Jon Rogers is Professor of Creative Technology at Northumbria University with a PhD in neural networks from Imperial College London. His research examines human relationships with emerging technologies through hands-on making and critical design practices. Rogers' work explores alternative narratives for digital futures through internet-connected objects and open hardware systems. He develops tangible interfaces that challenge conventional technology design paradigms and enable public participation in technological discourse. Recent research includes experimental prototyping platforms for open hardware development and methodological frameworks for reflecting on connected devices. His publications document innovative approaches to tangible computing and participatory design methodologies. Rogers founded the OpenDoTT doctoral training program exploring trust in IoT systems. He regularly organizes workshops at major technology conferences and maintains collaborations with international makerspaces and research institutions.
Dr. Daniel Allen is a Lecturer in Human Geography at Keele University's School of Life Sciences, specializing in human-animal relations. An IUCN/SSC otter specialist, his research on otter conservation and dog theft informs policy and advocacy. He led an Environment Agency project on otter predation perceptions and founded the 'Pet Theft Reform' campaign, influencing UK legislation (Pet Abduction Act 2024). His research examines human-animal bonds, including dog theft impacts and mental health benefits. Current projects include 'No pets! Young people’s transitions to campus living,' funded by the Society for Companion Animals Studies. Teaching includes modules on Nature Conservation, Space and Society, More-than-human Geographies, and Animals and Society. He supervises PhD student Emma Randall on eco-therapy. Awards include the Daily Mirror’s Peoples Pet Awards Special Recognition (2021) and the PM’s Points of Light Award (2022). Publications emphasize spatial justice, animal advocacy, and policy impacts, with recent work on pet theft trends and human-animal bonds in crises.
Dr. Nitya Lakshmanan is a Lecturer at the School of Computing, National University of Singapore. She specializes in Security, Mobile Security & Privacy, and Networking, with a focus on side channels in modern wireless systems. Her work has been recognized by the GSMA Hall of Fame for uncovering critical security vulnerabilities in mobile technology. Education: Bachelor's in Computer Science and Engineering from Mahatma Gandhi University, Kerala (2008) Master's from Anna University, Chennai (2011) Ph.D. from National University of Singapore (2022) Research Interests: 4G/5G security Networking Privacy risks in cellular networks Scientific Awards: GSMA Hall of Fame for discovering a new security vulnerability involving mobile phones Courses Taught: CS1010S Programming Methodology CS2107 Introduction to Information Security
Eitan Tadmor is a Distinguished University Professor at the Department of Mathematics and Institute for Physical Science & Technology at the University of Maryland. He holds the 2024 Chaire d'excellence at Sorbonne University's Fondation Sciences Mathématiques de Paris, and has served as Director of multiple research centers including the Center for Scientific Computation and Mathematical Modeling (2002-2016) and The Sackler Institute of Scientific Computation (1993-1996). Current: University of Maryland (2005-present) Previous: UCLA (1995-2002), Tel-Aviv University (1989-1995), CalTech (1980-1982) His research spans nonlinear conservation laws , entropy-stable schemes , collective dynamics , spectral methods , and multiscale modeling . He pioneered the spectral viscosity method and developed stability criteria for numerical schemes. Recent publications focus on swarm-based optimization , Euler-Poisson equations , and hydrodynamic alignment with over 15000 citations. His work on kinetic formulations and regularizing effects in PDEs has become foundational in computational mathematics. 2022 Norbert Wiener Prize (AMS-SIAM) 2022 Gibbs Lecturer (AMS) 2015 Peter Henrici Prize (SIAM-ETH) 2013-2021 Fellow of AMS/SIAM NSF grants (1999, 2008-2012, 2012-2020) He developed CentPack software for hyperbolic conservation laws and co-authored influential review papers on numerical methods and mathematical modeling. His collaborative work with institutions like IPAM, KI-Net, and ETH-ITS demonstrates international scientific leadership.
Federico Toschi is a Full Professor at Eindhoven University of Technology (TU/e), holding joint appointments in Applied Physics and Mathematics and Computer Science departments. His research focuses on multi-scale transport phenomena, combining statistical physics, fluid dynamics, and computational methods. He leads projects in the 4TU Centre for Multiscale Phenomena and EAISI. Education: PhD in Physics (University of Pisa, 1998) and academic background at Scuola Normale Superiore di Pisa. Interdisciplinary expertise in fluid dynamics turbulence, Lagrangian turbulence, crowd dynamics, and Lattice Boltzmann methods. Recipient of APS Fellow (2015), Euromech Fluid Mechanics Fellow (2012), and Ig Nobel Prize for Physics (2021). Research emphasizes turbulence modeling, pedestrian dynamics, and active matter, with applications in environmental flows and crowd management. His work bridges computational innovations with experimental validations. Recent articles explore kinetic data-driven turbulence modeling, pedestrian flow optimization, and turbulence effects in biological systems. Projects include digital twins for seismicity modeling and rarefied gas dynamics. Teaches fluid mechanics, computational physics, and chaos theory courses. Founded Flow Matters Holding BV, applying research to practical solutions.
Professor Karin Verspoor is the Dean of the School of Computing Technologies at RMIT University in Melbourne, Australia. She previously held roles as Director of Health Technologies and Deputy Head of the School of Computing and Information Systems at the University of Melbourne, and as Scientific Director of Health and Life Sciences at NICTA's Victoria Research Laboratory. Her research focuses on applying artificial intelligence methods to biomedical discovery and clinical decision support, particularly through natural language processing of clinical texts and biomedical literature. Affiliations: RMIT University (STEM College), Australian Alliance for Artificial Intelligence in Health (Victorian Node Lead) Industry Experience: Intelligenesis/Webmind Corp., Applied Semantics, Los Alamos National Laboratory, National ICT Australia Research Interests: Artificial Intelligence in Medicine Biomedical Natural Language Processing Health Informatics Computational Biology Cheminformatics Her work emphasizes cross-modal data integration, EHR analytics, and AI-driven clinical tools to address challenges in healthcare outcomes, musculoskeletal disorders, and infectious disease surveillance. Advising & Grants: Supervises research on AI-based decision-making frameworks, EHR data quality, and chemical knowledge extraction. Leads projects funded by initiatives like CANAIRI (Collaboration for Translational AI in Healthcare). Labs & Collaborations: Co-founder of the Australian Alliance for AI in Health, advancing national AI healthcare policy and translational research.
Professor Kylie Peppler is a dual Professor of Informatics and Education at the University of California, Irvine, leading the Creativity Labs and the Connected Learning Lab. Her research focuses on leveraging hands-on creativity—such as e-textiles, robotics, and traditional fiber crafts—to enhance STEM education, particularly for marginalized populations. She emphasizes the role of materiality in fostering systems thinking and equity in learning environments. Education: PhD in Education (UCLA), Postdoctoral training at UC Irvine, and prior roles at Indiana University. Her academic journey bridges psychology, art, and technology. Research Interests: Maker culture, computational thinking, STEAM integration, workforce development, and the impact of arts in education. She explores how tools like e-textiles and looms democratize access to STEM while addressing gender disparities. Key Projects: NSF-funded work on computational construction kits, Re-Crafting STEM initiatives, and Future of Work research using AR/VR for manufacturing training. Collaborations include Boeing, Inner-City Arts, and NYSCI. Awards: NSF Early CAREER Award, Mira Tech Educator of the Year, and Indiana Governor's Award. Her work is supported by NSF, Wallace Foundation, and industry partners. Grants & Labs: Over $10M in grants; directs labs advancing connected learning and equity through technology. Recent studies include virtual reality welding simulators and culturally sustaining arts practices. Labs/Teams: Creativity Labs (designing maker tools), Connected Learning Lab (digital equity), and partnerships with museums and industry to scale inclusive learning.
Ina Fiterau Brostean is an Assistant Professor in the College of Information and Computer Sciences at the University of Massachusetts Amherst, where she leads the Information Fusion Lab. Previously, she was a Postdoctoral Fellow at Stanford University's Mobilize Center (2015–2018) and earned her PhD in Machine Learning from Carnegie Mellon University (2015). Her research focuses on hybrid systems for multimodal data integration, particularly in healthcare, aiming to develop predictive models for clinical outcomes using time series, text, and images. Key areas include disease trajectory modeling, weakly-supervised transfer learning, and adaptive representation learning. Education: PhD in Machine Learning (Carnegie Mellon, 2015), MSc in Machine Learning (Carnegie Mellon, 2012), BEng in Computer Engineering (Politehnica Timisoara, Romania, 2009). Professional roles include teaching COMPSCI 651 (Optimization in Computer Science) and organizing NeurIPS workshops on Machine Learning in Healthcare. Research interests span machine learning methodologies for healthcare applications, including interpretable models, time series analysis, and dimensionality reduction. Notable achievements include the Marr Prize (ICCV 2015) and Star Research Award (SCCM 2016). Her lab collaborates on projects like predicting Alzheimer's disease progression and surgical outcomes using Bayesian networks and deep learning. Awards and recognitions include Rising Stars Workshop (2016), Manning IALS Research Award (2019), and GE Foundation Scholar Leader Award (2007). She actively contributes to the ML4Health community through leadership roles and workshop organization.
Dr. Hongli (Julie) Zhu is an Associate Professor in the Department of Mechanical and Industrial Engineering at Northeastern University's College of Engineering. Her research focuses on sustainable energy storage, multifunctional materials, and advanced manufacturing, with emphasis on developing environmentally friendly biomass-derived materials, all solid-state batteries, and flow batteries. She leads the ZHU Lab at Northeastern University, which is dedicated to creating safer, cheaper, and higher performance energy storage solutions while exploring multifunctional materials derived from nature. Dr. Zhu received her PhD from South China University of Technology and Western Michigan University (2004-2009). She conducted postdoctoral research at KTH Royal Institute of Technology in Sweden (2009-2011), focusing on biodegradable and renewable biomaterials from natural wood, followed by additional postdoctoral work at the University of Maryland (2012-2015), where she researched nanocellulose and energy storage. Dr. Zhu's research spans multiple disciplines at the intersection of materials science, energy storage, and sustainable manufacturing. Her work addresses critical challenges in energy storage technology, including developing all solid-state batteries, flow batteries, and high energy density battery systems. She has pioneered research in sustainable biomass-derived materials, particularly investigating cellulose, hemicellulose, and lignin for applications in bendable, implantable, and biocompatible electronics. Her lab also focuses on advanced manufacturing techniques, including high-speed roll-to-roll processing for emerging advanced materials and devices. Analysis of Dr. Zhu's publication record reveals a strong focus on next-generation battery technologies, particularly solid-state systems. Her research demonstrates significant contributions to understanding and improving lithium dendrite suppression, electrode architecture optimization, and interface stabilization in solid-state batteries. She has also made substantial advances in sustainable materials derived from natural resources, developing applications for cellulose nanostructured fibers, paper, and aerogel/hydrogel systems. MRS Communications Early Career Distinguished Presenters and JMR Distinguished Invited Speakers (2024) Selected in Stanford University List of Top 2% Scientists Worldwide (2021-2024) College of Engineering Faculty Fellow (2023) Soren Buus Outstanding Research Award (2022) Women in Materials Science, Advanced Materials (2021 and 2022) Women Scientists at the Forefront of Energy Research, ACS Energy Letters (2020) Innovator of the Year 2013, Maryland Jakob Wallenberg Scholarship, Sweden Dr. Zhu has secured significant research funding from various sources, including the National Science Foundation and Department of Energy. Her current projects include "Uncovering the mechano-electro-chemo mechanism of fresh Li in sulfide based all solid-state batteries through operando studies" (NSF), "Enabling Advanced Electrode Architecture through Printing Technique" (DOE), and "Engineering the Metal Sulfide Interface in All Solid State Batteries through Operando Study" (NSF). She collaborates with industry partners including Rogers Corporation and has developed patented technologies related to sustainable materials and energy storage. Dr. Zhu serves as Codirector of Advanced & Intelligent Manufacturing, Editor of Progress in Materials Science, and on the Editorial Advisory Board of Chemical Society Reviews. The ZHU Lab at Northeastern University is a highly interdisciplinary research group that bridges scales from the nanoscopic to macroscopic and system level. The lab's work has led to numerous patents, including "Natural fiber composites as a low-cost plastic alternative" and "Fire-retardant Nanocellulose Aerogel, and Methods of Preparation and Uses Thereof." The group focuses on making energy storage safer, cheaper, and higher performing while exploring multifunctional materials derived from nature, with particular emphasis on applying high-speed roll-to-roll manufacturing to emerging advanced materials and devices.
Professor Jerome Liang is a distinguished faculty member at Stony Brook University's Renaissance School of Medicine, holding professorships in Radiology, Biomedical Engineering, Electrical and Computer Engineering, and Computer Science. He serves as Co-Director of Radiology Research and has established himself as a leading expert in medical imaging reconstruction techniques. Dr. Liang's educational background includes a Ph.D. in Physics from City University of New York, postdoctoral training at Duke University, and fellowship at Albert Einstein College of Medicine. His undergraduate degree in Modern Physics was obtained from Lanzhou University in China. His primary research interests focus on advanced medical imaging techniques, particularly low-dose computed tomography image reconstruction, quantitative SPECT reconstruction, high-resolution PET imaging, tissue segmentation from multi-spectral images, computer-aided diagnosis systems, and virtual colonoscopy development. His work bridges engineering principles with clinical applications to improve diagnostic imaging capabilities while reducing radiation exposure. Analysis of his recent publications reveals a strong focus on machine learning applications in medical imaging, particularly in polyp classification, dual-energy CT spectral analysis, and virtual endoscopy. His research consistently aims to enhance diagnostic accuracy while optimizing radiation dose and improving visualization techniques for various medical conditions. 1981 China-US Physics Examination and Application Program (CUSPEA) Winner (Top 25 among 250,000 candidates) 1990 NIH First Investigator Award 1996 American Heart Association Established Investigator Award 1996 Radiological Society of North America Certificate of Merit Award 2002 SUNY Chancellor's Entrepreneur Award 2007 IEEE Society Fellow 2011-2013 SBU, BNL and CSHL Certificates of Excellence in Research and Invention 2013 Stony Brook School of Medicine Award for Excellence in Translational Research Dr. Liang has secured significant research funding including NIH/NCI R01 grants for "Advanced Virtual Colonoscopy for Early Cancer Screening" and "Radiogenomics of Colorectal Polyps." He currently leads active protocols including IRB 93995-MODCR005 focused on integrating virtual and optical colonoscopies with pathological analysis. His laboratory (IRIS - Imaging Research and Informatics) continues to advance medical imaging technology while mentoring the next generation of researchers in this critical field.
Lionel Levine is a Professor in the Department of Mathematics at Cornell University, affiliated with the College of Arts and Sciences. His academic research focuses on abelian networks, interacting particle systems, and the emergence of complex patterns from simple rules. He has held prestigious fellowships, including the Simons Fellowship and Sloan Research Fellowship, and has been honored with an endowed professorship. Levine's work bridges probability theory, combinatorics, and statistical physics, with notable contributions to the study of sandpile models and internal diffusion-limited aggregation (IDLA). Education: Ph.D. in Mathematics (2007), University of California, Berkeley. Research Interests: Applied Mathematics, Combinatorics, Probability, Abelian Networks, Sandpile Models, and their intersections with computer science and statistical physics. His research explores how local rules generate large-scale structures, such as in abelian networks and sandpile models. Awards and Honors: Simons Fellowship, Sloan Research Fellowship, Endowed Professorship in the College of Arts and Sciences. Teaching: Courses include Probability Theory (MATH 6710/6720), Topics in Probability: Math for AI Safety (MATH 7710), and undergraduate mathematics courses like Strategy, Cooperation, and Conflict (MATH 1340). Grants and Funding: Supported by the National Science Foundation (NSF), Simons Foundation, Sloan Foundation, and Institute for Advanced Study. Collaborations: Collaborates with prominent researchers such as Yuval Peres, Cris Moore, and Jim Propp. His work has been published in leading journals like the Annals of Probability and Duke Mathematical Journal. Future Work: Continues investigating AI safety, causal models, and multi-agent learning, including research on mathematical frameworks for transformer circuits and hidden incentives in AI systems.
Professor James Scanlan is a Professor of Design within the Faculty of Engineering and Physical Sciences at the University of Southampton. He leads research in design, logistics, simulation, and optimization, focusing on aerospace systems and unmanned aerial vehicles (UAVs). His work is funded by BAE Systems, Airbus, Rolls-Royce, and the EPSRC. Previously, he held roles at BAe Systems and the University of the West of England, completing a PhD on aerospace design process modeling. He has launched a spin-off business commercializing design process research. Education: MSc in Aerospace Design (Salford University), PhD in Computer Modeling of Aerospace Design (University of the West of England). Research groups include the Computational Engineering and Design Group and the Centre for Defence and Security Research. External roles include membership in the US National Science Foundation and European Programme Committee for Value Driven Design. Teaching: Leads MSc courses in aerospace IGDS. Awards: 2008 Rolls-Royce R&T award for Creativity. Personal interests include squash, flying Piper Warrior aircraft, and BBC interviews on UAV civil applications.
Olivier Sigaud is a Full Professor at Sorbonne University, affiliated with the ISIR (Intelligent Systems and Robotics Institute) and the Machine Learning and Intelligent Autonomous Systems (MLIA) team. He holds an engineering degree from ISEN and dual PhDs in Computer Science (University of Paris XI, 1996) and Philosophy (University of Paris I, 2004). Previously employed at Dassault Aviation (1995–2001), he transitioned to academia as a Lecturer and later a Professor at LIP6 and ISIR. His research focuses on reinforcement learning, robotics, computational neuroscience of decision-making in animals, and human-robot interaction. Key contributions include advances in goal-conditioned reinforcement learning, intrinsically motivated agents, and human-in-the-loop systems. He has co-authored over 100 publications in top-tier conferences (NeurIPS, ICML) and journals, with recent work exploring large language model grounding, open-ended learning frameworks, and motor skill acquisition through interactive curricula. Notable projects include the CURIOUS framework for modular multi-goal RL and the DREAM architecture for open-ended robotic learning. His work bridges theoretical AI with practical robotics applications, emphasizing interdisciplinary collaboration between computer science and neuroscience.
Steven Devleminck is an Associate Professor in the Department of Computer Science at KU Leuven's Faculty of Engineering Technology, concurrently serving as coordinator of the School of Arts (Associated Faculty) in Brussels. His dual appointment bridges engineering and arts through the Human-Computer Interaction (HCI) group at Group T Leuven Campus and Unit Art & Technology in Brussels, with active membership in DigiSoc – KU Leuven Digital Society Institute. His research centers on human-centered computing and speculative design methodologies , with core expertise in tangible interaction for emotion regulation and multispecies futures. Key themes include biofuturing as co-creative response to climate crises, squeeze-based interfaces for workplace stress, and artistic AI collaborations. His work uniquely integrates computer science with choreography, film studies, and anthropology through projects like “Youth TikTok production as public pedagogy” and “Imagining the Post-Anthropocene in BioFutures Living Lab”. Analysis of recent publications reveals three dominant trajectories: (1) Advancement of squeeze interaction techniques for affective computing, (2) Development of biofuturing frameworks for multispecies speculation, and (3) Critical examinations of AI's role in artistic mediumship. Cross-cutting themes include post-anthropocentric design, climate-responsive technologies, and decolonial approaches to digital pedagogy. Devleminck actively mentors doctoral candidates including Ula Sickle (choreographic exhibitions) and J. Verbesselt (cinema studies), while leading major funded projects such as: Living Corpora (2025-2029): Pioneering human-AI collaboration in digital humanities as Co-promotor Experiential Futuring (2022-2026): Co-creative methodology for social media outage response as Co-promotor Deradicalizing the City (2021-2025): Urban intervention research as Promotor He serves on the Computer Science Department Council and Doctoral Committee for the Associated Faculty of Arts. His laboratory ecosystem spans the HCI Group T Leuven Campus for technical development, Brussels-based Unit Art & Technology for artistic integration, and BioFutures Living Lab for participatory multispecies experimentation. This tripartite structure enables transdisciplinary work connecting squeeze sensor engineering with climate futures speculation and museum interface design.
Will Perkins is an Associate Professor in the School of Computer Science at Georgia Institute of Technology. Previously, he held faculty positions at the University of Illinois at Chicago, the University of Birmingham (UK), and was an NSF Postdoc at Georgia Tech. He earned his PhD in 2011 from New York University's Courant Institute under Joel Spencer. His research focuses on algorithms, statistical physics, and discrete mathematics, particularly exploring algorithmic tractability of random computational problems, statistical physics spin models, and combinatorial methods derived from algorithmic intuition. Research Interests : Algorithms, statistical physics, combinatorics, phase transitions, random graphs, and Gibbs measures. His work bridges theoretical computer science and statistical mechanics, addressing questions about sampling, phase coexistence, and algorithmic barriers. Recent Activities : Director of the Algorithms and Randomness Center at Georgia Tech, Managing Editor of Combinatorial Theory , and Associate Editor of Random Structures and Algorithms and SIAM Journal on Discrete Mathematics . Upcoming engagements include the Rocky Mountain Summer Workshop (2024), Park City Mathematics Institute (2024), and conferences on Random Structures and Algorithms (2025). Teaching : Courses include Design and Analysis of Algorithms (CS 3510), Advanced Algorithms (CS 4540), and specialized topics like Statistical Physics in Algorithms and Combinatorics (CS 8803). He has taught across institutions, including at the University of Birmingham and University of Illinois at Chicago. Key Contributions : His work on phase transitions in combinatorial structures, algorithmic sampling in statistical physics models, and rigorous analysis of Gibbs measures has been published in top venues like FOCS, STOC, and Communications in Mathematical Physics. Notable results include hardness of sampling for anti-ferromagnetic Ising models and novel contour methods for Pirogov-Sinai theory.
