Prof. Yonina Eldar is a Professor of Electrical Engineering at the Faculty of Mathematics and Computer Science , Weizmann Institute of Science. She holds the Dorothy and Patrick Gorman Professorial Chair and serves as Head of the Manya Igel Center for Biomedical Engineering and Signal Processing . Her research bridges classical signal processing with modern deep learning techniques. Develops model-based deep learning frameworks combining domain knowledge with data-driven approaches Focuses on sub-Nyquist sampling for efficient data acquisition in radar, ultrasound, and communications Created hardware prototypes for time-encoding machines and modulo-ADC systems Innovates in joint radar-communication systems for autonomous vehicles Her work emphasizes algorithm unrolling to create interpretable neural networks with reduced training requirements. Publications demonstrate applications in: Medical imaging (ultrasound, ECG monitoring) Autonomous systems (automotive radar) Communication technologies (DFRC systems) Active in theoretical foundations of model-based deep learning, with recent work establishing mathematical guarantees for unfolded networks. Collaborations include Tsinghua University and industry partners for hardware validation.
Prof. Dr. Silke Schwandt is a Professor of Digital History at Bielefeld University's Faculty of History, Philosophy and Theology , Department of History. She leads the Digital History group and co-leads subprojects within the Collaborative Research Center 1288 (CRC 1288) 'Practices of Comparison. Ordering and Changing the World' and the SFB 1288 subproject INF. She serves as Founding Director of the Center for Uncertainty Studies (CeUS) and is a member of multiple interdisciplinary institutes including the Institute for Interdisciplinary Conflict and Violence Research (IKG) and Bielefeld Center for Data Science (BiCDaS) . Her research focuses on Digital Humanities methodologies Medieval English legal history (1286 Huntingdonshire Eyre) Knowledge production under uncertainty Historical semantics network analysis Virtual reality applications for historical research Epistemological challenges in digital historiography Recent research trends show significant engagement with Digital data flows in historical contexts Visualization epistemology Sustainable socio-technical systems Lexical analysis of historical hate speech Medieval court dynamics as social networks Her work bridges traditional historiography with computational methods through projects like SAIL and 3B (Bots Building Bridges) . She also contributes to academic governance as a member of Bielefeld University Press' steering committee and the Senate's University Professors group.
A. Ianniello is a researcher active in interdisciplinary domains spanning industrial design, human-robot interaction, and emerging materials engineering. Their work bridges speculative design methodologies with applied research in robotics, healthcare, and immersive technologies. Research Focus: Human-Robot Interaction, Speculative Design, Medical Simulation, Augmented Reality Collaborations: Academic-industry partnerships in systemic design and urban mobility Methodologies: Experiential counterfactuals, co-design frameworks, immersive environments Recent publications (2022-2025) demonstrate expertise in speculative design for sustainability, spatial robotic experiences, and systemic approaches to healthcare and urban planning. They explore intersections of technology, ethics, and human-centric resilience through design-led interventions in smart cities and rural regeneration.
Ulaş Tezel is an Associate Professor at the Institute of Environmental Sciences (IESc) at Boğaziçi University in Istanbul, Turkey. His office is located in HKC-305. He holds a Ph.D. from the School of Civil and Environmental Engineering at Georgia Institute of Technology (2009), and M.S. and B.S. degrees in Environmental Engineering from Middle East Technical University (2003 and 2001). His research focuses on micropollutant occurrence, biotransformation of organic pollutants, molecular genetics of bacterial biotransformation, and catabolic transposons/plasmids. Key projects include studying disinfectant biodegradation during the SARS-CoV-2 pandemic, IoT-driven AI for sustainable watershed management, and ecophylogenetic analysis of genetic elements. Recent publications address global pharmaceutical pollution, pesticide residues in rice, wastewater treatment using microbial reactors, river micropollutant categorization, and acetaminophen biotransformation modeling. He leads the BIOMIG lab ( BIOMIG ), focusing on environmental microbiology and biotechnology. His grants include projects funded by Boğaziçi University Research Fund, CHIST-ERA, and TÜBİTAK. Current initiatives emphasize integrating AI and IoT for environmental decision-making and developing bioremediation tools for wastewater treatment.
Yufei Li is a Professor at Xi'an Jiaotong University's School of Computer Science and Technology, Department of Computer Science. With an extensive publication record spanning from 2007 to 2025, Dr. Li has established himself as a prominent researcher in database systems, software engineering, and machine learning applications. His work demonstrates strong interdisciplinary connections between computer science and electrical engineering, particularly in power systems applications. Dr. Li's research interests span multiple domains of computer science and engineering. His primary focus is on database systems, where he has pioneered work in LLM-based database tuning systems like GPTuner. He also has significant contributions in software configuration and performance optimization, as evidenced by his CSAT framework. His research extends to computer vision applications for security screening and medical diagnostics, as well as electrical engineering applications in power systems and UAV control. This diverse portfolio demonstrates his ability to bridge theoretical computer science with practical engineering applications across multiple domains. Analysis of Dr. Li's recent publications (2023-2025) reveals a strong trend toward integrating large language models with traditional computer science domains. His work on GPTuner represents a significant advancement in applying LLMs to database tuning, while his research on QUITE demonstrates innovative approaches to query rewriting using LLM agents. There's also a clear pattern of applying advanced machine learning techniques to solve domain-specific problems across electrical engineering, medical diagnostics, and industrial quality control. The interdisciplinary nature of his work positions him at the forefront of AI integration across multiple engineering disciplines. Dr. Li has established a productive research group with numerous doctoral students and collaborators, particularly Jiale Lao, Yibo Wang, and Jianguo Wang who frequently appear as co-authors on his recent publications. His research has been consistently funded, as evidenced by the steady stream of publications across multiple high-impact venues including IEEE Access, Journal of Systems and Software, and CVPR. His work demonstrates strong industry relevance with applications in database management, software configuration, security screening, and power systems. Dr. Li leads a research team focused on database systems and AI integration, with strong connections to both computer science and electrical engineering domains. His group appears to specialize in applying cutting-edge machine learning techniques, particularly large language models, to solve longstanding problems in database management and software engineering. The team maintains active collaborations with researchers across multiple institutions, as evidenced by the diverse author lists on his publications.
Ali Zarezade is a researcher at the Max Planck Institute for Software Systems (MPI-SWS), focusing on interdisciplinary research spanning algorithms, machine learning, and social computing. His work integrates theoretical foundations with practical applications in cyber-physical systems, distributed networks, and human-centric AI. Key research areas include human-in-the-loop machine learning, optimal control strategies for social networks, and spatio-temporal modeling of social media activity. His methods often combine stochastic control theory, sparse regression techniques, and online algorithm design to address challenges in education technology, security, and information diffusion. Prior contributions involve developing algorithms like RedQueen and Cheshire for optimizing social network activity, probabilistic models for hyperspectral unmixing, and visual tracking systems resilient to occlusions. His research bridges computational theory with real-world systems, emphasizing scalable solutions for distributed and networked environments.
Babak J. Fard, PhD, is a Research Assistant Professor in the Department of Environmental, Agricultural & Occupational Health at the University of Nebraska Medical Center (UNMC) College of Public Health. He holds a PhD in Interdisciplinary Engineering and an M.Sc. in Civil & Environmental Engineering from Northeastern University. His research focuses on environmental data science techniques, including geospatial analysis, machine learning, and AI models, to study climate variables and their health impacts across geographical and temporal scales. He has conducted extensive work on drought-related health risks, heat vulnerability mapping, and satellite-derived environmental data applications. Dr. Fard is affiliated with the American Geophysical Union (AGU) and the American Meteorological Society (AMS). His recent research emphasizes climate change adaptation strategies, public health risk assessment, and community resilience in the face of extreme weather events. Notable projects include mapping heat vulnerability in Nebraska, evaluating drought's impact on respiratory/cardiovascular mortality, and developing decision-support tools for drought preparedness. His work integrates interdisciplinary approaches to address pressing environmental health challenges, with a focus on rural and urban disparities. Ongoing projects include analyzing PM2.5 health risks using satellite data and exploring links between severe weather and mental health outcomes.
Professor Isidori Emanuele is a Full Professor (First Band) at the Department of Human Motor Sciences and Health, University of Rome 'Foro Italico'. He holds key roles including Rector's Delegate for International Relations and Training of Academic and Non-Academic Staff, and Head of the General Pedagogy Laboratory. His research focuses on pedagogy, sports education, anti-doping strategies, dual career development for student-athletes, and inclusive education models. He teaches courses such as 'Ethics and Sport' and 'General and Sports Pedagogy' at undergraduate and graduate levels. His work bridges educational theory and practice, emphasizing interdisciplinary approaches to contemporary challenges like athlete well-being, anti-doping compliance, and global sports ethics. He leads international projects such as the ‘Para-Limits’ initiative supporting disabled student-athletes. His research spans cross-cultural studies (e.g., Romania-EU teacher burnout comparisons) and innovative methods like flipped classrooms and e-learning platforms. Publications reflect a commitment to holistic education, addressing topics from ancient Greek agonistic values to modern technologies in sports pedagogy. Despite no listed awards, his extensive contribution to academic leadership and curriculum innovation underscores his scholarly impact. Current initiatives include developing microcredentials in sports education and exploring AI-enhanced tutoring systems.
Jialun Chen serves as an Eric and Wendy Schmidt AI in Science Postdoctoral Fellow within the Department of Engineering Science at the University of Oxford, and is a member of Reuben College. His research focuses on leveraging machine learning for precise wave forecasting at large temporal and spatial scales to enhance coastal dynamics modeling and offshore operational safety. His educational background includes: PhD in Ocean Engineering (specialization: Machine Learning) from the University of Western Australia, with doctoral research on phase-resolved wave and motion forecasting for offshore infrastructure. Chen's research centers on machine learning applications in ocean engineering, specifically: Machine learning-based wave modeling and forecasting for coastal/offshore environments Short-term motion prediction of floating infrastructure to enable safe ship-to-ship transfers and offshore operations Integration of physical understanding with data-driven models for improved accuracy Analysis of his 2022-2024 publications reveals a progression from autoregressive models to sophisticated phase-resolved predictions incorporating physical constraints, with consistent emphasis on practical applications for offshore wind energy operations and marine safety. No scientific awards or honors are documented in the available information. Chen is supported by the Eric and Wendy Schmidt AI in Science Postdoctoral Fellowship and contributes to the TetraSpar Offshore Wind Turbine Project, where he develops machine learning solutions for safe floater-to-floater transfers. No student advisement roles are indicated. He operates within Oxford's Marine Renewable Energy research group under Professor Thomas Adcock, focusing on advancing offshore wind energy technologies through AI-driven operational safety solutions for floating infrastructure.
Bonnie Mak is an Associate Professor in the School of Information Sciences at the University of Illinois, with courtesy appointments in History and Medieval Studies. Her research focuses on manuscript studies, book history, and the history of information practices across medieval to digital eras. She holds a PhD in Medieval Studies from the University of Notre Dame, alongside MA and BAH degrees in Medieval Studies with a philosophy concentration from Queen’s University. Mak’s work bridges historical and contemporary information practices, exemplified by her book *How the Page Matters* (2011) and her forthcoming chapbook *Reproducing by Fragments*. She co-edits *The Routledge Handbook of Information History*, and organizes the “AI and the Human Condition” seminar series through the Humanities Research Institute. Her academic service includes roles on the Senate Committee on the Library since 2022. Research interests include knowledge production, material culture, and digital humanities. Her articles explore topics like information history methodology, document materiality, and the impact of digitization. Awards include eight recognitions as an excellent teacher at the University of Illinois and fellowships at Penn State and the Illinois Program for Research in the Humanities. Mak teaches courses on information science history, the history of the book, and cultural practices of knowledge. Her work engages with interdisciplinary collaborations, blending humanities and technology studies to address evolving information infrastructures.
Julian Padget is a Reader (equivalent to Associate Professor) in the Department of Computer Science at the University of Bath, with extensive affiliations across multiple research centers including the EPSRC Centre for Doctoral Training in Statistical Applied Mathematics (SAMBa), Water Innovation and Research Centre (WIRC), UKRI CDT in Accountable, Responsible and Transparent AI, Centre for Therapeutic Innovation, and Institute for Digital Security and Behaviour (IDSB). His work bridges computer science with energy systems, healthcare, and digital governance through interdisciplinary collaborations. Padget's research centers on multiagent systems , agent architecture , norm representation and reasoning , and fusing symbolic and statistical AI , with significant contributions to distributed ledgers, policy modeling, and narrative models. His fingerprint analysis reveals dominant connections to multi-agent systems (100%), web services (72%), and normative frameworks (48%), reflecting his focus on creating ethically aligned autonomous systems for complex socio-technical environments. Recent work demonstrates increasing integration of AI with energy infrastructure and biomedical applications. Analysis of his 15 most recent publications shows a clear trajectory toward operationalizing AI ethics, particularly in bias management and trust frameworks, while maintaining strong foundations in multiagent coordination. His energy sector research increasingly focuses on digital spine architectures for data sharing, and biomedical collaborations explore molecular imaging techniques for cancer research. The consistent thread across domains is the development of governance frameworks for autonomous systems. Padget actively supervises doctoral students with 23 supervised works documented and serves as Principal Investigator on major grants including EPSRC-funded energy network projects (2025-2026) and Innovate UK collaborations with the BBFC. His policy impact is evidenced by parliamentary testimony in March 2025 that generated coverage across 16 news outlets. Current projects emphasize AI for agile energy networks, value-aware agent architectures, and statutory compliance frameworks for online media. His laboratory ecosystem spans the IAAPS Innovation Bridge and Institute for Digital Security and Behaviour, focusing on translating theoretical agent frameworks into practical applications for energy systems, digital governance, and health interventions. The Water Innovation and Research Centre provides critical infrastructure for his energy-related simulations, while therapeutic innovation collaborations enable biomedical applications of his norm-representation frameworks.
Dr. Prathyush Sambaturu is a Research Fellow in the Department of Biology at the University of Oxford, affiliated with the Kraemer research group. His work focuses on computational and spatial epidemiology, network science, combinatorial optimization, and complex systems. He designs agent-based network models to study infectious disease dynamics and develops surveillance strategies like genomic or wastewater monitoring to detect outbreaks and infer epidemiological parameters such as the reproduction number. His research interests include understanding how human mobility and population structure influence disease spread, as well as creating scalable algorithms for epidemic control. He also explores the impacts of climate change on migration patterns and associated disease risks. His articles span advanced computational methods applied to public health challenges, including graph-based active learning, fair disaster containment policies, and GPU-optimized intervention strategies. No scientific awards are explicitly mentioned. His research advisory role and grants information are not detailed in the provided texts. He is part of the Kraemer group, contributing to interdisciplinary projects at the intersection of epidemiology, network science, and algorithm design.
Chris Eliasmith is a Professor jointly appointed in the Systems Design Engineering and Philosophy departments at the University of Waterloo, with a cross-appointment to Computer Science. He is the Founding Director of the Centre for Theoretical Neuroscience and holds the Canada Research Chair in Theoretical Neuroscience. His research focuses on mathematical modeling of neural systems, including the Neural Engineering Framework (NEF) and Semantic Pointer Architecture (SPA), which underpin the world’s largest functional brain simulation, Spaun. He leads the Computational Neuroscience Research Group (CNRG), advancing neuromorphic computing, spiking neural networks, and cognitive modeling. Education: PhD in Neuroscience & Psychology (2000), Washington University in St. Louis MA in Philosophy (1995), University of Waterloo BASc in Systems Design Engineering (1994), University of Waterloo Research: His work integrates theoretical neuroscience with practical applications in robotics, AI, and neuromorphic hardware. Key contributions include the NEF, Spaun, and innovations like the Legendre Memory Unit (LMU) and Spatial Semantic Pointers (SSPs). His lab explores adaptive neural systems, decision-making models, and biologically plausible machine learning algorithms. Teaching: Recent courses include SYDE 556 (Simulating Neurobiological Systems), SYDE 750 (Topics in Systems Modelling), and PHIL 356 (Intelligence in Machines, Humans, and Other Animals). Awards: 2015 NSERC Polanyi Prize Labs & Projects: The CNRG develops tools like Nengo for simulating large-scale neural systems and collaborates on neuromorphic hardware platforms such as Loihi. Current projects span neural robotics, cognitive architectures, and biologically inspired AI systems.
Dr. Stefan Schlobach is an Associate Professor at the Faculty of Science, Vrije Universiteit Amsterdam, affiliated with the Artificial Intelligence department, Network Institute, and Knowledge Representation and Reasoning group. His research focuses on Semantic Web, Linked Data, Ontology Engineering, and Hybrid Intelligence systems. He has published over 116 peer-reviewed works and supervised 6 PhD theses. Recent projects include robotic knowledge acquisition (ORKA ontology), explainable AI for security systems, and knowledge engineering for virtual heritage applications. He co-developed the LOTUS search framework for big linked data and contributed to the CoPla ontology for agriculture. His work bridges theoretical foundations (e.g., argumentation systems) with applied domains (e.g., cyber-physical systems, cultural heritage digitization). Research Interests: Semantic Web Technologies Knowledge Graphs & Ontology Engineering Hybrid Intelligence (Human-AI Collaboration) Robotic Perception Systems Explainable AI Entity Linking & Identity Resolution Key Contributions: Developed LOTUS adaptive search system (2016 prize-winning work) Created ORKA ontology for robotics knowledge acquisition Pioneered perceived-entity linking methodologies Advocated knowledge engineering principles for hybrid systems Teaching: Leads courses in Artificial Intelligence, Knowledge Representation, and Semantic Technologies at both undergraduate and graduate levels.
Fredrik Lindsten is a Senior Associate Professor in Machine Learning and Head of the Division of Statistics and Machine Learning at Linköping University's Department of Computer and Information Science (IDA). His research focuses on statistical machine learning, emphasizing probabilistic modeling and uncertainty quantification in methods such as approximate Bayesian inference, representation learning, and graph-based approaches. Applications span weather forecasting, materials science, biochemistry, and automotive industry challenges. Education: MSc (2008), PhD (2013) in Automatic Control from Linköping University; Postdoctoral roles at University of Cambridge, UC Berkeley, and University of Oxford. Affiliations: WASP (Wallenberg AI, Autonomous Systems and Software Program), ELLIIT (Lab for Information and Communication Technology). Research Interests: Lindsten’s work bridges statistical methodology and machine learning, particularly in quantifying uncertainty in predictions. His team explores method development across diverse applications, including spatio-temporal models and graph-based techniques. Recent projects include probabilistic weather forecasting with graph neural networks and cryo-EM reconstruction techniques. Publications: Over 25+ peer-reviewed articles, with recent highlights in Nature Methods , NeurIPS , and Physical Review Materials , focusing on Bayesian methods, generative models, and computational statistics. Awards: Ingvar Carlsson Award (Swedish Foundation for Strategic Research), Benzelius Award (Royal Society of Sciences in Uppsala). Grants & Teams: Supervises 7+ PhD students; active in collaborative projects funded by WASP, ELLIIT, and VR (Swedish Research Council). Labs/Teams: Leads the Division of Statistics and Machine Learning (STIMA) at IDA, fostering interdisciplinary research in probabilistic machine learning.
