Find Academic Supervisors

Yuriy BrunView profile

Professor

• software engineering
• self-adaptive systems
• software fairness and bias
• +4 more

Yuriy Brun is a Professor at the Manning College of Information & Computer Sciences at the University of Massachusetts Amherst. He leads research in software engineering with a focus on improving the construction of intelligent, self-adapting systems while ensuring fairness and correctness. His research spans several key areas: software fairness, self-adaptive systems, automated program repair, and formal verification. Brun's work has pioneered the field of software fairness, establishing it as a fundamental software engineering concern. His lab, LASER, conducts high-risk, high-impact research aimed at fundamentally improving how engineers build systems. Brun's publications demonstrate a strong focus on automating formal verification and ensuring fairness in machine learning systems. His recent work leverages large language models for proof synthesis and develops techniques for guaranteeing fairness constraints in software systems. His research has shown significant evolution from distributed systems and collaborative development toward formal verification and AI safety. NSF CAREER Award recipient IEEE Fellow Multiple ACM SIGSOFT Distinguished Paper Awards Google Inclusion Research Award recipient Amazon Research Award recipient Microsoft Research Software Engineering Innovation Foundation Award Brun actively mentors students, with current PhD advisees including Zhanna Kaufman, Hadeel Eladawy, and Abhishek Varghese. His former students have gone on to positions at institutions including Oregon State University, University of California San Diego, and Microsoft. He teaches courses including Introduction to Software Engineering and Software Engineering Project Management.

Tevfik BultanView profile

Professor

• software verification
• program analysis
• software engineering
• +1 more

Tevfik Bultan is a Professor and Chair of the Department of Computer Science at the University of California, Santa Barbara. His research focuses on software verification, program analysis, software engineering, and computer security. He directs the Verification Laboratory (VLab) and has authored over 100 refereed publications. Education Ph.D. in Computer Science, University of Maryland, College Park (1998) M.S. in Computer Engineering, Bilkent University (1992) B.S. in Electrical Engineering, Middle East Technical University (1989) Research Focus Bultan's work spans automated verification techniques, security vulnerability detection, quantitative program analysis, and symbolic execution. His lab develops tools for analyzing software systems with applications in cloud security, network protocols, and embedded systems. Awards and Honors ACM Distinguished Scientist (2016) NSF CAREER Award (2000) UCSB Outstanding Graduate Mentor Award (2016) ACM SIGSOFT Distinguished Paper Awards (2005, 2014) NATO Science Fellowship (1993) Professional Activities He has chaired program committees for top conferences including ICSE, FSE, and ASE. Currently serves as associate editor for ACM TOSEM and on steering committees for ISSTA, ASE, and ICSE. Regularly advises PhD students and postdoctoral researchers in verification and security. Laboratory Leads the Verification Laboratory (VLab) focusing on automated reasoning techniques for software systems. Current projects include symbolic analysis for vulnerability detection, quantitative information flow, and security policy verification.

Wing-Kwong ChanView profile

Associate Professor

• Software Engineering
• Deep Learning
• Blockchain Systems
• +4 more

Wing-Kwong Chan is an Associate Professor in the Department of Computer Science at City University of Hong Kong. With a background that includes industry experience as a software engineer, Dr. Chan returned to academia and has established himself as a leading researcher in software engineering with a focus on emerging technologies. Dr. Chan received his BEng, MPhil, and PhD all from The University of Hong Kong. His academic journey began with a hardware-oriented Computer Engineering degree before shifting to software engineering for his graduate studies. His research interests center on software engineering, particularly the technical aspects interfacing with machine learning, blockchain, and GPU technologies. He addresses challenges in program analysis and concurrency, with recent work focusing on deep learning model verification and robustness. His publications span top venues including TOSEM, TSE, ICSE, ESEC/FSE, and ASE. Dr. Chan's recent publications demonstrate a strong trend toward integrating software engineering principles with deep learning systems, particularly in verification, testing, and robustness of AI models. His work bridges theoretical software engineering concepts with practical applications in emerging technologies. Best Paper Award from COMPSAC'04 Best Paper Award from COMPSAC'08 Best Paper Award from COMPSAC'10 Best Paper Award from QSIC'11 Best Paper Award from QRS'16 Best Paper Award from ISET'18 CityU President's Award 2017 Dr. Chan has successfully advised numerous PhD and MPhil students, with alumni dating back to 2006. He has secured substantial research funding through multiple Hong Kong Research Grants Council projects, ITF grants, and international collaborations. His current research focuses on patch robustness certification for deep learning models, reflecting his ongoing commitment to advancing software engineering practices for emerging technologies. As Program Leader for the MSc in E-Commerce program from the CS Department, Dr. Chan also contributes significantly to academic administration and curriculum development at City University of Hong Kong.

Zhen DongView profile

Associate Professor

• Software Reliability
• Software Security
• Mobile Computing
• +4 more

Zhen Dong is an Associate Professor at Fudan University, China, specializing in software engineering with a focus on software reliability and security, particularly in mobile computing. Previously, he was a PostDoc and Senior Research Fellow at the National University of Singapore under the guidance of Abhik Roychoudhury. His educational background includes: PhD in Computer Science from Heidelberg University (2017), advised by Prof. Artur Andrzejak Dr. Dong's research centers on developing techniques and tools for improving software reliability and security. His work spans mobile application testing, Android security, flaky test detection, and vulnerability localization. He has made significant contributions to the field of software testing and analysis, with a particular emphasis on practical applications for mobile systems. His research bridges theoretical foundations with real-world software engineering challenges. Analysis of Dr. Dong's recent publications reveals a strong focus on leveraging AI/ML techniques for software engineering tasks, particularly using LLMs for test automation and program analysis. His work consistently addresses critical challenges in mobile computing, especially Android application reliability and security. There's a clear trajectory toward more sophisticated analysis techniques, from traditional testing methods to AI-driven approaches. His notable scientific achievements include: ACM Distinguished Paper Award at ICSE'20 Best Paper Award at AsiaCCS'21 (1/370 submissions) ASE'22 Distinguished Reviewer Award Dr. Dong serves on the Board of Distinguished Reviewers for ACM Transactions on Software Engineering and Methodology and has been an active member of numerous program committees for top software engineering conferences including ICSE, ASE, and ISSTA. His service to the academic community extends to reviewing for prestigious journals such as IEEE Transactions on Software Engineering and Methodology and ACM Transactions on Software Engineering and Methodology. His research has been supported through various academic channels, enabling him to maintain an active lab focused on software testing and analysis, particularly for mobile platforms. The lab has produced numerous tools and techniques that have influenced both academic research and industrial practice in software reliability.

Yu FengView profile

Associate Professor

• Programming Languages
• Program Synthesis
• Program Analysis
• +3 more

Yu Feng is an Associate Professor in the Computer Science Department at the University of California, Santa Barbara. He received his Ph.D. in Computer Science from the University of Texas at Austin in 2018 and his MS in Computer Science from Beihang University in 2008. His research focuses on programming languages, program analysis and verification, program synthesis, and security. Specifically, he aims to build automated tools that guarantee the desired behavior of systems in a sound, precise, and scalable way. He is particularly interested in applying program synthesis and analysis to tackle security and performance problems in mobile devices and blockchain applications. Professor Feng's recent publications demonstrate a strong focus on program synthesis for security applications, particularly in blockchain and zero-knowledge proofs. His work spans multiple top-tier conferences including PLDI, POPL, ASE, and CCS, showing consistent contributions to both programming languages and security communities. His research often combines logical reasoning with machine learning techniques to develop innovative solutions for complex problems in program analysis and synthesis. Ethereum Academic Award (2025, 2024, 2023) ACM SIGPLAN Distinguished Paper Award, PLDI 2022 ACM SIGCHI Best Paper Award, CHI 2021 Google Faculty Research Award (Security), 2021 ACM SIGSOFT Distinguished Paper Award, ASE 2020 DARPA HARDEN grant (2022-2025) NSF-SaTC grant (2019-2022) ACM SIGPLAN Distinguished Paper Award, PLDI 2018 Professor Feng actively mentors students in his research group, currently advising several PhD students including Yanju Chen, Junrui Liu, Hongbo Wen, and Hanzhi Liu. He has served on program committees for numerous top conferences including CCS, PLDI, OOPSLA, ICSE, ASPLOS, and POPL. His research is supported by prestigious grants including DARPA HARDEN and NSF-SaTC. His laboratory focuses on developing cutting-edge program synthesis and analysis frameworks that combine logical reasoning with machine learning. The team works on applications in blockchain security, zero-knowledge proofs, and secure protocol implementation.

Bernd FischerView profile

Professor

• Automated Software Engineering
• Program Verification
• Logic-based Techniques
• +7 more

Bernd Fischer is a Professor and the current Head of Division in the Division of Computer Science at Stellenbosch University, South Africa. Previously, he held positions at TU Braunschweig, NASA Ames Research Center, and University of Southampton, establishing a strong international academic background in software engineering and formal methods. Professor Fischer's research focuses on automated software engineering, particularly logic-based techniques. His work spans specification-based component reuse, program synthesis, and program verification, with current emphasis on annotation inference, software model checking, and human-oriented presentation of verification results. His research bridges theoretical foundations with practical applications, particularly in concurrent program verification, grammar-based testing, and fault localization techniques. His work has significant implications for improving software reliability and developer productivity. Analysis of his recent publications reveals a strong focus on concurrent program verification through lazy sequentialization techniques, with substantial contributions to tools like CSeq and ESBMC. His research demonstrates consistent innovation in software verification, particularly in addressing the challenges of concurrency, bounded model checking, and fault localization. The interdisciplinary nature of his work connects theoretical computer science with practical software engineering challenges. ASE 2012 Most Influential Paper Award ACM Distinguished Paper Award Best Presentation Award Professor Fischer has successfully advised PhD students including Gillian Greene, who defended her thesis on "Concept-Based Exploration of Rich Semi-Structured Data Collections," and Geoff Birch, who completed work on "Fast, Fully-Automated, Model-Based Fault Localisation and Repair with Test Suites as Specification." His mentoring approach integrates theoretical rigor with practical tool development. His research has been supported through various academic grants enabling the development of multiple software verification tools. Professor Fischer leads development of several important software engineering tools including AutoBayes for statistical program synthesis, ConceptCloud for interactive visualization of software repositories, CSeq for concurrent program verification, and ESBMC for software model checking. These tools represent significant contributions to the software engineering research community and have been recognized in international verification competitions.

Xiang GaoView profile

Associate Professor

• Program Analysis
• Software Security
• Automated Program Repair
• +4 more

Xiang Gao is a Pre-tenure Associate Professor in the School of Software at Beihang University, China. His research focuses on applying program analysis, test generation, and formal methods to improve software quality through automated bug fixing and program synthesis. He has established significant collaborations with Fujitsu Laboratories of America, Microsoft Research, and other leading institutions in the software engineering field, demonstrating strong industry-academia connections. Dr. Gao received his Bachelor's degree in Computer Science (Elite Class) from Shandong University in 2016, followed by a Ph.D. from the School of Computing at the National University of Singapore, where he also served as a Postdoctoral Fellow until December 2021. His educational background spans both Chinese and Singaporean academic institutions, providing him with a global perspective on software engineering research. His primary research interests span multiple cutting-edge areas of software engineering: Program Analysis techniques for detecting and fixing software bugs with formal methods Software Security vulnerabilities with focus on automated repair methods Automated Program Repair systems that generate high-quality patches without overfitting Program Synthesis for creating transformation rules from examples Software Engineering for Artificial Intelligence (SE4AI) to improve AI model reliability and security Mobile Software Engineering with particular attention to UI testing and automation Deep Learning Security including model protection and obfuscation techniques Dr. Gao's recent publication trajectory shows a strategic evolution toward integrating large language models with traditional software engineering approaches, particularly in test generation and program repair. His work on DNN modularization (NeMo, CNNSpliter, SeaM) represents an innovative approach to enhancing model reusability and security in resource-constrained mobile environments, addressing critical challenges in deploying AI on edge devices. His scientific contributions have been recognized with multiple prestigious awards: ACM SIGSOFT Distinguished Paper Award for "ProveNFix: Temporal Property guided Program Repair" at FSE'24 IEEE TCSE Distinguished Paper Award for "Investigating and Detecting Silent Bugs in PyTorch Programs" at SANER'24 ACM SIGSOFT Distinguished Paper Award for "Modularizing while Training: A New Paradigm for Modularizing DNN Models" at ICSE'24 Distinguished Artifact Award for "Automated Patch Backporting in Linux (Experience Paper)" at ISSTA'21 Dr. Gao actively mentors students at various levels, seeking "self-motivated Ph.D, master, undergraduate students and interns with strong programming skills" for his research projects. He serves on numerous program committees for top software engineering conferences including ICSE, ASE, ISSTA, and FSE, demonstrating his growing influence in the academic community. His research has been supported through collaborations with industry partners including Microsoft Research and Fujitsu Laboratories of America, translating theoretical advances into practical applications. His laboratory focuses on several key research projects including Automated Software Vulnerability Repair (with techniques like Fix2Fit, VulnFix, and ExtractFix that address the overfitting problem in program repair), Program Synthesis for Program Transformation (including Semi-supervised synthesis and FixMorph for automated patch backporting in Linux), and Software Engineering for Artificial Intelligence (with projects like CNNSpliter, SeaM, and Sensei that apply software engineering principles to improve AI model usability and robustness). These projects represent cutting-edge work at the intersection of traditional software engineering and modern AI techniques, addressing critical challenges in software reliability and security.

Yiling LouView profile

Associate Professor

• Software Engineering
• Artificial Intelligence
• Programming Languages
• +5 more

Yiling Lou is an incoming Assistant Professor at the Siebel School of Computing and Data Science, University of Illinois Urbana-Champaign (starting Spring 2026), currently serving as a Pre-tenure Associate Professor at Fudan University. Previously a Postdoctoral Fellow at Purdue University under Prof. Lin Tan, Dr. Lou holds a Ph.D. and B.S. in Computer Science from Peking University supervised by Prof. Lu Zhang and Prof. Dan Hao. Research interests span Software Engineering synergized with Artificial Intelligence and Programming Languages , specifically focusing on LLM4Code, Agent&SE, Vulnerability Detection, and Software Testing/Debugging. Current projects include AgentIssue-Bench for agent system maintenance and INFERROI for enhancing static analysis with LLMs. Research trends show increasing integration of LLMs with traditional SE techniques, particularly in code generation (ClassEval, CodeGen4Libs), debugging (interactive runtime comparison), and vulnerability detection. Recent work emphasizes practical applications in agent systems and resource leak detection. ACM SIGSOFT Distinguished Paper Award (ESEC/FSE 2023) IEEE TCSE Distinguished Paper Award (ICSME 2021) Advises a large research group including 7 Ph.D. and 8 MS students at Fudan University, actively recruiting for UIUC starting Fall 2026. Leads the LLM4Code workshop series and serves on numerous program committees including ICSE, ASE, and FSE. Currently organizing research on Code Agents, Code LLMs, and AI&Security with strong industry relevance. Coordinates the Siebel School research group at UIUC focusing on the intersection of AI and Software Engineering, with particular emphasis on developing robust agent systems for code maintenance and security applications.

Lala CeklicView profile

Research Fellow

• Ophthalmology
• Retinal Diseases
• Optical Coherence Tomography (OCT)
• +5 more

Lala Ceklic is a Research Fellow and Grader at the University of Bern's Department of Ophthalmology since 2011, based at Inselspital, Universitätsspital Bern. She specializes in retinal imaging and clinical research, with expertise in optical coherence tomography (OCT) and multimodal imaging for managing diabetic macular edema, retinal vein occlusion, and age-related macular degeneration. Her educational background includes: Ophthalmology residency at Inselspital, Universitätsspital Bern (2011-2016) Ophthalmology residency at University of Novi Sad (2004-2007) Training at International Council of Ophthalmology (2003-2004) Dr. Ceklic's research focuses on predictive biomarkers for treatment response in retinal diseases, with recent work examining gender-specific differences in disease presentation and real-world outcomes of anti-VEGF therapy. Her expertise in OCT analysis drives investigations into structural changes during disease progression and treatment, particularly for diabetic macular edema and retinal vein occlusion where personalized treatment approaches are critical. Analysis of her 2022-2024 publications reveals a strategic shift toward translational research , connecting imaging biomarkers with clinical outcomes. Her work increasingly addresses health disparities through gender medicine frameworks while maintaining technical rigor in imaging methodology. Dr. Ceklic's scientific contributions are recognized through: ARVO Kreissig Foundation Travel grant (2012) Helmerich Foundation Scholarship (2011) American Academy of Ophthalmology Rotary Program Scholarship (2010) Austrian-American/Soros Foundation Scholarship (2008) European Society of Ophthalmology Scholarship (2007) She actively contributes to the Fight Retinal Blindness! international registry and RADIANCE clinical trial through Bern's imaging reading center. Her collaborative network spans 15+ countries, enabling high-impact studies on treatment standardization. While direct student supervision isn't documented, her role in training graders for multicenter trials provides significant mentorship opportunities in clinical research methodology. Dr. Ceklic is integral to the university's retinal imaging core facility , which standardizes OCT analysis for clinical trials and supports drug development pipelines. Her team specializes in biomarker discovery from complex imaging datasets, directly influencing therapeutic decision-making in retinal medicine.

Michael PradelView profile

Professor

• Software Engineering
• Program Analysis
• Bug Detection
• +5 more

Michael Pradel is a full professor in the Computer Science Department at the University of Stuttgart and faculty member at CISPA Helmholtz Center for Information Security (effective September 2025), where he leads the Software Lab. He is also affiliated with the International Max Planck Research School for Intelligent Systems and the Stuttgart ELLIS Unit, reflecting his interdisciplinary research approach. His research interests focus on software engineering, particularly program analysis, bug detection, and the application of machine learning to developer tools. Pradel's recent work increasingly explores LLM-based approaches for program repair, code analysis, and automated software development, as evidenced by projects like RepairAgent and ExecutionAgent. Pradel's publication record shows a clear trend toward integrating AI techniques with traditional software engineering methods, with recent papers focusing on LLM applications for program repair, change validation, and quantum software analysis. His work bridges theoretical foundations with practical tool development, as seen in frameworks like DyLin for Python analysis and LintQ for quantum programs. Ernst-Denert Software Engineering Award Emmy Noether grant (1.3 million Euro) by the DFG ERC Starting Grant (1.5 million Euro) Multiple ACM SIGSOFT Distinguished Paper Awards ACM Distinguished Member recognition Pradel actively mentors PhD students, with recent graduates including Matteo (specializing in quantum software) and Luca (focusing on software evolution). His group has received significant funding and maintains strong industry connections, including past sabbaticals at Facebook. He serves in leadership roles for major conferences, including PC co-chair for FSE 2027, demonstrating his standing in the software engineering community. The Software Lab maintains active collaborations with institutions worldwide, including CMU, Google, KAIST, and several European universities.

• Computer Vision
• Big Data Analytics
• Generative AI in Education
• +2 more

Dr. Kaveh Kiani is a faculty member at the University of Salford's School of Science, Engineering & Environment, with a primary affiliation to the Informatics Research Centre. His academic profile demonstrates expertise spanning both theoretical statistics and practical AI applications across multiple industries. Doctoral degree in Applied Statistics Master's degree in Data Science Postgraduate Certificate in Academic Practice (PGCAP) Fellow of the Higher Education Academy (FHEA) Dr. Kiani's research focuses on computer vision, big data analytics, generative AI applications in education, and survival analysis. His work bridges statistical theory with practical implementations, particularly evident in medical AI applications for respiratory disease detection, skin cancer diagnosis, and prostate cancer research. His industry experience informs his approach to solving real-world problems in healthcare, construction sustainability, and financial services. His recent publications (2023-2025) demonstrate strong activity in applying deep learning techniques to medical imaging and developing novel frameworks for industry-specific challenges. The research shows a clear trajectory toward increasingly sophisticated AI applications with practical impact. Fellow of the Higher Education Academy (FHEA) awarded by Advance HE Endorsed by The Royal Academy of Engineering as a Global Talent in Data Science Dr. Kiani actively supervises PhD students and teaches across multiple postgraduate programs including MSc Data Science, MSc Artificial Intelligence, and MSc Internet of Things with Data Science. His teaching modules cover Big Data Tools and Techniques and Applied Statistics and Data Visualisation, reflecting his dual expertise in both theoretical foundations and practical applications of data science.

Conrad WattView profile

Assistant Professor

• WebAssembly
• Formal Verification
• Concurrent Programming
• +3 more

Conrad Watt is an Assistant Professor at Nanyang Technological University in Singapore. His research focuses on the formal verification and mechanisation of WebAssembly, particularly its concurrency and security features. He previously held a Research Fellow position at Peterhouse, University of Cambridge. His work bridges theoretical formal methods with practical systems implementation, contributing to standards proposals for WebAssembly's evolution. He co-chairs the W3C WebAssembly Community Group and has served on program committees for POPL, PLDI, and SPLASH. Education: PhD in Computer Science (University of Cambridge, 2021), supervised by Peter Sewell Research interests include mechanisation of programming language specifications, relaxed-memory concurrency, and domain-specific languages for formal semantics. His projects like SpecTec aim to unify WebAssembly's specification across documentation, implementations, and mechanisations. Selected contributions to WebAssembly include: Designing its initial concurrency specification Developing WasmRef-Isabelle as a verified interpreter and fuzzing oracle Creating Iris-Wasm for modular program verification Scientific recognition includes: ACM Doctoral Dissertation Award Honorable Mention EAPLS Best Dissertation Award He advises PhD students in WebAssembly-related topics and leads collaborations with industrial partners like Wasmtime. Current research explores irreducible control flow in WebAssembly, richer concurrency models, and performance optimization through mechanised specifications.

Bernd FinkbeinerView profile

Professor

• Synthesis
• Verification
• Monitoring of Reactive Systems
• +9 more

Bernd Finkbeiner is a Professor of Computer Science at Saarland University and a faculty member at the CISPA Helmholtz Center for Information Security . He leads the Reactive Systems Group, which merged with CISPA in 2020, focusing on formal methods for ensuring system safety and security. University: Saarland University Research Institution: CISPA Helmholtz Center for Information Security His research spans synthesis, verification, and monitoring of reactive systems , with applications to program repair, static/dynamic analysis, and security guarantees . Recent work includes coinductive proofs for hyperliveness, asynchronous hyperproperty verification, and symbolic execution for bug detection. Key trends in his publications include: Hyperliveness and fairness in reactive systems Symbolic execution for hyperbug identification Integration of synthesis and verification techniques Formal guarantees for cybersecurity and software reliability He contributes to academic communities through committee roles at conferences like POPL, ICFP, SPLASH, and VMCAI.

• Synthetic Biology
• Gene Editing
• Genomics
• +6 more

Farren Isaacs is a Professor of Molecular, Cellular and Developmental Biology at Yale School of Medicine with extensive affiliations across Yale University. His primary appointments include the Department of Molecular, Cellular and Developmental Biology, Microbiology, and the Yale Cancer Center. He also participates in the Center for RNA Science and Medicine, Genomics, Genetics and Epigenetics Program, Molecular Cell Biology, Genetics and Development, Yale Combined Program in the Biological and Biomedical Sciences (BBS), and Yale Ventures. Dr. Isaacs received his BS in Bioengineering from the University of Pennsylvania, followed by MS and PhD degrees in Biomedical Engineering and Bioinformatics from Boston University. He completed postdoctoral training at Harvard Medical School before establishing his independent research program at Yale. His research focuses on developing and applying genome engineering technologies to understand and reprogram biological systems. His lab has pioneered approaches like MAGE (Multiplex Automated Genome Engineering) and has made significant contributions to genome recoding, CRISPR-based editing, and synthetic genetic circuit design. Recent work includes engineering genomically recoded organisms with simplified genetic codes and developing precision multiplexed base editing techniques with potential therapeutic applications. Dr. Isaacs' publication record demonstrates sustained innovation in synthetic biology and genome engineering, with high-impact papers spanning from foundational work in the early 2000s to multiple 2025 publications in journals like Nature, Nature Communications, and Science. His research trajectory shows a clear progression from developing basic genome engineering tools to applying these technologies to increasingly complex biological problems. Beckman Young Investigator Award (2012) Rising Young Stars of Science (2008) Dr. Isaacs maintains an active research program with multiple current grants supporting work in genome engineering and synthetic biology. His lab trains graduate students and postdoctoral fellows in cutting-edge genomic technologies while fostering interdisciplinary collaborations across Yale and with external institutions. The lab operates across two locations - at Yale's main Science Hill campus and at the West Campus facility. The Isaacs Lab focuses on developing next-generation genome engineering technologies and applying them to fundamental biological questions and potential therapeutic applications. Current research directions include expanding the genetic code, developing precision editing tools, and creating synthetic biological systems with novel properties and enhanced biocontainment features.

Ranjit JhalaView profile

Professor

• Programming Languages
• Software Engineering
• Type Systems
• +3 more

Ranjit Jhala is a Professor of Computer Science Engineering at UC San Diego's Jacobs School of Engineering, where he leads the Programming Systems Group. His research spans Programming Languages and Software Engineering, focusing on building reliable systems through Type Systems, Model Checking, Program Analysis, and Automated Deduction. His current projects include Flux for Rust verification, Liquid Haskell refinement types, and techniques for analyzing timing channels. Professor Jhala teaches courses on Programming Languages (CSE 130) and Graduate Programming Languages (CSE 230), with extensive experience teaching compilers and verification topics. Professor Jhala advises several students including Alexander Bakst, Ben Cosman, and Marc Andrysco. Notable former students include Niki Vazou (Postdoc at Maryland), Ravi Chugh (University of Chicago), and Patrick Rondon (Google).