Yan Delaure is Associate Professor of Fluid Mechanics at Dublin City University's School of Mechanical and Manufacturing Engineering and Deputy Director of the DCU Water Institute. His research focuses on multiphase flows, environmental hydraulics, and computational fluid dynamics applications in wastewater treatment and marine systems. Research includes microbubble dynamics for aeration, fluid-structure interactions in deformable systems, and biomimetic antifouling solutions. Recent publications explore advanced simulation methods for turbulent flows and additive manufacturing process optimization.
Colby Haggerty is an Assistant Professor at the Institute for Astronomy (IfA Mānoa) at the University of Hawaiʻi at Mānoa. He specializes in computational plasma physics, focusing on magnetospheric, heliospheric, and astrophysical systems. His research emphasizes collisionless plasma shocks, magnetic reconnection, and kinetic plasma turbulence. He holds a Ph.D. in Plasma Physics from the University of Delaware (2017) and conducted postdoctoral work at the University of Chicago (2017–2021). His work bridges theory, numerical simulations, and observational data analysis using advanced computational tools like Python, C++, Fortran, and MPI/OpenMP frameworks. Research Interests: He investigates collisionless plasma shocks and energetic particle acceleration (e.g., Earth’s bow shock, coronal mass ejections), plasma instabilities, magnetic reconnection dynamics, and the role of turbulence in energy dissipation. His studies often involve hybrid and particle-in-cell (PIC) simulations to model cosmic phenomena like supernova remnants and solar wind interactions. Articles & Trends: His recent publications highlight advancements in understanding shock-drift acceleration mechanisms, the saturation of plasma instabilities (e.g., Bell instability), and scaling laws for magnetic reconnection in asymmetric and relativistic regimes. Collaborations with institutions like NASA Goddard, Columbia University, and the University of Chicago underscore his interdisciplinary approach. He has also contributed to developing Python-based plasma physics tools (e.g., PlasmaPy) for the scientific community. Grants & Impact: His CAREER award (2024) supports studies on collisionless magnetic reconnection as a heliospheric process. He emphasizes computational methods and educational outreach, reflecting his dual focus on advancing science and training future researchers. Labs & Teams: While no specific lab is named, his work relies on collaborative networks with leading institutions, leveraging state-of-the-art simulation infrastructure to tackle complex plasma problems.
Dr. Mohamed Youssef is an Associate Professor in the Department of Electrical, Computer and Software Engineering at Ontario Tech University. He holds a PhD (Electrical and Computer Engineering) from Queen’s University (2005). His primary affiliation is the Faculty of Engineering and Applied Science, with research focusing on propulsion systems, power electronics, railway systems, and renewable energy technologies. Education: PhD, Electrical and Computer Engineering, Queen’s University (2005) MSc, Power Electronics, Concordia University (2001) MSc, Electric Power and Machines, Ain Shams University (1999) BSc, Electric Power and Machines, Ain Shams University (1995) Research Interests: Dr. Youssef’s expertise spans propulsion systems for automotive and hyperloop technologies , power electronics for IoT and renewable energy , railway electromagnetic compatibility , and power system stability . His work emphasizes practical applications in electric vehicles, smart grid integration, and sustainable energy systems. He leads the PEDAL (Power Electronics and Drives Laboratory) at Ontario Tech. Awards and Recognition: Recipient of the NSERC Post-doctorate Scholarship (2006) Best Paper Award at IECON 2004 Award of Merit from Ontario Center of Excellence (2006) Nominated for the Howard Alper Prize (2007) Professional Activities: He serves as a reviewer for IEEE Transactions on Power Electronics , IEEE Transactions on Industrial Electronics , and others. He has held roles as Technical Chair at IEEE SEGE 2015 and Track Chair at IEEE SEGE 2016. Current affiliations include Senior Member of IEEE and Chair of the IEEE Power Electronics Chapter in Toronto. Labs and Teams: He directs the PEDAL Lab , focusing on advanced power electronics and electric vehicle technologies. Collaborations include Bombardier Transportation and Armstrong Pumps.
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.
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.
Zeljko Pantic is an Associate Professor in the Department of Electrical and Computer Engineering at North Carolina State University. He holds a Ph.D. from NC State (2013) and B.S./M.S. degrees from the University of Belgrade (1998/2007). Before joining NC State in 2019, he served as an Assistant Professor and Associate Director of the Electric Vehicle and Roadway Research Facility at Utah State University. He is actively involved in editorial roles for IEEE Transactions on Transportation Electrification and serves on the IEEE IAS Transportation Systems Committee. Education: Ph.D., Electrical Engineering, North Carolina State University (2013) M.S., Electrical Engineering, University of Belgrade (2007) B.S., Electrical Engineering, University of Belgrade (1998) Research: Dr. Pantic specializes in electrified transportation systems, wireless power transfer (WPT), power converter design, and DC microgrid technologies. His work addresses challenges in EV charging infrastructure, magnetic circuit optimization, and energy conversion principles for transportation electrification. Recent projects include autonomous wireless charging systems for UAVs, marine DC microgrids, and road-embedded DWPT solutions. Awards & Recognition: 2019 IEEE JESTPE Second Prize Paper Award 2017 Outstanding Teacher of the Year (USU) 2012 NC State Mentored Teaching Assistantship Award Advisees & Grants: While specific student names are not listed, Dr. Pantic has advised graduate students on projects spanning WPT systems, EV infrastructure, and battery management. His work has been supported by grants focusing on dynamic charging, magnetic materials, and autonomous observatory nodes. Labs & Facilities: He leads research at NC State's Electric Vehicle and Roadway facility, focusing on roadway-integrated wireless charging and high-power WPT systems. Collaborations include ocean observatory development and autonomous system integration.
Hui Cao is the John C. Malone Professor of Applied Physics, Professor of Physics, and Professor of Electrical Engineering at Yale University. Her research focuses on mesoscopic physics, complex photonic materials, nanophotonics, and biophotonics, with experimental investigations into unconventional lasers, coherent light control, and disordered photonic systems. She leads a lab exploring applications in speckle-based imaging, deep-tissue optics, and chip-scale spectrometers. Education: Ph.D. in Physics from Stanford University (1997). Awards include the William E. Lamb Medal (2015), Guggenheim Fellowship (2013), and fellowships from the American Physical Society and Optical Society of America (2007). Research emphasizes random lasers, microcavity lasers, and wavefront shaping to control light in diffusive media. Key innovations include a disordered photonic chip spectrometer and methods to suppress nonlinear instabilities in fiber amplifiers. Awards: 12 major honors including AAAS Fellowship and multiple endowed professorships Patents: 3 core photonic technologies including random laser imaging and fiber amplifier control systems Lab Activities: Developing novel optical devices leveraging disorder and nonlinear effects
Rute C. Sofia is Industrial IoT Head at fortiss – the Bavarian research institute for software-intensive systems – and Invited Associate Professor at Universidade Lusófona de Humanidades e Tecnologias (ULHT). She is also an Associate Researcher at ISTAR, Instituto Universitário de Lisboa (Iscte-IUL). Previously she co-founded and served as Scientific Director of COPELABS/ULHT (2013-2017) and was Senior Researcher there from 2010-2019. Education Ph.D. in Computer Science, University of Lisbon, 2004 Visiting Scholar, Northwestern University (ICAIR) & University of Pennsylvania, 2000–2003 M.Sc. in Computer Science, University of Lisbon, 1999 B.Eng. in Computer Engineering, University of Coimbra, 1995 Research Interests Her work spans network architectures and protocols , Internet of Things (IoT) , edge and in-network computing , deterministic wired/wireless industrial networks , and network mining . A current focus is on resilient, AI-driven orchestration across the IoT–Edge–Cloud continuum for 6G and Industrial IoT systems. Scientific Awards & Recognition ACM Europe Councilor (2021–2025) ACM Senior Member & IEEE Senior Member IEEE ComSoc N2Women Awards co-chair (2020–2021) Highly Cited Paper Award, Applied Sciences MDPI (2023) Labs, Teams & Grants She currently leads the Industrial IoT competence field at fortiss, coordinating projects such as SemComIIoT (semantic communications for IIoT) and the open-source ns-3 DetNetWiFi framework. Earlier she co-founded the Portuguese startup Senception Lda (2013-2019) and the research unit COPELABS , driving EU H2020 initiatives like UMOBILE and shaping national strategies for cyber-physical systems.
Professor Jorge E. Viñuales is the Harold Samuel Chair of Law and Environmental Policy at the University of Cambridge. He founded the Cambridge Centre for Environment, Energy and Natural Resource Governance (C-EENRG) and holds affiliations with the University of Cambridge Conservation Research Institute . His academic roles include directing PhD programs, three MPhil programs, and serving as head of research. His research focuses on international environmental law , energy law , and investment law . Recent work explores climate policy , energy transition economics , and resource governance through complex systems modeling. His interdisciplinary research with C-EENRG colleagues has been published in Nature Climate Change , Global Environmental Change , and Climate Policy . Notable scientific recognition includes the Choice Outstanding Academic Title 2021 for his co-edited volume on the UN Friendly Relations Declaration. He serves as Chairman of the Compliance Committee of the UN-ECE/WHO-Europe Protocol on Water and Health, and as co-General Editor of the ICSID Reports (CUP) and Cambridge Studies on Environment, Energy and Natural Resources Governance (CUP).
Lisa Wills serves as Assistant Professor of Computer Science at Duke University's Trinity College of Arts & Sciences and holds a joint appointment in Electrical and Computer Engineering at the Pratt School of Engineering since 2019. Her research bridges computer architecture and domain-specific applications, with a focus on hardware acceleration for computationally intensive fields. Dr. Wills earned her Ph.D. from Columbia University in 2014. Her academic journey reflects a deep commitment to advancing hardware-software co-design methodologies for real-world computational challenges. Her research centers on developing efficient hardware accelerators for big data analytics, particularly in genomics, graph processing, and database systems. She pioneers frameworks that simplify accelerator deployment while tackling critical bottlenecks in genomic data analysis, protein structure prediction, and privacy-preserving computing. Current work focuses on hardware-aware machine learning systems and energy-efficient architectures for emerging AI applications. Analysis of her publication record reveals a clear trajectory: from foundational work in database processing units (2014-2016) to specialized genomic accelerators (2019-2021), then evolving toward ML-enhanced design automation (2022-2023) and cutting-edge architectural abstractions (2024-2025). Her research consistently targets the intersection of hardware efficiency and domain-specific computational demands, with increasing emphasis on AI/ML workloads. Google ML and Systems Junior Faculty Award (2025) Dr. Wills actively mentors doctoral students including Chris Kjellqvist (lead architect of Beethoven accelerator framework), Mason Ma (PyTFHE FHE framework), and Mansi Choudhary (COCOSSim accelerator simulator). Her research is supported by significant grants including the NSF AI Institute: Athena ($20M, 2021-2027), Meta-funded ProSE accelerator project (2023-2026), and NSF CAREER award (2021-2026), totaling over $25M in active funding. She directs the APEX Lab (Application-driven Programmable Efficient Accelerated Systems), which develops open-source frameworks like Beethoven for FPGA/ASIC accelerator deployment and focuses on lowering barriers for non-hardware researchers to leverage custom acceleration in genomics, AI, and big data applications.
James Massey is a Senior Research Fellow at the University of Cambridge, affiliated with the Department of Engineering under the Energy Group. His research focuses on computational fluid dynamics (CFD), turbulent reacting flows, and hydrogen combustion, supported by funding from Mitsubishi Heavy Industries (MHI). He holds a PhD in Engineering (2015-2019) and an MEng in Mechanical Engineering (2011-2015) from The University of Manchester. PhD in Engineering, University of Cambridge (2015-2019) MEng in Mechanical Engineering, The University of Manchester (2011-2015) His work spans hydrogen combustion , thermo-acoustics , and large eddy simulation (LES) , targeting emissions prediction, flame stabilization, and combustion instability. Key themes include mitigating CO/NOx emissions, analyzing swirl-stabilized flames, and developing skeletal mechanisms for hydrogen-hydrocarbon blends. Recent publications emphasize multi-regime combustion modeling , thermo-acoustic instability analysis , and machine learning applications in LES. His research often involves cross-institutional collaboration with MHI and contributions to combustion physics through DNS and LES frameworks. Sugden Award (2024) for best paper in The Combustion Institute British Section James contributes to teaching as a lecturer for courses like 4A13 Combustion and Engines (2023-2025) and ETB-1 Clean Fossil Fuels (2022-2023). He is a fellow of Robinson College and an active member of the Institute of Physics Combustion Physics Group and The Combustion Institute British Section.
Michael E. McHenry is a Professor of Materials Science and Engineering at Carnegie Mellon University's College of Engineering. He holds appointments with multiple research centers including the Data Storage Systems Center, Engineering Research Accelerator, Materials Research Science and Engineering Center, and Wilton E. Scott Institute for Energy Innovation. Dr. McHenry received his BS in Metallurgical Engineering and Materials Science from Case Western Reserve University in 1980, his PhD in Materials Science and Engineering from MIT in 1988, and completed a postdoctoral fellowship at Los Alamos National Laboratory. His research focuses on soft magnetic nano-composites for power and energy applications, with particular expertise in metal amorphous nanocomposites (MANCs) for high-efficiency electric motors and power systems. His work spans advanced materials processing, magnetic properties under various conditions, and rare earth materials criticality. His research portfolio demonstrates a clear progression toward practical applications of magnetic materials, particularly in high-power density, high-efficiency motors that can operate at high rotational speeds with minimal energy loss. His publications reveal a strong focus on translating fundamental materials science into engineering solutions for energy conversion, with significant emphasis on rare earth-free alternatives and high-frequency applications. IEEE Distinguished Lecturer (2013) TMS Awardee for Research Excellence (2014) Subject of TMS Symposium in Honor of M. E. McHenry (2016) NATO Series Lecturer on Rare Earth Criticality (2016/17) Dr. McHenry has co-founded CorePower Magnetics Inc. with Paul Ohodnicki and Samuel Kernion, commercializing soft magnetic technologies with applications in grid modernization and electric vehicles. His extensive publication record and leadership in major research initiatives including a MURI on high-temperature magnetic materials and an ARPA-E program demonstrate significant impact in both academic and industrial contexts. He has served in various leadership roles for Magnetism and Magnetic Materials and Intermag Conferences, and continues to advise on rare earth scarcity issues for organizations like NATO.
Jeff Zhang is an Assistant Professor in the School of Electrical, Computer and Energy Engineering at Arizona State University. He joined ASU in January 2023 after completing a postdoctoral fellowship at Harvard University. His research spans deep learning, computer architecture, embedded systems, and EDA, with particular emphasis on energy-efficient and fault-tolerant design for AI/ML systems and hardware accelerators. Education: Ph.D., New York University M.Eng., B.Eng., Hunan University Dr. Zhang's research bridges theoretical machine learning with practical hardware implementation, developing novel architectures that optimize performance, power consumption, and reliability. He has pioneered approaches for hardware acceleration of large language models, efficient sparse matrix operations, and novel memory technologies for AI workloads. His work has received multiple awards including IEEE Top Picks in Test and Reliability (2023) and IEEE Micro Best Paper Award (2022). His recent publications demonstrate a strong trend toward heterogeneous computing, with significant work in chiplet-based AI accelerators, photonic computing for AI, and 2.5D/3D integration techniques. The research spans from high-level compiler frameworks to circuit-level innovations, with a consistent theme of co-designing algorithms and hardware for optimal AI performance. His work on the SODA toolchain has been particularly influential in bridging Python to silicon. Scientific Awards: IEEE Top Picks in Test and Reliability, IEEE ITC, 2023 Best Paper Award, IEEE Micro, 2022 Best Paper Award Candidate, IEEE DATE, 2022 Best Presentation Award Nomination, ACM SIGDA DATE PhD Forum, 2020 Best Paper Award Nomination, IEEE VLSI Test Symposium, 2018 Ernst Weber Ph.D. Fellowship, New York University, 2015, 2016 Dr. Zhang actively mentors a diverse group of graduate and undergraduate students, with several alumni now working at leading technology companies including Apple, TSMC, and Ansys. His research is supported by prestigious grants from NSF, Sandia National Labs, and industry partners. He serves on technical program committees of numerous top conferences and has organized special sessions on emerging topics like Gen AI for Chip Design and LLM-Aided Design. Dr. Zhang leads a vibrant research group that collaborates extensively with industry partners and national laboratories. Current projects focus on next-generation AI hardware, including chiplet-based systems, photonic accelerators, and novel memory technologies for large language models. His group has developed several open-source tools and frameworks, including the SODA toolchain for bridging Python to silicon.
Dr. Frank Loh is a researcher at the Department of Computer Science III, University of Würzburg, specializing in energy efficiency, network performance, and Quality of Experience (QoE) in communication networks. His work focuses on optimizing LoRaWAN deployments, serverless computing, and edge-cloud environments, with an emphasis on reducing message collisions and improving resource utilization. He actively contributes to methodologies for gateway placement, traffic modeling, and energy consumption metrics. Research Areas Energy Efficiency in Communication Networks Quality of Service (QoS) and Quality of Experience (QoE) LoRaWAN Network Planning Edge and Serverless Computing Network Resource Analysis Recent Publications 2025: Energy modeling for 6G base stations 2025: Server cluster resilience via Markov models 2024: Serverless computing in edge-cloud environments 2024: LoRaWAN channel access optimization
Hua Ge is a Professor in the Department of Building, Civil and Environmental Engineering at Concordia University's Faculty of Engineering and Computer Science. She holds a Tier II Concordia University Research Chair in High Performance Building Envelope for Climate Resilient Buildings and leads extensive research in building science and climate adaptation. Her research focuses on wind-driven rain analysis , hygrothermal performance of building envelopes , advanced building facades , innovative wood-frame construction , and low-energy buildings . Current work examines climate change impacts on wind-driven rain loads, urban micro-climate effects, climate-resilient building envelopes, dynamic facades, and low-carbon healthy buildings. Her methodology combines large-scale laboratory testing, field monitoring, and computational modeling. Her 15 most recent publications demonstrate strong trends in nature-based climate resilience solutions , overheating risk mitigation in educational buildings , advanced hygrothermal modeling of wood-frame systems , and carbon sequestration strategies for buildings. The work spans multiple sub-disciplines including computational fluid dynamics, life cycle assessment, stochastic modeling, and field validation studies across Canadian climates. Tier II Concordia University Research Chair (CURC) in High Performance Building Envelope for Climate Resilient Buildings Professional Engineers of Ontario American Society of Heating, Refrigerating and Air-conditioning Engineers ASHRAE TC4.4 Building materials and building envelope performance (Subcommittee Chair) Professor Ge has supervised 42 graduate students (26 PhD, 16 MASc), including current advisees working on nature-based solutions, climate-resilient envelopes, and building integrated photovoltaics. Her research is supported by Concordia University Research Chair funding and collaborative projects with institutions like BCIT. She directs activities at Concordia's Building Envelope Test Facility and contributes to national standards through ASHRAE.
