جست‌وجوی استاد و پژوهشگر

Tzu-Mao Liمشاهده پروفایل

استادیار

• Computer Graphics
• Rendering Algorithms
• Differentiable Programming
• +۵ مورد دیگر

Tzu-Mao Li is an Assistant Professor in the Department of Computer Science and Engineering (CSE) at the University of California, San Diego (UCSD), affiliated with the Center for Visual Computing. His research focuses on differentiable graphics algorithms, combining classical visual computing with modern machine learning techniques. He holds a Ph.D. from MIT CSAIL under Frédo Durand and a postdoc at MIT and UC Berkeley with Jonathan Ragan-Kelley. His work spans rendering, programming languages for graphics, Monte Carlo methods, and inverse problems. Education: B.S. and M.S. from National Taiwan University (2011-2013), advised by Yung-Yu Chuang. Ph.D. from MIT CSAIL (Computer Graphics Group), advised by Frédo Durand. Postdoctoral research at MIT and UC Berkeley with Jonathan Ragan-Kelley. Research Interests: Differentiable rendering, Monte Carlo integration, programming language design for visual computing, physical simulation, adversarial machine learning, and applications in computer vision and robotics. Key areas include rendering algorithms (path tracing, bidirectional methods), optimization techniques (MCMC, gradient-based), and neural representations (SDFs, neural fields). Publications focus on advancing rendering algorithms, differentiable systems, and applications in inverse problems. Notable contributions include edge sampling for differentiable rendering, warped-area sampling, and diffusion models for BSDF sampling. Awards: ACM SIGGRAPH 2020 Outstanding Doctoral Dissertation Award, multiple Best Paper Awards at SIGGRAPH, and oral presentations at ICCV. Teaching: Courses include CSE 167 (Computer Graphics), CSE 168 (Rendering), and CSE 272 (Advanced Image Synthesis), emphasizing physically-based methods and programming.

Ashley Montanaroمشاهده پروفایل

استاد

• Quantum Computation
• Quantum Algorithms
• Quantum Information Theory
• +۳ مورد دیگر

Ashley Montanaro is Professor of Quantum Computation in the School of Mathematics at the University of Bristol, and co-founder of the quantum software startup Phasecraft. He is a member of the Quantum Information Theory research group at Bristol. His research focuses on the theory of quantum computing, with particular interest in quantum algorithms, computational complexity, quantum query and communication complexity, and classical algorithms. His work spans both theoretical foundations and practical applications of quantum computing. Montanaro's research output shows significant trends toward quantum algorithms for optimization problems, quantum computational supremacy, and bridging theoretical advances with practical implementation challenges. His publications span foundational quantum information theory to applied quantum algorithms, demonstrating a versatile research program that connects computer science with quantum physics. Among his professional activities, Montanaro served on the QIP steering committee (2016-2018) and was an editor for the Quantum journal until 2019. He has been active in conference organization, serving on program committees for ITCS 2018, AQIS 2017 and 2015, QIP 2015, and TQC 2014 and 2013, reflecting his standing in the quantum computing research community. He has supervised numerous PhD students including Josh Blake, Jorja Kirk, Sheila Perez Garcia, Sami Boulebnane, Jan Lukas Bosse, Lana Mineh, Joao F. Doriguello, Chris Cade, Sam Pallister, and Stephen Piddock. His teaching includes Quantum Computation (MATHM0023) which he has taught since 2014 and Advanced Quantum Information Theory which he taught in 2015 and 2016. As co-founder of Phasecraft, Montanaro is actively translating theoretical quantum computing advances into practical software solutions, positioning him at the intersection of academic research and quantum technology commercialization.

Seth Lloydمشاهده پروفایل

استاد

• Quantum Information Science
• Quantum Computation
• Quantum Communications
• +۵ مورد دیگر

Seth Lloyd is a Professor of Mechanical Engineering at the Massachusetts Institute of Technology (MIT), where he directs the Center for Extreme Quantum Information Theory (xQIT). His work bridges theoretical physics, quantum information science, and complex systems theory. He has made significant contributions to the foundations of quantum computing and quantum information processing. Lloyd received his education from prestigious institutions: B.A. from Harvard College (1982) M.Phil from Cambridge University (1984) as a Marshall Scholar Ph.D. in Physics from Rockefeller University (1988) Lloyd's research focuses on quantum information science, particularly quantum computation and quantum communications. He has pioneered work in quantum analog computation, quantum error correction, and quantum metrology. His research explores how quantum mechanics can be harnessed for information processing tasks, with applications ranging from quantum computing to understanding biological processes like photosynthesis. Lloyd is also known for his work on complex systems and the relationship between information and physical systems, arguing that the universe itself can be viewed as a quantum computer. His publication record shows a clear progression from foundational quantum computing work to applications in quantum machine learning and quantum biology. The most recent articles reveal a strong focus on quantum algorithms for machine learning, quantum metrology, and the intersection of quantum mechanics with biological systems. His work on the HHL algorithm for solving linear systems has been particularly influential in quantum machine learning, though its practical advantages have been debated following Ewin Tang's classical algorithms. Lloyd has received numerous scientific honors: Lindbergh Fellow (1994) Finmeccanica Professorship (1996) Edgerton Prize (2001) Fellow of the American Physical Society (2007) Quantum Communication Award (2012) International Quantum Communication Award (2012) Throughout his career, Lloyd has mentored numerous students and researchers in quantum information science. He has secured significant research funding for his work in quantum computing and complex systems. His research has been supported by various foundations and government agencies interested in advancing quantum technologies. Lloyd has also been involved in interdisciplinary collaborations, particularly with biologists studying quantum effects in photosynthesis. Lloyd directs the Center for Extreme Quantum Information Theory (xQIT) at MIT, which brings together researchers from physics, computer science, and engineering to tackle fundamental challenges in quantum information processing. His lab has been at the forefront of developing theoretical frameworks for quantum computing and exploring practical implementations of quantum information protocols.

Andrew Childsمشاهده پروفایل

استاد

• Quantum Algorithms
• Quantum Information Processing
• Quantum Simulation
• +۲ مورد دیگر

Andrew Childs is a Professor at the University of Maryland, affiliated with the Department of Computer Science and the Institute for Advanced Computer Studies (UMIACS). He serves as Director of the NSF Quantum Leap Challenge Institute for Robust Quantum Simulation (RQS) and is a Fellow at the Joint Center for Quantum Information and Computer Science (QuICS). His research focuses on quantum algorithms for simulating physical systems, algebraic problems, and quantum walk protocols, with applications in quantum computing and computational complexity. University of Maryland Institute for Advanced Computer Studies (UMIACS) Joint Center for Quantum Information and Computer Science (QuICS) NSF Quantum Leap Challenge Institute for Robust Quantum Simulation Childs' research spans quantum simulation, quantum Fourier transform, phase estimation, and Hamiltonian dynamics. He has developed techniques to reduce quantum computational resources for simulating quantum systems and explored limitations of quantum computers through hidden subgroup problems and non-unitary dynamics. His publications cover diverse areas including quantum walk optimization, Hamiltonian simulation methods, and applications to cryptography and condensed matter physics. Recent works address spatial search algorithms, product formulas for commutators, and quantum routing protocols. As an educator, Childs has taught courses on quantum algorithms and information processing at both the University of Maryland and University of Waterloo, with lecture notes and materials spanning multiple years. Contact: amchilds@umd.edu | Office: ATL 3359 | Affiliated with University of Maryland's quantum research institutes.

Alexey Gorshkovمشاهده پروفایل

استاد مدعو

• Quantum Optics
• Quantum Information Science
• Condensed Matter Physics
• +۶ مورد دیگر

Alexey Gorshkov is an Adjunct Professor at the University of Maryland (UMD) affiliated with the Joint Quantum Institute (JQI) and the Quantum Information and Computer Science Laboratory (QuICS). His primary academic role is in theoretical physics, focusing on quantum optics, quantum information science, and condensed matter physics. He leads a research group exploring quantum magnetism with alkaline-earth atoms, driven-dissipative systems, topological matter, and strongly interacting photons. His work bridges AMO (atomic, molecular, and optical) systems with high-energy and condensed matter physics, emphasizing quantum simulation and novel quantum technologies like precise clocks and quantum computers. Education details are not explicitly listed, but his research collaborations with institutions like JQI and UMD suggest advanced academic training in theoretical physics. His research interests revolve around understanding and controlling quantum many-body systems, particularly in far-from-equilibrium scenarios, entanglement dynamics, and dissipation effects. He has contributed to studies on Rydberg atoms, quantum routing protocols, and error mitigation in quantum simulators. Recent articles highlight his work on quantum protocols for verifying speedups, time-independent information flow, and entanglement dynamics. His group's achievements include demonstrating one-dimensional anyons and developing methods for correlated noise estimation with quantum sensors. Awards and grants are not explicitly mentioned in the provided text, but his prolific publication record indicates sustained research impact. Labs and teams associated with him include the JQI and QuICS, where he collaborates on experimental and theoretical projects. Graduate student and postdoc positions are available in his group, focusing on areas like quantum magnetism and topological systems. His work often involves close ties with experimental groups, emphasizing practical applications of theoretical breakthroughs.

Nuno Loureiroمشاهده پروفایل

دانشیار

• Plasma Physics
• Magnetic Reconnection
• Turbulence
• +۴ مورد دیگر

Nuno Loureiro is an Associate Professor at the Department of Nuclear Science and Engineering at MIT, with a secondary appointment in the Physics Department. He obtained his PhD in Physics from Imperial College London in 2005 and held postdoctoral positions at Princeton University and the UK’s Culham Centre for Fusion Energy before joining MIT in 2016. His research focuses on plasma physics , particularly theory and simulations of astrophysical and laboratory plasmas , including magnetic reconnection, turbulence, and kinetic effects. His work bridges classical plasma dynamics with emerging quantum computing applications. The 15 most recent publications highlight advancements in quantum algorithms for plasma simulations , magnetic reconnection mechanisms , and turbulence dynamics across relativistic and non-relativistic plasmas. Topics include plasmoid-mediated inverse energy transfer, data-driven fluid closures, and ion-acoustic instability impacts. 2015 Thomas H. Stix Award (American Physical Society) NSF CAREER Award

Aram Harrowمشاهده پروفایل

استاد

• Quantum Information Science
• Quantum Algorithms
• Quantum Computing
• +۳ مورد دیگر

Aram Harrow is a Professor of Physics at the Massachusetts Institute of Technology (MIT) , affiliated with the MIT Center for Theoretical Physics and MIT Center for Quantum Engineering . He focuses on quantum information science and quantum algorithms , with additional interests in representation theory and optimization . His recent work explores quantum computing applications in chemical physics and statistical mechanics . Undergraduate and graduate degrees in Physics at MIT Faculty positions: MIT (2013-present), University of Washington (2010-12), University of Bristol (2005-10) Research Interests: His work bridges quantum information theory and many-body physics , including: Quantum algorithm design for chemistry and optimization Quantum circuit complexity and t-designs Entanglement dynamics in quantum systems Quantum-classical hybrid computing models Key Publications: Recent articles demonstrate quantum speedups for biomolecular free energy calculations , Hamiltonian simulation , and jet clustering algorithms . His research combines quantum complexity theory with practical implementations on near-term quantum devices. Scientific Awards: 2023 Simons Investigator 2018 APS Bennett Award 2017 IEEE Best Paper Award 2016 Kavli Frontiers Fellow Mentorship: He advises current PhD students Shankar Balasubramanian , Angus Lowe , and Norah Tan , with 12 former advisees including Anand Natarajan and Saeed Mehraban . His 2026 recruitment seeks one new graduate student.

Barbara Krausمشاهده پروفایل

استاد

• Quantum Information Theory
• Quantum Computing
• Entanglement and Quantum Correlations
• +۴ مورد دیگر

Prof. Dr. Barbara Kraus is the Chair of Quantum Algorithms and Applications at the Technical University of Munich (TUM), affiliated with the TUM School of Natural Sciences. She previously held academic positions at the University of Innsbruck, where she founded her research group in 2010. Education : Physics and Mathematics at the University of Innsbruck; Post-doctoral work at MPI for Quantum Optics and University of Geneva. Her research focuses on foundational problems in quantum information theory, particularly entanglement in multipartite systems, quantum simulation, and verification of quantum processors. She develops theoretical tools for quantum many-body systems and explores applications in quantum computing, emphasizing error characterization and experimental validation. Recent publications highlight advancements in Hamiltonian learning, symmetry-resolved entanglement detection, and multipartite state transformations. Her work bridges theoretical quantum physics with practical implementations, including Rydberg platforms and quantum metrology. Key Awards : START Prize (2010), Ignaz L. Lieben Award (2013), Boltzmann Prize (2011), Südtiroler Sparkasse Research Prize (2019). She supervises doctoral students and postdocs in quantum information theory, with a focus on stabilizer states, quantum networks, and entanglement measures. Her courses at TUM include Quantum Information , Quantum Algorithms , and workshops on entanglement manipulation.

John F. Cannyمشاهده پروفایل

استاد

• Artificial Intelligence
• Robotics
• Human-Computer Interaction
• +۵ مورد دیگر

John F. Canny is the Paul and Stacy Jacobs Distinguished Professor of Engineering at the University of California, Berkeley, in the Department of Electrical Engineering and Computer Science (EECS). He joined the faculty in 1987 and is affiliated with the Berkeley Artificial Intelligence Research Lab (BAIR), Berkeley Center for New Media (BCNM), and other research institutes. His research focuses on artificial intelligence, robotics, human-computer interaction, and computational geometry. Canny is renowned for developing the Canny edge detector and contributions to motion planning, privacy-preserving algorithms, and real-time simulation. Education: B.Sc. in Computer Science and Theoretical Physics (Adelaide University, 1979), B.E. (Hons) in Electrical Engineering (Adelaide University, 1980), M.S. and Ph.D. in Electrical Engineering (MIT, 1983 and 1987). Research Interests: His work spans AI, robotics (including universal planar manipulation), HCI (e.g., MultiView video conferencing), and security (privacy in collaborative filtering). He has pioneered algorithms for edge detection, non-linear FEM simulation, and computational algebra. Awards: ACM Fellow (2020), Okawa Research Grant (2003), AAAI Classic Paper Award (2002), NSF PYI (1989), Packard Fellowship (1988), and ACM Doctoral Dissertation Award (1987). Advising & Grants: Advised over 30 Ph.D. students, many of whom hold prominent roles in academia and industry. His grants include NSF and Packard funding for foundational research in robotics and computational methods. He currently teaches CS188: Introduction to Artificial Intelligence. Labs/Teams: Leads research at the Berkeley Institute of Design (BiD), focusing on RISC robotics, activity-based computing, and privacy-enhanced telepresence systems.

Jonathan M. Bakerمشاهده پروفایل

استادیار

• Quantum Computing
• Quantum Computer Architecture
• Quantum Error Correction
• +۴ مورد دیگر

Jonathan M. Baker is an Assistant Professor in the Department of Electrical and Computer Engineering at The University of Texas at Austin, holding the Advanced Micro Devices Chair in Computer Engineering. His research centers on quantum computer architecture with emphasis on practical quantum error correction implementation across the quantum computing stack. His educational background includes a Ph.D. in Computer Science from the University of Chicago (advised by Fred Chong) and dual B.S. degrees in Mathematics and Chemistry and Computer Science from the University of Notre Dame. Baker's research spans quantum compilation, logic synthesis, multi-radix architectures, and error mitigation for both near-term and fault-tolerant quantum systems. His work addresses critical challenges in quantum hardware-software co-design, with particular focus on optimizing quantum circuits for real-world hardware constraints and noise characteristics. Current projects emphasize qudit-based computing, neutral atom architectures, and efficient error correction implementations. His publication record shows strong focus on quantum architecture innovations, with recent work exploring qudit advantages, modular chiplet designs, and dynamic noise adaptation. Key trends include hardware-aware compilation techniques, communication optimization across quantum systems, and practical approaches to fault tolerance. Best Paper Award Runner Up, MICRO 2020 IEEE Micro Top Pick, 2020 (Virtualized Logical Qubits) IEEE Micro Top Pick, 2020 (Extending Frontier with Qutrits) IEEE Micro Top Pick, 2021 (Emerging Technologies) Best Poster Award, MICRO 2018 Baker actively mentors graduate students in quantum computing architecture research and serves on conference review committees including MICRO and ASPLOS. His teaching includes specialized quantum systems courses at UT Austin and online EdX modules covering quantum computation fundamentals and architecture. He collaborates with the Duke Quantum Center and maintains strong industry connections through the AMD Chair position, focusing on bridging academic research with practical quantum computing implementations.

Daniel Grierمشاهده پروفایل

استادیار

• Quantum Complexity Theory
• Quantum Computing
• Computational Complexity
• +۳ مورد دیگر

Daniel Grier is an Assistant Professor jointly appointed in the Computer Science and Engineering and Mathematics departments at the University of California, San Diego (UCSD). His research focuses on quantum complexity theory , particularly exploring near-term quantum computing paradigms and proving quantum advantage over classical systems. He holds a Ph.D. from MIT and was previously a postdoctoral fellow at the University of Waterloo’s Institute for Quantum Computing. Education: Ph.D. in Computer Science, MIT B.S. in Computer Science and Mathematics, University of South Carolina Research Interests: Grier’s work bridges theoretical computer science and quantum computing, emphasizing algorithm design, complexity class separations, and foundational questions about quantum supremacy. He studies how low-depth quantum circuits, boson sampling, and other near-term technologies can achieve computational tasks classically deemed intractable. Recent Article Trends: His publications explore efficient quantum state learning (e.g., classical shadows), hardness results for quantum sampling problems (e.g., bipartite Gaussian boson sampling), and circuit lower bounds (e.g., depth-2 QAC circuits). These contributions highlight his focus on rigorously defining quantum computational advantages. Awards: None explicitly listed in the text. Advising & Grants: Advises at least one student, Jackson Morris. His research is supported by grants exploring quantum complexity and algorithm design. Teaches advanced courses on quantum complexity theory, computability, discrete mathematics, and quantum computing fundamentals. Labs/Teams: Maintains an active lab focused on quantum complexity theory, collaborating with colleagues on topics like interactive protocols and shallow quantum circuits.

• Computer Hardware
• FPGA Acceleration
• Artificial Intelligence
• +۸ مورد دیگر

Professor Wayne Luk is a Professor of Computer Engineering at the Department of Computing, Faculty of Engineering, Imperial College London. He leads the Programming Languages and Systems Section and the Custom Computing Research Group, and directs the EPSRC Centre for Doctoral Training in High-performance Embedded and Distributed Systems and the Centre for Advanced Financial Engineering. He previously served as a Visiting Professor at Stanford University from 2006 to 2009. His research spans FPGA acceleration, quantum computing, deep learning optimization, and algorithm-hardware co-design, with affiliations to the CRUK Convergence Science Centre and the Engineering Secure Software Systems group. His research interests include computational modeling for particle physics, causal discovery in agent-based systems, and high-throughput digital electronics. Notable contributions include FPGA-accelerated algorithms for neural networks, quantum circuit simulation, and Bayesian optimization frameworks. His work emphasizes practical applications of reconfigurable hardware in fields like medical imaging, high-energy physics, and financial systems. Professor Luk is a Fellow of the Royal Academy of Engineering, IEEE, and BCS. His publications focus on advancing hardware-aware machine learning, FPGA-based acceleration techniques, and scalable design methodologies. His research bridges theoretical computer science with applied engineering, addressing challenges in real-time systems, embedded computing, and next-generation computing architectures. His academic leadership includes directing interdisciplinary centers and training programs, fostering collaboration across computing, engineering, and physics. Current projects explore quantum computing tools, causal inference systems, and high-performance graph neural networks for particle physics applications.

Henry Yuenمشاهده پروفایل

دانشیار

• Quantum Computing
• Complexity Theory
• Cryptography
• +۲ مورد دیگر

Henry Yuen is the Srivani Family Associate Professor of Computer Science at Columbia University, with affiliations in the CS Theory group, Data Science Institute, and Columbia Quantum Initiative. His work bridges theoretical computer science and quantum information, focusing on the computational foundations of quantum systems. Yuen received his BA in Mathematics from the University of Southern California (2010) and PhD in Computer Science from MIT (2016). His research explores quantum complexity theory, quantum cryptography, and quantum information through both theoretical frameworks and practical applications. His recent publications reveal a strong focus on quantum pseudorandomness, quantum circuit complexity, and quantum cryptographic primitives. The research trajectory demonstrates increasing sophistication in connecting quantum computational models with fundamental questions in complexity theory, particularly through the lens of quantum interactive proofs and quantum state complexity. His work frequently intersects with operator algebras and quantum information theory, reflecting the interdisciplinary nature of modern quantum computing research. NSF CAREER award Sloan Fellowship Yuen actively mentors graduate students, with several PhD students having graduated to postdoctoral positions or faculty roles. His service includes co-organizing the Columbia CS Theory Seminar and serving on program committees for major conferences including QIP, STOC, and ITCS. He has organized multiple workshops on quantum computing challenges and cryptography. As a member of the Columbia Quantum Initiative, Yuen contributes to Columbia's growing quantum ecosystem, collaborating across departments to advance both theoretical foundations and potential applications of quantum information processing.

Brian Leeds DeMarcoمشاهده پروفایل

استاد

• Quantum Simulation
• Condensed Matter Physics
• Atomic, Molecular, and Optical Physics (AMO)
• +۴ مورد دیگر

Professor Brian Leeds DeMarco is a leading academic in the Department of Physics at the University of Illinois Urbana-Champaign (UIUC), serving as the IQUIST Director and Bardeen Faculty Scholar. He specializes in Atomic, Molecular, and Optical Physics, with a focus on quantum simulation using ultracold atoms in optical lattices to address problems in condensed matter physics. His research has led to groundbreaking discoveries, including the first quantum-degenerate Fermi gas and three-dimensional Anderson localization of matter, earning him prestigious awards like the NSF CAREER Award and Sloan Fellowship. DeMarco holds a B.A. in Physics from SUNY Geneseo (1996) and a Ph.D. from the University of Colorado Boulder (2001). His postdoctoral work at NIST with David Wineland advanced quantum computing with trapped ions. At UIUC since 2003, he has held roles such as Associate Head for Undergraduate Programs and currently chairs NASA's Fundamental Physical Sciences Standing Review Board. His research explores quantum simulation, quantum computing architectures, and ultracold matter. Notable achievements include identifying phase-slip crossover in optical lattices and pioneering 3D Anderson localization. He has published over 30 high-impact articles, with recent work focusing on quantum simulators and hybrid quantum systems. Awards: APS Fellow, University of Illinois Scholar, NASA Review Board Chair, Defense Sciences Study Group member. Grants: NSF, ONR, Sloan Foundation support for quantum simulation and ultracold atom research. DeMarco leads the DeMarco Group, advancing quantum technologies and training students in experimental physics. His lab’s work on quantum testbeds and trapped ion processors positions him at the forefront of quantum information science.

Aleksander Kubicaمشاهده پروفایل

استادیار

• Quantum Information Science
• Quantum Error Correction
• Fault Tolerance
• +۳ مورد دیگر

Aleksander Kubica is an Assistant Professor of Applied Physics at Yale University, specializing in quantum information science with a focus on quantum error correction and fault tolerance. His research bridges quantum many-body physics and topological codes, particularly exploring applications in superconducting circuits and quantum architectures. He holds a Ph.D. from the California Institute of Technology and a B.S. from the University of Warsaw. Dr. Kubica's work addresses foundational challenges in scalable quantum computing, including optimizing error correction protocols, developing fault-tolerant architectures, and analyzing the intersection of quantum metrology with error mitigation. Recent contributions include advancements in erasure qubits, correlated noise decoding, and topological code adaptations. His research often involves interdisciplinary approaches, combining theoretical physics with algorithm design and hardware-efficient solutions. Key themes in his publications include improving error correction thresholds, designing low-overhead quantum architectures, and exploring novel decoding strategies for topological codes. While no specific awards are listed, his active research trajectory and contributions to quantum computing indicate significant scholarly engagement in the field.