جست‌وجوی استاد و پژوهشگر

Andrea Coladangeloمشاهده پروفایل

استادیار

• Quantum Computation
• Quantum Cryptography
• Quantum Learning Theory
• +۳ مورد دیگر

Andrea Coladangelo is an Assistant Professor at the Allen School of Computer Science & Engineering , University of Washington , co-leading the Quantum group and contributing to the Theory and Crypto groups. He coordinates the NSF-funded Quantum@UW REU program , fostering undergraduate research in quantum information. Previously, he was a postdoctoral researcher at UC Berkeley and the Simons Institute , advised by Umesh Vazirani , following a PhD in Computer Science at Caltech under Thomas Vidick . His academic journey began with a B.A. in Mathematics from Oxford and a Master in Mathematics from Cambridge . He co-founded qBraid , a platform for quantum computing education. Research Interests : Andrea explores the intersection of quantum computation and cryptography , focusing on foundational questions in entanglement , quantum correlations , and quantum learning theory . His work investigates quantum pseudorandomness , device-independent security , quantum algorithms , and quantum copy-protection , often leveraging computational assumptions to bridge quantum information theory with cryptographic applications. Scientific Awards : 2025 Google Research Scholar Program Award in Quantum Computing 2023 CSE Undergraduate Teaching Award for his course on quantum computation 2019 Best Student Paper Award at QIP Teaching & Outreach : Andrea designed and taught CSE 434: Intro to Quantum Computation (Spring 2023, 2024, 2025), CSE 534: Quantum Information and Computation (Autumn 2023), and CSE 599C: Quantum Learning Theory (Winter 2025). He also delivered lectures at the 22nd Bellairs Crypto Workshop (2024) and led a quantum programming tutorial using qBraid . Labs & Teams : As co-leader of the Quantum group at the Allen School, he collaborates with researchers in Theory and Crypto , advancing quantum computing through interdisciplinary projects and educational initiatives.

David Duvenaudمشاهده پروفایل

دانشیار

• Machine Learning
• Generative Models
• Bayesian Inference
• +۵ مورد دیگر

David Duvenaud is an Associate Professor at the University of Toronto , holding a Canada Research Chair in Generative Models and a Schwartz Reisman Chair in Technology and Society . He is cross-appointed to the Department of Computer Science and Department of Statistical Sciences . A Sloan Research Fellow and founding member of the Vector Institute , his work bridges deep probabilistic models , AI safety , and scientific computing . PhD in Machine Learning (University of Cambridge, 2014) Postdoc in Hyperparameter Optimization (Harvard University, 2016) Co-founded Invenia (energy forecasting company) His research spans foundational Neural Ordinary Differential Equations (NeurIPS 2018 Best Paper) and Automatic Chemical Design (ACS Central Science 2018) to recent work on AGI governance (2025) and AI safety (2024). Key contributions include stochastic variational inference , implicit differentiation frameworks , and antisymmetrization layers for quantum Monte Carlo. Recent publications (2024-2025) focus on systemic existential risks from AI , many-shot jailbreaking attacks , and epistemic uncertainty quantification . His group trains energy-based models with scalable MCMC samplers and develops invertible neural architectures (e.g., Residual Flows NeurIPS 2019). He also explores human-AI alignment through LLM Processes (NeurIPS 2024) and Sycophancy in Language Models (ICLR 2024). Canada Research Chair (2025) NSERC Grant (2025) Sloan Research Fellow (2021) Schwartz Reisman Chair (2021) Best Paper Award (NeurIPS 2018) Distinguished Paper Award (ICFP 2021) His students include James Requeima , Jesse Bettencourt , and Raymond Douglas . He teaches courses on Statistical Methods for Machine Learning and Differentiable Inference . Current work (2025) investigates systemic human disempowerment through incremental AI capabilities and sabotage risk mitigation via hyperparameter-aware evaluations.

• Computational Physics
• Machine Learning
• Applied Mathematics
• +۷ مورد دیگر

Andrew M. Stuart is a Professor at the California Institute of Technology's Division of Engineering and Applied Science. His research bridges computational mathematics, machine learning, and physical modeling, focusing on inverse problems, partial differential equations, and multiscale systems. He has pioneered methodologies integrating Gaussian processes, Kalman inversion, and neural operators for scientific computing. His recent publications highlight innovations in competitive protein dimerization networks, nonlinear Bayesian inference, and operator learning. Articles span applications in materials science, geophysics, and biochemical signal processing, emphasizing data-driven discovery of differential equations and scalable algorithms for high-dimensional problems. Stuart's work addresses challenges in structural error modeling, uncertainty quantification, and graph-based learning, with implications for climate modeling and dynamical systems. Despite extensive contributions, the scraped data does not specify students, awards, or contact details.

Margaret P. Chapmanمشاهده پروفایل

استادیار

• Risk-Averse Control Theory
• Stochastic Control Theory
• Safety Analysis for Dynamical Systems
• +۴ مورد دیگر

Margaret P. Chapman is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Toronto's Faculty of Applied Science and Engineering. She leads the DATA Lab (Decision Analysis for Trustworthy Autonomy), focusing on risk-averse and stochastic control theory with applications to environmental and human health. Education: B.S. and M.S. in Mechanical Engineering from Stanford University (2012, 2014), Ph.D. in Electrical Engineering and Computer Sciences from UC Berkeley (2020, advised by Claire Tomlin) Her research bridges robust and stochastic optimal control via risk measure theory, emphasizing safety-critical applications in healthcare and sustainable cities. Key challenges include scalable risk-sensitive control methods, integrating physics-based and data-driven models for safety analysis, and promoting technologies that enhance planetary and human well-being. Recent publications focus on risk-averse autonomous systems, CVaR-based safety analysis, and multi-time-scale modeling for cancer treatment. She has advised students in both graduate and undergraduate research roles, including NSERC awardees and thesis participants. Awards: US National Science Foundation Graduate Research Fellowship, Berkeley Fellowship, Terman Engineering Scholastic Award, Leon O. Chua Award She teaches courses like ECE 557 (Linear Control Theory) and ECE 1643 (Risk-Averse Control with Learning). Her invited talks span institutions such as MIT, Princeton, and Georgia Tech, highlighting risk-sensitive analysis and control for trustworthy autonomy.

• Artificial Intelligence
• Strategic Reasoning in Multi-Agent Systems
• Formal Verification
• +۶ مورد دیگر

Aniello Murano is a Professor of Computer Science at the Department of Electrical Engineering and Information Technologies , University of Naples Federico II. He serves as Scientific Director of the ASTREA (Automated Strategic Reasoning) Laboratory and leads cutting-edge research in Artificial Intelligence, Strategic Reasoning, Multi-Agent Systems , and Formal Verification . Research Interests : Strategic reasoning under perfect/imperfect information, specification/verification/synthesis of reactive systems, temporal/modal logics, automata theory, parity games, game theory, mechanism design, and formal languages. Notable Projects : PNRR Research Unit Coordinator (2023-2025) on Resilient AI, PRIN 2020 Unit Coordinator (RIPER: Resilient AI-Based Self-Programming and Strategic Reasoning), H2020-MSCA SEAL (Principal Coordinator). Awards & Honors : JPMorgan Faculty Research Award (2022), Royal Society Award (2016), Best Paper PRIMA (2015), INDAM Project Leader (2023), Italian Scientific Habilitation (2017-2018). Students & Postdocs : Supervised 6 PhD students (e.g., Silvia Stranieri, Vadim Malvone) and mentored postdocs such as Munyque Mittelmann and Bastien Maubert. Laboratory : Leads ASTREA Lab, focusing on automated strategic reasoning and resilient AI systems.

Irina Rishمشاهده پروفایل

استاد

• Machine Learning
• Neural Scaling Laws
• Emergent Behaviors in AI
• +۱۳ مورد دیگر

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

Peter Zollerمشاهده پروفایل

استاد

• Theoretical Physics
• Quantum Optics
• Many-Body Quantum Physics
• +۳ مورد دیگر

Peter Zoller is a Professor of theoretical physics at the University of Innsbruck and Scientific Director at IQOQI Innsbruck (Austrian Academy of Sciences). His research focuses on quantum optics, many-body quantum physics, and quantum information science, with a strong emphasis on quantum simulation of gauge theories and atomic systems. He has trained 34 PhD students and hosted 57 postdoctoral researchers, fostering collaborations between theory and experiment. His group, the Zoller Group, explores quantum phenomena such as lattice gauge theories, entanglement dynamics, and topological order using advanced quantum simulation techniques. Key research interests include atomic physics, quantum gases, and applications of quantum technologies to high-energy physics problems. Recent work addresses string breaking in quantum simulators, entanglement Hamiltonians, and scalable architectures for fermionic quantum processors. Collaborations span institutions like Harvard, MIT, and the University of Innsbruck’s experimental teams. His contributions bridge foundational physics with cutting-edge quantum technologies, aiming to solve problems inaccessible to classical methods.

Stephen Pankavichمشاهده پروفایل

استاد

• Partial Differential Equations
• Kinetic Theory
• Mathematical Biology
• +۴ مورد دیگر

Stephen Pankavich is a Professor and Department Head in the Department of Applied Mathematics and Statistics at the Colorado School of Mines. He holds a PhD in Mathematical Sciences from Carnegie Mellon University, with research focused on partial differential equations, kinetic theory, and mathematical biology. His work bridges theoretical analysis and computational methods, addressing challenges in plasma dynamics, epidemiological modeling, and multiscale systems. Education: PhD, Mathematical Sciences, Carnegie Mellon University (2005) MS, Mathematical Sciences, Carnegie Mellon University (2001) BS, Mathematical Sciences, Carnegie Mellon University (2000) Research interests include the analytical and numerical study of collisionless plasmas, HIV dynamics, and epidemiological models. He has received awards such as the W.M. Keck Mentorship Award and the Colorado School of Mines Alumni Teaching Award. His articles explore topics like plasma decay rates, HIV therapy models, and particle-tracking algorithms. He has advised over 20 graduate and undergraduate students, contributing to impactful research in applied mathematics and computational science.

• Databases
• Web Data Management
• Logical Methods in Computer Science
• +۴ مورد دیگر

Michael Benedikt is a Professor of Computer Science at the University of Oxford and a Governing Body Fellow of University College. He holds the role of Director of the Advanced MSc in Computer Science program. His research focuses on databases, Web data management, logical methods in computer science, and theoretical computer science. Benedikt's work intersects with artificial intelligence, machine learning, and algorithms, with contributions to query languages, data integration, and formal methods. Education: Ph.D. in Mathematics, University of Wisconsin, 1993 Prior roles: Distinguished Member of Technical Staff at Bell Laboratories (1994–2006), visiting researcher at Yahoo! Labs Research Interests: Databases and information exchange Web and Web 2.0 data management Logical methods in computer science Formal verification and query optimization Applications in AI and machine learning Key Projects: FOX : Query-driven data acquisition from web-based sources PDQ : Proof-driven query answering over web-based data TRANCE : Transforming nested collections efficiently Awards: Best Paper Award at ICALP 2017 (Track B) EPSRC Established Career Fellowship (2015–2020) Advising & Grants: Directed the MSc in Advanced Computer Science program Supervised PhD students including Chia-Hsuan Lu and past advisees such as Luying Chen and Ben Spencer Received funding for projects like the ERC DIADEM initiative Labs & Teams: Active in the Department of Computer Science’s research groups, including the Algorithms At Large and Databases teams.

• Computer Science
• Formal Methods
• Automata Theory
• +۳ مورد دیگر

Dr Daniel Hausmann is a Lecturer in the Department of Computer Science at the University of Liverpool, affiliated with the School of Electrical Engineering, Electronics and Computer Science. His research focuses on formal methods, automata theory, and game theory, with applications to reactive synthesis and modal fixpoint logics. Current academic rank: Lecturer Key research areas: Formal verification, automata theory, game-based synthesis algorithms Contributions: Development of tools like COOL-MC and COOL 2 for modal fixpoint logics Recent research trends involve solving complex games for system verification, optimizing symbolic algorithms, and advancing coalgebraic approaches to satisfiability checking. His work spans both theoretical foundations and practical implementations for formal verification systems. Notable collaborations include researchers at the intersection of computer science and logic (e.g., N. Piterman, L. Schröder). The University of Liverpool's Computer Science department supports his work through academic infrastructure and research partnerships.

• Physico-Chemical Phenomena
• Thermal-Fluids
• Interfaces
• +۱۰ مورد دیگر

Professor Kripa K. Varanasi is a faculty member in the Department of Mechanical Engineering at the Massachusetts Institute of Technology, operating within the School of Engineering. Her interdisciplinary research spans physico-chemical phenomena, thermal-fluids, interfacial science, and electrochemical systems, with applications in energy, water, biomedical technologies, and sustainable manufacturing. Alumnus of Indian Institute of Technology Madras (B.Tech, 1998), MIT (M.S. 2002, Ph.D. 2004) Former GE Global Research lead with multiple technology awards Her research program uniquely bridges fundamental surface science with scalable industrial solutions, evidenced by startup ventures like LiquiGlide, Infinite Cooling, and AgZen. Expertise lies in Nanostructured surfaces , electrochemical fluid control , and bio-inspired material design . The group's visible light-responsive TiO 2 surfaces enable transformative approaches to oil-water separation and microfluidic manipulation. Scientific recognition includes: NSF Career Award DARPA Young Faculty Award ASME Bergles-Rohsenow Heat Transfer Award MIT Graduate Advising Excellence Award Multiple startup competition victories Key publications reveal systematic innovations in optically modulated wetting , dye-sensitized interfacial engineering , and industrial fluid dynamics . The group's startups have won prestigious competitions including the MIT-100K and MassChallenge Diamond Winner distinction.

Justin Y. Shiمشاهده پروفایل

دانشیار

• Scalable Computing
• Blockchain Protocols
• Software Safety
• +۵ مورد دیگر

Justin Y. Shi is an Associate Professor at Temple University's Computer and Information Science Department. His research focuses on scalable computing, blockchain protocols, software safety, and infinitely scalable systems. He holds a PhD from the University of Pennsylvania and has held roles including Interim Director of the Center for Advanced Computing and Communications (1992-1998) and CIS Department Chair (2007-2009). B.S. Computer Engineering, Shanghai Jiao-tong University (1977) M.S. Software Engineering, University of Pennsylvania (1983) PhD in Concurrent Programming, University of Pennsylvania (1984) His research explores decoupling programs and data from physical devices to achieve fault tolerance and scalability. Key contributions include the Statistic Multiplexed Computing (SMC) paradigm and foundational work on parallel algorithms and distributed systems. He has authored patents on scalable parallel computing and high-performance blockchain systems. Teaching highlights include courses on quantum computing, cybersecurity, and fullstack programming. He has advised over 15 students, many now in industry and academia. Founder & CEO of Parallel Computers Technology Inc. (2000-2017) Co-founder of SMC Labs (2022-present) Active in editorial roles for IEEE Blockchain Technology Briefs and contributor to NSF workshops on cloud computing. His work has been exhibited at Supercomputing Conferences (1991-2018).

Alvaro Torralbaمشاهده پروفایل

دانشیار

• Symbolic Search
• Artificial Intelligence
• Machine Learning
• +۳ مورد دیگر

Alvaro Torralba is an Associate Professor in the Department of Computer Science at Aalborg University, Denmark, affiliated with the Technical Faculty of IT and Design. His research focuses on symbolic search, heuristic functions, and planning algorithms within artificial intelligence and machine learning. Notable projects include the ConAn initiative exploring contrastive analysis for state-space exploration. He has contributed extensively to classical planning, probabilistic planning, and automated planning competitions, earning awards such as the First Prize in the Agile Track of the 10th International Planning Competition (IPC’23). His work often bridges theoretical advancements with practical applications, including game-based network update synthesis and believable non-player character development. Research outputs include over 60 publications since 2011, with a focus on optimizing search algorithms and enhancing planning efficiency through techniques like operator-potential heuristics and bidirectional search strategies. His scientific contributions span algorithmic innovation, verification methodologies, and large-scale abstraction evaluation. Collaborations and datasets include foundational work on PDDL generators and pattern databases, with open-access resources available via Zenodo. As a program committee member and award-winning researcher, Torralba actively contributes to advancing the frontiers of AI planning and decision-making systems.

Sylvain Conchonمشاهده پروفایل

استاد

• Formal Methods
• Automated Deduction
• Program Verification
• +۱۰ مورد دیگر

Sylvain Conchon is a Professor at Université Paris-Saclay, affiliated with the Laboratoire Méthodes Formelles (LMF, UMR 9021) and the Toccata research team, a joint project with INRIA Saclay – Île-de-France. His work lies at the intersection of formal methods, automated reasoning, and software verification. Research Interests: His primary research areas include SMT solving, automated deduction, program verification, model checking for parameterized systems, and information flow analysis. He develops theoretical foundations and practical tools to ensure software correctness, particularly in safety-critical and concurrent systems. Publications and Tools: His recent work centers on projects like BPI IDemo ARGOS (2024) and PEPR Secureval (2022), focusing on industrial software verification and cybersecurity. He is the main developer of Alt-Ergo , an SMT-based theorem prover, and Cubicle , a model checker for parameterized systems. His publications span formal methods, logic in computer science, and static analysis, with a strong emphasis on tool building and application. Scientific Awards: Advising and Grants: He has advised several PhD students including Guillaume Girol, Alain Mebsout, and Mohamed Iguernelala. He has led and participated in numerous research projects funded by ANR, FUI, PEPR, and industrial partners, demonstrating sustained grant success in formal methods and verification. Labs and Teams: He is a core member of the Toccata team at INRIA Saclay and the LMF laboratory, where he collaborates on advancing the state-of-the-art in deductive program verification and automated reasoning.

• Applied Stochastic Analysis
• Mathematical Ecology
• Interacting Particle Systems
• +۳ مورد دیگر

Michela Ottobre is a Professor of Mathematics at Heriot-Watt University, affiliated with the School of Mathematical & Computer Sciences. She holds editorial roles at Foundations of Data Science and SIAM Review , and is a member of the European Mathematical Society and Edinburgh Mathematical Society. Her research focuses on applied stochastic analysis, mathematical ecology, and multiscale modeling, with applications to rewilding and ecological systems. Education & Career PhD in Mathematics, Imperial College London (2012) Postdoctoral roles at Warwick University and Imperial College London Joined Heriot-Watt as Assistant Professor (2014), promoted to Full Professor (2024) Research Interests Her work integrates stochastic processes, interacting particle systems, and PDEs to model ecological and biological phenomena. Key areas include: Stochastic multiscale models Mathematical ecology and rewilding Numerical methods for SDEs Grants & Awards Leverhulme Research Grant (2020–2023) EPSRC Standard Grant (2025, £550k) for multiscale methods Sir Edmund Whittaker Memorial Prize (2019) Collaborations & Outreach Organized workshops including the 11th International Conference on Stochastic Analysis (2023) and contributed to ecological modeling initiatives. Supervises PhD students in applied stochastic analysis and mathematical ecology.