Florina Piroi is a Senior Researcher at the Technische Universität Wien (TU Wien), affiliated with the Faculty of Informatics and the Department of Data Science. She holds the role of Senior Scientist in Data Science and is a Substitute Member of the Curriculum Commission for Business Informatics. Her primary research focuses on information retrieval, medical informatics, natural language processing, and patent text mining. She leads projects such as the CLEF-IP evaluation lab and contributes to initiatives like the DoSSIER and OS Trails research programs. Key research interests include longitudinal evaluation of machine learning models, reproducibility in NLP tasks, and knowledge graph applications in manufacturing. She has supervised PhD student Anindita M. Ningtyas, whose work addresses medical terminology accessibility for laypeople. Piroi collaborates on interdisciplinary projects, such as designing health datasets from medical forums and improving search systems for evolving corpora. Her notable contributions include benchmarking frameworks for IR systems, semantic translation tools for industry, and methods for analyzing electrical systems. She has published over 50 peer-reviewed articles in venues like SIGIR, CLEF, and ECIR, emphasizing practical applications in both academic and industrial contexts.
Giancarlo Pellegrino is a faculty member at CISPA Helmholtz Center for Information Security , where he leads the Application Security (AppSec) research group. His work focuses on web security, program analysis, and the security of emerging technologies like the metaverse/WebXR. He has developed tools such as YuraScanner (LLM-based), MAK (RL-crawlers), Black Widow, and CHARON for vulnerability detection. Research Interests: Autonomous vulnerability detection, cross-site request forgery (CSRF), DOM clobbering, web API security, and adversarial attacks on physical systems. Committee Roles: PC co-chair for USENIX Security 2025, vice PC chair for USENIX Security 2023-2024, and served on PCs for IEEE S&P, ACM CCS, and others. Scientific Awards: Best paper at CHI 2025 for 'Permission Rationales in the Web Ecosystem.' Distinguished paper at IEEE S&P 2024 for 'The Great Request Robbery.' Distinguished papers at IEEE S&P 2023 for DOM clobbering and cross-site information leaks. His work bridges practical tool development (e.g., JAW JavaScript analysis framework) and theoretical studies on emerging threats like RAG-based systems and perceptual ad-blocking security. He also investigates cyberattacks involving clickbait PDFs and social media account trading.
Stefanie Größbacher is a Junior Researcher at the Institute of Creative Media/Technologies within the University of Applied Sciences St. Pölten, Austria. She is affiliated with the Department of Media and Digital Technologies and actively contributes to the Media Computing Research Group . Her work focuses on innovative applications of digital technologies across diverse fields including healthcare, education, and cybersecurity. Größbacher holds a BSc and Dipl.-Ing. in engineering, and her research spans visual analytics , AI-driven systems , and user-centered design . She leads projects such as the Josef Ressel Center for Knowledge-Assisted Visual Analytics (industrial data analysis) and the FIVE Project (youth social media empowerment). She also collaborates on initiatives like Smart Companion (AI for autonomous living) and IoT4LAC (smart community solutions). Her technical contributions include designing museum apps , developing cybersecurity training games , and creating AI tools for content documentation . She has authored over 20 peer-reviewed publications since 2019, with a focus on usability testing, mixed reality education, and healthcare technology. Key Projects: Josef Ressel Center, FIVE, IoT4LAC, Smart Companion, MIRACLE Research Areas: Visual Analytics, Mixed Reality, Cybersecurity, Healthcare Technology Teaching Roles: Contributes to study programs in Creative Computing, Media Technology, and Digital Healthcare
Dieter W. Fellner is a distinguished Professor of Computer Science at Technical University of Darmstadt, Germany, where he serves as Director of the Fraunhofer Institute of Computer Graphics (IGD). He also holds a concurrent position as Professor of Computer Science and Founding Director of the Institute of Computer Graphics and Knowledge Visualization at Graz University of Technology, Austria. With a career spanning over three decades, Fellner has established himself as a leading figure in computer graphics, digital libraries, and related fields. Education: Diploma in Technical Mathematics, Graz (1981) Doktorate (Ph.D.) in Technical Mathematics, Graz (1984) Habilitation, Graz (1988) Professor Fellner's research spans multiple domains within computer science, with a primary focus on computer graphics and its applications. His work encompasses computational geometry, 3D modeling and rendering, virtual and augmented reality, and digital libraries with emphasis on cultural heritage preservation. He has made significant contributions to algorithms for integrating modeling and rendering processes, efficient visualization techniques, and generative modeling approaches. His research extends to practical applications in internet-based multimedia systems, where he coordinated a strategic initiative funded by the German Research Foundation that supported approximately 50 researchers across 21 groups from 1997 to 2005. An analysis of Professor Fellner's publication record reveals a consistent trajectory of innovation in computer graphics and digital document systems. His early work focused on foundational graphics algorithms and videotex systems, evolving toward more complex 3D document modeling, visualization techniques, and digital library architectures. A notable trend is his interdisciplinary approach, bridging computer graphics with applications in cultural heritage, bioinformatics, and brain-computer interfaces. His research demonstrates a progression from theoretical algorithms to practical implementations addressing real-world challenges in information visualization and knowledge management. Scientific Awards: Fellow of the Eurographics Association (2000) Member of the IST Advisory Group for the European Commission (ISTAG) (2007) Best Technical Paper Award (Günther Enderle Award) at Eurographics'98 Conference Honorary Doctorate from the University of Rostock (2019) Throughout his career, Professor Fellner has supervised numerous students and researchers, though specific names are not documented in the available sources. His leadership extends to significant grant activities, most notably coordinating the German Research Foundation's strategic initiative on distributed processing and mediation of digital documents from 1997 to 2005. This major project provided funding for approximately 50 researchers annually across 21 research groups, demonstrating his capacity to lead large-scale collaborative research efforts. He has also served on editorial boards of leading journals and program committees of international conferences, shaping the direction of research in his fields of expertise. Professor Fellner directs the Fraunhofer Institute of Computer Graphics (IGD) in Darmstadt, a prominent research institution focused on applied computer graphics. He also founded and chairs the Institute of Computer Graphics and Knowledge Visualization at Graz University of Technology. These institutions serve as hubs for interdisciplinary research, bringing together computer scientists, domain experts, and industry partners to advance the state of the art in visualization, digital libraries, and knowledge management systems. The teams under his leadership have produced influential work in 3D document processing, cultural heritage digitization, and advanced visualization techniques.
Federico Reuben is a Senior Lecturer in the Department of Music at the University of York, UK. He holds a PhD from Brunel University and has held prior academic roles including Senior Lecturer and Course Leader in Creative Music Technology at Falmouth University. His research focuses on composition, live electronics, improvisation, sound art, and intersections between music, technology, and interdisciplinary collaboration. He co-founded squib-box , an artist-led organization promoting avant-garde music and performance. Education: PhD in Music, Brunel University (supervised by Richard Barrett and Christopher Fox) Studies in composition at The Royal Conservatoire, The Netherlands (with Louis Andriessen, Richard Ayres, Gilius van Bergeijk, and Martijn Padding) Advanced studies in human-computer interaction, digital signal processing, and algorithmic composition at the Institute of Sonology Research Interests: Studio-based composition, live electronic performance, improvisation techniques, music computing, cross-disciplinary collaboration, contemporary music aesthetics, and critical studies of digital sound culture. His work spans acoustic, electroacoustic, and mixed-media compositions, robotic sound installations, and internet-based art. Recent Articles: Focus on algorithmic creativity, telematic performance systems, and ethical challenges in AI-generated music. Notable works include Deep learning’s shallow gains (2023) and Measuring algorithmic originality (2022). Grants & Projects: Principal Investigator for Sounding Inaudible Voices (ongoing) Modularity, Immediacy, and Exchange in Laptop Improvisation (ongoing) Co-investigator for squib-box netlabel and performance network Labs/Teams: Active in the Online Orchestra Project (latency research), the Music, Science, and Technology Research Cluster at York, and the Alternative Histories of Electronic Music initiative.
Shishir K. Shah serves as Professor and Chair of the Department of Computer Science within the College of Natural Sciences and Mathematics at the University of Houston. His leadership extends to university-wide committees including Chair of the Undergraduate Committee of the Faculty Senate. Education: B.S. in Mechanical Engineering, The University of Texas at Austin M.S. & Ph.D. in Electrical and Computer Engineering, The University of Texas at Austin Professor Shah's research centers on computer vision fundamentals , specializing in machine learning and statistical methods for image/data analysis. His work addresses critical challenges in human behavior analysis, person re-identification under occlusion and clothing changes, video analytics, biometrics, and microscope image analysis. The Quantitative Imaging Laboratory he directs develops solutions for real-world surveillance, educational technology, and medical imaging applications. His recent publications reveal strong focus trends in occlusion-robust person re-identification (addressing both clothing changes and visual obstructions) and educational video analytics . These works demonstrate innovative approaches using graph attention networks, cross-modality learning, and transformer architectures to solve persistent challenges in visual recognition. Scientific Awards: John C. Butler Teaching Excellence Award (2011) Department of Computer Science Academic Excellence Award (2010, 2016) Student Vuong Dustin Nguyen received Dan E. Wells Outstanding Dissertation Award Professor Shah actively mentors graduate students, with documented success in dissertation supervision. His research program includes significant grant activity supporting the Quantitative Imaging Laboratory. He serves as Associate Editor for Image and Vision Computing, IEEE Journal of Translational Engineering on Health and Medicine, and Pattern Recognition, demonstrating strong community engagement. The Quantitative Imaging Laboratory operates at the intersection of theoretical computer vision research and practical applications, with projects spanning educational technology (Videopoints lecture video portal), surveillance systems, and biomedical imaging. Current work emphasizes robustness to real-world challenges like clothing changes, occlusions, and viewpoint variations in visual recognition systems.
Rajib Rana is a Professor of Computer Science at the University of Southern Queensland, affiliated with the School of Mathematics, Physics and Computing. He holds a PhD from the University of New South Wales (UNSW), completed in 2011. His research focuses on AI-driven solutions for mental health, speech emotion recognition, and healthcare informatics, with a strong emphasis on machine learning, deep learning, and domain adaptation techniques. His work spans clinical applications, wearable sensor systems, and mobile health technologies. Dr. Rana has supervised numerous doctoral candidates, including studies on AI tools for youth mental health interventions, adversarial attack robustness in speech emotion systems, and data analytics for school mental health monitoring. His research outputs include over 50 peer-reviewed articles, with notable contributions in IEEE Transactions on Affective Computing, Computers in Biology and Medicine, and other leading journals. His technical expertise includes federated learning, compressive sensing-based encryption for IoT, and context-aware affect sensing via smartphones. He collaborates widely with clinical partners and industry, addressing challenges like mental health triage prioritization and ICU admission prediction during pandemics. His work bridges computational methods with real-world healthcare and educational applications.
Afshin Karimi is an External Doctoral Researcher in Data Science at the University of Hamburg Business School and DESY, specializing in AI applications for scientific infrastructure optimization. Education Master of Computer Engineering from Sharif University of Technology, with specialization in AI and Data Science Research Focus His primary work leverages Natural Language Processing (NLP) and Large Language Models (LLMs) to enhance XFEL laser operations through Retrieval-Augmented Generation (RAG) and Knowledge Graphs. This research significantly improves automation and decision-making in scientific experiments, directly increasing operational uptime and efficiency in high-energy physics environments. His interdisciplinary approach bridges data science with experimental physics to solve real-world infrastructure challenges. Team Affiliation He actively contributes to the Data Science Team at the University of Hamburg Business School, collaborating on cutting-edge projects that integrate artificial intelligence with business and scientific applications.
Larry Davis is a Professor in the Department of Computer Science and the Institute for Advanced Computer Studies (UMIACS) at the University of Maryland. He is affiliated with the Computer Vision Laboratory of the Center for Automation Research, where he previously served as head from 1981-1986. His research focuses on visual surveillance, human movement analysis, and advanced computer vision systems such as the Keck Laboratory for the Analysis of Visual Movement. Established in 1998, the Keck Lab uses a 64-camera array to study 3D human motion tracking and shape recognition. His work spans projects like codebook-based background subtraction for surveillance and clothing appearance models for persistent tracking. He leads interdisciplinary research on laser beam propagation through atmospheric turbulence and has secured significant grants, including a $4M Multidisciplinary Research Initiative contract. His recent publications emphasize AI-driven solutions for media forensics, generative models, and adversarial attacks on vision systems. Research contributions include innovations in neural rendering (FlexNeRF), personalized clothing compatibility frameworks, and systems for detecting deepfakes and video tampering. His work bridges theoretical advancements with real-world applications in security, healthcare, and retail technology.
Kai-Kristian Kemell is a Research Fellow in the Computing Sciences department, focusing on interdisciplinary research at the intersection of Artificial Intelligence, Software Engineering, and Ethical AI. His work explores practical applications of Large Language Models (LLMs), autonomous agents in software development, and regulatory frameworks like the EU AI Act. Research Interests : His primary areas include AI-driven software development tools, ethical implementation of AI systems, multi-agent systems for code refactoring, and systematic literature reviews on trustworthiness in LLMs. He investigates how LLMs can automate tasks like microservice generation from API definitions and assist early-stage software startups through prompt engineering. Collaborations : Active collaborations include projects with researchers like Prof. Petri Abrahamsson, focusing on topics such as RAG systems, AI ethics governance, and hybrid work practices in software engineering. His work bridges theoretical AI concepts with practical industrial applications. Publications : His 2025 publications emphasize autonomous software development agents, EU AI Act critiques, and ethical alignment strategies for LLM-based systems. Earlier work (2023–2024) addresses AI ethics implementation via user stories, hybrid work impacts, and MLOps challenges. Awards : No specific scientific awards mentioned in the provided texts. Grants & Labs : No explicit details on grants or affiliated labs, but his research frequently involves collaborative projects with industry and academic partners.
Cataldo Basile is an Associate Professor in the Department of Control and Computer Science (DAUIN) at the Polytechnic University of Turin, where he is a member of the College of Computer, Film and Mechatronics Engineering. He serves as Course Coordinator for the Master's Degree in Cybersecurity Engineering and teaches courses such as Software Network Security, Hacking Techniques, Cryptography, and Security Verification and Testing across multiple programs including Computer Engineering, Cybersecurity, and Quantum Engineering. His research interests span a broad spectrum of cybersecurity domains, with a strong focus on automotive cybersecurity , confidential computing , cloud-native security , and threat modelling . He actively leads and contributes to research projects involving quantum-resistant cryptography, ISO-21434 compliance, and self-assessment tools for automotive cybersecurity resilience. Cataldo is a core member of the TORSEC - Security Group (DAUIN) , and supervises several PhD students working on cutting-edge topics like Trusted Execution Environments and automatic cyber risk analysis. Recent publications highlight his work in automotive cybersecurity verification using retrieval-augmented generation, rule-based OT threat modelling, intent-based security for cloud networks, and threat analysis of C-ITS. His research bridges academic innovation with industrial applications through numerous commercial research contracts and sponsorships. Scientific Director , CyberChallenge.IT Program (2023–2025) Scientific Manager , Multiple commercial research projects (2021–2025) Research Group Member , Q-FENCE (2025–2028), EU-funded quantum-resistant cryptography initiative Patent Holder , Methodology for identifying protected assets in binary files Cataldo Basile leads a vibrant research ecosystem involving PhD supervision, student teams like pwnthem0le , and collaborations with industry partners such as REPLY, Drivesec, and aizoOn. He also oversees sponsored initiatives like m0lecon, a computer security conference and CTF competition, fostering practical cybersecurity education and innovation.
Jiatao Gu is an Assistant Professor in the Department of Computer and Information Science (CIS) at the University of Pennsylvania, with a planned start in Fall 2025. He also works as a part-time Staff Research Scientist at Apple (MLR) and previously served as a full-time Research Scientist at Meta AI (FAIR Labs). His academic journey includes a Ph.D. in Electrical and Electronic Engineering (2018) from the University of Hong Kong under Prof. Victor O.K. Li, and a B.Eng. in Electronic Engineering (2014) from Tsinghua University.
Dr. Chen Wang serves as an Assistant Professor in the Department of Statistics and Actuarial Science at the University of Hong Kong, with visiting appointments at the University of Cambridge Faculty of Economics during June-August 2024, July-August 2022, and July-December 2019. His academic position and active research output confirm his status as a current faculty member engaged in interdisciplinary statistical research. His core research focuses on: Random Matrix Theory for high-dimensional covariance estimation Time Series Analysis in complex stochastic systems High-dimensional Data Analysis methodologies These statistical frameworks provide foundational tools for modern data-intensive scientific domains. Analysis of his 2022-2025 publications reveals a strategic expansion into biomedical AI applications, particularly in single-cell genomics and spatial biology. His work demonstrates consistent innovation in developing AI agents for biological experimentation (e.g., PerTurboAgent for Perturb-seq, SpatialAgent) and advancing molecular design through diffusion models. This trajectory shows a deliberate integration of his statistical expertise with cutting-edge computational biology challenges. No scientific awards or honors were documented in the provided materials. The available information contains no details regarding graduate student supervision, research grant funding, or laboratory affiliations. His visiting positions at Cambridge suggest collaborative international research activities, but specific advising relationships or grant mechanisms remain unreported in the source text.
Prof Marcello Trovati is a Professor of Computer Science at Edge Hill University, leading the Computer Science Department. He holds a PhD in Mathematics from the University of Exeter (2007) and has extensive experience in academia and industry, including roles at IBM Research and Coventry University. His research focuses on Data Science, Artificial Intelligence, Big Data Analytics, and Cybersecurity. He leads projects like ALFIE (Ethical AI frameworks) and SME Water (Water Industry Innovation), and collaborates widely on topics like malware detection, sentiment analysis, and healthcare informatics. His work spans over 60 publications and 13 funded projects, emphasizing interdisciplinary applications of computational methods. Education: PhD in Mathematics (University of Exeter), MA in Mathematics (University of Aberdeen) Affiliations: Data and Complex Systems Research Centre Research Interests: Marcello’s work integrates mathematical modeling with AI, addressing challenges in cybersecurity (e.g., Android malware detection via FSSDroid), healthcare (IoT medical devices), and decision-making systems. He develops novel algorithms for data analysis, such as retrieval-augmented generation for knowledge assets and sentiment urgency detection for business intelligence. Projects & Grants: ALFIE: Ethical AI frameworks and AutoML (2024–2027) SME Water: Innovations in Water Industry (2024–2026) Arts4Us: Mental health support via arts (2024–2027) Collaborations: Cross-disciplinary work with institutions like IBM Research, University of Derby, and Coventry University. Projects involve experts in healthcare, cybersecurity, and social sciences.
Yuyan Wang is an Assistant Professor of Marketing at Stanford Graduate School of Business and the Kevin J. O’Donohue Family Faculty Scholar (2024-2025). She holds a PhD in Statistics from Princeton University (2016) and a BSc from the Special Class for the Gifted Young at USTC (2012). Before academia, she spent 7 years as a machine learning scientist/engineer at Uber and Google DeepMind. Her research focuses on the intersection of marketing, machine learning, and statistics, with a emphasis on improving AI systems' long-term values and fairness. Key contributions include optimizing multi-sided marketplaces, developing intent-based recommendation frameworks, and deploying solutions with global business impact. Awards include the Steven Shugan Best Junior Faculty Paper Award (2025) and CIST Best Paper Award (2022). She teaches Stanford's first AI-focused MBA course, MKTG321, which received exceptional student feedback (mean instruction rating: 4.9/5). Education Highlights: PhD in Statistics, Princeton University (2016) BSc in Statistics, USTC Special Class for Gifted Youth (2012) Research Themes: Recommender Systems & Personalization Algorithmic Fairness & Long-Term Optimization User Intent Modeling & Explainer Systems Multi-Sided Marketplace Algorithms Awards & Recognitions: Steven Shugan Best Junior Faculty Paper (AIM 2025) CIST Best Paper (2022) Top 10 ML Article (0.7% selection) for Uber's Food Discovery work Teaching & Mentorship: Course creator of 'Understanding AI Technologies for Business Problems' (GSB's first AI MBA course) Mentor for 3+ students in CS research programs targeting marginalized groups Industry mentor at Google Brain and Uber Key Collaborations: Google DeepMind Uber Industry partnerships with Netflix, Clari, OpenAI, etc.
