Qin Lin is an Assistant Professor in the Department of Engineering Technology at the University of Houston's Cullen College of Engineering. Their research focuses on autonomous systems, control theory, and safety-critical applications. Lin holds a Ph.D. in Computer Science from Delft University of Technology (2015-2019) and completed a postdoctoral fellowship at Carnegie Mellon University's Robotics Institute (2019-2021). Research interests include safe reinforcement learning, fault-tolerant control systems, and cybersecurity for industrial control systems. Lin has published extensively on topics like vehicle autonomy, exoskeleton safety, and disturbance rejection in robotics. Their work emphasizes practical applications of control theory in autonomous driving, robotics, and human-robot interaction. Recent publications highlight advancements in control barrier functions, latency-aware autonomous systems, and data-driven anomaly detection in ICS environments. Lin has been recognized for contributions to curriculum development in engineering technology and maintains active collaborations in automotive and robotics domains.
Dr. Muhammad Intizar Ali is an Assistant Professor in the School of Electronic Engineering at Dublin City University (DCU). He holds a PhD (with distinction) from Vienna University of Technology, Austria (2011) and has held roles including Adjunct Lecturer and Research Fellow at the Insight Centre for Data Analytics, NUI Galway. His primary research focuses on IoT, Data Analytics, Machine Learning, and Knowledge Graphs with applications in Smart Cities, Manufacturing, Farming, and Healthcare. Education: PhD in Computer Science, Vienna University of Technology (2007-2011) Research Interests: IoT and Edge Analytics Federated and Distributed Machine Learning Semantic Web and Knowledge Graphs Smart Manufacturing and Industry 4.0 Stream Processing and Real-Time Systems Recent Work Trends: His publications emphasize federated learning frameworks, IoT-enabled adaptive intelligence, and knowledge graph applications in industrial contexts. Recent projects include digital twin systems for predictive maintenance and ontology-driven manufacturing solutions. Grants & Projects: Lead Investigator in SFI-funded projects like MultiRoof (2025-2029) and Neuro-Symbolic AI for Building Management EU/Industry collaborations including Terrain-AI and Bentley-funded initiatives Labs & Teams: Active in DCU's Data Analysis and Machine Learning research groups, leading projects like Smart DCU Digital Twin for campus optimization.
Kevin Kelly is a Professor of Philosophy at Carnegie Mellon University and the Director of the Center for Formal Epistemology. His work bridges formal epistemology, computational learning theory, and philosophy of science, with a focus on Ockham's razor, belief revision, and the topology of inquiry. Key Research Areas: Ockham's Razor, Epistemology, Formal Learning Theory, Modal Epistemic Logic, and Interdisciplinary Applications of Topology. Grants: John Templeton Foundation grant for research on truth-finding efficiency and scientific simplicity. Scientific Awards: John Templeton Foundation grant (2018–2021) Kelly's publications emphasize connections between probabilistic reasoning and qualitative belief, solutions to the lottery paradox, and computational models of knowledge acquisition. His recent work explores lighting design, human-centric ergonomics, and machine learning epistemology, reflecting a deep interdisciplinary engagement with technology and science.
Celeste Kidd is an Associate Professor at the University of California, Berkeley, leading the Kidd Lab. Her research bridges computational modeling and behavioral experiments to study knowledge acquisition in young children, drawing from foundational theories by Piaget, Montessori, and Vygotsky. Education: Ph.D. in Cognitive Psychology from the University of Rochester Her work focuses on attention, curiosity, and learning dynamics, particularly how children explore and attend to information during development. She employs eye-trackers, touchscreens, and observational play studies to quantify learning processes. Recent publications highlight her exploration of belief formation, social learning, and the intersection of AI with cognitive development. Her findings inform educational technologies and clinical practices. Her lab emphasizes cross-cultural studies and computational models of curiosity, with experiments spanning infancy to adolescence. She investigates how predictability, physical cues, and linguistic structures shape cognitive growth.
Luciano Pomatto is a Professor of Economics at the California Institute of Technology (Caltech) and Executive Officer for the Social Sciences since 2025. He holds affiliations with the Ronald and Maxine Linde Institute of Economic and Management Sciences, the Center for Social Information Sciences (CSIS), and the Center for Theoretical and Experimental Social Sciences (CTESS). Education: B.A. (2007), M.A. (2009) from Universita di Torino; Ph.D. (2015) from Northwestern University. Academic Career: Assistant Professor at Caltech (2016–22), promoted to Professor in 2022. Editorial Roles: Associate Editor at the Journal of Political Economy , Journal of Economic Theory , and Journal of Mathematical Economics . His research focuses on decision-making under uncertainty, risk analysis, information theory, and Bayesian epistemology. Recent work explores stochastic dominance, ambiguity preferences, and the mathematical foundations of forecasting. The 15 most recent publications span Econometrica, American Economic Review, Journal of Economic Theory, and Journal of Political Economy. They reflect interdisciplinary trends bridging microeconomic theory with probability theory, cognitive science, and social decision-making. Scientific Awards: Brass Division Award (2022). Teaching includes courses on Game Theory, Foundations of Economics, and Advanced Economic Theory, with cross-departmental offerings in Social, Cognitive, and Decision Sciences. No student names or grant details are explicitly listed in the provided text.
Dr Elliot J. Crowley is a Senior Lecturer in Electronics and Electrical Engineering at the University of Edinburgh, serving as Discipline Programme Manager. He co-leads the Bayesian and Neural Systems research group. His research focuses on simplifying machine learning, automated ML, low-resource deep learning, and engineering applications. He holds an MEng in Engineering Science and a DPhil (PhD) from the University of Oxford, with postdoctoral experience at Edinburgh's School of Informatics. He leads the EPSRC New Investigator Award and participates in the dAIEdge Horizon Network. Notable contributions include foundational work in neural architecture search (NAS), probabilistic methods for model efficiency, and applications in computer vision. His courses, such as the Data Analysis and Machine Learning module, emphasize practical Python-based learning for engineering students. Key awards include an EPSRC grant and recognition through distinguished papers at ASPLOS 2021. His team includes current PhD students (Linus Ericsson, Miguel Espinosa) and former advisees (Chenhongyi Yang at Meta, Jack Turner at Qualcomm). Research spans from NAS algorithms to ethical machine learning practices, with a focus on bridging theoretical advances and real-world engineering challenges.
Dr. Gemma Newlands is a Departmental Research Lecturer in AI & Work at the Oxford Internet Institute (OII), University of Oxford, since September 2022. Her research focuses on the sociological impact of AI on work and organizations, emphasizing surveillance, occupational value, and sustainability in AI production. She holds a PhD from the University of Amsterdam (2023) and previously served as Assistant Editor at Big Data & Society . Her work is supported by grants from the Norwegian Research Council and European Research Council. Research Interests : AI & Work, Sustainable AI Production, Surveillance in Digital Labour, Occupational Prestige, and Social Theory. She leads the Research Programme on AI & Work and contributes to the Reasoning with Machines AI Lab. Awards : 2024 University of Oxford Divisional Teaching Excellence Achievement Award (shared with Dr. Fabian Stephany). Teaching : Courses include Qualitative Interviewing and Data Analysis and Internet and Society . Grants & Projects : Multiple funded projects on algorithmic management, gig economy dynamics, and occupational evaluation via AI. Supervises Recognised Student Noah Khan in Social Data Science. Labs/Teams : Active in OII research groups such as the Fair Digital Economies and Mind and Emerging Technologies Lab.
Miroslaw Bober is Professor of Video Processing at the University of Surrey, where he joined in 2011. He leads the Visual Media Analysis team within the Centre for Vision, Speech and Signal Processing (CVSSP) in the School of Computer Science and Electronic Engineering. His extensive industry experience includes 15 years as General Manager of the Mitsubishi Electric R&D Centre Europe and Head of Research for its Visual & Sensing Division. BSc and MSc in Electrical Engineering from AGH University of Science and Technology, Krakow, Poland (1990) MSc in Machine Intelligence with distinction from Surrey University (1991) PhD in Computer Vision from Surrey University (1995) Professor Bober's research focuses on novel techniques in signal processing, computer vision and machine learning with applications in industry, healthcare, big-data and security. His expertise particularly lies in image and video analysis and retrieval, including visual search, object recognition, and analysis of motion, shape and texture. His algorithms for shape analysis, image/video fingerprinting, and visual search are considered world-leading and have been selected for ISO International standards within MPEG, with applications used by organizations like the Metropolitan Police. His recent publication trends show a strong focus on hybrid network architectures, scene graph generation, medical imaging applications, and augmented reality publishing systems. His work spans both theoretical advancements in computer vision and practical implementations addressing real-world challenges in media, healthcare, and security domains. The research demonstrates a consistent pattern of bridging academic innovation with industrial applications, particularly in visual search technology and media analysis. Presidential Award for strengthening the TV business in Japan via innovative 'Visual Navigation' content access technology (2010) Mitsubishi Best Invention Award for Image Signature Technology (2008) Professor Bober serves as Programme Director for the MSc in Multimedia Signal Processing and Communications and holds various teaching and mentoring roles. He has secured over 30 research and industrial grants totaling more than £16M, including the BRIDGET FP-7 project (5.28 M€) as coordinator and PI, and the CODAM project (£1.05 M) as PI. His work with the BBC, Huawei, and other industry partners demonstrates strong industry-academia collaboration. As chair of MPEG technical work on Compact Descriptors for Visual Search (CDVS) and Compact Descriptors for Video Analysis (CDVA), Professor Bober leads international standardization efforts. His Visual Media Analysis team develops cutting-edge visual search and media analysis algorithms with applications across broadcast, security, and healthcare domains.
Dr. Juan Durán is an Assistant Professor at the Faculty of Technology, Policy and Management at TU Delft, specializing in the philosophy of science and technology. His work focuses on computer simulations, AI ethics, Big Data, and the epistemological challenges in computational science. Education: Bachelor/Master in Computer Science and Philosophy, National University of Córdoba (Argentina) PhD in Cluster of Excellence SimTech, University of Stuttgart (Germany) Research interests: Epistemic opacity and trust in algorithms Ethics of medical AI and machine learning Computational reliabilism as a framework for justification Dark data and scientific data governance Teaching roles include courses on ethics and engineering, IT and values, and philosophy of science. His publications include influential works like Computer Simulations in Science and Engineering (2014) and co-authored papers on AI trustworthiness and medical epistemology.
Dr. Christopher Gilliam is an Assistant Professor in Applied Signal Processing at the University of Birmingham's Department of Electronic, Electrical and Systems Engineering. He holds an MEng (1st Class Hons) in Electrical & Electronic Engineering (2008) and a Ph.D. in Signal Processing (2013), both from Imperial College London. Prior to joining Birmingham in 2022, he was a Postdoctoral Fellow at The Chinese University of Hong Kong (2013–2017) and a Research Fellow at RMIT University, Australia (2017–2022). Research Interests: Sensor signal processing, radar imaging, sampling theory, motion estimation, quantum navigation, and medical imaging. Labs: Microwave Integrated Systems Laboratory (MISL). Committees: Member of IEEE Signal Processing Society and APSIPA Technical Committees. His work focuses on advancing signal processing techniques for radar systems, navigation, and medical imaging. Recent research highlights include drone-based SAR imaging, motion correction in MRI, and fusion of classical/quantum sensors for inertial navigation. He is actively supervising PhD students and contributes to projects sponsored by DSTG. Publications span radar SLAM, probabilistic navigation algorithms, and deep learning-driven medical imaging solutions. His research bridges theoretical signal processing with practical applications in autonomous systems and healthcare.
Arno Siebes is Professor of Algorithmic Data Analysis in the Department of Information and Computing Sciences at Utrecht University's Faculty of Science. His research focuses on data mining methodologies, particularly pattern mining and Minimum Description Length (MDL) principles. Key research areas include: Developing efficient algorithms for pattern discovery Applying MDL to data characterization Creating interpretable models for complex datasets Addressing challenges in data science education Recent publications demonstrate applications in diverse domains including mobility analysis, genomic screening, and pandemic response. His work combines theoretical foundations with practical implementations for knowledge discovery.
Brandon Karchewski is an Associate Head (Undergraduate) and Teaching Professor in the Department of Earth, Energy and Environment at the University of Calgary. He earned his PhD in Civil Engineering from McMaster University and teaches courses including Engineering Geology, Computational Methods, and Natural Disasters. His research program focuses on: Computational methods in geophysics and geomechanics Geoscience education innovation Climate change impacts on frozen soils Inverse modeling applications Karchewski has pioneered virtual field experiences and developed open-source tools for modeling climate impacts on permafrost. His educational research examines metaphor use in geoscience communication and field pedagogy. Recent computational work includes Python-based permafrost modeling and stochastic inversion methods for biogeochemical transport. He leads the geophysics field school program emphasizing team-based learning. Award recognition includes: Geoscience Teaching Award (2019) Best Poster Award for teaching innovation research (2018) Team Teaching Excellence award (2016) Multiple teaching assistant awards
Paul Franzon is the Cirrus Logic Distinguished Professor and Associate Department Head for Graduate Affairs at the Department of Electrical and Computer Engineering, North Carolina State University. He holds a PhD and Bachelor's in Electrical Engineering and a Bachelor's in Physics/Mathematics from the University of Adelaide, Australia. His research focuses on quantum information science, machine learning-driven hardware design, 3D integration, and high-speed systems. Education: PhD in Electrical Engineering, University of Adelaide (1988) Bachelor's in Electrical Engineering, University of Adelaide (1984) Bachelor's in Physics and Mathematics, University of Adelaide (1982) Research Interests: Quantum computing and algorithm optimization AI-driven design automation for 3D integrated circuits High-speed communication systems Hardware security and FPGA acceleration Awards & Honors: IEEE Fellow (2006) Alcoa Foundation Distinguished Engineering Research Award (2005) NC State Alumni Distinguished Undergraduate Professor Award (2003) NSW Australia Expatriate Scientist Award (2003) Advising & Grants: Advised PhD student Priyank Kashyap (2023 graduate) Recipient of NSF Young Investigators Award (1993) Labs & Collaborations: Center for Advanced Electronics Through Machine Learning (CAEML) IEEE EPS Society (Associate Editor)
Salvatore Ruggieri is a Full Professor at the Department of Computer Science, University of Pisa, Italy. He coordinates the National PhD Program in Artificial Intelligence for Society and teaches in the Master Program in Data Science and Business Informatics. His research focuses on algorithmic fairness, explainable AI, causality, and discrimination discovery, with contributions to tools like YaDT, X-SPELLS, and SCube. He co-chaired FAT*2020 and contributed to projects such as NoBias and HumanE-AI. His work bridges theoretical foundations with practical applications in fairness, privacy, and societal impact. Education: Ph.D. in Computer Science (1999), University of Pisa. Research Interests: Algorithmic Fairness and Non-Discrimination Explainable AI (XAI) Causal Inference Methods Decision Tree Algorithms Social Network Analysis Key Contributions: Developer of YaDT (decision tree tool) and SCube (segregation discovery). Advocacy for ethical AI through policy frameworks and GDPR-compliant explanations. Scientific Awards: Recipient of the Best Ph.D. Thesis in Theoretical Computer Science (EATCS, 1999).
Alissa N. Antle is Professor at Simon Fraser University's School of Interactive Arts and Technology, where she directs the Tangible, Embodied, Child Interaction (TECI) Research Lab. A Fellow of the Royal Society of Canada's College of New Scholars, her research develops interactive technologies to support children's cognitive and emotional development. Antle's current projects include VR systems for emotion regulation, AI literacy tools, and ethical design frameworks for biowearables. Her work employs research-through-design approaches and mixed-methods evaluations, often involving vulnerable populations. She collaborates with educators, clinicians, and international partners to create technologies for literacy development, mental health support, and critical digital literacy. Antle has received the FCAT Distinguished Researcher award and funding from SSHRC, NSERC, and CFI to support her research program in child-centered technology design.
