Associate Professor Joshua San Miguel leads research in computer architecture and systems at the University of Wisconsin-Madison, with an affiliate role in Computer Sciences. His work focuses on energy-efficient computing for IoT devices, microarchitecture innovations, and networks-on-chip. He holds a PhD (2017) and BASc (2012) from the University of Toronto. Education: PhD in Electrical & Computer Engineering, University of Toronto (2017) BASc in Engineering Science (ECE), University of Toronto (2012) Research Interests: Approximate computing for energy harvesting systems Branch prediction and value prediction in processors Cache architectures and networks-on-chip for many-core processors Intermittent computing resilience His recent work emphasizes value-level parallelism (Carat/uSystolic), RTL simulation acceleration (TaroRTL), and personalized neural network inference (CAP’NN). His research has been recognized with the NSF CAREER Award (2021) and multiple IEEE Micro Top Picks. Grants & Advising: Active in supervising advanced independent studies and master’s/dissertation research. Extensive grant funding includes the NSF CAREER Award and the Grainger Faculty Scholarship. Labs & Teams: Leads research groups focused on approximate computing and energy-efficient architectures within the Electrical & Computer Engineering department.
Norm Murray is a Professor at the Canadian Institute for Theoretical Astrophysics (CITA) within the University of Toronto . With a Ph.D. from UC Berkeley (1986), his research spans nonlinear dynamics , planetary formation , solar system evolution , and active galactic nuclei . His work combines theoretical physics with observational data from radio telescopes, X-ray satellites, and cosmological simulations. Recent research focuses on galaxy formation (via FIRE simulations), dark matter interactions in dwarf galaxies, and AGN disk dynamics . He employs machine learning for planetary collision modeling and investigates the interplay of magnetohydrodynamics and radiative transfer in quasar environments. Publications highlight his expertise in computational astrophysics, spanning topics from cosmic molecular gas mapping to the stability of exoplanetary systems.
Dr. YANG Guomin is an Associate Professor of Computer Science at Singapore Management University's School of Computing and Information Systems, where he coordinates the BSc Cybersecurity Track. His research focuses on privacy-preserving cryptography, authentication systems, and secure IoT frameworks. Research spans cryptographic protocols for cloud security, blockchain applications, and federated learning with emphases on efficiency and practical implementation. Recent publications demonstrate innovations in threshold authentication, redactable blockchains, and privacy-aware communication protocols. Advisees include LI Huilin and WANG Jiaheng, with research projects examining hardware-enhanced encryption, biometric authentication policies, and space network security. Work consistently addresses tension between security guarantees and computational efficiency in distributed systems.
Matt J. Rutherford is an Associate Professor in the Department of Computer Science at the University of Denver, with a joint appointment in the Department of Electrical and Computer Engineering. He is Deputy Director of the Unmanned Systems Research Institute and a faculty fellow of Project X-ITE. His research focuses on autonomous systems, embedded systems, and software engineering, with extensive contributions to UAV navigation, control systems, and robotics. Rutherford holds a Ph.D. in Computer Science from the University of Colorado Boulder (2006), an MS (2001), and a BS in Civil Engineering from Princeton University (1996). His work emphasizes practical applications of software engineering principles in distributed and embedded systems. Notable projects include radar-based collision avoidance for UAVs, self-leveling landing platforms, and studies on electric vehicle charging impacts on power grids. Rutherford's research bridges theoretical computer science with real-world engineering challenges, particularly in unmanned systems and robotic autonomy. Key publications explore UAV flight control using neural networks, ground/ceiling effects in rotorcraft, and GPU-based real-time pose estimation. His contributions to model-driven systems and distributed testbed automation highlight long-term engagement with software reliability and scalable experimentation frameworks. Rutherford collaborates widely, including with institutions like the University of South Carolina and Politecnico di Torino. His interdisciplinary approach integrates robotics, aerospace engineering, and software engineering to advance autonomous system capabilities.
Marian Verhelst is a Professor at KU Leuven's Faculty of Engineering Science, renowned for her research in hardware-efficient computing and dedication to STEM education. Her work spans hardware acceleration for machine learning, edge AI, and in-memory computing, with a focus on energy optimization and algorithm-hardware co-design. Her research interests include: Designing flexible hardware for ultra-low-power edge AI systems Optimizing sparsity-aware architectures for deep learning workloads Advancing chiplet-based and 3D memory technologies Co-designing algorithms and hardware for probabilistic AI Pioneering STEM outreach through KU Leuven InnovationLab Recent publications (2023–2025) demonstrate strong trends in: Hardware-software co-optimization for edge ML systems Efficient data movement in heterogeneous accelerators Low-precision and sparse computation techniques RISC-V based customizable SoCs Sustainable AI accelerator design Awards & Honors: Young Academy of Europe Award (2021) for science communication and STEM advocacy She leads significant educational initiatives, including the KU Leuven InnovationLab which has engaged 150 schools and 13,000 students since 2014. The program develops hands-on STEM projects (e.g., AI-powered wheelchairs, sustainable energy systems) and provides teacher training to inspire youth in engineering.
Joshua Garcia is an Assistant Professor in the Informatics Department at the University of California, Irvine (UCI), within the Donald Bren School of Information and Computer Sciences. His research focuses on software architecture, automated testing, and cybersecurity, particularly in autonomous systems and mobile applications. He leads projects like DeltaDroid, Doppelgänger Test Generation, and Darcy, which address software vulnerability management, architectural consistency, and safety-critical systems. Key achievements include an NSF CAREER Award (2025), an NSF CRI Grant (2018), and a DARPA competition win (2024). His work is adopted by organizations like Boeing, Google, and NASA. Garcia collaborates internationally, involving institutions in Padova and researchers like Luca, Jessy Ayala, and Philipp. Research Interests: Software architecture evolution, automated exploit generation, autonomous vehicle testing, and accessibility in software development Grants: NSF CAREER ($500K+), NSF CRI ($1M+) Labs/Teams: HexHive Group, Autonomous Systems Testing Lab
Marina S. Leite is a Professor in the Department of Materials Science and Engineering at the University of California, Davis. Her research focuses on novel materials for renewable energy, optical devices, and materials under extreme environments. She leads the Leite Lab, pioneering work in perovskite photovoltaics, thermophotovoltaic emitters, and transient photonics using machine learning for accelerated materials discovery. Her group combines advanced characterization techniques with computational methods to address challenges in energy harvesting and optical material performance. PhD: Not explicitly listed in provided text Her research interests include: Machine learning-driven materials discovery Halide perovskites for stable solar cells High-temperature optical materials Transient photonics using magnesium-based systems Thermophotovoltaic emitter design Key research trends from recent articles emphasize AI integration for predicting material behaviors, environmental stressor impacts on optoelectronics, and alloy systems for dynamic optical properties. Her lab has developed methods for automated experimentation and spectral selectivity in emitters. 2025 Optica Fellow 2025 SPIE Fellow Advising: Supervises students like Hannah Darr. Active in DARPA cross-disciplinary projects and editorial roles in energy journals. Leads grants focused on machine learning in materials science and photonic device development. The Leite Lab collaborates on projects involving transient materials and high-temperature photonics. Future work includes scaling superabsorber technologies, developing eco-friendly Pb-free perovskites, and advancing AI tools for material property prediction.
Aiichiro Nakano is Professor of Computer Science with joint appointments in Physics & Astronomy, Quantitative & Computational Biology, and the Collaboratory for Advanced Computing and Simulations at USC. He holds a Ph.D. in physics from University of Tokyo (1989) and has authored over 485 refereed publications in scalable algorithms, scientific machine learning, and computational materials science. Research develops AI-driven simulation methods for materials discovery, quantum computing applications, and exascale molecular dynamics. Recent work focuses on foundation models for molecular simulations, high-energy-density polymers, and nanocatalysis under extreme conditions. Publications show consistent contributions to computational science infrastructure, with accelerating focus on machine learning interatomic potentials and quantum-classical computing integration. Research bridges theoretical development with high-performance computing implementations. National Science Foundation CAREER Award
Myriam M.A.H. Cloodt serves as an Associate Professor of Open Innovation and Entrepreneurship at the Department of Innovation, Technology Entrepreneurship & Marketing (ITEM) within the School of Industrial Engineering at Eindhoven University of Technology (TU/e). She is also affiliated with EAISI High Tech Systems as a University Researcher. Her academic career spans over two decades, with a continuous focus on understanding innovation processes in complex business environments. Dr. Cloodt earned her MSc in Business Economics from Maastricht University in 1997. After working as a lecturer for two years, she began her PhD research at the Department of Organization & Strategy, completing her dissertation at METEOR (Maastricht University) on measuring innovative performance of high-tech companies after mergers and acquisitions. She joined Eindhoven University of Technology in July 2004 as a researcher at the School of Technology Management, becoming Assistant Professor in June 2006, and was promoted to Associate Professor of Open Innovation and Entrepreneurship in August 2017. Her research focuses on open innovation, corporate entrepreneurship, and strategic management, with particular interest in corporate venturing, strategic technology alliances, mergers and acquisitions, network analysis, and value constellations within innovation ecosystems. Dr. Cloodt's work bridges academic theory and practical application, as evidenced by her participation in industry projects for companies and innovation ecosystems like the High Tech Campus Eindhoven. Her publications span prestigious journals including Research Policy, Journal of Product Innovation Management, and Technological Forecasting and Social Change, as well as book chapters for Oxford University Press. Analysis of her recent publications reveals a strong trajectory toward circular economy applications, particularly in plastics innovation, alongside continued work on innovation ecosystems, science parks, and military innovation contexts. Her research demonstrates methodological diversity, employing network analysis, case studies, and stated choice experiments to investigate complex innovation phenomena across different sectors and organizational contexts. Best Paper Award - Mature PhD Student at the ESU European University Network on Entrepreneurship Conference (2021) Dr. Cloodt actively supervises graduate students and has contributed to numerous research projects, including C-PlaNeT (Circular Plastics Network for training), SEE-V-Lab, and the Brabant Center of Entrepreneurship. Her professional engagements extend to developing publications for organizations such as Adviesraad voor het Wetenschaps- en Technologiebeleid (AWT) and Het Financieele Dagblad. In teaching, she contributes to the BSc program in Industrial Engineering & Management Science, the MSc program in Innovation Management, and the Certificate program in Technology Entrepreneurship & Management. Her work aligns with UN Sustainable Development Goals, particularly through contributions to circular economy research and sustainable innovation practices. Dr. Cloodt maintains active collaborations with industry partners and participates in significant academic conferences including the Annual World Open Innovation Conference where she serves on program committees.
Prof. Helge Stein is a Professor in the Department of Chemistry at the Technical University of Munich (TUM), leading the Professorship for Digital Catalysis. He holds a doctorate in mechanical engineering from Ruhr University Bochum (summa cum laude) and conducted postdoctoral research at Caltech before joining TUM in 2023. His research focuses on accelerating materials discovery and optimization through digital tools like machine learning, robotics, and data management, with applications in catalysis and battery systems. Stein has pioneered the development of Materials Acceleration Platforms (MAPs) to streamline experimental and computational workflows globally. Education: Bachelor/Master in Physics, Georg August University of Göttingen (2008–2013) Doctorate in Mechanical Engineering, Ruhr University Bochum (2017, summa cum laude) Postdoc, California Institute of Technology (2017–2020) Tenure-track Professor, Karlsruhe Institute of Technology (2020–2023) Research Interests: Integration of robotics and AI in materials research High-throughput experimentation for battery and catalysis systems Data-driven approaches to nonlinear material-behavior analysis Development of decentralized Materials Acceleration Platforms (MAPs) Key Awards: ACS Engineering Au Rising Star (2023) Kit Innovation Award (2023) Masao Horiba Award (2021) Eickhoff Prize (2018) Teaching: Leads courses on high-throughput methods, data management in chemistry, and digital catalysis seminars. Collaborates with interdisciplinary teams across TUM's School of Natural Sciences and the Munich Institute of Robotics and Machine Intelligence. Labs/Teams: Directs research groups focused on automated electrochemistry, materials robotics, and AI-driven battery design, with partnerships spanning academia and industry for global MAP implementations.
Davin Wallace is an Associate Professor at the University of Southern Mississippi's School of Ocean Science and Engineering, where he investigates coastal and marine system responses to storms, sea-level rise, and sediment dynamics across timescales ranging from days to millennia. His research employs field observations, laboratory analyses, and numerical modeling, with active sites across the Gulf of Mexico, Bermuda, and the Philippines. Education: Ph.D. in Earth Science, Rice University (2007-2010) B.S. in Geology and German, Tulane University (2003-2006) Research Focus: Dr. Wallace's work bridges paleotempestology, coastal geomorphology, and sedimentary processes. His research examines hurricane impacts on sediment structures, barrier island resilience under sea-level rise, and Holocene coastal evolution. He specializes in reconstructing historical storm patterns using geologic proxies and assessing anthropogenic influences on coastal erosion. Publication Trends: His recent articles demonstrate interdisciplinary approaches to coastal vulnerability, combining geophysical data, machine learning, and multiproxy sediment analysis. Work consistently addresses climate change impacts, with emphasis on sedimentation mechanisms during extreme events and Quaternary landscape evolution. Scientific Recognition: No major awards documented in provided sources. Academic Leadership: Leads the USM Coastal Evolution and Hazards Research Lab, supervising field campaigns and collaborative projects. Secured grants for Gulf Coast studies from NSF and NOAA. Mentors graduate students in geological oceanography programs and coordinates international research teams across field sites.
Gina O’Connor is a Professor of Innovation Management at Babson College’s Entrepreneurship division, joined in 2019 after 29 years at Rensselaer Polytechnic Institute’s Lally School of Management. Her research focuses on breakthrough innovation capabilities in large mature companies, with a particular emphasis on organizational design and talent management for innovation functions. Dr. O’Connor holds a PhD in Marketing and Corporate Strategy from NYU, an MBA from Saint Louis University, and a BA in Business from Saint Louis University. She has published extensively in journals like Journal of Product Innovation Management , Organization Science , and Harvard Business Review , where she argues that innovation requires more than R&D spending—it necessitates a distinct organizational function with dedicated people, processes, and metrics. Her work on breakthrough innovation has been recognized through the Crawford Fellow award (2018) and multiple best paper awards. Gina co-authored Grabbing Lightning: Building a Capability for Breakthrough Innovation , named a top business book by Strategy+Business . She speaks frequently at global conferences including ISPIM Berlin, Academy of Management, and Innov8rs events. Scientific awards include: Elsevier BV Top Scholars (2021), MIT Sloan Management Review’s 'Twelve Essential Insights of the Decade' (2017), and multiple teaching awards from Rensselaer Polytechnic Institute. She advocates for professionalizing innovation as a distinct career path and consults for US and European companies on strategic innovation systems.
Pedro Fonseca is an Assistant Professor at the Department of Computer Science, Purdue University. He leads the Reliable and Secure Systems Lab, focusing on building reliable and secure core software systems such as operating systems, hypervisors, and distributed systems. His research has been recognized with awards including the NSF CAREER Award and Google Faculty Research Awards. Before Purdue, he completed a postdoc at the University of Washington, working with Arvind Krishnamurthy, Hank Levy, and Xi Wang. He earned his PhD from MPI-SWS and the University of Saarland under Rodrigo Rodrigues. His academic contributions span over 30 peer-reviewed publications in top-tier conferences like SOSP, OSDI, EuroSys, and ASPLOS. He teaches courses including CS503 (Operating Systems), CS592 (Reliable and Secure Systems), and CS408 (Software Testing). He actively serves on program committees for major systems conferences including SOSP, OSDI, EuroSys, and ASPLOS.
Prof. G. Scott Watson is a Professor in the Department of Physics at Syracuse University, affiliated with the College of Arts & Sciences. His research focuses on the interplay between fundamental particle physics and cosmology, particularly early universe cosmology, inflationary models, dark matter/energy, and string theory applications. He holds a Ph.D. in Physics from Brown University (2005) and B.S. degrees in Mathematics and Physics from the University of North Carolina at Wilmington (2000). Key research interests include string phenomenology as a quantum gravity framework, probing inflationary scenarios through cosmic microwave background (CMB) studies, and exploring dark matter origins. He leads major projects like CMB-S4 and contributes to the CMBPol mission concept. Watson has received the American Physical Society Outstanding Referee Award (2021) and serves on high-profile collaborations such as the Inflation Probe Study Analysis Group (IPSAG). Teaching responsibilities include advanced courses like Quantum Field Theory, Relativity and Cosmology, and Quantum Mechanics II. He actively mentors students through independent studies and advises on graduate admissions. Watson has secured significant grants, including a Department of Energy-funded project on theoretical particle physics and cosmology (2013–2025) and NSF support for cosmic acceleration research (2018–2023).
Yannis Kevrekidis is a Professor at Princeton University with a distinguished career in computational mathematics and chemical engineering. He is currently a Hans Fischer Senior Fellow at the Technical University of Munich (TUM-IAS) and has held visiting positions at institutions like the Zuse Institute Berlin and Caltech. Education : National Technical University of Athens (Chemical Engineering) University of Minnesota (PhD in dynamical systems) Research Interests : Equation-Free and Variable-Free Modeling Complex Systems Dynamics Multiscale Computation Integration of Machine Learning with Scientific Computing Pattern Formation & Instability Analysis Key Article Trends : Advanced data-driven modeling of dynamical systems Manifold learning for reaction coordinates Projective integration methods Coarse-grained modeling across disciplines Applications in epidemiology, neuroscience, and fluid dynamics Scientific Awards : Guggenheim Fellowship Humboldt Research Award Computing in Chemical Engineering Award (AIChE) Bodossaki Academic Award Allan P. Colburn Award Collaborations : Extensive international collaborations with institutions in Germany, Austria, and the UK Key role in the Complex Systems Modeling and Computation focus group at TUM-IAS
