشیار

• Robotics
• Autonomous Systems
• Computer Vision
• +۳ مورد دیگر

Xieyuanli Chen is an Associate Professor at the National University of Defense Technology (NUDT), China. He holds a Dr.-Ing. (summa cum laude) from the University of Bonn (2022), a Master's in Robotics from NUDT (2017), and a Bachelor's in Electrical Engineering from Hunan University (2015). His research focuses on robot learning, perception, and navigation, with an emphasis on LiDAR-based SLAM, autonomous systems, and semantic perception. Education: PhD: University of Bonn, 2018-2022 (supervised by Prof. Cyrill Stachniss) Master's: NUDT, 2015-2017 Bachelor's: Hunan University, 2011-2015 Research interests include robotics, autonomous systems, computer vision, and LiDAR perception. He has authored over 90 papers in top venues like TRO, RSS, ICRA, and CVPR. He serves as an Associate Editor for IEEE RA-L, ICRA, and IROS, and is a member of the RoboCup Rescue Robot League Technical Committee. Awards include the RSS Pioneer Award (2021), Best-in-Class RoboCup awards, and recognition as a World’s Top 2% Scientist (2024). His work spans LiDAR localization, moving object segmentation, and efficient semantic mapping. He advises students in robotics and autonomous systems. Labs/Teams: Active in the PRBonn group (University of Bonn) and leads research at NUDT on LiDAR-based perception systems.

Alexei Koulakovمشاهده پروفایل

استاد

• Neuroscience
• Theoretical Neurobiology
• Applied Mathematics
• +۵ مورد دیگر

Alexei Koulakov is a Professor at Cold Spring Harbor Laboratory (CSHL) and the Charles Robertson Professor of Neuroscience. His research focuses on applying mathematical and computational approaches to unravel the principles of brain organization, particularly in sensory systems like olfaction and vision. Koulakov's work explores how neural circuits form during development, the role of genetic and experiential factors, and the evolutionary basis of brain architecture. Education: PhD in Physics from the University of Minnesota (1998). Key Research Areas: Olfactory system development, neural network modeling, and AI inspired by biological computation. Koulakov's recent publications emphasize cross-disciplinary integration of neuroscience and AI, including NeuroAI initiatives and DeepNose models predicting olfactory percepts. His team investigates how innate abilities are encoded genomically and how experience shapes neural networks. Scientific contributions include studies on primacy coding in olfaction, stochastic learning mechanisms , and high-throughput neural mapping . Awards include the Charles Robertson Professorship , reflecting his leadership in theoretical neuroscience. Koulakov collaborates extensively, with notable work on genomic bottlenecks , odor mixture interactions , and neural integrator models . His lab at CSHL is at the forefront of NeuroAI research, leveraging brain circuit insights to advance artificial intelligence.