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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.

• Quantum Computation
• Quantum Cryptography
• Quantum Learning Theory
• +3 more

Jenna Wise DiVincenzo is an Assistant Professor at the Elmore Family School of Electrical and Computer Engineering at Purdue University. She specializes in research areas such as software verification, formal methods, and programming languages, with a focus on gradual verification techniques that combine static and dynamic analysis. Her work emphasizes usability and scalability in verification tools, and she has contributed to projects like Gradual C0 and gradual null-pointer analysis. Dr. DiVincenzo earned her PhD in Software Engineering from Carnegie Mellon University (2023) and a BS in Mathematics and Computer Science from Youngstown State University (2017). She has interned at IBM Research, MIT Lincoln Laboratory, and the Software Engineering Research and Empirical Studies Lab at YSU. Her awards include the Google PhD Fellowship, NSF GRFP Fellowship, and 2022 Rising Star in EECS. Her research projects span theoretical advancements in gradual verification, empirical studies on usability, and practical tool development. She advises PhD students (e.g., Craig Liu, Conrad Zimmerman) and collaborates on initiatives like gradual verification for Rust and educational tools to teach verification concepts. Her work also explores leveraging large language models for specification generation and enhancing verification tool soundness through formal proofs.

• Software Verification
• Formal Methods
• Programming Languages
• +5 more

Daniel M. Roy is a Full Professor at the University of Toronto, with cross-appointments in the Department of Computer Science, Department of Statistical Sciences, and Department of Electrical and Computer Engineering. He serves as Research Director at the Vector Institute and holds the CIFAR Canada AI Chair. Research Focus: Foundational principles of prediction, inference, and decision-making under uncertainty across machine learning, statistics, mathematical logic, applied probability, and computer science. Scientific Contributions: Key work in learning theory, statistical network analysis, probabilistic programming, and information-theoretic frameworks for generalization. Awards: ICML 2024 Best Paper Award for "Information Complexity of Stochastic Convex Optimization" and promotion to Full Professor in 2024. Student Advising: Actively mentors Ph.D. candidates and postdoctoral researchers with strong quantitative backgrounds, particularly at the intersection of machine learning, statistics, and computer science. Email: daniel.roy@utoronto.ca

• Machine Learning
• Statistics
• Probabilistic Programming
• +7 more

Lijie Chen is an Assistant Professor in the Department of Electrical Engineering and Computer Sciences at UC Berkeley, where he is part of the Berkeley Theory Group. Previously, he was a Miller Research Fellow at UC Berkeley, hosted by Avishay Tal and Umesh Vazirani, and earned his Ph.D. from MIT under Ryan Williams. His research focuses on theoretical computer science, particularly computational complexity theory, with applications to quantum physics and AI safety. Education: Ph.D. in Computer Science from MIT (2022), B.Sc. from Yao Class at Tsinghua University. Research Interests: Complexity theory, quantum complexity, derandomization, circuit lower bounds, and foundational aspects of AI safety. Chen has made significant contributions to understanding fundamental questions in complexity theory, including circuit lower bounds and the connections between randomness and computation efficiency. His work often bridges theoretical insights with practical implications in quantum computing and algorithm design. Awards and Honors: Machtey Award for Best Student Paper (2019). Danny Lewin Best Student Paper Award (2019). Invited to SICOMP Special Issues for FOCS and STOC papers. He has organized workshops on complexity theory and derandomization, and his research has been recognized in venues like STOC, FOCS, and the Journal of the ACM.

• Theoretical Computer Science
• Computational Complexity Theory
• Quantum Complexity Theory
• +4 more

Prof. Roger Wattenhofer is a Full Professor at the Department of Information Technology and Electrical Engineering, ETH Zurich, Switzerland, and Deputy head of the Computer Engineering and Networks Lab. He holds a doctorate in Computer Science from ETH Zurich (1998) and has held positions at Brown University and Microsoft Research before returning to ETH. His research focuses on distributed computing, wireless networks, and algorithmic systems design, with contributions to Byzantine agreement protocols, blockchain technologies, and neural network architectures. He teaches courses such as Distributed Systems and Computational Thinking. Education: Ph.D. in Computer Science (ETH Zurich, 1998). Research interests include distributed systems, network algorithms, and the intersection of machine learning with distributed computing. His work spans both theoretical foundations and practical implementations, addressing challenges in fault tolerance, consensus mechanisms, and algorithmic efficiency. Recent publications explore topics like adversarial robustness in voting systems, privacy in reinforcement learning, and generative music models. He actively contributes to open-source frameworks and benchmarks for neural algorithmic reasoning.

• Distributed Computing
• Wireless Networks
• Distributed Systems
• +4 more

Michael Baldea is an Associate Professor in the Department of Chemical Engineering at the University of Texas at Austin . He holds a Ph.D. in Chemical Engineering from the University of Minnesota (2006), with prior degrees from 'Babeş-Bolyai' University in Romania (M.Sc. 2001, Diploma 2000). His research group develops theoretical and computational methods for Process and Energy Systems Engineering , focusing on integrated decision-making, performance optimization, and process intensification with industrial validation. Education: Ph.D., Chemical Engineering, University of Minnesota (2006) M.Sc., Interface Process Engineering, 'Babeş-Bolyai' University (2001) Diploma, Chemical Engineering, 'Babeş-Bolyai' University (2000) Research Thrusts: Integrated decision-making in chemical/energy supply chains Process performance monitoring and optimization Process integration and intensification Key applications include grid-responsive chemical plants, intensified distillation/column designs, and renewable energy integration for building systems. Scientific Awards: Frank A. Liddell, Jr. Fellowship NSF CAREER Award (2015-2020) Moncrief Grand Challenges Faculty Award (2014) AIChE Outstanding Young Researcher Award (2017) Implementation : His group has translated research into commercial tools through partnerships with industrial test beds and is working to integrate methods into commercial simulators. They explore predictive approaches for building energy management and strategic capital investment analysis in next-generation energy systems.

• Process Systems Engineering
• Energy Systems
• Nonlinear Optimization
• +3 more

Nadia Polikarpova is an Associate Professor in the Department of Computer Science and Engineering at the University of California, San Diego . She earned her PhD from ETH Zurich in 2014 under Bertrand Meyer , followed by postdoctoral research at MIT CSAIL with Armando Solar-Lezama . Her academic contributions have been recognized with prestigious awards including the 2020 Sloan Fellowship , 2020 Intel Rising Stars Award , and 2020 NSF CAREER Award . Polikarpova's research focuses on program synthesis , program verification , and type systems . She leads the Programming Systems group at UCSD and contributes to the IFIP Working Group 2.8 on Functional Programming since 2022. Her work spans foundational research and practical tools, including projects like Synquid , SuSLik , and Laurel that combine formal methods with machine learning for code generation. Her recent publications in venues like OOPSLA , NeurIPS , and ICFP reveal trends in AI-assisted programming , live programming environments , and formal verification . She has advised numerous PhD and Master’s students including Shraddha Barke , Zheng Guo , and Tristan Knoth , many of whom have moved to prominent academic and industry positions. Notable artifacts from her lab include tools like ColDeco for spreadsheet inspection and Superfusion for eliminating intermediate data structures. 2020 : Sloan Fellow 2020 : Intel Rising Stars Award 2020 : NSF CAREER Award 2021 : Distinguished Paper at POPL 2023 : Distinguished Artifact at PLDI 2023 : Distinguished Paper at OOPSLA Polikarpova actively contributes to academic service, serving on program committees for PLDI , POPL , and OOPSLA , and co-chairing the OOPSLA Review Committee in 2023. She has delivered keynotes at APLAS'20 and PLDI'24 , emphasizing the integration of large language models with formal methods.

• Program Synthesis
• Program Verification
• Type Systems
• +5 more

Vera Fischer is an Associate Professor and Privatdozent in the Department of Mathematics at the Faculty of Mathematics, University of Vienna. She has been an active researcher since 2008, with a strong publication record in mathematical logic and set theory. Her research centers on set-theoretic combinatorics , focusing on cardinal characteristics of the continuum, forcing, definability, and structures such as maximal almost disjoint families, cofinitary groups, and independent families. She investigates foundational questions in independence, spectra of combinatorial objects, and the interplay between definability and generic extensions. Her work often involves constructing models to separate cardinal invariants or to realize specific spectra under forcing. The recent publications show a consistent trend in combinatorial set theory , with a focus on tower spectra, tight families, mad families, and their behavior under various forcing notions like Cohen and Sacks forcing. Her research frequently explores the definability and destructibility of combinatorial families, often in collaboration with leading researchers such as Corey B. Switzer, Stefan Geschke, and Saharon Shelah. FWF START Prize, 2017 Förderungspreis der ÖMG, 2018 Förderungspreis, 2018 She leads and participates in research projects such as Comparing the Real Line to Combinatorics of the Uncountable and A Sacks-like model with large continuum , indicating active grant funding and supervision of research activities. She also organizes academic events like Colloquium Logicum and engages in public outreach on topics like infinity. Her research is conducted within the Set Theory Group at the University of Vienna, where she collaborates with other logicians and contributes to the academic community through publications, conferences, and mentorship.

• Set Theory
• Mathematical Logic
• Forcing
• +7 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.

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

Dr. Richard Fair is the Lord-Chandran Distinguished Professor of Engineering at Duke University, with a career spanning semiconductor physics, digital microfluidics, and lab-on-a-chip systems. His research group collaborates with faculty across Duke, Harvard, and Stanford in bioengineering, genomics, and environmental science to develop applications-driven microfluidic platforms. Ph.D. in Electrical and Computer Engineering, Duke University (1969) B.S.E.E., Duke University (1964) M.S.E.E., Pennsylvania State University (1966) Research interests focus on electrowetting-based microfluidics for biosensing, diagnostics, and synthetic biology applications. Key innovations include adaptive droplet routing , magnetic bead manipulation , and integrated optical sensors for real-time analyte detection in environmental and medical contexts. Recent publications emphasize deep reinforcement learning for biochip automation, fluorescent nucleosome detection , and inorganic ion analysis in aerosols. Collaborations with institutions like Advanced Liquid Logic and NSF-funded projects highlight his interdisciplinary approach. IEEE Third Millennium Medal (2000) Solid State Science and Technology Award (Electrochemical Society, 2003) Gordon E. Moore Medal (2009) Fellow, IEEE and Electrochemical Society Grants include NSF awards with Nan Jokerst and Krish Chakrabarty for adaptive lab-on-a-chip optical control, DARPA funding for genomic engineering platforms, and collaborations with the Desert Research Institute on airborne particle sensing. His lab develops scalable solutions for environmental monitoring, clinical diagnostics, and synthetic biology applications.

• Microfluidics
• Electrowetting Technology
• Lab-on-a-Chip Systems
• +7 more

Matthew Lakin is an Associate Professor with tenure in the Department of Computer Science at the University of New Mexico, with a courtesy appointment in the Department of Chemical & Biological Engineering. He is affiliated with the UNM Center for Biomedical Engineering and the School of Engineering, and collaborates extensively with the UNM Health Sciences Center and external institutions. Education: Ph.D., Computer Science, University of Cambridge, 2010 M.A. (Cantab), University of Cambridge, 2009 B.A. (Hons), Computer Science, University of Cambridge, 2005 Dr. Lakin's research focuses on molecular computing, DNA nanotechnology, synthetic biology, and formal verification of biomolecular circuits. He develops computational models and experimental systems for programmable biological devices, especially using heterochiral DNA to enhance stability in living cells. His work spans software tools for biodesign and experimental validation in mammalian systems, with applications in nanomedicine and biosensing. The recent publications highlight a strong trend in engineering robust, intelligent biomolecular systems. His work integrates machine learning concepts into chemical reaction networks, advances geometric modeling of DNA systems, and pioneers L-DNA-based circuits for intracellular applications. The research spans theoretical foundations, software tools, and wet-lab experimentation, emphasizing interdisciplinary innovation. Scientific Awards: Presidential Early Career Award for Scientists and Engineers (PECASE), 2025 NSF CAREER Award, 2021 UNM School of Engineering Junior Faculty Research Excellence Award, 2021 Multiple student awards under his mentorship, including the Outstanding Graduate Student Award and DNA28 Best Student Presentation recognition Dr. Lakin has advised numerous graduate and undergraduate students, including Ph.D. graduates in Biomedical Engineering and Computer Science. He leads major funded projects such as the NSF CAREER grant on heterochiral molecular computing, an EPSCoR Research Fellowship, and a $3M NSF grant on heavy metal biosensing in collaboration with Native American communities. He is also PI on multiple NSF grants related to synthetic cells and nucleic acid technologies. He directs the Lakin Lab for Programmable Biology, which operates within the Department of Computer Science and collaborates with Chemical & Biological Engineering and the Center for Biomedical Engineering. The lab emphasizes both computational modeling and experimental molecular biology, and runs an NSF-funded biotechnology summer camp in partnership with ¡Explora! science museum to strengthen STEM education in New Mexico.

• Molecular Computing
• DNA Nanotechnology
• Synthetic Biology
• +7 more

Michał Pilipczuk is an Associate Professor at the Institute of Informatics, Faculty of Mathematics, Informatics and Mechanics of the University of Warsaw. His research focuses on theoretical computer science, particularly algorithms on discrete structures, parameterized algorithms, structural graph theory, and logic in computer science. He leads the ERC-funded project "BOBR: Decomposition Method for Discrete Problems" and previously led a grant on optimality in parameterized complexity funded by the Polish National Science Center. His research interests include parameterized algorithms , structural graph theory , graph algorithms , and computational complexity . He has made significant contributions to the understanding of problems such as Independent Set in restricted graph classes, graph editing problems, and structural decompositions. The recent publications highlight a strong focus on structural graph theory and exact algorithms . Key themes include quasi-polynomial time algorithms for Independent Set in claw-free graphs, diameter computation in bounded genus graphs, and kernelization in trivially perfect graphs. His work often bridges combinatorial insights with algorithmic applications, particularly in the context of parameterized complexity. Principal Investigator, ERC Grant BOBR: Decomposition Method for Discrete Problems (2021–2026) Principal Investigator, Polish National Science Center Grant on Optimality in Parameterized Complexity (2014–2017) He has advised or collaborated with several researchers, including Marcin Wrochna and Marcin Pilipczuk. His work is published in top venues such as STOC, SODA, ESA, and ICALP.

• Algorithms on Discrete Structures
• Parameterized Algorithms
• Structural Graph Theory
• +4 more

Alexander Summers is an Associate Professor at the Department of Computer Science , University of British Columbia . He joined UBC in March 2020 after serving as a Senior Researcher (Oberassistent) at ETH Zurich from 2014-2020. His research bridges Programming Languages , Formal Methods , and Software Engineering , with a focus on automated verification tools for heap-based and concurrent programs. MSc Joint Mathematics and Computer Science, Imperial College London (2004) PhD Computer Science, Imperial College London (2009) Postdoc, ETH Zurich (2009-2014) Summers leads the Prusti Project , developing deductive verification tools for Rust, and contributes to the Viper Project for intermediate verification languages. His work addresses challenges in: Memory safety and concurrency verification Ownership models and aliasing control Automated reasoning with SMT solvers Resource-oriented programming specifications Debugging verification condition quantifiers Formal validation of verification infrastructure His research has been recognized with a Amazon Research Award and ACM SIGPLAN Distinguished Paper Awards . He teaches courses like Advanced Software Engineering and Program Verifiers and Program Verification , and supervises graduate students in formal verification and Rust-related research.

• Programming Languages
• Formal Methods
• Software Engineering
• +4 more

Tom Schrijvers is a Professor at the Department of Computer Science in the Faculty of Engineering Science at KU Leuven, Belgium. He leads the Programming Languages Group within the Declarative Languages and Artificial Intelligence (DTAI) research group. His research focuses on programming languages, particularly functional and logic programming, with special emphasis on Haskell, type systems, and algebraic effects. His research interests include: Functional Programming, especially Haskell Type Systems and Type Theory Algebraic Effects and Handlers Logic Programming, particularly Prolog Constraint Programming Domain-Specific Languages Programming Language Theory Prof. Schrijvers' recent research has focused on effect systems, staged programming, and language composition. His work on algebraic effect handlers has been particularly influential, providing new insights into how effects can be modularly composed and handled in functional languages. He has also made significant contributions to the understanding of type classes and their implementation in Haskell. His publications demonstrate a consistent focus on practical applications of programming language theory, with work spanning from foundational type theory to applied domain-specific languages for areas like fluorescence microscopy. His research often bridges the gap between theoretical programming language concepts and practical implementation concerns. Prof. Schrijvers has supervised numerous PhD students to completion, including Pieter Wuille, Benoit Desouter, George Karachalias, Steven Keuchel, Amr Saleh, Alexander Vandenbroucke, and Ruben Pieters. He currently supervises PhD students Klara Mardirosian, César Santos, Gert-Jan Bottu, Koen Pauwels, Birthe van den Berg, and Roger Bosman. His research group has received funding from various sources including EU projects like GRACeFUL. The Programming Languages Group at KU Leuven, which he leads, focuses on functional (Haskell) and logic (Prolog, Datalog, CLP) programming languages, as well as general programming language theory. The group has been active in numerous research projects and collaborations across Europe.

• Programming Languages
• Functional Programming
• Type Systems
• +6 more

Conrad Watt is an Assistant Professor at Nanyang Technological University (NTU), Singapore , specializing in WebAssembly, formal verification, and concurrency. He previously served as a Research Fellow at Peterhouse, University of Cambridge, and earned his PhD under Peter Sewell. Co-chair of the W3C WebAssembly Community Group Active in WebAssembly standards development, including concurrency specifications Developed mechanizations in theorem provers like Isabelle/HOL Collaborator with industry (wasmtime engine) and academic teams on verification tools Research Focus: Formal verification of low-level languages, concurrency models, and security mechanisms for WebAssembly. His work bridges theoretical rigor with practical applications, including WasmRef-Isabelle and threads projects. Recent Trends: 2025 publications explore separation logic automation and concurrency experiments, while 2024-2023 work emphasizes specification toolchains (SpecTec), verified interpreters, and memory-safe execution techniques. Scientific Awards ACM Doctoral Dissertation Award Honorable Mention EAPLS Best Dissertation Award Advising: Supervises PhD students Qiyuan Xu and Antanas Kalkauskas. Collaborates with researchers like Philippa Gardner and Jean Pichon-Pharabod.

• WebAssembly
• Formal Verification
• Programming Languages
• +4 more