جست‌وجوی استاد و پژوهشگر

Rob Dickersonمشاهده پروفایل

استادیار

• Programming Languages
• Formal Methods
• Relational Verification
• +۳ مورد دیگر

Rob Dickerson is an Assistant Professor of Computer Science at Augustana, specializing in programming languages and formal methods with a focus on practical software engineering applications. His research bridges theoretical principles and real-world implementation challenges. His primary research interests include relational verification , e-graph applications , and modular program logic . He develops techniques for aligning programs, inferring library specifications through data-driven methods, and verifying relational properties across multiple executions. Current projects include KestRel for program alignment and RHLE for relational ∀∃ properties. Key contributions span publications at premier venues including OOPSLA, APLAS, and PLDI workshops. His work on Elrond received a Distinguished Artifact Award at OOPSLA 2021. Notable publications examine relational verification (2025), e-graph-based alignment (2025), and modular verification of relational properties (2022). Distinguished Artifact Award (Elrond, OOPSLA 2021) Dickerson maintains active industry connections through prior work at Square and participates in academic service through venues like the PurPL seminar at Purdue. Outside academia, he engages in épée fencing and amateur piano/violin performance.

Benjamin Delawareمشاهده پروفایل

استادیار

• Programming Languages
• Formal Methods
• Program Verification
• +۵ مورد دیگر

Benjamin Delaware is an Assistant Professor in the Department of Computer Science at Purdue University since 2016. He earned his Ph.D. in Computer Science from the University of Texas at Austin in 2013 under William Cook and Don Batory, following an M.Sc. in Computer Science from Washington University in St. Louis (2007) and a dual B.S. in Computer Science and B.A. in Russian from Truman State University (2005). Research focuses on program synthesis , formal verification , and relational program properties . Key contributions include KestRel (relational verification), PALM (LLM-assisted proof automation), and Clotho (distributed system testing). His publications span top venues like OOPSLA, PLDI, and POPL, with recent work (2025) on coverage-type-guided synthesis and LLM-integrated proof repair . He has received multiple scientific awards , including a SIGPLAN Distinguished Paper Award (2023) and CRII Award from NSF (2018). Ben advises a research group producing graduates such as Qianchuan Ye (now at SUNY Buffalo) and Kia Rahmani (UT Austin postdoc). He teaches graduate courses on program reasoning (CS560) and programming language design (CS456/CS565), with earlier teaching experience at UT Austin. Active in academic service , he served as Program Committee Co-Chair for RocqPL 2026 and CoqPL 2025 , and as Diversity, Equity, and Inclusion Chair at POPL 2023. His grants include NSF funding for input generator verification (2023-2026) and Cisco research on privacy-preserving computation (2022-2023).

Jérôme Lerouxمشاهده پروفایل

پژوهشگر

• Formal Verification
• Vector Addition Systems
• Petri Nets
• +۷ مورد دیگر

Jérôme Leroux is a Directeur de Recherche (DR CNRS) at the Laboratoire Bordelais de Recherche en Informatique (LaBRI) , affiliated with the University of Bordeaux , France. His research focuses on formal verification of infinite-state systems, vector addition systems, Presburger arithmetic, and acceleration techniques for symbolic computation. Key research themes include: Vector Addition Systems (VASS) and Petri Nets Automata-based representations for Presburger arithmetic Abstract interpretation and CEGAR frameworks Verification of asynchronous distributed systems His recent publications highlight work on acceleration techniques for convex binary relations, serialized digit automata, and regular acceleration methods for number decision diagrams. These contributions are implemented in tools like the Talence Presburger Arithmetic Suite (TAPAS) and the FAST tool for symbolic verification. Scientific awards include a Best Paper Award at TURING'100 . He has advised several Ph.D. students and postdoctoral researchers, including Alexander Heußner and Thibault Hilaire (current Ph.D. candidate). Leroux is actively involved in organizing conferences like MFCS'23 and serving on program committees for venues such as VMCAI'26 .

Ramakanth Kavuluruمشاهده پروفایل

استاد

• Biomedical Informatics
• Text Mining
• Information Extraction
• +۳ مورد دیگر

Professor Ramakanth Kavuluru is affiliated with the University of Kentucky , where he holds positions in the Institute for Biomedical Informatics , Center for Computational Sciences , and Markey Cancer Center . His research focuses on Biomedical Informatics , Text Mining , Information Extraction , Biomedical Terminologies , Machine Learning , and Data Mining , contributing to the UN Sustainable Development Goal of Education. Education: Post Doctoral Scholar, Wright State University (2011) Doctor of Philosophy, University of Kentucky (2009) His recent research involves Natural Language Processing (NLP) applications in healthcare, including overdose surveillance , biomedical relation extraction , and COVID-19 recovery programs . Publications demonstrate expertise in large language models , deep neural networks , and clinical data systems . Scientific recognition includes the 2024 Fulbright Scholar award. His grant history shows leadership in National Institute on Drug Abuse and National Library of Medicine projects. Collaborations span 27 grants and 112 research outputs , including work with the National Cancer Institute on cancer prevention initiatives.

• Machine Learning
• Deep Learning
• Semantic Web

Dr. Shahin Mehdipour Ataee is an Assistant Professor at the State University of New York at Fredonia with teaching experience since 2007. His office hours are held Tuesdays (12:30-2:00 PM), Wednesdays (3:30-5:00 PM by appointment), and Thursdays (12:30-2:00 PM). Education: Ph.D. in Computer Engineering - Information Systems Track, Eastern Mediterranean University (2018) M.S. in Software Engineering, Qazvin Azad University (2010) B.S. in Computer Engineering - Software Track, Mazandaran University of Science and Technology (2007) His research focuses on Machine Learning , Deep Learning , and Semantic Web technologies, with applications in AI problem-solving, multimedia pattern recognition, and web service frameworks. Recent publications demonstrate expertise in logic-based systems and computational methods. Awards: Teaching Excellence Award 2024-2025 Teaching Excellence Award 2023-2024 Research Excellence Award 2022-2023 He teaches a broad spectrum of computer science courses including core subjects like Algorithms, Operating Systems, and specialized topics in AI/ML. While no active lab or grants are mentioned, his publication record indicates ongoing scholarly activity.

Youcef Derbalمشاهده پروفایل

دانشیار

• Complex Adaptive Systems
• Cancer Modeling
• Machine Learning in Oncology
• +۴ مورد دیگر

Dr. Youcef Derbal is an Associate Professor in the Department of Information Technology Management at Toronto Metropolitan University's Ted Rogers School of Information Technology Management. With a Ph.D. in Electrical and Computer Engineering from Queen's University, he brings extensive industry experience from robotics, telecommunications, and software sectors to his academic work. His research focuses on Complex Adaptive Systems, particularly in modeling cancer dynamics through agent-based simulations, machine learning applications for oncology, and computational frameworks for biological processes. Key research areas include tumor dynamics, cell signaling pathways, and tumor-infiltrating lymphocyte recruitment. Dr. Derbal teaches undergraduate courses in business information systems, programming, system analysis, database design, and client-server applications, along with graduate-level advanced project management. His publication record shows consistent output in computational biology, cancer modeling, and grid computing since 2006. Professional affiliations include: Association for Computing Machinery (ACM) Institute of Electrical and Electronics Engineers (IEEE) International Society for Computational Biology (ISCB) Administrative contributions include service on University Senate, chairing multiple departmental committees, leading the Business Technology Management curriculum development (2009-2014), and serving as School Director during 2013/2014.

Ali Sekmenمشاهده پروفایل

استاد

• Approximation Theory
• Sampling Theory
• High-Dimensional Data Analysis
• +۲ مورد دیگر

Dr. Ali Sekmen serves as Professor and Chair of the Department of Computer Science within the College of Engineering at Tennessee State University, where he has held leadership positions since joining in 1998. He additionally contributes to university governance as a member of the Tennessee State University Board of Trustees. His academic credentials include dual Ph.D. degrees from Vanderbilt University in Electrical Engineering (2000) and Mathematics (2012), complemented by an MS in Mathematics (2009) from Vanderbilt and undergraduate/postgraduate engineering degrees from Bilkent University. This unique interdisciplinary background fuels his research at the intersection of theoretical mathematics and practical computing. Dr. Sekmen's research program centers on Approximation Theory, Sampling Theory, High-Dimensional Data Analysis, Machine Learning, and Robotics, with particular emphasis on subspace segmentation algorithms and their real-world implementations. His work demonstrates a consistent pattern of bridging abstract mathematical concepts with tangible engineering applications, especially in robotics systems and data analysis frameworks. The publications spanning 2012-2019 reveal increasing focus on deep learning integration with traditional mathematical approaches for handling complex datasets. As an active Principal Investigator, Dr. Sekmen has secured substantial funding from major agencies including NSF, NASA, USDA, and the Department of Defense. His current projects include USDA-funded water infrastructure robotics and Army-sponsored subspace segmentation research, demonstrating sustained competitiveness in federal grant acquisition. His teaching portfolio spans foundational computer science courses to specialized topics in machine learning and robotics, reflecting his commitment to both theoretical and applied education.

Thomas W. Repsمشاهده پروفایل

استاد

• Program Analysis
• Abstract Interpretation
• Model Checking
• +۴ مورد دیگر

Thomas W. Reps is the J. Barkley Rosser Professor & Rajiv and Ritu Batra Chair Emeritus at the University of Wisconsin-Madison , where he has been a faculty member since 1985. He is also President of GrammaTech, Inc., and a co-founder of the company. Education: Ph.D. in Computer Science from Cornell University (1982), winner of the 1983 ACM Doctoral Dissertation Award. Reps’s research spans program analysis , abstract interpretation , model checking , and computer security . His recent work focuses on quantum computing verification , probabilistic program analysis , and symbolic methods for static analysis . His publications (over 225) include foundational contributions to program slicing (1988 paper with Horwitz and Binkley, cited >1,780 times), machine-code analysis (ETAPS Best-Paper Awards in 2004 and 2008), and programming environments (co-author of The Synthesizer Generator ). Key awards include the ACM SIGPLAN Programming Languages Achievement Award (2017) , Guggenheim and Packard Fellowships , and ACM Fellow (2005) . Students: Mentored award-winning graduates like Akash Lal (SIGPLAN Outstanding Dissertation) and Venkatesh Srinivasan (Outstanding Graduate Student Research Award).

Thomas Wiesمشاهده پروفایل

استاد

• Program Analysis
• Verification
• Automated Deduction
• +۴ مورد دیگر

Thomas Wies is a Professor of Computer Science at the Courant Institute of Mathematical Sciences, New York University, and Chair of the Computer Science Department. He is a member of the Analysis of Computer Systems Group and has a personal website. Affiliation: New York University Research Focus: Program Analysis, Verification, Automated Deduction, Concurrency Teaching: Principles of Programming Languages (undergraduate), Programming Languages (graduate), Rigorous Software Development Research Interests: His work centers on program analysis, verification of concurrent systems, automated deduction, and applications of separation logic. He develops tools like Raven, GRASShopper, and Sprout for verifying concurrent programs and search structures. Recent Articles (2025-2023): Focus on SMT-based verification, concurrent data structures, temporal safety effects, multiparty protocols, and separation logic extensions. These span conferences like OOPSLA, CAV, IEEE S&P, and TACAS. Scientific Awards: Best Paper at ISSRE 2019 Best Paper at ICFP 2014 Professional Activities: Organizer/Co-organizer of VerifyThis 2025, Program Chair roles in NETYS 2023, VMCAI 2022, and others. Active in PC roles for POPL, PLDI, CAV, and VMCAI. Students & Group: Advises PhD students including Elaine Li, Devora Chait-Roth, and Ekanshdeep Gupta. Group alumni include Kshitij Bansal, Siddharth Krishna, and Wei Wang.

Nate Fosterمشاهده پروفایل

استاد

• Programming Languages
• Networking
• Software-Defined Networking
• +۵ مورد دیگر

Nate Foster is a Professor of Computer Science at Cornell University's Bowers Computing and Information Science college. He also serves as a Visiting Professor at EPFL's Data Center Systems Laboratory during the 2023-24 academic year and as a Visiting Researcher at Jane Street. His research focuses on developing languages and tools that make it easy for programmers to build secure and reliable systems, with particular emphasis on software-defined networking. Dr. Foster's educational background includes: PhD in Computer Science from the University of Pennsylvania MPhil in History and Philosophy of Science from Cambridge University BA in Computer Science from Williams College Nate Foster's research spans multiple areas within programming languages and systems. His current work focuses on the design and implementation of languages for programming software-defined networks. He has also made significant contributions to bidirectional languages (also known as "lenses"), database query languages, data provenance, type systems, mechanized proof, and formal semantics. His interdisciplinary approach combines theoretical foundations with practical systems building, seeking to bridge the gap between formal methods and real-world network programming. An analysis of Foster's recent publications reveals a strong focus on network programming languages, particularly NetKAT and P4. His work consistently applies formal methods to networking problems, with increasing emphasis on verification, equivalence checking, and symbolic execution techniques. The research trajectory shows progression from foundational language design to practical verification tools, demonstrating how theoretical programming language concepts can solve real-world networking challenges. Dr. Foster has received numerous prestigious awards for his contributions: Sloan Research Fellowship NSF CAREER Award ACM SIGPLAN Robin Milner Young Researcher Award (2023) Most Influential POPL Paper Award Tien '72 Teaching Award Google Research Award Yahoo! Academic Career Enhancement Award Cornell Engineering Research Excellence Award Morris and Dorothy Rubinoff Award ACM SIGCOMM Rising Star Award As an active member of the programming languages community, Foster has advised numerous graduate students and secured significant research funding through his NSF CAREER award and Google Research Award. He has served in leadership roles for major conferences including PLDI, POPL, and ICFP, demonstrating his commitment to mentoring the next generation of researchers through programs like PLMW@PLDI. His collaborative approach is evident in his extensive co-authorship network across academia and industry. Foster leads research in the area of programming languages for networking, with particular focus on the NetKAT framework for network verification. His work bridges the gap between formal methods and practical networking systems, creating tools that have influenced both academic research and industry practice in software-defined networking. His collaborations with institutions like EPFL and industry partners like Jane Street demonstrate the real-world impact of his research agenda.

Chandrakana Nandiمشاهده پروفایل

استادیار

• Programming Languages
• Compilers
• Equality Saturation
• +۵ مورد دیگر

Chandrakana Nandi is the Director of US R&D at Certora and an affiliate assistant professor in the Department of Computer Science & Engineering at the University of Washington's College of Engineering. She completed her PhD at the University of Washington working with Zachary Tatlock and Dan Grossman in the PLSE research group. Her research focuses on building tools for scaling automated formal verification to real-world programs, particularly for DeFi applications. She works extensively with equality saturation techniques (egg project) and has made significant contributions to computational fabrication through projects like Carpentry Compiler, Szalinski, and LambdaCAD. Her work bridges programming languages, compilers, and digital fabrication, creating novel tools that transform how we design and manufacture physical objects. Nandi's publication record shows a strong trajectory in programming language techniques applied to verification and fabrication. Her work on equality saturation has become foundational in the field, with the egg library enabling state-of-the-art results in compiler optimization and program synthesis. Recent work has expanded into formal verification of smart contracts, demonstrating the versatility of her research approach across different domains. Distinguished Paper Award at OOPSLA 2021 Sigplan Research Highlight for POPL 2021 As Director of US R&D at Certora, she leads research efforts on verification tools for languages like WASM and techniques to help users write formal specifications more easily using mutation testing. She has served in numerous organizational roles for major programming languages conferences including as Workshops Co-Chair for ICFP 2025 and Committee Member for PLDI Review Committee. Nandi has established herself as a leader in the intersection of programming languages and computational fabrication, with her work on equality saturation becoming particularly influential across multiple subfields of programming languages research.

Olivier St-Cyrمشاهده پروفایل

دانشیار

• Human-Computer Interaction
• User Experience Design
• HCI Education
• +۷ مورد دیگر

Olivier St-Cyr is an Associate Professor, Teaching Stream and the Associate Dean, Teaching and Learning at the Faculty of Information (iSchool) at the University of Toronto, with a cross-appointment in the Department of Mechanical and Industrial Engineering. He has been instrumental in designing and deploying the user experience design concentration curriculum of the Master of Information (MI) degree since joining the Faculty of Information in 2016. Under his leadership, two novel learning spaces were designed and constructed at the Faculty of Information to facilitate active learning and technology-enhanced courses. Dr. St-Cyr's educational background includes: PhD in Industrial Engineering from the University of Toronto (2006) MASc in Systems Design Engineering from the University of Waterloo (2002) BA in Computer Science and Psychology from York University (2000) His research interests span multiple interconnected domains focused on improving how we teach and apply Human-Computer Interaction principles. At the core of his work is HCI/UX pedagogy and education , where he explores innovative teaching methods and curriculum development. He has co-founded EduCHI, an international community of practice for HCI/UX educators that has grown steadily over the past five years. His research also extends to UI/UX design for complex systems, particularly focusing on human factors for safety critical systems and cognitive work analysis . A significant portion of his recent work investigates active learning pedagogy and the design of technology-enhanced active learning classrooms , where he has designed and overseen the construction of specialized learning spaces at the Faculty of Information. An analysis of Dr. St-Cyr's publication trends reveals a strong focus on HCI education and pedagogy, with increasing emphasis on community building among HCI educators through initiatives like EduCHI. His earlier work focused more on ecological interface design and cognitive work analysis applied to complex systems, while his recent publications show a shift toward educational applications and curriculum development. The interdisciplinary nature of his work connects human factors engineering with educational theory, creating bridges between technical interface design and effective teaching practices. Dr. St-Cyr has received notable recognition for his teaching excellence: Outstanding Instructor Award, Faculty of Information, Master of Information Student Council (MISC), 2016-2017 University of Toronto Early Career Teaching Award, 2020-2021 In terms of academic leadership, Dr. St-Cyr serves in significant roles related to teaching and learning. He is the co-founder of EduCHI, which is part of the ACM conference on Human Factors in Computing Systems (CHI), and serves as a member of the ACM Education Advisory Committee representing SIGCHI. He has developed numerous courses across multiple departments including the Faculty of Information, Department of Mechanical and Industrial Engineering, and various computer science and engineering departments. His grant activity includes projects such as "Acquainting Canadian Seniors with State of the Art Dementia Prevention Strategies," funded by the Public Health Agency of Canada. Dr. St-Cyr has been instrumental in establishing learning environments that support his educational philosophy. Through his leadership, two specialized learning spaces were created at the Faculty of Information to facilitate active learning approaches and technology-enhanced instruction. These spaces enable faculty to incorporate studio sessions and hands-on learning experiences into their courses, reflecting his commitment to innovative educational approaches in the field of information studies and human-computer interaction.

Andrzej Murawskiمشاهده پروفایل

استاد

• Programming Languages
• Automata Theory
• Software Verification
• +۹ مورد دیگر

Professor Andrzej Murawski is a Tutorial Fellow at Worcester College , affiliated with the University of Oxford Department of Computer Science . His research spans the semantics of programming languages , automata theory , and software verification , integrating game semantics and probabilistic computation for modeling complex systems. Education: D.Phil., University of Oxford (2001) His work addresses higher-order recursion , concurrent programs , and probabilistic verification , including differential privacy and machine learning applications. Recent publications focus on reachability analysis , contextual equivalence , and automata over infinite alphabets . Key activities include organizing SIGLOG (Vice-Chair), editing SIGLOG News , and serving on LICS , POPL , and CONCUR program committees. Awards include the POPL 2025 Distinguished Paper Award . Students : Current advisees include Benedict Bunting and Haoxuan Yin; past students include Fabian Zaiser and David Hopkins.

Matteo Camilliمشاهده پروفایل

دانشیار

• Software Engineering
• Software verification
• Automated testing
• +۴ مورد دیگر

Matteo Camilli is an Associate Professor in the Department of Electronics, Information and Bioengineering (DEIB) at Politecnico di Milano, Italy, where he leads research in software engineering and verification. His academic journey includes positions as Assistant Professor at Free University of Bozen-Bolzano and postdoctoral research at the University of Milan and University of Bergamo. His educational background includes a PhD in Computer Science (2015), MSc in Computer Science (2012), and BSc in Computer Science (2009), all from the University of Milan. His doctoral research focused on combining advanced abstraction techniques and big data approaches to address state explosion problems in formal verification. Camilli's research primarily centers on software verification, testing, and methods to improve dependability of autonomous, cyber-physical, service-based, and ML-enabled critical systems. His work spans formal methods, model-based testing, uncertainty quantification, and design-time/runtime verification with applications to complex distributed systems. His recent publications reflect a growing focus on explainable self-adaptation, quality assurance for LLM-based systems, and managing uncertainty in adaptive systems. His publication record includes papers in top journals (TOSEM, TAAS, JSS, EMSE) and conferences (ICSE, ISSRE, ICST, ICSA). He serves on program committees for prestigious conferences including ICSE, ICSA, ICST, and ECSA, and is on the steering committee for the International Workshop on Formal Approaches for Advanced Computing Systems (FAACS). Camilli actively contributes to the academic community through conference organization, including serving as Program Committee Member for numerous conferences and as Program Co-Chair for the Software Architecture track at ACM SAC. He also serves as guest editor for special issues on automated testing and dependable AI systems. His teaching portfolio at Politecnico di Milano includes Software Engineering 2, Software Engineering for Automation, and Distributed Software Development. Previously at Free University of Bozen-Bolzano, he taught Systems Engineering and Verification and Reliability for Dependable Systems.

• Cognitive Neuroscience
• Visual Perception
• Perceptual Learning
• +۵ مورد دیگر

Professor Caspar M. Schwiedrzik is a Research Professor in the Department of Cognitive Neurobiology at the Faculty of Biology and Biotechnology, Ruhr University Bochum. He serves as a Principal Investigator at the Research Department of Neuroscience, where he leads a research group investigating the neural basis of learning and cognitive flexibility. His research focuses on understanding how organisms adapt to changing environments through perceptual learning mechanisms, with particular emphasis on the visual system. Professor Schwiedrzik employs a comparative approach across species using multimodal techniques including electrophysiology, neuroimaging, and computational modeling to identify fundamental principles of neural adaptation. His work bridges cognitive neuroscience, vision science, and computational approaches to unravel how the brain detects environmental changes and adjusts behavior accordingly. Analysis of recent publications (2015-2024) reveals a consistent research trajectory focused on visual perception, face processing, and learning mechanisms. The work demonstrates increasing methodological sophistication with a shift toward more complex questions about predictive coding, neural plasticity, and cross-species comparisons. Key trends include growing emphasis on top-down processing mechanisms, the role of statistical learning in visual adaptation, and the neural basis of perceptual generalization. Professor Schwiedrzik has established productive collaborations with leading neuroscience institutions, as evidenced by co-authorships with researchers from institutions including New York University. His research program appears well-funded, supporting multiple high-impact publications annually in top neuroscience journals. The lab maintains strong international connections while being firmly rooted in the neuroscience research ecosystem at Ruhr University Bochum. The lab focuses on understanding the brain's capacity for adaptation to environmental changes, particularly through visual learning mechanisms. Current projects investigate how predictions shape visual processing, how perceptual learning generalizes across different effectors, and how neural representations support flexible decision-making. This research provides fundamental insights into both normal cognitive function and potential pathways to maladaptive states when learning mechanisms fail.