Associate Professor Jarryd Pla is an experimental physicist and electrical engineer at the University of New South Wales, specializing in quantum information processing and quantum technologies. He holds a PhD in Electrical Engineering from UNSW (2013) and a first-class honors BEng in Photonic Engineering (2009). Current ARC Future Fellow Former Bragg Gold Medal recipient His research focuses on: Spin-based quantum computation in silicon Superconducting quantum circuits Quantum-noise-limited microwave amplifiers Hybrid quantum systems for quantum memory Quantum sensing and spectroscopy Recent publications highlight: Room-temperature maser amplifiers Kinetic inductance parametric amplifiers Coherent control of donor spins Quantum-limited electron spin resonance Scientific Awards: ARC Future Fellowship (2024-2028) Bragg Gold Medal His grants include: ARC DECRA (2019-2022): Superconducting hybrid quantum technologies ARC Discovery Project (2021-2024): Quantum sensing with semiconductor devices ARC Future Fellowship (2024-2028): Room-temperature diamond-based microwave detection
Professor George Chen of the University of Southampton is a leading expert in electrical insulation, dielectrics, and space charge measurement. He leads the Electrical Power Engineering research group and is a Fellow of IEEE. Research Focus : Electrical insulation, dielectrics, and HVDC cable systems Key Collaborations : Doctor Igor Golosnoy, Doctor Thomas Andritsch, Emeritus Professor Alun Vaughan Projects : Advanced XLPE for HVDC cables, Semicon material development, and power cable aging evaluation His recent work explores breakdown mechanisms in DC circuit breakers, thermal aging effects on dielectrics, and polymer design for high-temperature energy storage. Awards include IEEE Fellowship for contributions to dielectric performance improvements. PhD Students : Jing Nan, Luming Zhou
Mircea R. Stan is a Professor of Electrical and Computer Engineering at the University of Virginia, serving as Director of Computer Engineering and Virginia Microelectronics Consortium (VMEC) Professor. He leads the High-Performance Low-Power (HPLP) lab and is an associate director of the Center for Automata Processing (CAP). His research focuses on AI hardware, Processing in Memory, Low Power Design, Cyber-Physical Systems, and Spintronics. Education: Ph.D. (1996) and M.S. (1994) from UMass Amherst; Diploma (1984) from Politehnica University, Bucharest. Research interests include energy-efficient computing architectures, IoT systems, and emerging technologies like magnetic skyrmions and memristors. He has pioneered work on asynchronous stochastic computing, thermal-aware microarchitecture, and microfluidic cooling for 3D-ICs. Key awards include the 2024 A. Richard Newton Technical Impact Award, 2018 ISCA Influential Paper Award, and IEEE Fellow (2014). He has held editorial roles at IEEE TVLSI, IEEE TNano, and IEEE Design & Test. Notable contributions include the HPLP lab’s advancements in low-power logic computing, the VCRFID framework for Industry 4.0, and thermal-aware design tools like Hot-LEGO and Cool-3D.
Vittorio Curri is a Full Professor in Optical Communications and Networking at the Department of Electronics and Telecommunications (DET) of Politecnico di Torino , Italy. He is a founding member of the OptCom Group and PhotonLab and leads the PLANET research team. His work spans optical network modeling, AI-assisted control, open-source software (GNPy), and environmental sensing via optical fiber. He has led numerous EU and industry-funded projects and is a key figure in open optical networking. Research Interests: Multi-band optical transmission in single-mode fibers Physical layer aware networking Machine learning and AI for optical network optimization Environmental sensing using optical network telemetry Open and disaggregated optical networks Digital twin development (GNPy project) WDM and coherent transmission modeling The recent publications reflect a strong trend toward integrating AI and machine learning with digital twin technologies for real-time network control, anomaly detection, and performance optimization. There is a clear focus on wideband, multi-band, and converged metro-access networks , with modeling efforts extending to filtering effects, polarization, and nonlinear impairments. The research is highly applied, with strong industry collaboration and open-source contributions. Scientific Awards: Concorso "Galileo Feraris" (2002) JLT Best Paper Award (2014, 2015) FFABR 2017 Research Grant Advising and Grants: Prof. Curri has supervised 25 PhD students and numerous postdocs. He has served as Principal Investigator on major projects including WON (EU H2020) , ALLEGRO , NESTOR , RESTART , SENSEI , and SCIPIO . He leads the GNPy open-source project under TIP and has extensive collaborations with Synopsys, Open Fiber, INFINERA, and CESNET. His research is funded by EU programs (Horizon Europe, H2020), PNRR, and multiple industrial contracts. Labs and Teams: He leads the PLANET (Physical Layer Aware NETworking) team, a subgroup of the OptCom Group at PoliTo. The team focuses on simulation, modeling, and AI-driven control of next-generation optical networks. They collaborate closely with the LINKS Foundation and international partners including Soochow University and CESNET .
Mo-Yuen Chow is a Professor in the Department of Electrical and Computer Engineering at North Carolina State University (NCSU), holding the position since July 1999. He also serves as a Qiushi Chair Professor at Zhejiang University, China, and a Chang Jiang Visiting Chair Professor (2010–2013). His research focuses on Micro/Smart Grids Energy Management, Collaborative Distributed Control, and Battery Modeling. He holds significant IEEE awards, including Fellow status (2007) and the Dr.-Ing. Eugene Mittelmann Achievement Award (2020). Chow earned his Ph.D. in Electrical Engineering from Cornell University (1987), following an M.Eng. (1983) and B.S. (1982) from the University of Wisconsin-Madison. His work spans battery health monitoring, distributed control systems, and cyber-physical security in smart grids. Notable contributions include innovations in battery state-of-charge estimation and resilient microgrid management frameworks. His publications emphasize cybersecurity in distributed energy systems, AI-driven energy management, and fault diagnosis in battery systems. He has pioneered frameworks like the DEED-ADMM algorithm for multi-energy systems and developed models for solid electrolyte interface growth in lithium-ion batteries. Awards: Over 10 major IEEE awards, including recognition for service and education. Research Themes: Smart grids, distributed control, battery systems, and cyber-physical resilience. Labs/Projects: DC microgrid testbeds and collaborative distributed energy management systems.
Erik Brunvand is a Professor at the School of Computing , University of Utah, and holds an Adjunct Professor position in the Department of Electrical and Computer Engineering. He was a University Professor from 2014–2016 and is currently on leave at the National Science Foundation (CISE/CNS division) (Fall 2019–2021). His research focuses on computer architecture, VLSI systems, asynchronous circuits, and graphics processing, particularly GPU architectures for ray tracing. Teaching includes courses like Digital VLSI Design , Embedded Systems and Kinetic Art , and interdisciplinary classes like Making Noise: Sound Art and Digital Media . He has organized the ASYNC international symposium series and contributed to projects such as TRaX (ray tracing architecture) and ACK (asynchronous design framework). Grants include NSF awards for GPU architectures, ray tracing applications, and microengine-based control systems. His work spans hardware design, sustainable computing, and arts/technology collaborations, including kinetic art installations and educational initiatives like circuit bending.
Seyhan Onbaşıoğlu is a Professor in the Department of Mechanical Engineering at Istanbul Technical University (ITU), actively engaged in thermal engineering research with current projects extending to 2025. Affiliated with ITU's Ayazağa Campus in Istanbul, Turkey, the profile shows ongoing research leadership including principal investigator roles in multiple national projects. Research interests center on heat transfer enhancement, two-phase flow phenomena, and thermal system optimization, with fingerprint analysis indicating strong expertise in heat transfer (100%), flow distribution (94%), and experimental investigation (83%). Key focus areas include nucleate boiling, ice sublimation dynamics, and energy-efficient appliance design. Publication trends since 2016 emphasize experimental validation of thermal systems, with significant contributions to refrigeration technology, domestic appliance efficiency, and microchannel heat transfer. Recent works (2023-2025) increasingly integrate numerical modeling with experimental verification, particularly in suction muffler design and ice sublimation under humidity effects. h-index: 12 Total Citations: 129 (Scopus) Research Outputs: 27 publications Active Projects: 1 (2025) Completed Projects: 6 (2012-2022) As principal investigator, leads projects funded by TUBITAK, SRP, and TTO including refrigerator system development, steam generation technology, and ship diesel engine turbocharger design. Supervised 53 research works to date across thermal engineering applications. Current projects focus on refrigerator cooling system enhancement (2025) and ejector-integrated refrigeration design (2022).
Maris Ozols is an Assistant Professor at the University of Amsterdam and a researcher at QuSoft, affiliated with the Algorithms and Complexity department at Centrum Wiskunde & Informatica (CWI). His primary research focuses on quantum algorithms and quantum information theory, with significant contributions to quantum complexity, quantum cryptography, and quantum state discrimination. His research interests span quantum algorithms, quantum information theory, quantum cryptography, quantum complexity, and theoretical computer science. Ozols has developed fundamental techniques in quantum query complexity, quantum state discrimination, and quantum cryptographic security models. His work often bridges theoretical computer science with quantum information physics, demonstrating practical implications for quantum computing architectures. His publication record shows consistent output in top venues including Communications in Mathematical Physics, Leibniz International Proceedings in Informatics, and Quantum journal. Recent work (2022-2025) focuses on quantum state discrimination, quantum circuit optimization, quantum machine learning, and cryptographic applications of quantum algorithms. His research demonstrates strong theoretical foundations with practical implications for quantum computing development. Leverhulme Early Career Fellow (University of Cambridge) Ozols has secured research funding through multiple Netherlands Organisation for Scientific Research (NWO) grants including the Quantum Software Consortium (QSC) and Quantum Computation with Bounded Space projects. His collaborative work spans international institutions including the University of Waterloo (where he earned his PhD), University of Cambridge, IBM Research, and various European quantum computing groups. He maintains active research groups in quantum algorithms at both QuSoft and CWI, with recent focus on quantum machine learning applications and quantum cryptographic protocols.
Hacı Güzel GÜLEÇ is a Lecturer at Kastamonu University , affiliated with the Cide Rıfat Ilgaz Vocational School and Electronics and Automation Department . He has been in this position since 2010 and served as Department Head from 2013 to 2018. His academic journey includes a PhD in Electrical-Electronics and Computer Engineering (2022) from Düzce University , an MSc in Electrical and Electronics Engineering (2017) from Karabük University , and a BSc in Electronics and Communication Engineering (2003) from Süleyman Demirel University . His research focuses on Electromagnetics , Microwave and Antenna Technologies , Fuzzy Logic , and Semiconductors . He has published extensively on applications of Artificial Neural Networks (ANN) and ANFIS in energy systems, material science, and power electronics. Notable trends in his work include: Renewable energy optimization using ANN (e.g., solar and wind energy prediction). DC-DC converter designs for energy storage (e.g., Arduino-based battery charging systems). Electromagnetic field analysis in applied physics (e.g., solenoid modeling). Predictive modeling for composite materials (e.g., Cu-TiC composites). His collaborative efforts span institutions like Afyon Kocatepe University and Karabük University , working with researchers such as Ünal Kaya , Hüseyin Demirel , and Murat Caner . No formal awards or student advising records are publicly documented.
R. Iris Bahar is a Professor of Computer Science and Engineering at Colorado School of Mines, where she serves as Department Head of Computer Science since 2022. Previously, she held dual appointments as Professor of Engineering and Computer Science at Brown University for 26 years. Her research focuses on energy-efficient and reliable computing across system-level to device-level domains, with applications in near-data processing, robust machine learning for robotics, and noise-immune nanoscale circuit design. Research Interests include computer architecture, low-power design, and robotic system optimization. She pioneers hybrid generative-discriminative inference techniques for robot scene perception, achieving 40% accuracy improvements in occluded environments through FPGA-based Monte-Carlo sampling implementations. Her work on subthreshold CMOS noise modeling provides 1000x simulation speedups over SPICE-based methods. Near-Data Processing Architectures Approximate Computing Hardware/Software Co-design Nanoscale Noise Modeling Robust AI for Robotics Scientific Awards include the 2024 IEEE Undergraduate Teaching Award, 2019 Marie R. Pistilli Women in Engineering Achievement Award, and NSF CAREER award. She is an IEEE Fellow and ACM Distinguished Scientist. Funded Research spans NSF grants on thermal noise effects ($360k), durable data structures ($500k), and Brown SEED grants for autonomous robotics. Her lab trains 12+ students in hardware acceleration and probabilistic computing.
Dr. QUAN Chen is an Associate Professor at the School of Microelectronics, Southern University of Science and Technology (SUSTech), holding this position since May 2025 after serving as Assistant Professor (2019-2025) and Research Assistant Professor at the University of Hong Kong (2012-2018). A Shenzhen high-level overseas talent, he earned his PhD from the University of Hong Kong and conducts cutting-edge research in electronic design automation. His academic credentials include: Ph.D. from The University of Hong Kong (2010) Master's degree from The University of Hong Kong (2007) Bachelor's degree from Sun Yat-Sen University (2005) Dr. Chen's research pioneers advanced EDA algorithms for large-scale analog/RF circuit simulation, post-Moore multi-physics analysis, and AI-assisted design technologies. His work addresses critical challenges in nanodevice modeling and quantum computing circuits, resulting in over 50 publications in top venues like IEEE TCAD and DAC, plus four Chinese patents. Analysis of his recent publications reveals dominant trends in exponential integrator methods for transient simulation, model order reduction techniques, and physics-informed machine learning for reliability analysis. His work bridges numerical mathematics with practical EDA applications across analog circuits, quantum hardware, and emerging memory technologies. Key recognitions include: Wu Wenjun Artificial Intelligence Science and Technology Award, Second Prize (2020) ICCAD Best Paper Award Nomination (2012) Dr. Chen actively recruits Postdoctoral Fellows, Research Assistants, and Graduate Students while leading major funded projects including NSFC key/general programs and Guangdong Provincial R&D initiatives. His industry partnerships with Huawei, Empyrean, and Guowei Group translate theoretical advances into real-world EDA solutions. He directs a specialized research group at SUSTech focused on computational methods for next-generation circuit design, fostering innovation in simulation algorithms and multi-physics analysis through academic-industry collaboration.
Professor Francis Dawson is affiliated with the Department of Electrical and Computer Engineering at the University of Toronto , under the Faculty of Applied Science and Engineering . He has been active in teaching and research since 1988, focusing on energy systems, power converters, and electromagnetic modeling. PhD in Electrical Engineering (University of Toronto, 1988) MASc in Electrical Engineering (University of Toronto, 1985) BASc in Electrical Engineering (University of Toronto, 1982) BSc in Physics (University of Toronto, 1978) His research spans static power converters , energy storage systems , signal processing in power applications, and electromagnetic compatibility . Recent publications focus on fault current limiters , digital phase-locked loops , and plasma physics , with keywords indicating strong ties to Electrical Engineering and Materials Science . Scientific accolades include: IEEE Fellow Myron Zucker Student Design Project 1st Prize (2012) K. Yamamoto Best Student Paper Award (2007) F. Farahmand 3rd Best Paper Prize (2005) He has collaborated on industrial projects in power electronics and contributed to the Power Group at the University of Toronto.
Marianne Fyhn is a Professor at the University of Oslo's Section of Physiology and Cell Biology within the Faculty of Mathematics and Natural Sciences. She leads the Centre for Integrative Neuroplasticity (CINPLA), a strategic research initiative integrating experimental biology with computational physics/mathematics to study brain information processing and plasticity. PhD in Neuroscience (NTNU, 2000-2005) MSc in Physiology (University of Tromsø, 1997-1999) Bachelor in Arctic Biology (University Courses in Svalbard, 1995-1996) BSc in Biology (University of Bergen/Oslo, 1992-1995) Her research focuses on neural plasticity mechanisms in cortical structures, using large-scale neuronal recordings and transcranial two-photon microscopy to study synaptic and population code changes during sensory learning. Key findings include discovering grid cells in mice and demonstrating how experience modifies cortical circuits for long-term memory. Recent publications analyze perineuronal nets' role in memory stabilization, grid cell conformal mapping, and topological population coding in visual cortex. These works reveal intersections between neural circuit dynamics , computational modeling , and neurodegenerative processes . 2008: European Brain and Behaviour Society Award 2007: Eppendorf-Science Prize in Neurobiology 2006: Donald B. Lindsley Prize & I.K. Lykke Award Fyhn serves as course manager for advanced courses including MBV1020 - Physiology and MBV4340 - Advanced Neurobiology . Her lab develops educational tools like Neuronify for neural circuit simulation and open-source platforms for electrophysiological data analysis.
Venkata Virajit Garbhapu (Viru) is a Research Fellow at the CONNECT Centre, Trinity College Dublin, specializing in optical networks with emphasis on physical layer optimization and cross-layer integration. Education: PhD, Institut Polytechnique de Paris (CIFRE scholar with Huawei) Laura Magistrale, Politecnico Di Milano His research focuses on maximizing network capabilities through physical layer impairment-aware frameworks, demonstrated by 52.8% training time reduction in distributed AI systems using 800G DCI links and superior failure recovery via optical circuit switching. Key contributions include per-channel power allocation algorithms balancing linear/nonlinear impairments and feasible C-band integration of classical/QKD signals through Raman noise-aware wavelength allocation. His work centers on developing sophisticated simulators enabling cross-layer optimization for functionalities like QKD and packet switching. Analysis of his 2019-2025 publications reveals a dominant trend toward AI-driven network automation, with increasing emphasis on digital twins and large language models for lifecycle management, alongside quantum-classical signal coexistence. The research consistently bridges theoretical algorithms with experimental demonstrations, showing progression from transfer learning for failure detection to advanced AI/quantum integration. No scientific awards were mentioned in available sources. His PhD was supported by a CIFRE industrial scholarship with Huawei, and he participated in the European QSNP project. No student advisement details are provided, though his BONSAI research addressed optical network failure management using transfer learning to overcome data scarcity. He operates within the CONNECT Centre ecosystem at Trinity College Dublin while maintaining active collaboration with Huawei Paris Research Center, reflecting strong industry-academia integration in optical communications research.
Pierre Lewden is an Associate Professor at the University of Bordeaux , affiliated with the Bioelectronics group under the School of Electrical and Electronic Engineering. His research focuses on event-based computing, memristors, and hardware neural networks, with a particular emphasis on neuromorphic engineering and nanotechnologies.
