API Discovery Professors and Supervisors

Nick HagarView profile

Assistant Professor

Northwestern UniversityUnited States

Nick Hagar is a postdoctoral scholar at Northwestern University working on the Generative AI in the Newsroom Initiative and an incoming assistant professor at the University of Minnesota. His research focuses on collective attention dynamics in digital ecosystems, particularly examining how AI impacts journalism and media landscapes. Current Position: Postdoctoral Scholar at Northwestern University Upcoming Position: Assistant Professor at University of Minnesota Previous Experience: New York Times, Meta, Patreon Education: PhD from Northwestern University, Medill School of Journalism Hagar's research explores how people discover information online, why certain content becomes popular, and how AI systems affect news distribution. His work combines computational methods, data science, and network analysis to study platform dynamics in social media and news ecosystems. He has particular expertise in analyzing TikTok, Reddit, Facebook, and Substack as attention markets. His recent publications demonstrate a strong focus on the intersection of AI and journalism, with particular attention to local news systems, news recommendation algorithms, and how generative AI is transforming newsroom practices. His research shows how platform algorithms often demonstrate 'algorithmic indifference' toward news content, particularly on platforms like TikTok. Featured in Business Insider, The New Yorker, and Nieman Lab Regular contributor to media discussions on AI and journalism Active participant in academic conferences including ICWSM and ICA Hagar has developed several open-source tools including a Substack API, Common Crawl Genealogy project, and Archive Check CLI for collecting website data from various archives. His work bridges academic research with practical journalism applications, focusing on how computational methods can enhance news production and distribution.

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Alejandra VasquezView profile

Research Fellow

Lund UniversitySweden

Alejandra Vasquez is a visiting research fellow at Lund University's Faculty of Engineering, affiliated with the Department of Pure and Applied Biochemistry. She co-discovered novel lactic acid bacteria in honeybees in 2005 with Tobias Olofsson, creating a research program spanning medical biotechnology, microbiology, and bee health. Research Interests: Her work focuses on antimicrobial properties of honeybee-specific bacteria for functional food development and antibiotic alternatives. Research areas include: Honeybee microbiome dynamics Lactic acid bacteria therapeutic applications Pathogen inhibition mechanisms Genomic evolution of symbiotic bacteria Wound healing microbiome therapies Article Trends: Her publications (2017-2025) reveal progressive discovery of novel bacterial species like Bifidobacterium kimbladii and Bifidobacterium mellis , with applications ranging from bee health (Paenibacillus inhibition) to human clinical trials for chronic rhinosinusitis. Collaborative work extends to genome biology and systematic microbiology journals. Funding: Research supported by grants from Formas, VR (Swedish Research Council), and other external sources.

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Ranjit JhalaView profile

Professor

National and Kapodistrian University of AthensGreece

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.

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Nadia PolikarpovaView profile

Associate Professor

National and Kapodistrian University of AthensGreece

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.

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Vassilis RoutsisView profile

Lecturer

University College LondonUnited Kingdom

Vassilis Routsis serves as a Lecturer in Technologies in the Digital Humanities at University College London's Department of Information Studies, holding a dual appointment as Senior Research Fellow. His primary responsibility involves managing the UK Data Service's census flow data project, working closely with the Office for National Statistics (ONS) to disseminate 2011 and 2021 UK census data. Currently, he leads the development of next-generation tools including sophisticated APIs with subsetting capabilities and user-friendly interfaces for census data discovery and extraction. Routsis earned his PhD from UCL with research focused on Informational Privacy and Self-Disclosure Online: A Critical Mixed-Methods Approach to Social Media . His academic background includes a BA in Sociology and an MSc in Social and Political Theory, establishing a robust foundation in social sciences that informs his interdisciplinary approach to digital humanities. His research spans the intersection of technology and social sciences, with particular emphasis on data privacy, census data analysis, and AI applications for social research. Routsis currently leads the ESRC-funded CORDIAL-AI project exploring how Large Language Models can improve access to complex datasets, and co-leads the Census Innovation at CeLSIUS project focused on enhancing access to restricted UK Census data through synthetic datasets and improved user guidance. Routsis teaches postgraduate courses in programming, databases, and AI, leading modules including 'Server-Side Programming and Structured Data,' 'Social Media Analytics,' 'Database Theory and Practice,' and 'Developing Dynamic Web Applications.' His recent publications demonstrate a clear trend toward making complex data more accessible through innovative technological solutions, particularly focusing on census flow data and AI methodologies for data retrieval. ESRC-funded CORDIAL-AI project lead Census Innovation at CeLSIUS project co-lead Member of Royal Statistical Society Peer reviewer for Digital Humanities Conference and iConference As supervisor for Master's and PhD students in Digital Humanities and Data Science programs, Routsis brings extensive technical expertise across multiple programming languages including Python, JavaScript, PHP, SQL, Java, C#, and R. His work at the UCL Centre for Languages and International Education as a software and web engineer further demonstrates his commitment to developing pedagogical tools that support academic communities.

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Jerzy BrzezińskiView profile

Professor

Poznan University of TechnologyPoland

Professor Jerzy Brzeziński is a distinguished academic at Poznań University of Technology, where he serves in the Faculty of Computer Science and Telecommunications within the Institute of Computer Science. With a career spanning several decades, he has established himself as a leading researcher in distributed systems and networking, maintaining active research productivity with publications as recent as 2022. Professor Brzeziński's research interests focus on distributed systems, peer-to-peer networks, service-oriented architecture, mobile ad hoc networks, and distributed shared memory systems. His work bridges theoretical foundations with practical applications, particularly in areas like gaming platforms, secure data exchange, and reliable communication protocols. Recent publications demonstrate continued innovation in sybil-proof data exchange mechanisms and broadcast protocols for mobile networks. Analysis of his publication trends reveals an evolution from foundational work on memory systems and consistency models in the early 2000s toward more applied research in peer-to-peer systems, gaming applications, and sustainable computing in recent years. His work consistently addresses core challenges in distributed computing while adapting to emerging technological contexts. Professor Brzeziński has supervised at least 10 doctoral students throughout his career, including Andrzej Stroiński, Michał Kalewski, and Łukasz Piątkowski. His mentorship has produced significant contributions to the field, with students researching topics like Petri Net models, reliable broadcast protocols, and server synchronization. His research is characterized by strong collaborative networks, frequently working with colleagues at Poznań University of Technology and former students. This collaborative approach has resulted in numerous high-impact publications in reputable journals and conference proceedings across the distributed systems domain.

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Wynn MeyerView profile

Assistant Professor

Lehigh UniversityUnited States

Wynn Meyer is an Assistant Professor in the Department of Biological Sciences at Lehigh University, leading the Meyer Lab since joining the faculty in fall 2020. Her research integrates genomic variation analysis to unravel evolutionary history and adaptive mechanisms across species, with particular emphasis on mammalian dietary specialization. Education: PhD in Human Genetics, University of Chicago Postdoctoral Research, University of California, Berkeley Postdoctoral Research, University of Pittsburgh Research Focus: Dr. Meyer specializes in convergent evolution through the lens of population genetics and comparative genomics . Her lab investigates how repeated dietary adaptations (e.g., herbivory) in diverse mammalian lineages reveal underlying genetic mechanisms, employing advanced bioinformatics for large-scale genomic analysis. Current projects explore gene regulatory evolution in liver metabolism and metabolic disease susceptibility. Publication Trends: Her scholarly output demonstrates consistent innovation in evolutionary genomics methodology and cross-species analysis. Key themes include the genetic basis of convergent traits (evident in marine mammal detoxification systems and primate pigmentation), development of computational tools like RERconverge, and exploration of fundamental population genetics principles. Work frequently bridges theoretical frameworks with empirical genomic data. Awards: AAAS Science & Technology Policy Fellow at NIH Advising & Collaboration: Dr. Meyer actively mentors PhD students in Cell/Molecular Biology and Evolutionary Biology concentrations, supervises undergraduate researchers, and maintains cross-lab collaborations (notably with Kowalko Lab on cavefish and tetra fish projects). Her team includes postdoctoral researchers, graduate students, and K-12 teacher participants through dedicated recruitment initiatives. Lab Structure: The Meyer Lab operates from Lehigh's Iacocca Hall with dedicated computational resources for genomic analysis. Current research leverages bat formations for data visualization and maintains active projects on mammalian dietary genomics, cavefish evolution, and liver gene regulation. The lab emphasizes interdisciplinary training and science-policy engagement.

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Michał WójcikView profile

Lecturer

Gdańsk University of TechnologyPoland

Michał Wójcik serves as Assistant Lecturer in the Department of Computer Systems Architecture within the Faculty of Electronics, Telecommunications and Informatics at Gdańsk University of Technology. Concurrently, he holds a technical role as Senior Specialist – Design Programmer in the Team for Development and Maintenance of Central University Systems (CUI), operating from Building A of the Faculty (room 523) with contact number 583471867. His research bridges computer science with environmental and business applications, focusing on Software Engineering (RESTful systems, Jakarta EE), High Performance Computing (parallel application simulation), and Environmental Informatics (marine forecasting, benthic habitat mapping). Key projects include the FindFISH numerical forecasting platform for fisheries and MERPSYS simulation environment for HPC systems, demonstrating strong interdisciplinary collaboration. Analysis of his 2017-2024 publications reveals evolving expertise from core HPC simulation toward applied environmental informatics, with recent work emphasizing open innovation competence profiling and marine ecosystem modeling. His publications consistently address real-world problems through computational solutions, particularly in Baltic Sea environmental monitoring and multi-partner innovation ecosystems.

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陳恭View profile

Professor

National Chengchi UniversityTaiwan

陳恭 is a Professor at the Department of Information Management, College of Business, National Chengchi University. With expertise spanning blockchain technology, information security, and software engineering, he has established himself as a leading academic in Taiwan's fintech research community. His work bridges theoretical computer science with practical business applications, particularly in digital finance and secure information systems. Dr. 陳恭 earned his PhD in Computer Science from Yale University (1989-1994), following Bachelor's and Master's degrees from National Taiwan University. His academic journey at National Chengchi University spans over two decades, progressing from Associate Professor to his current position as Professor, with significant administrative roles including Department Chair and Director of the Computer Center. Professor 陳恭's research primarily focuses on blockchain and smart contracts, Open API technology and security, social big data analysis, and programming languages. His work demonstrates a clear evolution from foundational programming language research to cutting-edge blockchain applications. He has made significant contributions to secure multi-party computation, aspect-oriented programming, and blockchain-based systems, with publications in top journals and conferences across computer science, public administration, and business innovation. An analysis of Professor 陳恭's recent publications (2016-2024) reveals a strong concentration on blockchain technology and its financial applications, particularly in smart contracts, consensus algorithms, and secure data dissemination. His research increasingly integrates with IoT systems and demonstrates practical applications in financial technology, showing a clear trajectory from theoretical foundations to real-world business solutions. His notable achievements include: Senior Excellent Teacher (20 years) from National Chengchi University Multiple Distinguished Professor appointments Special Outstanding Talent Award from the National Science Council Multiple Internationalization Excellent Research Awards Professor 陳恭 has secured numerous research grants from both governmental agencies like the Ministry of Science and Technology and private sector organizations including financial institutions and technology companies. His projects often focus on blockchain applications, fintech innovation, and information security, demonstrating strong industry-academia collaboration. He is actively involved in establishing National Chengchi University as a leading center for blockchain research in Taiwan, contributing to research centers focused on fintech innovation and digital transformation. His work has fostered significant collaborations between academia and industry in the rapidly evolving field of digital finance.

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Ali MesbahView profile

Professor

Max Planck Institute for Security and PrivacyGermany

Ali Mesbah is a Professor in the Department of Electrical and Computer Engineering at the University of British Columbia (UBC), where he leads the SALT lab. His research focuses on software engineering with emphasis on AI-driven software analysis, software testing, and software evolution. Previously, he was a Visiting Research Scientist at Google during 2017-2018. Dr. Mesbah received his BSc/MSc (2003) and PhD (2009) degree cum laude in Computer Science from the Delft University of Technology (TUDelft). After completing a postdoctoral fellowship with the Software Engineering Research Group at TUDelft and a Visiting Researcher position at Fujitsu Laboratories of America, he joined UBC in 2011. His research interests span software engineering with particular focus on AI-driven software analysis, software testing, software evolution, program comprehension, fault localization and repair. His work has significant applications in web application testing, JavaScript analysis, and automated program repair. He has pioneered techniques for testing modern web applications, analyzing JavaScript code, and leveraging AI for software maintenance tasks. His recent publications demonstrate a clear evolution toward integrating large language models with traditional program analysis techniques, focusing on test generation, bug repair, and understanding multi-hunk patches. His work bridges theoretical software engineering concepts with practical applications, particularly in web technologies and AI-assisted development. Amazon Research Award (2023) Killam Accelerator Research Fellowship (KARF) (2020) Killam Faculty Research Prize (2019) NSERC Discovery Accelerator (DAS) award (2016) ACM Distinguished Paper Awards at ICSE (2009, 2014) IEEE Distinguished Paper Award at ICST (2018) Best Paper Award at ESEM (2015) Best Paper Award at ICWE (2013) Dr. Mesbah has advised numerous PhD and MASc students, many of whom have gone on to positions at leading technology companies including Google, Amazon, Apple, Microsoft, and SAP. His research has been supported by various grants including the Amazon Research Award and NSERC funding. He leads the SALT lab at UBC, which focuses on software analysis, testing, and learning, with current research directions including AI-driven software engineering, web application testing, and program repair. The lab maintains active collaborations with industry partners and academic institutions worldwide.

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Christoph TreudeView profile

Associate Professor

Max Planck Institute for Security and PrivacyGermany

Christoph Treude is an Associate Professor of Computer Science at Singapore Management University. His academic journey includes senior lecturer positions at the University of Melbourne and the University of Adelaide, and postdoctoral research at McGill University, the University of São Paulo, and the Federal University of Rio Grande do Norte. Treude's research focuses on improving software quality and developer efficiency through better access to relevant information. His methodology combines empirical studies with tool development that considers natural language artifacts in software repositories. His work spans several key areas of software engineering: Empirical studies of developer behavior and practices Integration of large language models in software development Human-AI collaboration frameworks Software documentation quality and maintenance Reproducibility in scientific software Security practices in code reviews His recent publications demonstrate a strong focus on the intersection of artificial intelligence and software engineering, examining how AI technologies can enhance developer productivity while maintaining code quality and security. His research often involves large-scale empirical studies across various software ecosystems and developer communities. Treude has received significant recognition for his contributions: ARC Discovery Early Career Research Award (2018-2020) Four best paper awards, including two ACM SIGSOFT Distinguished Paper Awards He serves on the Editorial Boards of IEEE Transactions on Software Engineering and Springer's Empirical Software Engineering journal, and holds the role of Open Science Editor for Elsevier's Journal of Systems and Software. Treude has chaired major conferences including ICSME 2020, ICPC 2023, and TechDebt 2023, and regularly participates in software engineering conference program committees. His research has been funded by industry leaders including Google, Facebook, and DST, and he has authored over 150 scientific articles with more than 250 co-authors.

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Hongyu ZhangView profile

Professor

Max Planck Institute for Security and PrivacyGermany

Hongyu Zhang is a Professor and Dean of the School of Big Data and Software Engineering at Chongqing University, China, and an Honorary Professor at The University of Newcastle, Australia. Previously, he served as a Lead Researcher at Microsoft Research Asia and an Associate Professor at Tsinghua University, China. He received his PhD from the National University of Singapore in 2003. His academic journey spans prestigious institutions, combining industry research experience with academic leadership. Dr. Zhang's research interests focus on intelligent software engineering, software analytics, data-driven software engineering, software fault management, testing and debugging, and software maintenance and reuse. His work centers on improving software quality and productivity by mining and analyzing vast amounts of software data. Over the years, he has developed innovative methods that apply data mining, machine learning (including deep learning), and information retrieval techniques to extract knowledge from software data and solve complex software engineering problems. His research spans three major areas: intelligent programming (code search, code summarization, code generation), intelligent quality prediction (defect prediction, cloud failure prediction, performance prediction), and intelligent fault detection and diagnosis (log-based fault detection, crash-based fault localization, bug report analytics). His recent publications demonstrate a clear trend toward integrating large language models and deep learning techniques with traditional software engineering practices. The research spans intelligent programming assistance, code security, UI automation, distributed systems optimization, and performance analysis. His work increasingly focuses on practical applications of AI in software engineering, with emphasis on real-world impact in industrial settings, particularly in microservices, cloud systems, and large-scale software development environments. 8 ACM Distinguished Paper Awards Best Paper Award: How Long Will it Take to Mitigate this Incident for Online Service Systems? David Lorge Parnis Fellowship Senior Member of IEEE Distinguished Member of ACM Distinguished Member of CCF Fellow of Engineers Australia (FIEAust) Recognized in The Australian's Top Researchers special edition as leading researcher in Software Systems World's Top 2% Scientists (career-long) Dr. Zhang has successfully advised numerous PhD and Master's students who have gone on to prominent positions at leading technology companies and academic institutions worldwide. His research has been supported by significant grants including Australian Research Council Discovery Projects (as Lead CI) and multiple National Science Foundation of China projects. His work has made tangible impacts in industry, most notably through the Microsoft Developer Assistant project which received over 450K downloads in 2016. He leads research groups focused on intelligent software engineering and software analytics, with strong collaborations between Chongqing University, The University of Newcastle, and Microsoft Research. His teams develop practical tools for code intelligence, log analysis, and fault diagnosis that are deployed in real-world online service systems.

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Qing HuangView profile

Associate Professor

Max Planck Institute for Security and PrivacyGermany

Qing Huang is an Associate Professor in the School of Computer and Information Engineering at Jiangxi Normal University in Nanchang, China. His academic career focuses on bridging software engineering with artificial intelligence, particularly through the application of large language models to enhance software development processes. His research interests span multiple interconnected domains: Software Engineering Knowledge Graphs Human-Computer Interaction Programming Languages Artificial Intelligence applications in software development Dr. Huang's recent work demonstrates a strong focus on leveraging large language models (LLMs) to address fundamental challenges in software engineering. His research explores how AI can enhance code generation, API understanding, software testing, and knowledge representation in programming contexts. A significant portion of his work investigates the intersection of knowledge graphs and LLMs to create more intelligent software development tools. His publications reveal a consistent pattern of innovation in applying cutting-edge AI techniques to practical software engineering problems, with particular emphasis on improving developer productivity through better tooling and knowledge management. Dr. Huang has made notable contributions to the field of prompt engineering for software development tasks, exploring how natural language interfaces can serve as "APIs" for human-AI interaction. His work on AI Chains represents a novel approach to connecting human developers with LLM capabilities through structured knowledge representations. Additionally, he has conducted significant research on smart contract analysis, code reuse, and type inference in partial code contexts. His scientific contributions have been recognized through publications at major software engineering conferences including ASE and ICSE, with multiple papers accepted across different tracks (Research Papers, Journal-First, Tool Demonstrations). Dr. Huang serves as a Program Committee member for ASE 2025 in the Research Papers track, demonstrating his standing in the software engineering research community. Dr. Huang collaborates extensively with researchers from various institutions, including Data61 (Australia), Nanyang Technological University, and other Chinese universities. His work demonstrates strong interdisciplinary connections between traditional software engineering and emerging AI technologies.

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