جستجوی استاد راهنمای دانشگاهی

• Trustworthy Artificial Intelligence
• Privacy-Preserving Systems
• Adversarial Robustness
• +۴ مورد دیگر

Mathias Lécuyer is an Assistant Professor in the Computer Science Department at the University of British Columbia (UBC) , part of the Faculty of Science . His research focuses on trustworthy AI systems, with emphasis on privacy (differential privacy), adversarial robustness, and causal machine learning. He leads the Systopia Lab and collaborates with groups such as UBC S&P, TrustML, and CAIDA. Education: PhD in Computer Science from Columbia University (2019), MSc from Columbia University (2013) and École Polytechnique (2011). Research Interests: Ensuring rigorous guarantees for AI systems via differential privacy, robustness against adversarial attacks, and causal inference. His work spans theoretical foundations and practical implementations in areas like privacy-preserving systems, federated learning, and transparent machine learning. Awards: SOSP Distinguished Artifact Honorable Mention (2024), Google Research Award (2022), Bourse Carnot Fellowship (2011), and UBC’s top teaching evaluations (2021). Advising & Service: Supervised over 20 students (PhD, MSc, undergrad) across privacy and machine learning topics. Serves on PCs for top conferences (OSDI, S&P, NeurIPS) and organizes workshops like TrustML @ UBC. Actively mentors high school students in ML research through outreach programs. Labs/Teams: Leads the Systopia Lab at UBC, focusing on AI safety and privacy. Collaborates with MSR, Google, and industry partners on practical system implementations.

Dan Kowalمشاهده پروفایل

دانشیار

• Bayesian Statistics
• Functional Data Analysis
• Mixed Data Modeling
• +۶ مورد دیگر

Dan Kowal is an Associate Professor in the Department of Statistics and Data Science at Cornell University, joining in 2024. His research focuses on Bayesian models for large/dependent data, mixed data modeling, and interpretable uncertainty quantification. Key areas include public health, environmental justice, epidemiology, and economics. He holds a PhD from Cornell University (2017) and previously served as an Assistant Professor at Rice University. Awards include the Blackwell-Rosenbluth Award (2021), Army Research Office Young Investigator Award (2020), and Lindley Prize Honorable Mention (2024). Notable grants include NSF funding for adaptive dependent data models (2022–2025) and Army Research Office support for Bayesian prediction methods (2020–2022). His work addresses racial inequities in statistical modeling and has been published in top journals like JASA and Bayesian Analysis. He advises multiple PhD students and develops R packages (e.g., SeBR, countSTAR) for Bayesian regression and data synthesis. Teaching roles include Bayesian Statistics at both undergraduate and graduate levels.

Somil Bansalمشاهده پروفایل

استادیار

• Autonomous Systems
• Robotics
• Control Theory
• +۳ مورد دیگر

Somil Bansal is an Assistant Professor in the Department of Aeronautics and Astronautics at Stanford University, part of the School of Engineering. Previously, he served as an Assistant Professor in the Electrical and Computer Engineering (ECE) department at the University of Southern California. He holds a B.Tech. from IIT Kanpur, an MS, and a Ph.D. from UC Berkeley’s EECS department. Research Focus: Development of mathematical tools and algorithms for safety-critical autonomous systems, emphasizing learning-enabled systems’ safety. Key Collaborations: Waymo, Skydio, Google, Boeing, NASA AMES/JPL. Awards: NSF CAREER Award, Eli Jury Award, RSS Pioneer Award, and Outstanding Graduate Instructor Award. His research integrates control theory and machine learning to ensure safety in autonomous systems, focusing on safe learning frameworks, anomaly detection, and real-time safety guarantees. He leads the Safe and Intelligent Autonomy (SIA) Lab, which explores applications in robotics, autonomous driving, and aerospace systems. Teaching: Courses include Introduction to Control Design Techniques and Principles of Safety-Critical Autonomy. He advises doctoral students and supervises research projects in his lab.

Wojciech Jaroszمشاهده پروفایل

دانشیار

• Computer Graphics
• Rendering
• Light Transport Simulation
• +۳ مورد دیگر

Wojciech Jarosz is an Associate Professor of Computer Science at Dartmouth College, affiliated with the College of Engineering and Computer Science. His research focuses on computer graphics, particularly light transport simulation, rendering algorithms, and digital fabrication. He co-founded the Visual Computing Lab and previously led the rendering group at Disney Research Zürich. Jarosz holds a Ph.D. and M.S. from UC San Diego and a B.S. from the University of Illinois Urbana-Champaign. His educational background includes studies in computer science and engineering, with a strong emphasis on graphics and rendering. Research interests span light transport simulation, Monte Carlo methods, appearance capture, and fabrication. Notable achievements include the Eurographics Young Researcher Award (2013) and the NSF CAREER Award (2019). Jarosz's work integrates theoretical rigor with practical applications, such as real-time rendering techniques and volumetric light transport. His lab develops tools for artistic authoring, including intuitive metaphors for volumetric lighting in animated films. Recent projects explore wave-optics BSDF models, optical heterodyne rendering, and unifying radiative transfer models. Key awards include the SIGGRAPH 2024 Best Paper Award and Neukom Institute prizes. His teaching includes courses on rendering algorithms, computer graphics, and computational photography. Jarosz collaborates with industry (e.g., Disney, NVIDIA) and advocates for diversity in computer graphics research.

Marcelo Mattarمشاهده پروفایل

استادیار

• Computational Neuroscience
• Decision-Making
• Reinforcement Learning
• +۳ مورد دیگر

Marcelo Mattar is an Assistant Professor of Psychology and Neural Science at New York University, leading the Mattar Lab. His research focuses on the neural computations underlying memory, decision-making, and reinforcement learning. He holds a Ph.D. in Psychology from the University of Pennsylvania and has held academic positions at NYU, UC San Diego, and postdoctoral roles at Princeton University and the University of Cambridge. His work bridges computational neuroscience and artificial intelligence, aiming to model how the brain uses internal models for planning and decision-making. Education: Ph.D. in Psychology (Computational and Cognitive Neuroscience), University of Pennsylvania, 2016 M.A. in Statistics, University of Pennsylvania, 2016 B.A. in Electronics Engineering, Instituto Tecnologico de Aeronautica, Brazil, 2010 Research Interests: The lab develops mathematical models of learning and decision-making, leveraging reinforcement learning, Bayesian statistics, and neural networks. Experiments involve human behavioral studies and neuroimaging, with collaborations in animal electrophysiology and computational psychiatry. Key Contributions: His work explores how episodic memory and hippocampal replay support flexible decision-making. Recent studies highlight parallels between human cognition and AI systems, such as language models' metacognitive abilities and brain-inspired algorithms. Awards: Newton International Fellowship, Royal Society (2018–2019) Lab Team: The lab includes postdocs, PhD students, and undergraduates from diverse fields like cognitive science, neuroscience, and computer science. Current members are listed on the lab's website. Lab Location: Meyer Hall, 6 Washington Place, New York, NY 10003.

Xiaolei Fangمشاهده پروفایل

دانشیار

• Statistical Learning
• Deep Learning
• Federated Learning
• +۳ مورد دیگر

Xiaolei Fang is Associate Professor in the Edward P. Fitts Department of Industrial and Systems Engineering at North Carolina State University. His research develops advanced statistical learning, deep learning, and optimization methods for industrial applications involving high-dimensional data, with particular focus on condition monitoring, failure prognostics, and system performance optimization. He holds a PhD in Industrial Engineering and MS in Statistics from Georgia Tech. Professor Fang's research integrates machine learning with industrial engineering to solve complex problems in predictive maintenance, quality control, and energy systems. His methodological innovations include federated learning approaches for privacy-preserving prognostics, distributionally robust machine learning models, and tensor-based statistical methods for manufacturing quality diagnostics. He has received multiple prestigious awards including the ISE Outstanding Research Award (2024), Sigma Xi Best PhD Thesis Award (2019), and SAS Data Mining Best Paper Award (2016). His research has been funded by NSF, Cisco Systems, and the US Department of Energy. Professor Fang teaches courses in Quality Design & Control, Statistical Models for Systems Analytics, High-Dimensional Data Analytics, and Optimization Models. He has supervised 9 PhD students to completion and currently advises 7 graduate students working on projects spanning federated learning for prognostics, tensor-based quality control, and machine learning applications in manufacturing and energy systems.

Anshumali Shrivastavaمشاهده پروفایل

دانشیار

• Large Scale Machine Learning
• Randomized Algorithms for Big Data
• Graph Mining
• +۳ مورد دیگر

Anshumali Shrivastava is an Associate Professor of Computer Science, Electrical and Computer Engineering, and Statistics at Rice University, affiliated with the George R. Brown School of Engineering. His research focuses on large-scale machine learning, randomized algorithms for big data, and graph mining. He holds a PhD from Cornell University (2015) and an MSc from the Indian Institute of Technology Kharagpur (2008). His research interests span scalable deep learning, efficient neural network inference, and probabilistic algorithms. He has pioneered techniques in compressed learning, hashing-based search, and distributed optimization for handling massive datasets. Notable contributions include methods for accelerating LLM inference, memory-efficient quantization, and graph processing algorithms. Teaching: Probabilistic Algorithms, Large-Scale ML, and Machine Learning Seminars Awards: Charles W. Duncan Jr. Achievement Award (2023), Young Faculty Research Award (2021), NSF CAREER Award (2017), and multiple best paper awards His work bridges algorithm design with practical applications in recommendation systems, genomics, and edge computing. Current efforts focus on sustainable AI, hardware-aware compression, and efficient training/inference pipelines for large models.

Ingo Bojakمشاهده پروفایل

استاد

• Computational Neuroscience
• Neurodynamics
• Neural Population Models
• +۳ مورد دیگر

Ingo Bojak is a Professor at the School of Psychology and Clinical Language Sciences , University of Reading. His research focuses on computational neuroscience, neurodynamics, and neural population models. He explores topics such as biological mistake-making, EEG analysis, and the effects of anesthesia on brain activity. Bojak serves as an associate editor for Neurocomputing and related journals. His work bridges theoretical frameworks with experimental data, emphasizing cross-scale biological phenomena and neural network dynamics. Bojak’s research interests include understanding spontaneous neural oscillations, cortical activity modeling, and the integration of EEG/fMRI data. He has contributed to advancements in neural field theory and Bayesian uncertainty quantification. His studies on biological mistakes highlight adaptive mechanisms across biological systems. Related affiliations include collaborations with the School of Biological Sciences at the University of Reading. Recent publications emphasize theoretical biology, computational neuroscience, and interdisciplinary approaches to understanding neural systems. His work often addresses functional adaptation through error-driven mechanisms and explores the interplay between neural excitability and inhibition.

Stefanie Czischekمشاهده پروفایل

استادیار

• Quantum many-body systems
• Quantum condensed matter
• Quantum computation and simulation
• +۴ مورد دیگر

Dr. Stefanie Czischek is an Assistant Professor in the Department of Physics at the University of Ottawa, leading the APRIQuOt research group focused on artificial and physically realizable intelligence for quantum applications. She joined uOttawa in 2022 after postdoctoral work at the University of Waterloo. Her research bridges quantum technologies and neural networks, with expertise in quantum simulation, neuromorphic computing, and machine learning applications in quantum physics. Research Interests: Quantum computation/simulation using neural networks Neuromorphic hardware implementations Quantum many-body systems Machine learning for quantum control and tomography Her publications demonstrate strong interdisciplinary focus, combining quantum physics with cutting-edge ML techniques. Recent works explore transformer models for quantum simulation, neural network quantum states, and quantum sensing applications. The research shows consistent evolution toward hardware-algorithm co-design for quantum problems. Awards: Springer Thesis Award (2020) for doctoral research on neural-network simulation of quantum systems. Research Group & Advising: Leads the APRIQuOt lab with 1 postdoc, 6 graduate students, and 1 undergraduate. Current projects include large language models for quantum states, quantum optimal control via reinforcement learning, and neuromorphic quantum simulations. The group collaborates with experimental teams and maintains strong industry-academia partnerships.

Benjamin Grimmerمشاهده پروفایل

استادیار

• Continuous Optimization
• Algorithms for Machine Learning
• Adversarial Optimization
• +۳ مورد دیگر

Benjamin Grimmer is an Assistant Professor in the Department of Applied Mathematics and Statistics at Johns Hopkins University. He is affiliated with the Mathematical Institute for Data Science (MINDS) and the Data Science & AI Institute. His research focuses on designing and analyzing algorithms for continuous optimization, particularly in nonconvex, nonsmooth, and adversarial settings. Grimmer’s work bridges classical optimization theory and modern machine learning challenges, leveraging computer-assisted proof techniques to advance algorithmic foundations. He earned his PhD in Operations Research from Cornell University, advised by Jim Renegar and Damek Davis. His doctoral work was supported by a National Science Foundation fellowship. Grimmer has held research positions at Google and the Simons Institute, exploring adversarial optimization and continuous-discrete optimization interfaces. His current work is supported by the Air Force Office of Scientific Research and a 2024 Alfred P. Sloan Fellowship. Research interests include algorithm design for stochastic/nonconvex/nonsmooth optimization, computer-aided proof methods, and meta-optimization tools like stepsize schedules. His recent studies, including work on gradient descent acceleration via long steps, were highlighted in Quanta Magazine (2023). Education: PhD in Operations Research, Cornell University (advisor: Jim Renegar and Damek Davis) Awards: Alfred P. Sloan Fellowship in Mathematics (2024) National Science Foundation Graduate Fellowship (PhD support) Dr. Grimmer advises a research group including PhD candidates Ning Liu, Thabo Samakhoana, Alan Luner, Yue Wu, and others. His lab explores optimization algorithms through both theoretical and applied lenses, collaborating closely with industry and academic partners.

Hector Geffnerمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Automated Planning
• +۵ مورد دیگر

Hector Geffner is an Alexander von Humboldt Professor at RWTH Aachen University, leading the Chair of Machine Learning and Reasoning. He specializes in automated planning, machine learning, and reasoning, with a focus on representation learning for acting and planning. His work bridges symbolic and model-based AI, emphasizing general policies and subgoal decomposition. Education & Background : PhD from UCLA (1989), prior roles at IBM Watson Research Center and Universidad Simón Bolívar. Former ICREA researcher and professor at Universitat Pompeu Fabra (2001–2022). Research Interests : Classical and probabilistic planning, reinforcement learning, knowledge representation, and applications in robotics. His ERC-funded RLeap project explores learning generalized policies and symbolic representations for effective decision-making. Teaching : Courses include 'Actions and Planning in AI' and 'Social and Technological Change', emphasizing interdisciplinary AI applications. Awards & Recognition : Alexander von Humboldt Professorship (2023), AAAI/EurAI Fellowships, and editor of influential works on Judea Pearl’s contributions to AI. Grants & Projects : Advanced ERC grant (2020–2025), Humboldt Foundation support, and RWTH funding for research on planning and reasoning. Labs & Teams : Heads the Chair of Machine Learning and Reasoning at RWTH, focusing on interdisciplinary research in AI, robotics, and planning algorithms.

Sherry Tongshuang Wuمشاهده پروفایل

استادیار

• Human-Computer Interaction
• Natural Language Processing
• Artificial Intelligence
• +۶ مورد دیگر

Sherry Tongshuang Wu is an Assistant Professor at Carnegie Mellon University's School of Computer Science, with primary appointments in the Human-Computer Interaction Institute (HCII) and secondary affiliation with the Language Technology Institute (LTI) . Trained at the University of Washington under Jeffrey Heer and Dan Weld, she bridges HCI and NLP to study human interactions with AI systems across diverse user groups. Her educational background includes a Ph.D. (2016-22) and M.S. (2016-18) in Computer Science and Engineering from the University of Washington, and a B.Eng. (2012-16) from Hong Kong University of Science and Technology. Industry experience includes research internships at Google Brain, Microsoft Research, and Apple. Wu's research focuses on three interconnected pillars: Real-world AI Evaluation (developing frameworks like SPHERE for systematic assessment), Task-specific AI Test & Distill (optimizing general-purpose models for specific use cases), and Human-AI Task Delegation (designing optimal collaboration between humans and AI). Her work emphasizes practical deployment, user-specific net gains, and error recovery mechanisms. Analysis of her 15 most recent publications reveals a strong trend toward evaluation frameworks (35%), human-AI collaboration systems (30%), and specialized model distillation (25%), with growing emphasis on educational applications (10%). Key methodological themes include checklist-based evaluation, perspective-aware retrieval, and structural analysis of AI outputs. Google Academic Research Award (2024) Amazon Research Awards (2024) AIED 2024 Best Paper Award ACL 2020 Best Paper Award Rising Stars in EECS Workshop (2020) Wu actively mentors 19 students across PhD, Master's, and undergraduate levels, with notable projects including synthetic data generation, LLM literacy tools, and retrieval system optimization. She leads significant grant-funded work through Amazon Research Awards and Google Academic Research Awards, focusing on deployable model generation and human-AI collaboration frameworks. Her lab develops practical tools like Promp2Model and SPHERE that bridge theoretical research with industry applications.

Nikolai Matniمشاهده پروفایل

استادیار

• Machine Learning
• Control Theory
• Robotics
• +۱۰ مورد دیگر

Nikolai Matni is an Assistant Professor in the Department of Electrical and Systems Engineering at the University of Pennsylvania. He holds a secondary appointment in the Department of Computer and Information Science and is a member of the Applied Mathematics and Computational Sciences (AMCS) graduate group. His research focuses on integrating learning, optimization, and control for safety-critical and data-driven cyber-physical systems, with applications in robotics, autonomy, and distributed control. Secondary Appointment: Computer and Information Science (University of Pennsylvania) Labs/Centers: GRASP Lab, PRECISE Center His research bridges machine learning, robust control, and autonomous systems, particularly in developing guaranteed-safe strategies for cyber-physical systems. He emphasizes the importance of reliability and robustness in learning-based control for applications like self-driving vehicles and agile robots, where failures could be catastrophic. Recent publications highlight advancements in vision-based robotic control, neural ODEs for motion planning, adversarial exploration strategies, and stability-constrained learning. These works span conferences such as ICRA, CoRL, WACV, IROS, and L4DC, with a focus on safety-critical applications. Notable scientific awards include the NSF CAREER Award, George S Axelby Award (for his work on System Level Synthesis), AFOSR YIP award, and Google Research Scholar Award. He was also elevated to IEEE Senior Member. Matni advises Ph.D. students in Computer and Information Science (CIS), Electrical and Systems Engineering (ESE), and Applied Mathematics and Computational Sciences (AMCS). His teaching includes courses like ESE 2030 (Linear Algebra with Engineering/AI applications) and others focused on control theory and robotics.

Brad Knoxمشاهده پروفایل

دانشیار

• Reinforcement Learning
• Human-Computer Interaction
• Artificial Intelligence
• +۴ مورد دیگر

Brad Knox is a Research Associate Professor in the Department of Computer Science at the University of Texas at Austin . His work bridges machine learning, human-computer interaction, and computational cognitive science, with a focus on developing systems that learn from human feedback. Key research areas: Reinforcement Learning, Human-AI Interaction, Reward Design, Autonomous Systems Notable contributions: TAMER framework for human-guided learning, empirical studies on reward misdesign, and human preference modeling for autonomous agents Research Trends : His recent work (2023-2025) emphasizes reward alignment, safety in autonomous systems, and preference-based learning frameworks. Earlier studies (2012-2020) established foundational methods for integrating human feedback into reinforcement learning architectures and exploring behavioral signatures in decision-making. Scientific Honors : Bert Kay Dissertation Award (2013) Victor Lesser Distinguished Dissertation Award (IFAAMAS, Runner-up, 2013) NSF SBIR Grant (PI, 2016) NSF Graduate Research Fellowship (2008-2011) IEEE Intelligent Systems AI 10 to Watch (2013) Teaching & Leadership : Knox served as Principal Lecturer for MIT's Interactive Machine Learning course (2013) and held organizational roles at major conferences including Reinforcement Learning Conference (Scheduling Chair, 2025) and RLDM workshop (Co-chair, 2022).

Aswin Sankaranarayananمشاهده پروفایل

استاد

• Computational Photography
• 3D Shape Estimation
• Imaging Systems
• +۴ مورد دیگر

Aswin Sankaranarayanan is a Professor in the Department of Electrical and Computer Engineering at Carnegie Mellon University (CMU) , where he leads the Image Science Lab . His research focuses on computational photography , 3D shape estimation , and novel imaging system design . He earned his Ph.D. in Electrical and Computer Engineering (2009) from the University of Maryland and completed a postdoctoral fellowship at Rice University (2012) . Research Themes: Developing imaging systems that exploit low-dimensional signal models to overcome traditional sensing limitations Co-design of optics and processing algorithms for efficient sensing Application of non-linear signal models to high-dimensional data Advancing compressed sensing and big data processing techniques Scientific Recognition: SIGGRAPH 2023 Best Paper Award (Split-Lohmann Multifocal Displays) CVPR 2019 Best Paper Award (Fermat Paths for NLOS Reconstruction) NSF CAREER Award (2017) Dean’s Early Career Fellowship (2018-2021) Herschel Rich Invention Award (2016) Technical Contributions: His recent publications reveal expertise in non-line-of-sight shape reconstruction , VR/AR display systems , and biomedical imaging . Collaborations span institutions like University College London and University of Toronto.