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Qirun ZhangView profile

Associate Professor

• Program Analysis
• Programming Languages
• Software Engineering
• +4 more

Qirun Zhang is the Catherine M. and James E. Allchin Early Career Associate Professor in the School of Computer Science at Georgia Institute of Technology. His research focuses on program analysis, compiler optimization, and formal language theory, with numerous publications in top-tier programming language and software engineering conferences including PLDI, POPL, OOPSLA, and FSE. He teaches courses on compilers, program analysis, and software testing. Dr. Zhang's research interests center on improving software reliability and security through advanced program analysis techniques. He approaches problems from perspectives including computational complexity, analytic combinatorics, graph theory, and formal languages. His work often bridges theoretical foundations with practical applications in compiler design and program verification. His recent publications show a strong focus on context-free language reachability, Dyck-language based analyses, and SMT solving techniques. His research demonstrates consistent innovation in making program analysis more precise while maintaining scalability, with applications ranging from debug information validation to software debloating and type inference. PLDI Distinguished Paper Award (2020) SIGSOFT Distinguished Paper Award (2023) OOPSLA Distinguished Artifact Award (2022) Dr. Zhang actively mentors PhD and MS students, with current advisees including Camille Bossut and Benjamin Mikek. His service to the academic community includes Artifact Evaluation Co-Chair roles for PLDI 2025 and 2026, and program committee membership for numerous top conferences including PLDI, POPL, and OOPSLA. He leads research projects including SLOT, Context-Free Language Reachability with Transitive Redundancy Elimination, and Debug Information Validation.

Gilbert BernsteinView profile

Assistant Professor

• Computer Graphics
• Programming Languages
• High-Performance DSLs
• +7 more

Gilbert Bernstein is an Assistant Professor in the Computer Science & Engineering department within the College of Engineering at the University of Washington. His research bridges computer graphics and programming languages, with a focus on high-performance domain-specific languages. Previously, he was a post-doctoral scholar at UC Berkeley and MIT working with Jonathan Ragan-Kelley, and received his PhD from Stanford University under Pat Hanrahan. His research interests span Computer Graphics, Programming Languages, High-Performance DSLs, Physical Simulation, Geometry & Topology, Differentiable Programming, Hardware DSLs, Tools for Artists, Fabrication, and Human-Computer Interaction. Bernstein develops languages and compilers that enable efficient computation for creative applications, physical simulations, and graphics rendering systems. His recent publications reveal strong trends in differentiable programming for graphics applications, domain-specific languages for hardware acceleration, and computational approaches to traditional crafts like quilting and knitting. His work consistently combines formal language theory with practical applications in graphics and fabrication. Bernstein actively mentors students across multiple institutions including current advisees Felix Hahnlein (UW Postdoc), Ryan Zambrotta (UW PhD), Haoran Peng (UW PhD), and previous students including Alex Reinking (UC Berkeley PhD 2022, now at Qualcomm) and MacKenzie Leake (Stanford PhD 2021, now at Adobe Research). His lab works on diverse projects including debugging CAD programs, compilers for finite element methods, semantics for knitting machines, algebraic scheduling of tensor programs, and exocompilers for hardware accelerators. Bernstein also collaborates on DSLs for networking, Counterstrike bots, gradient-based optimization, memory management, hardware design, and garment design tools.

Rajiv GuptaView profile

Professor

• Parallel Computing
• Compilers
• Computer Architectures
• +4 more

Rajiv Gupta is a Distinguished Professor and the Amrik Singh Poonian Professor of Computer Science at the University of California, Riverside (UCR), where he serves as Associate Dean for Academic Personnel in the Bourns College of Engineering (BCOE). He is a member of the RIPLE research group and has co-authored 327 papers with an h-index of 69 and over 16,600 citations. His extensive service includes chairing major conferences such as FCRC 2015, PPoPP 2020, ASPLOS 2011, and PLDI 2008. Professor Gupta's research focuses on Programming, Compiler, Runtime & Architectural Support for Parallel & Distributed Heterogeneous Systems and Software Tools for Monitoring and Managing Runtime Behavior . His work spans graph analytics with scalability and performance, understanding and managing the dynamic behavior of parallel programs, software speculation for irregular parallelism, dynamic program analysis for secure and reliable computing, and compiler optimizations with architectural support. His research has significant applications in high-performance computing, GPU programming, and distributed systems. Analysis of his recent publications reveals a strong focus on graph processing systems, with particular emphasis on evolving and streaming graph analytics. His work addresses critical challenges in memory management for large-scale graph processing, hardware acceleration for graph algorithms, and optimization techniques for concurrent and distributed graph computations. The research demonstrates a progression from foundational compiler and architecture work to increasingly sophisticated systems for handling modern data-intensive computing challenges. Fellow of the ACM (2009) Fellow of the IEEE (2008) Fellow of the AAAS (2011) NSF Presidential Young Investigator Award (1991) UCR Doctoral Dissertation Advisor/Mentor Award (2012) Multiple best paper awards across major conferences Two students won ACM SIGPLAN Outstanding Doctoral Dissertation Award Five advisees received NSF CAREER Award Professor Gupta has supervised 42 PhD students to completion and currently advises several doctoral candidates. His advising success is reflected in his students' achievements, including multiple award-winning dissertations and significant career accomplishments in academia and industry. His research has been supported by numerous grants from NSF, DARPA, and industry partners, enabling sustained investigation into parallel computing systems. The RIPLE research group under his leadership has produced influential work that bridges theoretical foundations with practical system implementations. As the leader of the RIPLE research group at UC Riverside, Professor Gupta oversees a vibrant team focused on innovative approaches to parallel and distributed computing. The group maintains strong collaborations with industry partners and other academic institutions, contributing to the development of next-generation computing systems. Current projects include GRASP (Graph Analytics with Scalability & Performance) and research on understanding and managing the dynamic behavior of parallel programs, reflecting the group's continued focus on cutting-edge computing challenges.

Ranjit JhalaView profile

Professor

• Programming Languages
• Formal Verification
• Software Engineering
• +3 more

Ranjit Jhala is a Professor of Computer Science Engineering in the Jacobs School of Engineering at the University of California, San Diego. His research focuses on building reliable computer systems through programming languages and software engineering techniques. His primary research interests include Programming Languages, Formal Verification, and Software Engineering. He draws from and contributes to areas such as Type Systems, Model Checking, Program Analysis, and Automated Deduction, bridging theoretical foundations with practical implementations for real-world software development. Prof. Jhala's publication record shows a consistent trajectory in refinement type systems, evolving from Liquid Haskell to Flux for Rust, while also exploring neurosymbolic approaches to error repair and type error diagnosis. His work demonstrates a commitment to making formal verification techniques accessible to practitioners. He leads the Programming Systems Group at UCSD, mentoring graduate students and collaborating with researchers across the programming languages community. His service includes General Chair roles for POPL 2018 and PLDI 2022, reflecting his leadership position in the field. Prof. Jhala is also known for his mentoring activities, including talks on academic presentation skills and participation in ICFP's mentoring programs for students and early-career researchers.

Mayur NaikView profile

Professor

• Programming Languages
• Artificial Intelligence
• Neurosymbolic Programming
• +5 more

Mayur Naik is the Misra Family Professor in the Department of Computer and Information Science at the University of Pennsylvania's School of Engineering and Applied Science. He holds office in Room 642B, Amy Gutmann Hall and maintains an active research program focused on the intersection of programming languages and artificial intelligence. Before joining UPenn, he was faculty at Georgia Institute of Technology and a researcher at Intel Labs, Berkeley. Naik received his PhD in Computer Science from Stanford University in 2008 under Alex Aiken, a Masters from Purdue University in 2003 under Jens Palsberg, and a Bachelors from BITS Pilani in 1999. He grew up in Goa, India. His primary research interests center around neurosymbolic programming, which combines symbolic reasoning with machine learning to create more accurate, interpretable, and domain-aware AI systems. His group develops language design, learning algorithms, and compiler optimizations in this space, with their most mature effort being the Scallop neurosymbolic programming language and compiler toolchain. He also conducts research in trustworthy AI for healthcare applications and AI-enabled programming tools that improve programmer productivity. Analysis of his recent publications shows a strong trend toward neurosymbolic programming frameworks (Scallop, TorchQL), LLM-assisted program analysis (IRIS), and applications of these techniques to security, healthcare, and computer vision. His work consistently bridges theoretical foundations with practical implementations, often releasing open-source systems. Misra Family Professor (endowed chair, effective July 2024) Multiple distinguished paper awards (PLDI 2019, FSE 2015, PLDI 2014) Test-of-Time Paper Awards (FSE 2013, FSE 2012, EuroSys 2011) His student Elizabeth Dinella won the 2025 ACM SIGSOFT Outstanding Dissertation award Naik has advised numerous PhD students who have gone on to faculty positions at top institutions including Peking University, University of Toronto, Ashoka University, Bryn Mawr College, and Johns Hopkins University. His research is supported by grants from NSF, Google, Amazon, and other industry partners. His lab maintains active collaborations with clinicians and bioinformatics researchers to apply neurosymbolic programming to healthcare problems. His research group, which includes current PhD students and postdocs, develops practical open-source systems and applies them to diverse domains including computer vision, cybersecurity, medicine, and bioinformatics. The group maintains strong industry connections with Google, Microsoft, Amazon, and other tech companies.

Kunle OlukotunView profile

Professor

• Computer Architecture
• Parallel Programming
• Chip Multiprocessors
• +4 more

Kunle Olukotun is a Professor of Electrical Engineering and Computer Science at Stanford University's School of Engineering, where he has been faculty since 1991. He directs the Stanford Pervasive Parallelism Lab (PPL) and co-leads the Transactional Coherence and Consistency (TCC) project. His research focuses on computer architecture, parallel programming environments, and scalable parallel systems. Key areas include chip multiprocessors (CMPs), transactional memory systems, domain-specific languages (DSLs) for heterogeneous computing, and hardware-software co-design for machine learning workloads. His work bridges theoretical foundations with practical systems implementation. Notable contributions include the Stanford Hydra research project (one of the first chip multiprocessors with thread-level speculation), founding Afara Websystems (acquired by Sun Microsystems), and developing the Niagara processor architecture. His DSL frameworks like Green-Marl and Spatial enable efficient graph analysis and hardware acceleration. His publications reveal strong trends in parallel systems evolution: from foundational CMP research (2000s) to transactional memory (2004-2010), then DSLs for heterogeneous computing (2010-2015), and currently foundation model systems (2023-2025). Subfield analysis shows consistent focus on hardware-software co-design, sparse computation, and compiler techniques across decades. ACM Fellow (2006) for contributions to multiprocessors on a chip and multi-threaded processor design Best Paper Award at IEEE International Symposium on Workload Characteristics (IISWC '10) for EigenBench Olukotun actively mentors researchers through the Stanford Pervasive Parallelism Lab (PPL), which seeks to proliferate parallelism across application domains. His projects have secured significant industry partnerships, including the acquisition of his startup Afara Websystems by Sun Microsystems. Current research focuses on compiler frameworks for foundation model systems and hardware acceleration for sparse machine learning workloads, supported by collaborations with major tech companies. He leads the Stanford Pervasive Parallelism Lab (PPL), which develops compiler and runtime systems for heterogeneous architectures. The lab's work spans DSLs, hardware acceleration, and parallel programming models, with strong industry ties to companies like NVIDIA and Google. Current initiatives include the Mosaic compiler framework and Stardust architecture for sparse tensor computation.

Fernando Magno Quintão PereiraView profile

Associate Professor

• Compilers
• Code Generation
• Compiler Optimizations
• +3 more

Fernando Magno Quintão Pereira is an Associate Professor at the Federal University of Minas Gerais (UFMG), Brazil, specializing in compiler design and program analysis. His academic journey began with a Ph.D. from UCLA in 2008 under Jens Palsberg's supervision, establishing his foundation in compiler research. His research focuses on compilers , with core expertise in code generation , compiler optimizations , and static program analyses . Recent work explores quantum compilation, binary analysis, and security-aware compilation techniques. His publications reveal consistent contributions to major conferences including PLDI, CGO, and SPLASH, with emphasis on practical optimization frameworks and theoretical compiler advancements. Analysis of his 15 most recent publications (2020-2026) shows dominant themes in binary optimization (e.g., AnghaBench), security-aware compilation (e.g., Memory-Safe Elimination of Side Channels), and emerging architecture support (e.g., Quantum Computing Compilation). His work bridges theoretical compiler principles with real-world systems challenges. He actively contributes to the academic community through: Program committees for PLDI (2020-2025), CGO (2021-2026), and SPLASH conferences Leadership roles including CGO Finance Chair (2026) and PLDI Diversity & Inclusion Co-Chair (2023-2024) Organizing JENSFEST 2024 and serving on multiple conference steering committees Pereira maintains an active research group evidenced by continuous publication output and conference leadership, with his personal website ( homepages.dcc.ufmg.br/~fernando/ ) serving as a hub for his academic activities.

Nadia PolikarpovaView profile

Associate Professor

• Program Synthesis
• Program Verification
• Type Systems
• +2 more

Nadia Polikarpova is an Associate Professor in the Computer Science and Engineering Department at the University of California, San Diego. She leads the Programming Systems group and serves as a member of IFIP Working Group 2.8 on Functional Programming since 2022. Her academic journey includes a PhD from ETH Zurich (Switzerland) under Bertrand Meyer's supervision in 2014, followed by postdoctoral work at MIT CSAIL with Armando Solar-Lezama. Her research interests center around program synthesis, program verification, and type systems, with a focus on building practical tools that enhance software security and reliability. Polikarpova's work bridges theoretical foundations with real-world applications, particularly in the emerging area of AI-assisted programming. Her recent publications demonstrate a strong trajectory in program synthesis techniques, with increasing integration of machine learning approaches. The research spans from foundational type-driven synthesis methods to practical applications for validating AI-generated code and synthesizing heap-manipulating programs. Her work frequently appears in top-tier programming languages venues including PLDI, POPL, ICFP, and OOPSLA. 2020 Sloan Fellow 2020 Intel Rising Stars Award 2020 NSF CAREER Award Distinguished paper awards at PLDI'21, ICFP'20, and POPL'19 Best paper award at FM'15 Polikarpova actively mentors PhD students and has advised numerous graduates who now work at Microsoft Research, University of Michigan, and various tech companies. She teaches core programming languages courses including CSE 130 and specialized graduate courses on program synthesis (CSE 291). Her service to the community includes program committee roles for major conferences and co-chairing the Haskell conference in 2022.

Koushik SenView profile

Professor

• Software Engineering
• Programming Languages
• Formal Methods
• +3 more

Koushik Sen is a Professor in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley. His academic career spans major contributions to software engineering and programming languages research through active participation in premier conferences including PLDI, ICSE, and ISSTA. His research focuses on Software Engineering , Programming Languages , and Formal Methods , with particular emphasis on developing software tools that enhance programmer productivity and software quality. Key research thrusts include automated test generation , symbolic execution , fuzzing techniques , and program synthesis . His work bridges theoretical foundations with practical tool development for real-world software verification challenges. Analysis of his publication record reveals consistent contributions to automated testing methodologies, with recent work integrating machine learning (particularly large language models) into traditional program analysis techniques. His research shows strong continuity in improving software reliability through innovative input generation and vulnerability detection approaches. As an active academic leader, he has served as General Chair for MAPL (2020), Program Chair for ISSTA (2017), and committee member for numerous top-tier conferences including PLDI, ICSE, and SPLASH across multiple years. His academic advising manifests through collaborative publications with students on topics like test corpus expansion (Bonsai Fuzzing), visualization synthesis (VizSmith), and smart contract auditing (ItyFuzz), though specific student names aren't listed in the source material. His research has been supported through conference participations and likely associated grants given his extensive publication record.

Maria HalkidiView profile

Associate Professor

• Data Mining
• Machine Learning
• Recommender Systems
• +6 more

Maria Halkidi is an Associate Professor in the Department of Digital Systems at the University of Piraeus, Greece, where she conducts cutting-edge research in data mining and machine learning with over two decades of academic experience. Her educational background includes: Bachelor's degree in Informatics from the University of Piraeus (1997) Master's degree from Athens University of Economics and Business (1999) Ph.D. from Athens University of Economics and Business (2003) Dr. Halkidi's research focuses on fundamental and applied aspects of data mining, particularly recommender systems, graph data mining, cluster validity assessment, and distributed data mining. Her work bridges theoretical frameworks with practical implementations in sensor networks, social media, and real-time analytics, contributing significantly to algorithmic development in these domains. Analysis of her recent publications reveals a strong trajectory toward fairness and diversity optimization in recommender systems, alongside innovative approaches to graph clustering quality assessment and scalable sentiment analysis. Her research consistently addresses real-world challenges in dynamic data environments, with increasing emphasis on multi-stakeholder optimization and privacy-aware recommendation frameworks. Scientific awards: No specific awards or honors were documented in the provided materials. Dr. Halkidi has participated extensively in National and European-funded research projects, including a prestigious Marie-Curie fellowship at the University of California, Riverside. She serves on program committees for major international conferences in data mining and machine learning, demonstrating active community engagement. While specific graduate students aren't listed in the source materials, her professorial role indicates ongoing mentorship of Master's and Ph.D. candidates. She maintains active research affiliations with the Network oriented systems & services lab and the DataStories research group at the University of Piraeus, where she collaborates on interdisciplinary projects involving big data analytics, social network analysis, and distributed systems.

• Machine Learning
• Data Analytics
• Social Media Analysis
• +5 more

Dr. Paraskevas Koukaras is an Academic Scholar at the International Hellenic University (IHU) and a Postdoctoral Research Associate at the Information Technologies Institute (ITI) of the Centre for Research and Technology - Hellas (CERTH), affiliated with the School of Science and Technology at IHU. His educational background includes: IT Engineer from the Department of Informatics, Alexander Technological Educational Institute of Thessaloniki (ATEI) PhD in Interdisciplinary data science methods using machine learning for enhanced knowledge acquisition from the School of Science and Technology, International Hellenic University (IHU) Dr. Koukaras' research spans social media analytics, energy systems, and machine learning. His work focuses on energy load forecasting and optimization, data analytics, information modeling, and graph mining in heterogeneous networks. He applies these techniques to address challenges in public health, financial markets, and building energy efficiency through prescriptive analytics. His recent publications (2020-2023) demonstrate a strong focus on applying machine learning to real-world problems, particularly in social media analysis for public health during the COVID-19 pandemic, energy forecasting, and fake news detection. His work consistently employs multi-model approaches leveraging both traditional and deep learning techniques across healthcare, finance, and energy domains. Dr. Koukaras has participated in major European research projects including eDREAM (H2020), DRIMPAC (H2020), PRECEPT, SmartWins, easySRI, and SMACCs, focusing on demand response technologies and energy ecosystems. He contributes to academic training through teaching courses in data science and computing despite no formal advisees being listed. He operates within interdisciplinary research teams at ITI/CERTH and IHU's School of Science and Technology, collaborating on AI applications for energy efficiency, healthcare support, and social media analytics in residential and industrial contexts.

Angelo SifalerasView profile

Professor

• Mathematical Programming
• Network Optimization
• Heuristic Methods
• +2 more

Angelo Sifaleras is a Full Professor at the Department of Applied Informatics, School of Information Sciences, University of Macedonia (Thessaloniki, Greece). He also serves as an Adjunct Professor of Quantitative Methods at the Hellenic Open University. His academic credentials include a Ph.D. in Applied Informatics (University of Macedonia, 2007) and a B.Sc. in Mathematics (Aristotle University of Thessaloniki, 1999). Research Focus: Professor Sifaleras specializes in Mathematical Programming , Network Optimization , and Heuristic Methods . His work integrates computational optimization with real-world logistics, supply chains, and sustainable systems. Key research themes include metaheuristic algorithms for vehicle routing, pollution-aware logistics, and parallel computing solutions for network analysis. Publication Trends: His 15 most recent articles emphasize hybrid metaheuristics (e.g., combining reinforcement learning with Variable Neighborhood Search), sustainable supply chain modeling, and high-performance computing applications. Dominant domains include operations research, environmental optimization, and educational algorithm visualization. Teaching & Service: He instructs courses in Combinatorial Optimization, Linear Algebra, and Heuristic Methods, employing tools like CPLEX, Gurobi, and SageMath. As an editor for Operations Research Forum and the Yugoslav Journal of Operations Research , he supports academic discourse in optimization. A Senior ACM member, he contributes to conferences like ICVNS and ITS.

Panagiotis PapadimitriouView profile

Associate Professor

• Software-Defined Networking
• Network Function Virtualization
• Cloud Networking
• +6 more

Panagiotis Papadimitriou serves as an Associate Professor at the Department of Applied Informatics, University of Macedonia in Thessaloniki, where he leads the NetCloud research group. Previously, he held positions as Assistant Professor at Leibniz University Hanover's Institute of Communications Technology and as a Post-Doctoral Researcher at Lancaster University (UK) and Telecom SudParis (France), with additional research experience at the University of Cambridge. His research spans Internet architectures and technologies with emphasis on Network Function Virtualization (NFV), Software-Defined Networking (SDN), Time-Sensitive Networking (TSN), Internet-of-Things (IoT), and Edge Computing. His work includes ML-assisted NFV orchestration (SFC-graph embedding, transformation, and multi-domain partitioning), network slicing orchestration, programmable dataplanes with P4, deterministic networks, datacenter network fabrics, and edge computing. Notable research outcomes include distributed network processing architectures (MIDAS, DistNSE), multi-provider service mapping mechanisms (Nestor), open-source NFV orchestration platforms (TeNOR), and orchestration platforms for cross-slice communication (MESON). His recent publications show a strong focus on In-Band Network Telemetry, Service Function Chain optimization, Time-Sensitive Networking implementations, and cross-slice communication systems, demonstrating his leadership in network virtualization and edge computing research areas. His work consistently addresses practical implementation challenges while advancing theoretical foundations in network management. Scientific Recognition: Senior Member of IEEE Associate Editor of IEEE Transactions on Network and Service Management Counselor of the IEEE Student Branch at University of Macedonia As Principal Investigator of the Horizon Europe NEPHELE project and previous EU-funded projects (NECOS, T-NOVA, CONFINE), he has secured over 1M euro in research funding. He actively contributes to the academic community through conference organization roles including IEEE ICC NGNI 2025, IEEE ICIN 2024, and IFIP/IEEE CNSM 2022 as General Co-Chair. The NetCloud research group he leads maintains a dedicated edge computing facility used for both research and teaching, and is known for its commitment to open-source software and community service through academic network resources. His educational background includes a Ph.D. in Electrical and Computer Engineering from Democritus University of Thrace (2008), an M.Sc. in Information Technology from University of Nottingham (2001), and a B.Sc. in Computer Science from University of Crete (2000).