Xiang Fu is a researcher in the Department of Computer Science at Hofstra University, with affiliations to institutions including Georgia Southwestern State University, University of California, Santa Barbara, and Massachusetts Institute of Technology. His work spans formal verification of web services, software security, zero-knowledge proofs, and automated testing frameworks. His research interests include: Web Services Formal Verification Software Security Zero-Knowledge Proofs Automated Testing Software Engineering Recent publications focus on zero-knowledge auditing for financial systems, formal verification of web services, and secure software analysis. Notable tools developed include APOGEE for automated grading and WISEngineering for scalable online learning. No explicit scientific awards are documented in the provided data.
Anthony Widjaja Lin is a Full Professor (W3) in Theoretical Computer Science (Automated Reasoning) and Max-Planck Fellow at University of Kaiserslautern-Landau, Germany. Previously, he was an Associate Professor in Programming Languages at Oxford University Department of Computer Science and Governing Body Fellow at Kellogg College (2016-2019), and an Assistant Professor at Yale-NUS, Singapore (2014-2016). He completed his PhD in Informatics at University of Edinburgh in 2010 under Leonid Libkin (supervisor) and Richard Mayr (co-advisor). Dr. Lin's educational background includes: PhD in Informatics, University of Edinburgh (2010) MSc, University of Toronto BSc (Honours), Melbourne University Dr. Lin's research focuses on automated reasoning, particularly over strings, formal language theory, learning/synthesis, and foundations of machine learning. His work has significant applications in software verification, program synthesis, querying graph databases, and computer security. He leads the development of the OSTRICH string solver, which won the QF_S (Single Query Track) in SMT-COMP 2023. His research has evolved from foundational work on string constraint solving to applications in verification of string-manipulating programs and more recently to connections with machine learning models like transformers. Dr. Lin has received numerous prestigious awards including an ERC Consolidator Grant (2023), Amazon Research Award (2021), ERC Starting Grant (2017), Google Faculty Award (2017), and the LICS Kleene Award (2010). Dr. Lin has advised several PhD students to completion, including Pascal Bergsträßer, Chih-Duo Hong, and Xuan-Bach Le, who have gone on to become Assistant Professors at institutions like National Chingchi University and Nanyang Technical University. He currently supervises multiple PhD students and postdocs working on string solving, automated reasoning, and verification. Dr. Lin leads the AV-SMP project (Algorithmic Verification of String-Manipulating Programs), which was supported by an ERC Starting Grant (2017-2022) and an Amazon Research Award (2021). His research group develops tools like OSTRICH, SLOTH, and CertiStr for string constraint solving and verification.
Aaron Williams is an Associate Professor in the Computer Science Department at Williams College. He holds a Ph.D. in Computer Science from the University of Victoria (2009) and prior degrees from the University of Waterloo. Before joining Williams, he served as an Assistant Professor at Bard College at Simon’s Rock (2014–2018) and held postdoctoral positions at Guelph, Carleton, and McGill Universities. His research focuses on algorithms and combinatorics, with a recent emphasis on retrogame archaeology—examining how retro video games managed computational constraints. He collaborates extensively with undergraduate students on topics like puzzle complexity. Williams' educational background includes a BMath (2001) and MMath (2003) in Computer Science from Waterloo, followed by industry experience as a programmer at Corel and Autodesk. His work bridges theoretical computer science with practical applications, particularly in combinatorial generation and Gray codes. He teaches courses at Williams and maintains an active research agenda in computational puzzles and historical game analysis. His research interests span algorithm design, combinatorial enumeration, and the intersection of computer science with retro gaming. He has published widely on topics like universal cycles, de Bruijn sequences, and NP-hard puzzles.
Florentina Voboril is a Research Fellow in the Algorithms and Complexity group at Technische Universität Wien. Her research explores how Large Language Models can solve Constraint Programming instances efficiently, with additional focus on SAT-solving and algorithm design. She investigates applications of AI in combinatorial optimization problems and develops innovative approaches to algorithmic challenges. Voboril has developed methods for SAT-based local improvement in string problems and generates streamlining constraints using LLMs. Her work bridges theoretical computer science with practical applications in computational biology and AI-assisted programming.
Benjamin Monmege is an Associate Professor at Aix-Marseille Université (AMU), holding the habilitation to direct research. He is affiliated with the Laboratoire d'Informatique et Systèmes (LIS) and part of the MOdelisation and VErification research team. His work focuses on formal methods for software verification and synthesis, with special emphasis on quantitative aspects of formal languages, automata theory, game theory, and grammatical inference. He contributed to tools like MightyL (for MITL logic translation) and QuantiS (for quantitative specifications verification). Recent research includes robust controller synthesis in timed automata, dynamics on routing games, and decidability in weighted timed games. He has advised three PhD students: Damien Busatto-Gaston, Théodore Lopez, and Julie Parreaux. Monmege teaches in AMU's Computer Science and Interactions Department, co-responsibilizing the Portail Descartes. Courses include 'Logique' (BSc), 'Automates' (MSc), and foundational computer science modules. His international collaborations include postdoctoral work at ULB (2015) and participation in MOVE seminars. His research has been published in venues like Logical Methods in Computer Science, FSTTCS, and CONCUR, with notable work on algorithmic game theory, real-time systems, and formal verification techniques.
Xiaokang Qiu serves as Associate Professor in Purdue University's Elmore Family School of Electrical and Computer Engineering, specializing in Programming Languages and Software Engineering with core expertise in program verification, program synthesis, and automated deduction. His research establishes critical bridges between enumerative and deductive synthesis methodologies, developing novel frameworks for verified program generation. Key contributions include string transformation synthesis with concurrency guarantees, bit-vector manipulation optimization via syntax-guided enumeration, and network design automation through comparative learning techniques. This work consistently advances formal verification foundations while addressing practical software engineering challenges. Publication trends from 2017-2025 reveal escalating complexity in synthesis targets—from basic data-structure manipulations to concurrent string operations and network configurations. His approach increasingly integrates machine learning elements with formal methods, demonstrating how query-based learning can drive near-optimal system design while maintaining provable correctness guarantees across diverse computational domains.
Dr. Maxence Corman is a postdoctoral researcher at the Max Planck Institute for Gravitational Physics (Albert Einstein Institute) in Potsdam, Germany, where he works in the Department of Astrophysical and Cosmological Relativity. His research bridges gravitational physics and cosmology, with a strong emphasis on numerical relativity and high-performance computing. Ph.D. in Theoretical Physics, Perimeter Institute for Theoretical Physics (2019–2023) M.Sci. in Physics and Astronomy, University of Glasgow (2016–2019) B.Sc. in Physics with Astrophysics, University of Glasgow (2014–2016) Dr. Corman's research focuses on testing general relativity in strong-gravity regimes through numerical simulations. His work explores black hole mergers in modified gravity theories , nonsingular bouncing cosmologies , robustness of inflation under inhomogeneous initial conditions , and flux compactifications in string theory as mechanisms for cosmic acceleration. He investigates how primordial black holes behave during cosmological bounces and whether inflation can emerge from highly inhomogeneous states. His methodology relies heavily on solving Einstein's equations numerically to model gravitational wave signals and spacetime dynamics in extreme conditions. He has studied binary black hole mergers in Einstein-scalar-Gauss-Bonnet gravity, producing waveforms that deviate from general relativity and could be detectable by future observatories like LISA. He also explores higher-dimensional theories and forecasts how space-based gravitational wave detectors can constrain extra dimensions through multi-messenger astronomy. Dr. Corman is passionate about promoting women in physics and envisions gender parity across all academic levels within the next two decades. He encourages young women to pursue physics authentically and without pretense. He has no listed scientific awards or students at this stage of his career. Dr. Corman leads or contributes to several key research projects, including: Numerical simulations of black hole mergers in modified gravity Dynamics of primordial black holes in bouncing cosmologies Nonlinear evolution of flux compactifications in string theory Testing inflationary robustness under extreme inhomogeneities Forecasting constraints on extra dimensions using LISA-era observations
Haniel Barbosa is a tenured Assistant Professor in the Department of Computer Science at Universidade Federal de Minas Gerais (UFMG), Brazil. His research focuses on improving SMT solvers for formal verification and enhancing their trustworthiness through proof certificates, as detailed in his work on projects like Lean-SMT and Carcara. He also serves as a senior technical lead for the state-of-the-art SMT solver cvc5 and actively collaborates with institutions like the University of Iowa, Stanford University, and Inria Nancy. Barbosa's research is supported by grants from the Defense Advanced Research Projects Agency (DARPA), CAPES, and Amazon Web Services. He mentors a diverse team of postdoctoral scholars, PhD students, and MSc students, including Caio Raposo, Tomaz Mascarenhas, Pedro Saccomani, and Bruno Andreotti. His teaching portfolio includes courses like Introduction to Computational Logic, Theory and Practice of SMT Solving, and Formal Methods. His publications span topics such as SMT proof production, proof reconstruction, and syntax-guided synthesis, with a focus on scalable algorithms, higher-order logic extensions, and industrial-strength solver development. Key trends in his work include formal verification, automated reasoning, and the intersection of logic with software engineering. Scientific awards include the Distinguished Tutorial Paper Award at FM 2024 and the Best Tool Paper Award at TACAS 2022 . Barbosa also contributes extensively to academic service as a steering committee member for SBMF, PC chair for LSFA and SBMF conferences, and organizer for SMT-COMP. His outreach includes invited tutorials at ATVA 2024, Dagstuhl Seminars, and workshops on SMT solving.
Dr. Arjun Radhakrishna is a Researcher at Microsoft in the PROSE team, specializing in program synthesis and formal methods . His work focuses on developing tools to ensure correctness and optimize soft specifications like performance and energy consumption in concurrent and embedded systems. He previously held post-doctoral positions at the University of Pennsylvania and completed his PhD at IST Austria under Prof. Thomas Henzinger.
David Brumley is a Professor of Electrical and Computer Engineering at Carnegie Mellon University with a courtesy appointment in the Computer Science Department. He previously served as Director of CyLab, CMU's Security and Privacy Institute, from 2015 to 2017. His research focuses on developing systems that automatically check software for exploitable bugs using program analysis with security-specific properties. Brumley received his Ph.D. in Computer Science from Carnegie Mellon University, an MS in Computer Science from Stanford University, and a BA in Mathematics from the University of Northern Colorado. Before his academic career, he served as a Computer Security Officer for Stanford University from 1998-2002. Brumley's research focuses on software security techniques that provide users with guarantees. His work sits at the intersection of model checking, formal methods, compilers, and logic, all applied to security problems. He develops efficient symbolic execution, reasoning about bit-level arithmetic in finite fields, sound decompilation, and decision procedures. His research also extends to network security and applied cryptography, focusing on efficient protocols, signature schemes, and privacy-preserving cryptography. A key aspect of his work involves binary code analysis, which allows reasoning about the security of code that actually executes. Brumley's publication record shows a consistent progression from theoretical foundations in program analysis to practical security systems. His work spans symbolic execution, fuzzing, exploit generation, and binary analysis. The Mayhem Cyber Reasoning System, which won the DARPA Cyber Grand Challenge, represents the culmination of his research vision for automated vulnerability detection and patching. His publications demonstrate how theoretical advances in program analysis can be translated into real-world security tools. USENIX Security Best Paper Awards (2003, 2007) International Conference on Software Engineering Distinguished Paper Award (2014) NSF CAREER Award (2010) United States Presidential Early Career Award for Scientists and Engineers (PECASE) (2010) Sloan Foundation Award (2013) DARPA Cyber Grand Challenge Winner ($2,000,000) (2016) Brumley has mentored numerous PhD students who have gone on to successful careers in academia and industry, including co-founders of ForAllSecure. He served as faculty mentor for the CMU Hacking Team Plaid Parliament of Pwning (PPP), which has been ranked #1 internationally and won DefCon 2013. His research has been supported by significant grants including DARPA programs and the NSF CAREER award. He also runs PicoCTF, an annual computer security contest for high school students that has become one of the largest cybersecurity education initiatives of its kind. Brumley leads the development of security systems through both academic research and commercialization. He is the CEO of ForAllSecure, which commercializes the Mayhem system developed through his academic research. His work bridges the gap between theoretical security research and practical security tools used by industry, creating a pipeline from academic innovation to real-world impact.
Florin Silviu Manea is a Professor for Theoretical Computer Science at the Institute of Computer Science, University of Göttingen, Germany. He has held this W3-Professor position since 2022, funded by the Heisenberg programme of the DFG. Prior to this, he was a W2-Professor for Fundamentals of Computer Science at the same university (2019-2022). His academic career includes research positions at Kiel University (2011-2019) and an Alexander von Humboldt fellowship at the University of Magdeburg (2009-2011). Dr. Manea received his PhD in December 2007 from the University of Bucharest with the distinction Summa cum Laude. His PhD thesis was titled "Networks of bio-processors" and was supervised by Victor Mitrana. He also earned a Master of Science (2005) and Bachelor of Science in Computer Science (2003) from the University of Bucharest, both with perfect GPA scores of 10/10. Professor Manea's research focuses on the theoretical foundations of computer science, with particular emphasis on string algorithms and combinatorics. His work explores the intricate relationships between formal language theory, automata theory, and practical applications in string solving. He has made significant contributions to understanding pattern matching with variables, combinatorial properties of strings, and the computational aspects of word equations. His research bridges theoretical concepts with practical applications in areas like bioinformatics and programming language theory. Analysis of Professor Manea's recent publications reveals a strong focus on string algorithms, particularly in the areas of pattern matching, word equations, and combinatorics on words. His work often explores the computational complexity of string problems while developing efficient algorithms to solve them. A recurring theme is the investigation of repetitions, palindromes, and gapped structures in strings, with applications ranging from DNA sequence analysis to programming language design. His research demonstrates a consistent pattern of advancing both the theoretical understanding and practical applications of string algorithms. Professor Manea has received several prestigious awards for his contributions to computer science: Lehrpreis 2020 (Teaching Award) by CS-students from Göttingen The "Tudor Tanasescu" Prize of the Romanian Academy (2009) Honorable Mention at ACM International Collegiate Programming Contest World Finals (2004, 2007) As an advisor, Professor Manea has successfully supervised multiple PhD students to completion with highest honors, including Maria Kosche (2023), Stefan Siemer (2024), and Tore Koß (2024), all achieving Summa cum Laude distinctions. He currently leads a research group focused on theoretical computer science at the University of Göttingen. His research is supported by significant funding, including a DFG grant for "String Constraint Solving: Combinatorial, Algorithmic, and Language-Theoretic Approaches" and continued Heisenberg-programme funding until 2027 for his project "Combinatorial String Solving." Professor Manea leads the Theoretical Computer Science research group at the University of Göttingen, which focuses on string algorithms, combinatorics, and computational models. His group actively collaborates with researchers worldwide and has organized several major conferences including DLT 2024, NCMA 2024, and CSL 2022. The group offers supervision for BSc and MSc theses on both theoretical and applied topics related to their research areas.
Jedidiah McClurg is an Assistant Professor in the Department of Computer Science at Colorado State University, with prior faculty appointments at Colorado School of Mines and the University of New Mexico. He received his Ph.D. in Computer Science from the University of Colorado Boulder in 2018, where he was a member of the CUPLV research group under the supervision of Pavol Cerny. His research focuses on programming languages, program synthesis, verification, and their applications in networking, compilers, and distributed systems. His educational background includes an M.S. in Computer Science from Northwestern University (2013) and a B.S. in Electrical Engineering from the University of Iowa (2009). He has completed internships at Microsoft Research (RiSE Group, 2014) and Rockwell Collins (2011, 2013, 2004). McClurg’s research interests include programming languages, formal verification, software synthesis, software-defined networking, compilers, and system security. His work aims to develop tools and techniques that help programmers write more secure, reliable, and efficient code, especially in safety-critical domains. He has led multiple NSF-funded projects, including FMitF and CRII grants, totaling over $1 million in funding. His recent publications span high-impact venues such as PLDI, CAV, DISC, and SOSR, with topics ranging from neural network optimization and regular expression synthesis to network program verification and FEC code generation. These works reflect a consistent trend toward automating correctness, improving performance, and enabling scalable solutions in systems and networking. NSF CRII: SHF: Foundations for Stateful Network Programming ($175,000) NSF FMitF: Game Theoretic Updates for Network & Cloud Functions ($355,000 for him) NSF FMitF: Robust Enforcement of Customizable Resource Constraints ($250,000 for him) NSF GRFP (awarded to student Lauren Baker) He has advised multiple graduate and undergraduate students, many of whom have secured positions at leading tech companies such as Google, Apple, and Amazon. He is actively involved in academic service, having served on program committees for PLDI, SOSR, CAV, and others, and as a reviewer for journals like IEEE/ACM Transactions on Networking (ToN) and ACM Transactions on Software Engineering (TSE). He also contributes to open-source research via GitHub and maintains a strong online academic presence.
Tevfik Bultan is a Professor in the Department of Computer Science at the University of California, Santa Barbara, where he leads the Verification Laboratory (VLab). He has been actively teaching undergraduate and graduate courses including CS 160 (Translation of Programming Languages), CS 267 (Automated Verification), and CS 272 (Software Engineering), along with specialized seminars on topics like Neural Network Verification and Quantitative Verification. Ph.D. in Computer Science, University of Maryland, College Park (1998) M.S. in Computer Engineering and Information Science, Bilkent University (1992) B.S. in Electrical and Electronics Engineering, Middle East Technical University Professor Bultan's research focuses on software verification, static analysis, model checking, and security, with particular emphasis on quantitative information flow, side channel analysis, and string analysis. His work bridges theoretical foundations with practical applications in web software, service-oriented computing, and concurrency. He has pioneered techniques for detecting bugs in identity and access management policies and quantifying information leakages in crypto libraries, which earned him Amazon Research Awards in 2017 and 2023. His recent publications and student dissertations reveal a strong trend toward quantitative approaches to security analysis, with increasing focus on automated techniques for detecting and mitigating side channels in various contexts including network communications, cryptographic libraries, and access control systems. His work combines formal methods with practical security applications, often developing novel constraint solving and model counting techniques. Amazon Research Award (2017): Automatically Detecting Bugs in Identity and Access Management Policies Amazon Research Award (2023): Detecting and Quantifying Information Leakages in Crypto Libraries Professor Bultan has advised numerous PhD students who have gone on to academic positions at institutions like Stevens Institute of Technology, Harvey Mudd College, and King Saud University, as well as industry positions at companies including Amazon, Google, Microsoft, and Intel. His laboratory, the Verification Laboratory, has received funding from various sources to support research in software verification and security analysis. The lab focuses on developing practical verification techniques that can be applied to real-world software systems.
David Becerra is a Senior Lecturer in the School of Computer Science at McGill University, where he joined in 2020. His expertise spans computational biology, algorithms, and programming competitions, with a focus on interdisciplinary applications in life sciences. Dr. Becerra earned his PhD in Computer Science with emphasis in Bioinformatics from McGill University in 2017 under Professor Jérôme Waldispühl. He completed a postdoctoral fellowship at the Terrence Donnelly Centre for Cellular & Biomolecular Research (2018-2020) supervised by Professor Philip Kim. His undergraduate and master's degrees are from Universidad Nacional de Colombia, where he worked with Professor Luis Niño. His research integrates machine learning, bio-inspired computing, and optimization to solve molecular biology problems at sequential and structural levels. Key areas include protein structure prediction, molecular docking, sequence alignment, and algorithm development for computational biology. He applies these techniques to model complex life science phenomena through computational simulation and data mining. Analysis of his 15 most recent publications reveals a strong trajectory in computational structural biology, with increasing use of deep learning (particularly graph neural networks) for protein design and folding. His work consistently bridges theoretical algorithms with practical biological applications, emphasizing multi-objective optimization approaches. Qualified for ICPC World Finals (2020, 2021) Dr. Becerra actively mentors students through undergraduate research projects (COMP396/400/401), course instruction (COMP204/251), and programming competition teams. He fosters an inclusive environment where students develop algorithmic confidence and interpersonal skills through collaborative problem-solving. He leads McGill's competitive programming initiative, which has created a thriving community preparing students for international competitions like the ICPC. This program emphasizes both technical excellence and team cohesion, resulting in consecutive world final qualifications.
Amatur Rahman is a Research Fellow in the Szpiech Lab within the Department of Biology at Pennsylvania State University. His research focuses on computational genomics, bioinformatics algorithms, and data compression techniques for large-scale genomic datasets. He is affiliated with the Wartik Laboratory at 360 Science Drive, University Park, PA, and can be reached at aur1111@psu.edu. His research interests span genomic data analysis, including sequence search optimization, genome assembly artifact detection, CRISPR/Cas9 guide RNA design, and IoT applications in environmental monitoring. His work bridges algorithm development with practical genomic challenges, emphasizing efficiency and scalability. Rahman's publications highlight contributions to bioinformatics tools, such as the K-mer File Format and GPU-accelerated methylation calling pipelines. His research also addresses IoT constraints, like adaptive sensing on limited data plans and low-cost air quality monitoring devices like AQBox. His articles reflect a trend toward solving computational bottlenecks in genomics while maintaining interdisciplinary applications in environmental and biomedical fields.
