Travis B. Thompson, Ph.D. is an Assistant Professor in the Department of Mathematics and Statistics at Texas Tech University, leading the TM4 (Texas Tech Translational and Theoretical Mathematical Modeling and Machine Learning in Medicine) research group. His academic journey includes postdoctoral work at Rice University, Simula Research Laboratory, and the University of Oxford, focusing on mathematics applied to neurodegenerative diseases. Education: Ph.D. in Mathematics from Texas A&M University (2013) Dr. Thompson develops theoretical mathematical models and applies scientific computing and machine learning to study neurological pathologies, particularly Alzheimer’s disease. His work explores complex biological processes on networks, translational healthcare applications, and nutritional security implications. Current research trends integrate neuroimaging data with finite element simulations to model tau progression , amyloid beta dynamics , and glymphatic clearance in age-related diseases. Scientific awards and honors were not explicitly mentioned in the provided materials. Dr. Thompson’s interdisciplinary approach connects computational neuroscience with biomedical engineering , utilizing techniques like diffusion tensor imaging and level set methods to analyze pathological protein spread and brain tissue mechanics . The TM4 research group focuses on network neurodegeneration , personalized medicine , and machine learning diagnostics . Their work spans from microfluidic cancer detection to computational modeling of brain clearance mechanisms , addressing challenges in both neurodegenerative diseases and biomedical engineering through rigorous mathematical frameworks.
Xiao Wang is a research assistant and PhD student in the Cyber-Physical Systems Group at the Technical University of Munich since 2019. She holds a Master of Science in Mechanical Engineering from the same university (2018) and a Bachelor of Engineering in Vehicle Engineering from Tongji University, China. Her research focuses on Motion Planning for Autonomous Vehicles , Formal Methods , and Safe Reinforcement Learning . She has supervised multiple theses exploring topics like constrained RL, online verification, imitation learning, and safety falsification for autonomous systems. Her teaching roles include exercises and practical courses on Artificial Intelligence and Motion Planning for Autonomous Vehicles since 2018. Her publications (2020–2023) span journals like Transactions on Machine Learning Research and conferences such as ITSC and FISITA , addressing challenges in safe RL, control barrier functions, and naturalistic traffic rule violations. She has also contributed to integrating the Apollo framework with the CommonRoad motion planning environment. Key research areas: Safe Reinforcement Learning, Motion Planning, Formal Verification, Autonomous Driving, Control Barrier Functions, Trajectory Prediction
Linus Peitz is a research academic affiliated with multiple prestigious UK institutions including the School of Human Sciences at the University of Greenwich (primary affiliation), the School of Social Policy, Sociology and Social Research at the University of Kent, and the Centre for the Study of Social Cohesion at the University of Oxford. His work bridges criminology, social psychology, and public health through innovative sport-based interventions in correctional settings. Dr. Peitz specializes in gender-specific prison programming, with particular expertise in football-based rehabilitation initiatives like the Twinning Project. His research examines how sport interventions affect prisoner wellbeing, social identity, and desistance from crime, with recent focus on women's unique experiences in prison settings. His work identifies critical barriers including reproductive health challenges, fitness limitations, and institutional barriers that disproportionately affect incarcerated women. Analysis of Peitz's publication trends reveals a consistent focus on the psychological and social mechanisms through which sport interventions facilitate rehabilitation. His research demonstrates that football programs create valuable social connections, improve mental health outcomes, and foster prosocial behaviors, though with gender-specific variations in engagement and impact. His work emphasizes the importance of trauma-informed approaches and gender-responsive programming in correctional settings. Peitz collaborates extensively with researchers across UK institutions and has received funding from significant sources including UKRI Future Leaders Grants. His research methodology typically employs qualitative approaches including thematic analysis of semi-structured interviews with program participants, providing rich insights beyond what quantitative metrics can capture. His current research examines how sport-based interventions like the Twinning Project (operating across five countries and four continents) can be optimized for different populations within the criminal justice system, with particular attention to creating pathways for sustained engagement beyond prison walls.
Professor Michael D. Jenkinson holds the Sir John Fisher / RCS England Chair of Surgical Trials at the University of Liverpool , where he is also Professor of Neurosurgery. His work focuses on meningioma management, brain metastases, and interventional clinical trials, with funding from MRC, Brain Tumour Charity, and NIHR. PhD in Neuroscience (Imaging and Biology of Oligodendroglial Tumours) Consultant Neurosurgeon at The Walton Centre (2010–present) Chief Investigator on NIHR-funded trials: ROAM, STOP 'EM, SPRING, ReStart Research Interests: Meningioma growth modeling and personalized monitoring Seizure prophylaxis in neurosurgery Surgical trials methodology Quality of life assessments in neuro-oncology Leadership Roles: Chair, SBNS Academic Committee Chair, EORTC Meningioma Committee Member, NIHR HTA Prioritisation Committee Co-Investigator on PROSSPER and FUTURE GB trials
David Kaufman serves as a Clinical Associate Professor in the Department of Health Informatics at the School of Health Professions, SUNY Downstate Health Sciences University, a position held since 2020. Previously, he was Associate Professor at Arizona State University's Department of Biomedical Informatics, Associate Research Scientist at Columbia University, and Lecturer at UC Berkeley's Graduate School of Education. Educational background: Bachelor's degree in Psychology from McGill University Master's degree in Educational Psychology from McGill University PhD in Educational Psychology from McGill University With 20+ years of expertise, Dr. Kaufman specializes in human factors evaluation of health technologies. His research integrates cognitive science with clinical informatics to optimize EHR-mediated workflows, enhance eHealth literacy across diverse patient populations, and develop consumer-facing health IT solutions. Key projects include evaluations of computerized provider order entry systems, diabetes telemedicine platforms, and emergency management systems during the COVID-19 pandemic, with recent collaborations involving the Mayo Clinic. Professional recognition: Fellow of the American College of Medical Informatics (FACMI) Dr. Kaufman maintains active academic partnerships including a Visiting Professorship at the University of Victoria. His work bridges theoretical human-computer interaction research with practical implementations in clinical settings, focusing on usability challenges for low-literacy patient populations and clinician workflow integration.
Mogens Fosgerau is a Professor at the Department of Economics, University of Copenhagen, with a research focus on discrete choice theory, rational inattention, transportation and urban economics, congestion modeling, and entropy-based frameworks. He has held an ERC Advanced Grant (2017-2023) and completed a Grand Solutions project for the Innovation Fund Denmark (2016-20). Education: Mathematical Economics (Aarhus University, 1990), PhD in Mathematics (University College London, 1992). Current affiliations: Department of Economics (University of Copenhagen), Faculty of Social Sciences. Former roles: Guest Professor at DTU (2022-2023), member of the Commission for Green Transition of Passenger Cars (2019-2021). His research explores the intersection of information theory and discrete choice models, addressing complex substitution patterns and endogeneity issues through generalized entropy frameworks. He applies these models to transportation planning, urban economics, and climate policy analysis. Recent publications focus on perturbed utility models, inverse product differentiation logit, and rational inattention in spatial choice contexts. His work bridges theoretical econometrics with practical transport and environmental policy challenges. Awards: Recipient of the 2021 Transportation Science Meritorious Service Award. Former Editor-in-Chief of Economics of Transportation (2012-2020). Advising and Grants: Leads research projects funded by the European Research Council and Innovation Fund Denmark. Has participated in policy committees including the Danish Environmental Economic Council (2019-2025) and the Committee on Public Transport Mobility (2023-24).
Professor Marilyn Lennon is a leading academic in the Department of Computer and Information Sciences at the University of Strathclyde , holding the title of Professor in Digital Health and Care. She founded and directs the Digital Health and Wellness Group (DHaWG) , focusing on multidisciplinary research for designing, evaluating, and implementing digital technologies that enhance individual and population health and wellbeing. First class BSc in Psychology (University of Glasgow, 1998) PhD in Computing Science (University of Glasgow, 2002) Postgraduate diploma in academic practice (Fellow of Higher Education Academy) With over two decades of experience in Human-Computer Interaction (HCI) , usability, and user-centered design, her research spans wearable and mobile technologies for home healthcare, including remote monitoring (predictive falls monitoring), remote diagnosis (colon capsule technology evaluations), and smart home solutions for social care. She specializes in addressing technical and social barriers to real-world health technology implementation, emphasizing robust yet rapid evaluation methods. Recent publications highlight her work in 3D healthcare visualization, telehealth implementation, and medication management systems. Her research combines lab-based usability testing with real-world ('in the wild') evaluations of technologies like smart home systems and telehealth solutions. Key trends include cross-disciplinary collaboration, accessibility for sensory-impaired individuals, and AI-driven healthcare innovations. Scientific Awards MyCity: Glasgow - Gamechanger Award (Gold Medal) 2025 Images of Research 2025 Shortlist Emerald Outstanding Paper Award 2013 Best Paper Award (most replicable scientific paper) 2013 As course director for the Masters in Digital Health Systems , she has supervised 5 PhD students (2 completed) and over 100 honors projects. Current projects include the No Need to Fall initiative funded by the Health Foundation (£400k) and the PREMIO project co-creating clinical trials in advanced breast cancer. She maintains active collaborations with NHS, charities, and international institutions like the University of Waterloo.
Kristin Y. Pettersen is a Professor at the Department of Technical Cybernetics, Norwegian University of Science and Technology (NTNU), and a Professor II at the Norwegian Defence Research Institute (FFI). She is a co-founder of Eelume AS, a company specializing in underwater robotics solutions. Education: Civil Engineering and PhD in Technical Cybernetics from NTNU Her research focuses on advanced control systems for marine and underwater vehicles, particularly snake robots and autonomous underwater vehicles (AUVs). Key areas include formation control, path following, adaptive guidance algorithms, and safety-critical control in dynamic environments. Recent work explores machine learning integration and energy-shaping techniques for robust locomotion. Publications highlight trends in Model Predictive Control (MPC) , Collision Avoidance , and Task-Priority Operational Space Control for redundant and underactuated systems. Her work bridges theoretical control theory with practical applications in marine robotics, including autonomous inspections and cooperative transport. Labs/Teams: Collaborates with NTNU's Faculty of Information Technology and Electrical Engineering and co-founded Eelume AS, advancing subsea robotic manipulation technologies.
Prof. Dr. Ralf Plattfaut is a Professor for Information Systems and Transformation Management at the University of Duisburg-Essen since 2023. He heads the Process Innovation & Automation Lab , focusing on digital transformation, business process management, and robotic process automation (RPA). Prior to this, he was Professor for Business Informatics at the South Westphalia University of Applied Sciences (2019–2023), and a management consultant at McKinsey & Company (2013–2019). Current Role: Professor (since 2023) Lab Leadership: Process Innovation & Automation Lab Consulting: Management consultant and keynote speaker Research Interests span digital transformation, IT governance, RPA adoption, and postcolonial perspectives in information systems. His work addresses organizational challenges in process automation, agile methodologies, and human-AI collaboration. Recent Publications (2024–2025) examine paradoxes in agile transformations, postcolonial IT governance in NGOs, AI adoption in healthcare, and behavioral barriers like status quo bias. These studies often employ computational grounded theory and cross-organizational case analyses. Scientific Recognition includes multiple Best Paper and Best Conference Paper nominations at ICIS, HICSS, and IFIP E-Government Conferences, as well as recognition by Wirtschaftswoche as a top German-speaking management researcher under 40.
Jovan Stojkovic is an incoming Assistant Professor at the Department of Computer Science at the University of Texas at Austin, set to join in Fall 2026. Prior to his appointment at UT Austin, he will spend a year at Meta working with the AI and Systems Co-design group. His research focuses on cloud computing and datacenters, with particular emphasis on cloud-native workloads and machine learning inference. Education: PhD in Computer Science from the University of Illinois at Urbana-Champaign, advised by Professor Josep Torrellas Undergraduate studies at the School of Electrical Engineering, University of Belgrade, Serbia, where he was recognized as the best student of the Computer Engineering and Information Theory Department every year from 2017-2020 Research Interests: Jovan's research focuses on cloud computing and datacenters , with two primary domains: Cloud-native workloads , such as microservices and serverless computing. He investigates how to co-design novel hardware platforms and software systems that deliver orders-of-magnitude improvements in performance, energy efficiency, and resource utilization for these emerging workloads. Machine Learning (ML) inference , particularly large language models (LLMs). His work addresses the challenges of ML inference through smart scheduling, workload placement, and system-level configuration tuning to reduce energy, power, and thermal overheads while maintaining performance and accuracy guarantees. Publication Trends: Jovan's publications demonstrate a strong focus on optimizing cloud infrastructure for emerging workloads. His research spans across serverless computing, microservices, and large language model inference. A clear trend emerges in his work: addressing the performance, energy efficiency, and resource utilization challenges of modern cloud workloads through innovative hardware-software co-design approaches. His most recent work shows increasing focus on LLM inference optimization, particularly in the areas of thermal management, power efficiency, and scheduling for many-adapter environments. Awards and Honors: HPCA Best Paper Award (2025) IEEE MICRO Top Picks Honorable Mention (2024) 6 patents with IBM and Microsoft on: Serverless systems, Processor overclocking in the cloud, and Energy-efficient LLM inference W. J. Poppelbaum Memorial Award (2025) for hardware and architecture innovation Mavis Future Faculty Fellowship (2024–2025) Invited to present at 11th Heidelberg Laureate Forum (2024) Kenichi Miura Award (2022) for excellence in High Performance Computing Multiple student travel grants to ISCA, MICRO, ASPLOS, and HPCA Advising and Grants: Jovan is actively seeking prospective PhD students for his research group at UT Austin. His research has been supported through collaborations with major tech companies including IBM, Microsoft, and Meta. His six patents with IBM and Microsoft demonstrate the practical impact of his research in serverless systems, processor overclocking, and energy-efficient LLM inference. His work on serverless computing (MXFaaS, EcoFaaS) and LLM inference optimization has received significant recognition in top-tier computer architecture conferences. Research Groups: During his PhD at UIUC, Jovan worked with Professor Josep Torrellas on cloud infrastructure research. He has collaborated extensively with researchers at IBM Research (particularly Hubertus Franke) and Microsoft (particularly Íñigo Goiri and Ricardo Bianchini). His upcoming position at UT Austin will establish his independent research group focused on cloud computing and datacenter systems. His year at Meta working with the AI and Systems Co-design group will further strengthen his expertise in AI infrastructure.
Giorgia Ramponi is an Assistant Professor with Tenure Track at the Faculty of Business, Economics and Informatics at the University of Zurich. She is also an affiliated professor at the ETH AI Center and the Data Science and AI, Computer Science and Engineering department at Chalmers University of Technology. Her educational background includes a Ph.D. in Information Technology from Politecnico di Milano (completed June 2021 with honors), advised by Marcello Restelli, and a Master of Science in Computer Science with Honours Programme (110/110 cum laude) from la Sapienza (July 2017), advised by Flavio Chierichetti and Alessandro Panconesi. Dr. Ramponi's research focuses on machine learning and mathematical modeling, with particular emphasis on reinforcement learning and multiagent learning. Her work bridges theoretical foundations with practical applications, exploring how learning algorithms can make optimal decisions in complex environments. She has made significant contributions to areas including inverse reinforcement learning, multi-agent systems, constrained Markov decision processes, and human-AI interaction through preference learning. Her recent publications demonstrate a strong trend toward addressing fundamental challenges in reinforcement learning, particularly in multi-agent settings, constrained optimization, and learning from human feedback. Her work combines theoretical rigor with practical applications across robotics, economics, and decision-making systems. Hassler Research Grant for "Unified Feedback Integration Framework for Reinforcement Learning" Dr. Ramponi actively contributes to the academic community through conference participation, invited lectures (including at the Mediterranean Machine Learning Summer School), and teaching. She designed and taught the "Data Science and Machine Learning" course for the ETH-Ashesi Master program. She is also a member of the ELLIS community, which connects excellence in AI research across Europe. Her research group focuses on developing frameworks for reinforcement learning with various feedback types, including preferences, rewards, and demonstrations. The group aims to advance the theoretical understanding of learning algorithms while addressing practical challenges in real-world applications.
Tony Cookson is a Professor of Finance and the Michael A. Klump Endowed Professor at the Leeds School of Business , University of Colorado Boulder, where he has served since 2013. His research spans empirical finance , household and corporate financial decision-making , and the impact of social media and legal institutions on financial behavior. He has published in top journals like the Journal of Finance , Journal of Financial Economics , and Management Science , focusing on topics from investor disagreement to fracking-induced debt repayment . Education : Ph.D. in Economics (University of Chicago), M.S. in Statistics and Applied Economics (Montana State University), B.S. in Economics (Montana State University) His research interests include: How social media shapes investor sentiment and trading patterns The economic consequences of fracking and casino policy Legal institutions and their role in credit market development Behavioral finance through LLM-driven investor personas His scientific awards include the Best Paper in Investments and Asset Pricing (MFA 2023) , NASDAQ Best Paper in Asset Pricing (WFA 2021) , and Finalist for TIAA Paul A. Samuelson Award (2021) . He serves as Editor at the Review of Corporate Finance Studies and Associate Editor at multiple top journals.
Simon Oya is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of British Columbia (UBC), Faculty of Applied Science. He holds a PhD in Information Technologies and Communications from the University of Vigo (Spain) and was previously a postdoctoral fellow at the Cryptography, Security and Privacy (CrySP) group at the University of Waterloo. His educational background includes: BSc, MSc, PhD from University of Vigo (Spain) Simon Oya's research focuses on designing and evaluating privacy-enhancing technologies with strong privacy and utility guarantees. He approaches privacy problems from a statistical perspective, using theoretical tools from signal processing and information theory to quantify privacy leakage and develop effective defenses. His primary research areas include: Privacy-preserving searchable encryption Machine learning privacy (particularly membership inference attacks) Anonymous communication systems Location privacy Differential privacy His publication record demonstrates a consistent focus on analyzing and improving privacy mechanisms across various domains. His recent work has particularly emphasized the intersection of machine learning and privacy, as well as advancing techniques for searchable encryption. His research methodology typically involves developing statistical models to understand privacy leakage and designing optimization-based approaches to improve privacy-utility tradeoffs. His notable scientific contributions include developing attacks against searchable encryption schemes to better understand their privacy limitations, and designing improved privacy mechanisms for location-based services. His work on statistical disclosure attacks against anonymous communication systems has also been influential in the field. As an educator, he teaches CPEN 442: Introduction to Cybersecurity at UBC. He actively seeks motivated graduate students interested in privacy research, particularly those with strong backgrounds in statistics, machine learning, or optimization.
Dr. Shabnam Sadeghi Esfahlani is an Associate Professor in Robotics at the School of Engineering and the Built Environment, Anglia Ruskin University , where she serves as Deputy Leader of the BORI research group and leads the Automation & Robotics MSc program. Her interdisciplinary expertise spans mechatronics, artificial intelligence, virtual reality, and serious games , with a focus on applications for rehabilitation, medical training, and autonomous systems . As a Chartered Engineer and Senior Fellow of the Higher Education Academy , she has secured significant funding from Innovate UK, Horizon 2020, and GCRF , with grants exceeding £3 million. Education PhD in Mechanical Engineering, Anglia Ruskin University BSc (First Class) in Statistics & Mathematical Science, Shahid Beheshty University Her research integrates AI with robotics for societal impact, exemplified by the open-source SROBO ground robot and projects like Rehabgame and the Assistive Feeding Robot . She has published over 45 peer-reviewed articles and contributes to academic communities as a journal guest editor and conference organizer . Key collaborations include IET, IMechE, and the Nuffield Foundation as a mentor for young students. Scientific Awards & Recognitions: Chartered Engineer (CEng), Engineering Council UK Senior Fellow (SFHEA), Higher Education Academy Student-Voted 'Made a Difference Award' (2018) Post-Graduate Certificate in Higher Education
Professor Ola Isaksson is a faculty member in Product Development at Chalmers University of Technology, where he leads the Systems Engineering Design research group. With over 40 research projects nationally and internationally, his work bridges academic research and industrial application, particularly in aviation and transport-related manufacturing sectors. His expertise spans digitalization, sustainability, and advanced manufacturing methods in product development. Ola Isaksson received his PhD in Computer Aided Machine Design from Luleå University of Technology in 1999. Prior to his academic career, he had a specialist career at GKN Aerospace Engine Systems (formerly Volvo Aero) in Trollhättan, focusing on design and product development until 2015. Professor Isaksson's research focuses on developing new product development capabilities to address societal and industrial needs through digitalization and advanced manufacturing. His primary interests include platform-based development, Set Based Engineering, multidisciplinary engineering methods, Value-driven development, and knowledge-intensive system support. He has particular expertise in additive manufacturing integration, design space exploration, and sustainability transition in product development. Analysis of Professor Isaksson's recent publications reveals a strong focus on integrating digital technologies with sustainable manufacturing practices. His work demonstrates a progression from traditional design methodologies toward AI-assisted design, digital twins, and advanced data analytics. Key thematic areas include additive manufacturing implementation, design margin management, sustainability integration, and aerospace component optimization, reflecting his commitment to bridging theoretical research with industrial applications. Professor Isaksson is one of the founders of the Swedish Product Development Academy and maintains active membership in the Design Society, ASME, and SIG PM, reflecting his significant contributions to the field of engineering design. With over 100 scientific publications and leadership in more than 40 research projects, Professor Isaksson has established himself as a leading figure in product development research. His work frequently involves close collaboration with industry partners, particularly in the aviation sector, securing substantial research funding for projects addressing digitalization, sustainability, and advanced manufacturing challenges. Professor Isaksson leads the Systems Engineering Design research group at Chalmers University of Technology. His team focuses on developing methodologies for complex product development, with particular emphasis on digital tools, sustainability integration, and manufacturing innovation. The group maintains strong industry connections, especially with aerospace manufacturers, facilitating the translation of research into practical applications.
