Professor Alasdair McDonald holds the Chair in Renewable Energy Technology at the School of Engineering, University of Edinburgh . His work focuses on the integrated electrical-magnetic-mechanical modeling and design of large electrical machines for offshore renewable energy systems , particularly wind turbine powertrains . He previously served as a Lecturer, Senior Lecturer, and Reader in Wind Turbine Technology at the University of Strathclyde. Education: PhD in Structural Analysis of Low-Speed, High-Torque Generators (University of Edinburgh, 2008) MEng (Hons) in Integrated Electrical & Mechanical Engineering (University of Durham, 2004) Research Interests: Design of permanent magnet electrical machines for wind and marine energy Lightweight generator structures and advanced manufacturing methodologies Condition monitoring using SCADA and vibration data Cost of energy optimization for offshore renewables Projects: STREAM 1: Innovations in Forth/Tay Offshore Wind Clusters (EPSRC, 2025-2029) Wind2DC: Medium Voltage DC Power Take-Off Systems (EPSRC, 2023-2026) PV054: Modular Generators for Floating VAWTs (EPSRC & SeaTwirl AB, 2023) Media Contributions: Quoted in research media about floating hydrogen production systems (2025)
Patrik Hilber is a Professor at KTH Royal Institute of Technology, working in the Division of Electromagnetic Engineering and Fusion Science within the School of Electrical Engineering and Computer Science (EECS). He serves as Deputy Director of First and Second Cycle Education at EECS and heads the QED AM research group. He is also a board member of YH-electrical engineering. Research Interests: His research focuses on reliability engineering, asset management, maintenance optimization, and smart grid technologies in electric power systems. Key areas include transmission and distribution systems, dynamic line and transformer rating, wind power integration, multiobjective optimization, condition monitoring, and data quality in power systems. He applies advanced modeling and data-driven approaches to improve power system planning, operation, and resilience. The recent trends in his publications (2020–2025) highlight a strong emphasis on dynamic rating technologies (DLR and DTR), data quality and machine learning applications in outage analysis, reliability-centered planning for wind farms and distribution systems, and the integration of renewable energy and electric vehicles. His work bridges theoretical modeling with practical utility applications. Teaching and Academic Leadership: He is examiner and course responsible for several degree projects in electrical engineering, power systems, and energy innovation. He also teaches courses on reliability evaluation, asset management, and innovation in electric power engineering. Publications and Books: He has authored a book titled Reliability Analysis and Asset Management Applied to Power Distribution (2014) and a book chapter on cable segment replacement optimization. His scholarly output includes numerous peer-reviewed articles in leading journals such as IEEE Transactions on Power Systems , Reliability Engineering & System Safety , and Applied Energy . Education: He holds a Ph.D. (2008), a Licentiate degree (2005), and an M.Sc. (2000), all from KTH. He became a Docent (Associate Professor) in 2014.
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.
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.
Professor Bill O'Neill is a Fellow in Engineering at Downing College and holds the Professor of Laser Engineering position at the University of Cambridge. He leads the Centre of Industrial Photonics and focuses on cutting-edge laser-based manufacturing technologies. BSc (Essex) MSc (Essex) MA PhD (Imperial) His research spans high-power laser applications in materials processing, including aerospace alloys, medical alloys, ceramics, and polymers. He investigates ultra-short laser-matter interactions (femtosecond pulses) for thermal-free machining and nanofabrication techniques aimed at developing personal factory systems. Key projects include electron backscattered diffraction analysis (EBSD) validation of material responses and £10M UK research council-funded innovations. Publications highlight advancements in laser microstructuring, x-ray collimation, and cold gas dynamic spray technologies. His work trends toward precision manufacturing, materials science, and photonics applications. As head of the Centre of Industrial Photonics, O'Neill's team explores nanoparticle manipulation for on-demand fabrication of complex products, envisioning a future where desktop printers create micro/nano devices.
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
Songbin Gong is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Illinois at Urbana Champaign, where he has been a faculty member since August 2013. He was promoted from Assistant Professor to Associate Professor in August 2019 and holds the Intel Alumni Fellowship. His research is centered at the Micro and Nanotechnology Lab, where he leads the Integrated RF Microsystems research group. Professor Gong's research focuses on RF and microwave photonics, microwave acoustics, and Micro-Electro-Mechanical Systems, with particular expertise in lithium niobate-based devices. His work spans the development of acoustic resonators, filters, and transducers operating from VHF to Sub-THz frequencies. Recent publications demonstrate significant advances in high-frequency acoustic devices, including GHz resonators with high electromechanical coupling and low loss characteristics. His research has direct applications in 5G communications, wireless sensing, and imaging systems. Gong has established himself as a leader in the field of RF MEMS and acoustic devices, with numerous high-impact publications in top journals including IEEE Transactions on Microwave Theory and Techniques, Journal of Microelectromechanical Systems, and Optics Express. His work shows a clear progression toward higher frequency operation, improved device performance, and novel integration approaches for next-generation communication systems. Among his notable achievements is the development of thin-film lithium niobate devices that overcome traditional frequency limitations of MEMS resonators, enabling operation beyond 10 GHz. This work addresses critical challenges in 5G and future wireless technologies where conventional approaches face scaling limitations. IEEE Ultrasonics Early Career Investigator Award DARPA Young Faculty Award 2014 NASA Early Career Faculty Award 2017 Intel Alumni Fellow 2017-present Multiple Best Paper Awards at major conferences including International Ultrasonic Symposium and International Microwave Symposium Professor Gong actively mentors graduate and undergraduate students, with several of his PhD students achieving notable success, including Ruochen Lu who joined UT Austin as a tenure-track assistant professor. His research group has secured significant funding from agencies including DARPA and NASA, supporting cutting-edge work in RF microsystems. The group maintains strong industry connections, particularly with Intel, reflecting the practical relevance of their research to commercial communication technologies. The Gong Research Group leverages micro/nano electro mechanical systems (N/MEMS), integrated photonic, and compound semiconductor technologies to develop chip-scale hybrid microsystems for RF communication, sensing, and imaging applications. Their current work focuses on pushing the boundaries of acoustic device performance while maintaining compatibility with standard semiconductor manufacturing processes.
Johan Meyers is a full Professor at KU Leuven's Faculty of Engineering Science, Department of Mechanical Engineering, where he heads the Applied Mechanics and Energy conversion (TME) research unit. He serves as a contact person for TME and is an active member of the KIES – KU Leuven Institute for Energy and Society. His administrative roles include membership on the Council of the Faculty of Engineering Science, the Mechanical Engineering Department Council and Board, and chairing the HPC Steering Committee. Professor Meyers' research focuses on turbulent flow simulation and optimization, with particular emphasis on wind energy applications, atmospheric pollutant dispersion, and computational methods. His work spans Direct Numerical Simulation (DNS), Large-Eddy Simulation (LES), and model reduction techniques for applications in energy engineering. Current research categories include flow control & optimization, wind farm engineering, and atmospheric pollutant dispersion modeling, with specific applications in radioactive release scenarios and wind turbine system optimization. His recent publications demonstrate a strong trend toward wind energy applications, particularly in optimizing wind farm layouts and operations through advanced computational methods. The research shows significant emphasis on Large-Eddy Simulation techniques to study atmospheric boundary layer interactions with wind farms, with growing interest in hybrid wind-solar energy systems and the effects of surface temperature heterogeneity on flow patterns. His work increasingly integrates machine learning approaches to enhance computational efficiency in wind farm modeling. Professor Meyers actively supervises numerous PhD students including Bon, T., Janssens, N., Jamaer, S., and ALREWENY, A., among others. His research is supported by multiple ongoing projects through 2028, including 'Wind-farm co-design in the North-Sea basin given climate and market uncertainty' and 'Reconstruction of turbulence from partial observations,' primarily funded by research councils and industry partnerships. He leads the Turbulent Flow Simulation and Optimization (TFSO) research group, which develops efficient supercomputing simulation tools for turbulent flow applications in energy engineering. The group specializes in wind farm optimization, atmospheric pollutant dispersion modeling, and airborne wind energy systems, with a particular focus on LES studies of wind farm interactions with the atmospheric boundary layer.
Daniel J. Sorin is a Professor of Electrical and Computer Engineering at Duke University's Pratt School of Engineering, where he also serves as Associate Chair of Education. He holds joint appointments in both the Electrical and Computer Engineering department and Computer Science department, and is recognized as a Bass Fellow for his contributions to education and research. His research focuses on computer architecture with specific expertise in memory systems, cache coherence protocols, fault tolerance, and verification-aware design. Dr. Sorin's work bridges theoretical computer architecture with practical implementations, often incorporating coding theory to solve architectural challenges. His research group has made significant contributions to automated protocol generation, hardware acceleration, and robot motion planning systems. Dr. Sorin's publications reveal a consistent focus on memory consistency models, cache coherence protocols, and verification techniques. His recent work has expanded into robot motion planning acceleration, FPGA resource management, and novel error correction techniques for emerging memory technologies. The trend shows increasing interdisciplinary work connecting computer architecture with robotics and machine learning applications. Program Chair of HiPEAC 2017 Co-chair of IEEE Micro's Top Picks selection committee (2016) Lois and John L. Imhoff Distinguished Teaching Award (2011) NSF CAREER Award recipient IEEE Micro Top Pick awards (2011, 2015) ACM Senior Member As an advisor, Dr. Sorin has mentored numerous PhD students who have gone on to successful careers at leading technology companies including Google, Microsoft, Oracle, and Nvidia. His research group maintains strong industry connections and has produced influential work in cache coherence protocols, memory systems, and fault-tolerant architectures. He has also authored the widely-used textbook 'A Primer on Memory Consistency and Cache Coherence' (2nd edition). Dr. Sorin leads an active research laboratory focused on next-generation computer architecture challenges, with ongoing projects in hardware acceleration, memory systems, and robot motion planning. His group collaborates with researchers across multiple disciplines including robotics, coding theory, and semiconductor design.
Lisa Wu Wills is an Assistant Professor in the Department of Computer Science and Electrical and Computer Engineering at Duke University, leading the APEX Lab (Application-driven Programmable Efficient Accelerated Systems Lab). Her research focuses on hardware acceleration for big data analytics in genomics, graphs, and databases to advance healthcare and natural sciences. Education: Ph.D. in Computer Science, Columbia University, 2014 Research Interests: Dr. Wills pioneers computer architecture and hardware-software co-design to create efficient accelerators for emerging applications. Her work targets genomics , graph analytics , and database systems , emphasizing simplified hardware deployment and energy efficiency for scientific breakthroughs in healthcare and AI. Publication Trends: Her 2022-2025 publications reveal a strong focus on open-source frameworks (Beethoven, PyTFHE) for accelerator development, hardware acceleration in privacy-preserving computing, and optimization for large language models. Key themes include transfer learning for EDA, domain-specific architectures for genomics, and energy-efficient image processing. Scientific Awards: Google ML and Systems Junior Faculty Award (2025) Advising and Grants: Dr. Wills mentors three PhD students: Chris Kjellqvist (Beethoven framework architect), Mason Ma (PyTFHE lead for FHE applications), and Mansi Choudhary (COCOSSim simulator creator). Her 2025 Google award funds research on accelerating vector databases and retrieval-augmented generation for LLMs. Labs and Teams: She directs the APEX Lab at Duke, developing tools like Beethoven (open-source accelerator composer) and PyTFHE for hardware-software integration, enabling domain scientists to leverage custom acceleration with minimal hardware expertise.
Dr. Charles Rougé is a Senior Lecturer in Water Resilience at the Department of Civil and Structural Engineering, School of Mechanical, Aerospace and Civil Engineering, University of Sheffield. He holds an MSc and PhD, and his career spans top institutions in France, the US, Canada, and the UK. 2018–present: University of Sheffield (Lecturer → Senior Lecturer) 2023–2026: Principal Investigator, EPSRC-funded project on water-energy systems under climate change and energy transition Research Focus: Modelling complex water resource systems to enhance resilience against climate change, with a growing emphasis on water-energy nexus challenges. His work integrates hydrology, power systems engineering, economics, and decision theory. Key Trends: 15 most recent articles span climate-perturbed hydrological models, water-energy system coupling, socio-hydrology applications, and transboundary water governance. Many showcase interdisciplinary approaches to water infrastructure flexibility and uncertainty quantification. Scientific Awards: 2019 Quentin Martin Best Practice Award (JWRPM) 2015 Editor's Citation for Excellence (WRR) Grants: EPSRC grant (UKRI) for 'Flexible design and operation of water resource systems' (2023–2026) Team Leadership: Leads the 'Water resilience' research group at Sheffield, mentoring early-career researchers in water system sustainability and low-carbon energy transition.
Alessandro Aliakbargolkar is a Professor at the Department of Space Systems Design under the School of Aerospace Engineering at Skolkovo Institute of Science and Technology (Skoltech). His research focuses on Federated Satellite Systems, CubeSat constellations, and Spacecraft Systems Architecture, with applications in Earth observation, messaging services, and networked satellite systems. He has an extensive publication record in these areas, including work on technology roadmapping and digital twin implementation. Key Research Areas: Satellite federation and resource sharing CubeSat constellation design Network performance optimization Integration of systems engineering models with AI Selected Trends: Recent work explores digital twin technologies for CubeSats, federated satellite network analysis, and large language model applications in spacecraft design. Publications often combine theoretical frameworks (e.g., network theory) with practical implementations (e.g., LoRa-based messaging services). ORCID Profile: 0000-0001-5993-2994
Professor James Im serves as Professor of Materials Science in the Departments of Earth and Environmental Engineering and Applied Physics and Applied Mathematics at Columbia University, with an office at 1106 S.W. Mudd (Mail Code 4701). His academic career spans over three decades at Columbia, where he progressed from Assistant Professor (1991-1994) to Associate Professor (1995-2002), and ultimately to full Professor (2002-present), including a tenure as Chair of the Materials Science and Engineering Program (2002-2014). His educational background includes a B.S. with Distinction in Materials Science from Cornell University (1984) and a Ph.D. in Electronic Materials from MIT (1989), followed by postdoctoral research at Caltech (1989-1991). Cornell University: B.S. Materials Science (1984) MIT: Ph.D. Electronic Materials (1989) Caltech: Postdoctoral Scholar (1989-1991) Im's research centers on ultra-rapid phase transitions in beam-irradiated thin films, specifically focusing on laser crystallization of silicon films , energy-beam-induced melting and solidification , and nucleation in discontinuous phase transitions . His work employs experimental, computational, and theoretical approaches to develop innovative semiconductor materials for advanced displays, solar cells, and integrated circuits. Notably, his invention of Sequential Lateral Solidification (SLS) technology has been licensed to major display manufacturers (Samsung, LG, Sharp) and implemented in products by Apple, Blackberry, and Nokia. Current research focuses on advancing the Spot-Beam Crystallization (SBC) platform using fiber lasers for next-generation microelectronics. His publication record spans environmental aerosol studies (2019-2024), oilfield operations technology (2002-2014), and foundational atmospheric research (1980s), reflecting interdisciplinary expertise bridging materials science, environmental engineering, and petroleum technology. The most recent works emphasize low-cost sensor development and aerosol monitoring. Professional recognition includes membership in prestigious societies: Bohmisch Physical Society Sigma Xi Alpha Sigma Mu Materials Research Society American Physical Society Im's research group maintains strong industry connections through technology licensing and collaborative projects, particularly in display manufacturing. His leadership as former department chair demonstrates administrative commitment alongside scientific innovation. The laboratory leverages state-of-the-art laser systems and beam delivery optics for materials development, with recent focus shifting toward environmental monitoring applications while maintaining core semiconductor research.
Phillip J Ansell is an Associate Professor in the Department of Aerospace Engineering at the University of Illinois. He serves as the Director of the Center for High-Efficiency Electrical Technologies for Aircraft (CHEETA), focusing on advancing sustainable aviation through innovative propulsion and energy systems. His research interests include aerodynamics optimization, hydrogen propulsion, electric aircraft integration, and cryogenic technologies. Ansell has received prestigious awards such as the AFOSR Young Investigator Award (2015), ARO Young Investigator Award (2017), and the Lawrence Sperry Award (2023), recognizing his contributions to sustainable aviation and flow control technologies. His work spans interdisciplinary areas like hydrogen fuel cell systems, airfoil design, and electrified aircraft architectures. Recent research emphasizes sustainable aviation frameworks, cryogenic hydrogen storage, and propulsion-airframe integration. Ansell has collaborated on projects involving distributed propulsion systems, wind energy optimization, and advanced plasma actuators for flow control. His leadership in CHEETA drives innovations in superconductivity and high-temperature superconducting components for next-generation aircraft. Notable contributions include studies on laminar flow control, transonic aerodynamics, and the technical challenges of integrating MW-scale hydrogen propulsion systems. His publications reflect a blend of theoretical modeling, experimental validation, and systems engineering approaches to address aviation's sustainability challenges.
Moinuddin Qureshi is a Professor of Computer Science at Georgia Institute of Technology, affiliated with the School of Computer Science and involved in the Online Master of Science in Computer Science (OMSCS) program. He holds a Ph.D. and M.S. from the University of Texas at Austin. His research focuses on computer architecture, memory systems, hardware security, and quantum computing, with notable contributions to mitigating rowhammer vulnerabilities and advancing quantum error correction. Previously, he was a Research Staff Member at IBM T.J. Watson Research Center (2007–2011), where he contributed to caching algorithms for Power-7 processors. He has held leadership roles, including Program Chair of MICRO 2015 and Selection Committee Co-Chair of Top Picks 2017. His work has been recognized with prestigious awards, including the 2019 Persistent Impact Prize and multiple best paper awards. Key research areas include secure memory design (e.g., rowhammer mitigation techniques like MINT and Moat), quantum computing (e.g., Flag-Proxy Networks and Élivágar), and hardware vulnerability analysis (e.g., Roguerfm attacks and COAXIAL memory systems). His publications span 2009–2025, addressing topics like error correction, secure tracking, and quantum annealing optimization. Awards include membership in ISCA, MICRO, and HPCA Hall of Fame, alongside contributions to conferences like HiPC and IEEE MICRO. His work bridges theoretical advancements with practical implementations in both classical and quantum domains.
