Gias Uddin is an Associate Professor at York University's Lassonde School of Engineering and an Adjunct Professor at the University of Calgary . His research bridges Software Engineering (SE) and Artificial Intelligence (AI) , focusing on AI Trustworthiness Assessment (SE4AI) and AI-Driven Productivity Tools (AI4SE) . PhD in Software Engineering & AI, McGill University (2018) MSc in Software Engineering, Queen’s University (2008) BSc in Computer Science & Engineering, Bangladesh University of Engineering and Technology (2004) His research explores: Metamorphic Relations for LLM Hallucination Detection AI-Enhanced Software Documentation Foundational Models for Runtime System Modernization Developer-Centric AI Tooling Recent article trends show expertise in LLM Trustworthiness , Low-Code Platforms , and IoT Developer Communities . Awards include Distinguished Paper at FSE 2025 , multiple IBM Champion recognitions, and York Research Award . He leads the Data Intensive Software Analytics (DISA) Lab and mentors PhD students in SE-AI Intersections .
Yu Chen is an Associate Professor in the School of Information Systems and Technology at San José State University’s Lucas College and Graduate School of Business. She holds a PhD in Computer and Communication Sciences from the Swiss Federal Institute of Technology at Lausanne (EPFL), and has held postdoctoral positions at the University of California, Irvine. Her work bridges human-computer interaction, AI, and social impact, with a strong focus on education and inclusive innovation. PhD, Computer and Communication, EPFL, Switzerland MS, Security and Mobile Computing, Aalto University, Finland MS, Security and Mobile Computing, Norwegian University of Science and Technology (NTNU), Norway BE, Information Security, Huazhong University of Science and Technology (HUST), China Her research centers on human-AI interaction , AI for social good , emerging technology education , and health informatics . She is particularly interested in how chat-based interfaces can support student success, career advising, and community problem-solving. She emphasizes design thinking, empathy, and interdisciplinary collaboration in her pedagogy. Her recent publications span AI education, chatbot design, inclusive learning, and ethical AI. A recurring theme is the use of conversational agents to empower students and address societal challenges such as food insecurity, housing, and mental health. The research demonstrates a strong commitment to experiential learning and real-world impact. Collaborative Research: Broadening Inclusive Participation in AI Undergraduate Education (NSF) Girls in AI: AI Education for K-12 Girls (SJSU RSCA) Innovation Farm: AI-Powered Social Innovation (SJSU CEP) AI Counselors and Tutors for Student Development (SJSU RSCA) Yu Chen has received numerous honors, including the SJSU College of Business Distinguished Undergraduate Teaching Award (2024), multiple Master Teacher Awards, and recognition for research excellence at AMCIS and ICIS. She was also awarded the Erasmus Mundus Scholarship and a Swiss National Science Foundation fellowship. She actively mentors undergraduate students in research, many of whom have co-authored peer-reviewed publications and won awards in student competitions. Her lab, the AI for Social Good initiative, fosters a collaborative, student-driven environment where interdisciplinary teams prototype AI solutions for community challenges. She advocates for trust in students, no-code tools, and education that is relevant, creative, and socially responsible.
Dr Michael Boemo is an Assistant Professor at the University of Cambridge, holding dual appointments in the Department of Pathology and Department of Genetics. He leads research at the intersection of computational biology, DNA replication, and cancer genomics, developing machine learning tools to analyze replication stress and genomic instability. Academic Background: BA in Mathematics (Rutgers University), PhD in Physics (University of Oxford) Research Focus: Genomic instability in cancer, DNA replication/repair defects, computational modeling using machine learning and high-performance simulations Teaching: Lectures in Natural Sciences Tripos (mathematical biology, genetics, systems biology), module organizer for cancer biology and biological modeling His research group leverages nanopore sequencing and AI to map replication fork dynamics, revealing how stalled forks generate mutations in cancer cells and pathogens. Recent work examines extrachromosomal DNA replication vulnerabilities and transcription-replication conflicts. Dr Boemo collaborates across computational biology and cancer research domains, with publications spanning journals like Nature Methods, Cell, and PLoS Computational Biology. His lab develops tools such as DNAscent for replication fork analysis and explores therapeutic targeting of replication stress.
Hamish van der Ven is an Assistant Professor of Sustainable Business Management of Natural Resources at the University of British Columbia (UBC) within the Department of Wood Science and Faculty of Forestry. He leads the Business, Sustainability and Technology Lab and maintains affiliations with the Environmental Governance Lab at the University of Toronto, the Earth System Governance Project, and the United Nations Forum on Sustainability Standards. PhD in Political Science from University of Toronto Previous roles at McGill University and Yale University Research focuses on sustainable supply chain governance, eco-labeling, transnational environmental governance, corporate social responsibility, and digital technology impacts His recent work analyzes indirect climate impacts of generative AI and social media, transparency mechanisms in fast fashion, stakeholder influence on sustainability standards, and governance dynamics in buyer-driven supply chains. His 2019 book Beyond Greenwash? Explaining Credibility in Transnational Eco-Labeling (Oxford University Press) critiques eco-labeling efficacy. Van der Ven's research has been funded by grants including SSHRC Insight Grant (2025-2030), SSHRC Explore Grant (2023), SSHRC IDG Grant (2021-2023), FRQSC Collaborative Grant (2018-2021), and SSHRC Postdoctoral Fellowship (2016-2017). He examines both broad trends in global environmental governance and specific case studies across agriculture, aquaculture, and retail sectors.
Yan Chen is an Assistant Professor at the Virginia Tech College of Engineering , where he leads the PRIME Lab (Programming with Intelligent Machines & Environments) . His work focuses on creating interactive Human-AI systems to enhance real-time data analysis and programming education, particularly addressing barriers in collaborative learning environments. University of Toronto (Postdoctoral Fellow) University of Michigan (Ph.D., Information Science) University of Colorado, Boulder (BS/MS in Applied Math & Electrical & Computer Engineering) His research bridges Human-Computer Interaction (HCI) and Computer Science Education , with a focus on real-time data analysis , AI-driven programming assistance , and scalable learning tools . He employs LLMs and human-centered design to simplify complex computational processes, enabling data workers to detect critical patterns efficiently. Recent publications highlight trends in generative AI for education , proactive AI programming support , and collaborative analytics . Key themes include real-time classroom insights , intergenerational smartphone learning , and automated feedback systems . Scientific recognition includes: 🏆 Best Paper at L@S 2024 🏅 Best Paper Honorable Mention at CHI 2023 🏅 Best Paper Honorable Mention at UIST 2022 🏆 Best Short Paper at VL/HCC 2020 He mentors a team of PhD and MS students in projects spanning AI-assisted education, web automation, and collaborative coding tools, with active recruitment for future research directions.
Aravind Machiry is an Assistant Professor at Purdue University's Electrical and Computer Engineering Department and a founding member of the Purdue Systems and Software Security (PurS3) Lab . His research focuses on system security, particularly vulnerability detection, prevention, and secure system development using static/dynamic program analysis, fuzzing, type systems, and machine learning. Designing practical solutions for software and embedded system security Recipient of NSF CAREER and Amazon Research awards Active participant in SPLASH 2025 as OOPSLA Review Committee member His recent work includes automated vulnerability detection in embedded software, spatial memory safety enhancements, and security analysis of GitHub workflows. He has received recognition for his research through multiple distinguished paper awards and industry funding. Selected scientific awards include NSF CAREER Award (2024) Amazon Research Award (2022) Test of Time Award at FSE 2023 for DynoDroid Distinguished Paper Award at OOPSLA 2022 for 3c Qualcomm Innovation Fellowship (2025) His research team has developed frameworks like ARGUS for taint analysis of CI/CD workflows and FuzzUEr for UEFI interface fuzzing, discovering hundreds of critical vulnerabilities in open-source projects and thousands of command injection flaws in GitHub repositories.
Christian Wolff is a University Professor and Chair of Media Informatics at the Institute for Information and Media, Language and Culture at the University of Regensburg. Since April 2022, he has served as the founding Dean of the Faculty of Computer Science and Data Science, while maintaining secondary membership in the Faculty of Languages, Literature and Cultural Studies (SLK). His academic career spans over three decades with significant contributions to multiple disciplines at the intersection of computer science and humanities. Wolff's research interests center around multimedia and multimodal information systems, electronic publishing, and text technology, particularly text mining. His work bridges computer science with digital humanities, legal informatics, and social media analysis. Recent publications demonstrate a strong focus on large language models, sentiment analysis applications across various domains, legal technology innovations, and virtual reality research for cognitive studies. His interdisciplinary approach has produced significant contributions in both technical and humanities domains. His recent publication trends reveal a strategic shift toward applied AI research, particularly in legal technology (LegalTech), social media analysis, and sentiment analysis using large language models. The publications show increasing collaboration across disciplines, connecting computer science with law, political science, literature, and psychology. His work on the digital basis document for legal proceedings represents a major practical application of his research in the German justice system. East Bavarian Cultural Prize Doctoral Award of the University of Regensburg Wolff has led numerous interdisciplinary research projects connecting computer science with humanities and legal studies. His leadership extends to institutional roles including Dean of Research, Vice Dean, and Dean of Faculty positions. He has been instrumental in establishing the new Faculty of Computer Science and Data Science at the University of Regensburg, demonstrating significant impact on institutional development and research infrastructure. Wolff directs research initiatives focused on text technology, digital humanities, and legal informatics. His work with the INDIGO - Internet and Digitization Eastern Bavaria initiative and the TRIO project demonstrates commitment to regional technology transfer and innovation. The interdisciplinary nature of his research groups connects computer scientists with legal scholars, linguists, and social scientists to address complex digital transformation challenges.
Jessica Hullman is the Ginni Rometty Professor of Computer Science at Northwestern University's McCormick School of Engineering and a Faculty Fellow at the Institute for Policy Research. Her research develops theoretical frameworks and interfaces for human-AI collaboration, focusing on uncertainty quantification, statistical modeling, and decision-making in domains like scientific research and AI-assisted analysis. Education: PhD in Information (Visualization), University of Michigan (2013) MS in Information Analysis, University of Michigan (2008) BA in Comparative Studies, Ohio State University (2003) Tableau Postdoctoral Fellowship, UC Berkeley (2015) Research Focus: Hullman's work bridges formal models of rational inference (e.g., Bayesian decision theory) with real-world applications. Key areas include: human-AI complementarity in decision-making, visualization of uncertainty, statistical reform, and LLM applications in behavioral science. Her research consistently addresses the alignment of data-driven interfaces with human cognitive capabilities. Publication Trends: Recent work demonstrates a strong emphasis on human-AI collaboration frameworks, decision-theoretic evaluation of visualizations, and methodological rigor in machine learning and social science. Key themes include uncertainty quantification (conformal prediction, privacy tradeoffs), behavioral experiments in AI-assisted tasks, and critical analyses of scientific practices. Awards & Honors: Microsoft Faculty Fellow (2019) Google Faculty Award NSF CAREER, Medium, and Small Awards Multiple best paper/honorable mention awards at top HCI/visualization venues (CHI, VIS) Funding & Labs: Principal Investigator for NSF-funded projects including HCC: Medium on visualization tools. Previously affiliated with University of Washington's Interactive Data Lab and DataLab. Current research includes NSF-supported work on improving data visualization for reasoning about analytical assumptions.
Ingvill Rasmussen is a Professor at the Department of Education, Faculty of Educational Sciences, University of Oslo (UiO), where she has been employed since 2010. Her academic profile demonstrates expertise in the intersection of digital technology and educational practices, with particular emphasis on classroom dialogue, collaborative learning, and microblogging applications in educational settings. She teaches courses including PED2103 (Development and learning), PED1001 (Learning and teaching), and specialized courses on digital interactions and learning sciences research in the digital age. Dr. Rasmussen's research examines how new technologies transform communication and learning processes. Her work investigates conversations, collaboration, and knowledge production between students and teachers, with special attention to internet and web-based environments. She explores how teachers can effectively guide students in digital learning environments and how students develop necessary self-regulation skills to manage attention across analog and digital activities. Her academic interests explicitly address how ubiquitous technology demands new skills in regulating attention – learning to disconnect, put away devices, and switch between analog and digital activities. Her publication record from 2018-2025 reveals a consistent research trajectory focused on microblogging as a tool for enhancing classroom dialogue. These works examine how digital tools create spaces for student voice, support collaborative meaning-making, and facilitate productive educational interactions across diverse subject areas. Her research shows increasing sophistication in analyzing the nuanced ways technology mediates educational dialogue. Reviewer for prominent international journals including International Journal of Learning, Culture and Social Interaction; International Journal for Educational Research; and British Journal of Educational Technology Library Board, UiO (2014-2022) Employee representative, Department Board (2016-2020) Co-chair and organizer of the Nordic ISCAR conference (2007) Dr. Rasmussen completed her PhD at UiO (2000-2005) on 'Project work and ICT - studying learning as participation trajectories,' followed by postdoctoral work on the TWEAK project (2006-2010). She is currently involved in multiple research projects including AI-supported science conversations, EU Kids Online V (EUKO), and the Samtavla innovation project as a key member of the Living and Learning in the Digital Age (LiDA) research group.
Stuart E. Middleton is a Professor in the Electronics and Computer Science (ECS) department at the University of Southampton, where he has been employed since 2003. His research bridges artificial intelligence with practical applications in social science, mental health, and security domains. He leads multiple research projects funded by DTP and CISDnS CDT, focusing on multimodal natural language processing and large language models for social good applications. Professor Middleton's research interests center on Natural Language Processing, Large Language Models, and Human-in-the-loop AI systems. His work spans mental health applications (particularly suicide risk detection and mood change analysis), social media analysis for crisis mapping, geoparsing for location extraction, and argument mining in political discourse. He has developed numerous open-source NLP projects and datasets including CPIQA for climate science, ConversationMoC for mental health monitoring, and M-Arg for multimodal argument mining. His research demonstrates how AI can effectively support human decision-making in critical domains like mental healthcare, defense applications, and crisis management. His recent publications reveal a strong trend toward applying LLMs to high-impact societal challenges, particularly in mental health monitoring and climate science verification. He has pioneered methods for detecting suicidal ideation in social media, identifying moments of mood change, and developing context-aware question answering for climate papers. His work consistently emphasizes the importance of human oversight in AI systems, with numerous publications on responsible AI, regulation, and human-in-the-loop approaches. Ranked 1st in ECAL-2024 shared task on suicidal ideation detection Ranked 1st in NAACL-2022 shared task on suicide risk and mood change classification Winner of 'best paper' award at WWW2002 Semantic Web Workshop Professor Middleton actively supervises PhD students through multiple funded projects including 'Multimodal Natural Language Processing for Computational Social Science', 'Large Language Models for Military Veteran Mental Health', and 'Large Language Models for Human/AI Information Foraging to Combat Digital Human Trafficking into Terrorism'. He has secured significant funding from UKRI, DSTL, and other sources to support his research in responsible AI applications. He organizes major workshops including the RAI UK Workshops on Responsible AI for Mental Health and AIUK workshops on AI for Data Rescue and Defense applications. His research group maintains numerous GitHub repositories with open-source NLP tools and datasets that have been widely adopted by the research community.
Jeffrey K. Mullins is an Assistant Professor in the Department of Information Systems at the Sam M. Walton College of Business, University of Arkansas. His research explores the intersection of emotion, cognition, and ethics in information systems, with a focus on how digital technologies blur boundaries between work and play through gamification and metaverse development. Ph.D. in Information Systems, University of Arkansas Director of Ph.D. Program, Walton College Research interests span: Emotion and cognition in digital environments Ethics of biometric and gamification technologies Metaverse development and Web3 transitions Work-life convergence in IS-enabled contexts His publications in MIS Quarterly , Journal of Business Ethics , and Journal of the Association for Information Systems reflect these themes. Recent work examines AI biometrics policy and immersive learning applications. 2024 Walton College Executive MBA Teacher of the Year 2020 ACM SIGMIS Doctoral Dissertation Award 2016 Outstanding Lecturer Award With over a decade of IT experience at a Fortune 100 firm, he teaches graduate courses in IT management and multivariate analysis while maintaining certifications like ERPsim Level 2 Training.
Prof. Dr.-Ing. Udo Fiedler is a faculty member at the Technical University of Central Hesse (THM), Department of Business Administration and Economics, where he serves as Head of the Production Engineering Laboratory and Member of the Senate. His academic work focuses on manufacturing engineering with specialization in high-speed machining, production processes, and machine tools. His research interests include: High-Speed Machining (HSC) and precision manufacturing Green machining of sintered parts in the green state Process optimization using statistical experimental design Machine tool technology and NC programming Industry 4.0 applications in manufacturing education Process monitoring and control for increased manufacturing safety Prof. Fiedler's publication record demonstrates an evolution from fundamental machining processes toward integrating AI with traditional manufacturing. His recent work shows strong emphasis on applying artificial intelligence to quality prediction, optimizing green machining processes, and implementing Industry 4.0 concepts through learning factory approaches, bridging traditional manufacturing engineering with modern digital technologies. His significant scientific contributions include: Development of methods for NC programming of complex workpieces Research on stability lobe diagrams for milling processes Studies comparing different production methods including HSC, EDM, and generative processes Work on mechatronic tool holders for process monitoring Applications in the ophthalmic industry for precision machining of spectacle lenses Prof. Fiedler teaches multiple courses at THM including Factory Planning/Ergonomics, Handling and Assembly Technology, Innovative Manufacturing Processes, and Machine Tools at the bachelor's level, and Learning Factory 1 and 2 at the master's level. He leads current research projects including Klag-Robotics (2023-2025), Loewe Project OST (2018-2021), and GrünSpan (2014-2015), demonstrating sustained research activity across multiple manufacturing domains.
Jodi Halpern is a full-time Professor at the University of California, Berkeley, and a co-founder of the Kavli Center for Ethics, Science and the Public (2022). She serves as Faculty Director of BERGIT (Berkeley Group for Ethics and Regulation of Innovative Technologies) and actively consults for organizations including the Federal Reserve Bank of San Francisco, Kaiser Permanente, Intel, Salesforce, and the Mayor’s Office of Los Angeles. Halpern’s work bridges psychiatry, philosophy, and bioethics to address empathy in leadership, AI ethics, and gene editing. Professor at UC Berkeley Co-founded Kavli Center (2022) Faculty Director of BERGIT Consults for tech, healthcare, and government entities Halpern’s research focuses on empathic curiosity as a tool for conflict resolution and trust-building in healthcare, post-war reconciliation, and AI ethics. Her current projects include Engineering Empathy (investigating AI’s impact on emotional relationships), Gene Editing from Bench to Bedside (exploring scientists’ beliefs about gene editing), and Remaking the Self in the Wake of Illness (narratives of adaptation to illness). She integrates philosophical analysis with empirical research in psychology, neuroscience, and behavioral economics. Her notable scientific awards include the 2022 Guggenheim Fellowship in Health and Medicine, the 2019 Chancellor’s Chair at UC Berkeley, and the Yale University Award for the best PhD of significance to humanity across disciplines. Halpern received her MD and PhD through the NIH Medical Scientist Training Program Award , completed psychiatry residency at UCLA, and held fellowships at Princeton, Greenwall, and Townsend centers.
Liu Ye is a Professor at The University of Queensland (UQ) within the School of Chemical Engineering. She leads the Greenhouse Gas (GHG) research program at UQ Urban Water Engineering and has secured over AU$10M in competitive research funding. Her work focuses on achieving net-zero emissions through innovations in urban wastewater systems, with collaborations spanning 15+ water utilities, governments, and technology firms like Suez and Veolia. Research Areas: Greenhouse gas mitigation, sludge minimization, biogas production, advanced biological nutrient removal, online process control, and resource recovery from wastes. Key Awards: Research Innovation Award (Australia Water Association), UQ Foundation Research Excellence Award, EAIT Faculty Teaching Excellence Award, and Fellow of the Royal Society of Chemistry (RSC). Teaching: Courses include CHEE2020 (Process Equipment and Control), CHEE2501 (Environmental Systems Engineering I), CHEE4012 (Industrial Wastewater Management), and thesis supervision. Leadership: Associate Editor for Environmental Science: Water Research and Technology , member of IWA Strategic Council, and contributor to industry peak bodies like WSAA and WaterRA. Recent Articles: Her 2025 publications highlight strategies for balancing energy recovery with GHG emissions, systematic frameworks for N2O quantification, and advancements in phosphorus removal. Earlier works (2024–2022) explore electrochemical iron applications, ferric salt enhancements, and hybrid modeling techniques combining mechanistic and deep learning approaches. Scientific Impact: Her research has driven a 35% reduction in N2O emissions at an Adelaide treatment plant, saving $50M annually, and pioneered free nitrous acid (FNA) technology for biofilm control and sludge reduction.
Prof. Dr. Matthias Rarey is a computer scientist and Professor at the University of Hamburg's Center for Bioinformatics. He holds a Ph.D. in Computer Science from the University of Bonn (1996) and has been leading the Algorithmic Molecular Design working group since 2002. His research focuses on molecular design algorithms, cheminformatics tools, and 3D bioinformatics. Co-founder of BioSolveIT GmbH Former cheminformatics group leader at Fraunhofer SCAI Former researcher at SmithKline Beecham and Roche Bioscience Head of Helmholtz Data Science Graduate School DASHH Director of Center for Data and Computing in Natural Science (CDCS) Research interests span algorithmic molecular design, cheminformatics, structure-based drug discovery, and machine learning applications in bioactivity prediction. His group developed widely used tools like FlexX, PoseView, and SpaceLight for molecular modeling and fragment space analysis. Recent publications focus on geometric pattern matching in protein-ligand interfaces, combinatorial fragment space encoding, adverse drug reaction network analysis, and efficient shape-based virtual screening. The work emphasizes scalable algorithms for billion-sized compound libraries and integration of machine learning with traditional cheminformatics approaches. Scientific awards include: GMD Award 1996 (Best Dissertation) GMD Award 2000 (Best Project) NRW Wissenschaftspreis 2002 Corwin Hansch Award 2005 Emerging Technologies Award 2011 Norddeutscher Wissenschaftspreis 2020 Academic leadership roles: Founding director of Center for Bioinformatics Co-founder of M.Sc. Bioinformatics and B.Sc. Computing in Science programs Chair of doctoral committee at Faculty of Computer Science Member of EMBL-EBI's Molecular and Cellular Structure advisory board Former Associate Editor of Journal of Chemical Information and Modeling
