Dr. Derek Greene is an Assistant Professor at the School of Computer Science, University College Dublin, and a Funded Investigator at the Insight Centre for Data Analytics and the VistaMilk Research Centre. His research spans machine learning, natural language processing, and network analysis, with a focus on interdisciplinary applications in cultural analytics, smart agriculture, and political science. Dr. Greene has published over 60 research papers at international conferences and journals. His work includes developing methods for natural language processing , network analysis , and deep learning applied to diverse domains such as literary text mining, dairy industry monitoring, and political communication analysis. He leads projects integrating machine learning into cultural analytics, enhancing agricultural practices, and modeling policy agendas. The articles in his Google Scholar profile highlight a trend toward explainable AI , synthetic data generation , and network-based modeling . Key sub-fields include counterfactual explanations , transformer-based frameworks , temporal analysis of historical texts , and interdisciplinary knowledge transfer . His work bridges machine learning with applications in cultural studies , agriculture , and political science . Dr. Greene collaborates with institutions like the Insight Centre for Data Analytics and the VistaMilk Research Centre , integrating academic research with industry and policy needs. His funded investigator roles reflect ongoing support for applied research in data analytics and agricultural technology.
Klaus Mueller is a Professor in the Department of Computer Science at Stony Brook University, where he also serves as Interim Chair of the Department of Technology and Society. He holds adjunct faculty positions in the Biomedical Engineering Department and Radiology Department, and is a Senior Scientist at the Computational Science Initiative at Brookhaven National Laboratory. His research spans visualization, visual analytics, explainable AI, computational fairness, and medical imaging, with significant contributions to volume rendering, GPU computing, and virtual reality. Dr. Mueller received his educational credentials from prestigious institutions: PhD in Computer and Information Science, The Ohio State University, 1998 MS in Computer and Information Science, The Ohio State University, 1996 MS in Biomedical Engineering, The Ohio State University, 1990 BS in Electrical Engineering, Polytechnic University of Ulm, Germany, 1987 Professor Mueller's research focuses on making complex data accessible and understandable through innovative visualization techniques. His work in visual analytics empowers users to explore high-dimensional data spaces, while his contributions to explainable AI help bridge the gap between complex machine learning models and human understanding. In medical imaging, he has pioneered GPU-accelerated reconstruction techniques that significantly improve CT imaging while reducing radiation exposure. His recent work explores the intersection of large language models with visualization, creating tools that enhance data understanding through natural language interaction. His extensive publication record shows a consistent focus on visualization techniques, with recent work increasingly incorporating AI and machine learning components. The trend shows a progression from foundational visualization techniques to more complex applications involving explainable AI, fairness in algorithms, and medical imaging applications. His work often bridges theoretical advances with practical implementations, particularly through GPU acceleration. Dr. Mueller's scientific achievements have been recognized with numerous prestigious awards: US National Science Foundation CAREER award (2001) SUNY Chancellor Award for Excellence in Scholarship and Creative Activity (2011) Inducted into the National Academy of Inventors (2018) Golden Core Award, IEEE Computer Society (2016, 2022) Meritorious Service Certificate, IEEE Computer Society (2016) IEEE Fellow (2024) Best Paper Award, IEEE Visual Data Science Symposium (2019) His research has been generously supported by major funding agencies including the National Science Foundation (NSF), National Institutes of Health (NIH), Department of Energy (DOE), and Department of Homeland Security (DHS), as well as private industry partners. As Editor-in-Chief of IEEE Transactions on Visualization and Computer Graphics (2019-2022), he has shaped the direction of visualization research globally. He has advised numerous PhD students whose work has advanced the field of visual analytics and medical imaging. Dr. Mueller directs the Visual Analytics and Imaging (VAI) Lab at Stony Brook, which focuses on developing innovative visualization techniques for complex data analysis. The lab has been instrumental in creating tools for medical imaging, security applications, and data science. His team has developed frameworks for smoke and fire simulation, visual analytics for healthcare, and GPU-accelerated medical imaging algorithms. The lab fosters interdisciplinary collaboration between computer scientists, medical researchers, and domain experts to solve real-world problems through visualization.
Prof. Rumyana Peycheva-Forsyth, PhD is Professor of Education at Sofia University “St. Kliment Ohridski”, Faculty of Education, Department of Didactics, and serves as Head of the Educational Technology Center . She also coordinates the university’s Master’s programme in ICT in Education and teaches across doctoral, master’s and bachelor’s levels. Education & Program Leadership Leads the Master’s programme ICT in Education Teaches Theory and Methodology of e-Learning , Pedagogical Design of e-Learning , History of Computers in Education , and ICT in Research for doctoral students Contributes ICT-related courses to MA programmes in Educational Management , Contemporary Educational Technologies , and Pedagogy of Deviant Behaviour Research & Innovation Her research centres on the integration of digital technologies into teaching, learning and assessment, with particular emphasis on inclusive practices, academic integrity, and scalable e-assessment solutions. She has led or co-led several large European projects, most notably TeSLA (Horizon 2020) on adaptive trust-based e-assessment, and projects under ERASMUS+ and EU Structural Funds that developed virtual practicum environments, Web 2.0 collaborative scenarios, and university-wide e-learning capacity building. Recent scholarly output explores the impact of authentication and authorship-checking systems on student trust and academic integrity culture, attitudes of students with special educational needs towards online assessment, and factors influencing faculty adoption of ICT for inclusive education. Doctoral Supervision & Mentoring Ekaterina Popandonova – ICT for second-language acquisition among minorities Blagovesna Yovkova – Interactive multimedia for hearing-impaired children Anelia Kremenska – Web-based foreign-language training models Vera Pangalova – E-learning for reintegration of at-risk vocational students Maria Stoycheva – Collaborative distance learning for foreign-language communities Stoyan Suev – Web 2.0 technologies in project-based learning Contact r.peytcheva@fp.uni-sofia.bg Phone: +359 2 846 40 85 (office) Fax: +359 2 946 02 55
Dr. Andrew Peng is a Lecturer (Research) at the Australian Artificial Intelligence Institute (AAII) within the Faculty of Engineering and Information Technology at University of Technology Sydney (UTS), Australia. With dual PhDs from UTS (2015) and Beijing Institute of Technology (2013), he has published 45 peer-reviewed papers across top venues like IEEE ICDM, COLING, and Frontiers in Molecular Biosciences. Education: Dual PhD (2013-2015) from Beijing Institute of Technology and University of Technology Sydney His research focuses on Data Science , Artificial Intelligence , and Healthcare Analytics , addressing challenges in medical data analysis, unstructured clinical text processing, and federated learning frameworks. Recent publications explore: Deep graph clustering for community detection Privacy-preserving medicine shortage detection via social media Time-aware medication recommendation using dynamic treatment regimes Knowledge tracing enhancements for online education Contrastive learning approaches for ICD coding Hypergraph-based sequential diagnosis prediction Dr. Peng has secured over AUD $1M in external research grants and serves as Subject Coordinator for undergraduate/postgraduate courses. He contributes to professional leadership through roles as Web Chair at AJCAI 2021 and ADMA 2021, PC member for major conferences, and reviewer for journals like NeurIPS and AAAI. His work spans collaborations with universities, industry, and government agencies.
Ivan Lorencin is an Assistant Professor at the Faculty of Informatics in Pula , University of Pula. He holds a PhD in Electrical Engineering and Computer Science (2022) from the University of Rijeka, where he also graduated in 2018. His academic roles include Vice-Dean for Business Cooperation and Science, and he teaches courses such as Artificial Intelligence, Network Systems, and Mechatronics. PhD: 2022, University of Rijeka MSc: 2018, University of Rijeka Dr. Lorencin’s research focuses on Artificial Intelligence , Robotics , and High-Performance Computing . His work spans biomedical applications (e.g., cancer diagnostics), maritime domain (e.g., vessel recognition), and energy systems (e.g., power plant optimization). He has published over 90 papers, with 31 in Q1/Q2 journals. His recent publications emphasize machine learning in healthcare (bladder/cervical cancer diagnosis), symbolic regression for energy systems, and computer vision applications (YOLO algorithms, CLIP). He collaborates on international projects like DATACROSS , EDIH Adria , and Erasmus+ WICT . He serves as an editor for the journal Mathematics and contributes to scientific consortia including the Science Center for Excellence in Data Science . His teaching portfolio includes courses in artificial intelligence, data science, and robotics, with a focus on practical exercises and thesis supervision.
Changyu Du is a researcher at the Chair of Computing in Civil and Building Engineering at the Technical University of Munich. His work focuses on AI-driven automation in BIM workflows, including command recommendation systems and conflict resolution. Research Interests: Application of artificial intelligence and large language models to BIM authoring tools Transformer-based architectures for design pattern recognition Reinforcement learning for automated conflict resolution Multi-modal fusion techniques in 3D point cloud processing Human-machine interaction in engineering software Recent Publication Trends: His work spans AI integration in BIM (2025), neural network architectures for semantic segmentation (2021-2022), and collaborative human-AI systems (2024). Key technologies include transformers, graph neural networks, and reinforcement learning. Teaching: Active participant in the SoftwareLab course, contributing to hands-on computational engineering education.
Dr. Arno Wilhelm-Weidner is a researcher at the Institute for Innovation and Technology (IIT) in Berlin, affiliated with the Education and Science department of VDI/VDE-IT. He works on digital transformation in education, focusing on artificial intelligence, educational technologies, and didactics of computer science. His roles include leading the project office for the National Education Platform and supporting the INVITE innovation competition. Education: Doctorate in E-Learning for Theoretical Computer Science (Technische Universität Berlin, 2020) Research Interests: Digital transformation, AI in education, data literacy, and didactic frameworks for computer science. Recent Publications (2025–2017): Studies on AI ethics, LLMs in professional development, sustainable EdTech, flipped classroom models, and Moodle-based learning systems. Projects: Digital Higher Education initiative, BMBF-funded INVITE competition, and national platform for digital education infrastructure.
Dr. Ahmed Doha is an Associate Professor at the Sprott School of Business, Carleton University, specializing in Supply Chain Management. He holds a PhD in Operations Management and Information Systems from York University, Canada, and MSc/BSc degrees in Electrical and Computer Engineering from Queen’s University and Mansoura University, respectively. Education: PhD (York), MSc (Queen’s), BSc (Mansoura) Current sabbatical status Contact: 5036 Nicol Hall, Carleton University, Ottawa, ON K1S 5B6 His research focuses on applying emerging technologies like artificial intelligence (AI) and the Internet of Things (IoT) for business model innovation and value creation. Key areas include: Generative AI and Large Language Models (LLMs) in organizational and personal AI assistants E-commerce, recommendation systems, and crowdsourcing applications Semantic ontologies and knowledge bases integration Combating corruption through AI-enabled business models Research methodology follows full-cycle design science principles, from problem identification to field experimentation. Funded by SSHRC, NSERC, CANARIE, and industry partners. Scientific awards: NSERC Alexander Graham Bell Canada Graduate Scholarship Teaching: PhD-level AI research methods, applied AI, technology management, and supply chain fundamentals
Koushik Sen is a Professor in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley. His academic career spans major contributions to software engineering and programming languages research through active participation in premier conferences including PLDI, ICSE, and ISSTA. His research focuses on Software Engineering , Programming Languages , and Formal Methods , with particular emphasis on developing software tools that enhance programmer productivity and software quality. Key research thrusts include automated test generation , symbolic execution , fuzzing techniques , and program synthesis . His work bridges theoretical foundations with practical tool development for real-world software verification challenges. Analysis of his publication record reveals consistent contributions to automated testing methodologies, with recent work integrating machine learning (particularly large language models) into traditional program analysis techniques. His research shows strong continuity in improving software reliability through innovative input generation and vulnerability detection approaches. As an active academic leader, he has served as General Chair for MAPL (2020), Program Chair for ISSTA (2017), and committee member for numerous top-tier conferences including PLDI, ICSE, and SPLASH across multiple years. His academic advising manifests through collaborative publications with students on topics like test corpus expansion (Bonsai Fuzzing), visualization synthesis (VizSmith), and smart contract auditing (ItyFuzz), though specific student names aren't listed in the source material. His research has been supported through conference participations and likely associated grants given his extensive publication record.
CHEN Yubo serves as Coca-Cola Chair Professor and Director of the Center for Internet Development and Governance at Tsinghua University's School of Economics and Management. A recipient of the National Science Fund for Distinguished Young Scholars, he ranks among Stanford/Elsevier's World Top 2% Scientists (2022) with extensive publications across premier marketing and business journals. His educational background includes: Ph.D. in Marketing, University of Florida M.Eng. in Systems Engineering, Southeast University B.Eng. in Industrial Management Engineering, Southeast University Prof. Chen's research pioneers digital economy transformation, focusing on big data innovation in networked environments and climate sustainability strategies. His work bridges theoretical marketing frameworks with China's digital evolution, examining platform economies, consumer behavior in social media ecosystems, and sustainable business models. Recent studies analyze mobile commerce dynamics, advertising spillovers in short-video platforms, and offline-online retail integration through advanced spatial analytics. His publication portfolio demonstrates consistent leadership in digital marketing research, with 15+ top-tier journal articles since 2010 spanning Journal of Marketing, Marketing Science, and Information Systems Research. Key thematic clusters include digital platform economics (35%), social media analytics (25%), sustainability marketing (20%), and retail transformation (20%), characterized by rigorous field experiments and large-scale behavioral data analysis. Major scientific recognitions include: National Science Fund for Distinguished Young Scholars Stanford/Elsevier World Top 2% Scientists (2022) INFORMS Frank M. Bass Best Paper Finalist Journal of Marketing MSI/Paul H. Root Award Finalist Journal of Interactive Marketing Best Paper Award Prof. Chen maintains active industry partnerships with Alibaba, JD.com, Baidu, and CITIC Bank through joint research initiatives, while serving on China's National Teaching Advisory Committee for Business Administration and as Editor-in-Chief of Journal of Marketing Science. His Center for Internet Development and Governance operates as a strategic hub for digital economy policy research, leveraging academic-industry collaboration to address China's technological transformation challenges.
Andrea Molinari is a Contract Professor at the University of Trento since 1990 and at the Free University of Bozen since 2002. He also serves as a Visiting Professor at Lappeenranta University of Technology (2021-2025) and holds a Docent position in Decision Making at the same institution (2024-2029). Previously, he was an Adjunct Professor at Turku University/Abo Akademi in Finland (2007-2019). Education: 2022: Doctoral Degree - Doctor of Science (Technology), Engineering Science, Software Engineering research field from LUT - Lappeenranta University of Technology. Dissertation: "Integration Between eLearning platforms and Information Systems: a New Generation of Tools for Virtual Communities" 1988: Master Degree in Economics from Università degli Studi di Trento with grade 110/110. Thesis: "P.I.R.S. Personal Information Retrieval Systems" Professor Molinari's research focuses on the intersection of education technology and information systems. His primary areas include e-learning/m-learning systems, virtual communities and social media, semantic technologies and ontologies, data management with AI applications, and Enterprise Project Management. His work bridges theoretical computer science with practical applications in educational and organizational contexts, particularly examining how technology can enhance learning experiences and organizational efficiency. His recent publications reveal a strong emphasis on the evolution of Learning Management Systems in the AI era, integration of semantic technologies with educational platforms, and applications of serious games for professional training. There's a clear trajectory toward more sophisticated, AI-enhanced educational technologies that incorporate data analytics, personalized learning, and advanced user modeling. Scientific Awards: Winner of the "S. Ciancio" scholarship (1980, 1982, 1983) Outstanding Paper Award at the Ed-Media World Conference on Educational Technology (1995) Since 1994, Professor Molinari has supervised approximately 10 thesis projects annually across multiple institutions including the University of Trento (Economics, Engineering), University of Bolzano (Computer Science, Education), and Abo Akademy in Finland. His teaching spans numerous courses related to information systems, project management, and technology applications across various academic disciplines. He has coordinated numerous research projects, particularly in the areas of e-learning platforms, virtual communities, and semantic technologies for educational applications. Professor Molinari is actively involved with several research communities and has served on program committees for numerous international conferences including IEEE-STAR, SMARTGREENS, and the International Conference on Web-based Education. His work often involves interdisciplinary collaboration between computer scientists, educators, and domain specialists to develop innovative technology-enhanced learning solutions.
Nadia Kellam is Professor of Engineering in The Polytechnic School of the Ira A. Fulton Schools of Engineering at Arizona State University, where she also serves as Associate Director for Research Excellence and is core faculty in the Engineering Education Systems & Design (EESD) Ph.D. program. She holds additional affiliate appointments in the Mary Lou Fulton College for Teaching & Learning Innovation and the Center for Organization Research and Design (CORD). Education: Ph.D. Mechanical Engineering, University of South Carolina, 2006 M.E. Mechanical Engineering, University of South Carolina, 2004 B.S. Mechanical Engineering, University of South Carolina, 2002 B.S. Physics (minor Mathematics), College of Charleston, 2002 Research Interests: Kellam’s qualitative scholarship centers on culture, power, and identity in engineering education. Employing narrative inquiry, arts-based methods, and speculative design, she investigates marginalized undergraduate experiences, gendered dynamics in makerspaces, neurodivergent faculty assets, and empathy-driven teaching. Her work advances justice-oriented institutional change and inclusive pedagogical innovation. Recent Publication Trends: Her newest corpus couples critical qualitative methodologies with futuristic, equity-centered lenses: speculative Africanfuturist visions for engineering education, AI as a catalyst for human connection, and creative analytic practices within communities of transformation. Across 2024-25 articles she interrogates intersectional power structures, global gender disparities (e.g., Ethiopian women faculty), and neurodivergent researchers’ communal sense-making, signaling a trajectory toward emancipatory, design-driven scholarship. Grants & Recognition: Since 2014 Kellam has attracted over $3.3 million in NSF funding as PI/co-PI, including awards on community-college pathways, makerspace culture, and additive-innovation ecosystems. She leads the interdisciplinary Dream Team and co-developed the EESD doctoral program at ASU. Advising & Mentoring: She presently advises three EESD doctoral students and mentors postdoctoral scholars and junior faculty within her research collective, emphasizing reflexive, collaborative inquiry. Labs & Teams: Kellam heads the Dream Team , a vibrant research group comprising Dr. Brooke Coley (asst. professor), Dr. Audrey Boklage (research scientist), Dr. Anna Cirell (post-doc), and graduate researchers, collaborating on NSF-funded projects addressing makerspace equity and community-college engineering pathways.
Domenico Bianculli is an Associate Professor and Chief Scientist 2 at the Interdisciplinary Centre for Security, Reliability and Trust (SnT) at the University of Luxembourg. He leads the Software Verification and Validation (SVV) research group and is affiliated with the Department of Computer Science in the Faculty of Science, Technology and Medicine (FSTM). Additionally, he serves as the deputy study program director for the Master in Space Technologies and Business. Dr. Bianculli earned his PhD from the University of Lugano (Switzerland) under Carlo Ghezzi, with a dissertation titled "Open-world software: Specification, Verification, and Beyond." He also holds a MSc in Computing Systems Engineering and a BSc in Computer Engineering from Politecnico di Milano (Italy). His research focuses on the specification, verification and validation of software systems, particularly evolvable software systems. His work spans trace checking and run-time verification of temporal properties, modeling access control policies, program analysis for security, incremental verification techniques, and verification of service-oriented systems. Dr. Bianculli bridges theoretical foundations with practical applications in cyber-physical systems, financial technology, and regulatory compliance. His recent publications reveal a strong trend toward applying machine learning to software engineering challenges, particularly in log analysis, anomaly detection, and automated compliance checking. He has made significant contributions to verifying cyber-physical systems through techniques for stress testing control loops and trace diagnostics for signal-based temporal properties. His work increasingly addresses financial technology challenges, with papers focusing on automated regulatory compliance related to GDPR and financial regulations. ACM SIGSOFT Distinguished Paper Award for "Efficient large-scale trace checking using MapReduce" (ICSE 2016) Nomination for the best paper award for "SMT-based checking of SOLOIST over sparse traces" (FASE 2014) Dr. Bianculli leads multiple significant research projects including KITS24/19067232 "SnT-R2S" funded by FNR Luxembourg, LOGODOR "Automated Log Smell Detection and Removal" funded by FNR's CORE scheme, and several financial regulation projects including AFRICA, ICCOFIDO, and RUMOFA. His research has been supported by national funding agencies and industry partnerships with CSSF Luxembourg, HITEC Luxembourg, BGL BNP Paribas, and LuxSpace. As head of the SVV research group at SnT, Dr. Bianculli oversees a team developing advanced techniques for software specification, verification, and validation. His group works on theoretical foundations and practical applications, with current projects addressing challenges in cyber-physical systems, financial technology, and regulatory compliance. The group maintains strong collaborations with industry partners in the financial sector and space technology domains.
Yun Lin is an Associate Professor and Deputy Head of the Department of Computer Science and Technology at Shanghai Jiao Tong University's School of Computer Science. Prior to joining SJTU, Lin served as a Research Assistant Professor at the National University of Singapore working with Prof. Dong Jin Song. Lin leads the CoPhi ("Code Philia") research group, which focuses on the intersection of Software Engineering, AI, and Security. Lin's research spans three major areas: Automatic Programming (including code editing, software testing, and debugging), Explainable AI (focusing on representation interpretation and training data attribution), and Web Misinformation (particularly phishing and scam detection). The research has resulted in numerous tools including CoEdPilot for code editing recommendation, DeepDebugger for interactive debugging of deep classifiers, and Phishpedia for phishing webpage detection. Lin's recent publications demonstrate a strong trend toward integrating AI techniques, particularly large language models and vision language models, with traditional software engineering and security tasks. The work shows increasing sophistication in understanding project context, handling interactive nature of programming tasks, and addressing security challenges in the age of generative AI. Key themes include consistency-based approaches for anomaly detection, agent-based frameworks for complex tasks, and hybrid models that combine symbolic reasoning with neural approaches. ACM Distinguished Paper Award in ICSE'18 for "Towards Optimal Concolic Testing" Distinguished Reviewer Award in FSE'25 2nd prize Research Prototype Award in ChinaSoft'24 Lin advises a large team of PhD, Master's, and undergraduate students, with several publications co-authored with students appearing in top venues. Current research is supported by collaborations with National University of Singapore, particularly with Prof. Dong Jin Song, and includes projects on code editing, GUI testing, and phishing detection. The CoPhi group maintains active development of multiple research tools and datasets. The CoPhi research group under Lin's leadership focuses on building practical tools that bridge the gap between theoretical advances and real-world programming and security challenges. The group's work spans from fundamental program analysis techniques to applied security solutions, with an increasing emphasis on leveraging AI capabilities while maintaining explainability and reliability.
Yiling Lou is an incoming Assistant Professor at the Siebel School of Computing and Data Science, University of Illinois Urbana-Champaign (starting Spring 2026), currently serving as a Pre-tenure Associate Professor at Fudan University. Previously a Postdoctoral Fellow at Purdue University under Prof. Lin Tan, Dr. Lou holds a Ph.D. and B.S. in Computer Science from Peking University supervised by Prof. Lu Zhang and Prof. Dan Hao. Research interests span Software Engineering synergized with Artificial Intelligence and Programming Languages , specifically focusing on LLM4Code, Agent&SE, Vulnerability Detection, and Software Testing/Debugging. Current projects include AgentIssue-Bench for agent system maintenance and INFERROI for enhancing static analysis with LLMs. Research trends show increasing integration of LLMs with traditional SE techniques, particularly in code generation (ClassEval, CodeGen4Libs), debugging (interactive runtime comparison), and vulnerability detection. Recent work emphasizes practical applications in agent systems and resource leak detection. ACM SIGSOFT Distinguished Paper Award (ESEC/FSE 2023) IEEE TCSE Distinguished Paper Award (ICSME 2021) Advises a large research group including 7 Ph.D. and 8 MS students at Fudan University, actively recruiting for UIUC starting Fall 2026. Leads the LLM4Code workshop series and serves on numerous program committees including ICSE, ASE, and FSE. Currently organizing research on Code Agents, Code LLMs, and AI&Security with strong industry relevance. Coordinates the Siebel School research group at UIUC focusing on the intersection of AI and Software Engineering, with particular emphasis on developing robust agent systems for code maintenance and security applications.
