Thomas Degueule is a CNRS researcher in the Software Engineering group at LaBRI (Bordeaux, France). His research focuses on software evolution , modularity , and comprehension , with a particular emphasis on software libraries , APIs , software ecosystems , and domain-specific languages (DSLs) . Prior to his current role, he worked as a postdoctoral researcher at CWI (Amsterdam) and earned his PhD at IRISA (Rennes, France). Research Themes : Software evolution, API analysis, dependency management, empirical software engineering, DSL engineering Projects : ANR JCJC ALIEN (PI), H2020 CROSSMINER (WP leader), ALE (associate member), LEOC Clarity (member), ANR INS GEMOC (member), ITEA2 MERgE (member) Software Tools : Roseau, Maracas, BreakBot, Melange, Eclipse GEMOC Studio, K3, Alex, Scava His recent publications highlight trends in API breaking changes , dependency evolution , DSL interoperability , and automated software analysis (see articles list for details). He also contributes to software education through tools like MOON and Immediate Feedback for Jupyter Notebooks. Thomas has supervised PhD students including Gustave Monce (University of Bordeaux), Lina Ochoa (now assistant professor at Eindhoven University), Corentin Latappy (post-doc at University of Bordeaux), and Christophe Casseau (lecturer at University of Bordeaux). He actively participates in academic service, serving on program committees for conferences such as ICSE , ASE , ICSME , ISSTA , and SLE , and organizing workshops like SLEBoK and MLE 2021 .
Dr. Shirin Nilizadeh is an Associate Professor in the Department of Computer Science and Engineering at The University of Texas at Arlington's College of Engineering. She leads the Security and Privacy Research Lab, conducting interdisciplinary research at the intersection of cybersecurity, privacy, machine learning, and social media analysis. Her work addresses critical societal issues related to online security, privacy, and safety through data-driven approaches. Dr. Nilizadeh received her PhD in Computer Science from Indiana University in 2014, followed by MS in Computer Science from Amirkabir University (2007) and BS in Computer Engineering from Islamic Azad University (2004). Her research focuses on security and privacy in systems and social networks, employing techniques from machine learning and big data analytics. She takes a highly interdisciplinary approach, integrating AI, NLP, social sciences, and public health to address societal issues in cybersecurity and privacy. Her research objectives include: (1) detecting and characterizing emerging threats in online social networks like social engineering attacks, misinformation, and online hate speech; (2) advancing the adversarial robustness and fairness of ML and NLG systems; and (3) studying humans' online behaviors through data-driven interdisciplinary research. Analysis of her recent publications reveals a strong focus on AI-generated security threats, particularly phishing scams using LLMs, NFT fraud detection, social media toxicity analysis, and content moderation systems. Her work bridges theoretical security research with practical applications, often addressing real-world security challenges through innovative technical solutions. Among her notable scientific achievements are the prestigious NSF CAREER award (2023), Comcast Innovation Awards (2022 and 2024), College of Engineering Outstanding Early Career Research award (2024), and IEEE SP 2024 Distinguished Paper Award. Her work has also received best paper and technical poster awards at eCrime 2021 and NDSS 2022. Dr. Nilizadeh has successfully mentored numerous doctoral and master's students while securing significant research funding, including multiple NSF grants and Comcast Innovation Fund awards. She leads a vibrant research group that has produced impactful work cited in official reports submitted to The Supreme Court and the EU Committee on Civil Liberties, Justice, and Home Affairs. Her lab has also received coverage from WIRED, MIT Technology Review, Orange's Hello Future, and Communications of the ACM. She serves on numerous program committees for top international conferences including ACM CCS, USENIX Security, and POPETS, and has organized outreach programs like OurCS@DFW to broaden participation of underrepresented students in computing.
Ruben Martins is an Assistant Professor at Carnegie Mellon University's School of Computer Science and serves as the program director of the Master of Science in Computer Science (MSCS) . His research focuses on the intersection of constraint programming, program synthesis, analysis, and verification, with recent work aiming to make formal methods tools more accessible through automated reasoning. Ruben earned his Ph.D. with honors from the Technical University of Lisbon, Portugal (2013) , followed by postdoctoral research at the University of Oxford (2014-2015) and UT Austin (2015-2017) . Research Interests : Ruben's work bridges constraint programming and program synthesis , with applications in software verification , optimization , and automated reasoning . He has developed award-winning tools like Open-WBO , a modular MaxSAT solver that has won gold medals in international competitions. His publications span top-tier venues such as POPL , PLDI , FSE , SAT , and CP , often addressing real-world challenges from program analysis to network security. Scientific Awards include: Distinguished Paper Award at PLDI 2018 Distinguished Paper Award at FSE 2021 Distinguished Paper Award at SAT 2022 Gold medals for Open-WBO in MaxSAT competitions Teaching & Advising : Ruben mentors Ph.D., Master’s, and undergraduate students in research projects related to program synthesis, formal methods, and constraint solving. He teaches courses such as Bug Catching: Automated Program Verification and Advanced Topics in Logic: Automated Reasoning and Satisfiability , emphasizing hands-on experience with tools like Why3. His advising spans topics from AI-driven program repair to network protocol verification , fostering collaboration across disciplines.
Stephen Humphry is a Senior Honorary Research Fellow at the Graduate School of Education, The University of Western Australia. He joined UWA in 2006 and has maintained continuous research funding through Australian Research Council Linkage grants since 2008. His work focuses on educational assessment, measurement, and evaluation, with significant contributions to large-scale testing programs including NAPLAN and WALNA. Humphry holds a Bachelor of Psychology from Western Australia and a PhD from Murdoch University. His expertise spans psychometrics, Rasch measurement, and quantitative methods in educational assessment. Dr. Humphry's research has increasingly focused on developing novel approaches that enable classroom teachers to reliably assess students in areas not amenable to large-scale testing. His work on pairwise comparison methods, Rasch analysis, and construct validity has advanced the field of educational measurement. He has published extensively on topics including narrative assessment, writing rubrics, and scale development. His recent publications demonstrate a strong trend toward innovative assessment methodologies, particularly in narrative writing and developmental attributes. These works emphasize practical applications for classroom teachers while maintaining rigorous psychometric standards. The research shows increasing sophistication in handling complex assessment challenges through pairwise comparison techniques and ordered exemplars. Dr. Humphry has supervised 7 research projects and has been awarded 25 research grants, including the current "International collaboration in teaching and learning of Einsteinian physics" project funded by the Australian Research Council until 2025. He has also led multiple NAPLAN-related research projects for the Australian Curriculum Assessment & Reporting Authority. His industry experience includes leading the Central Analysis of Data project for NAPLAN (2011-2013) and serving as Psychometrician for the Western Australian Department of Education's large-scale assessment program prior to joining UWA. His research has led to commercial applications including Brightpath Assessments, used by several hundred schools across Australia.
Dr. Yanjun Zhang is an Honorary Research Fellow at the School of Electrical Engineering and Computer Science, The University of Queensland. His research focuses on privacy-preserving technologies, federated learning, cybersecurity in IoT systems, and machine learning security. He holds a PhD in Privacy-Preserving Sharing for Genome-Wide Analysis from The University of Queensland (2021). Education: PhD in Information Technology, School of Information Technology and Electrical Engineering, The University of Queensland (2021) Research Interests: Designing secure collaborative machine learning frameworks Defending against adversarial attacks in cyber-physical systems Privacy preservation in distributed genomic and medical data analysis Compliance and ethics in virtual personal assistant applications Key Contributions: Developed privacy-preserving federated learning frameworks (AgrAmplifier, PrivColl) Conducted foundational studies on evasion attacks in IoT systems Created datasets for analyzing malicious browser extensions and Alexa skills Labs/Teams: Active contributor to UQ Cyber initiatives, including the 2021-2022 Seed Funding project on federated deep learning for medical imaging.
Marcel Böhme is a faculty member at the Max Planck Institute for Security and Privacy (MPI-SP) , leading the Software Security research group. His work focuses on foundational advancements in fuzzing , statistical program analysis, and scalable vulnerability discovery. Education: PhD from National University of Singapore (NUS) Research interests span: Statistical and causal frameworks for software testing Efficiency/Scalability of automated testing Fundamental limits of vulnerability detection Practical fuzzing technology (e.g., Entropic in LibFuzzer) Recent publications highlight trends in: Machine learning for security analysis Privacy-preserving statistical methods Future-proof security frameworks Protocol fuzzing with large language models Scientific accolades include: ERC Consolidator Grant (2024) NUS Outstanding Young Alumni Award (2022) ARC DECRA (2019) Multiple ACM Distinguished Paper Awards He serves as: Spokesperson for Research Group Leaders at Max Planck Society Guest Editor-in-Chief for ACM TOSEM PC Chair for ASE'25 and ISSTA'26
Shixiang (Woody) Zhu is an Assistant Professor in Data Analytics at the Heinz College of Information Systems and Public Policy, Carnegie Mellon University. He holds a PhD in Machine Learning from Georgia Institute of Technology (2022) and B.S./M.S. in Computer Science from Beijing University of Posts and Telecommunications (2017). His research bridges machine learning, operations research, and statistics, focusing on sequential modeling, human-AI collaboration, and energy systems operations. He has received awards including the IEEE Power & Energy Society Best Paper Award (2025) and was a finalist for the INFORMS Wagner Prize (2021). Education : PhD in Machine Learning, Georgia Tech (2017–2022) B.S./M.S. in Computer Science, BUPT (2010–2017) His research emphasizes spatio-temporal data analysis , decision making under uncertainty , and applications to energy systems, healthcare, and public policy. Notable projects include optimizing police zone design (Wagner Prize finalist) and enhancing grid resilience through robust optimization. He actively collaborates with institutions like Argonne National Laboratory and NSF-funded projects. Awards : Best Paper Award, IEEE Power & Energy Society (2025) Gen-AI Fellows (2024) Finalist, INFORMS Wagner Prize (2021) Advising & Grants : Advises PhD students Zekai Fan, Wenbin Zhou, and others Recipient of Block Center Seed Grant (2024), NSF funding (2024) His work spans energy resilience, public policy optimization, and causal inference in social systems. He co-leads the INFORMS Data Mining Society and reviews for top journals like Operations Research and Management Science.
Marina Milovanović is a Professor at the University of Singidunum, Faculty of Informatics and Computing, Department of Mathematics. She holds dual doctoral degrees from the Faculty of Science, University of Kragujevac (Department of Mathematics, 2014) and Faculty of Entrepreneurial Business, Union University (2008), along with Master's and Bachelor's degrees from the Faculty of Mathematics, University of Belgrade (2000-2005 and 1995-2000 respectively). Faculty of Science, University of Kragujevac, Department of Mathematics (PhD, 2014) Faculty of Entrepreneurial Business, Union University (PhD, 2008) Faculty of Mathematics, University of Belgrade (Master's, 2000-2005) Faculty of Mathematics, University of Belgrade (Bachelor's, 1995-2000) Svetozar Marković High School, science and mathematics major (1991-1995) Professor Milovanović specializes in Mathematics Education and Educational Technology, with particular expertise in interactive multimedia applications for teaching mathematics. Her research consistently bridges theoretical mathematics with practical educational technology solutions, evolving from traditional multimedia approaches to incorporating cutting-edge AI and machine learning techniques. She has authored multiple books including 'Interactive multimedia in mathematics teaching' (2015) and collections of solved mathematics problems for entrance exams. Her recent publication record through 2025 demonstrates active engagement in interdisciplinary research, particularly at the intersection of educational technology, artificial intelligence, and practical applications in fields ranging from software engineering to medical diagnostics. Her work shows a clear trajectory from foundational educational technology research toward more sophisticated AI-enhanced learning systems. Professor Milovanović has made significant contributions to semantic web applications in education, particularly through Moodle LMS enhancements, and has explored SCADA applications in industrial contexts. Her collaborative research spans multiple countries and institutions, reflecting an international scholarly network. She has extensive experience developing computer tools for engineering education and has published on diverse topics including petroleum industry processes, environmental management, and financial mathematics. Her work demonstrates consistent application of computational approaches to solve domain-specific problems across multiple disciplines.
Claire Le Goues is an Associate Professor in the School of Computer Science at Carnegie Mellon University , affiliated with the Software and Societal Systems Department (formerly Institute for Software Research). She holds a Ph.D. and M.S. in Computer Science from the University of Virginia and a B.A. in Computer Science from Harvard College. Her research focuses on software engineering with emphasis on program analysis , transformation , and search-based repair . She leads the squaresLab group and co-directs the REUSE@CMU summer program. Her work spans automated program improvement (stochastic/formal approaches), software assurance, quality metrics, and systems from open source to robotics. Recent scientific awards include the ACM FSE 2025 Test of Time Award Honorable Mention and the Presidential Early Career Award for Scientists and Engineers (PECASE) . She mentors students in software engineering and actively collaborates on projects like SearchRepair and GenProg , supporting empirical benchmarks such as ManyBugs and IntroClass . She teaches software engineering and program analysis at undergraduate, master’s, and doctoral levels, addressing challenges in scaling modern systems. Her lab focuses on software repair , code transformation , and AI-driven testing .
Dr. Jie Li is a dual-career academic and creative professional with a PhD in Industrial Design Engineering from Delft University of Technology. As an HCI/UX researcher in industry and Adjunct Professor at multiple institutions, she bridges academia and practice through work on Extended Reality (XR) , Human-AI interactions , and user experience evaluation . Her ACM Interactions column 'Bits to Bites' explores interdisciplinary research methodologies. Education: MSc in Industrial Design Engineering, Delft University of Technology PhD in Industrial Design Engineering, Delft University of Technology (2019) Her research spans social VR platforms , AI-augmented cognition , and privacy-preserving emotion detection , with recent publications analyzing LLM-assisted game design , harassment detection in VR , and XR's impact on remote collaboration . She has received Best Demo Awards (2020, 2022) and the ACM Best Paper Award (2018). Notable trends in her work include emerging immersive technologies (XR, 6DoF displays), human-AI collaboration frameworks , and cross-domain applications from medical VR clinics to cultural heritage experiences . Her advocacy for synthetic UX research and asynchronous co-creation tools reflects industry-academia hybrid innovation. Scientific Awards: Best Demo Award (2020, ACM TVX/IMX 2020) Best Demo Award (2022, ACM Multimedia) ACM Best Paper Award (2018, ACM TVX) While maintaining active roles in CHI conference committees and guest lecturing , Jie also operates a Delft-based creative cake design business , demonstrating her commitment to interdisciplinary exploration and 'slash career' balance between technical research and artistic practice.
Michael Oberst is an Assistant Professor of Computer Science at Johns Hopkins University's Whiting School of Engineering, affiliated with the Malone Center for Engineering in Healthcare and the Data Science and AI Institute. His research focuses on developing reliable machine learning systems for healthcare decision-making, emphasizing causal inference and robust performance across diverse clinical settings. Key research themes include: Ensuring ML system reliability comparable to FDA-approved medical tools Causal reasoning in observational healthcare data Robustness to dataset shifts across hospitals Algorithmic fairness under unobserved confounding Medical adaptation of large language models Recent publications (2025-2024) demonstrate trends in prediction-powered inference, clinical validation frameworks, and robustness evaluation methods. His work appears in top ML venues (NeurIPS, ICML, UAI, EMNLP) and translational medicine journals. Michael holds a BS in Statistics from Harvard University and a PhD in Computer Science from MIT, with postdoctoral training at Carnegie Mellon University's Machine Learning Department. His group actively seeks PhD students and postdocs for developing trustworthy AI solutions in healthcare.
Alastair F. Donaldson is a Professor and Director of Research in the Department of Computing at Imperial College London, where he leads the FastPL research group. His academic career spans over a decade at Imperial, progressing from Lecturer (2011-2014) to Senior Lecturer (2014-2017), Reader (2017-2020), and Professor (2020-present). He has also held significant industry positions, including Founder and Director of GraphicsFuzz Ltd. (acquired by Google in 2018), Senior Software Engineer at Google (2018-2021), and Visiting Researcher at both Google and Microsoft Research Redmond. Donaldson earned his PhD from the University of Glasgow under Alice Miller, following a BSc (hons, First Class) in Computing Science and Mathematics. His postdoctoral work included an EPSRC Postdoctoral Research Fellowship at the University of Oxford and a Research Fellowship at Wolfson College Oxford. His research focuses on formal analysis, software testing and programming languages techniques for improving software reliability, with special emphasis on high-performance systems. Donaldson's work bridges theoretical foundations with practical applications, particularly in compiler testing, GPU programming verification, and metamorphic testing. His research has significantly influenced both academia and industry, as evidenced by the acquisition of his startup GraphicsFuzz by Google. Analysis of his recent publications reveals a strong focus on fuzz testing techniques applied across diverse domains including compilers, GPUs, cryptographic protocols, and large language models. His work consistently combines formal methods with practical testing approaches, addressing challenges in compiler correctness, memory models, and API verification across multiple platforms. 2017 BCS Roger Needham Award EPSRC Early Career Fellowship Best Paper Award, EuroSys 2024 Best Paper Award, MET 2021 Best Paper Award, IWOCL 2019 Best Paper Award, IISWC 2019 Best Paper Award, ICST 2016 ACM SIGSOFT Distinguished Paper Award, ISSTA 2023 ACM SIGSOFT Distinguished Paper Award, FSE 2017 ACM SIGPLAN Most Influential OOPSLA Paper Award, 2022 (for GPUVerify) As Director of Research in the Department of Computing, Donaldson oversees research strategy and development. His FastPL research group investigates novel techniques for programming, testing and reasoning about high performance systems. He has served on numerous program committees and held leadership roles including PLDI Steering Committee Chair (2022-2025) and PACM-PL Advisory Board member. His industry engagement includes testifying as an Expert Witness in the IBM UK Ltd v LzLabs GmbH & Ors case. The FastPL research group, which Donaldson leads, focuses on formal analysis, software testing and programming languages. The group has made significant contributions to compiler testing, GPU verification, and metamorphic testing techniques, with practical impact demonstrated by the acquisition of GraphicsFuzz. Current research directions include fuzzing for zero-knowledge proof circuits, randomized testing of decompilers, and systematic testing of large language models for code generation.
GÜÇLÜ ŞEKERCİOĞLU is an Associate Professor at Akdeniz University, Faculty of Education, Department of Educational Sciences. Currently serving as Director of the Center for Measurement, Evaluation, Certification Research and Application and Director of the Institute of Educational Sciences since 2022, they also hold the position of Vice Dean of the Faculty of Education (2022-2025). Their academic career spans over 15 years at Akdeniz University, progressing from Research Assistant (2010-2011) to Assistant Professor (2011-2019) and currently Associate Professor (2019-present). Academic background includes: Doctorate (2003-2009): Ankara University, Institute of Educational Sciences, Measurement and Evaluation Postgraduate (1998-2001): Ankara University, Institute of Educational Sciences, Measurement and Evaluation Undergraduate (1993-1997): Ankara University, Faculty of Educational Sciences, Psychological Services in Education Dr. Şekercioğlu's research focuses on the intersection of psychometrics, educational measurement, and statistical analysis in educational contexts. Their work demonstrates deep expertise in measurement invariance, differential item functioning, scale development and validation, and advanced statistical methods in education. They have developed specialized knowledge in analyzing international assessments like PISA and TIMSS, with particular attention to cross-cultural measurement equivalence. Their research methodology combines rigorous quantitative approaches with practical applications in educational settings, contributing significantly to the understanding of how psychological constructs can be reliably measured across diverse populations. Analysis of their publication record reveals a strong emphasis on measurement theory and its practical applications in educational contexts. Their work consistently addresses critical issues in cross-cultural assessment, scale adaptation, and the psychometric properties of educational instruments. A notable trend is the application of advanced statistical techniques to solve practical measurement problems in education, particularly focusing on how tests function across different language groups and cultural contexts. They have made significant contributions to understanding measurement invariance in international assessments like PISA, with implications for fair and valid cross-national comparisons. Dr. Şekercioğlu has supervised 8 theses at various levels and serves on numerous doctoral and master's thesis committees. Their research is supported by multiple grants, including several TÜBİTAK projects addressing critical educational issues such as child sexual abuse prevention, nursing education assessment, and language acquisition among immigrant children. They actively contribute to the academic community through peer review activities for journals like Educational Sciences: Theory & Practice and Hacettepe Education Faculty Journal.
Dr John Pill is a Lecturer in Language Testing at Lancaster University’s School of Social Sciences, holding the Trinity College London Lectureship in Language Testing . His research focuses on testing language for specific purposes, speaking assessment, language assessment literacy, and test impact across academic and healthcare domains. He actively collaborates with Trinity College London on research and development projects. Current Teaching (2025-26): MA in Language Testing by distance (LING504, LING506) PhD Supervision: All aspects of language testing, particularly test constructs and stakeholder perspectives Research Groups: Language Testing Research Group (LTRG) Research Interests span technical and social dimensions of language testing. Technically, he examines test development for domain-specific language use (e.g., medical contexts) and construct validity. Socially, he investigates test impact through stakeholder views, including professional registration bodies and multilingual faculty at the American University of Beirut. His work bridges discourse community analysis with practical testing frameworks. Recent publications (2025) address societal implications of language testing, comparative judgment methodologies in EFL writing assessment, and fairness in testing legacy. Earlier work (2016-2024) includes healthcare communication standards, academic-to-workplace writing transitions, and Trinity College London Graded Examinations alignment with China’s English standards.
WANG, Yushi is currently a Junior Researcher (Assistant Professor) at the Future Robotics Organization of Waseda University , with prior roles in the Faculty of Science and Engineering (2018–2021). His research focuses on robotics, tactile sensing, and actuator design. Education: Ph.D. in Science and Engineering from Waseda University (2015–2018). Research interests include Humanoid Robotics , Force/Torque Control , and Soft Robotics , particularly for applications in tactile sensing and safety mechanisms . Recent work explores Permanent Magnet Elastomer (PME)-based sensors and Series Clutch Actuators , enabling safer human-robot interactions and adaptive compliance. His publications span conferences like IROS , AIM , and SII , addressing challenges in 3-axis force measurement , collision safety , and material testing . He has taught courses such as 理工学基礎実験 and メカニカルエンジニアリングラボA (2018–2021). Professional memberships include IEEE , IEEE WIE , and the Japan Robotics Society . His work also involves patents for haptic interfaces and torque limiters , with grants like the 若手研究 (Young Researcher Grant) (2021–2023).
