جست‌وجوی استاد و پژوهشگر

Mehrdad Moharramiمشاهده پروفایل

استادیار

• Reinforcement Learning
• Markov Decision Processes
• Random Graphs
• +۳ مورد دیگر

Mehrdad Moharrami is an Assistant Professor in the Department of Computer Science at the University of Iowa . He specializes in reinforcement learning, Markov decision processes, and random graph models for economics and computational systems. Education : BSc in Mathematics and Electrical Engineering from Sharif University of Technology; MSc in Electrical Engineering and Mathematics from University of Michigan; PhD in Electrical Engineering from University of Michigan (2020) His research focuses on robust reinforcement learning algorithms under distributional shifts and network structure analysis using parameterized random graphs. Recent work explores societal systems modeling and risk-sensitive control frameworks. Moharrami's publications demonstrate interdisciplinary expertise across computer science, mathematics, and network science , with notable contributions to: Risk-sensitive reinforcement learning (ICML 2025, Mathematics of Operations Research 2024) Random graph modeling (COLT 2025, Random Structures & Algorithms 2024) Network optimization (IEEE Transactions on Network Science 2024, IEEE Transactions on Information Theory 2023) Scientific recognition includes: Rackham Predoctoral Fellowship (2019) NSF MPS Workshop participation (2022) Multiple conference best paper nominations Iranian National Olympiad medals (2007-2008) As co-organizer of major workshops (SIGMETRICS 2022, Performance 2023), he actively contributes to academic community development while maintaining active referee roles for top journals like IEEE/ACM Transactions on Networking and Mathematics of Operations Research.

Cynthia Rushمشاهده پروفایل

دانشیار

• Mathematical Statistics
• High-Dimensional Inference
• Approximate Message Passing
• +۷ مورد دیگر

Cynthia Rush is an Associate Professor in the Department of Statistics at Columbia University, where she conducts research at the intersection of mathematical statistics, information theory, and high-dimensional data analysis. She is affiliated with the Graduate School of Arts and Sciences and is based in the School of Social Work building in New York City. Ph.D. in Statistics, Yale University, 2016 B.S. in Mathematics, University of North Carolina at Chapel Hill Her research focuses on the development and analysis of algorithms for high-dimensional inference, particularly using Approximate Message Passing (AMP), with applications in sparse regression, signal processing, and machine learning. She integrates tools from information theory, statistical physics, and applied probability to understand fundamental limits and performance of modern statistical methods. Her recent publications span topics such as SLOPE regularization, community detection, power posteriors, and massive multiple access in communications. These works appear in top journals including IEEE Transactions on Information Theory, Annals of Statistics, and NeurIPS. Her research shows a consistent trend toward rigorous theoretical analysis of high-dimensional statistical algorithms and their practical implications. Notable scientific recognition includes being a finalist for the JKW Student Paper Award at IEEE ISIT in 2016. She has delivered numerous invited talks at leading institutions and conferences worldwide, reflecting her active role in the theoretical data science community. JKW Student Paper Award Finalist, IEEE ISIT 2016 She advises and collaborates with researchers in statistics, machine learning, and information theory, though formal doctoral advisees are not listed. Her work is supported by theoretical grants and collaborations across engineering and mathematical sciences. She is involved in the broader academic community through seminars, workshops, and program committees, contributing to the advancement of rigorous statistical learning. Cynthia Rush leads a research group focused on high-dimensional inference and algorithmic analysis, often collaborating with researchers at institutions like Yale, Princeton, and international centers in Europe and Asia. Her lab emphasizes theoretical foundations with practical relevance in data science and communications.

Paul Leeمشاهده پروفایل

استاد مهمان

• Computer Science
• Storage Systems
• Performance Optimization
• +۳ مورد دیگر

Paul Lee is a Visiting Professor at the University of California, Berkeley, affiliated with the African Diaspora Scholar program. His role involves lecturing and visiting faculty responsibilities. While no explicit departmental or school affiliation is stated, his research focuses on storage systems, performance optimization, and networking. His work spans computer science disciplines, addressing challenges in I/O management, virtualization, and real-time systems. Lee's research interests center around optimizing storage and network architectures, with a focus on resource allocation, quality-of-service mechanisms, and scalable solutions. He has contributed to areas such as throttling algorithms for I/O streams, virtualization frameworks, and memory management techniques. His publications from 1988 to 2018 reflect a sustained focus on performance-related challenges in computer systems, with notable work on storage virtualization, kernel memory allocation, and real-time system implementations. No scientific awards are explicitly mentioned in the provided texts. Lee has no listed advisees or grants in the current data. His work appears primarily technical, centered around system-level optimizations without explicit mention of labs or interdisciplinary teams.

Matthew Flattمشاهده پروفایل

استاد

• Programming Languages
• Functional Programming
• Compiler Design
• +۳ مورد دیگر

Matthew Flatt is a Professor in the School of Computing at the University of Utah . His primary research focuses on programming languages, specifically in the design and implementation of Racket and related systems like PLT Redex . He leads work on macros, domain-specific languages (DSLs), compiler design, and concurrency mechanisms. Flatt has developed influential tools such as Scribble (a documentation system), Honu (for algebraic syntax), and Places (message-passing parallelism in Racket). He also contributes to educational materials, including How to Design Programs . He teaches courses like CS 3520/6520: Programming Languages , emphasizing interpreters-based learning and functional programming. His research integrates practical implementation with theoretical foundations, supported by grants and collaborations in systems programming and education. Flatt has held visiting roles and sabbaticals, including at Northeastern and Northwestern Universities, and conducted research in Beijing during 2006-2007. His work bridges language design, implementation, and real-world applications.

Matteo Ciminiمشاهده پروفایل

استادیار

• Programming Languages
• Formal Methods
• Type Systems
• +۷ مورد دیگر

Matteo Cimini is an Assistant Professor in the Miner School of Computer & Information Sciences at the Kennedy College of Sciences, University of Massachusetts Lowell. He teaches courses in programming languages, algorithms, and computer science foundations across multiple semesters from 2017 through 2025. Dr. Cimini earned his Ph.D. in Computer Science from Reykjavik University (2012), where his dissertation focused on Contributions to the Meta-Theory of Structural Operational Semantics. He also holds an M.S. in Computer Science from the University of Bologna (2008). His research centers on programming language theory, with particular expertise in formal methods, type systems, and language semantics. Dr. Cimini's work bridges theoretical foundations with practical language design, focusing on verification, gradual typing, and concurrency. His research has been published in top-tier programming languages conferences including POPL, ICFP, and PLDI. Dr. Cimini's recent publications demonstrate a strong focus on language-oriented programming, with his "Lang-n-X" series of systems exploring innovative approaches to language design, verification, and analysis. His work often combines theoretical rigor with practical implementation, creating tools that enable new ways of working with programming languages. Best Paper Award at TAP 2023 for "Testing Languages with a Languages-as-Databases Approach" Dr. Cimini actively mentors students, with recent advisees including Ph.D. student Joan and master's student Eswar, who successfully defended a thesis on Lang-n-Prove. He serves on program committees for numerous prestigious conferences including IFL 2025, SLE 2025, FORTE 2025, ICE 2025, and has been involved with conferences dating back to 2015. Dr. Cimini leads the development of the "Lang-n-X" series of systems, which includes tools like Lang-n-Prove (for language proofs), Lang-n-Send (for language communication), and Lang-n-Assert (for language logics). These systems form an integrated framework for language design, verification, and analysis, representing a cohesive research vision that has evolved through consistent publication output.

Nobuko Yoshidaمشاهده پروفایل

استاد

• Theory of Computing
• Concurrency
• Type Theory
• +۲ مورد دیگر

Nobuko Yoshida is a Professor in the Department of Computer Science at the University of Oxford. Her research centers on theoretical foundations of concurrent and distributed systems, with a focus on type theory and programming languages. Affiliation: University of Oxford Research Interests: Theory of Computing, Concurrency, Type Theory, Programming Languages, Distributed Systems Conference Involvement: Active in POPL, ECOOP, ICFP, OOPSLA, PPDP, and ST30 tracks Her recent work analyzes multiparty session types, asynchronous/synchronous communication models, timed protocols, and type refinements for distributed systems. Key contributions include advancements in protocol specification, endpoint projection, and formal verification of concurrent systems. Yoshida has authored or co-authored publications at leading venues including ECOOP, ST30, and OOPSLA. Her articles emphasize compositional reasoning for multiparty sessions, hybrid protocol designs, and robustness in distributed environments with crash-stop failures. These works intersect with subfields such as session type theory, formal verification, and specification-agnostic implementation techniques.

Armando Solar-Lezamaمشاهده پروفایل

دانشیار

• Software Synthesis
• High-Performance Computing
• Information Flow Security
• +۳ مورد دیگر

Armando Solar-Lezama is an Associate Professor at the Massachusetts Institute of Technology (MIT) where he leads the Computer Aided Programming Group within CSAIL. His research bridges programming languages, formal methods, and machine learning to advance program synthesis techniques for real-world applications. His primary research focuses on software synthesis with applications spanning high-performance computing, information flow security, and probabilistic programming. He investigates how to automatically generate correct and efficient programs from specifications, with recent work emphasizing neurosymbolic approaches that combine neural networks with symbolic reasoning for complex synthesis tasks. Analysis of his publication trends (2021-2025) reveals a strategic evolution from foundational synthesis algorithms toward practical applications in probabilistic programming and security-critical systems. His work consistently integrates machine learning with formal verification, demonstrating growing emphasis on coarse-to-fine synthesis methods and knowledge compilation techniques for discrete probabilistic models. Award highlights: SIGPLAN Milner Award As leader of MIT's Computer Aided Programming Group, Solar-Lezama mentors graduate researchers and collaborates across institutions. His extensive service as Program Chair for POPL 2025 and committee roles at premier conferences (PLDI, POPL, SPLASH) demonstrates significant community leadership. While specific grant details aren't provided, his sustained publication output in top venues indicates robust funding support. He directs the Computer Aided Programming Group at MIT CSAIL, which develops tools for program synthesis, verification, and analysis. Current projects focus on neurosymbolic programming frameworks, probabilistic program synthesis, and security-aware compilation techniques that integrate information flow control with high-performance computing optimizations.

Jiawei Chenمشاهده پروفایل

پژوهشگر ارشد

• Recommender Systems
• Information Retrieval
• Machine Learning
• +۵ مورد دیگر

Jiawei Chen is a "Hundred Talent" Research Fellow at Zhejiang University's College of Computer Science and Technology, with over 60 publications in top-tier venues including WWW, SIGIR, KDD, and NeurIPS. His research focuses on advancing recommender systems through innovative approaches to debiasing, graph-based learning, and large language model integration. His educational background includes: Ph.D. in Computer Science, Zhejiang University (2017-2020) Master's in Computer Science, Zhejiang University (2014-2017) Bachelor's in Micro-electronic, University of Electronic Science and Technology of China (2010-2014) Chen's research centers on solving fundamental challenges in recommender systems, particularly addressing popularity bias through spectral analysis and causal inference. His work bridges graph mining, knowledge representation, and large language models to develop robust recommendation frameworks. Recent contributions include pioneering graph transformers for ranking optimization and counterfactual reasoning to burst filter bubbles. His influential surveys on recommendation debiasing and deep clustering have established foundational taxonomies for the field. Analysis of his 2023-2025 publications reveals three dominant trends: (1) Deepening causal approaches to bias mitigation through counterfactual interventions and distributionally robust optimization, (2) Advancing graph-based architectures with sign-aware transformers and uncertainty-aware structure learning, and (3) Integrating large language models through knowledge distillation techniques to enhance sequential recommendation. His work consistently targets high-impact solutions to popularity bias while maintaining strong theoretical grounding. His research excellence has been recognized with: Best Paper Award at WSDM 2025 for spectral analysis of popularity bias amplification Best Paper Honorable Mention at SIGIR 2023 for offline reinforcement learning in recommendation Chen actively mentors future researchers and seeks self-motivated graduate students for projects in recommendation systems, LLMs, and graph mining. His extensive service as program committee member for WWW, AAAI, KDD, and SIGIR—along with reviewing for IEEE TNNLS, TKDE, and TOIS—demonstrates significant community leadership. He has released valuable community resources including the KuaiRec and KuaiRand datasets for unbiased recommendation research. While specific lab affiliations aren't detailed, Chen maintains strong collaborative ties with researchers across institutions, particularly with Prof. Xiangnan He's group. His work frequently involves large-scale industrial datasets and open-source tools like EasyRL4Rec, indicating leadership in practical recommendation system development and community resource sharing.

• automata theory
• logic
• games
• +۳ مورد دیگر

Anca Muscholl is a Professor at the University of Bordeaux, affiliated with LaBRI (Laboratoire Bordelais de Recherche en Informatique), a prominent computer science research laboratory in France. Her research focuses on theoretical aspects of computer science, particularly in automata theory, logic, games, verification and synthesis, distributed systems, and foundations of databases. Her research interests span across several key areas in theoretical computer science: Automata theory, with particular focus on string transducers and weighted automata Formal verification and synthesis of distributed systems Logic in computer science, including two-variable logic on data words Communication models and message passing systems Web services composition and negotiation protocols Professor Muscholl has been actively involved in the theoretical computer science community through numerous editorial and leadership roles. She currently serves on the editorial boards of TheoretiCS, Logical Methods in Computer Science, and LIPIcs. She is also a member of the Council of the European Association for Theoretical Computer Science (EATCS) and the Comité national de la recherche scientifique (section 06). Her recent publications demonstrate continued productivity and influence in her field, with significant contributions to string transducers, distributed systems verification, and automata theory. Her work shows a consistent focus on theoretical foundations with practical applications in verification and distributed computing. Her professional service includes significant leadership roles such as: Steering committee chair of the EATCS International Colloquium on Automata, Languages and Programming (2018-2024) Editorial board member of Discrete Mathematics & Theoretical Computer Science (2001-2023) Editorial board member of Information Processing Letters (2006-2015) Steering committee member of the Symposium on Theoretical Aspects of Computer Science (STACS) (2008-2014) Executive committee member of GDR Informatique-Mathématique (2017-2023) Professor Muscholl has made substantial contributions to the theoretical computer science community through her research, service, and mentorship. Her work bridges theoretical foundations with practical applications in verification and distributed systems, contributing to the advancement of formal methods and their applications.

Mario Románمشاهده پروفایل

پژوهشگر

• Category Theory
• Functional Programming
• Probabilistic Programming
• +۵ مورد دیگر

Mario Román is a Research Associate at the Department of Computer Science, University of Oxford , affiliated with the Compositional Systems and Methods Group at Tallinn University of Technology. His work bridges category theory , functional programming , and probabilistic programming , focusing on formal semantics and mathematical notation. His research interests include monoidal and premonoidal categories , Markov categories , and coinductive methods for dataflow programming. He explores the algebraic structures underlying quantum programming and stochastic processes , often applying graphical calculi and string diagrams. Recent work trends, as seen in his publications, emphasize distributive monoidal categories for program logics, effectful Mealy machines for bisimulation, and partial Markov categories for probabilistic reasoning. His collaborations span institutions in Europe and Asia, including Tallinn, Oxford, and Tokyo. Scientific Awards : Kleene Award, 'Monoidal Streams for Dataflow Programming', LiCS’22 Distinguished Paper, 'Effectful Mealy Machines: Bisimulation and Trace', LiCS’25 Best BSc Thesis, Spanish Royal Mathematical Society, 2018 International Mathematical Olympiad Honorary Mention, 2012 Mario has contributed to community initiatives as a member of the Compositionality Journal Executive Board and served on program committees for Applied Category Theory and Mathematical Foundations of Programming Semantics .

Raghavan Komondoorمشاهده پروفایل

دانشیار

• Programming languages
• Program analysis
• Software engineering
• +۹ مورد دیگر

Raghavan Komondoor is an Associate Professor in the Department of Computer Science and Automation at the Indian Institute of Science (IISc), Bangalore. His research focuses on programming languages, program analysis, and software engineering, with a strong emphasis on developing automated tools that help programmers understand, verify, and transform programs quickly and reliably. His primary research interests include: Programming languages and program analysis Software verification and testing Static analysis techniques Null dereference verification Points-to analysis Web application analysis ORM-based controller verification Memory optimization in Java programs Code clone detection and elimination Dr. Komondoor has developed several influential research tools including NPEDetector for null dereference verification, Ross for verifying Java program safety, DFAS for static analysis of asynchronous systems, PageModeler for web application analysis, and ORMInfer for verification of ORM-based controllers. His research spans over a decade with publications in top-tier software engineering conferences including ASE, ICSE, ISSTA, and SPLASH. His recent publications demonstrate a consistent focus on formal methods and program verification, with significant contributions to symbolic fixpoint algorithms, ORM controller verification, and controller synthesis over infinite state spaces. These works build upon his earlier foundational research in points-to analysis, web application analysis, and null pointer verification. Dr. Komondoor's service contributions to the academic community include: Program Committee Co-Chair for ATVA 2025 Organizing co-chair for ISEC 2024 Chair of PhD Symposium committee for ISEC 2020 Program committee membership for ICSE (2018-2025), ASE (2019-2020), ISSTA (2022), and numerous other major conferences He has successfully mentored numerous PhD and M.Tech students, many of whom now work at leading organizations including Microsoft, Siemens Research, Goldman Sachs, and academic institutions. Currently, he advises multiple PhD students and M.Tech researchers in the Programming Languages Laboratory at IISc. He teaches courses on Program Analysis and Verification, Principles of Distributed Software, and Formal Methods in Software Engineering, and maintains active research projects with open positions for research staff.

• Distributed Systems
• Erlang Programming Language
• Scalability Engineering
• +۴ مورد دیگر

Natalia Chechina is an Honorary Research Fellow at the School of Computing Science, University of Glasgow, specializing in distributed systems with a particular focus on the Erlang programming language and its applications in building reliable and scalable systems. Her research interests span several critical areas in distributed computing: Distributed systems architecture and design Scalability and reliability engineering Erlang ecosystem development Robotics communication systems Server software engineering Performance optimization in distributed environments Dr. Chechina's extensive publication record from 2009-2021 demonstrates consistent contributions to advancing distributed computing capabilities, particularly through her work on SD Erlang (Scalable Distributed Erlang). Her research bridges theoretical analysis with practical implementation, addressing fundamental challenges in building systems that maintain functionality despite failures. She has made significant contributions to understanding how to construct robust server infrastructure that can scale effectively while preserving reliability across diverse application domains. Her collaborative work, frequently with Phil Trinder and other colleagues at the University of Glasgow, spans robotics, instant messaging systems, and general server infrastructure. The cross-disciplinary nature of her research is evident in publications addressing robot team coordination and communication, demonstrating how distributed systems principles can solve complex challenges in multiple domains.

Carlo Lucibelloمشاهده پروفایل

استادیار

• Neural Networks
• Machine Learning
• Statistical Inference
• +۳ مورد دیگر

Carlo Lucibello is an Assistant Professor of Computer Science at Bocconi University since 2018. He holds a PhD in Physics from Sapienza University (2015), supervised by Giorgio Parisi and Federico Ricci-Tersenghi. His research bridges statistical physics and machine learning, focusing on analytical methods for neural networks and optimization problems. Roles: Faculty member in Computing Sciences Department Affiliations: Bocconi University Education: PhD in Physics from Sapienza University (2015) Research interests include neural networks, statistical inference, disordered systems, and combinatorial optimization. He contributes to open-source projects like GraphNeuralNetworks.jl, Flux.jl, and Zygote.jl for Julia. Teaching includes courses on machine learning, programming, and complex systems. He actively develops physics-inspired algorithms for learning and optimization. Publications span topics in statistical physics and machine learning, with recent work on associative memory capacity and neural network theory. His collaborative projects include contributions to Julia's ecosystem and theoretical advancements in message-passing algorithms.

Felix Stutzمشاهده پروفایل

پژوهشگر

• Formal Methods
• Concurrent and Distributed Systems
• Security Protocols
• +۳ مورد دیگر

Felix Stutz is a researcher in the Security and Trust of Software Systems (SaToSS) group at the University of Luxembourg, led by Sjouke Mauw. He holds a PhD from the Max Planck Institute for Software Systems (MPI-SWS), where he was advised by Rupak Majumdar and Damien Zufferey. His research focuses on formal methods for concurrent/distributed systems, security protocols, and message-passing program analysis. Education: PhD in Computer Science, MPI-SWS (2022) Masters degree from University of Saarland Research internship at Imperial College London with Emanuele D’Osualdo and Philippa Gardner Research Interests: Felix specializes in formal verification of distributed systems, cryptographic protocols, and algorithmic security analysis. His work bridges theoretical foundations with practical tools like the PaSh shell script parallelizer and Lemma9 cryptographic protocol verifier. Professional Highlights: Collaborated with MIT CSAIL on PaSh (2022), contributed to tools like AMGC for session types and Heimdall for Erlang scheduling. His research emphasizes tool-supported verification for complex systems.

• Theoretical Computer Science
• Formal Verification
• Software Systems Verification
• +۵ مورد دیگر

Georg Zetzsche is a tenure-track faculty member at the Max Planck Institute for Software Systems (MPI-SWS) in Kaiserslautern, Germany, where he heads the Models of Computation group. He joined MPI-SWS in November 2018 after postdoctoral positions at IRIF, Université Paris-Diderot (2017-2018) and LSV Cachan (2015-2017). He received his PhD in Computer Science from Universität Kaiserslautern under the supervision of Prof. Dr. Roland Meyer in 2015, with a dissertation in the Concurrency Theory Group. His research focuses on theoretical foundations of verification and synthesis of software systems, with particular emphasis on decidability and complexity issues of infinite-state systems. His work explores synthesis of finite-state abstractions of infinite-state systems (including separability problems, downward closures, and Parikh images), as well as decision problems for infinite groups where he applies methods from verification to gain insights on how to devise infinite-state models with pleasant analysis properties. Zetzsche's research output demonstrates expertise across formal verification, theoretical computer science, and programming language theory. His publications reveal a consistent focus on vector addition systems, reachability analysis, language theory, and decidability questions. His work often bridges theoretical foundations with practical verification problems, particularly in the context of infinite-state systems where traditional verification techniques face significant challenges. 2025 Salomaa Prize ERC Starting Grant for project FINABIS EATCS Best Paper Award at ETAPS 2023 EAPLS Best Paper Award at ETAPS 2021 Distinguished Paper Award at POPL 2021 EATCS Distinguished Dissertation Award He actively serves on program committees for major conferences including LICS 2026, MFCS 2025, DLT 2024, ICALP 2024, POPL 2024, LICS 2023, and many others. He has organized events such as Theorietag 2023 on automata and formal languages. His current group at MPI-SWS includes PhD students Pascal Baumann, Pascal Bergsträßer, Irmak Sağlam, Lia Schütze, and Yousef Shakiba, along with postdocs Moses Ganardi and Chris Köcher.