Gourav Datta is an Assistant Professor in the Department of Electrical, Computer, and Systems Engineering at Case Western Reserve University (CWRU). He holds a PhD from the University of Southern California (USC) and a B.Tech. from IIT Kharagpur. His research focuses on energy-efficient algorithm-hardware co-design for machine learning at the edge, with specializations in In-Sensor Computing, Neuromorphic Computing, and Embedded Machine Learning. Prior to CWRU, he worked as an Applied Scientist at Amazon AGI, contributing to Amazon Nova's video understanding capabilities. Education: PhD in Electrical Engineering, USC (2023) B.Tech. in Instrumentation Engineering, IIT Kharagpur (2018) Research Interests: Energy-efficient computer vision and multimodal deep learning In-sensor computing and neuromorphic architectures Efficient model deployments on FPGAs/microcontrollers Multi-modal KV cache compression and retrieval-augmented generation Awards: 2024 William Ballhaus Best PhD Dissertation Award 2023 USC ECE Best RA Award 2022 Qualcomm Innovation Fellowship finalist 2018 USC Annenberg Fellowship Academic Service: Program committee member for ISQED, VLSID, DAC (2024–2025) Reviewer for IEEE journals and conferences including ICASSP, ICLR, and WACV Labs/Teams: Leads research on edge computing and neuromorphic systems at CWRU's ECSE department, collaborating with industry partners like Edge Impulse.
Prof. Sacha van Albada is a Research Professor and Group Leader of Theoretical Neuroanatomy at the Institute for Advanced Simulation (IAS-6, Computational and Systems Neuroscience) at Forschungszentrum Jülich. Her research focuses on constructing and analyzing large-scale spiking neural network models of the cerebral cortex, integrating anatomical and physiological data to understand brain dynamics. Key areas include predictive connectomics, cortical microcircuit organization, and neuromorphic computing applications. Her work emphasizes multi-scale modeling across cell-type interactions, network architectures, and system-level brain functions. She develops computational tools like the NEST simulator for distributed neural network simulations. Recent projects involve modeling neuron-astrocyte interactions and exploring how cortical hierarchies and top-down signals modulate neural activity patterns. She also engages in ethical considerations of brain-inspired AI systems. Dr. van Albada collaborates extensively with experimental neuroscientists and HPC experts, contributing to initiatives like the Human Brain Project. Her models have been applied to study visual and somatosensory cortices, motor cortex dynamics, and resting-state network behavior. Despite no explicit mention of awards/grants, her high publication output and leadership role indicate significant academic impact.
Dr. Sarah Sharif is an Assistant Professor in the School of Electrical and Computer Engineering at the University of Oklahoma. She leads the QNET laboratories, focusing on quantum devices, nanophotonics, and optical systems. Her research spans AMO engineering for next-generation communication and sensing, as well as mid-infrared nanomaterial innovations. Dr. Sharif holds a Ph.D. in Electrical Engineering from Louisiana State University (LSU), with minors in Physics and a graduate certificate in Materials Science. Her postdoctoral work at the University of Illinois Urbana-Champaign explored quantum light-matter interactions. She has over a decade of industrial R&D experience and contributed to LIGO’s gravitational wave research (2018–2020). Professional memberships include APS, OSA, Optica, and IEEE. She is the new Optica leader for Quantum Optical Science & Technology (2023). Her lab’s mission emphasizes diversity in STEM, fostering inclusive innovation in quantum and photonic technologies. Current research includes quantum convolutional neural networks (QCNNs), aerosol scattering mitigation in free-space optics, and mid-IR nanophotonic optimization. Active collaborations with industry and academia drive applied projects in neuromorphic computing, UAV systems, and smart manufacturing.
Paulo Flores holds the position of Associate Professor at the Department of Electrical and Computer Engineering within the Instituto Superior Técnico of the University of Lisbon. His academic career is deeply rooted in Electronics and Digital Systems, with a focus on VLSI design, FPGA optimization, and hardware acceleration for bioinformatics applications. He has contributed extensively to research in multiplierless constant multiplication algorithms, low-power circuit design, and quaternary logic implementations. Notable achievements include the SAT Competition 2014 and 2013 Bronze Medals for innovative contributions in formal methods and algorithmic efficiency. His teaching responsibilities include advanced courses in digital systems design, electronic engineering projects, and laboratory guidance in topics like operational amplifiers and FIR filters. Flores actively collaborates with research units like INESC-ID and has supervised multiple academic projects in electronic engineering and digital signal processing. Research interests span across circuit testing, embedded systems optimization, and multi-valued logic architectures. His work frequently explores trade-offs between computational efficiency and energy consumption in hardware implementations.
Pedro Filipe Zeferino Tomás is an Associate Professor at the University of Lisbon's College of Engineering, Department of Electrical and Computer Engineering. He is affiliated with INESC-ID research institute and teaches courses on Large Scale Computing, Computer Architecture, and High Performance Computing. His academic career includes a PhD in Neural Code modeling (2009), a Master's in DSP hardware structures (2006), and a Bachelor's in Bio-Inspired Artificial Retina design (2003). His research focuses on neuromorphic engineering, parallel computing architectures, and biosensing applications. Key areas include GPU optimization, transprecision arithmetic, neural prosthetics, and participatory urban biosensing. He has pioneered bio-inspired systems for visual neuroprostheses and developed energy-efficient hardware accelerators for machine learning. His recent work integrates biosensors into urban studies, exploring participatory methods for analyzing affective geographies and walkability. Over 60 peer-reviewed publications demonstrate his contributions to high-performance computing, neural modeling, and interdisciplinary urban technology. Grants: Multiple EU-funded projects in neuromorphic systems and urban biosensing Labs: Leads the Heterogeneous Computing Lab at INESC-ID and collaborates with neural engineering teams
Dhritiman Bhattacharya is an Assistant Professor in the Department of Electrical & Computer Engineering at Rowan University, affiliated with the Henry M. Rowan College of Engineering. He holds a Ph.D. in Mechanical and Nuclear Engineering from Virginia Commonwealth University (2020) and a B.Sc. in Electrical and Electronic Engineering from Bangladesh University of Engineering and Technology (2013). Prior to his current role, he was a Postdoctoral Fellow in the Department of Physics at Georgetown University. His research focuses on overcoming energy limitations in CMOS-based computing through spintronic innovations, leveraging magnetic nanostructures and non-volatile materials to develop novel neuromorphic and memory technologies. Key areas include voltage-controlled skyrmions, magneto-ionic devices, and neuromorphic computing architectures. He has published over 25+ journal articles and received the 2021 Best Paper Award at the ASME Smart Materials Conference, alongside recognition as an Outstanding Reviewer for the Institute of Physics. His articles explore topics such as magnetic nanostructure design, strain-mediated switching, and reservoir computing with frustrated nanomagnet arrays. Notable contributions include studies on 3D interconnected nanowire networks and physically secure logic locking mechanisms. Professional memberships include IEEE and the American Physical Society.
Prof. Dr.-Ing. Cristóbal Curio is a Professor of Cognitive Systems at the Faculty of Informatics, Reutlingen University since 2014. He also serves as Prodekan for Research. His academic career includes roles at Max Planck Institute for Biological Cybernetics (2004-2013) and Ruhr-University Bochum. Research focuses on cognitive architectures, deep learning, and experimental methodologies applied to robotics, computer vision, and biomedical engineering. Key projects include EU-funded HEIDI and AIDA initiatives, and BMBF KI Delta Learning. Education: 1992-1998: Electrical Engineering and Computer Science degrees at Ruhr-University Bochum and Purdue University 1998-2003: PhD in Computer Science, Ruhr-University Bochum 2014: Habilitation, University of Tübingen Teaching: Courses include Cognitive Systems (Master's), Advanced Image Processing, and Applied Artificial Intelligence. Labs: Leads the Angewandte Künstliche Intelligenz and Ambient Assisting Cloud Lab . Research interests span cognitive systems, neural networks, and human-centric computing with applications in healthcare and autonomous systems. Active in EU and national research programs, publishing extensively on topics like robotic pose estimation, medical image analysis, and human-robot interaction.
Yifan (Evan) Peng is an Assistant Professor at the University of Hong Kong, jointly affiliated with the Departments of Electrical & Electronic Engineering and Computer Science. He leads the WeLight Lab, focusing on interdisciplinary research at the intersection of Optics, Graphics, Vision, and Artificial Intelligence. His work emphasizes computational imaging systems, holography, and human-centered visual technologies. Education: PhD in Computer Science from the Imager Lab, University of British Columbia Postdoctoral Research Scholar at Stanford University's Computational Imaging Lab Visiting Student Researcher at KAUST's Visual Computing Center and Stanford MS & BS in Optical Science and Engineering from Zhejiang University Research Interests: His research explores computational optics, holographic displays, VR/AR/MR systems, and low-level vision techniques. Recent efforts include developing snapshot hyperspectral imaging systems, lighting-robust machine vision, and neural rendering frameworks for dynamic scenes. He investigates hardware-software co-design in imaging systems and explores applications in medical imaging and mixed reality. Publications: Recent work focuses on advancing holographic display technologies, neural rendering, and hybrid optical-computational imaging systems. Key contributions include metasurface-based AR displays, speckle reduction techniques, and learned optical systems for hyperspectral imaging. His research bridges physical optics with digital algorithms to achieve high-quality imaging and display solutions. Grants & Advising: Hosts visiting scholars and collaborates with industry partners like Ford, Sony, and Intel. Openings exist for PhD students, postdocs, and research assistants in computational imaging and optics. Labs & Teams: Leads the WeLight Lab at HKU, collaborating with global institutions on projects like neural holography and diffractive optics. Active in conferences like SIGGRAPH, CVPR, and ISMAR as program committee member.
Dr. Xiaojuan Qi is an Assistant Professor in the Department of Electrical and Electronic Engineering at The University of Hong Kong. Her research focuses on 3D vision, deep learning, and AI applications in medical imaging and science. She holds memberships in IEEE and CCF. Education: PhD, The Chinese University of Hong Kong (2018) BEng, Shanghai Jiao Tong University (2014) Postdoctoral Researcher, University of Oxford (prior to HKU) Research Interests: Dr. Qi develops scalable deep learning algorithms for medical and natural image analysis, 3D scene understanding, and neural network behavior in out-of-distribution scenarios. Her work emphasizes label efficiency, geometric reasoning, and cross-modal integration. Lab Contributions: As head of the Computer Vision and Machine Intelligence Lab (CVMI Lab), she explores open-world intelligence, 3D reconstruction, and AI applications in robotics, autonomous systems, and healthcare. Recent lab highlights include 10+ ICCV/CVPR/NeurIPS papers in 2023-2025. Awards: ImageNet Semantic Parsing Challenge (1st Place) Outstanding Reviewer Awards (ICCV 2017/2019) CVPR Doctoral Consortium Travel Award Advising & Grants: Supervises ~20 PhD students (listed in detail). Active in securing grants for embodied AI, medical imaging, and edge computing hardware. Lab Collaborations: Focuses on interdisciplinary projects with Intel, Oxford, and Toronto groups, emphasizing 3D vision, neuromorphic computing, and AI-driven medical solutions.
JUAN FCO GUERRERO MARTINEZ is a Professor in the Department of Electronic Engineering at the Universitat de València, School of Engineering. He is affiliated with the Group for Digital Design and Processing (GPDD), where he conducts research at the intersection of biomedical engineering, signal processing, and hardware systems. His work integrates real-time embedded systems with clinical applications in cardiology and neuroscience. His research interests include biomedical signal processing , cardiac electrophysiology , machine learning for healthcare , FPGA-based real-time systems , and neuromorphic computing . He has extensively studied ventricular fibrillation, EEG/ECG analysis, and neural signal classification, often applying advanced computational techniques to improve clinical diagnostics and interventions such as deep brain stimulation. The 15 most recent publications reflect a strong trend toward real-time, hardware-accelerated biomedical systems , particularly using FPGAs for neural networks and signal classification. His work bridges theoretical signal processing with practical implementations in medical devices, emphasizing efficiency, accuracy, and clinical applicability. Themes include the use of time-frequency analysis, KNN classifiers, and spiking neural networks for detecting arrhythmias and brain activity patterns. He has supervised research theses and contributed to educational tools in signal processing and biomedical engineering. His academic leadership is evident in curriculum development and educational software, such as MATLAB-based tools for data analysis. While no formal awards are listed, his sustained publication record and leadership in research groups underscore his scholarly impact. Guerrero Martinez leads or contributes to research on embedded systems for medical diagnostics , neural engineering applications , and educational innovations in engineering . His lab, associated with the GPDD group, focuses on co-designing hardware and software for adaptive biomedical systems, supporting both research and teaching in electronic and biomedical engineering.
Ernst Niebur is a Professor of Neuroscience at Johns Hopkins University, affiliated with the Mind/Brain Institute and the Department of Neuroscience within the Zanvyl Krieger School of Arts and Sciences. He is actively involved in multiple interdisciplinary graduate training programs, including Neuroengineering, Neuroscience, Electrical and Computer Engineering, Psychological and Brain Sciences, Visual Neuroscience, and the Institute for Computational Medicine. His research lies at the intersection of computational and systems neuroscience, focusing on the development of quantitative models of brain function grounded in neurophysiology, anatomy, and behavior. Key research interests include selective attention, perceptual grouping, neural synchrony, decision-making, and neuromorphic implementations of cognitive functions. He has pioneered models of proto-object-based visual saliency and border ownership in visual cortex, often in collaboration with experimental neurophysiologists. The analysis of his recent publications (2017–2024) reveals a strong focus on computational models of attention, saliency, and decision-making, with increasing integration of neuromorphic and robotic applications. His work spans cognitive neuroscience, vision science, and neuroengineering, frequently employing biologically realistic neural network models and analyzing spike train dynamics. There is a consistent trend toward modeling higher-order cognitive functions using system-level approaches that incorporate temporal coding and network dynamics. Ernst Niebur has not been mentioned as having received formal scientific awards in the provided text, but his extensive publication record in high-impact journals such as PLoS Computational Biology, Journal of Neuroscience, Vision Research, and IEEE Transactions demonstrates significant scholarly impact. He advises graduate students and has mentored numerous former lab members, contributing to training in neuroscience and neuroengineering. While no specific grants are listed, his involvement in multiple NIH-funded training initiatives suggests active participation in funded research. His lab collaborates across disciplines, particularly with engineers and neurophysiologists, and explores both biological and artificial implementations of neural computation. His lab focuses on constructing and testing computational models of neural systems, particularly those involved in attention and perception. The lab integrates theoretical modeling with empirical data, maintaining close ties with experimental groups. Research themes include dynamic visual saliency, neuromorphic hardware implementations, and neural mechanisms of decision-making.
Gaetano Di Caterina is a Senior Lecturer in the Department of Electronic and Electrical Engineering at the University of Strathclyde, within the Faculty of Engineering. He is a Fellow of the Higher Education Academy (HEA) and leads the Neuromorphic Sensor Signal Processing Lab as part of the Centre for Signal & Image Processing (CeSIP). He holds key academic roles, including Course Director for the MSc in Machine Learning and Deep Learning (joint with Computer and Information Sciences) and Adviser of Studies for Year 1 of the Computer and Electronic Systems undergraduate program. He is currently the Leonardo Lecturer (2023–2027) in a strategic partnership between Strathclyde and Leonardo UK Ltd. PG Certificate in Learning and Teaching in Higher Education, University of Strathclyde (2021–2024) Doctor of Engineering, University of Strathclyde (2009–2013) Master of Engineering, University of Strathclyde (2006–2009) Bachelor of Engineering, Università degli Studi di Napoli Federico II (2001–2005) His research centers on neuromorphic engineering, machine learning, deep learning, digital signal processing, and embedded systems, with applications in surveillance, medical diagnostics, robotics, and space and underwater sensing. He applies these techniques to video analytics, EMG and speech processing, medical imaging, and event-based vision systems. His work often integrates spiking neural networks and neuromorphic sensors to improve robustness in low-SNR and real-time environments. His recent publications reflect a strong trend in leveraging neuromorphic computing and deep learning for challenging real-world problems—such as turbulence mitigation in imaging, voice disorder classification, underwater and space object detection, and brain signal analysis. These works span computer vision, biomedical engineering, signal processing, and AI, demonstrating a highly interdisciplinary approach focused on practical, deployable solutions. His scientific recognition includes: Best Paper Award, IEEE, 27 Aug 2022 IEEE Brain Data Bank Challenges and Competitions, 31 Oct 2017 He is actively involved in research funding and supervision, serving as Principal Investigator on multiple projects funded by the Air Force Office of Scientific Research and Leonardo UK Ltd. He supervises PhD students and postdoctoral researchers in the Neuromorphic Sensor Signal Processing Lab. His professional activities include hosting academic visitors and participating in strategic AI workshops at Strathclyde. He is currently accepting PhD students and continues to expand his work in applied AI and neuromorphic technologies. He leads the Neuromorphic Sensor Signal Processing Lab within the CeSIP research group, fostering innovation in sensor fusion, spiking neural networks, and real-time embedded AI systems. The lab engages in both theoretical and applied research, collaborating with industry and defense partners to develop next-generation intelligent sensing solutions.
Adrian M. Ionescu is a Professor at the Nanoelectronic Devices Laboratory (Nanolab) within the School of Engineering at École Polytechnique Fédérale de Lausanne (EPFL) , Switzerland. His work spans nanoelectronics , quantum computing , and energy-efficient technologies , with a focus on steep-slope transistors , ferroelectric materials , and digital twins for healthcare . He leads major European projects like DIGIPREDICT and Milli-Tech , pioneering low-power devices for IoT and quantum systems . Education: B.S./M.S. in Electronics and Telecommunications (1989, Politehnica University Bucharest); PhD in Microelectronics (1994, Bucharest) and Physics of Semiconductor Devices (1997, Grenoble INP). Research Interests: Beyond CMOS devices, quantum qubits , edge AI sensors , ferroelectric negative capacitance , and lab-on-skin technology for biofluid analysis . Scientific Awards: IEEE Cledo Brunetti Award (2024) IEEE George Smith Award (2017) IBM Faculty Award (2013) André Blondel Medal (2009) Swiss Academy of Sciences (SATW) Member (2015) Leadership: Founder of Nanolab; former Director of EPFL's Doctoral Program in Microsystems and Microelectronics; Editor of IEEE Transactions on Electron Devices; Technical Chair of IEEE conferences. Grants & Projects: Advanced ERC Grant for Milli-Tech (energy-efficient DIGIPREDICT (digital twins for disease prediction), and SINERGIA NEMO (neuromimetic memristors). Students: Supervised over 40 PhD students, including Ehsan Ansari, Fabio Bersano, and Vanessa Conti, working on quantum dots , sweat biomarkers , and van der Waals heterostructures . Lab Focus: Nanolab develops low-power nanoelectronics , quantum computing architectures , and sustainable fabrication processes using non-toxic materials .
Abdullah Al-Mahboob is a Staff Scientist at the Center for Functional Nanomaterials, Brookhaven National Laboratory, and an Adjunct Assistant Professor at State University of New York – Farmingdale State College and CUNY-Queensborough Community College. His research focuses on surface and interface science, 2D materials, and quantum heterostructures. Education: Doctor of Science (Tohoku University, Japan), Master of Science/Master of Philosophy (Jahangirnagar University, Bangladesh). His work leverages spectro-microscopy and robotic fabrication tools like CFN QPress to study dynamical phenomena in 2D quantum materials, including interlayer energy transfer, excitonic structure, and surface magnetism. He has published in journals such as Nano Letters , Nature , and Physical Review B , with a focus on 2D materials, heterostructures, and surface dynamics. Dr. Al-Mahboob has held academic and research roles across institutions including Yale University, University of Liverpool, and Tohoku University. He utilizes techniques like Low-Energy Electron Microscopy (LEEM), X-ray Photoemission Electron Microscopy (XPEEM), and time-resolved angle-resolved photoemission spectroscopy (TR-ARPES) to investigate quantum and nanoscale properties.
Liam Mc Daid is a Professor of Computational Neuroscience at the School of Computing, Engineering and Intelligent Systems, Ulster University. He serves as Research Director for Computing, Engineering and Intelligent Systems within the Faculty of Computing, Engineering and Built Environment. His research spans computational neuroscience, neural networks, and biomedical engineering, with a focus on hardware implementations and fault detection in systems like RISC-V. He contributes to UN Sustainable Development Goals related to health and technology. Recent publications highlight his work on spiking neural networks for fault detection, deep learning in medical imaging, and biotech assays for cardiac diagnostics. His research has been cited over 1,600 times, with an h-index of 23. Scientific awards include the 2011 Adaptive Routing Strategies Prize, the 2010 Northern Ireland Science Park 25K Biotechnology Award, and InventNI’s 2019 Life and Health Startup of the Year. His work has been featured in media outlets addressing asthma management and health tech innovation.
