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Joey W HustonView profile

Professor

• High Energy Physics
• Quantum Chromodynamics (QCD)
• Particle Phenomenology
• +7 more

Joey W Huston is a Professor at the Department of Physics & Astronomy, Michigan State University, and a Visiting Professor at the Institute for Particle Physics Phenomenology, Durham University. He has over 600 publications with >30,000 citations, including 9 papers with >500 cites each. Positions: MSU Research Foundation Professor (1998-present), Visiting Professor at Durham (2003-present) Education: Ph.D. (1983) and B.S. (1976) from University of Rochester and Carnegie-Mellon University His research focuses on Quantum Chromodynamics (QCD) , Parton Distribution Functions , and Jet Physics . He contributes to Higgs Boson studies, Supersymmetry , and Dark Matter Searches via the ATLAS experiment at the LHC. Recent articles (2025) highlight advancements in Jet Flavour Tagging , Top-Quark Mass Measurement , and Exotic Higgs Decays , alongside computational innovations like Neural Simulation-Based Inference and Cloud Resource Optimization . Scientific Awards: APS Fellow (2025), Distinguished Visitor by Scottish Universities Physics Alliance (SUPA) Prof. Huston has co-spoken for the CTEQ collaboration and organized workshops at Les Houches, Kavli Institute, and Fermilab. He authored the Handbook on Perturbative QCD and is writing a book on QCD at the LHC for Oxford University Press.

Jonathan M. BakerView profile

Assistant Professor

• Quantum Computing
• Quantum Computer Architecture
• Quantum Error Correction
• +4 more

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.

Yongmin LiuView profile

Professor

• Nano Optics
• Nanomaterials Engineering
• Plasmonics
• +5 more

Yongmin Liu is a Professor in Mechanical and Industrial Engineering and Electrical & Computer Engineering at Northeastern University, and a member of the Cross-College Magnetics Center. He holds a PhD in Applied Science and Technology from UC Berkeley (2009), with earlier degrees from Nanjing University. His research focuses on nano-optics, metamaterials, plasmonics, and their applications in optical devices and systems. His interdisciplinary work bridges engineering, physics, and AI, with notable contributions to metasurface design and optical neural networks. Education: PhD, Applied Science and Technology, UC Berkeley (2009) M.S. and B.S., Physics, Nanjing University (2003, 2000) Research Interests: Nano-optics, nanoscale materials engineering, metamaterials, plasmonics, and applied physics. His group develops novel optical materials and devices for applications like super-resolution imaging, efficient light harvesting, and biomedical detection. Recent projects include AI-driven photonic materials design and meta-optical neural networks. Key Achievements: Recipient of the Søren Buus Outstanding Research Award (2024) NSF CAREER Award (2017) and ONR Young Investigator Award (2016) Elected SPIE Fellow (2023) and Optica Fellow (2023) Lab & Collaborations: Head of the Yongmin Liu Research Group, collaborating with institutions like Georgia Tech and Purdue University. Recent grants include a $1.5M NSF DMREF grant for AI-driven photonic materials and a $468K NSF grant for meta-optical neural networks.

Jonathan A. KelnerView profile

Professor

• Algorithms
• Spectral Graph Theory
• Combinatorial Optimization
• +7 more

Jonathan A. Kelner is a Professor of Applied Mathematics in the Department of Mathematics at the Massachusetts Institute of Technology (MIT) and a member of the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL). His research focuses on applying techniques from pure mathematics to solve fundamental problems in algorithms and complexity theory, with the goal of developing practical algorithms for real-world questions. Dr. Kelner received his undergraduate degree from Harvard University and his Ph.D. in Computer Science from MIT in 2006. Before joining the MIT faculty, he spent a year as a member of the Institute for Advanced Study. His educational background has provided a strong foundation for his interdisciplinary research spanning mathematics and computer science. His research interests include combinatorial optimization, mathematical programming, spectral graph theory, distributed computing, machine learning, computational geometry and topology, computational biology, signal processing, and random matrix theory. Kelner's work demonstrates how deep theoretical insights can lead to practical algorithmic improvements, particularly in graph algorithms and optimization problems. His approach often involves connecting seemingly disparate areas of mathematics to create novel algorithmic techniques. Analysis of his recent publications reveals a strong focus on spectral graph theory, optimization algorithms, and the Sum-of-Squares method. His work frequently addresses fundamental questions in theoretical computer science with practical implications for algorithm design. A notable trend in his research is the development of nearly-linear-time algorithms for various graph problems, which represents significant improvements over previous approaches. NSF CAREER Award Alfred P. Sloan Research Fellowship NEC Award for Research in Computers and Communication Sprowls Doctoral Dissertation Award Best Student Paper Award at STOC 2004 Best Paper Award at STOC 2011 Kokusai Denshin Denwa Junior Faculty Chair 2008 Harold E. Edgerton Faculty Achievement Award 2011 School of Science Award for Excellence in Undergraduate Education 2012 Professor Kelner has been actively involved in mentoring students and has received recognition for his teaching excellence, including the School of Science Award for Excellence in Undergraduate Education in 2012. His research has been supported by prestigious grants including the NSF CAREER Award. He has collaborated extensively with researchers across multiple institutions, often working with other leading figures in theoretical computer science to produce groundbreaking results in algorithm design. At MIT, Kelner is part of both the Mathematics Department and CSAIL, positioning him at the intersection of theoretical mathematics and practical computer science. This dual affiliation reflects the interdisciplinary nature of his work, which bridges pure mathematical theory with concrete algorithmic applications.

Daniel GrierView profile

Assistant Professor

• Quantum Complexity Theory
• Quantum Computing
• Computational Complexity
• +3 more

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.

Alejandro RodriguezView profile

Professor

• Nanophotonics
• Quantum Optics
• Nonlinear Optics
• +7 more

Alejandro Rodriguez is a Professor of Electrical and Computer Engineering at Princeton University, serving as Director of Education Program MIRTHE+. He leads the Nanophotonics Design and Computation Group , focusing on theoretical and computational exploration of nanostructured materials for novel optical phenomena. His research bridges classical and quantum optics, emphasizing nonlinear optics, fluctuation-induced forces, and topological photonics. Education: Ph.D., Massachusetts Institute of Technology (2010) B.S., Massachusetts Institute of Technology (2006) Research interests include: Low-power nonlinear optics and frequency conversion Casimir forces and thermal radiation in structured media Optimization of photonic devices via inverse design Quantum and thermal fluctuation effects in nanostructures Awards: Presidential Early Career Award (2019) National Science Foundation CAREER Award (2015) Multiple international recognitions for contributions to nanophotonics Advising and Grants: Supervises graduate students in photonics and materials science Recipient of innovation grants for AI, robotics, and wireless communications advancements Labs/Teams: Active in Princeton's Materials Institute (PMI) and collaborates with global research networks in quantum science and nanophotonics.

• Plasma Physics
• Nonlinear Dynamics
• Electromagnetic Theory
• +3 more

Thomas M. Antonsen Jr. is a Distinguished University Professor at the University of Maryland, holding joint appointments in the Department of Electrical and Computer Engineering and the Department of Physics. He is affiliated with the Institute for Research in Electronics & Applied Physics (IREAP), Maryland Energy Innovation Institute, and the Institute of Physical Science and Technology. His research focuses on plasma physics, nonlinear dynamics, and high-power coherent radiation sources. Antonsen earned his B.S., M.S., and Ph.D. in electrical engineering from Cornell University (1973–1977) and has held visiting positions at institutions such as the University of California, Santa Barbara, and the École Polytechnique in France. **Education:** B.S., Electrical Engineering, Cornell University, 1973 M.S., Electrical Engineering, Cornell University, 1976 Ph.D., Electrical Engineering, Cornell University, 1977 **Research Interests:** Antonsen’s work spans magnetically confined plasmas, laser-plasma interactions, and advanced vacuum electronics. He has pioneered adjoint methods for optimizing beam-wave interaction systems and contributed to the development of high-power microwave amplifiers. His recent projects include wave chaos in complex systems and machine learning applications in nonlinear dynamics. **Awards & Honors:** James Clerk Maxwell Award (American Physical Society, 2023) IEEE Marie Sklodowska-Curie Award (2022) University of Maryland Distinguished University Professor (2017) IEEE Fellow (2012) **Teaching & Mentorship:** Antonsen teaches courses such as Physics 132 (Biophysics), Electrodynamics, and Plasma Physics. He mentors graduate students in plasma physics and vacuum electronics through his research groups at IREAP and the Bright Beams Collective. **Labs & Collaborations:** His research is supported by grants from the Department of Energy, NASA, and the Office of Naval Research. Key collaborations include the National Institute of Standards and Technology (NIST) and the European XFEL facility.

Aleksander KubicaView profile

Assistant Professor

• Quantum Information Science
• Quantum Error Correction
• Fault Tolerance
• +3 more

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.

Juan GarayView profile

Professor

• Cryptography
• Information Security
• Cryptographic Protocols
• +7 more

Juan Garay is a Professor in the Department of Computer Science & Engineering at Texas A&M University, affiliated with the College of Engineering. His research focuses on cryptography, information security, and distributed systems, with notable contributions to cryptographic protocols, blockchain technologies, and consensus mechanisms. He holds a leadership role in advancing theoretical and applied aspects of secure computation and network security. Research Interests: Cryptography and Information Security Secure Multiparty Computation Cryptocurrencies and Blockchain Protocols Consensus Algorithms Distributed Computing Game Theory in Cryptography Publications highlight his work on the Bitcoin Backbone Protocol, secure multiparty computation, and post-quantum cryptographic systems. He actively contributes to conferences and workshops in cryptography and distributed systems. He advises graduate students in computer science and engineering, though specific advisee names are not listed. His work is supported by grants from the National Science Foundation (NSF) and other institutions, focusing on secure protocols and distributed systems. Office: Peterson Building (PETR 429) Contact: garay@cse.tamu.edu

• Security
• Cryptography
• Theoretical Computer Science
• +3 more

Yan Huang is an Associate Professor in the Department of Computer Science at Indiana University Bloomington, focusing on security and cryptography. His work bridges theoretical foundations with practical systems, emphasizing cryptographic protocols with strong security guarantees for generic computation. University: Indiana University Bloomington School: College of Arts and Sciences Department: Computer Science Academic Rank: Associate Professor His research combines theoretical computer science , program analysis , artificial intelligence , and software engineering to address real-world security problems. Key areas include secure computation , zero-knowledge proofs , and privacy-preserving technologies . Recent publications, such as Phecda (SP'25) and Dubhe (USENIX Security'23), highlight advancements in post-quantum cryptography , AES verification , and privacy-preserving deep packet inspection , reflecting his focus on scalable and practical cryptographic solutions. He has advised notable students including Changchang Ding (PhD, Computer Science) and Ruiyu Zhu (PhD, now at Facebook), and served on program committees for top conferences like ACM CCS and CRYPTO .

W. Brent LindquistView profile

Professor

• Computational Finance
• Financial Mathematics
• Porous Media Flow
• +2 more

W. Brent Lindquist is a Professor in the Department of Mathematics and Statistics at Texas Tech University, affiliated with the TTU Mathematical Finance Program. His contact details include office location in the Mathematics & Statistics building (Room 104), phone (+1 806 834 2348), and email brent.lindquist@ttu.edu. His research spans computational financial mathematics, porous media flow, neuroscience applications, and quantum electrodynamics. Key contributions include dynamic asset pricing with market microstructure integration, pore-scale flow modeling using 3D micro-tomography, automated neuron morphology identification, and QED computations for electron magnetic moments. Recent work emphasizes ESG factor incorporation into financial models. Analysis of 2023–2025 publications reveals a dominant focus on sustainable finance, particularly ESG-integrated option pricing and portfolio optimization. Methodologies include random forests for market microstructure analysis, skew random walks for volatility modeling, and Lévy processes for Bitcoin dynamics. Cross-cutting themes involve hedonic real estate models with ESG factors and unified asset pricing frameworks bridging classical finance theories.

Ananth GramaView profile

Professor

• Parallel Computing
• Distributed Systems
• Machine Learning
• +7 more

Ananth Grama is the Samuel D. Conte Distinguished Professor of Computer Science and Associate Director of the Center for Science of Information at Purdue University. He holds a faculty position in the Department of Computer Science, College of Science. His research focuses on parallel computing, distributed systems, machine learning, and their applications in complex systems such as materials modeling and clinical analytics. He teaches advanced courses like CS525 (Parallel Computing) and CS314 (Numerical Methods). Research interests span parallel algorithms, fault-tolerant learning, quantum machine learning, and data-driven healthcare analytics. Recent work addresses fundamental limits of generative models, online learning under noisy conditions, and clinical outcome predictions. His projects include DOE-funded research on critical element recovery and NIH grants for hearing assessment technologies. Notable contributions include over 50 peer-reviewed publications since 2022, with recent papers appearing at ICLR, NeurIPS, and ICML. Current postdocs include Changlong Wu (collaborating with Wojciech Szpankowski) and Luopin Wang (with Nadia Atallah). He advises seven graduate students and oversees multidisciplinary research teams.

Brandon ReagenView profile

Assistant Professor

• Computer Architecture
• Hardware Acceleration
• Machine Learning
• +3 more

Brandon Reagen is an Assistant Professor in the Department of Electrical and Computer Engineering at New York University's Tandon School of Engineering, with affiliations in Computer Science, the Center for Advanced Technology in Telecommunications (CATT), and the NYU Center for Cybersecurity (CCS). He holds a PhD in Computer Science from Harvard (2018) and undergraduate degrees in Computer Systems Engineering and Applied Mathematics from the University of Massachusetts, Amherst (2012). His research focuses on computer architecture, hardware acceleration for deep learning and privacy-preserving computation, and VLSI design. He pioneered efficient deep learning accelerator designs through unsafe optimizations and contributed to benchmarking frameworks like Aladdin and MachSuite. His work spans privacy-preserving machine learning, secure computing systems, and hardware-software co-design for cryptographic protocols. Key achievements include the NSF CAREER Award (2024) and Siebel Scholar recognition (2018). His research centers on advancing secure computing through innovations like zero-knowledge proof accelerators (e.g., zkSpeed), fully homomorphic encryption frameworks (Orion), and entropy-guided privacy techniques for large language models. He leads interdisciplinary efforts at CATT and CCS to bridge hardware design and cybersecurity challenges. Reagen's contributions include over 50 publications in top-tier conferences (e.g., ISCA, ASPLOS, MLSys) and industry collaborations at Facebook AI. His work emphasizes practical solutions for encrypted computation efficiency, privacy-preserving inference, and scalable secure systems.

David SchusterView profile

Associate Professor

• Condensed Matter Physics
• Quantum Computing
• Superconducting Circuits
• +3 more

David Schuster is an Associate Professor of Physics at the University of Chicago. His primary research focuses on experimental condensed matter physics, with a particular emphasis on circuit quantum electrodynamics (cQED), superconducting qubits, and quantum information science. He leads the Schuster Lab, which explores quantum systems, hybrid quantum technologies, and topological materials. Education: Ph.D. in Physics from Yale University (2007), advised by Robert Schoelkopf. His doctoral work pioneered advancements in circuit QED, demonstrating strong coupling between superconducting qubits and microwave resonators. Research Interests: The lab investigates superconducting quantum circuits, topological photonics, quantum sensors for dark matter, and scalable quantum computing architectures. Projects include developing fluxonium qubits, autonomous error correction, and hybrid systems involving trapped electrons on helium. Key Contributions: Published in Nature , Science , and Physical Review Letters on topics like topological circuits, photon blockade, and dark matter detection using superconducting cavities. Collaborates with groups at Stanford, Purdue, and other institutions on quantum technologies. Students and Collaborators: Advises numerous graduate and undergraduate students, including prominent alumni who have transitioned to postdocs and industry roles. Lab members present at major conferences like the APS March Meeting. Labs: Schuster Lab at the University of Chicago, with access to state-of-the-art facilities like the Pritzker NanoFabrication Facility. Collaborates with the Awschalom, Cleland, and Houck groups on hybrid quantum systems and materials science.

Henry HoffmannView profile

Professor

• Self-aware & Adaptive Computing
• Computer Systems
• Computer Architecture
• +6 more

Henry Hoffmann is a Professor and Liew Family Chair in the Department of Computer Science at the University of Chicago. His research focuses on self-aware computing systems that adapt to meet goals like power efficiency, performance, and security. He leads the SEEC project and has contributed to advancements in computer architecture, embedded systems, and quantum computing. Hoffmann received the PECASE (2019), DOE Early Career Award (2015), and was inducted into the Samsung Hall of Fame for discovering vulnerabilities in SmartTVs. He holds a PhD from MIT (2013) and has co-founded Config Dynamics (2019). His work bridges control theory, machine learning, and traditional computer systems to create adaptive solutions for modern computing challenges. Education: PhD in Electrical Engineering and Computer Science from MIT (2013), SM (2003), and B.S. (1999) with highest honors from UNC Chapel Hill. Professional experience includes roles at Tilera Corporation and MIT Lincoln Laboratory. Research Interests: Self-aware systems, adaptive resource management, quantum computing optimization, and cybersecurity. His SEEC framework enables systems to autonomously adapt to constraints like energy and performance. Recent work explores applying adaptive techniques to AI/ML models for energy-efficient inference and security. Awards: Over $19M in research funding, 100+ publications, and leadership roles in NSF Expedition EPiQC (quantum computing). Named Chair of UChicago CS Department (2023-2024). Labs/Teams: Systems Group, EPiQC (quantum computing), and CERES (unstoppable computing systems). Current students include Jerry Ding and Ryien Hosseini. Notable alumni include Yi Ding (now faculty at Purdue) and Nikita Mishra.