Jeffrey Young is a Principal Research Scientist at Georgia Institute of Technology, working with the Partnership for Advanced Computing Environments (PACE) and leading Georgia Tech’s Open Source Program Office. His research focuses on high-performance computing (HPC), computer architecture, and novel accelerators including GPUs, FPGAs, and Arm/RISC-V processors. He leads next-generation computing strategy at PACE and directs the NSF-funded CRNCH Rogues Gallery testbed, which explores post-Moore accelerators like neuromorphic and near-memory systems. His work bridges hardware-software co-design and scientific software engineering. Recent research trends show expertise in quantum programming (Qwerty/ASDF), heterogeneous computing (Cupbop), and memory system optimization across GPUs, FPGAs, and CPUs. He has contributed to exascale workflows (HIPLZ), safe HPC libraries, and UAV co-simulation frameworks. Scientific Awards: NSF-funded CRNCH Rogues Gallery testbed (2020-2024) Education: Ph.D. in Computer Architecture (2013), advised by Dr. Sudhakar Yalamanchili Labs & Initiatives: Director, CRNCH Rogues Gallery testbed Co-Director, Georgia Tech Center for Scientific Software Engineering Director, Georgia Tech Open Source Program Office
Dr. Weilu Gao is an Assistant Professor in the Department of Electrical & Computer Engineering at the University of Utah. He holds a B.S. from Shanghai Jiao Tong University (2011) and a Ph.D. from Rice University (2016), followed by postdoctoral research there until 2019. Before joining Utah, he worked as a Photonics Designer at Lightmatter Inc. (2019–2020). His research focuses on photonics/optoelectronics of nanomaterials, including carbon nanotubes and 2D materials, with applications in computing, sensing, and energy. He has over 90 publications and 5,800+ citations. Research interests include reconfigurable photonics for machine learning, chiral photonic materials, and scientific computing using optical neural networks. Key projects involve developing diffractive optical neural networks (DONNs) for PDE-solving and energy-efficient computing, programmable chiral heterostructures, and wafer-scale aligned carbon nanotube architectures. His work bridges nanomaterial science with optical engineering, emphasizing scalable fabrication and cross-disciplinary applications. Notable achievements include publishing in Nature Communications , Advanced Photonics Research , and ACS Photonics . He leads the Weilu Gao Lab, which actively collaborates on NSF-funded projects (e.g., 2022 NSF award for carbon nanotube-based semiconductors). Professional activities include organizing workshops on chiral photonics and presenting at conferences like ECS Meetings. Grants include NSF funding for semiconductor research and collaborations with institutions like the University at Buffalo and Tokyo Metropolitan University. His lab recruits students and postdocs in scientific computing, photonics, and nanomaterials.
Albi Mema is a researcher affiliated with the Chair of AI Processor Design (AI-Pro) at Technische Universität München (TUM). His work focuses on emerging technologies for AI applications, including neuromorphic hardware, reliability engineering, and quantum computing. University: Technische Universität München Department: Chair of AI Processor Design (AI-Pro) Key research areas include: Emerging Technologies for AI Neuromorphic Hardware Reliability in Semiconductor Devices Quantum Computing RISC-V Architecture Machine Learning Computer-Aided Design His recent publications address fault-tolerant hyperdimensional computing, analog computing for AI, FeFET-based neuromorphic systems, and compact majority gate design using FDSOI technology. No scientific awards are mentioned in the provided text.
Freja Stær Hincheli serves as a Lecturer at the Department of Computer Science , University of Copenhagen. Her work intersects multiple domains within machine learning, with a particular emphasis on quantum-inspired algorithms, medical imaging, and sustainable AI development. Keywords : Machine Learning, Quantum Computing, Medical Imaging, Natural Language Processing, Computational Biology Key Collaborations : SCIENCE AI Centre Her research spans quantum-enhanced neural networks, explainable AI for medical diagnostics, and energy-aware model design. Recent publications highlight applications in cross-cultural recipe adaptation, emotion-aware dialogue systems, and climate-conscious AI strategies. The Machine Learning Section at DIKU focuses on theoretical foundations and applications including medical image analysis , biological data modeling , and quantum computing , aligning with her contributions.
James Shackleford serves as Associate Professor and Interim Associate Dean for Enrollment Management and Graduate Education in the Department of Electrical and Computer Engineering at Drexel University. His research bridges medical image processing, high performance computing, and emerging neuromorphic architectures with significant contributions to radiation therapy applications. Education: PhD in Electrical Engineering, Drexel University, 2011 MS in Electrical Engineering, Drexel University BS in Electrical Engineering, Drexel University Research Focus: Professor Shackleford's work centers on GPU-accelerated medical image registration (forming the core of the open-source Plastimatch software), real-time tumor motion management for radiation therapy, and digital spiking neuromorphic systems . His research integrates computer vision, machine learning, and embedded systems to solve clinical imaging challenges. Publication Trends: Recent work (2020-2024) reveals dual research trajectories: (1) advancing deformable image registration through CycleGAN-based domain adaptation for CT auto-segmentation in radiation oncology, and (2) pioneering neuromorphic computing with configurable hardware architectures, dataflow-based compilers, and resource-aware neural network mapping. These streams converge on high-performance solutions for medical imaging and efficient neural processing.
Malachy Mc Elholm serves as a Lecturer in Electrical Engineering at Ulster University's Faculty of Computing, Engineering and the Built Environment, based at Magee Campus in Londonderry. His research integrates hardware engineering with neuromorphic computing to address critical system reliability challenges. His research specializes in fault detection mechanisms for RISC-V architectures and self-repairing hardware systems using spiking neural networks . Key contributions include novel watchdog mechanisms for real-time fault tolerance in processors and neuromorphic solutions for structural health monitoring. Work spans hardware security , transient fault mitigation , and biologically inspired computing , with applications from processor integrity to civil infrastructure resilience. Recent publications (2020-2025) reveal a concentrated focus on applying spiking neural networks to RISC-V fault detection, yielding three 2025 papers on "Smart Watchdog" systems. Parallel work extends these principles to structural health monitoring, demonstrating cross-domain applicability from computer architecture to agricultural engineering through dairy farm energy studies. Scientific Awards No awards documented in available information Advising and Grants Advises PhD researcher David Simpson on RISC-V fault detection systems. Leads public engagement initiatives including a 2025 NI survey on dairy farm renewable energy generation and a 2021 pilot study comparing energy consumption in robotic versus conventional dairying , reflecting applied research in sustainable technology transfer. Labs and Teams Core member of Ulster University's neuromorphic computing research collective collaborating with Professors Harkin and McDaid. The team develops spiking neural network hardware for real-time critical applications, maintaining active projects in processor security and infrastructure monitoring with significant industry partnerships.
Stephen B. Furber is an ICL Professor of Computer Engineering in the Department of Computer Science at the University of Manchester. His research spans advanced processor technologies, focusing on low-power system design, asynchronous digital systems, and neuromorphic computing systems like the million-core SpiNNaker platform. Research Focus: Systems-on-chip, Networks-on-chip, Neural systems engineering Academic Leadership: Head of Department of Computer Science (2001-2004) Scientific Recognition: CBE for services to computer science Fellow of the Royal Society and IEEE Faraday Medal recipient Wolfson Research Merit Award recipient
Peter Bienstman is a full professor at Ghent University, working in the Department of Information Technology (INTEC) where he has been since 1997. He is affiliated with the Photonics Research Group and also collaborates with imec. His research spans nanophotonics, neuromorphic computing, and biosensing applications. Bienstman received his electrical engineering degree from Ghent University in 1997 and completed his Ph.D. at the same institution in 2001. His doctoral work focused on "Rigorous and efficient modelling of wavelength scale photonic components." His research interests primarily revolve around nanophotonics and its applications, with specific focus areas including: Photonic Reservoir Computing for neuromorphic information processing Optical label-free biosensors based on ring resonators TE/TM biosensors for measuring conformational changes SiN biosensors operating in the visible spectrum Optical spiking neurons and neuromorphic architectures Nanophotonic information processing systems Analysis of his recent publications reveals a strong focus on advancing photonic reservoir computing for practical applications, particularly in communications signal processing and biomedical sensing. His work demonstrates how photonic systems can implement neuromorphic computing paradigms with energy efficiency advantages over traditional electronics. Recent trends show increasing integration of phase-change materials and exploration of quantum-inspired photonic computing approaches. Bienstman has received significant recognition for his work, most notably an ERC Starting Grant for the Naresco-project: "Novel paradigms for massively parallel nanophotonic information processing." This prestigious European grant supports his innovative research at the intersection of photonics and computing. As an advisor, Bienstman has supervised numerous doctoral students to completion and currently mentors a large research group with nine active PhD students and two postdoctoral researchers. His research is supported by multiple grants that enable the development of novel photonic computing architectures and biosensing platforms. The group's work bridges fundamental photonics research with practical applications in communications, healthcare, and computing. The Photonics Research Group at Ghent University, where Bienstman works, maintains state-of-the-art facilities for nanophotonic device design, fabrication, and characterization. The group collaborates extensively with imec and other international research institutions, creating a vibrant ecosystem for advancing photonic technologies from fundamental research to potential commercial applications.
Vijaykrishnan Narayanan is a Professor at the College of Engineering , Pennsylvania State University, with affiliations in both Computer Science & Engineering and Electrical Engineering departments. He co-directs the Microsystems Design Lab and leads the Architecture, Benchmarking, and Circuits Thrust at the DARPA/SRC LEAST Center. Education : Bachelors (1993) from University of Madras; Ph.D. (1998) from University of South Florida His research focuses on Power Aware Computing , Computer Architecture , Embedded Systems , and Emerging Device Integration . Recent work explores steep-slope devices for energy efficiency, nonvolatile processors for ambient energy harvesting, and neuromorphic architectures using hybrid VO₂-MOSFET oscillators. Key article trends span post-CMOS technologies (tunnel FETs, VO₂ devices), low-power design , and bio-inspired signal processing . Scientific Awards : IEEE Fellow, ACM Fellow, IEEE Transactions on VLSI Best Paper, IEEE Micro Best Paper Patents : Dynamically-configurable hardware architecture for audience analytics
Eva LAGUNAS is an Assistant Professor and Deputy-Head of the SIGCOM research group at the Interdisciplinary Centre for Security, Reliability and Trust (SnT) , University of Luxembourg. Her expertise lies in Non-Terrestrial Communication Systems , focusing on radio resource management and wireless networks optimization. She holds a Ph.D. and M.Sc. in Telecommunications Engineering from the Polytechnic University of Catalonia (UPC), Barcelona. Key Affiliations & Roles: Principal Investigator (PI) of the FNR CORE project VARRAY-5G (Vehicular Phase Array Antennas for 5G) PI of the CHIST-ERA project SHIELD (Secure Distributed Learning & Smart Contract Ecosystems) Co-PI in the ESA ARTES-funded NeuroSat project (Neuromorphic Processors for SatCom) Deputy-Head of SIGCOM group and active contributor to the TelecomAI-Lab (AI-driven satellite communication) Event organizer: Special Session on ML for NTN at IEEE ICMLCN 2025, Track Chair at EUCNC 2025 Research Interests: Non-Terrestrial Networks (NTN), 6G integration, satellite-ground network convergence, AI-driven optimization, reconfigurable intelligent surfaces (RIS), and neuromorphic computing for onboard processing. She also leads efforts in vehicular communications and energy-efficient satellite payloads. Recent Achievements: 2025: Elected to the EURASIP Board of Directors 2024: Listed in the 100 Brilliant and Inspiring Women in 6G list 2023: Co-authored NeuroSat project's IEEE publication on neuromorphic computing for SatCom Labs & Projects: TelecomAI-Lab : Developing neuromorphic hardware (e.g., Intel Loihi2, BrainChip AKIDA) for satellite applications ESA NeuroSat : Pioneering neuromorphic processors for onboard SatCom resource management Grants & Funding: Secured Luxembourg National Research Fund (FNR) and CHIST-ERA grants for projects like VARRAY-5G and SHIELD, totaling over €2M in research investment.
William Henrich Due serves as a Lecturer at the Department of Computer Science (DIKU), University of Copenhagen, within the Machine Learning section. His work intersects with the SCIENCE AI Centre and leverages the department's high-performance compute cluster for research in quantum computing, sustainable AI, and medical applications. Research focuses span quantum machine learning (biomolecular simulations, photonic processors), sustainable AI systems (energy efficiency, climate impact), and clinical applications (EEG analysis, medical imaging). His recent publications reveal strong activity in quantum-classical hybrid systems, with 8/15 recent papers addressing quantum computing challenges. The work emphasizes practical implementations in medical imaging and resource-constrained environments. His research aligns with DIKU's Machine Learning section priorities including medical imaging biomarkers and sustainable computing. Key infrastructure includes TreeSense for remote sensing and the department's dedicated compute cluster. No scientific awards were explicitly documented in the provided materials. Due contributes to DIKU's teaching mission as a Lecturer while engaging with the SCIENCE AI Centre's interdisciplinary initiatives. His work connects with medical imaging applications and quantum computing infrastructure development. Active in the Machine Learning section's research ecosystem, his work intersects with medical imaging analysis and quantum computing applications, utilizing specialized resources like TreeSense for environmental monitoring.
Rasha Karakchi serves as a Lecturer in the Department of Computer Science and Engineering at the University of South Carolina's Molinaroli College of Engineering and Computing, where she teaches diverse courses while maintaining an active research program in hardware acceleration and embedded systems. Her academic foundation includes: Ph.D. in Computer Science and Engineering, University of South Carolina (2020) M.E. in Computer Engineering, University of South Carolina (2016) Dr. Karakchi's research centers on high-performance reconfigurable embedded systems, with particular focus on hardware acceleration for automata processing, spiking neural networks, and genomic sequence alignment. Her work bridges theoretical computer science with practical hardware implementation, emphasizing energy efficiency and real-time performance in security-critical applications. Analysis of her 2023-2025 publications reveals a dominant research trajectory applying machine learning to optimize hardware configurations for domain-specific tasks. Key thematic clusters include ML-enhanced automata processors for pattern matching, lightweight encryption engines for embedded security, and specialized architectures for spiking neural network acceleration—all demonstrating consistent innovation in hardware-software co-design for computationally intensive workloads. Her research excellence has been recognized through: SPARC Award (South Carolina's Program to Advance Research and Creativity)
Scott Mahlke is a Professor and Associate Chair in the Department of Electrical Engineering and Computer Science at the University of Michigan's College of Engineering. He is affiliated with both the Advanced Computer Architecture Laboratory and the Software Systems Laboratory. Dr. Mahlke joined the University of Michigan in 2001 after completing his Ph.D. at the University of Illinois and working at HP Laboratories. Ph.D., University of Illinois Former Researcher, HP Laboratories Dr. Mahlke's research spans compilers, computer architecture, and high-level synthesis, with particular focus on overcoming challenges in performance, power consumption, and reliability for next-generation computer systems. His work integrates hardware and software co-design approaches to address fundamental limitations in modern computing platforms. His research has evolved from traditional compiler and architecture topics toward increasingly incorporating machine learning acceleration, autonomous systems, and reliability engineering. Analysis of his recent publications (2021-2025) reveals a strong trend toward hardware-software co-design for emerging workloads, particularly in autonomous systems, neural network acceleration, and reliability-aware computing. His work demonstrates consistent innovation in bridging compiler technology with architectural innovations to solve real-world performance and efficiency challenges. Dr. Mahlke has received significant recognition for his contributions to the field: National Science Foundation CAREER Award (2003) for "Compiler-Directed Synthesis of Application Specific Processors" Morris Wellman Faculty Development Assistant Professor appointment (2004) ISCA Most Influential Paper Award (2006) for the 1991 paper "IMPACT: An Architectural Framework for Multiple Instruction Issue Processors" Young Alumni Award from the University of Illinois ECE Department (2007) As an educator, Dr. Mahlke has taught core computer systems courses including EECS 370 (Introduction to Computer Organization), EECS 483 (Compiler Construction), and EECS 583 (Advanced Compilers) since joining Michigan. His teaching philosophy follows Yale Patt's 10 commandments for teaching, emphasizing understanding over memorization, genuine respect for students, and taking responsibility for course content. He has received mixed but generally positive student evaluations, with students noting both his deep subject matter expertise and areas for improvement in lecture delivery. Dr. Mahlke maintains active research leadership through his affiliations with the Advanced Computer Architecture Laboratory and Software Systems Laboratory, where his team continues to explore innovative approaches to compiler and architecture challenges in modern computing systems.
Pengcheng Xu is a Researcher at the Technical University of Munich's Chair of Circuit Design under Prof. Ralf Brederlow, specializing in analog and mixed-signal circuit design. His work spans energy harvesting systems, neuromorphic hardware, and wireless sensor technologies, with strong industry connections including prior roles at Huawei and Fraunhofer EMFT. Education: Bachelor of Physics, Shanghai Normal University (2013) Master of Integrated Circuit Engineering, Tongji University (2016) Ph.D. in Electrical Engineering, Université catholique de Louvain (2021) Exchange Student, University of Erlangen-Nuremberg (2015) Xu's research focuses on practical applications of circuit design including RF energy harvesting for battery-less IoT sensors, neuromorphic accelerators for edge computing, and precision analog systems for electrochemical/ mechanical stress sensing. His work bridges theoretical circuit innovation with real-world implementation in semiconductor processes from 28nm FDSOI to emerging memory technologies. His publications demonstrate consistent high-impact contributions to IEEE journals and conferences including JSSC, ISSCC, and ESSCIRC, with particular expertise in impedance-aware rectifier design and low-power circuit architectures. Xu holds a pending European/US patent for RF energy harvesting systems. Awards and Recognition: Shanghai Outstanding Graduate Award (2013, 2016) Chinese Government Award for Outstanding Self-Funded Students Abroad (2020) Chinese National Scholarship (2012, 2014, 2015) Meritorious Winner, Mathematical Contest in Modeling (2013) Xu actively contributes to the academic community as IEEE Young Professionals Germany Chair (2023-2024), IEEE Design Automation Conference TPC member (2022-2024), and reviewer for multiple IEEE journals. He supervises student theses in analog circuit design and neuromorphic hardware through TUM's Chair of Circuit Design, which maintains strong industry partnerships with semiconductor companies.
Neslihan Serap Şengör is a Professor at the Department of Electronics and Communication Engineering, Istanbul Technical University . Her work bridges Artificial Intelligence , Neuroscience , and Circuits and Systems Theory . Research areas include: Neuromorphic computing with Intel Loihi Basal ganglia and motor control modeling Spiking neural networks for hardware Cognitive process simulation Projects focus on: Hardware implementation of motor learning Computational models for Parkinson's disease Cortex structure simulation on neuromorphic chips Contact: sengorn@itu.edu.tr
