Matthew Kay is an Associate Professor in the Department of Communication Studies at Northwestern University's School of Communication, with a secondary appointment in Computer Science. He serves as Co-Director of Graduate Studies for the PhD in Technology and Social Behavior program. His research focuses on human-computer interaction and information visualization, specializing in uncertainty communication, usable statistics, and personal informatics. He employs mixed-method approaches including behavioral analysis, interactive system development, and visualization technique evaluation to address real-world data interpretation challenges. Analysis of his recent publications reveals dominant themes in visualization literacy development, uncertainty representation for decision-making, and health informatics applications. His work consistently bridges theoretical frameworks with practical implementations, particularly in educational assessment tools and election forecast visualizations. Professor Kay co-directs the Midwest Uncertainty Collective (MU collective), a research group advancing uncertainty communication methodologies. Previously faculty at the University of Michigan School of Information, he maintains active contributions to visualization tool development including the ggdist R package for uncertainty visualization.
Baris Fidan is a Professor in Mechanical & Mechatronics Engineering at the University of Waterloo, with cross appointments in System Design Engineering and Electrical & Computer Engineering. He is a senior member of IEEE and AIAA. His research focuses on cooperative/adaptive control, autonomous systems, multi-agent networks, and vehicular control applications. He leads the Cooperative & Adaptive Mechatronic Systems (CAMS) Lab, which develops control strategies for autonomous vehicles, robotic systems, and intelligent transportation. Education: PhD in Electrical Engineering, University of Southern California (2003) Masters in Electrical & Electronic Engineering, Bilkent University (1998) Bachelor's in Electrical & Electronic Engineering & Mathematics, Middle East Technical University (1996) Research Interests: His work spans adaptive control theory, sensor networks, multi-agent coordination, autonomous vehicle networks, and biomedical systems control. He emphasizes practical applications in intelligent transportation, robotic navigation, and distributed system optimization. Grants & Projects: He has led major grants including NSERC Discovery Programs on cooperative mechatronic systems and 3D autonomous vehicle coordination. Industrial projects include autonomous driving strategies, vehicle control optimization, and high-precision gear manufacturing technologies. Labs/Teams: Directs the CAMS Lab, which collaborates on projects involving distributed motion planning, sensor localization, and autonomous vehicle networks. Current projects address challenges in urban autonomous driving, cooperative robotic systems, and resilient sensor networks.
David Martens is a Professor of Data Science at the University of Antwerp , where he directs the Applied Data Mining Research Group within the Faculty of Business and Economics . He also serves as Chair of the Department of Engineering Management and Director of the Antwerp Center on Responsible AI . His academic work spans data mining , interpretable machine learning , and the societal impact of AI . PhD in Applied Economic Sciences (KU Leuven, 2008) Director, Antwerp Center on Responsible AI Chair, Department of Engineering Management Martens' research focuses on responsible AI and data ethics , with applications in finance, public policy, and behavioral analysis. His recent publications emphasize counterfactual explanations , LLM interpretability , and privacy implications in AI systems. His articles reveal trends in Explainable AI (XAI) , including narrative-driven explanations , graph neural networks , and ethical challenges like monetization risks and algorithmic bias. Keywords span Computer Science , Artificial Intelligence , and Behavioral Data . Martens is a leading voice in data science ethics , authoring the book Data Science Ethics: Concepts, Techniques, and Cautionary Tales (Oxford University Press, 2022). He combines academic rigor with industry experience, having consulted for banks, telecom firms, and startups in fraud detection and digital advertising .
Raquel Fernández is Full Professor of Computational Linguistics and Dialogue Systems at the University of Amsterdam, where she leads the Dialogue Modelling Group at the Institute for Logic, Language & Computation (ILLC). As Vice-Director for Research at ILLC and a Fellow of the ELLIS Society, she bridges computational linguistics, cognitive science, and artificial intelligence through her research on language use in multimodal and conversational contexts. PhD in Computational Linguistics from King's College London Prior research positions at University of Potsdam and Stanford University's CSLI Her work explores how cognitive constraints, social interaction, and perception shape language use, with a focus on: Visually-grounded language processing Multimodal dialogue modeling Model uncertainty and calibration Language grounding in multimodal data Language learning and semantic change Dialogue reference resolution Recent publications analyze multimodal reasoning limitations, cross-lingual knowledge consistency, and uncertainty modeling in dialogue systems. She has received multiple accolades including an ERC Consolidator Grant , NWO VENI/VIDI/Aspasia fellowships , and EMNLP/GenBench awards . Outstanding Paper Award (EMNLP 2023) Best Data Award (GenBench Workshop 2023) ELLIS Society Fellow ERC Consolidator Grant #819455 recipient NWO VENI/VIDI/Aspasia awardee As a leader in academic service, she serves on the SIGDAT Executive Committee and chairs multiple conference committees. Her lab develops models for multimodal dialogue, visual storytelling, and grounded language understanding.
Professor Guy Wallis is a Professor and Director of Research at the School of Human Movement and Nutrition Sciences, Faculty of Health, Medicine and Behavioural Sciences at the University of Queensland. He is also an Affiliate of the Centre for Sensorimotor Performance. His work bridges visual neuroscience, computational modeling, and applied human factors research, with significant contributions to understanding visual recognition and visuomotor behavior. Education: Bachelor (Honours) of Engineering in Electrical and Electronic Engineering from Imperial College London PhD in Visual Neuroscience from University of Oxford, UK Prof. Wallis's research program combines computational modeling with behavioral studies, many conducted in computer-controlled virtual environments. His work spans visual neuroscience, object recognition, visuomotor control, and simulator-based training. He has made significant theoretical contributions to understanding how visual recognition is achieved in biological systems and how everyday visuomotor tasks are regulated, challenging existing paradigms and offering new insights. His recent publications reveal a strong focus on virtual reality applications, visual-motor integration challenges, and cross-species cognitive studies. There's a clear trend toward investigating how virtual environments can be optimized for training and assessment, with particular attention to visual perception limitations and how humans adapt to these environments across diverse contexts from surgical training to aviation. Scientific Awards and Recognitions: Elected Fellow of the Queensland Academy of Arts and Sciences (2022) ARC Medical Research Advisory Group (2022-2024) ARC College of Experts (2019-2021) CSIRO CSS Human Research Ethics Committee member (2020-2022) UQ Health and Behavioural Sciences Faculty, HDR Supervision Award (2018) ARC Future Fellowship (2011-2014) ARC QEII Fellowship (2003-2007) UQ Postdoctoral Fellowship (2001-2003) Prof. Wallis has successfully secured funding from major organizations including the Australian Research Council, the Human Frontier Science Program, and the Wellcome Trust. His industry partnerships span diverse sectors such as construction training, mining, healthcare, and aerospace. His research has led to the development of novel training programs for health professionals, impacted the design of man-machine interfaces for mining equipment, and informed the design parameters for pilot training systems. As Director of Research, he oversees the research direction of the School of Human Movement and Nutrition Sciences, fostering interdisciplinary collaborations and supporting early-career researchers through his leadership in the Centre for Sensorimotor Performance.
Adam Drazin is a Tutor in the Department of Anthropology at University College London (UCL), specializing in design anthropology, material culture, and consumption. He works at the intersection of academia and public engagement, collaborating with NGOs, corporate clients, and research clusters such as the ‘Business of Fashion, Textiles and Technology’ at the London College of Fashion. His research explores domesticity, care ethics, and material practices in Romania, Ireland, and the UK. Research Focus: Design anthropology, material culture, domestic consumption, and transnational home-making practices. Geographic Scope: Romania, Ireland, UK, with attention to Romanian diaspora communities. Editorial Roles: Co-editor of Home Cultures , former editor of Journal of Material Culture . Projects: Studio of Material Life, ethnographic consultancy via UCL’s Ethnographic Insights Lab. His recent work emphasizes textile innovation, care ethics in domestic spaces, and critiques of material cultures within design. Publications include Design Anthropology in Context (2021) and The Social Life of Materials (2015). He mentors postgraduate researchers and contributes to public anthropology through consultancy.
Soumya Dutta is an Assistant Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology Kanpur (IITK), where he leads the INSIGHT: Intelligent Scientific and Visual Computing of Big Data Research Group. He joined IIT Kanpur in October 2022 after working as a Scientist II at Los Alamos National Laboratory (LANL) from July 2019 to August 2022, and previously as a Postdoctoral Research Associate at LANL from June 2018 to July 2019. His educational background includes a Ph.D. and M.S. in Computer Science and Engineering from The Ohio State University (2011-2018), where he was part of the GRAVITY research group, and a B.Tech. in Electronics and Communication Engineering from West Bengal University of Technology, India (2005-2009). Research Interests: Dr. Dutta's research focuses on the intersection of machine learning, visual computing, big data, and high-performance computing. His primary research areas include Machine Learning for Visual Computing and Image Analysis, Big Data Visualization and Analytics, Data Science and HPC, Machine Learning for Scientific Computing, and Explainability and Interpretability of AI Models. His work addresses various big data characteristics including the 5 Vs: Volume, Velocity, Variety, Veracity, and Value. He develops techniques that make complex machine learning models more interpretable and explainable, enabling their effective adoption in real-life applications across scientific domains, social media, IoT, healthcare, and industry applications. Dr. Dutta's research group has secured multiple funded projects including: DAVi: An Intelligent Data Analytics and Visualization Framework (funded by ISRO), Intelligent Visual Computing of Extreme-scale Data for Accelerating Scientific Discovery (IIT Kanpur Initiation Grant), Enabling Interactive Big Data Analytics and Visualization at Exascale (SERB), Development of AI-Enabled National Portal for Efficient Search of Missing People (C3iHub), and Proactive and Generalized Deepfake Defense Mechanisms (C3iHub). Best Reviewer, Honorary Mention Award for IEEE Transactions on Visualization & Computer Graphics (TVCG), 2021 Best Paper Award at ISAV 2021, co-located with Supercomputing (SC) LAAP Award at Los Alamos National Laboratory, 2021 Best Paper Award at TopoInVis 2019 Best Paper Award at ISAV 2018, co-located with Supercomputing (SC) Best Poster Award in 12th Annual CSE Student Poster Exhibition, The Ohio State University, 2018 Best Poster Award in 11th Annual CSE Student Poster Exhibition, The Ohio State University, 2017 Best Paper Honorable Mention Award at IEEE Visualization Conference (IEEE VIS) 2016 Dr. Dutta actively mentors a large group of students including Ph.D., M.Tech., and B.Tech. students. His current Ph.D. students include Shanu Saklani, Sankhadeep Bhowmick, Ananya Chaturvedi, Arpita Santra, Anubhav Dixit (co-supervised), and Robin Shah. He has supervised numerous M.Tech. students with thesis topics ranging from uncertainty-aware neural networks to deepfake detection. Dr. Dutta currently teaches courses including CS360 - Introduction to Computer Graphics and CS661 - Big Data Visual Analytics. The INSIGHT research group collaborates internationally with researchers from Meta, Oak Ridge National Laboratory, and National Taiwan Normal University. The group's work focuses on building machine learning and data science-based solutions to analyze large-scale multifaceted data in a scalable way, enabling interactive and interpretable analytics of complex data from scientific simulations, social media, IoT, healthcare, and other application domains.
Dr. Konstantin (Kostia) M. Zuev serves as Teaching Professor in the Computing + Mathematical Sciences Department at California Institute of Technology , where he has made significant contributions to network science and computational statistics since 2016. His dual PhDs in Mathematics (Moscow State University, 2008) and Civil Engineering (HKUST, 2009) underpin his interdisciplinary research spanning differential geometry, stochastic simulation, and network dynamics. Education PhD in Mathematics, Lomonosov Moscow State University (2008) PhD in Civil Engineering, Hong Kong University of Science & Technology (2009) His research focuses on network science , particularly course-prerequisite networks and complex financial systems , with recent work extending to network navigability in cosmological models and rare event simulation. Over his career, he has developed innovative Bayesian inference methods and geometric preferential attachment theories while maintaining active collaborations across mathematics, physics, and biomedical domains. Recent publications highlight network analysis in education ( 2023 ), hyperbolic graph theory ( 2024 ), and pandemic-informed cancer mortality studies ( 2023 ). His 15 most recent articles demonstrate methodological innovations across disciplines including statistics, physics, finance, and cosmology. Scientific recognition includes Humboldt Research Fellowship (2021) Carver Mead Seed Fund Grant (2023) ASCIT Teaching Award (2018, 2023) Northrop Grumman Teaching Excellence Prize (2019) As Graduate Option Representative for Information and Data Sciences at Caltech and faculty advisor for multiple student organizations including the Caltech Karate Club and Caltech Chess Club , he actively bridges academic rigor with community engagement through outreach initiatives like the virtual math education channel and university math circles for K-12 students.
Alan Stocker is a Professor in the Department of Psychology at the University of Pennsylvania, affiliated with the Neuroscience Graduate Group, Bioengineering Graduate Group, and Computational Neuroscience Initiative. His research focuses on understanding how prior beliefs and expectations shape visual perception, combining theoretical models with psychophysical and physiological experiments. Key areas include optimal inference frameworks, neural coding efficiency, and decision-making under uncertainty. Education: PhD in Physics (ETH Zurich, 2002); MSc in Biomedical Engineering and Materials Science (ETH Zurich, 1995). Prior appointments include postdoctoral work at New York University and ETH Zurich. Research Interests: Behavioral Neuroscience, Cognitive Neuroscience, Sensation and Perception. Specific projects explore statistical properties of visual environments, subjective expectations from past decisions, and neural computations underlying perception. Advising: Current advisees include Cheng Qiu (Postdoc), Ansh Soni, and Mengting Fang (Graduate Students). Courses taught: Perception, Visual Neuroscience, and Probabilistic Models in Perception and Cognition. Labs/Teams: Leads the Computational Perception and Cognition (CPC) Laboratory, emphasizing theory-driven experimentation. Collaborates widely across disciplines including bioengineering and electrical systems engineering.
Howie Choset is a Professor of Robotics at the Robotics Institute, Carnegie Mellon University. He directs the Undergraduate Robotics Minor and leads the Biorobotics Laboratory, where his research focuses on snake robots, motion planning, and medical robotics. He is also affiliated with the Manufacturing Futures Institute. Ph.D., Mechanical Engineering, California Institute of Technology (1996) M.S., Mechanical Engineering, California Institute of Technology (1995) B.S.E., Computer Science and Engineering, University of Pennsylvania (1990) B.S., Economics, The Wharton School of Business (1990) Choset's research centers on robotics for confined and complex environments, particularly through the development of snake robots. His work integrates mechanism design, path and motion planning, and estimation to enable applications in surgery, manufacturing, infrastructure inspection, and search and rescue. He is a pioneer in medical robotics and has co-founded Medrobotics to commercialize minimally invasive surgical robots. His recent publications highlight a strong focus on ergodic exploration, multi-agent systems, motion planning under uncertainty, and medical robotics. Themes include optimizing robot trajectories for information gathering, solving complex path planning problems with dynamic obstacles, and advancing autonomous systems for disaster response and space exploration (e.g., the EELS robot for Enceladus). MIT Technology Review Top 100 Innovators under 35 (2002) Best Paper Award, RIA (1999) Best Paper Award, ICRA (2003) Best Paper, IEEE Bio Rob (2006) Best Video, ICRA (2011) Nominations for best papers at ICRA, IROS, and CLAWAR Choset has advised numerous students, many of whom have won top awards. His lab has received significant funding for robotics research, including projects in surgical robotics, additive manufacturing, and autonomous exploration. He is the lead author of the textbook Principles of Robot Motion and is actively involved in educational innovation through custom robotics labs. He leads the Biorobotics Laboratory at CMU, which develops advanced robotic systems like snake robots and the EELS (Exobiology Extant Life Surveyor) robot for NASA missions. The lab collaborates with industry and government agencies on applications ranging from surgery to space exploration.
Dylan Campbell is a Lecturer in Computing at the Australian National University (ANU), affiliated with the ANU College of Systems & Society. His research focuses on computer vision, optimization, and robotics, particularly in 3D vision and deep learning applications. He has held prior roles as a Research Fellow at the University of Oxford’s Visual Geometry Group and ANU’s Australian Centre for Robotic Vision. Campbell holds a PhD from ANU (2018) and a BE in Mechatronic Engineering from UNSW (2012). Research interests include geometric sensor alignment, neural radiance fields, and differentiable optimization layers. He actively supervises students (7 PhD/DPhil, 3 MEng, 9 honours) and teaches advanced courses in computer vision and robotics. Notable awards include the Marr Prize Honourable Mention (2017) and the IEEE Australia Council Postgraduate Student Paper Competition (2018). He has organized workshops at ECCV and CVPR, served as a reviewer for top conferences like CVPR/ICCV/ECCV, and contributed to datasets like SEED4D and RefRef. His work emphasizes efficient training of neural networks and leveraging symmetries in data for long-range connections.
Arash Arami is an Associate Professor in the Department of Mechanical and Mechatronics Engineering at the University of Waterloo, cross-appointed in Systems Design Engineering. He directs the Neuromechanics and Assistive Robotics Laboratory and maintains affiliations with Waterloo Robohub, the Centre for Bioengineering and Biotechnology, Waterloo AI institute, and KITE institute at Toronto Rehab Institute. He earned his Doctorate in Electrical Engineering from EPFL (2014), Master of Science from University of Tehran (2009), and Bachelor of Science from University of Tabriz (2006), all in Control Engineering. His research in Assistive Robotics and Rehabilitation Engineering integrates Machine Learning with Neuromechanics to develop intelligent systems for human movement analysis. Key focus areas include exoskeleton control algorithms, wearable sensor systems, and neural control modeling for rehabilitation applications. Recent publications demonstrate interdisciplinary work spanning robotics, biomedical engineering, and materials science, with emphasis on real-time human locomotion prediction, exoskeleton-human interaction, and data-driven health monitoring solutions. Dr. Arami serves as Chair of the NSERC Scholarship Committee (2021-2023) and mentors graduate students through the Mechatronics Exchange Study program. His teaching includes core courses in control systems, robot manipulators, and biomechanical engineering. The Neuromechanics and Assistive Robotics Laboratory fosters collaborations with clinical partners at Toronto Rehab Institute, focusing on translating robotic innovations into practical rehabilitation tools through interdisciplinary teamwork.
Patricio Vela is a Professor at the School of Electrical and Computer Engineering , Georgia Institute of Technology , specializing in geometric perspectives for control theory and computer vision. His research focuses on computer vision integration for semi-autonomous systems, nonlinear control of robotic systems, and biologically inspired mechanics. Education: B.S. (1998) and Ph.D. (2003) from Caltech Research Areas: Autonomy, Robotics, Computer Vision, Control Theory Key Contributions: Geometry-based control systems, visual navigation frameworks, SLAM benchmarking Recent publications highlight advances in vision-based motion planning , 6D pose tracking , and safe navigation policies for autonomous robots. His work bridges geometric mechanics with deep learning for robust perception and control in dynamic environments. Awards: HENAAC Most Promising Engineer (2005) Contact: pvela@gatech.edu | Office: TSRB 441 | Phone: 404.894.8749
Renée J. Miller is a Professor and Canada Excellence Research Chair in Data Intelligence at the Cheriton School of Computer Science, University of Waterloo. Her research focuses on data integration, data management, and open data systems. She holds a PhD in Computer Science from the University of Wisconsin-Madison and bachelor’s degrees in Mathematics and Cognitive Science from MIT. Her work addresses challenges in data preparation, integration, and curation, aiming to reduce the burden on data scientists. She co-authored foundational papers on data exchange and schema mapping, earning the ICDT Test-of-Time Award (2013) and the Alonzo Church Award (2020). Miller has led major initiatives like the NSERC Business Intelligence Network and the International Very Large Data Base Foundation. Her grants include NSERC Accelerator Awards and funding from IBM, SAP, and Microsoft. Notable students include Ariel Fuxman (SIGMOD Dissertation Award winner) and Oktie Hassanzadeh (IBM PhD Fellow). Her research group, the Miller Lab, develops tools like Clio for schema mapping and systems for data lake exploration (RONIN, JOSIE).
Dr. Ehsan Abbasnejad is an Associate Professor at Monash University's Department of Data Science and Artificial Intelligence, and holds adjunct positions at the Australian Institute for Machine Learning (AIML, University of Adelaide) and the Centre for Augmented Reasoning (CAR). He specializes in foundational AI, focusing on vision-language tasks, adversarial machine learning, and reinforcement learning. His work bridges theory with real-world applications in agriculture, energy, healthcare, and sports. Education: PhD in Computer Science from Australian National University (ANU). Research Interests: Machine Learning Theory and Adversarial Defenses Neural Network Robustness and Generalization Multimodal Learning (Vision-Language) Continual and Transfer Learning Applications in Energy, Healthcare, and Robotics Awards: Finalist for Australian AI Academic/Researcher of the Year (2024) Multidisciplinary competition wins (e.g., OzMineral Explorer Challenge) Advising & Grants: Australian Research Council (ARC) Discovery Project on Reinforcement Learning CSIRO's Next Generation Graduate Fund Accepting PhD students in foundational AI and applications Labs & Teams: Director of Foundational Machine Learning & Reasoning at Monash, leading global teams in AI competitions and industry collaborations (Microsoft Research, NEC Labs America).
