Aleksandar Mijatović is a Professor of Probability at the Department of Statistics, University of Warwick, and Deputy Head of Department for Research. He was previously Chair in Probability at King's College London and Reader in Probability at Imperial College London. His research focuses on probability theory, stochastic processes, mathematical finance, numerical stochastics, and data science. He holds a Ph.D. in low-dimensional topology from Trinity College Cambridge and worked as a quantitative analyst in foreign exchange derivatives before academia. Research interests include stochastic analysis of processes with jumps, simulation methods (e.g., Monte Carlo), stochastic control, and applications in finance. He is a Fellow of the Alan Turing Institute and maintains a YouTube channel, Prob-AM, explaining his research. His work often bridges theoretical probability with practical applications in finance and data science. Key publications explore topics like reflected Brownian motion, Lévy processes, branching processes, and stochastic gradient descent. Collaborations with institutions like King’s College London and Imperial College London highlight his academic networks. His contributions span theoretical advancements and computational methodologies, with applications in risk management, option pricing, and algorithm development.
Dr. Ke Chen is a Senior Lecturer in the School of Computer Science at The University of Manchester, leading the Machine Learning and Perception (MLP@UoM) Lab. His research focuses on machine learning, deep learning, reinforcement learning, and their applications in intelligent systems, computer vision, and audio/speech processing. He has supervised over 50 PhD students and holds editorial roles in journals like Neural Networks and IEEE Transactions on Neural Networks . Dr. Chen has received awards such as the NSFC Distinguished Principal Young Investigator Award (2001) and JSPS Research Award (1993). Education: PhD in Computer Science (1990), with academic positions at institutions including Peking University, The Ohio State University, and Kyushu Institute of Technology. His professional activities include roles in IEEE Computational Intelligence Society committees and external examiner roles at universities like Essex. Research interests span computational cognitive systems, biometric authentication, and video game AI. Key contributions include advancements in deep architectures, reinforcement learning, and speaker-specific feature extraction. His lab, MLP@UoM, explores topics like explainable AI and transfer learning. Recent publications (2024) include work on goal-conditioned reinforcement learning and bias-resilient algorithms. He is actively involved in international conferences, serving as a keynote speaker and program committee member.
Dr. Wenqi Shi serves as an Assistant Professor at the Peter O’Donnell Jr. School of Public Health at UT Southwestern Medical Center. Her research focuses on the integration of artificial intelligence with healthcare, particularly advancing algorithms and systems for precision medicine. She specializes in working with multi-modal patient data including EHRs, medical notes, imaging, and genomics, with dedicated applications in pediatric healthcare, cancer, and rare diseases. Her research interests include developing large language models for translational medicine, creating agentic AI and generative models for biomedical discovery, and establishing responsible AI practices to enhance clinical outcomes. Publication trends show extensive work in explainable AI, clinical decision support systems, and multi-modal data integration, with recent emphasis on retrieval-augmented language models and causal inference methodologies. Dr. Shi obtained her Ph.D. from the Georgia Institute of Technology prior to joining UT Southwestern.
Len Gelman is a Professor and Chair in Signal Processing and Condition Monitoring at the University of Huddersfield's Department of Engineering within the School of Computing and Engineering. He also serves as Director of the Centre for Efficiency and Performance Engineering. His research focuses on advanced signal processing techniques for fault diagnosis in electromechanical systems, vibration analysis, and predictive maintenance. He is actively involved in PhD supervision and has authored over 100 publications, achieving 1499 citations and an h-index of 21. Key research areas include digital twin technology, nonlinear spectral analysis, and machine learning for industrial diagnostics. His work addresses challenges in non-stationary signal processing, motor current signature analysis, and condition monitoring under varying operating conditions. Collaborations include interdisciplinary projects with the Centre for Efficiency and Performance Engineering. Recent studies highlight innovations in fault diagnosis frameworks for rotating machinery, conveyor belt systems, and wind turbines. His contributions bridge theoretical advancements with practical industrial applications, emphasizing explainable AI and adaptive diagnostics. Gelman's research has been presented at major conferences like the World Congress on Engineering and published in specialized journals. Education: Not explicitly stated in the provided text. Awards: High citation count and h-index reflect his significant academic impact. Grants/Advising: Supervised 2 PhD projects; accepting new students in diagnostic engineering and condition monitoring. Labs/Teams: Leads the Centre for Efficiency and Performance Engineering and collaborates with the Department of Engineering's research groups.
Alessio Malizia is a Professor of User Experience Design at the University of Hertfordshire, UK, and concurrently holds a full Professorship at the Computer Science Department of the University of Pisa, Italy. His research focuses on Human-Centered Systems, Human-Centered AI, and Design Fictions, emphasizing ethical AI, user participation, and the integration of visual programming languages. He has held roles at institutions including Sapienza University of Rome, IBM, Silicon Graphics, and Xerox PARC, where he researched neural networks and gestural interfaces. His work bridges academia and industry, addressing challenges in healthcare, agriculture, and education. Malizia’s career includes visiting research at Xerox PARC, associate professorship at the University Carlos III of Madrid, and senior lecturer roles at Brunel University London. He is an ACM Distinguished Speaker and actively promotes democratizing AI through tools like BlocklyBias and PyFlowML. His research spans touchless interfaces, collaborative design methodologies, and AI explainability. Key contributions include frameworks for socio-technical process modeling in agriculture, tools for bias detection in AI data, and visual languages fostering user participation in ML systems. His work on telemedicine and healthcare AI emphasizes co-design and trusted AI systems. Recent studies explore end-user development for smart environments, digital agriculture, and computational thinking education through tools like TAPASPlay. Awards: ACM Distinguished Speaker Key Interests: Human-AI Interaction, UX Design, AI Ethics, IoT Ecosystems, and Co-Creation Methodologies Notable Projects: Figmant, ModeLLer, and the Chatbot Usability Scale validation His grants and collaborations focus on improving AI transparency, user-centered design processes, and participatory innovation in technology adoption across sectors.
Nelson Nicolas Higuera Ruiz is a PreDoc Researcher at the Vienna University of Technology, affiliated with the Faculty of Informatics' Knowledge-Based Systems research group. His work bridges logic programming and deep learning for explainable AI. Research Focus: Neurosymbolic AI, Visual Question Answering (VQA), Answer Set Programming (ASP), and hybrid reasoning systems Projects: Leads optimization research in the LCS (2017–2025) project, developing neurosymbolic approaches for intelligent systems Key Contributions: Pioneering adaptive large-neighbourhood search algorithms for ASP optimization, modular neurosymbolic architectures, and contrastive explainability frameworks for VQA Collaborations: Active in international workshops and conferences including IJCAI, AAAI, and CLeaR, frequently collaborating with researchers like Thomas Eiter and Johannes Oetsch Publications: Focus on neurosymbolic integration, optimization algorithms, and explainability across AI, logic programming, and computer vision domains
Caner Özer is a Researcher affiliated with Istanbul Technical University's Department of Artificial Intelligence and Data Engineering and the University of Twente's MIA group. He holds a PhD in Computer Engineering from Istanbul Technical University (2020), an MSc in Telecommunication Engineering (2017-2020), and a BSc in Electronics and Communications Engineering (2013-2017). His research focuses on medical imaging AI, explainable artificial intelligence (XAI), deep learning applications in healthcare, and computer vision techniques for artifact detection in medical imaging. He has conducted visiting research at the University of Twente (2024) and serves on academic committees at Istanbul Technical University. Research interests include developing explainable models for mammogram analysis, enhancing medical image quality assessment via transformers and neural networks, and addressing challenges in cardiovascular MRI segmentation through motion artifact detection. His work bridges deep learning theory with practical clinical applications, emphasizing transparency and accuracy in AI-driven medical diagnostics. Notable contributions include cross-domain artifact correction for cardiac MRI, joint CNN-RNN models for intracranial hemorrhage detection, and XAI methods for chest X-ray analysis. His research has been published in top-tier venues with a focus on medical imaging and deep learning advancements.
Rui Ponte Costa is an Associate Professor at the University of Oxford's Department of Physiology, Anatomy & Genetics (DPAG). He leads the Neural & Machine Learning group, focusing on computational models of learning in the brain by integrating AI principles. His research emphasizes cortical circuits, neuromodulation, and subcortical regions to understand credit assignment mechanisms. He holds a PhD in Computational Neuroscience and Machine Learning from the University of Edinburgh and has held postdoctoral positions at the University of Oxford, University of Bern, and McGill University. His group's work bridges theoretical and experimental neuroscience, collaborating with institutions like MILA and Google DeepMind. Education: Bachelor's in Computer Science, University of Coimbra (Portugal) PhD in Computational Neuroscience, University of Edinburgh (UK) Research Interests: Neural mechanisms of learning and plasticity Machine learning inspired by biological systems Cerebro-cerebellar interactions Neuromodulatory systems' role in reinforcement learning Collaborators include Christopher Summerfield, Timothy Behrens, and Yarin Gal. His lab's work has been published in Nature Communications , Cell Reports , and top machine learning venues. He co-organizes the Oxford NeuroTheory Forum and has pioneered models explaining cortical dynamics in task acquisition and consolidation. Advising and Grants: No specific grants listed, but active in collaborative research networks. Advising focuses on PhD students in computational neuroscience and AI. Labs/Teams: Neural & Machine Learning Group at DPAG, part of Oxford's broader neuroscience and AI ecosystem.
Suresh Venkatasubramanian is a Professor at Brown University, previously at the University of Utah's School of Computing. His research focuses on algorithmic fairness, automated decision systems, computational geometry, and the societal impacts of AI. He co-founded the FAT* conference and sits on the ACLU of Utah board. He holds a B.Tech from IIT Kanpur and a Ph.D. from Stanford University. Education : B.Tech (Computer Science, IIT Kanpur), Ph.D. (Stanford University). Key Roles : Member of Computing Community Consortium Council, Research Advisory Council for NYC's FTA Tool, and First Judicial District of Pennsylvania. Research interests emphasize fairness, accountability, and transparency in algorithms. Notable contributions include work on predictive policing biases, Shapley-value critiques, and information access gaps. Awards include an NSF CAREER Award and an ICDE Test-of-Time Award. Grants : Mozilla Foundation, NSF BIGDATA, DARPA A4V. Teaching : Advanced Algorithms, Ethics of Data Science, and courses on algorithmic fairness. Service roles include organizing conferences like FAT* and ALENEX, and advising on algorithmic governance in criminal justice systems.
Shahin Jabbari is an Assistant Professor in the Computer Science Department at the College of Computing & Informatics, Drexel University, where he is a member of the EconCS research group. His research lies at the intersection of machine learning, game theory, and algorithmic fairness, with a focus on ethical AI and its societal implications. Prior to Drexel, he was a CRCS postdoctoral fellow at Harvard University's School of Engineering and Applied Sciences, hosted by Milind Tambe, and affiliated with the EconCS group. Education: PhD in Computer and Information Science, University of Pennsylvania (2013–2019), advised by Michael Kearns Master's in Computing Science, University of Alberta, advised by Robert Holte and Sandra Zilles Bachelor's in Computer Engineering, Sharif University of Technology His research interests center on machine learning, algorithmic fairness, and game theory, particularly focusing on how AI systems can be designed to be more equitable, interpretable, and robust. He investigates ethical aspects of algorithmic decision-making, aiming to ensure AI technologies contribute positively to society. His work often integrates human behavior modeling and experimental validation, especially in cybersecurity and public health domains. His recent publications span top venues including ICML, NeurIPS, AAAI, AAMAS, PNAS, and TMLR. The research trends show a consistent focus on fairness in AI, explainability, robustness, and strategic interactions in complex systems. Topics include fair influence maximization, adaptive phishing training, cyber deception games, and ethical machine learning frameworks. These works reflect a multidisciplinary approach combining theoretical rigor with real-world applicability. Scientific Awards and Recognitions: Best Paper Finalist, AAMAS 2021 Best Paper, GameSec 2020 Spotlight Presentation, ICML 2021 Best Paper, KI 2012 Shahin Jabbari actively contributes to the academic community through advising, teaching, and service. He teaches graduate courses such as CS 589: Responsible Machine Learning and CS 590: Privacy. He has served on the senior program committees of ICML and NeurIPS, is an Action Editor for TMLR, and has reviewed for numerous top-tier conferences and journals. He mentors students through research projects and invites prospective PhD candidates to apply through Drexel’s formal channels. He is involved in the Drexel Computer Science Theory Reading Group and contributes to advancing responsible AI practices. He is affiliated with the EconCS group at Drexel, which focuses on economic and computational aspects of AI, including game theory, mechanism design, and multi-agent systems. His lab integrates tools from machine learning, behavioral modeling, and optimization to develop AI systems that are not only intelligent but also fair and trustworthy. Future work is expected to further explore human-AI collaboration, ethical AI deployment, and policy-aware algorithm design.
Andreas J. Kassler is a Full Professor of Computer Science at Karlstad University, Sweden, where he has been since 2005. He co-chairs the Distributed Systems and Communication (DISCO) group and focuses on networking, cloud computing, and wireless networks. His research includes software-defined networking, future internet architectures, and network optimization. He has authored/co-authored over 130 peer-reviewed publications, holds 6 patents, and serves on editorial boards of journals like Journal of Internet Engineering . Education : Ph.D. in Computer Science, Universität Ulm (2002) Docent (Habilitation), Karlstad University (2007) M.Sc. in Mathematics/Computer Science, Universität Augsburg (1995) Research Interests : Software Defined Networking (SDN) Programmable Dataplanes Wireless Mesh Networks Time-Sensitive Networking (TSN) Edge Computing Machine Learning for Network Optimization Recent Directions : His work spans TSN scheduling, hybrid P4 solutions for 5G, and explainable AI in energy communities. He explores network resilience, latency optimization, and multi-objective control in microgrids. Service Contributions : Track co-chair for VTC 2015 General chair for Wired/Wireless Internet Communications (WWIC) 2013 Editor-in-Chief of IARIA Journal on Advances in Internet Technology Labs/Teams : Leads DISCO group at Karlstad University. Collaborates with global teams on projects like mmWave backhaul networks and SDN-enabled industrial control systems.
Dr. Cheng-Chew Lim is a Professor in the School of Electrical and Mechanical Engineering at the University of Adelaide. He specializes in control theory, autonomous systems, and multi-agent reinforcement learning. His research focuses on trusted autonomous systems, secure cyber-physical networks, and decentralized decision-making models. He has published over 300 articles and supervised 50+ PhD and master’s students. Dr. Lim teaches courses in control systems, autonomous systems, and engineering project management. He has held editorial roles, including Associate Editor for IEEE Transactions on Systems, Man, and Cybernetics, and is actively involved in professional associations like the IEEE Control and Aerospace Electronic Systems Joint Chapter. His current projects include physics-informed neural networks for medical imaging, secure distributed autonomous systems, and resilient formation control under cyberattacks. Dr. Lim has secured research grants from ARC and industry partnerships, emphasizing practical applications in robotics, cybersecurity, and smart systems.
Tania Cerquitelli is a Full Professor in the Department of Control and Computer Science (DAUIN) at Politecnico di Torino, where she leads research in data science, concept-drift management, and inclusive AI technologies. She is a member of SmartData@PoliTO, the GEDI Observatory for Gender Equality, and serves in leadership roles related to social affairs and community policies at the university level. She also acts as a scientific advisor for the partnership with Accenture. Her research interests span Data Science , Concept-Drift Management , Database Systems , Conversational Data Science , and Industry 4.0 . She applies AI and machine learning to industrial, societal, and ethical challenges, particularly in promoting inclusive communication and gender equality in research. The most recent publications highlight her work in explainable AI, concept drift detection, multimodal diagnostics, and AI for social good. Her research integrates machine learning, natural language processing, and computer vision to address real-world problems in manufacturing, healthcare, agriculture, and education. She is an Associate Editor for several prestigious journals including Expert Systems with Applications , Computer Networks , Future Generation Computer Systems , and Knowledge and Information Systems . She has served on the program committees of major conferences such as ECML PKDD, EDBT/ICDT, and ACM KDD, and has been a reviewer and selection committee member for ETH Zurich and EMPA. She actively supervises PhD students and teaches a wide range of courses including Data Science and Database Technologies, Business Intelligence for Big Data, and Gender and Diversity in Research. She is involved in multiple national and international research projects such as E-MIMIC, WEBFARE, and EnABLES, focusing on inclusive AI, smart data, and industrial applications. Her lab affiliations include the DBDM - Database and Data Mining Group (DAUIN) and the Interdepartmental Center SmartData@PoliTO - Big Data and Data Science Laboratory , where she contributes to advancing data science methodologies and their societal impact.
Nicole A. Benedek is an Associate Professor in the Department of Materials Science and Engineering at Cornell University, part of the College of Engineering. Her research group focuses on theoretical and computational approaches to understanding and designing functional materials, particularly complex oxides and perovskites. She integrates principles of crystal chemistry, symmetry, and density functional theory to uncover mechanisms underlying material properties and to guide the discovery of new materials with targeted functionalities. Her research interests include nonlinear phononics, ultrafast optical control of lattice dynamics, ferroelectricity, magnetism, and thermal transport in materials. She is particularly interested in how materials behave out of equilibrium and how external stimuli such as light can induce dramatic changes in their properties. This work has implications for low-power electronics, data storage, and dynamic optical devices. The recent publications from her group reflect a strong trend in controlling material symmetries and properties using light, especially through infrared and Raman resonant excitation. Her work bridges theory and experiment, often in collaboration with synthetic chemists, to validate predictions and discover new polar and multiferroic materials. She has made key contributions to understanding negative thermal expansion, light-induced phase transitions, and hybrid improper ferroelectricity. Scientific Awards: NSF CAREER Award, National Science Foundation (2015) Ralph E. Powe Junior Faculty Enhancement Award (2014) Journal of Materials Chemistry Emerging Investigator (2016) Australian Postgraduate Award (2003) Dr. Benedek advises graduate students in materials science and engineering and has mentored PhD candidates such as Ethan T. Ritz and Tucker Swenson. Her research is supported by the National Science Foundation (including the MRSEC program), the Department of Energy, and the Cornell Center for Materials Research. She leads the Benedek Group, which develops theoretical frameworks to explain and predict material behavior, emphasizing design rules for next-generation functional materials. The Benedek Group collaborates extensively with experimentalists, notably with Michael A. Hayward at Oxford University, to synthesize and characterize predicted materials. Their joint work has led to the discovery of new ferroelectric Dion-Jacobson phases and a deeper understanding of polar distortions in layered perovskites. The group combines computational modeling with physical insight to push the boundaries of materials design.
Dr. Stella Pytharouli is a Senior Lecturer in Civil and Environmental Engineering at the University of Strathclyde. With over 20 years of expertise in structural and ground deformation monitoring/analysis, her research focuses on subsurface characterization and slope instability early warning systems through microseismic monitoring, geodetic technologies, and machine learning integration. MEng (2002) - University of Patras MSc (2004) - University of Patras PhD (2007) - University of Patras Her research combines advanced signal processing with geodetic monitoring (terrestrial/aerial) to develop AI-driven solutions for UK landslide sites. Key areas include: Microseismic monitoring of weak seismic events Geometric and kinematic analysis of ground deformations Integration of geotechnical data with machine learning Climate change impact on slope stability Low-cost sensor development for environmental monitoring Recent publications highlight her work on AI-based seismic classification models, tiltmeter applications, and 3D reconstruction techniques. Her group includes 4 PhD students and 1 postdoc. Scientific recognitions include: Geophysical Research Letters front cover selection (2011) EOS Research Spotlight (2019) Lampadarios Prize from Academy of Athens (2009) TOPCON Award for young researchers (2008) As Director of Postgraduate Research (2020-present), she supervises PhD students and teaches land surveying modules. Current projects address slope stability analysis, climate change correlations, and seismic data automation.
