Dr. Dave Murray-Rust is an Associate Professor in Human-Algorithm Interaction Design at TU Delft's Faculty of Industrial Design Engineering. He explores the intersection of humans, data, and AI through design research, focusing on ethical AI systems and sociotechnical interactions. His work bridges computer science, design theory, and digital sociology, addressing challenges like algorithmic fairness and human-AI collaboration. He leads initiatives such as the AI Futures Lab and Data-Centric Design Lab, advancing methods for leveraging behavioral data in design processes. His research emphasizes experiential AI frameworks, metaphors for designers, and the legibility of AI systems. He has been honored with awards including Best alt.HRI 2024 and a CHI 2023 Best Paper Award for contributions to fairness perceptions in algorithmic decision-making. Murray-Rust teaches courses like the Speculative Design Studio and collaborates on projects like DCODE (Designing the Future of AI) and the BrightSky Project. His work extends to public engagement through installations like GeoPact and explorations of blockchain's societal impact. He holds an Honorary Fellowship at the University of Edinburgh.
Nancy B. Kurland serves as the Clair R. McCollough Professor of Business Administration at Franklin & Marshall College, where she has taught since 2010 after previous appointments at California State University-Northridge, Pepperdine University, and the University of Southern California. Her academic leadership focuses on integrating sustainability and social responsibility into business education through innovative curriculum development and community engagement. Her educational foundation includes: BA in Political Science from Penn State MBAs from Cornell University and Catholic University of Leuven, Belgium Doctorate in Management from University of Pittsburgh, Katz Graduate School of Business Professor Kurland's research pioneers the integration of ecological sustainability into business curricula while examining organizational forms that reconcile profit with purpose. She investigates community socio-emotional wealth in Lancaster County farming communities, benefit corporation governance models, and the tensions within 'buy local' movements regarding ethical consumption. Her work bridges theoretical frameworks with practical applications in supply chain sustainability and crisis management, emphasizing how organizations can drive social good through structural innovation and community partnerships. Analysis of her recent publications reveals a strategic shift toward hybrid organizational models and pandemic-responsive business-society dynamics. She has developed comprehensive typologies for sustainability education while exploring mission alignment in social enterprises and the evolution of benefit corporations as vehicles for systemic change. Her research consistently connects classroom innovation with real-world impact through community-engaged scholarship. Her scientific recognition includes: Best Paper Awards (2023, 2021, 2015) Kevin E. Ruble Fellowship in Conscious Capitalism (2017) Professor Kurland actively mentors students through close classroom relationships and community-based learning initiatives. She co-founded F&M's Center for Sustained Engagement with Lancaster and CSUN's Institute for Sustainability, creating platforms for student-faculty collaboration on local food systems, farming preservation, and sustainability networks. Her grant work focuses on developing interdisciplinary approaches to campus sustainability and ethical consumption. She leads the Center for Sustained Engagement with Lancaster, which connects F&M students with Lancaster County communities through sustainability projects, farming preservation research, and local food system development. Her work with the Institute for Sustainability at CSUN established foundational frameworks for campus-wide sustainability integration, demonstrating her commitment to translating academic research into community impact.
Professor David Wagg is a Professor of Nonlinear Dynamics and Departmental Director of Research and Innovation at the School of Mechanical, Aerospace and Civil Engineering, University of Sheffield. His research focuses on nonlinear structural dynamics, digital twins, vibration suppression, and real-time hybrid testing. He holds a BEng and PhD from University College London and previously served as a Professor at the University of Bristol (2008–2013). Notable awards include the EPSRC Advanced Research Fellowship (2004–2009). Education: BEng and PhD in Nonlinear Dynamics from University College London. Research Interests: Digital twins for dynamics applications, nonlinear structural dynamics, vibration control, real-time hybrid testing, and identification methods for nonlinear dynamics. His work emphasizes applying nonlinear models and control strategies to engineering challenges like wind turbines and large civil infrastructure. Grants & Leadership: Co-Investigator for EPSRC grants on CITCoM and Digitwin, coordinator of the Marie Curie ETN DyVirt, and PI for the EPSRC programme on Engineering Nonlinearity (2012–2017). He co-authored Nonlinear Vibration with Control (2015) and edited books on structural dynamics. Lab/Teams: Involved in the Laboratory for Verification and Validation (LVV) and leads research groups focused on digital twin applications, inerter-based systems, and structural health monitoring.
Ibrahim Sabek is an Assistant Professor in the Thomas Lord Department of Computer Science at the University of Southern California (USC), with a courtesy appointment in the Dornsife Spatial Sciences Institute. He leads the Next-generation Data-Intensive Systems Group (NexDIG) and previously held postdoctoral positions at MIT's Data Systems Group and an NSF/CRA Computing Innovation Fellowship. He earned his PhD in computer science from the University of Minnesota, Twin Cities in 2020, with recognition for his dissertation's excellence. His research focuses on integrating machine learning and quantum computing into data management systems, emphasizing scalable systems design, algorithms, and data structures. Notable awards include the Google Systems and ML Junior Faculty Award (2025), the NSF/CRA Computing Innovation Fellowship, and the Best Demo Award at ACM SIGSPATIAL 2024. His work spans quantum-augmented database engines, learned query optimizers, and causal inference for system debugging. He actively serves on program committees for top conferences like VLDB and SIGMOD, and organizes workshops such as Q-Data. Current courses include CSCI 543 on modern data management, emphasizing prerequisites like CSCI-485/585. Key contributions include frameworks like LIMAO, TurboReg, and Flash, addressing challenges in spatial probabilistic modeling and scalable regression. His research bridges machine learning, quantum computing, and traditional database systems, with applications in spatial data analysis and system optimization.
Prof. Dr. Ben Wagner is a Professor of Media, Technology & Society at Inholland University, Director of TU Delft's AI Futures Lab on Rights and Justice, and Professor of Human Rights & Technology at IT:U. His work bridges social sciences, technology, and human rights, focusing on digital governance, AI ethics, and societal impacts of technological change. He holds a PhD from the European University Institute (2013) and has led institutions like the Center for Internet & Human Rights (Viadrina) and the Sustainable Computing Lab (WU Wien). Key initiatives include Inholland's Digital Rights Research Team (DRRT), Sustainable Media Lab (SML), and contributions to the European Cloud for Heritage OpEn Science (ECHOES). His research emphasizes designing accountable tech systems, digital rights frameworks, and sustainable digital infrastructures. Recent work addresses gaps between legal/ethical guidelines and public sector data practices, AI governance across nations, and audit mechanisms for platform transparency. Awards include the 2023 Best Paper Award at HICSS for AI governance research and a 2013 Best Student Paper at Internet Science. Collaborations span academia, governments, and industries to shape equitable tech policies. Active in advisory roles for ENISA, Patterns Journal, and the UKRI Trustworthy Systems Hub.
Maneesh Agrawala is the Forest Baskett Professor of Computer Science and Director of the Brown Institute for Media Innovation at Stanford University. He is also a consulting AI Scientist at Roblox. His research lies at the intersection of computer graphics, human-computer interaction (HCI), and visualization, with a focus on cognitive design principles for improving audio/visual media. He leads a vibrant research group and has advised numerous PhD students and postdocs. His research interests include: Computer Graphics Human-Computer Interaction Visualization and Visual Communication Cognitive Design Principles Generative AI and Diffusion Models Interactive Video and Sketch-based Interfaces Data and Information Visualization His recent publications (2023–2025) reflect a strong trend toward leveraging generative models—particularly diffusion models—for video and image synthesis, editing, and personalization. Key themes include controllable generation (e.g., SparseCtrl, ControlNet), sketch-to-image translation, relightable texturing, and tools that enhance visual storytelling and data communication. His work integrates cognitive science with computational tools to build systems that support human creativity and understanding. His scientific honors include: MacArthur Foundation Fellowship (2009) Alfred P. Sloan Foundation Fellowship (2007) NSF CAREER Award (2007) SIGGRAPH Significant New Researcher Award (2008) Allen Distinguished Investigator Award (2014) Induction into the SIGCHI Academy (2021) ACM Fellow (2022) Okawa Foundation Research Grant (2006) A dedicated mentor, Agrawala has advised a large cohort of students and postdocs, many of whom have gone on to influential positions in academia and industry. His lab develops tools for video editing (e.g., AnimateDiff, ControlNet), sketch-based design, and visualization (e.g., EmphasisChecker), often bridging theory with practical applications in media and education. He continues to be a leading figure in visual computing and interactive systems.
Dr Robin Crockett is the University Academic Integrity Lead at the University of Northampton, based in the Academic Registry. He is a mathematician-ethicist actively engaged in research and professional development in academic integrity, document forensics, and the detection of contract cheating and AI-generated text. He is a member of the European Network for Academic Integrity (ENAI), co-founder of the Midlands Integrity Group (UK), and has advised UK policymakers on legislation to ban essay mills. He holds Chartered Scientist and Chartered Mathematician status. MPhil, The Management of Electricity Supplies via Storage as Hydrogen, Cranfield University Master, Energy Conservation and the Environment, Cranfield University PhD, Electrostatic Damage to Semiconductor Devices, University of Southampton Master, Natural & Electrical Sciences, University of Cambridge Bachelor, Natural & Electrical Sciences, University of Cambridge Dr Crockett's research centers on document forensics and academic integrity, with core interests in Fourier theory, time-series analysis, and stylometry for identifying contract cheating. His work increasingly addresses the challenges posed by generative artificial intelligence in education. He applies mathematical and statistical methods to analyze linguistic cues, writing styles, and embedded information in student submissions. His recent publications highlight a strong trend toward understanding and mitigating academic misconduct in the AI era. Topics include AI-text detection uncertainties, forensic stylometry, and policy development for generative AI misuse. Earlier work includes environmental research on radon remediation and signal processing applications in telecommunications. Chartered Scientist Chartered Mathematician Dr Crockett has supervised PhD students, including Believe Nwamae in Computing. He has secured internal research funding, such as the Small Grants Scheme for Early Career Researchers at the University of Northampton for a project on AI-synthesized text detection. He has been an Academic Visitor at Loughborough University and served on the Turnitin Advisory Board, indicating active collaboration and external engagement. He frequently presents at academic events and contributes to policy discussions. He is affiliated with research networks including the European Network for Academic Integrity (ENAI) and the European Geosciences Union (as a former Scientific Officer). His work is supported by institutional and collaborative projects focused on advancing machine discernment of academic misconduct.
Dr. Wan Renjie is an Assistant Professor in the Department of Computer Science at the Faculty of Science, Hong Kong Baptist University (HKBU). He holds a BEng in Network Engineering from the University of Electronic Science and Technology of China and a PhD from Nanyang Technological University (NTU), Singapore. Prior to joining HKBU, he was a Wallenberg-NTU Presidential Postdoctoral Fellow (2020–2022) and a guest researcher at Peking University (2019–2020). His research focuses on computational photography, 3D vision, AI security, digital watermarking, and neural representations . He explores robustness and security in vision models, especially concerning NeRFs and 3D Gaussian Splatting, and develops methods for low-light enhancement, reflection removal, and domain adaptation. Dr. Wan has published in top-tier venues including TPAMI, IJCV, CVPR, ICCV, NeurIPS, AAAI, and ECCV . His recent work emphasizes copyright protection for neural 3D models , adversarial attacks in multimodal and event-based systems, and medical image reconstruction. He is actively mentoring PhD students and research assistants. VCIP 2020 Best Paper Award Outstanding Reviewer, ICCV 2019 He teaches courses such as Introduction to AI and ML (COMP3057) , AI Application Development (COMP3065) , and Python for Data Analysis and Machine Intelligence (COMP7035) . Dr. Wan leads a dynamic research group with ongoing projects on watermarking, 3D reconstruction, and AI security, and he is currently recruiting new PhD students and research assistants.
Oliver Nash is a researcher at Imperial College London, actively engaged in the formalisation of advanced mathematical concepts. His work bridges geometry and computational logic, with a focus on rigorous proof systems. Research Interests: Oliver's research lies at the intersection of geometry and the formalisation of mathematics. He specialises in using proof assistants to verify deep mathematical results, including topics such as the h-principle, sphere eversion, and Lie algebras. His work contributes to the growing field of certified mathematics, ensuring correctness through machine-checked proofs. Publication Trends: His recent publications at the CPP conference demonstrate a consistent focus on formalising foundational results in differential geometry and algebra. These works reflect a trend toward computational trust in mathematical proofs, particularly in complex geometric transformations and algebraic structures. Professional Activities: Oliver is an active contributor to the Certified Programs and Proofs (CPP) conference, presenting cutting-edge work in formal verification. He maintains a personal website detailing both his academic contributions and early explorations in computer graphics, such as raytracing and radiosity rendering from the early 2000s. Labs and Projects: While specific lab affiliations are not mentioned, his work is closely aligned with research groups in formal methods and interactive theorem proving, likely involving tools like Lean, Coq, or Isabelle.
Geoffroy Couteau is a CNRS research scientist at IRIF (Institut de Recherche en Informatique Fondamentale), Université Paris Cité, where he conducts research in theoretical and applied cryptography. He obtained his PhD from École Normale Supérieure de Paris in 2017 under the supervision of David Pointcheval and Hoeteck Wee, followed by a postdoctoral position at Karlsruhe Institute of Technology (KIT) from 2017 to 2019. His primary research interests include secure multiparty computation, zero-knowledge proofs, and the theoretical foundations of cryptography, with a particular emphasis on pseudorandom correlation generators and efficiency improvements in cryptographic protocols. He has made significant contributions to fine-grained cryptography, non-interactive zero-knowledge proofs, and post-quantum secure computation. The recent publications reflect a strong trend toward foundational advances in secure computation, with increasing focus on efficiency, practicality, and connections to complexity theory and learning theory. His work often bridges theoretical hardness assumptions with practical protocol design. ERC Starting Grant (2023) for project OBELiSC (Overcoming Barriers and Efficiency Limitations in Secure Computation) Geoffroy Couteau has advised numerous PhD and master’s students, including Dung Bui, Clément Ducros, Eliana Carozza, and Ulysse Léchine. He has also hosted many visiting students and postdocs, fostering a vibrant research group. He has served on the program committees of major conferences such as EUROCRYPT, CRYPTO, TCC, and PKC. He is currently leading research in cryptography at IRIF and is involved in postdoctoral hiring for projects in advanced cryptographic primitives. He maintains a research blog and resource collection for students, including LaTeX templates, a probability cheat sheet, and curated answers to common cryptography questions.
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 .
Prof. Dr. Katja Rösler is a Professor of Automotive Engineering at the Institute of Mechanical Engineering, Ruhr West University of Applied Sciences since March 2012. Her career spans academic research and industrial development with key positions at TU Braunschweig, Volkswagen AG, and Fraunhofer Institute. Education: Industrial Mathematics degree completed under standard period Doctorate: Engineering (Driver Modeling) from TU Braunschweig, 2008 Her research focuses on automotive engineering with special emphasis on modeling/simulation, vehicle dynamics, driver assistance systems, accident research, alternative drives, and mobility concepts. She actively combines simulation with experimental verification and has significant involvement in Formula Student projects. Recent publications highlight her work in intelligent mobility systems (2018-2020), with particular attention to electromobility, accessibility solutions for elderly/disabled populations, and micromobility analysis. Earlier works established her expertise in driver modeling, vehicle measurement technology, and simulation-experiment correlation. Labs: Automotive Engineering Lab Teaching: Mechanics (Statics, Strength of Materials, Dynamics), Vehicle Dynamics, Driver Assistance Systems
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.
Yingfei Xiong is an active Associate Professor at Peking University, China, specializing in software engineering and programming languages. With a consistent research trajectory from 2013 through 2026, Xiong has established themselves as a prominent figure in the software engineering research community, regularly contributing to top-tier conferences including SPLASH, ICSE, ASE, and PLDI. Dr. Xiong's research primarily focuses on program synthesis, automated program repair, and software analysis techniques. Their work bridges theoretical programming language concepts with practical software engineering applications, particularly in developing novel approaches for code generation, bug fixing, and program optimization. The research demonstrates strong interdisciplinary connections between traditional software engineering, programming languages theory, and emerging AI techniques. Analysis of Xiong's publication trends reveals a clear evolution in research focus, beginning with foundational work in API transformations and program adaptation around 2013-2016, shifting toward program repair techniques from 2017-2020, and most recently incorporating machine learning and neural approaches into program synthesis and repair (2021-2026). The work consistently addresses practical challenges in software development while maintaining theoretical rigor, with increasing integration of AI techniques in recent years. Dr. Xiong has served in various leadership roles across the software engineering conference ecosystem, including program committee membership and session chair positions at major conferences. Their extensive service demonstrates recognition by peers as a subject matter expert in software engineering and programming languages research. While specific grant information isn't detailed in the provided text, the sustained publication record suggests successful research funding.
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.
