جستجوی استاد راهنمای دانشگاهی

Jin Geمشاهده پروفایل

استادیار

• Liver Disease
• Liver Transplantation
• Cirrhosis
• +۶ مورد دیگر

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.

Jessica B. Rubinمشاهده پروفایل

استادیار

• Hepatology
• Gastroenterology
• Liver Transplantation
• +۴ مورد دیگر

Jessica B. Rubin, MD, MPH, is Assistant Professor of Medicine at the University of California, San Francisco (UCSF) School of Medicine, where she holds dual clinical appointments in the Divisions of Gastroenterology and Transplant Hepatology. She directs an NIH-funded research program at the intersection of hepatology, pain management, and health-services science, with the overarching goal of improving quality of life and outcomes for patients living with liver disease. Education & Training: B.S., Biomedical Engineering, Yale University (2009) M.D., Weill Cornell Medical College (2014) M.P.H., Health Care Management, Icahn School of Medicine at Mount Sinai (2012) Residency: Internal Medicine, UCSF (2017) Fellowships: Gastroenterology (2020) & Transplant Hepatology (2021), UCSF Research Focus: Dr. Rubin’s scholarly work centers on three synergistic themes: (1) developing safe, effective analgesic strategies for patients with chronic liver disease and cirrhosis; (2) identifying and mitigating gender-based disparities across the liver-transplant continuum; and (3) leveraging large administrative and clinical datasets to inform evidence-based pain and opioid-stewardship policies. Her portfolio encompasses retrospective cohort analyses, prospective quality-improvement initiatives, and predictive-modeling projects that integrate electronic health-record data with patient-reported outcomes. Across 50+ peer-reviewed publications since 2007—including 10 in 2024 alone—her work consistently explores how systemic factors (race, sex, insurance status, social determinants) intersect with clinical variables (cirrhosis severity, transplant candidacy, pain phenotypes) to influence morbidity, mortality, and healthcare utilization. Recent influential articles illuminate racial differences in primary sclerosing cholangitis, cannabis-related effects on transplant outcomes, and the complex interplay among pain, mental health, and quality-of-life metrics in pre- and post-transplant populations. Honors & Awards: American Association for the Study of Liver Diseases (AASLD) Anna S. Lok Advanced/Transplant Hepatology Award (2020) American Society of Transplantation Women’s Health Community of Practice Travel Award (2019) UCSF Department of Medicine Outstanding Resident Research Award (2017) European Association for the Study of the Liver Young Investigator Award (2016) Leonard P. Tow Humanism in Medicine Award & Gold Humanism Honor Society (2014) Alpha Omega Alpha (2013) Multiple Weill Cornell and Yale academic prizes and honors Professional Memberships & Service: Dr. Rubin is an active member of the American Association for the Study of Liver Diseases, American Gastroenterological Association, American College of Gastroenterology, American Society of Transplantation, and Northern California Society for Clinical Gastroenterology. She serves as ad-hoc reviewer for leading journals and participates in national consensus panels on pain management in liver disease.

Adway Girishمشاهده پروفایل

دانشگاهی

• Information Theory
• Coding Theory
• Machine Learning
• +۹ مورد دیگر

Adway Girish is a third-year Ph.D. candidate and Doctoral Assistant at the School of Computer and Communication Sciences (IC), École Polytechnique Fédérale de Lausanne (EPFL), Switzerland. He is affiliated with the Information Processing Group (IPG) and the Laboratory for the Theory of Information (LTHI), working under the supervision of Prof. Emre Telatar. He has also collaborated with leading researchers including Michael Gastpar, Hyeji Kim, and Shlomo Shamai. His educational background includes a B.Tech. in Electrical Engineering with honors and a minor in Mathematics from the Indian Institute of Technology Bombay (IITB), completed in 2022. Adway's research centers on information theory and its applications in communication, security, and machine learning. He is particularly interested in foundational aspects of information measures, entropy optimization, and the theoretical underpinnings of modern deep learning architectures such as transformers and large language models. His recent work explores rate-distortion frameworks for prompt compression, learning dynamics in transformers, and entropy-constrained communication channels. His research bridges theoretical rigor with practical implications in AI and communication systems. The trend in his recent publications shows a shift from classical signal processing and micro-Doppler analysis during his undergraduate years to advanced topics in information theory and machine learning during his Ph.D. His contributions are published in top-tier venues such as ISIT, NeurIPS, ICLR, and ICML workshops, indicating a strong trajectory in theoretical computer science and applied mathematics. Scientific Awards and Recognitions: ICLR 2025 Spotlight Paper (awarded to top 5% of accepted papers) Oral presentation at ICML 2024 Workshop on Theoretical Foundations of Foundation Models (selected as one of top 4 out of 58 submissions) Adway advises no students currently, as he is himself a doctoral candidate. However, he plays an active role in collaborative research projects involving multiple co-authors across institutions. He has not received specific mention of external grants in the provided text, but his position as a Doctoral Assistant at EPFL suggests institutional funding. His collaborations with renowned researchers suggest involvement in larger research initiatives and potential access to grant-supported projects. He is a core member of the Information Processing Group (IPG) at EPFL, a research lab focused on theoretical and applied aspects of information science, including coding, communication, learning, and data analysis. The group fosters interdisciplinary research and hosts regular seminars, candidacy reviews, and internal presentations, all of which Adway actively participates in.

Elizabeth Polgreenمشاهده پروفایل

استادیار

• Programming Languages
• Program Synthesis
• Formal Verification
• +۷ مورد دیگر

Elizabeth Polgreen is a Lecturer (~Assistant Professor) in the School of Informatics at the University of Edinburgh, where she conducts research in programming languages and formal methods, particularly program synthesis and verification. She is affiliated with the Laboratory for Foundations of Computer Science and contributes to the Programming Languages and Software Engineering research area. Her research focuses on formal program synthesis, leveraging techniques such as symbolic reasoning, genetic algorithms, and large language models to improve the scalability and automation of software verification. She explores applications in code modernization, tensor program lifting, and explainable AI. Her work bridges programming languages, AI, and formal methods to build trustworthy systems. Her recent publications (2020–2025) span top venues including PLDI, CGO, AAAI, CAV, OOPSLA, and NeurIPS, showing a strong trend in neuro-symbolic synthesis, grammar inference, and verification of AI and low-resource code. She has secured competitive funding, including a Royal Academy of Engineering fellowship and an Amazon Research Award. Scientific awards include: Royal Academy of Engineering Research Fellowship Amazon Research Award Best Paper Award at GPCE Distinguished Paper Award from CGO She advises multiple PhD students, including Yixuan Li, Alexander Brauckmann, and José Wesley de Souza Magalhães, often in collaboration with Professor Mike O'Boyle. Her research is supported by grants from the Royal Academy of Engineering and Amazon. She has delivered keynotes, tutorials, and a TEDx talk, contributing actively to outreach and education in formal methods. She leads a research group within the Laboratory for Foundations of Computer Science, focusing on trustworthy systems through synthesis and verification. Her team includes PhD students and collaborators working on cutting-edge topics in program analysis, AI for code, and compiler design.

• Applied Optimization
• Wireless Internet of Things
• Machine Learning
• +۶ مورد دیگر

Carlo Fischione is a Full Professor at KTH Royal Institute of Technology in the School of Electrical Engineering and Computer Science, Division of Network and Systems Engineering, Stockholm, Sweden. He is a Fellow of IEEE, KTH Digital Futures, and the Italian Academy DASP, and a Distinguished Lecturer of the IEEE Communications Society. He holds a PhD and Laurea (Summa cum Laude) in Engineering from the University of L’Aquila, Italy, and has held research positions at MIT, Harvard, and UC Berkeley. PhD in Electrical and Information Engineering, University of L’Aquila (2005) Laurea in Electronic Engineering, Summa cum Laude, University of L’Aquila (2001) His research focuses on applied optimization, wireless Internet of Things, and machine learning , with particular emphasis on federated learning, over-the-air computation, and spectrum sharing in 5G/6G networks. He leads a vibrant research group and has supervised numerous PhD and postdoctoral researchers, many of whom now hold faculty or senior research positions globally. His recent publications reflect a strong trend toward machine learning in distributed and resource-constrained networked environments , especially focusing on communication efficiency, privacy, and scalability. Key themes include federated learning over fading channels, over-the-air computation, and AI-aided wireless channel prediction. IEEE Fellow IEEE Distinguished Lecturer, Communications Society IEEE Communication Society S. O. Rice Award (2018) Best Paper Award, IEEE Transactions on Industrial Informatics (2007) Starting Grant, Swedish Research Council (2008) Prof. Fischione has advised numerous students who have gone on to prominent academic and industry roles. He has secured significant research funding from the Swedish Research Council, SSF, KAW Foundation, Vinnova, and EU Horizon programs, leading projects such as MALEN, SAICOM, TAIRCOMP, and WIDCOMP. He is also the founding General Chair of IEEE ICMLCN and a co-founder of ELK.Audio, demonstrating strong industry and innovation engagement. He leads multiple research initiatives and labs focused on networked machine learning, including groups working on federated learning, wireless AI, and edge intelligence. His team actively contributes to advancing the theoretical and practical foundations of machine learning over networks.

Foutse Khomhمشاهده پروفایل

استاد

• Software Engineering
• Software Maintenance and Evolution
• Trustworthy Artificial Intelligence
• +۴ مورد دیگر

Foutse Khomh is a Professor of Software Engineering at Polytechnique Montréal where he leads the SWAT Lab on software development, deployment, maintenance and evolution of AI intensive and cloud based software systems. He holds prestigious positions as a Canada Research Chair Tier 1 on Trustworthy Intelligent Software Systems, a Canada CIFAR AI Chair on Trustworthy Machine Learning Software Systems at Mila - Quebec Artificial Intelligence Institute, and a FRQ-IVADO Research Chair on Software Quality Assurance for Machine Learning Applications. Dr. Khomh received his Ph.D. in Computer Science from the University of Montreal under the supervision of Yann-Gaël Guéhéneuc, with the Award of Excellence. He also holds a Master's degree in Software Engineering from the National Advanced School of Engineering (Cameroon) and a Master's degree (D.E.A) in Mathematics from the University of Yaounde I (Cameroon). Professor Khomh's research focuses on ensuring the reliability, fairness, and ethical alignment of machine learning-powered systems throughout their entire lifecycle. His work specifically addresses concepts of equity, fairness, diversity, identity, and social inclusion to enhance user confidence in AI applications. He is developing techniques and tools to ensure that AI applications comply with proposed regulations, particularly in sectors such as healthcare, transportation, security, and customer service. His groundbreaking research on software maintenance and evolution, particularly on the impact of developers' design decisions on software quality, has been cited more than 3,600 times according to Google Scholar. His extensive publication record shows a strong focus on trustworthy AI systems, with recent work concentrating on deep learning testing, quality assurance for machine learning systems, privacy protection in software logs, and the challenges of developing and maintaining reliable AI-powered applications. His research bridges software engineering principles with artificial intelligence to create more dependable and trustworthy systems. 2025 IEEE CS TCSE New Directions Award Arthur B. McDonald Fellowship Canada Research Chair Tier 1 on Trustworthy Intelligent Software Systems CS-Can/Info-Can Outstanding Young Computer Science Researcher Prize for 2019 Multiple IEEE TCSE Most Influential Paper (MIP) Awards Multiple Best Paper Awards at international conferences Canada CIFAR AI Chair Professor Khomh has supervised or co-supervised seven PhD students, eleven MSc students, and ten undergraduate students. Three of his PhD students were nominated for the Best PhD Thesis Award of Polytechnique Montréal, with one winning the prize for Best Computer Science and Software Engineering PhD Thesis. His research has drawn exceptional support, with over $2.5 million in research funding from diverse sources including NSERC Discovery, NSERC Discovery Supplement Award, FRQ-IVADO Research Chairs, and Mitacs. He leads the R3AI project, which received a $124.5M grant from the Canada First Research Excellence Fund to develop robust, reasoning, and responsible AI. As the leader of the SWAT Lab, Professor Khomh directs research on software development, deployment, maintenance and evolution of AI-intensive and cloud-based software systems. He serves on the program committees and editorial boards of several top international conferences and journals in software engineering, including the Editorial Board of IEEE Software. He has held leadership positions as General co-chair of FSE 2026 and SANER 2025, and has served as program co-chair for numerous conferences including SSBSE 2024, ICPC 2019, and ICSME 2018.

• Natural Language Processing
• Legal Informatics
• Greek Language Processing
• +۴ مورد دیگر

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.

Kamila Misiejukمشاهده پروفایل

پژوهشگر

• Learning Analytics
• Educational Data Analysis
• Computer-Supported Collaborative Learning
• +۷ مورد دیگر

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
• +۴ مورد دیگر

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.

Konstantinos Krampisمشاهده پروفایل

دانشیار

• Bioinformatics
• Artificial Intelligence
• Machine Learning
• +۶ مورد دیگر

Konstantinos Krampis is an Associate Professor in the Department of Biological Sciences at Hunter College, City University of New York (CUNY). He holds joint appointments as Faculty in CUNY's Computer Science and Biology Ph.D. programs, Director of Bioinformatics and Biostatistics Core at the Cancer Health Disparity Partnership (Hunter-Temple/FCCC), and Adjunct Faculty at Weill Cornell Medical College's Institute of Computational Biomedicine. Education: PhD in Bioinformatics, Virginia Tech MSc/BSc in Molecular Biology, University of Athens Research Focus: Dr. Krampis leads research at the intersection of artificial intelligence and genomics. His work centers on developing scalable bioinformatics solutions using cloud computing and high-performance clusters, with applications in personal genomics, cancer research, and health disparities. Current projects involve Large Language Models (LLMs), AI interpretability, and machine learning for genomic data science. He has established CUNY's Next-Generation Sequencing facility and bioinformatics computing infrastructure. Funding & Impact: Principal Investigator on multiple NIH and NSF grants focused on bioinformatics tool development. Research enables portable, scalable genomic analysis across computing platforms. Teaching: Regularly teaches courses including Computational Molecular Biology (BIO 42500), Machine Learning for Bioinformatics (BIO 47106), Python for Bioinformatics (BIO 47105), and mentors PhD students through CUNY's capstone program.

Rui Abreuمشاهده پروفایل

استاد

• Software Quality
• Software Testing
• Debugging
• +۵ مورد دیگر

Rui Abreu is a Full Professor at the Faculty of Engineering of the University of Porto, Portugal. His academic career spans multiple institutions including IST, ULisbon where he served as an Associate Professor, and the University of Porto where he previously held the position of Assistant Professor. He has also been a Visiting Researcher at Google NYC (2019-2020) and a member of the Model-Based Reasoning group at PARC's System and Sciences Laboratory. Dr. Abreu earned his Ph.D. in Computer Science - Software Engineering from the Delft University of Technology, The Netherlands, and his M.Sc. in Computer and Systems Engineering from the University of Minho, Portugal. His research revolves around software quality with emphasis on automating testing and debugging phases of the software development life-cycle as well as self-adaptation. With extensive expertise in both static and dynamic analysis algorithms, his work spans traditional software engineering, quantum software testing, and security-focused development practices. His recent publications demonstrate a strong focus on applying AI techniques to software engineering problems, quantum software testing, and security vulnerability detection. The trend shows increasing integration of large language models in debugging tools and expanding research into quantum software verification. Dr. Abreu has received significant recognition for his work, including 6 Best Paper Awards and a Distinguished Paper Award at ESEC/FSE 2019. His research has attracted considerable attention in the software engineering community. As an entrepreneur, he co-founded DashDash in January 2017, a platform that allows users to create web applications using only spreadsheet skills. The company secured $9M in Series A funding in May 2018. His industry experience complements his academic work, particularly in the areas of practical software tools and applications. Dr. Abreu has been actively involved in numerous conferences, serving as General Chair for ICST 2020, Program Committee member for multiple tracks across various conferences, and as Steering Committee member for ICST. His leadership in the software engineering community is evident through his extensive service and contributions to the field.

Wei Wangمشاهده پروفایل

استاد

• Big Data Analytics
• Machine Learning
• Data Mining
• +۵ مورد دیگر

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 Suمشاهده پروفایل

پژوهشگر ارشد

• Software Engineering
• Artificial Intelligence
• Automated Testing
• +۳ مورد دیگر

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).

Ludwig Felderمشاهده پروفایل

پژوهشگر

• Artificial Intelligence
• Human-Computer Interaction
• Software Engineering
• +۱۲ مورد دیگر

Ludwig Felder is a Doctoral Candidate & Research Associate at the Technical University of Munich (TUM), affiliated with the Chair of Software Engineering & AI under the School of Computation, Information and Technology . His research focuses on Human-LLM Interaction & Collaboration and Development Tools for LLM-Powered Applications . Education: Master of Science in Human-Computer Interaction, Ludwig-Maximilians-Universität München Master of Science in Computer Science, Ludwig-Maximilians-Universität München Bachelor of Science in Media Informatics, Ludwig-Maximilians-Universität München Research Trends: His work explores the intersection of large language models (LLMs), graphical user interface (GUI) testing, and software development tools. Topics include LLM-based agents for app review, EU AI Act compliance, code vulnerability detection via knowledge graphs, and multimodal reasoning for UI/code generation. His research emphasizes practical applications of LLMs in software engineering and human-centric AI systems.

• AI/ML Decision-making under Uncertainty
• Software Engineering for Autonomous and Self-adaptive Systems
• Software Engineering for AI/ML Systems
• +۵ مورد دیگر

Dr. Nelly Bencomo is an Associate Professor in the Department of Computer Science at Durham University. She leads the SE@Durham research team and is Principal Investigator (PI) for the EPSRC-funded Twenty20Insight project, which focuses on explainability in intelligent systems. Her interdisciplinary work bridges Software Engineering, Requirements Engineering, Design Thinking, and Machine Learning, with applications to Digital Twins and human-machine teaming under uncertainty. Education : PhD from Lancaster University (UK) Research interests center on AI/ML decision-making under uncertainty, software engineering for autonomous and self-adaptive systems, and runtime modeling frameworks. Her work integrates Bayesian surprise theory, Markov processes, and non-functional requirements analysis to address challenges in system adaptability and human-AI collaboration. Recent publications emphasize uncertainty quantification in adaptive systems, digital twin lifecycle management, and explainable AI. Articles span topics like automated traceability for LLM-generated code, uncertainty flow diagrams, and SPECTRA's Markovian framework for NFR tradeoffs. Scientific Awards include: Best Paper & Software Artefact Awards (2024) Women for Innovation Recognition (2024) 10-Year Influential Paper Awards (2019) Best Paper REFSQ 2013 Leverhulme & Marie Curie Fellowships She supervises PhD students and has served as ACM Distinguished Speaker (2022), MODELS/SEAMS PC Co-Chair (2022), and IEEE TCSE Executive Committee member (2020-). Her labs focus on models@run.time, digital twins, and adaptive system governance.