جست‌وجوی استاد و پژوهشگر

Konstantinos Mamourasمشاهده پروفایل

استادیار

• Programming Languages
• Formal Methods
• Runtime Verification
• +۶ مورد دیگر

Konstantinos Mamouras is an Assistant Professor at the Department of Computer Science, Rice University. His research focuses on programming abstractions for data streams, runtime verification for cyber-physical systems, program semantics, and Kleene Algebra with Tests. Rice University, Houston, TX Department of Computer Science Research Interests : Design of programming abstractions for streaming data Runtime verification of cyber-physical systems Formal methods in program verification Kleene Algebra with Tests (equational theories) Efficient automata-based processing Quantitative monitoring frameworks Recent Scientific Contributions : 2025: Membership Testing for Semantic Regular Expressions (PLDI 2025) 2024: HybridSA GPU regex acceleration (OOPSLA 2024) 2024: Efficient regex with lookaround (POPL 2024) 2023: Bit Vector Automata for regex (OOPSLA 2023) 2021: Quantitative monitoring framework (RV 2021) Scientific Awards : 🏆 Distinguished Paper Award, PLDI 2024 🏆 Best Paper Award, EuroSys 2021 🏆 EATCS Best Theory Paper Award, FoSSaCS 2015 Advising : Mentored PhD student Lingkun Kong (2023) Advised MSc student Zhifu Wang (2022) Current advisees: Alexis Le Glaunec and Agnishom Chattopadhyay Teaching : Spring 2023: IoT Programming and Data Analysis (COMP 418/518) Fall 2022: Reasoning about Algorithms (COMP 382) Spring 2021: Reasoning about Software (COMP 403/503)

Sami Daviesمشاهده پروفایل

پژوهشگر

• Theoretical Computer Science
• Combinatorial Optimization
• Algorithm Design
• +۵ مورد دیگر

Sami Davies is a Research Scientist in Theoretical Computer Science at the Simons Institute and the Department of Electrical Engineering and Computer Sciences (EECS) at the University of California, Berkeley. He earned his PhD in Mathematics from the University of Washington in 2021 under the supervision of Thomas Rothvoss, with work spanning the Department of Mathematics and the Allen School theory group. Prior to Berkeley, he was a postdoctoral researcher in the theory group at Northwestern University. His research lies at the intersection of theoretical computer science and combinatorial optimization. Key interests include scheduling algorithms, clustering, hypergraph matchings, approximation algorithms, and algorithmic applications in machine learning. His work often addresses fundamental problems in algorithm design with practical implications in resource allocation, data analysis, and network optimization. The most recent publications reflect a strong trend in developing efficient combinatorial algorithms for clustering and scheduling problems, particularly under complex constraints such as communication delays or fairness objectives. There is a growing integration of machine learning concepts into traditional algorithmic frameworks, as seen in works on predictive flows and trace reconstruction. His research is consistently published in top-tier venues including NeurIPS, ICML, SODA, FOCS, and COLT, indicating high impact and recognition within the theoretical CS community. Sami is actively engaged in academic service and outreach. He has served as a mentor in the Washington Directed Reading Program and co-taught the 'Math in Society' course through FEPPS, which provides education to incarcerated individuals. He has also taught yoga at the University of Washington. He enjoys bouldering, running with his dog, vegan cooking and baking, and practicing yoga. He completed a 200-hour yoga teacher training in 2019. Contact: Email: samidavies@berkeley.edu , davies@berkeley.edu

Panagiotis Charalampopoulosمشاهده پروفایل

استادیار

• Algorithms
• Data Structures
• String Algorithms
• +۳ مورد دیگر

Panagiotis Charalampopoulos is a Lecturer (Assistant Professor) in the Department of Informatics at King's College London, Faculty of Natural, Mathematical & Engineering Sciences. He previously served as a Lecturer in the School of Computing and Mathematical Sciences at Birkbeck, University of London, and was a postdoctoral fellow at the Efi Arazi School of Computer Science, Reichman University (IDC Herzliya), hosted by Shay Mozes and Oren Weimann (University of Haifa). Education: Ph.D. in Computer Science, King's College London, UK (2016–2020) M.Sc. in Advanced Computing, King's College London, UK (2015–2016) B.A. in Mathematics, University of Cambridge, UK (2010–2013) His research lies in the field of algorithms and data structures, with a strong focus on string algorithms and planar graphs. These interests are rooted in theoretical computer science and discrete mathematics, aiming to develop efficient computational methods for complex data structures. His work bridges foundational theory with practical algorithmic design, particularly in scenarios involving large-scale string processing and graph-based modeling. While no publications are listed in the provided text, his research trajectory suggests a concentration in theoretical aspects of computer science, particularly algorithmic efficiency and structural properties of strings and planar networks. Scientific Awards: No awards mentioned in the provided text. Dr. Charalampopoulos has supervised or co-supervised research during his Ph.D. under Tomasz Radzik and Solon P. Pissis, and undertook research internships at the Institute of Informatics, University of Warsaw (hosted by Jakub Radoszewski) and at IDC Herzliya. These experiences highlight international collaboration and mentorship in high-level algorithmic research. No specific grants are mentioned, but his postdoctoral and Ph.D. work indicate engagement with research funding structures. He has been affiliated with research groups at King's College London, Birkbeck, Reichman University, and the University of Warsaw, contributing to a collaborative academic network in algorithms research. No dedicated lab or team is explicitly mentioned, but his work aligns with theoretical computer science research groups in these institutions.

Viktor Kunčakمشاهده پروفایل

دانشیار

• Program Synthesis
• Program Verification
• Automated Reasoning
• +۳ مورد دیگر

Viktor Kunčak is an Associate Professor with tenure at the École Polytechnique Fédérale de Lausanne (EPFL) in the School of Computer and Communication Sciences. He leads the Laboratory for Automated Reasoning and Analysis (LARA) and has been at EPFL since 2007 after completing his PhD at the Massachusetts Institute of Technology (MIT). His educational background includes: PhD from Massachusetts Institute of Technology (MIT), 2007 Professor Kunčak's research focuses on bridging the gap between human goals and computational realizations through program synthesis , verification , and automated reasoning . His work has significant applications in software development, formal methods, and programming languages. He is particularly known for developing practical tools like Leon and Stainless that implement theoretical advances in automated reasoning. His recent publications demonstrate a consistent focus on advancing program synthesis techniques, verification frameworks, and formal methods. The research trajectory shows progression from foundational theoretical work to practical applications and tools that can be used by developers. Many papers focus on making verification and synthesis more accessible and efficient for real-world programming tasks. His notable scientific achievements include: ACM SIGSOFT Distinguished Paper Award for work on automated testing European Research Council (ERC) Grant of 1.5M EUR (2012) Communications of the ACM Research Highlight for a PLDI paper Professor Kunčak has supervised 13 completed PhD theses and teaches courses on functional and parallel programming, compilers, and verification at EPFL. He has also co-taught a popular MOOC on Parallel Programming that reached over 100,000 learners worldwide. His research has been supported by significant funding including a 5-year ERC grant. He has served in leadership roles for major conferences including as program co-chair for FMCAD 2014 and VMCAI 2012. He leads the Laboratory for Automated Reasoning and Analysis (LARA) at EPFL, which develops tools like Stainless for program verification. The lab has established an international presence through collaborations including a European COST Action to establish standardized formats for verification and synthesis (Rich Model Toolkit).

• Quantum Chromodynamics (QCD)
• Lattice Field Theory
• High Energy Physics
• +۵ مورد دیگر

Bastian Brandt is a researcher at the Faculty of Physics, Bielefeld University , focusing on Quantum Chromodynamics (QCD) and Lattice Field Theory . His work explores the effects of extreme magnetic fields on QCD topology, thermodynamics, and anomalous transport phenomena. He is affiliated with the Computer Simulations and Lattice Field Theory Group and the Bielefeld Graduate School in Theoretical Sciences . Brandt's research interests include: QCD Thermodynamics under extreme conditions Topological Susceptibility and axion-photon coupling Chiral Effects like the Chiral Separation Effect Phase Transitions in QCD with isospin/baryon chemical potentials Early Universe Cosmology and primordial black hole formation His recent publications highlight trends in lattice simulations , rewighting techniques , and continuum limit studies . He collaborates on projects like TRR 211/2 (DFG-funded) to study Magnetized and Isospin-Asymmetric QCD Matter and Software Development Centers for lattice field theory. Brandt's work bridges theoretical physics with computational methods , advancing understanding of QCD in extreme environments and its cosmological implications.

Artem Sokolovمشاهده پروفایل

استاد

• Machine Translation
• Natural Language Processing
• Structured Prediction
• +۴ مورد دیگر

Artem Sokolov serves as an Honorary Professor in the Department of Computational Linguistics at Heidelberg University and as a Research Scientist at Google Berlin. His primary research focuses on machine translation and structured prediction within natural language processing. Previously, he held positions at Amazon, the Statistical NLP Group at Heidelberg University led by Prof. Stefan Riezler, LIMSI, and Orange Labs in France, contributing to advancements in statistical and neural machine translation systems. He earned his PhD in Computer Science and Artificial Intelligence from the IRTCITS research center in Kyiv. His doctoral thesis investigated randomized algorithms for locality-sensitive embeddings of the Levenstein edit distance, establishing foundational work for efficient string similarity search in computational linguistics and intrusion detection systems. Dr. Sokolov's research expertise spans machine translation, imitation learning, bandit algorithms, and weakly supervised learning. He has pioneered methods for learning from partial feedback in structured prediction tasks, particularly addressing exposure bias in sequence generation and multi-facet evaluation of translation systems. His work bridges theoretical machine learning with practical NLP applications, emphasizing robustness against noisy data and scalable optimization techniques for real-world deployment. Analysis of his recent publications reveals trends toward scalable influence functions for model interpretability, multi-attribute control in machine translation, and rigorous auditing of multilingual datasets. His research consistently intersects natural language processing, machine learning optimization, and data quality assessment, with increasing emphasis on ethical AI considerations and efficient learning from weak supervision signals. Scientific awards include: 1st place at ECML/PKDD Discovery Challenge 2010 (English quality task) 2nd place at ECML/PKDD Discovery Challenge 2010 (general task) 2nd place in Semi-Supervised Feature Learning Challenge at NIPS 2011 3rd place in Semi-Supervised Feature Learning Challenge at NIPS 2011 As co-Principal Investigator for the 2015-2017 grant "Weakly Supervised Learning of Cross-Lingual Systems", Dr. Sokolov developed techniques for learning cross-lingual rankings from weakly supervised data sources like patent citations and Wikipedia hyperlinks. He has mentored students through teaching advanced courses including Imitation Learning, Stochastic Learning, and Statistical Machine Translation at Heidelberg University, supervising seminar projects on structured prediction and optimization algorithms. Dr. Sokolov is an active member of the Statistical NLP Group at Heidelberg University and collaborates with research teams at Google Berlin. His current work focuses on advancing production-scale machine translation systems through scalable inverse reinforcement learning and robust training methodologies, building on his extensive background in both academic research and industrial applications.

Raju Venugopalanمشاهده پروفایل

استاد

• Quantum Chromodynamics
• High Energy Physics
• Color Glass Condensate
• +۵ مورد دیگر

Raju Venugopalan is a distinguished theoretical physicist serving as a Distinguished Scientist at Brookhaven National Laboratory (BNL) and Director of the BNL EIC Theory Institute since October 2022. He also holds an Adjunct Professor position at Stony Brook University since March 2009 and previously served as Group Leader of the BNL Nuclear Theory group from June 2010 to September 2021. His career spans over two decades at BNL, where he was awarded tenure in June 2002. Venugopalan received his B.S. from the University of Chicago in June 1987 and his Ph.D. from Stony Brook University in August 1992. Following his doctorate, he completed postdoctoral appointments at the Theoretical Physics Institute at the University of Minnesota (1992-1994), the National Institute for Nuclear Theory at the University of Washington (1994-1996), and as a Danish Research Council Fellow at the Niels Bohr Institute in Copenhagen (1997-1998). His research primarily focuses on Quantum Chromodynamics (QCD) at high energies, where he has made seminal contributions to the development of the Color Glass Condensate (CGC) effective theory. Venugopalan has pioneered numerical and analytical techniques in real-time classical-statistical field theory and co-invented the IP-Glasma model, which provides the most successful description of high-energy heavy-ion collisions to date. He has also been an early proponent of the Electron-Ion Collider (EIC), having organized the first eRHIC workshop at BNL in December 1999. His work explores interdisciplinary connections between high-energy QCD and other areas of physics, ranging from Planck scale phenomena to ultracold atomic systems. Venugopalan's recent publications demonstrate a strong focus on the intersection of QCD, gravity through double copy formalisms, quantum information aspects of QCD (particularly entanglement), and quantum computing applications to field theory problems. His work on the QCD-gravity double copy in Regge asymptotics represents a cutting-edge research direction connecting strong force physics with gravitational phenomena. He has also been increasingly active in quantum information applications to nuclear physics, particularly in studying entanglement in QCD systems. Co-PI of Simons Foundation Collaboration on Confinement and QCD strings Co-author of Phys. Rev. D 50th Anniversary Milestone Paper (2020) Suffolk County NY Distinguished Asian American Award (2019) BNL Science & Technology Award (2018) Humboldt Research Award (Humboldt Prize, 2016) Fellow of the American Physical Society (2007) Venugopalan has supervised numerous undergraduate, master's, and Ph.D. students throughout his career. He has secured significant research funding including co-PI roles on DOE Topical Theory Collaborations (BEST, TMD, SURGE) and the Simons Foundation Collaboration on Confinement and QCD strings. His leadership extends to chairing the Steering Committee of the Joint BNL/Stony Brook Center for Frontiers in Nuclear Science and serving on multiple national and international advisory committees. As Director of the BNL EIC Theory Institute, Venugopalan leads theoretical efforts supporting the development of the Electron-Ion Collider, a major DOE project currently under construction at BNL. He also contributes to quantum computing initiatives through his role as Theory sub-Thrust co-Leader for the Co-design Center for Quantum Advantage (C2QA) at BNL.

• Probabilistic Programming
• Deep Learning
• Artificial Intelligence
• +۵ مورد دیگر

Jan-Willem van de Meent is an Associate Professor at the University of Amsterdam , where he co-directs the AMLab with Max Welling. He holds an Assistant Professor position (on leave) at Northeastern University , continuing to advise students and collaborate remotely. His research focuses on combining probabilistic programming and deep learning to develop models that generalize from limited data. Key areas include inductive biases through physical simulators, causal structures , and symmetries , with applications in robotics , NLP , healthcare , and physical sciences . Recent work includes Variational Flow Matching for graph generation Equivariant neural models for physical systems Entropy coding of complex data structures Goal-contrastive reinforcement learning for robotics Awards NSF CAREER award (2021) Students & Postdocs Robin Walters (Postdoctoral Fellow) Ondrej Biza (Ph.D. Candidate) Babak Esmaeili (Ph.D. Candidate) Sam Stites (Ph.D. Candidate) Hao Wu (Ph.D. Candidate) Xiongyi Zhang (Ph.D. Candidate) Heiko Zimmermann (Ph.D. Candidate) Jered McInerney (Ph.D. Candidate) Eli Sennesh (Ph.D. Candidate)

• Gravitational Wave Astronomy
• Laser Interferometry
• Space Interferometry
• +۳ مورد دیگر

Dr. Melanie Ast is a Junior Scientist/Postdoc at the Max Planck Institute for Gravitational Physics (AEI) in Hannover, Germany. Her research focuses on gravitational wave astronomy, laser interferometry, and space-based interferometry. She contributes to advanced detector technologies like LISA and analyzes gravitational wave signals from compact binary mergers. Her work involves improving interferometric techniques to suppress noise and enhance detection capabilities. Her research areas include testing general relativity through gravitational wave observations, studying neutron star mergers and their electromagnetic counterparts, and developing methods for multi-messenger astronomy. She has collaborated on projects analyzing data from Advanced LIGO, Virgo, and KAGRA detectors, contributing to key studies of GW170817 and other historic gravitational wave events. Dr. Ast's publications emphasize high-precision measurement techniques, such as stray light suppression in interferometers and phase-reference distribution systems. Her work bridges experimental physics with theoretical astrophysics, advancing the field of gravitational wave detection and its applications to cosmology and fundamental physics.

• Theoretical Physics
• Quantum Many-Body Systems
• Quantum Phase Transitions
• +۵ مورد دیگر

Prof. Markus Heyl is a Professor of Theoretical Physics III at the University of Augsburg, affiliated with the Faculty of Mathematics, Natural Sciences, and Materials Engineering. Prior to this role, he held positions as a Leibniz Group Leader at the Max-Planck Institute for the Physics of Complex Systems and a postdoctoral fellow at institutions such as TU Munich and IQOQI Innsbruck. His research focuses on quantum many-body systems, dynamical quantum phase transitions, and the application of machine learning techniques to quantum physics. Heyl's academic journey includes a PhD from Ludwig-Maximilians-Universität München under Prof. Stefan Kehrein, followed by postdoctoral work exploring quantum dynamics, localization phenomena, and quantum simulation. He currently leads a vibrant research group comprising postdoctoral researchers and PhD students investigating topics like neural quantum states, adaptive quantum algorithms, and non-equilibrium quantum systems. Teaching responsibilities include courses on 'Machine Learning in Physics' and advanced seminars on quantum many-body theory. His recent workshops, such as 'Connecting Physics and Computer Science' (2024) and 'Bridging the Divide' (2023), reflect his interdisciplinary approach to advancing quantum research. Key research themes include dynamical criticality, Trotterization optimization for NISQ devices, and the interplay between quantum chaos and integrability. His work bridges theoretical insights with experimental validations, leveraging tools like neural networks and classical networks to model complex quantum systems.

• Combinatorics
• Algebra
• Geometry
• +۳ مورد دیگر

John Machacek is an Assistant Professor of Mathematics and Computer Science at Hampden-Sydney College. His academic journey includes a PhD in Mathematics from Michigan State University (advised by Michael Shapiro), postdoctoral positions at the University of Oregon and York University, and prior visiting roles at Hampden-Sydney. He specializes in combinatorics with applications to algebra, geometry, topology, and theoretical computer science. His research explores topics such as cluster algebras, matroid theory, graph theory, and scheduling problems. Key educational milestones include a B.S. from the University of Minnesota-Twin Cities (Honors Thesis advised by Victor Reiner) and postdoctoral collaborations with Patricia Hersh and Nantel Bergeron. His Erdős number is 3, reflecting collaborative work in combinatorial mathematics. Research trends in his articles focus on algebraic and geometric combinatorics, including studies on Grassmannians, matroid structures, scheduling algorithms, and discrete dynamical systems. His work bridges pure combinatorics with interdisciplinary applications in physics and computer science. No scientific awards are explicitly listed, though his extensive contributions to MathOverflow and collaborative projects highlight his scholarly engagement. He advises no listed students but actively participates in academic communities across platforms like MathOverflow and Google Scholar. His research outputs span diverse subfields, emphasizing both foundational theory and applied problem-solving.

• Gravitational Wave Astronomy
• Astrophysics
• Quantum Engineering
• +۳ مورد دیگر

Dr. Alexander Wanner holds management roles at several institutions within Leibniz University Hannover, including the QUEST-Leibniz Research School, Quest Centre for Quantum Engineering and Space-Time Research, QuantumFrontiers, and the HITec Institute of Technology. His email is alexander.wanner@quest.uni-hannover.de . His research focuses on gravitational wave detection, quantum engineering, and detector technology. He contributes to projects like the Einstein-Elevator facility and the AEI 10m prototype interferometer, emphasizing precision measurement and noise reduction in gravitational wave observatories. Wanner's work bridges astrophysics and experimental physics, addressing topics such as binary coalescence modeling, cosmic string constraints, and multi-messenger observations. He collaborates with LIGO-Virgo and ANTARES networks, advancing understanding of gravitational wave sources and their astrophysical implications. His role in managing interdisciplinary research initiatives highlights expertise in coordinating large-scale projects and integrating cutting-edge technologies for fundamental physics research.

• Applied Mathematics
• Optimization
• Computational Mathematics
• +۳ مورد دیگر

Yair Censor is a distinguished Professor in the Department of Mathematics at the University of Haifa. His work focuses on applied mathematics, particularly in optimization, projection methods, and computational algorithms with applications in medical physics and engineering. He has developed foundational techniques like the superiorization method, which balances feasibility-seeking and objective function reduction in iterative algorithms. Research Interests: Optimization theory, convex feasibility problems, superiorization, medical imaging algorithms, and parallel computing. Collaborations: Extensive work with Gabor T. Herman, Aviv Gibali, Reinhard Schulte, and others in computational methods for radiation therapy and tomography. His recent publications (2023–2025) emphasize feasibility-seeking algorithms, superiorization in floorplanning, and perturbation resilience. While no explicit awards or students are listed in the provided text, his contributions to iterative methods and medical physics remain influential.

Michael Stillmanمشاهده پروفایل

استاد

• Computational Algebraic Geometry
• Commutative Algebra
• String Theory
• +۳ مورد دیگر

Michael Stillman is a Professor of Mathematics at Cornell University, affiliated with the Nonlinear Algebra research group at the Max Planck Institute for Mathematics in the Sciences (MPI MiS). His work focuses on computational algebraic geometry, commutative algebra, and their applications to theoretical physics, particularly string theory. He is a principal author of the open-source computer algebra system Macaulay2, which underpins much of his research in algorithm development and computational methods for algebraic structures. Stillman's research includes tackling computationally intensive problems in string theory, such as calculating nef cones and semigroups for Calabi-Yau varieties linked to toric geometry, and optimizing cohomology computations for line bundles on toric varieties. His recent collaboration with Mastroeni and Schenck explored quadratic Gorenstein algebras and the Koszul property.

Maximilian Wiesmannمشاهده پروفایل

پژوهشگر ارشد

• Algebraic Geometry
• Computational Geometry
• Combinatorics
• +۴ مورد دیگر

Maximilian Wiesmann is a mathematician working as an ELBE Postdoctoral Fellow at the Center for Systems Biology Dresden, Germany. Previously, he completed his PhD at the Max-Planck-Institute for Mathematics in the Sciences in Leipzig under the supervision of Eliana Duarte and Bernd Sturmfels. Mentored by Aida Maraj and Ivo Sbalzarini at CSBD, his work bridges pure mathematics with applications in computational biology and quantum information. His educational background includes: PhD in Mathematics, Max-Planck-Institute for Mathematics in the Sciences, Leipzig (2025) MASt in Pure Mathematics, University of Cambridge (2022) B.Sc. in Mathematics, University of Bonn (2021) Wiesmann's research focuses on the intersection of algebraic geometry and computational mathematics, particularly in nonlinear algebra applications. His work spans theoretical investigations in toric geometry and combinatorics to practical implementations in quantum information and machine learning. He has developed significant software tools including the EulerStratifications.jl Julia package, which accompanies his research on hypersurface families. His publications demonstrate a consistent trajectory from foundational mathematical structures to their applications in statistical modeling and computational science. Analysis of his 11 most recent publications reveals a strong focus on algebraic statistics and geometric approaches to statistical problems, with increasing attention to machine learning applications. His work connects pure mathematical structures with practical computational frameworks, particularly through the lens of nonlinear algebra. The development of specialized software like EulerStratifications.jl demonstrates his commitment to making theoretical advances accessible for practical computation. Wiesmann has served as a tutor in linear algebra and foundational mathematics at the University of Bonn during winter terms 2019/20 and 2020/21. He also completed a Quantum Science Summer Internship at Riverlane in Cambridge, UK in 2021, and was a visiting student at the Institute for Mathematical and Statistical Innovation at the University of Chicago in September and October 2023. At CSBD, Wiesmann is part of a vibrant research environment focused on systems biology, where he applies his expertise in algebraic geometry to biological modeling problems. His work with the Nonlinear Algebra Community on Zenodo demonstrates his commitment to open science and community building in his field.