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Sarah NadiView profile

Associate Professor

• Software Engineering
• Empirical Software Engineering
• Mining Software Repositories
• +7 more

Sarah Nadi is an Associate Professor of Computer Science at New York University Abu Dhabi (NYUAD), where she also serves as the Associate Program Head for Undergraduate Studies in Computer Science. She holds an adjunct position at the University of Alberta and co-directs the SANAD lab, which focuses on building tools to enhance software developer productivity through empirical and data-driven methods. Education: BSc, The American University in Cairo MMath, University of Waterloo PhD, University of Waterloo Her research lies at the intersection of software engineering and empirical methods, with a strong emphasis on mining software repositories (MSR) to understand and improve software development practices. Key research themes include API misuse detection, library selection and migration, code recommender systems, and the role of large language models in software engineering. She investigates how developers use APIs, especially in data-centric Python libraries and cryptography, and builds tools like MuDetect and CogniCrypt to prevent misuse. Her work on library migration includes creating benchmarks such as PyMigBench and analyzing real-world migration patterns in Python. Her recent publications show a growing focus on AI in software engineering, with empirical evaluations of tools like GitHub Copilot and LLMs for test generation. She has also contributed significantly to understanding software variability in systems like Linux and Android, and to software integration challenges such as merge conflicts and refactoring-aware merging. Her work often combines static analysis, data mining, and user studies to build practical, evidence-based tools. Notable recognitions include a Best Paper Award at CASCON 2009 for her work on CMDB-based root cause analysis. She has published extensively in top-tier venues including FSE, ICSE, TSE, ASE, and MSR. Her research is supported by active grants, and she is currently recruiting fully funded PhD students for Fall 2025. Best Paper Award, CASCON 2009 Sarah Nadi mentors students through capstone projects and research supervision. She teaches core courses such as Software Engineering (CS-UH 2012), Special Topics in Computer Science (CS-UH 3260), and the Capstone Project in Computer Science (CS-UH 4001). She leads the SANAD lab, which develops tools for API analysis, library comparison, and developer support using empirical and machine learning techniques. Her lab’s projects include LibComp, MUBench, MuDetect, and CogniCrypt, and she emphasizes open science through publicly available datasets and tools.

Jin GeView profile

Assistant Professor

• Liver Disease
• Liver Transplantation
• Cirrhosis
• +6 more

Jin Ge, MD, MBA is an Assistant Professor in the Department of Medicine at the University of California, San Francisco (UCSF) School of Medicine. He serves as a gastroenterologist and transplant hepatologist in the Liver Transplant Program, with affiliations at the UCSF Bakar Computational Health Sciences Institute and the UCSF-UC Berkeley Joint Program in Computational Precision Health. After earning a bachelor's degree in engineering at Princeton University (2008), he obtained combined MD and MBA degrees from the University of Pennsylvania (2015) before completing residency in internal medicine and fellowships in gastroenterology and transplant hepatology at UCSF (2018-2022). His work focuses on applying clinical informatics, artificial intelligence, and data science to enhance outcomes for patients with advanced liver disease through innovative technologies and decision support systems. Liver disease management Transplant hepatology Clinical decision support systems Large language models Health equity in organ allocation Electronic health record analytics His research output spans 15+ peer-reviewed publications covering topics from AI-guided liver transplantation allocation to NLP-driven cirrhosis prognosis , with multiple first-author studies in Hepatology and Liver Transplantation . Recent work includes developing a liver disease-specific chat interface using retrieval-augmented generation and implementing clinical decision support systems through human-centered design. APASL 2023 Best Oral Presentation AASLD 2021 Anna S. Lok Award UCSF 2019 Clinical Fellow Award University of Pennsylvania 2015 Palmer Scholar 20+ professional recognitions across academia As a practicing clinical researcher, he directs the Division of Gastroenterology's Clinical AI initiatives and maintains active involvement with the National COVID Cohort Collaborative (N3C) Consortium. His clinical practice addresses complex hepatology cases requiring multidisciplinary management strategies.

Qiang GuoView profile

Senior Lecturer

• Formal Methods
• Software Engineering
• Distributed Systems
• +5 more

Dr Qiang Guo is a Senior Lecturer in Computer Science at Teesside University, affiliated with the Department of Computing & Games within the School of Computing, Engineering and Digital Technologies. He holds a PhD in Computer Science from Brunel University London and has previously worked as a Research Associate/Fellow at the University of Sheffield, Robert Gordon University, and the University of Cambridge, as well as a Senior Software Engineer at MasterCard. PhD, Computer Science, Brunel University London (2023) Master, Computational Intelligence, University of Plymouth Bachelor, Industrial and Electrical Automation, Xi'an University of Architecture and Technology PGCert in Higher Education, Teesside University His research focuses on Formal Methods in Software Engineering , particularly model-checking and testing of Distributed and Concurrent Systems . He integrates Artificial Intelligence techniques into Software Conformance Testing and explores Algorithmic Skeleton-based Parallel Computing for software engineering applications. His work addresses verification challenges in Erlang/OTP systems, extended finite state machines, and timed components. Dr Guo’s publications highlight a trajectory in Formal Verification , Automated Testing , and Optimization Techniques . Key themes include the application of Genetic Algorithms to finite state machine testing, Process Algebra (μCRL/mCRL) for telecommunications systems, and Testability Transformations to improve structural coverage. Trends reflect interdisciplinary bridges between Software Engineering and AI . As a Principal Investigator (PI) for the Innovate UK-funded project MC-IoT: Model Checking IoT Systems (2024-25, £31,907), he leads research in IoT system verification. He also contributed as Co-Investigator (Co-I) to the Parrot AI Enterprise Project (2023, £40k). Dr Guo is affiliated with the Centre for Digital Innovation , where he contributes to advancing software engineering methodologies. His work involves collaboration with academic and industrial partners to address real-world challenges in system verification and distributed computing.

Nathan W. CasseeView profile

Researcher

• Software Engineering
• Sentiment Analysis
• Technical Debt
• +3 more

Nathan W. Cassee is a University Researcher at Eindhoven University of Technology (TU/e) in the Department of Mathematics and Computer Science. His work focuses on software engineering, sentiment analysis, and technical debt prioritization, combining empirical studies with natural language processing. Education: PhD Thesis (2024) on "Sentiment in Software Engineering" Research Themes: Technical Debt, Sentiment Polarity, Software Maintainability, and Empirical Studies Recent Publications: Explore sentiment's impact on code quality, self-admitted technical debt, and security questions using empirical methods Teaching: Involved in the course "Automata, Language Theory and Complexity" (2018-2025)

• Natural Language Processing
• Legal Informatics
• Greek Language Processing
• +4 more

Ion Androutsopoulos is a Professor in the Department of Informatics at Athens University of Economics and Business (AUEB), where he leads the AUEB NLP Group. With over 180 publications spanning three decades, he is a prominent figure in Natural Language Processing research, particularly known for his work bridging NLP with legal informatics, Greek language processing, and biomedical applications. His research interests focus on several interconnected areas of Natural Language Processing: Legal Informatics : developing NLP systems for legal document analysis, legal judgment prediction, and legal reasoning, with recent work including GreekBarBench and Archimedes-AUEB systems Greek Language Technology : creating specialized tools for Modern Greek processing, including GR-NLP-TOOLKIT and Greeklish transliteration systems Biomedical Text Mining : working on diagnostic captioning and medical image analysis through participation in ImageCLEFmedical challenges Financial NLP : developing systems like EDGAR-CRAWLER for financial document analysis and XBRL tagging His recent publications (2023-2025) show an increasing focus on Greek-specific NLP resources and practical applications of large language models in legal reasoning. His work often combines theoretical NLP advances with practical implementations, particularly through his leadership of the AUEB NLP Group which regularly participates in international evaluation campaigns. Professor Androutsopoulos has supervised numerous PhD students who have become active researchers in their own right, including John Pavlopoulos, Prodromos Malakasiotis, and Ilias Chalkidis. His collaborative network spans multiple institutions and disciplines, reflecting the interdisciplinary nature of modern NLP research.

Pawel MordakaView profile

Researcher

• Plant Metabolism
• Synthetic Biology
• Chloroplast Engineering
• +3 more

Dr. Pawel Mordaka is a Postdoctoral Research Associate in the Department of Plant Sciences at the University of Cambridge. His research focuses on synthetic biology, genetic engineering, and plant metabolism, with a particular emphasis on chloroplast engineering and metabolic pathway optimization in model organisms like Chlamydomonas reinhardtii and Clostridium . Department: Plant Sciences Academic Rank: Researcher Email: pmm63@cam.ac.uk His work spans synthetic biology tool development (e.g., CpPosNeg selection systems, Start-Stop DNA assembly), transgene regulation in chloroplasts, and bioengineering of microbial-plant interactions. Recent publications highlight advancements in genetic code compression, promoter engineering, and heterologous pathway reconstruction for biofuel and biocatalysis applications. Dr. Mordaka's research intersects molecular plant biology and industrial biotechnology, leveraging algal chloroplasts as testbeds for radical metabolic rewiring. His methodologies include VIGS protocols, PVX expression vectors, and terminator analysis to refine transgene expression. Contact: pmm63@cam.ac.uk

Asztalos MárkView profile

Associate Professor

• Model-Driven Engineering
• Software Engineering
• Graph Rewriting Systems
• +3 more

Márk Asztalos is an Associate Professor at the Budapest University of Technology and Economics , affiliated with the Faculty of Electrical Engineering and Informatics and the Department of Automation and Applied Informatics . He leads research in the Visual Modeling Group, focusing on model-driven engineering, graph rewriting systems, and domain-specific languages. Research Interests: Model transformation verification, text-based modeling, graph pattern matching, and cloud/mobile system modeling. Contact: E-mail: Asztalos.Mark@aut.bme.hu , Office: Q.B226, Department of Automation and Applied Informatics, BME. His publications emphasize model transformation verification (2010-2015), graph rewriting techniques for pattern matching (2015-2017), and domain-specific language design (2014). Recent work (2020) analyzes model integration challenges in model-driven methodologies. Contact details: Address: Budapest 1117, Magyar tudósok krt. 2, Hungary Phone: +36 (1) 463-3702

Kamila MisiejukView profile

Researcher

• Learning Analytics
• Educational Data Analysis
• Computer-Supported Collaborative Learning
• +7 more

Kamila Misiejuk is a Postdoctoral Researcher at the Center of Advanced Technology for Assisted Learning and Predictive Analytics (CATALPA) within FernUniversität Hagen since October 2024. She previously served as a Senior Researcher and PhD Fellow at the Centre for the Science of Learning and Technology (SLATE) , University of Bergen (2017-2024), where she developed expertise in learning analytics and network modeling. Her research focuses on Interdisciplinary applications of Epistemic Network Analysis (ENA) and Transition Network Analysis (TNA) Designing data-driven educational tools for assessment and feedback Evaluating generative AI in academic writing and peer assessment Studying ethical implications of learning analytics dashboards Key trends in her 15 most recent publications (2024-2025) include Systematic reviews of generative AI and dashboard effectiveness Development of network analysis frameworks for collaborative learning Investigations into human-AI interaction dynamics and idiographic analytics Methodological tutorials in educational data visualization and R programming She contributes to professional networks as: Board Member , International Society for Quantitative Ethnography (ISQET, since 2021) Committee Chair , ISQET Resources Committee (2021-2023) Member , Society for Learning Analytics Research (SoLAR, since 2018)

• Artificial Intelligence
• Cybersecurity
• Security of AI
• +4 more

Isabel Cecília Correia da Silva Praça Gomes Pereira is a Coordinator Professor at the School of Engineering of the Polytechnic Institute of Porto (ISEP), where she serves as Director of the Master on Informatics Engineering and Advisor of ISEP Presidency for R&D. She is also a Senior Researcher at GECAD (Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development), a research unit ranked as Excellent by the Portuguese Science & Technology Foundation. Dr. Praça holds a PhD in Electrical and Computer Engineering - Industrial Informatics from the University of Trás-os-Montes e Alto Douro, and completed her post-doctoral studies in Artificial Intelligence - Multi-Agent Systems with support from the Portuguese National Science Foundation (SFRH/BPD/30111/2006). Her academic journey includes progressive appointments from Assistant to Adjunct Professor and finally to Coordinator Professor at ISEP. Her research focuses on Artificial Intelligence applied to cybersecurity and security of AI , with significant contributions to applying AI in various domains including cybersecurity (projects like AIDA, SAFE, VESTA), industry (SeCoIIA, Cyberfactory), and energy systems (SPET, MAS-Society). She leads a research team at GECAD working on these topics and has strong connections with over 100 companies and technology transfer centers across more than 30 countries. Analysis of her recent publications reveals a strong trend toward AI security, with particular emphasis on adversarial attacks against AI systems, secure implementation of AI in critical domains, and privacy-preserving AI techniques. Her work spans both theoretical foundations and practical applications across multiple sectors including energy, healthcare, and network security, demonstrating her ability to bridge academic research with real-world applications. Dr. Praça has been recognized as an expert by several prestigious organizations including the European Union Agency for Cybersecurity (ENISA), where she contributes to working groups on Security of AI and the European Cybersecurity Skills Framework. She also serves as an expert for NATO's Defence Innovation Accelerator for the North Atlantic (DIANA) and represents ISEP in the European Cybersecurity Organization (ECSO). As an educator, she teaches Machine Learning and Multi-agent Systems in the MSc in AI program, and Security of Communications and Infrastructures in the Cybersecurity branch of the MSc in Informatics. She currently supervises 4 PhD students in AI security and privacy and has guided 45 Master's theses, demonstrating her commitment to developing the next generation of researchers and practitioners in her field. Her research is supported by numerous international and national grants, including multiple Horizon Europe and Horizon 2020 projects where she serves as Principal Investigator or Co-PI. She has participated in over 35 R&D projects totaling significant funding, with a strong track record of translating research into practical applications through industry partnerships.

Tolga AyavView profile

Professor

• Formal Methods for Software Testing
• Hardware Component and On-Chip System Tests
• Real-Time and Fault-Tolerant Embedded Systems
• +5 more

Professor Tolga Ayav is affiliated with the Department of Computer Engineering at Izmir Institute of Technology , where he has served as faculty since 2006. He received his BSc in Electrical and Electronics Engineering (1995) from Dokuz Eylül University, MSc in Computer Engineering (1999) from Izmir Institute of Technology, and PhD in Computer Engineering (2004) from Ege University. He was a researcher at INRIA Rhone-Alpes in 2005. Education BSc: Electrical and Electronics Engineering, Dokuz Eylül University (1995) MSc: Computer Engineering, Izmir Institute of Technology (1999) PhD: Computer Engineering, Ege University (2004) Research Interests include formal methods for software testing, hardware component and on-chip system tests, real-time and fault-tolerant embedded systems, blockchain applications, and machine learning. His work focuses on: Formal verification techniques Real-time system optimization Hardware-software co-design Blockchain-based security solutions Mathematical modeling in testing Recent Publications span topics like: Deep learning for livestock monitoring Blockchain in IoT security Fourier expansion in fault analysis Optimized data replication strategies Finite state machine testing Scientific Awards include: Best Paper Award at IEEE CBDCom 2016 for cloud data replication research Teaching includes courses such as: CENG 312: Computer Networks CENG 523: Advanced Topics in Real-Time Systems CENG 215: Circuits and Electronics Projects he has led include: TÜBİTAK TEYDEB: Spectrally Efficient Small Cell Base Station Design BAP: Dedicated Server for Physical Network Applications Lab Leadership : He leads the DCS Research Group at Izmir Institute of Technology, focusing on distributed computing and real-time systems.

• Geometallurgy
• Mineral Processing
• Geochemistry
• +3 more

Dr. Julie Hunt is a geologist and geometallurgist at the Centre for Ore Deposit and Earth Sciences (CODES) within the School of Natural Sciences at the University of Tasmania. As of February 1, 2025, she transitioned to adjunct status while maintaining active research collaborations. Her work bridges geology and mineral processing with a focus on geometallurgy, critical metals, and sustainable resource extraction. Her research interests center on geometallurgy, mineral processing/beneficiation, geochemistry, and critical metals. Dr. Hunt specializes in understanding the geological controls on mineral processing behavior, with particular expertise in IOCG (Iron Oxide Copper Gold) deposits, tungsten systems, and Tasmanian mineral resources. Her work integrates geological, mineralogical, and geochemical data to optimize resource extraction while minimizing environmental impacts. Dr. Hunt's recent publications reveal a strong focus on critical metals in Tasmanian deposits, geometallurgical characterization of various ore systems, and environmentally sustainable production methods. Her work demonstrates how geological understanding can directly inform processing decisions, particularly for complex deposits where traditional approaches may be inefficient. She has secured significant funding for research projects related to geometallurgy and critical metals, demonstrating the practical applications of her work for the mining industry. Dr. Hunt actively supervises doctoral and masters students, with completed supervision including Karla Alejandra Morales Leiva, Nathaly Estefania Guerrero Ramirez, Cassady Lee Harraden, Paula Andrea Montoya Lopera, Angela Jane Escolme, Stacey Leichliter, and Natalee Kate Bonnici. Her current doctoral supervision includes projects on Nautanen IOCG deposit characterisation, Critical Metal Potential in Savage River Magnetite Deposit, and Pathways to Production for Critical Metals at the Kara W deposit.

• eLearning
• Human-Computer Interaction (HCI)
• Software Engineering
• +5 more

Miguel A. Redondo is a Full Professor at the Department of Tecnologías y Sistemas de Información, University of Castilla-La Mancha (Spain), since 2019. He holds a PhD in Computer Science and Engineering from the same university (awarded in 2002). Research Focus : eLearning, Human-Computer Interaction (HCI), Software Engineering, and eye-tracking assessment. Affiliation : Universidad de Castilla-La Mancha, Campus de Ciudad Real, Spain. His academic career centers on designing model-driven and AI-powered tools to enhance programming education. Key contributions include the GreedEx Tab system for algorithm learning and the COLLECE 2.0 collaborative programming environment. He has pioneered the integration of genetic algorithms for optimizing group learning dynamics and developed immersive systems for algorithmic thinking through augmented/virtual reality. His work spans educational technology , collaborative software , and adaptive learning frameworks . Recent publications emphasize m-learning usability (2024), machine learning in healthcare (2023), and personality-driven group organization (2021). He frequently collaborates with researchers like Ana I. Molina and Yoel Arroyo on topics such as educational data mining and visual modeling languages . While no formal student list or awards are provided in the source text, his leadership in model-driven development for CSCL tools has been cited in numerous works across IEEE, ACM, and Springer venues.

Wei WangView profile

Professor

• Big Data Analytics
• Machine Learning
• Data Mining
• +5 more

Wei Wang is the Leonard Kleinrock Chair Professor in Computer Science and Computational Medicine at University of California, Los Angeles (UCLA) , and the founding Director of the Scalable Analytics Institute (ScAI) . She is also a core faculty member of the UCLA Jonsson Comprehensive Cancer Center, Institute for Quantitative and Computational Biology, and Bioinformatics Interdepartmental Graduate Program. Education: Ph.D. in Computer Science, UCLA (1999) M.S. in Systems Science and Industrial Engineering, SUNY at Binghamton (1995) Research Interests: Dr. Wang’s research spans big data analytics , machine learning , data mining , natural language processing , bioinformatics , computational biology , and AI for science . Her work focuses on designing scalable algorithms for complex pattern discovery in large-scale, heterogeneous data, with applications in medicine, genomics, and scientific discovery. Scientific Awards & Honors: ACM Fellow (2020) IEEE Fellow (2022) NSF CAREER Award (2005) Microsoft Research New Faculty Fellow (2005) Phillip and Ruth Hettleman Prize (2007) Okawa Foundation Research Award (2013) ACM SIGKDD Service Award (2016) IEEE ICDM Outstanding Service Award (2012) IBM Invention Achievement Awards (2000, 2001) Multiple Best Paper Awards at SIGKDD, ICDE, and ACM BCB Advising & Research Team: Dr. Wang currently advises 15 PhD students and 1 postdoctoral fellow . Her lab focuses on AI-driven scientific discovery, scalable analytics, and interdisciplinary applications in biomedicine and materials science. Labs & Institutes: Director, Scalable Analytics Institute (ScAI) , UCLA Member, UCLA Jonsson Comprehensive Cancer Center Member, Institute for Quantitative and Computational Biology Member, Bioinformatics Interdepartmental Graduate Program

Yanqi SuView profile

Research Fellow

• Software Engineering
• Artificial Intelligence
• Automated Testing
• +3 more

Yanqi Su is a postdoctoral researcher at the TUM School of Computation, Information and Technology (CIT) at the Technical University of Munich, affiliated with the Chair of Software Engineering & AI . She will soon complete her Doctorate at The Australian National University under Prof. Zhenchang Xing, following academic training at Nanjing University of Aeronautics and Astronautics. Education : PhD (in progress, ANU), Master’s/Bachelor’s in Computer Science and Technology (Nanjing University of Aeronautics and Astronautics) Research Focus : Her work centers on enhancing exploratory testing through Knowledge Graphs and Large Language Models (LLMs) , with notable contributions to test scenario generation , automated exploratory testing , and bug-component triaging . She has discovered ~300 bugs in applications like Firefox (desktop/Android), many resolved in collaboration with developers. Publication Trends : Her research spans LLM applications for GUI/code analysis , automated security testing , and AI ethics . Key subfields include model context protocols , vulnerability detection , code optimization , and human-AI collaboration in software development. Scientific Recognition : Recipient of the Distinguished Paper Award at ASE 2021 , her work appears in top-tier venues (ICSE, ASE, TSE, TOSEM).

• Cyber-Physical Systems
• Interval Analysis
• Robotics
• +5 more

Alexandre Chapoutot is a Lecturer and researcher at ENSTA Paris within the Computer Science and Systems Engineering Unit (U2IS). He leads the Semantics of Hybrid Systems (SSH) research group and serves as Head of the 2nd year specialization of the engineering cycle in Computer Science. His academic work bridges theoretical computer science with practical engineering applications, focusing on the rigorous analysis and verification of complex systems. Chapoutot's research interests span the verification of cyber-physical systems, with a strong emphasis on interval analysis and mobile robotics. He has made significant contributions to the static analysis of programs through abstract interpretation, particularly for improving the accuracy of floating-point arithmetic calculations. His work bridges theoretical computer science with practical engineering applications, developing methods that ensure the reliability and safety of autonomous systems. The research has direct applications in robotics, control systems, and safety-critical embedded software where numerical precision and system correctness are paramount. His recent publications reveal a strong trend toward the integration of formal methods with practical robotics applications. The work spans theoretical foundations in hybrid systems verification, practical implementations for robotics navigation and control, and innovative approaches to combining different verification techniques. A significant portion of his recent work focuses on Signal Temporal Logic applications, trajectory optimization, and the development of verified algorithms for autonomous systems. The publications demonstrate a consistent progression from theoretical foundations to practical implementations with real-world robotics applications. Chapoutot actively supervises multiple PhD students working on cutting-edge research topics at the intersection of formal methods and robotics. His research group collaborates extensively with other institutions and researchers, as evidenced by the numerous co-authored publications across various domains. He contributes to the development of the DynIbex software library, which provides validated solutions for ordinary differential equations using Runge-Kutta methods. His laboratory work centers around the Semantics of Hybrid Systems (SSH) group, which develops theoretical frameworks and practical tools for the analysis and verification of cyber-physical systems. The group's research spans formal methods, numerical analysis, and robotics, with a strong emphasis on developing mathematically rigorous approaches that can be applied to real-world engineering problems.