Cathy Wu is the Class of 1954 Career Development Associate Professor in Civil and Environmental Engineering at MIT, affiliated with the Institute for Data, Systems, and Society (IDSS). Her research bridges machine learning, optimization, and urban systems, with a focus on mixed autonomy systems in mobility. She holds degrees from MIT (B.S., M.Eng in EECS) and a Ph.D. from UC Berkeley (EECS). Education: B.S. and M.Eng in Electrical Engineering and Computer Science, MIT (2012-2013) Ph.D. in Electrical Engineering and Computer Science, UC Berkeley (2018) Research Interests: Reinforcement Learning and Machine Learning Large-scale Optimization and Control Theory Mobility Systems and Urban Infrastructure Implications of AI and Automation Her work emphasizes interdisciplinary collaboration, involving transportation, computer science, and public policy. She founded the Interdisciplinary Research Initiative within the ACM Future of Computing Academy to advance cross-disciplinary computing research. Key Projects: Includes Flow (open-source RL framework for traffic control), eco-driving incentive mechanisms, and mixed autonomy traffic optimization. Her articles address congestion mitigation, autonomous vehicle integration, and scalable supervision strategies. Awards: Recipient of fellowships, best paper awards, and teaching honors (specific names unlisted). Engagement: Collaborations with institutions like Microsoft Research, OpenAI, and Caltrans. Active in policy-oriented initiatives and education through IDSS programs.
Prof. Juan Alonso is the Vance D. and Arlene C. Coffman Professor and James & Anna Marie Spilker Chair in the Department of Aeronautics & Astronautics at Stanford University. He directs the Aerospace Design Laboratory (ADL), focusing on high-fidelity computational methods for aerospace system design. His expertise spans transonic/supersonic/hypersonic aircraft, rotorcraft, and launch vehicles. Alumni include record-holding teams for human-powered watercraft and lightweight unmanned aerial vehicles. Education: PhD (1997) from Princeton University in Mechanical & Aerospace Engineering; M.A. (1993) Princeton; B.S. (1991) MIT Aeronautics/Astronautics. Research emphasizes multi-disciplinary optimization, numerical methods, and parallel computing applied to advanced aircraft design, sustainable aviation, and UAS systems. Notable contributions include computational design frameworks like SU2 and SUAVE, and initiatives in curriculum development for engineering education. Recent work focuses on: GPU-accelerated CFD solvers, multi-fidelity surrogate models (e.g., VortexNet), contrail simulation frameworks, and battery degradation modeling for electric aircraft. Active in urban air mobility and high-fidelity trajectory optimization for hypersonic systems. Labs/Teams: Aerospace Design Laboratory (ADL) leading open-source computational tools development. Involved in NASA-funded projects and industry partnerships for advanced propulsion systems.
Daniel A. McAdams is the Robert H. Fletcher Professor in Mechanical Engineering at Texas A&M University and serves as the NSF Program Director of Convergent Activities. His research develops design theory and methodology with focus on functional modeling, bio-inspired design, and technology evolution. Educational Background: PhD in Mechanical Engineering from University of Texas at Austin MS in Mechanical Engineering from California Institute of Technology BS in Mechanical Engineering from University of Texas at Austin His research investigates innovation in concept synthesis through computational methods, bio-inspired design approaches, and technology evolution applied to product development. Current projects include function-sharing principles in biological systems, digital twin architectures, and patent mining for technology forecasting. Recent publications explore applications of speculative fiction in design ideation, graph-theoretic approaches for digital twins, and automated assessment in engineering education, demonstrating cross-disciplinary innovation across design science. Awards and Honors: ASME Design Theory and Methodology Award Distinguished Achievement Award for Student Relations Multiple Faculty Fellow awards Design Studies Best Paper Award Outstanding Faculty Mentor Award He leads the Product Synthesis Engineering Lab, advancing design methodologies for complex engineered systems through computational approaches and biological analogies.
Carlos Salomon Gallo is a Professor and NHMRC Investigator Fellow (EL2) at The University of Queensland's Centre for Clinical Research, affiliated with the School of Biomedical Sciences. He directs the Centre for Extracellular Vesicle Nanomedicine and leads the Exosome Biology Laboratory. A globally recognized key opinion leader in extracellular vesicles (ranked 3rd worldwide by Expertscape), his research focuses on EV biology for diagnostic and therapeutic applications in ovarian cancer, gestational diabetes, preeclampsia, and other obstetrical syndromes. His research integrates proteomics (SWATH-MS), miRNA analysis, and advanced isolation techniques to develop liquid biopsies. Core interests include: EV biomarker discovery and validation for early disease detection. Mechanisms of EV-mediated signaling in metabolic and oncological pathologies. Engineering EVs for targeted drug delivery and CRISPR-Cas therapeutics. Clinical translation of EV-based diagnostics (IVDs) and therapeutics. Analysis of his recent articles reveals a dominant focus on EV profiling in pregnancy complications (gestational diabetes, preeclampsia) and oncology (ovarian cancer), utilizing multi-omics approaches. Key trends include developing high-sensitivity EV biosensors, understanding hypoxia-induced EV signaling, and exploring 3D models for EV research. He has received significant recognition, including: NHMRC Emerging Leadership Fellow NHMRC Investigator Fellow (EL2) He leads the Exosome Biology Laboratory and the UQ Centre for Extracellular Vesicle Nanomedicine, fostering cross-disciplinary collaboration. His work involves extensive national and international partnerships, evidenced by leadership roles in the Centre for Clinical Diagnostics and over 20 invited international talks in 5 years. He actively mentors HDR students and contributes to global EV research standards (MISEV2023).
Kevin Baum is a computer scientist currently serving as the deputy head of the Neuro-Mechanistic Modelling (NMM) department at the German Research Center for Artificial Intelligence (DFKI) since January 2023, and head of the Centre for European Research in Trusted AI (CERTAIN) at DFKI since December 2023. Based at the Saarland Informatics Campus in Saarbrücken, Germany, he completed his doctorate in philosophy in March 2024, combining technical expertise with philosophical depth. His work bridges computer science with ethics, focusing on making AI systems transparent and accountable to human users. Dr. Baum's research program centers on interdisciplinary questions concerning the explainability and transparency of AI systems. His work spans multiple significant projects including the Explainable Intelligent System (EIS) initiative and project E7 of the Transregional Collaborative Research Centre 248 "Foundations of Perspicuous Software Systems" (CPEC). He has developed frameworks for understanding stakeholder perspectives on explainable AI and investigated how different information types about automated systems affect user perceptions of fairness and justice. His approach consistently combines theoretical foundations with practical implementations across diverse contexts. Analysis of his publication trends reveals a clear trajectory from theoretical foundations in machine ethics toward practical implementations of explainability requirements in real-world contexts. His work demonstrates increasing focus on human oversight effectiveness, fairness monitoring, and ethical considerations across various AI applications. He has made significant contributions to both academic discourse and practical AI development guidelines, with publications spanning computer science, philosophy, psychology, and human-computer interaction venues. Award for Ethics for Nerds lecture series As a research leader, Dr. Baum contributes to shaping AI development practices through his departmental leadership and interdisciplinary collaborations. His current work with CERTAIN focuses on establishing European research standards for trusted AI development and deployment, emphasizing the practical implementation of ethical requirements in AI systems. He maintains active collaborations across multiple institutions and disciplines, reflecting his commitment to bridging technical and philosophical considerations in AI development. At DFKI, he leads research that combines neuroscientific insights with AI development to create more interpretable systems. The NMM department focuses on both theoretical research on explainable AI foundations and practical applications in various domains, with particular attention to how different stakeholders understand and require explanations from AI systems.
Moez Limayem is a Professor at the University of South Florida's Muma College of Business, specializing in Information Systems. His research focuses on digital platforms, user behavior analysis, and technology adoption in e-commerce and healthcare contexts. He has published extensively in top journals like the Journal of Management Information Systems and MIS Quarterly , addressing topics such as recommender systems, virtual collaboration, and the impact of emerging technologies like service robots in hospitality. Limayem has contributed to conferences including ICIS and ECIS, and his work bridges theoretical insights with practical applications in business informatics. His recent projects explore multi-sided platforms, habit formation in technology use, and the ethical implications of mobile phone overuse. Key research areas include: Design and management of digital platforms User behavior in virtual environments Technology adoption in organizations Impact of color and media on learning outcomes Limayem has collaborated with institutions like the University of Alberta and the Polytechnic Montréal, demonstrating cross-disciplinary engagement. His work often emphasizes the integration of information systems into academic and business contexts, as seen in his case study on USF's Muma College of Business. He has also investigated challenges in data collection methods and innovative alternatives to traditional student sample studies.
Professor Brian Surgenor is a faculty member at Queen's University's Department of Mechanical and Materials Engineering, part of the Smith Engineering faculty. He holds a B.Sc. (1977), M.Eng. (AECL/Whiteshell), and Ph.D. (1983) in Mechanical Engineering from Queen's University. His research focuses on machine vision systems for automation, autonomous vehicle navigation, and mechatronic system design education. He has held key administrative roles including Department Head (1993-2002), Associate Dean (2008-2013), and Vice-Dean (2013-2016). His work emphasizes interdisciplinary innovation, such as the Mitchell Hall design project and contributions to Ingenuity Labs. Education: B.Sc. Mechanical Engineering, Queen's University (1977) M.Eng. Engineering Physics, McMaster University (AECL/Whiteshell) Ph.D. Mechanical Engineering, Queen's University (1983) Research interests include: - Pneumatic servosystems - Intelligent algorithms for machine vision - Off-road autonomous vehicle systems - Mechatronics education methodologies - Hybrid powertrain systems for vehicles His recent publications (2017–2024) explore autonomous systems, machine vision applications, and fuel cell hybrid technologies. Notable trends include advancements in UAV-based infrastructure inspection, terrain-adaptive autonomous driving, and low-cost machine vision solutions for small part sorting. His work bridges theoretical control systems with practical industrial automation challenges. He has contributed to laboratory design for CDIO curricula and pioneered mechatronics education through problem-based learning. His administrative leadership has shaped Queen's engineering graduate programs and research infrastructure. Currently involved in Ingenuity Labs, fostering cross-disciplinary innovation.
Chun Ouyang is a Professor at Queensland University of Technology (QUT) in the School of Computer Science within the Faculty of Science. With an extensive publication record spanning over two decades from 2002 to 2025, Professor Ouyang has established themselves as a leading researcher in Business Process Management, Process Mining, and Explainable AI. Their work bridges theoretical foundations with practical applications across healthcare, finance, and industrial sectors. Professor Ouyang's research interests primarily focus on Business Process Management systems, Process Mining techniques, Explainable Artificial Intelligence, and Healthcare Process Analysis. Their work has evolved from foundational BPMN/BPEL translation research in the early 2000s to sophisticated process mining approaches in the 2010s, and most recently to cutting-edge Explainable AI applications in clinical and business contexts. They have developed novel methodologies for process querying, predictive process analytics, and XAI evaluation frameworks that have significantly advanced the field. Their research consistently emphasizes practical applicability while maintaining strong theoretical foundations, with publications in top-tier journals and conferences including IEEE Transactions, Springer journals, and major BPM conferences. Analysis of Professor Ouyang's recent publications (2023-2025) reveals a strategic research trajectory that integrates traditional process mining with modern AI techniques, particularly focusing on explainability and trustworthiness. Their work demonstrates a consistent pattern of addressing real-world challenges through rigorous methodological development, with increasing emphasis on healthcare applications, clinical decision support systems, and the ethical implications of AI deployment. The publications show strong interdisciplinary collaboration patterns, particularly with medical researchers and industry partners. Professor Ouyang has mentored numerous PhD students and early-career researchers who have gone on to establish themselves in the BPM and AI communities. Their research group at QUT has secured multiple competitive grants supporting innovative work in process analytics and AI. They maintain active collaborations with leading researchers globally, including Catarina Pinto Moreira, Arthur ter Hofstede, and Moe Wynn. Professor Ouyang leads the Process Analytics Research Group at QUT, which focuses on developing advanced techniques for business process analysis, prediction, and optimization. The group maintains strong industry connections with healthcare providers, financial institutions, and government agencies, ensuring their research has practical impact. Current projects include developing trustworthy AI systems for clinical decision support, cross-organizational process analysis frameworks, and next-generation process mining techniques for complex, distributed systems.
Eric Coatanea is a Professor at Tampere University, affiliated with the Automation Technology and Mechanical Engineering department within the Faculty of Engineering and Natural Sciences. His research focuses on manufacturing systems, causal graph networks, multi-disciplinary optimization, and AI integration in engineering design. He holds a BSc from University of West Brittany (1990), MSc from INSA Toulouse (1993), and teaching certification from Ecole Normale Supérieure (1994). Research interests include modeling manufacturing systems, additive manufacturing (e.g., Directed Energy Deposition), causal graphs for decision-making, and systems engineering. Notable awards include the Chevalier des Palmes Académiques (2018) and Marie-Curie Fellowship (2006–2008). Recent publications emphasize optimization algorithms combining AI (e.g., L-ANN-GWO), viscosity effects in 3D printing, and causal graph applications in MDO. His work bridges theoretical frameworks with practical engineering challenges, focusing on early design synthesis and sustainable manufacturing. Commitments include editorial roles (Journal of Integrated Design & Process Science), NSERC Canada advisory, and board memberships (Dynavio Cooperative Oy, Selko Oy). Current projects include LILIAM, ÄVE, and DIGITBrain initiatives.
Dr. Susan D. Hovorka is a Research Professor at the Bureau of Economic Geology, The University of Texas at Austin, specializing in geological techniques for environmental applications. She focuses on subsurface permeability dynamics in both tight and highly transmissive systems, with a primary emphasis on geological carbon sequestration and CO₂ storage security. Ph.D. in Geology (1990), The University of Texas at Austin M.A. in Geology (1981), The University of Texas at Austin B.A. in Geology (1974), Earlham College Her research addresses critical challenges in carbon geological storage, including: Characterizing salt formations as containment materials Analyzing carbonate fabrics for karst aquifer flow understanding Field CO₂ injection experiments for sequestration assessment Developing composite confining systems for secure CO₂ retention The articles she has contributed to since 2002 demonstrate a consistent focus on: Carbon capture and storage (CCS) technologies Reservoir pressure dynamics and fault permeability Permit-ready site workflows and risk mitigation Geological analogs from petroleum systems Dr. Hovorka actively collaborates with institutions like the Gulf Coast Carbon Center (GCCC) and participates in international CCS initiatives. Her work integrates sedimentology, geophysics, and environmental policy to advance subsurface carbon management solutions.
Florent Krzakala is a Full Professor at École polytechnique fédérale de Lausanne (EPFL) in Switzerland, holding positions across multiple departments including the School of Basic Sciences (SB), School of Engineering (STI), and specifically within the Department of Physics (IPHYS) and Department of Electrical Engineering (IEM). He leads the Information, Learning and Physics Laboratory (IdePHICS) and maintains an office at ELD 239, Station 11, 1015 Lausanne. His research bridges statistical physics and computational disciplines, with significant contributions to understanding the theoretical foundations of machine learning and optimization problems. Dr. Krzakala received his MSc in Physics from Orsay, France in 1999, followed by a PhD in Statistical Physics from Orsay, Paris XI, France in 2002, and completed a postdoctoral position at Roma La Sapienza in 2004. This strong foundation in physics has informed his interdisciplinary approach to computational problems. His research interests span Statistical Physics, Machine Learning, Probability and Statistics, Computer Science, Information Theory, Inference on Graphs, Random Constraint Optimization, and Computational Optics. Krzakala's work focuses on applying methods from statistical physics to problems in theoretical computer science, probability, and machine learning. He investigates how concepts from disordered systems and phase transitions can illuminate computational barriers in optimization and inference tasks. His research has particular relevance for understanding the behavior of neural networks, compressed sensing, and high-dimensional statistical models. Analysis of his recent publications reveals a strong trend toward understanding the fundamental limits of learning in high-dimensional settings, with particular emphasis on phase transitions, statistical-to-computational gaps, and the theoretical properties of deep learning architectures. His work frequently bridges rigorous mathematical analysis with practical machine learning applications, demonstrating how insights from statistical physics can inform algorithm design and theoretical understanding in AI. Krzakala actively mentors the next generation of researchers, supervising numerous PhD students whose work continues to advance these interdisciplinary fields. His laboratory serves as a hub for researchers exploring the intersection of physics and computation, fostering collaborations across traditional disciplinary boundaries. He teaches advanced courses including Fundamentals of Inference and Learning, Statistical Physics, and Statistical Physics for Optimization & Learning, which examine the connections between physical principles and computational methods. His educational materials, including lecture notes on statistical physics methods in optimization and machine learning, have become valuable resources for students and researchers worldwide. As founder and scientific advisor of the startup Lighton, Krzakala has also demonstrated a commitment to translating theoretical insights into practical applications, particularly in the realm of optical computing for machine learning tasks.
Ingo Jahn is a Professor at The University of Queensland's School of Engineering. His academic career spans roles including R&D at Rolls-Royce (2007–2012) and academic positions at The University of Queensland (2012–2022). He holds an MEng (Oxford, 2005) and PhD (Oxford, 2011). Education: MEng in Engineering, University of Oxford (2005) PhD in Aerospace Engineering, University of Oxford (2011) Research Interests: Hypersonics: vehicle design, glide trajectory optimization, and aerothermodynamics Fluid Dynamics: computational methods, turbulence, and flow control Control Systems: model predictive control and co-design frameworks Thermodynamics: heat transfer in supercritical CO2 cycles and thermal protection systems His work bridges theoretical and experimental approaches, with a focus on hypersonic vehicle integration and propulsion systems. Publications: Recent articles emphasize hypersonic vehicle co-design, fluid-structure interaction, and experimental methods. Key themes include trajectory optimization, thermal management, and advanced simulation techniques. Grants & Awards: No awards explicitly listed, but extensive industry collaboration (e.g., Rolls-Royce) and leadership in high-impact projects indicate significant recognition. Supervision: Currently supervising 8 doctoral students on topics like hypersonic co-design, unstart prevention in ramjets, and scramjet trajectory optimization. Affiliations: Institute for Advanced Engineering and Space Sciences, AIAA, ASME. Active in conferences like AIAA SciTech and Global Power and Propulsion Society events.
Dr. Anwar Ali is a Lecturer in the Department of Electronic and Electrical Engineering at Swansea University's Bay Campus, affiliated with the School of Aerospace, Civil, Electrical and Mechanical Engineering. He holds an M.S. in Electronic Engineering (2010) and a Ph.D. in Electronic and Communication Engineering (2014) from Politecnico di Torino, Italy. His research focuses on: Power electronic converters and conditioning systems Embedded systems for aerospace applications Analog/mixed-signal circuit design Satellite technologies including power management Attitude determination and control systems Thermal modeling of aerospace systems Dr. Ali has authored over 50 publications with recent works concentrated in satellite power systems, thermal analysis of spacecraft, machine learning applications in healthcare/robotics, and energy harvesting techniques. His research demonstrates consistent innovation in small satellite technologies and cross-disciplinary applications of electrical engineering principles. He currently supervises PhD projects on: Wireless power transfer for implantable medical devices Integrated power and attitude control optimization for small spacecraft and teaches modules including Analogue Design, Software Engineering, Embedded System Design, and Integrated Circuit Design.
Fabian Spill is a Professor of Applied Mathematics, specializing in interdisciplinary research that bridges mathematical modeling with biomedical applications. His work spans cancer biology, metabolic pathways, and data-driven optimization for sustainable systems. Research Focus: Mathematical modeling of cancer spheroids, immune-inflammatory dynamics, and epigenetic age regression. Collaborations: Engages in cross-disciplinary projects with institutions like the Medical Research Council and Innovate UK. Projects: Leads initiatives in systems-mechanobiology, stem cell behavior prediction, and energy-efficient building design. Recent publications highlight his contributions to hybrid computational models for collagen stiffening in tumors, lactate signaling in inflammation, and interpretable machine learning frameworks for age estimation. His research aligns with UN Sustainable Development Goals, particularly those addressing health and sustainable cities.
Maria Garlock is the Daniel Tsui Professor in Engineering at Princeton University, serving as Co-Director of the Program in Architecture and Engineering and Head of Forbes College. Her roles also include membership in the Executive Committee of the Council on Science and Technology, Associated Faculty in the School of Architecture, and Associated Faculty in the Program in Latin American Studies. Garlock holds a PhD in Structural Engineering (Lehigh University, 2002), an MS in Civil Engineering (Cornell University, 1993), and a BS in Civil and Environmental Engineering (Lehigh University, 1991). Her research focuses on resilient structural design for extreme hazards like fires, earthquakes, and storm surges. She explores both isolated and cascading multi-hazard scenarios while also analyzing historical structural designs (e.g., Félix Candela’s thin-shell concrete umbrellas) and improving STEM education for non-technical majors through innovative teaching methods, including MOOCs and scale model exhibitions. Recent work emphasizes coastal defense systems using hyperbolic-paraboloid forms and steel-concrete girder performance under shear stress. Garlock has received notable honors including the ASCE SEI Fellowship (2016 T.R. Higgins Lectureship), President’s Award for Distinguished Teaching (2012), and the Emerson Electric Co. Faculty Advancement Award (2006). In education and grants, she teaches courses like Structures and the Urban Environment and Advanced Design of Steel/Concrete Structures , and has secured government funding for STEM literacy initiatives. Her research collaborations include the BRITE Pivot project and studies on Cuba’s historic National School of Ballet domes. She also leads efforts in deploying kinetic umbrellas as flood barriers and advancing probabilistic models for fire fragility in multi-hazard contexts. Garlock’s work bridges engineering and art, exemplified by her preservation studies of Candela’s architectural masterpieces and pedagogical innovations that emphasize creativity in structural design.
