Youssef M. Marzouk is the Breene M. Kerr (1951) Professor of Aeronautics and Astronautics at MIT and co-director of the MIT Center for Computational Science and Engineering (CCSE). He is affiliated with the MIT Schwarzman College of Computing, the Statistics and Data Science Center, and the Aerospace Computational Design Laboratory. His research focuses on computational science and engineering, with an emphasis on uncertainty quantification, Bayesian modeling, data assimilation, and machine learning applied to physical systems. He holds a Ph.D. in Mechanical Engineering from MIT (2004), preceded by S.M. (1999) and S.B. (1997) degrees in Aeronautics and Astronautics from the same institution. Marzouk’s work bridges computational mathematics, statistical inference, and fluid dynamics, addressing challenges in energy systems and environmental modeling. He has received numerous awards, including the 2018 AIAA Associate Fellowship and the 2012 MIT Class of 1942 Career Development Chair. His teaching spans computational mathematics, fluid dynamics, and uncertainty quantification. Key collaborations involve the MIT CCSE and external institutions, with funding from DOE and NSF. He advises students on topics like stochastic modeling and inverse problems, and his research lab explores advanced computational methods for high-dimensional systems.
Dr. Debraj Roy is a Visiting Professor at the University of Amsterdam (UvA), affiliated with the Faculty of Science, Mathematics and Computer Science and the Informatics Institute. His research focuses on agent-based modeling, socio-economic dynamics, environmental resilience, and blockchain technology. He investigates complex systems such as urban slums, disaster recovery, and climate adaptation using computational methods like remote sensing and machine learning. His work bridges theory and practice, offering insights into policy design for sustainable development and social equity. Key research interests include slum dynamics, poverty traps, and the application of blockchain oracles for decentralized systems. He employs advanced techniques such as global sensitivity analysis and manifold learning to explore multi-scale socio-environmental challenges. His recent articles highlight trends in carbon pricing, flood risk valuation, and multi-agent systems. Earlier work concentrated on urban inequality in cities like Bangalore and Mexico City, leveraging geospatial and statistical tools. No scientific awards or grants are explicitly listed. His advising and team collaborations are unspecified in the provided text.
John Miller is a Professor of Economics and Social Science at Carnegie Mellon University (CMU) and a Research Professor at the Santa Fe Institute. His work focuses on complex adaptive systems, computational modeling, and social dynamics. He holds a Ph.D. in Economics from the University of Michigan (1988) and has held academic positions since 1990. Miller’s research explores emergent patterns in social systems through agent-based models, experimental economics, and nonlinear dynamics. His research interests span complex adaptive systems, game theory, auction markets, and behavioral economics. Notable contributions include foundational work on computational social science, the Standing Ovation Problem, and cooperative behavior analysis. Miller has authored influential books such as Complex Adaptive Systems: An Introduction to Computational Models of Social Life and A Crude Look at the Whole . He has received awards including the Elliot Dunlap Smith Award for Teaching Excellence and has led initiatives like the Open Learning Initiative. Miller’s academic leadership roles include Director of Graduate Studies at CMU and Faculty Director of the Omidyar Fellows Program at Santa Fe Institute. His work bridges economics, computer science, and interdisciplinary complexity research.
Dr. Victoria C. P. Chen is a Professor in the Industrial, Manufacturing, and Systems Engineering (IMSE) department at The University of Texas at Arlington (UTA), where she has served since 2002. She previously held positions at the Georgia Institute of Technology from 1993-2001. Dr. Chen has held several leadership roles at UTA, including Interim Department Chair (2012-2014), Director of the Center on Stochastic Modeling, Optimization, & Statistics (COSMOS) (2008-2012, and again from 2017-present), and Director of Doctoral Studies (2019-present). She was also the George & Elizabeth Pickett Professor from 2015-2017 and was inducted into the UT Arlington Academy of Distinguished Teachers in 2019. Dr. Chen is actively involved with INFORMS (Institute for Operations Research and the Management Science), where she currently serves as Secretary on the Executive Board. Dr. Chen earned her B.S. in Mathematical Sciences from The Johns Hopkins University, and her M.S. and Ph.D. in Operations Research and Industrial Engineering from Cornell University. Her academic journey includes visiting professorships at the University of Genoa, Italy, and Iowa State University. Dr. Chen's research utilizes statistical perspectives to create new methodologies for operations research problems appearing in engineering and science. Her expertise includes the design of experiments, statistical modeling, and data mining, particularly for computer experiments and stochastic optimization. Through her statistics-based approach, she has developed computationally-tractable decision-making methods for many high-dimensional complex systems. Her work spans multiple domains including sustainability, energy, water management, healthcare, and law enforcement. Specific application areas include inventory forecasting, airline optimization, water reservoir networks, wastewater treatment, air quality monitoring, green building design, nurse assignment systems, and pain management programs. Her recent publications demonstrate continued innovation in mixed integer programming for electric vehicle charging stations, vacuum ultraviolet spectroscopy prediction, and sustainable building education. Senior Member, Institute for Operations Research and the Management Sciences (INFORMS) (2024) Data Mining Prize (Lifetime Achievement Award), INFORMS Society on Data Mining (2023) College of Engineering Teaching Award, UT Arlington (2021) Third Place Award, C3.ai COVID-19 Grand Challenge (2020) Academy of Distinguished Teachers, University of Texas at Arlington (2019) George & Elizabeth Pickett Professorship (2015-2017) As an educator and mentor, Dr. Chen has advised over 25 doctoral students across diverse research topics in operations research and systems engineering. She has secured substantial research funding from multiple sources including the National Science Foundation (over $1.5 million in active projects), Environmental Protection Agency, National Institute of Justice, and industry partners like Luminant and Dallas-Fort Worth International Airport. Her current research projects focus on decision analytics for sustainable urban environments, optimization for Texas water management, and statistical methods for pain management programs. She has served as Principal Investigator or Co-PI on more than 20 externally funded research projects totaling over $3 million in funding. Dr. Chen co-founded the Center on Stochastic Modeling, Optimization, & Statistics (COSMOS) at UTA with Dr. H. W. Corley. This research center brings together faculty and students from multiple disciplines to address complex problems through advanced statistical and optimization methods. She also leads interdisciplinary research teams working on projects related to sustainable infrastructure, energy systems, and healthcare optimization, frequently collaborating with researchers from civil engineering, environmental science, and medical fields.
Matthew Holden is an Associate Professor in the School of Computer Science at Carleton University. He holds a PhD (2018) and MSc (2014) from Queen's University and a BScH (2012) from Western University. His research focuses on Surgical Data Science, applying machine learning to surgical time-series data from operating rooms and simulations to improve patient outcomes and surgical training. Key areas include real-time decision support, performance assessment, and surgical efficiency through domain-knowledge integration. Research interests emphasize machine learning for surgical workflows, skill assessment via sensor data (e.g., motion tracking, EEG), and computer-assisted interventions. Notable work includes automated proficiency evaluation in cataract surgery, ultrasound-guided procedures, and neurosurgical training. His contributions span medical robotics, surgical education, and clinical decision support systems. Publications highlight advancements in surgical workflow anticipation, tool detection, and skill metrics across domains like ophthalmology, emergency medicine, and neurology. Holden advocates for interdisciplinary approaches combining computational methods with clinical expertise to enhance healthcare delivery.
Tor Söderström is a Professor at the Department of Education at Umeå University. His research focuses on learning and development in sports, computer simulation training, and professional knowledge development in police education. He has contributed extensively to understanding talent identification in sports, particularly in Swedish football, and the efficacy of simulation-based training methodologies in law enforcement education. His research spans topics including athlete development trajectories, the impact of physiological testing on elite athlete performance, and the role of childhood athletic ability in long-term sports participation. Notable projects include a study on dropout processes in Swedish football talent systems and analyses of Swedish gym-goers' training patterns over two decades. Publications highlight interdisciplinary approaches, combining sports science, sociology, and educational technology. Recent work explores children’s rights in sports through scoping reviews and examines athlete retention strategies from adolescence into adulthood. His research often emphasizes practical applications in training design and policy development for both sport and professional education sectors. No scientific awards are explicitly mentioned in the provided texts. His work has involved collaborations with institutions like the Swedish Football Association and police education programs, focusing on scenario-based training methodologies and competency development in high-stakes professions.
Johanna Pirker serves as an Associate Professor at the Institute of Human-Centred Computing, Graz University of Technology, where she holds teaching authorization in Applied Computer Science. Her work bridges academic research with practical applications in interactive technologies, maintaining active consultation hours for students every Monday morning. Her research centers on human-centered computing with emphases on virtual/augmented reality systems, serious game design, and AI-driven interactive experiences. She investigates player behavior, user experience optimization, and therapeutic/educational applications of immersive technologies across diverse contexts including rehabilitation, engineering education, and social platforms. Recent 2025 publications reveal strong trends in AI integration for gaming ecosystems (toxicity detection, dialogue systems), VR-based educational tools across disciplines, and cross-cultural analyses of gaming communities. Her work consistently combines experimental user studies with novel system development to address real-world challenges. While specific grant details and student advising records aren't documented in source materials, her extensive publication output across venues like FDG and iLRN indicates active leadership in interdisciplinary collaborations focused on advancing immersive technologies for societal benefit.
Brittany Tower holds positions as Assistant Director for Simulation and Skills Learning at the School of Nursing, Sam Houston State University (SHSU), and serves as a Clinical/Simulation Adjunct in Critical Care. She earned an MSN in Nursing Education from Texas A&M University-Corpus Christi and a BSN from the same institution. Her professional experience includes 6+ years in clinical and simulation instruction, including roles as an Emergency/Trauma nurse at Ben Taub Hospital in Houston and leadership in hospital process improvement initiatives. Research & Expertise: Her current focus centers on enhancing clinical judgment and decision-making through simulation-based education. She is a member of the International Nursing Association for Clinical Simulation (INACSL) and actively contributes to refining simulation programs for standardized, effective training. Additional Roles: She has held adjunct roles at both SHSU and Texas A&M University-Corpus Christi, emphasizing clinical instruction in critical care settings. Her work includes mentoring new nurses and redesigning simulation curricula to align with industry standards.
Professor Roy Pea is the David Jacks Professor of Education & Learning Sciences at Stanford University, with a courtesy appointment in Computer Science. He served as Director of the H-STAR Institute (2007-2021) and founded Stanford’s PhD program in Learning Sciences and Technology Design. His research focuses on technology-enhanced learning, social foundations of human learning, and interdisciplinary applications of digital tools. Stanford University, School of Education Graduate School of Education Department Courtesy appointment in Computer Science His work spans complex domains like concussion education, climate change learning, and AI-driven mental health interventions. He co-authored the 2010 National Education Technology Plan and co-edited key texts including Video Research in the Learning Sciences and AI in Education . His NSF-funded LIFE Center (2004-2014) advanced learning science theories. Recent publications address: (1) linguistic framing of concussions and reporting behavior, (2) AI chatbots for mental health, (3) "engineering fiction" to reduce climate change abstractness, and (4) immersive AR/LLM learning experiences. His research integrates data science, psychology, and educational technology. Fellow, American Academy of Arts and Sciences (2019) Inaugural Fellow, International Society of the Learning Sciences (2018) Honorary Doctorate, The Open University (2018) Best Bridging Paper, EDM 2014 LAK13 Best Paper Award (2013) Roy mentors doctoral and master’s students in learning sciences, advising on topics related to technology, cognition, and equity. He contributes to digital education policy through roles on advisory boards for organizations like NSF, NIH, and the Joan Ganz Cooney Center. His patents include methods for digital video analysis and collaborative learning systems.
Associate Professor Mary Jeanette Ignacio is a faculty member at the Alice Lee Centre for Nursing Studies, Yong Loo Lin School of Medicine, National University of Singapore (NUS), where she has served since 2008. She currently serves as the Undergraduate Programmes Director for Year 1 and specializes in simulation-based pedagogy. Doctor of Philosophy (Health Professions Education), Maastricht University Bachelor of Science (Nursing), St Dominic Savio College, Philippines Doctor of Medicine (MD), University of Santo Tomas, Philippines Bachelor of Science (Psychology), University of the Philippines Diliman Her research focuses on stress management , cognitive integration , collaborative learning , and human factors in healthcare education. She has pioneered simulation-based teaching strategies across undergraduate and postgraduate nursing curricula and developed innovative approaches to link pathophysiology with clinical practice. Recent publications highlight her work in virtual reality simulations , systematic reviews on gender discrimination in nursing , and interprofessional educational interventions . She has received multiple teaching excellence awards and secured grants for simulation-based learning projects. NUS Teaching Excellence Award (Individual) 2023 NUS Long Service Award 2023 National University of Singapore Teaching Excellence Award 2017 Her funded projects include grants from the Centre for Development of Teaching and Learning (NUS), Singapore Millennium Foundation, and Sigma Theta Tau International Honors Society of Nursing Research. She actively contributes to advancing simulation pedagogy , virtual hospital design , and affective fidelity in simulations to improve clinical learning outcomes.
Dr. Laura B. Balzer is an Associate Professor of Biostatistics at the University of California, Berkeley. Her work focuses on causal inference, machine learning, and addressing methodological challenges in both randomized trials and observational studies, particularly in global health contexts. She leads collaborations in East Africa, focusing on HIV elimination and community health in rural regions. Her research emphasizes translating academic findings into real-world impact. Education: PhD in Biostatistics, UC Berkeley (2015) MPhil in Computational Biology, University of Cambridge (2009) BS in Applied Mathematics, University of Vermont (2008) Research Interests: Dr. Balzer’s work addresses causal inference in complex settings, including semi-parametric methods, measurement challenges, and dependence structures. Her global health projects target HIV prevention, tuberculosis transmission, and hypertension management in sub-Saharan Africa. She designs interventions like the SEARCH Dynamic Choice model, which offers flexible HIV prevention options, and evaluates community health worker programs. Publications highlight her contributions to HIV/AIDS research, including studies on PrEP uptake, viral suppression in adolescents, and tuberculosis-HIV co-infection. Methodologically, she advances causal inference frameworks to handle missing data and clustered designs. Awards: While no specific awards are listed, her work has been funded by initiatives like the SEARCH trials, reflecting its scientific and public health significance. Advising & Grants: Balzer collaborates with multidisciplinary teams in Uganda and Kenya, focusing on translational research. Her grants support interventions linking statistical innovation to healthcare delivery improvements in resource-limited settings. Labs/Teams: Her research is embedded within global health partnerships, particularly within the SEARCH trials network, which integrates biostatistics with clinical and community-based implementation.
Pietro Liò is Full Professor in the Department of Computer Science and Technology at the University of Cambridge, where he leads research in Artificial Intelligence and Computational Biology as part of the AI group and the Cambridge Centre for AI in Medicine. He holds additional affiliations as Fellow and Council member of Clare Hall College, member of Ellis (European Lab for Learning & Intelligent Systems), and member of Academia Europaea. Professor Liò earned dual PhDs in Complex Systems and Non Linear Dynamics from the University of Florence and in Theoretical Genetics from the University of Pavia, Italy. His educational background bridges theoretical computer science with biological sciences, forming the foundation for his interdisciplinary research approach. His research focuses on developing Artificial Intelligence and Computational Biology models to understand disease complexity and advance personalized medicine. Current work emphasizes Graph Neural Network modeling for integrating multi-scale, multi-omics, and multi-physics data; combining deep learning with mechanistic approaches; explainability in medical AI; and developing AI-based medical digital twins and personal decision support systems. His work spans from fundamental algorithm development to clinical applications, with particular emphasis on translating computational advances into medical solutions. Analysis of his recent publications reveals strong activity in geometric deep learning , explainable AI for healthcare , and multi-omics integration , with increasing focus on clinically applicable tools that maintain both predictive power and interpretability. Member of Academia Europaea Listed among Top Italian Scientists by VIA-Academy Professor Liò has mentored over 40 PhD students and postdoctoral researchers, including notable names such as Petar Velickovic, David Buterez, and Chaitanya Joshi. His research is supported through collaborations with the Cambridge Centre for AI in Medicine and various international partnerships. He serves on departmental committees including Student Complaints and Postdoc Mentoring, and has completed equality and diversity training essentials. He leads research within the Artificial Intelligence group at Cambridge, focusing on creating computational frameworks that bridge biological complexity with clinical applications through advanced machine learning techniques.
Dr. Joseph Moore is an Assistant Professor in the Department of Mechanical Engineering at Johns Hopkins University (JHU), serving as Director of the Agile and Intelligent Robotics (AIRO) Laboratory. He is affiliated with the Laboratory for Computational Sensing and Robotics (LCSR), the Institute for Assured Autonomy (IAA), and holds a Bridging Faculty appointment in the Research and Exploratory Development Department (REDD) at JHU/APL. His research focuses on computational control, machine learning, and robotics to enable agile systems operating in complex environments. Dr. Moore previously served as Robotics Group Chief Scientist at JHU/APL, leading projects on hybrid unmanned aerial-aquatic vehicles and aerobatic fixed-wing systems. He has secured funding as Principal Investigator (PI) for ONR, DARPA, and ARL programs, particularly in post-stall maneuvering control and multi-robot coordination. His work emphasizes robust control strategies for autonomous systems in constrained environments. Research interests include aerial robotics, optimization, and learning-based control. Notable contributions involve NMPC-based systems, UAV navigation, and adaptive control for uncertain environments. His recent articles highlight advancements in swarm coordination, morphing-wing UAVs, and PAC-NMPC frameworks. Dr. Moore advises students such as Mark Gonzales and Adam Polevoy. Key grants include ONR/DARPA-funded projects on post-stall flight control and Army-funded multi-robot coordination efforts. His lab (AIRO) and collaborations (LCSR, IAA) drive applied and theoretical robotics research.
Ambarish Kulkarni is an Assistant Professor in the Department of Chemical Engineering at the University of California, Davis. His research focuses on multi-scale molecular modeling, data science for materials discovery, catalysis, and separations. He combines quantum chemistry methods (e.g., wave function theory, density functional theory) with classical simulations and machine learning to design novel materials for applications in catalysis, energy storage, and environmental remediation. Specific areas of interest include methane activation, CO 2 capture, and heterogeneous electrocatalysis. His work bridges theory and experiment, collaborating with experimental groups to validate computational findings. Notable projects include: Developing catalysts with atomically dispersed metals for enhanced reactivity Designing zeolite materials for selective chemical transformations Creating machine learning workflows to accelerate material discovery Recent research highlights the role of water in CO 2 adsorption mechanisms, the dynamic behavior of confined nanoparticles, and redox-cycling phenomena in zeolite-embedded catalysts. His computational tools like the Multiscale Atomic Zeolite Simulation Environment (MAZE) enable detailed analysis of complex material behaviors. No scientific awards are explicitly listed in the provided information. His advising activities and grants are not detailed in the current data, but his extensive publication record indicates active research collaboration and funding support.
Professor Edward Palmer is a faculty member in the School of Education at the University of Adelaide, serving as Director of the Unit of Digital Education and Training and Acting Deputy Head of School. His research focuses on technology's role in education and training, particularly in virtual/extended realities, AI-driven assessment, and personalized learning approaches. He has secured over $3 million in funding from government and industry partners, with projects addressing AI ethics, VR applications in medical training, and MOOC design. Education & Roles: Holds academic leadership positions and directs digital education initiatives. Research: Investigates AI in education, VR for situational awareness, and innovative assessment methods. Active in collaborative projects with industry and defense sectors. Grants & Funding: Secured significant grants for ventures in AI ethics, VR training simulations, and digital health hubs. Labs/Teams: Leads the Unit of Digital Learning and Society, collaborating with postdocs like Daniel Lee on AI and VR projects. His work emphasizes practical applications, such as medical procedure training in VR and adaptive learning systems. He mentors HDR students and postdocs in AI-driven training scenarios and digital education innovation.
