جست‌وجوی استاد و پژوهشگر

• Artificial Intelligence
• Bioinformatics
• Neuromorphic Computing
• +۱۲ مورد دیگر

Gianvito Urgese is an Associate Professor at the Interuniversity Department of Territorial Sciences, Planning and Policies (DIST) at Politecnico di Torino, where he is also a member of the EDA research group and the SmartData@PoliTO Big Data and Data Science Laboratory. His academic and research activities are deeply integrated into the Department of Control and Computer Engineering (DAUIN), reflecting his interdisciplinary focus on computer engineering and data science. His research spans artificial intelligence, bioinformatics, neuromorphic computing, edge computing, embedded systems, and Industry 4.0. He investigates optimized task-specific algorithms, designs heterogeneous software-hardware architectures for bioinformatics acceleration, and develops computational paradigms for neuromorphic platforms. His work also extends to digital lifecycle management in Industry 4.0, aligning with Sustainable Development Goals 9, 11, and 12. His recent publications reveal a strong trend in neuromorphic computing and quantum-inspired optimization, with contributions to benchmarking frameworks (NeuroBench), neuron-based encoding tools (WiN-GUI), and quantum annealing methods. These works are published in high-impact journals such as Nature Communications , IEEE Transactions on Emerging Topics in Computing , and Science Translational Medicine , indicating a multidisciplinary and high-impact research profile. Urgese is actively involved in supervising PhD students and teaching graduate-level courses such as Neuromorphic Computing and Engineering, Applied AI and Machine Learning, and System-on-Chip Architecture. He serves on doctoral colleges and course committees, demonstrating leadership in academic governance. Scientific and Research Leadership: Principal Investigator (Scientific Manager) in multiple commercial research projects on data analytics, fog computing, and firmware design (2019–2025). Supervision of PhD research on neuromorphic systems, bioinformatics algorithms, and AIoT solutions. Active contributor to European-funded initiatives in neuromorphic and Industry 4.0 domains. He collaborates with multidisciplinary teams, including researchers at the Candiolo Cancer Institute and participants in the Telluride Neuromorphic Cognition Engineering workshop, highlighting the collaborative and applied nature of his work.

Mostafizur Rahmanمشاهده پروفایل

استادیار

• Beyond CMOS Computing
• Nanoscale 3-D Integrated Circuits
• Neuromorphic Computing
• +۳ مورد دیگر

Mostafizur Rahman is an Assistant Professor in the School of Science and Engineering at the University of Missouri-Kansas City. His research focuses on nanoscale computing paradigms, including Beyond CMOS computing with emerging devices, neuromorphic systems mimicking mammalian brain functions, and 3-D integrated circuits with advanced thermal management. He holds a Ph.D. in Electrical and Computer Engineering from the University of Massachusetts Amherst. His expertise spans nanoscale 3-D integrated circuits, crosstalk-based computing architectures, and experimental prototyping of novel nanodevices. Key contributions include developing Skybridge-3D-CMOS technology for fine-grained 3-D integration and exploring crosstalk computing as a foundation for reconfigurable digital logic. He investigates thermally aware circuit design, addressing challenges in transistor-level 3-D integration and scalability. Research trends in his articles emphasize scalable crosstalk computing (7nm to 180nm nodes), FPGA security through bitstream analysis, and neuromorphic systems leveraging wave interference functions. His work bridges nanoscale device physics with system-level design, targeting applications in high-density, energy-efficient computing systems. No scientific awards are explicitly documented in the provided materials. Advising and grants details are not included, but his labs focus on experimental prototyping of nanoscale fabrics and advanced 3-D IC technologies.

Aboozar Taherkhaniمشاهده پروفایل

مدرس ارشد

• Artificial Intelligence
• Data Science
• Big Data
• +۱۲ مورد دیگر

Dr. Aboozar Taherkhani is a Senior Lecturer in the School of Computer Science and Informatics at De Montfort University, UK, within the Faculty of Computing, Engineering and Media. His research integrates computer science and neuroscience, focusing on artificial intelligence, deep learning, and spiking neural networks. He is affiliated with the Institute of Artificial Intelligence (IAI) at DMU and has previously served as a full-time Research Fellow at Nottingham Trent University. His research interests include: Artificial Intelligence and Machine Learning Deep and Spiking Neural Networks Multimodal Data Analysis Image and Nonlinear Signal Processing Robotics, IoT, and Natural Language Processing Activity Recognition and Computational Neuroscience The recent publications highlight a strong trend in deep learning applications across domains such as healthcare (breast ultrasound segmentation, bone scaffolds), agriculture (hydroponics), robotics (3D reconstruction, gesture recognition), and privacy-preserving AI. His work emphasizes both theoretical advancements in neural models and practical implementations on edge devices and multimodal systems. Dr. Taherkhani has secured research funding through the Leverhulme Trust Research Project Grant RPG-2016-252 on multimodal data spaces. While no specific awards are listed, his extensive publication record in top journals reflects scholarly impact. He teaches courses such as Intelligent Mobile Robots, Natural Language Processing, Deep Neural Networks, and programming in C++, Python, and C#. He leads a research lab at DMU and supervises graduate students, though specific advisee names are not provided. His lab focuses on AI, robotics, and embedded intelligent systems.

Giacomo Spiglerمشاهده پروفایل

استادیار

• Deep Learning
• Deep Reinforcement Learning
• Meta-Learning
• +۶ مورد دیگر

Dr. Giacomo Spigler is a Tenured Assistant Professor at Tilburg University's Department of Cognitive Science and Artificial Intelligence (TSHD). He co-directs the AI for Robotics Lab (AIR-Lab) and the MindLabs Robotics & Avatars Lab. His research focuses on deep reinforcement learning for robotics, meta-learning, and evolutionary robotics, with applications in human-robot interaction and autonomous systems. Education: Ph.D. in Computational Neuroscience, University of Sheffield (2018) M.Sc. Cognitive Science (Computational Neuroscience), University of Edinburgh (2014) B.Sc. Computer Engineering, Sant’Anna School of Advanced Studies and University of Pisa (2012) Research Interests: Spigler explores artificial general intelligence, continual learning, and biohybrid systems. He develops algorithms for robotics that bridge simulation and real-world deployment, emphasizing embodied cognition and intuitive engineering principles. Key Projects: Co-PI of the STEADFAST project (Swarm Technology for Military/First Responder Operations) Developed the Tilburg Hand robot platform for reinforcement learning research Teaching: Courses include Deep Reinforcement Learning (MSc), Calculus (BSc), and Elements of Machine Learning (BSc AI Minor), with a focus on hands-on robotics applications.

Matthew Guthausمشاهده پروفایل

استاد

• Electronic Design Automation (EDA)
• VLSI Design
• Machine Learning for EDA
• +۵ مورد دیگر

Matthew Guthaus is a Professor at UC Santa Cruz, where he serves as lead Principal Investigator of the VLSI Design and Automation Lab and is a member of the Hardware Systems Collective. He founded the VLSI Design Automation (VLSI-DA) laboratory in 2006, focusing on physical design, circuits, and algorithms research for electronic design automation (EDA). His research spans low-power circuits, thermal-aware design, reliability, and machine learning applications in EDA. Guthaus's research interests center on advancing EDA through modern AI techniques, with active projects including GATMesh (GNN-based clock mesh analysis), GAT-Steiner (GNN-driven Steiner tree prediction), OpenGCRAM (open-source gain cell compiler), and effective capacitance modeling with GNNs. His work emphasizes open-source tool development and hardware innovation. Recent publications (2021-2025) demonstrate strong focus on machine learning integration within EDA workflows, open-source silicon initiatives, and memory design optimization. Key trends include graph neural networks for physical design challenges, democratization of EDA tools, and co-optimization techniques for AI hardware. Scientific awards include: NSF CAREER Award (2011) for resonant clocking research Guthaus leads the VLSI Design and Automation Lab, which develops open-source EDA tools like OpenRAM and collaborates within the Hardware Systems Collective. He contributes to NSF initiatives on integrated circuit education and workforce development, authoring workshop reports in 2021-2022. He will present recent work on OpenROAD optimizations at ORConf 2025.

Venkattraman Ayyaswamyمشاهده پروفایل

دانشیار

• Stochastic Methods
• Non-equilibrium Fluid Mechanics
• Plasma Physics
• +۳ مورد دیگر

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++.

• Unconventional Computing
• Liquid Robotics
• Soft Robotics
• +۹ مورد دیگر

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.

• Neuroelectronic Interfaces
• Bioelectronics
• Tissue Engineering
• +۳ مورد دیگر

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.

• Ultra-low power digital circuit design
• Energy-efficient AI processor design
• Compute-in-memory
• +۵ مورد دیگر

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.

Yang Yangمشاهده پروفایل

دانشیار

• Inverse Problems
• Medical Imaging
• Data Science
• +۴ مورد دیگر

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.

• Spiking Neural Networks
• Memristors
• Neuromorphic Computing
• +۵ مورد دیگر

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.

• Neural Networks
• Networks on Chips
• Hardware Engineering
• +۳ مورد دیگر

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.

• Internet of Things
• Neural Networks
• Smart Manufacturing
• +۳ مورد دیگر

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.

• Computational Neuroscience
• Neural Networks
• Computer Hardware
• +۳ مورد دیگر

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 Landryمشاهده پروفایل

دانشیار

• Complex Systems
• Network Science
• Dynamical Systems
• +۵ مورد دیگر

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.