Dr. Venkattraman Ayyaswamy is an Associate Professor in the Mechanical Engineering Graduate Group at the University of California, Merced's School of Engineering. His research focuses on non-equilibrium fluid mechanics, plasma physics, and computational modeling of micro/nanostructured systems. Ph.D., Aeronautics & Astronautics, Purdue University (2012) M.S., Aeronautics & Astronautics, Purdue University (2009) B.S., Aerospace Engineering, IIT Madras (2007) Key research areas include stochastic methods for engineering applications, microfluidics, plasma modeling using OpenFOAM, and plasma-biomaterial interactions. His group employs computational/theoretical approaches while maintaining experimental capabilities. Recent publications analyze microwave microplasma behavior, ionization dynamics, and continuum breakdown criteria. The 15 most recent works span plasma characterization techniques, computational frameworks (plasmaFoam, SOMAFOAM), and applications in agriculture/environmental systems. Teaching includes courses in fluid mechanics, aerodynamics, and cold plasma theory. The lab actively recruits graduate students with programming skills in FORTRAN or C++.
Dr. Noushin Raeisi Kheirabadi serves as a Research Fellow at the Faculty of Environment and Technology (FET) at the University of the West of England (UWE), Bristol. She is actively engaged in the European Innovation Council FETOpen project COgITOR: Colloid Cybernetic Systems, focusing on learning and computing in colloidal systems. Her research spans Unconventional Computing, Liquid Robotics, Soft Robotics, Bio-inspired computing, Colloids, Energy Harvesting, Sustainable Technologies, Nanomaterials, Smart Materials, Renewable and Sustainable Energy, Nanocomposites, and 2D Materials. She leads investigations into liquid robotics, neuromorphic computing in colloids, energy harvesting via triboelectric nanogenerators based on two-dimensional nanostructures, and semiconductor processing for microdevices. Her work bridges nanotechnology, sustainable energy systems, and bio-inspired computational paradigms. Dr. Raeisi Kheirabadi collaborates with Huddersfield University and the European Innovation Council and SMEs Executive Agency (EISMEA) on the COgITOR project. No scientific awards were mentioned in the provided information. Her research contributes to sustainable technologies through nanomaterials innovation and unconventional computing approaches, with potential applications in energy-efficient robotics and environmental monitoring systems.
Francesca Santoro is a Professor jointly appointed at RWTH Aachen University (where she heads the Neuroelectronic Interfaces Lab) and Forschungszentrum Jülich (IBI-3 research group). She specializes in neuroelectronic interfaces, bioelectronics, and tissue engineering, with a focus on treating neurodegenerative diseases using chip-based technologies. Education: PhD in Electrical Engineering & Information Technology, RWTH Aachen/Forschungszentrum Jülich (2014) Master’s in Biomedical Engineering, University of Naples Federico II (2010) Bachelor’s in Biomedical Engineering, University of Naples Federico II (2008) Research: Her work bridges bioelectronics and regenerative medicine, emphasizing neural interface design, neuromorphic devices, and nanotechnology for brain repair. Recent innovations include light-mediated bioelectronics and organic electrochemical neurons that mimic biological systems. Publications: Her 15 most recent articles (2020–2025) cluster around neurohybrid systems, nanotechnology-driven neural interfaces, and organic neuromorphic devices, reflecting a consistent focus on bioelectronic solutions for neurological disorders. Awards: ERC Starting Grant (2020) Falling Walls Science Breakthrough in Engineering (2021) MIT Innovator Under 35 Europe/Italy (2018) Leopoldina Early Career Award (2022) Heart Rhythm Society Fellowship (2016) Leadership: She founded the Tissue Electronics Lab at the Italian Institute of Technology (2017–2021), co-founded BRYLA, and leads interdisciplinary teams developing next-generation neural interfaces.
Dr. Longyang Lin is an Assistant Professor at the School of Microelectronics, Southern University of Science and Technology (SUSTech) in Shenzhen, China. He received his Ph.D. from the National University of Singapore in 2018 and has been with SUSTech since May 2021. His research focuses on cutting-edge integrated circuit design with emphasis on ultra-low power systems, hardware security, and cryogenic circuits. Education: Ph.D., National University of Singapore, 2018 M.Sc., Lund University, 2011-2013 B.Sc., Umeå University & B.Eng., Shenzhen University, 2007-2011 Dr. Lin's research spans ultra-low power digital circuit design , energy-efficient AI processor design , compute-in-memory , on-chip sensor fusion , and cryogenic CMOS circuit design . His work addresses critical challenges in battery-less systems, hardware security, and energy-efficient computing. He has developed innovative solutions for self-powered sensor nodes, widely energy-scalable VLSI systems, and secure integrated circuits that operate across extreme temperature ranges. His recent publications demonstrate a strong focus on hardware security, ultra-low power design, and biomedical applications. The research shows a clear progression toward more integrated systems that combine sensing, processing, and communication in highly energy-constrained environments. Many papers address the challenge of maintaining performance while drastically reducing power consumption, with several targeting sub-nanowatt operation. Scientific Awards: Takuo Sugano Award for Outstanding Far-East Paper, ISSCC 2022 ISSCC Demonstration Session Certificate of Recognition, 2022 ISSCC Demonstration Session Certificate of Recognition, 2020 IEEE SSCS Singapore Chapter Award, 2017 & 2018 ISSCC Student Travel Grant Award, 2017 Dr. Lin actively mentors students and researchers, with openings for Research Assistant Professors, Postdoctoral Fellows, Research Assistants, and Graduate Students. His research group benefits from ample funding and regular tape-out opportunities (3-5 per year across different process nodes). He serves as Associate Editor for IEEE Transactions on VLSI Systems and has authored or co-authored over 40 publications, including 10 in IEEE JSSC, 5 in ISSCC, and 12 in VLSI Circuits conference. His laboratory focuses on integrated circuit design for ultra-low power applications, with specialized expertise in hardware security, energy harvesting systems, and cryogenic electronics. The research environment supports tape-outs in advanced CMOS processes, enabling practical validation of theoretical designs.
Matthias Probst is a Researcher at the Department of Information Security, Technical University of Munich (TUM), specializing in hardware security and neural network implementations. His work bridges cryptographic engineering with emerging computing paradigms, focusing on vulnerabilities in neuromorphic hardware and side-channel analysis of neural networks. Research Focus: Probst investigates Side-Channel Analysis techniques against neuromorphic systems, particularly Spiking Neural Networks (SNNs) and Physical Unclonable Functions (PUFs). His recent publications reveal expertise in Hardware security countermeasures (DOMREP series) Fault injection methodologies (Switch-Glitch) Secure neural network acceleration Quantum-resistant hardware implementations His work demonstrates how timing and electromagnetic emanations can extract secrets from emerging hardware architectures. Publication Trends: Analyzing Probst's 2019-2025 publications shows increasing focus on neural network security (60% of recent work), with growing emphasis on neuromorphic hardware (40% since 2022). His research uniquely combines traditional side-channel analysis with next-generation computing architectures, revealing critical vulnerabilities in spiking neuron implementations and PUF-based security primitives. Key themes include timing-based attacks on loop PUFs, electromagnetic fault injection sweet spots, and masked neural network accelerators. Collaboration Network: Probst consistently collaborates with TUM's Chair of Information Security team led by Prof. Georg Sigl, particularly with Manuel Brosch and Michael Gruber. His 2024 EMDRIVE Architecture paper involves cross-institutional work spanning embedded diagnostics to edge computing.
Yang Yang is an Associate Professor in the Department of Computational Mathematics, Science and Engineering at Michigan State University. Their research bridges inverse problem theory with computational innovation for medical and geophysical imaging technologies. Ph.D. in Mathematics (2014), University of Washington B.S. in Mathematics (2009), Zhejiang University Research Focus : Theoretical development of inverse boundary value problems in acoustics, optics, and elasticity using PDE theory, microlocal analysis, and differential geometry Geometric inverse problems for metric and vector field identification in Riemannian/Lorentzian geometries Algorithm design for tomographic reconstruction with applications in Ultrasound Computed Tomography, Photoacoustic Tomography, and Optical Tomography Scientific Contributions : NSF CAREER award recipient (2023) Developed non-iterative boundary control algorithms for acoustic inverse problems Established uniqueness/stability results for transversely isotropic perturbations in elasticity Created mathematically-justified imaging algorithms for media with unknown acoustic properties Publication Trends : Recent work focuses on conscious learning frameworks to address post-selection misconduct in AI, emergent Turing machines for autonomous programming, and ethical implications of deep learning's performance data. These align with their core expertise in inverse problem analysis and computational innovation.
Jean Tomas is an Associate Professor at the University of Bordeaux, affiliated with the Laboratoire de l'intégration, du matériau au système (IMS). He is a member of the Bioelectronics research group and Team 2HC within IMS, a prominent research laboratory focused on materials, systems integration, and micro/nanoelectronics. His work bridges the gap between theoretical neural models and practical hardware implementations. Dr. Tomas's research focuses on neuromorphic engineering with particular emphasis on spiking neural networks and memristive systems. His work spans from fundamental circuit design of biomimetic neurons to practical applications in low-power computing and intelligent sensors. He has made significant contributions to the hardware implementation of spiking neural networks using memristive technologies, exploring how these systems can achieve efficient computation with minimal energy consumption. His research also extends to electronic microassembly processes and power electronics. The analysis of his publication record reveals a consistent trajectory from early work on analog neural circuit design toward increasingly sophisticated neuromorphic systems. His recent publications demonstrate expertise in mixed-mode spiking neural networks, passive memristive arrays, and their application to neuromorphic cameras. His work addresses critical challenges in hardware neural networks, including the effects of line resistance in crossbar arrays and strategies for on-the-fly learning in resource-constrained environments. Dr. Tomas actively collaborates with researchers across multiple institutions, including CNRS@CREATE Ltd. and various university laboratories. His work has been supported by significant research initiatives including ANR-19-CHIA-0003 (GrAI - Green Artificial Intelligence) and the European Project ULPEC (Ultra-Low Power Event-Based Camera). He is deeply involved in the Bioelectronics research ecosystem at IMS, contributing to both fundamental research and practical applications. His work connects circuit design with system-level implementation, particularly in the development of energy-efficient neuromorphic sensors that can process information at the edge of computing networks.
Aqib Javed serves as a Teaching Fellow (Lecturer) in Electrical Engineering at Ulster University's School of Computing, Engineering and Intelligent Systems, based at the Derry~Londonderry campus. His research focuses on applying spiking neural networks to solve critical challenges in networks-on-chip architectures, structural health monitoring systems, and emerging healthcare technologies. Dr. Javed's primary research domains include neural networks, networks-on-chip optimization, hardware engineering implementations, structural health monitoring, deep learning methodologies, and machine learning applications. His work demonstrates particular expertise in developing neuromorphic computing solutions for real-time systems where conventional AI approaches face latency or power constraints. Analysis of his publication history reveals a clear research trajectory: early work concentrated on networks-on-chip traffic prediction (2020-2021), expanded into structural health monitoring hardware systems (2020-2021), then progressed to neuromorphic datasets for sensory fusion (2023), with recent publications pivoting toward edge intelligence applications in cardiac healthcare (2025). This evolution shows increasing specialization in deploying spiking neural networks at the hardware edge for time-sensitive applications. No scientific awards were documented in the available materials. Information regarding student advising responsibilities or research grant acquisitions was not present in the source documentation. Dr. Javed maintains active research collaborations within Ulster University's neuromorphic engineering group, working closely with Professor Jim Harkin, Professor Liam McDaid, and Dr. Jinghai Liu on hardware acceleration projects for artificial intelligence systems, with laboratory work centered around FPGA implementations of spiking neural network architectures.
Kyle Madden serves as a Research Fellow at Ulster University within the School of Computing, Engineering and Intelligent Systems at the Derry~Londonderry campus. His work bridges theoretical computer science with industrial applications, focusing on intelligent systems development for manufacturing and IoT environments. Education: PhD in Computer Science (awarded June 2025) from Ulster University with thesis: “Spiking neural networks for detecting denial-of-service attacks in networks-on-chip” supervised by Dr. Jim Harkin and Dr. Liam Mc Daid. Dr. Madden’s research spans cutting-edge domains in neuromorphic engineering and industrial IoT. He pioneers frameworks for translating spiking neural networks to FPGA hardware while optimizing power efficiency, develops cloud-based IoT systems for industrial decision-making, and applies computational intelligence to 3D printing quality control. His work integrates user experience considerations in smart manufacturing interfaces and advances event-based sensor data processing through neuromorphic event alarm systems. Recent publications (2024-2025) reveal strong thematic focus on deploying AI solutions in real-world industrial contexts. Key trends include hardware acceleration of neural networks for manufacturing, cloud-IoT integration using AWS infrastructure, and human-centered design for industrial IoT applications. His research consistently addresses practical implementation challenges in sensor data processing and neural network deployment. Scientific Awards: No awards documented in available information Dr. Madden has no listed advisees in the provided materials. His research outputs indicate collaborative project involvement, particularly in EU-funded industrial IoT initiatives and neuromorphic computing consortia, though specific grant details aren’t disclosed. Current work appears integrated within Ulster University’s Computer Science research group focusing on intelligent systems. He actively contributes to research teams developing neuromorphic event-based processing systems and industrial IoT frameworks. Recent work on FrostRune demonstrates leadership in creating asymmetric translational pipelines from high-level neural models to FPGA deployment, positioning him within hardware-aware AI research communities.
Professor Liam McDaid serves as Interim Research Director at Ulster University's School of Computing, Engineering and Intelligent Systems (Magee Campus, Derry~Londonderry). His leadership spans computational neuroscience and neuromorphic engineering projects addressing global challenges through the UN Sustainable Development Goals framework. Research focuses on computational neuroscience and neural engineering , with expertise in spiking neural networks, astrocyte modeling, hardware implementations (FPGAs/Networks-on-Chip), and biomedical applications. His work integrates computer hardware with biological systems for fault-tolerant computing and medical diagnostics. Key publications reveal trends in neuromorphic hardware for real-time fault detection (RISC-V systems), medical AI for cardiac/adrenal diagnostics, and event-driven sensing . Research bridges computer engineering with neuroscience to solve healthcare infrastructure challenges. Prize 'Biotechnology' category in Northern Ireland Science Park 25K Award (2011) IgniteNI's global Accelerator Programme (2021) Life and Health Startup Company of the Year 2019 (InventNI) Prize Asthma (2019) Leads major projects including AI-EPOCMON (cardiac diagnostics), Nervous Systems (neural interfaces), and microwave thermal therapy for hypertension. Supervises PhD researchers in neural engineering while directing industry collaborations with healthcare technology focus. Maintains active partnerships across Europe in neuromorphic computing and medical device development. Directs research teams advancing spiking neural networks for hardware security and medical applications, with recent work on adrenal segmentation pipelines and RISC-V watchdog mechanisms. Future initiatives focus on expanding AI-driven point-of-care diagnostics and neuromorphic infrastructure monitoring systems.
Nicholas Landry is an Associate Professor of Biology at the University of Virginia with a courtesy appointment in the School of Data Science. His research examines contagion dynamics on complex systems, focusing on how higher-order network structures influence disease and information spread through interdisciplinary approaches combining network science, dynamical systems, and open-source development. His educational background includes a B.S. in Mechanical Engineering from the University of New Hampshire (2014), M.S. and Ph.D. in Applied Mathematics from the University of Colorado Boulder (2020, 2022), and postdoctoral training at the Vermont Complex Systems Center (2022-2024). Landry's core research investigates dynamical processes on higher-order networks, Bayesian inference for network structure reconstruction, and open-source software development. He maintains the XGI and HyperContagion libraries to advance cross-disciplinary research in network science, with applications spanning epidemiology, social contagion, and governance systems. His work emphasizes the critical role of group interactions in altering contagion dynamics compared to traditional pairwise models. Analysis of recent publications reveals accelerating focus on hypergraph structures, real-world dataset integration (particularly Bluesky social media), and computational frameworks for governance modeling. The research trajectory shows increasing methodological sophistication in handling higher-order interactions while maintaining strong connections to empirical social and biological systems. Scientific recognition includes: Small Data Analytics Resource Award from UVA's Data Analytics Center for "Analyzing large-scale social networks through the Bluesky social media platform" Landry actively mentors graduate researchers including Kuankuan Hu, Abhay Gupta, and Ahmed Ahmed through the Computing for Global Challenges program. His lab secured the Small Data Analytics Resource Award and participates in EXPAND fellowship collaborations with Professors Kasimatis and Brodie, integrating network methods into sexual conflict research and behavioral phenotyping. The Landry Lab, established August 2025, collaborates extensively with UVA's Biocomplexity Institute, Quantitative Collaborative, and School of Data Science. Current expansion includes postdoc Daniel Kaiser and multiple PhD students, with active development of open-source tools and participation in international conferences like NetSci 2025 where Landry organized the "Software and Data for Supporting Network Science" workshop.
Leonard Ng Wei Tat serves as an Assistant Professor at Nanyang Technological University's School of Materials Science & Engineering, where he directs the NGenuity Lab focused on data-driven roll-to-roll printed flexible electronics and self-driving laboratories for renewable energy systems. His work bridges advanced manufacturing, nanomaterials, and artificial intelligence to develop sustainable energy solutions. Ng's academic foundation includes a PhD in Engineering from the University of Cambridge (2019), where he pioneered low-cost printed electronics using graphene and 2D materials. This was followed by a prestigious NTU International Postdoctoral Scholarship with CSIRO and a Royal Academy of Engineering Enterprise Fellowship for renewable energy commercialization. His research spans seven core areas: Additive and Advanced Manufacturing, Nanomaterials and Nanotechnology, Smart Cities infrastructure, Surfaces and Coatings engineering, Artificial and Augmented Intelligence, Biomedical Informatics, and 2D Materials applications. Current projects emphasize machine-learning enabled inverse design for high-throughput manufacturing of photovoltaics, with particular focus on stability enhancement and efficiency optimization of printed solar cells. Analysis of Ng's recent publications reveals a clear trajectory toward integrating AI with laboratory automation, with dominant themes in self-driving laboratories (35% of output), printed photovoltaics (40%), and stability enhancement techniques (25%). His work demonstrates exceptional translational impact, with multiple industry features including PV Magazine coverage of his perovskite solar cell breakthroughs. Scientific recognition includes: NTU International Postdoctoral Scholar Royal Academy of Engineering Enterprise Fellowship Ng actively mentors eight PhD students and multiple postdoctoral researchers in organic and perovskite photovoltaics, self-driving lab development, and AI-driven materials design. His group secures substantial funding through competitive mechanisms including Nanyang PhD Graduate Scholarships, SINGA Scholarships, and industry partnerships, supporting both fundamental research and commercial translation efforts. The NGenuity Lab operates as a dynamic hub for innovation, featuring specialized facilities for roll-to-roll printing, photovoltaic characterization, and AI-driven experimental automation. Current initiatives focus on democratizing laboratory robotics through low-cost 3D printing and developing closed-loop optimization systems for industrial-scale renewable energy manufacturing.
Shubhadeep Bhattacharjee is an Assistant Professor in the Department of Electrical Engineering at the Indian Institute of Technology Hyderabad (IITH) . He leads the Advanced Electron Devices Lab , where his team explores cutting-edge research in low-power semiconductor devices, neuromorphic hardware, and quantum materials. Research Interests His research spans three major domains: Classical Transistors: Investigating sub-thermionic 2D transistors to overcome the Boltzmann limit and enable ultra-low voltage operation. Neuromorphic Devices: Developing CMOS-compatible memristive and synaptic devices for energy-efficient artificial and spiking neural networks. Quantum Devices: Exploring twisted 2D heterostructures for emergent quantum phenomena and ferroelectric memory applications. Funded Projects Dr. Bhattacharjee is the Principal Investigator (PI) or Co-PI on several nationally and internationally funded projects: BRNS, DAE, GoI: Scalable co-integration of 2D materials for spiking neural networks (3 years) SERB, GoI: Tunable synaptic plasticity in MoS₂ transistors for low-power SNNs (2 years) IITH Seed Grant: Heterogenous integration of neuromorphic devices with 2D semiconductors (2 years) ASCENT, Europe: BEOL-compatible MoS₂ devices (2 years) SPARC, MoE, GoI: India-US collaborative workforce development in semiconductor manufacturing (Co-PI, 2 years) Lab & Collaborations Dr. Bhattacharjee’s lab emphasizes interdisciplinary collaboration, combining cleanroom nanofabrication, electrical characterization, and device modeling. The lab is open to PhD, postdoc, and project students with backgrounds in electrical engineering, physics, materials science, and chemistry.
Dr. Shubham Sahay is an Assistant Professor in the Department of Electrical Engineering at the Indian Institute of Technology Kanpur. His research focuses on solid-state logic and memory devices, neuromorphic computing, and hardware security. He completed his Ph.D. in Electrical Engineering from IIT Delhi in 2018 and his B.Tech in Electronics Engineering from IIT BHU, Varanasi in 2014. Ph.D. in Electrical Engineering, IIT Delhi, 2018 B.Tech in Electronics Engineering, IIT BHU, Varanasi, 2014 Dr. Sahay's research interests span hardware platforms for neuromorphic computing, hardware security primitives, novel device architectures for scaling CMOS technology, analytical and compact modeling of semiconductor devices, non-volatile memories, and spintronics. His work bridges the gap between theoretical device physics and practical applications in computing and security domains, with a particular emphasis on memory technologies and their applications in emerging computing paradigms. His publications reveal a strong focus on memory technologies, particularly NAND flash and emerging memory devices like OxRAM. Dr. Sahay has made significant contributions to neuromorphic computing architectures and hardware security primitives that leverage inherent device variability. His work demonstrates expertise in both theoretical modeling and practical implementation of advanced semiconductor devices. List of golden reviewers for IEEE Transactions on Electron Devices (TED) 2017-2020 I.I.T. (B.H.U.), Varanasi Gold Medal 2014 Late Prof. Nagesh Chandra Vaidya Gold Medal 2014 Dr. (Late) Nandita Saha Roy Memorial Gold Medal 2014 C. Raja Gopal Memorial Gold Medal 2014 Dr. Ayyagari Sambasiva Rao Prize 2014 Late Prof. Manoranjan Sengupta Platinum Jubilee Merit Award 2014 Dr. Sahay serves as an editor for IETE Technical Review and is an active member of IEEE, where he also holds the position of Vice-chair for IEEE EDS, UP Section. His research has been supported by various funding sources, though specific grant details are not provided in the available information. Dr. Sahay teaches courses including EE698P (Memory Technology and Neuromorphic Computing), EE370 tutorial (Digital Electronics), EE210 tutorial (Microelectronics - I), and EE201 tutorial (Introduction to Electronics), contributing to both undergraduate and graduate education in electrical engineering.
Chin Shen Ong is a Researcher in the Department of Physics and Astronomy at Uppsala University, specializing in Materials Theory. His work focuses on theoretical investigations of electronic, magnetic, and structural properties of novel materials, particularly two-dimensional systems and interfaces. He maintains an active research program with consistent publications in high-impact journals including Nature, Physical Review Letters, and Nano Letters. Dr. Ong's research spans multiple areas of condensed matter physics and materials science, with particular expertise in graphene-based systems, 2D materials, and their interfaces. He employs advanced computational methods, primarily density functional theory, to investigate electronic structure, magnetism, and transport properties. His work has significant implications for next-generation electronics, spintronics, and neuromorphic computing applications, with several publications addressing fundamental mechanisms for resistive switching and memory devices. His recent publications reveal a strong focus on materials with potential applications in quantum technologies and advanced electronics. Key research themes include the investigation of magneto-optical effects in ultrafast magnetization dynamics, crystal structures of novel compounds, pressure-driven transitions in nanomaterials, and surface modifications of semiconductors with bismuth. His work combines theoretical modeling with experimental validation through extensive international collaborations. Dr. Ong actively collaborates with researchers across multiple institutions, contributing to interdisciplinary projects that bridge theoretical physics with materials science and nanoelectronics. His research demonstrates sophisticated understanding of quantum mechanical principles and computational methods, with practical implications for developing energy-efficient computing architectures that overcome limitations of traditional CMOS technology.
