Find Academic Supervisors

Keshab K. ParhiView profile

Professor

• VLSI Signal Processing
• Machine Learning
• Biomedical Signal/Image Processing
• +6 more

Keshab Parhi is the Distinguished McKnight University Professor and inaugural Erwin A. Kelen Chair in Electrical Engineering at the University of Minnesota's College of Engineering. He has been a permanent faculty member since 1988 and serves as Editor-in-Chief of the IEEE Circuits and Systems Magazine. B.Tech., IIT Kharagpur (1982) M.S., University of Pennsylvania (1984) Ph.D., UC Berkeley (1988) His research spans VLSI architecture design for signal processing, machine learning , biomedical systems , homomorphic encryption , and DNA/molecular computing . Key projects include high-speed transceivers, low-power bio-signal classification, and novel cryptographic architectures. Recent publications focus on homomorphic encryption for privacy-preserving AI, hyperdimensional computing for energy-efficient classification, and neural network accelerators with pipelined architectures. Scientific Awards IEEE Fellow (1996) IEEE Kiyo Tomiyasu Award (2003) IEEE W.R.G. Baker Prize (2001) ACM Fellow (2020) AIMBE Fellow (2022) National Academy of Inventors (2020) Dr. Parhi has advised 52 PhD students and 65+ MS/MEng theses, while leading the Parhi Research Group. His work contributes to UN SDGs through advancements in low-power computing and medical diagnostics .

Paula López MartínezView profile

Professor

• Computer Vision
• Electronic Design of Intelligent Devices
• Energy Harvesting
• +4 more

Paula López Martínez is a Full Professor at the University of Santiago de Compostela (USC) and Deputy Director of the Singular Research Centre on Intelligent Technologies (CiTIUS) . Since June 2024, she has held the Televés Microelectronics Chair at USC. Her work bridges Computer Vision and Electronic Design of Intelligent Devices , with a focus on CMOS Image Sensors and Biomedical Applications . Her research spans Energy Harvesting , Device Modeling , and Low-Power Embedded Systems . Key projects include SEMIoTICS (low-power AI at the edge), MISEL (multispectral vision), and NANOEATERS (nanotechnology transfer). She has authored over 100 peer-reviewed papers and served in IEEE leadership roles, including Secretary of the Sensory Systems Technical Committee and Treasury of IEEE Women in Engineering Spain (2006–2008). Recent publications highlight Analog Computing-In-Memory , HDR CMOS Sensors , and Energy-Efficient DC-DC Converters . Her work integrates Photovoltaic Energy Harvesting with Biomedical Device Design , emphasizing Self-Powered Systems and Dynamic Vision Sensors . She employs Verilog-AMS and ATLAS Simulations for CMOS Photodiode Modeling , addressing Crosstalk and Shot Noise in pixel arrays. Notable collaborations include Victor M. Brea, Diego Cabello, and Miguel Heredia Conde. She contributes to IEEE Transactions and Q1-Ranked Journals , with a focus on Hardware Acceleration via FPGA and Custom IC Design . Her expertise spans Thermoelectric Generators , Smart Energy Systems , and Radiation-Hard Transistors .

• Analysis
• Geometry
• Probability
• +3 more

Nicholas Marshall is an Assistant Professor in the Department of Mathematics at Oregon State University. His work bridges analysis, geometry, and probability with strong applications in data science. Current faculty: Oregon State University Postdoctoral training: Princeton University (NSF Fellowship) Doctoral training: Yale University Undergraduate education: Clarkson University His research focuses on: Interplay between geometric structures and probabilistic models Development of numerical methods for high-dimensional data Applications in cryo-electron microscopy and hyperdimensional computing Analysis of stochastic algorithms and convergence properties Recent publications indicate significant contributions to: Harmonic expansion techniques Equivariant function learning Momentum-accelerated optimization methods Binary hyperdimensional geometry NSF Postdoctoral Fellowship (Princeton) He actively mentors graduate and undergraduate students, including Wyatt Whiting, Peter Cowal, Heather Fogarty, and Seth Alderman. Teaching appointments include advanced courses in probability theory, numerical linear algebra, and mathematics of data science.

• Neuromorphic Computing
• Embodied Artificial Intelligence
• Robotics
• +6 more

Dr. Yulia Sandamirskaya is the Head of Research Center "Cognitive Computing in Life Sciences" at Zurich University of Applied Sciences (ZHAW), focusing on neuromorphic computing applications for embodied artificial intelligence. Her work bridges computational neuroscience and robotics, emphasizing neural-dynamic architectures for real-time decision-making, learning, and sensorimotor integration in autonomous agents. Key Research Areas: Neuromorphic hardware, dynamic neural fields, spiking neural networks, spatial language modeling, and autonomous sequence generation. Projects: Developed controllers for UAVs and robotic arms using event-based vision sensors, explored on-chip unsupervised learning, and designed models for spatial language interpretation in robots. Scientific Contributions: Her publications span robotics conferences and journals like Science Robotics and Frontiers in Neurorobotics , addressing topics such as path integration, obstacle avoidance, and cognitive architectures. Recent work (2024) includes visual odometry with resonator networks and hyperdimensional scene factorization on neuromorphic chips. Advising: Supervised multiple MSc theses at ETH Zurich and NSC/INI programs, mentoring students on neuromorphic navigation, spiking networks, and tactile learning. Collaborated with institutions like ETH Zurich, University of Queensland, and INI Bochum. Labs & Collaborations: Leads the "Neuromorphic Computing Applications: Embodied AI" group at ZHAW, partnering with INIvation (Zurich) and Jörg Conradt (KTH) on neuromorphic hardware implementations. Projects integrate cognitive models with robotic platforms, emphasizing energy efficiency and low-latency interaction.

• Educational Technologies
• Health Informatics
• Human-Computer Interaction
• +7 more

Sergio Gago-Masague is a Teaching Professor at the University of California, Irvine (UCI), where he serves in the Department of Computer Science within the School of Information and Computer Sciences. He directs the Engaging Technology and Application Design Lab, focusing on cross-disciplinary research in assistive and educational technologies. Previously, he worked as a Research Scientist at UCI’s California Institute for Telecommunications and Information Technology and lectured in informatics before transitioning to his current role in 2018. His research spans health informatics, human-computer interaction, software engineering, and computer science education. Key projects include developing the Pain Buddy mHealth tool for pediatric cancer pain management, the Maestro platform for teaching AI robustness, and predictive models for improving CS student retention. He has pioneered the use of gamification in education and embedded systems for health monitoring. Education: Ph.D. in Product Engineering, Universitat Politècnica de Catalunya Recent work emphasizes ethical AI in music industry applications, bias mitigation in student admissions algorithms, and leveraging large language models (LLMs) in post-pandemic education. His articles reflect trends in wearable health tech (e.g., transdermal alcohol prediction via hyperdimensional computing) and CS education innovations like customized student-project matching in capstone courses. Recipient of the 2024 UCI Celebration of Teaching Award and a 2023 ICS Awards honoree, Gago-Masague has also contributed to institutional initiatives like the HSI Project for early academic interventions. His lab fosters collaborations across disciplines to design socially impactful technologies.

• In-Memory Computing
• AI Hardware Acceleration
• Edge Computing
• +5 more

Shaahin Angizi is an Assistant Professor in the Department of Electrical and Computer Engineering at the New Jersey Institute of Technology (NJIT). His research focuses on next-generation computing systems, emphasizing energy-efficient architectures, in-memory computing, and secure AI hardware. He leads multiple National Science Foundation (NSF)-funded projects, including initiatives on robotic vision systems, in-cache AI acceleration, and edge computing. His work spans hardware security, emerging memory technologies, and interdisciplinary applications of AI in healthcare and IoT. Notable projects include developing secure imaging sensors (SenGuard/iSEW), adversarial attack defenses for neural networks, and AI-driven accelerator design automation using large language models (LLM-IMC/TPU-Gen). Angizi has authored over 136 publications and secured 4 active NSF grants between 2022-2027. His research bridges device-level innovations (e.g., magnetoelectric FETs) with system-level designs, aiming to reduce computational carbon footprint while enhancing reliability. Key Projects: Infrared Retinomorphic Vision (2024-2027) Toward Opportunistic In-Cache AI Acceleration (2023-2025) Integrated Sensing & Normally-off Computing (2022-2026) Research Themes: Processing-in-Memory Architectures Edge Intelligence Hardware Hardware Security & Trust Non-Volatile Memory Systems Angizi’s contributions include pioneering sensor-embedded watermarking (iSEW), adversarial attack analysis on LLMs, and energy-efficient in-sensor processing frameworks (PISA/HyperSense). His work frequently explores synergies between photonics, analog computing, and AI to create sustainable high-performance systems.

Lei YangView profile

Assistant Professor

• Hardware/Software Co-Exploration for Neural Network Architectures
• Embedded Systems
• Automated Machine Learning
• +4 more

Dr. Lei Yang is an Assistant Professor in the Department of Information Sciences and Technology at George Mason University, focusing on Hardware/Software Co-Exploration for Neural Network Architectures, Embedded Systems, and High-Performance Computing. Previously, she served as an Assistant Professor at the University of New Mexico and held post-doctoral and research scholar positions at Notre Dame, UC Irvine, and the University of Pittsburgh. Research Interests: System-Level Optimization for Applied Machine Learning Automated Machine Learning (AutoML) Federated Learning for Medical AI and Drug Discovery Quantum Neural Networks and Dark Silicon Many-Core Systems Thermal-Aware and Energy-Efficient Computing Award Trends: 50+ publications in premier venues like DAC, ICCAD, and IEEE Transactions, with multiple Best Paper Awards and Nominations. Notable recognitions include the 2021 IEEE TCAD Donald O. Pederson Best Paper Award and the 2017 ICCD Best Paper Award. Honors & Service: General Chair, SIGDA Student Research Forum at ASP-DAC 2023 Organizer, E2ML Workshop at GLVLSI 2021 Registration Chair, ICCD 2021 TPC Member, DAC 2022, ICCAD 2021, and IEEE SOCC 2020

Minxuan ZhouView profile

Assistant Professor

• Systems
• High Performance Computing
• Parallel Computing
• +5 more

Minxuan Zhou is an Assistant Professor of Computer Science at Illinois Institute of Technology. His research focuses on systems, high-performance computing, and parallel computing with an emphasis on processing-in-memory (PIM) acceleration, fully homomorphic encryption (FHE), and hyperdimensional computing. He holds a Ph.D. and M.S. in Computer Science from the University of California San Diego, and a B.S. in Computer Science and Technology from Beihang University. His work spans hardware-software co-design, cryptographic acceleration, and energy-efficient computing systems. Research Interests Processing-in-Memory (PIM) architectures for accelerating machine learning and graph processing Hardware acceleration for secure computation (FHE) Hyperdimensional computing frameworks for edge AI Thermal-aware design and memory optimization in 3D systems Recent Research Trends Recent publications emphasize co-optimization techniques for PIM architectures, FHE hardware acceleration, and privacy-preserving machine learning. His work often bridges theoretical algorithm design with practical hardware implementations using novel memory technologies like ReRAM and 3D-stacked memory. Grants & Labs Engaged in cutting-edge research through collaborations on projects involving PIM accelerators and encrypted computation systems. His lab focuses on developing full-stack solutions for next-generation computing architectures.

Víctor Manuel Brea SánchezView profile

Associate Professor

• Computer Vision
• Electronic Design of Intelligent Devices
• CMOS-3D Hardware Implementation
• +2 more

Víctor Manuel Brea Sánchez is an Associate Professor at the Research Center on Intelligent Technologies (CiTIUS) within the University of Santiago de Compostela (Spain). His work focuses on hardware implementations for computer vision and CMOS-3D technologies . Research Areas : Computer Vision, Electronic Design, Low-Power Embedded Systems Projects : AZOR (Search, Location, Rescue), Power Management Unit Chip for Energy Harvesting His recent publications emphasize CMOS-based vision sensors , analog computing-in-memory , and spatio-temporal feature extraction . Trends include deep learning acceleration, event cameras, and hardware for real-time object detection. Scientific contributions include the Best Paper Award at the 2003 European Conference on Circuit Theory and Design. Collaborations with researchers like Manuel Mucientes and Paula López highlight interdisciplinary efforts in AI-driven hardware design.

Ozgur SinanogluView profile

Professor

• Hardware Security
• Integrated Circuit Design
• VLSI Circuits
• +7 more

Ozgur Sinanoglu is a Professor of Electrical and Computer Engineering at New York University Abu Dhabi (NYUAD) and a Global Network Professor at NYU Tandon School of Engineering. He holds a B.S. in Electrical and Electronics Engineering and Computer Engineering from Bogazici University (1999), and an M.S. and Ph.D. in Computer Science and Engineering from UC San Diego (2001, 2004). His research focuses on hardware security, design-for-trust, and VLSI circuit reliability. Education: B.S., Electrical and Electronics Engineering & Computer Engineering, Bogazici University, 1999 M.S., Computer Science and Engineering, UC San Diego, 2001 Ph.D., Computer Science and Engineering, UC San Diego, 2004 Research Interests: Design-for-Security in VLSI circuits Logic locking and hardware obfuscation Hardware Trojans and side-channel attacks Secure chip manufacturing and testing His work has led to over 200 publications and 20 patents. He directs the NYUAD Center for Cybersecurity and the Design-for-Excellence Lab, funded by NSF, DoD, Intel, and Mubadala Technology. Awards include the NYUAD Distinguished Research Award (2021) and induction into the UAE's Mohammed bin Rashid Academy of Scientists (2021). Grants and Leadership: Lead PI on NSF and DoD-funded projects Directed ACM ASIACCS 2017 in Abu Dhabi Associate Editor for IEEE TIFS, TETC, and JETC Labs and Teams: Center for Cybersecurity (CCS) Design-for-Excellence Lab (DfX)

M. Hassan NajafiView profile

Assistant Professor

• Emerging Digital Design Methodologies
• Artificial Intelligence
• Stochastic Computing
• +8 more

M. Hassan Najafi is an Assistant Professor in the Department of Electrical, Computer, and Systems Engineering at Case School of Engineering, Case Western Reserve University. His research focuses on emerging digital design methodologies, stochastic and unary computing, hyperdimensional computing, edge computing, and low-power VLSI design. He holds a PhD in Electrical Engineering from the University of Minnesota (2018), an M.S. in Computer Architecture from the University of Tehran (2014), and a B.S. in Computer Engineering (Hardware specialization) from the University of Isfahan (2006). Education: PhD Electrical Engineering, University of Minnesota, 2018 M.S. Computer Architecture, University of Tehran, 2014 B.S. Computer Engineering (Hardware), University of Isfahan, 2006 His research interests span artificial intelligence, computer architecture, and machine learning , with a focus on energy-efficient computing systems. He explores novel paradigms like hyperdimensional computing and processing-in-memory to advance edge computing applications. His work aims to bridge theoretical innovations with practical hardware implementations. Dr. Najafi has received prestigious awards including the NSF CAREER Award (2024), GLSVLSI Best Paper Awards (2023-2024), and the DAC Outstanding Dissertation Award (2018). He holds the Lockheed Martin Corporation Endowed Professorship (2023) and the ULL Francis Patrick Clark/BORSF Professorship (2022). His professional affiliations include Senior Member status in IEEE. While specific grants and advising details are not detailed here, his work emphasizes interdisciplinary collaboration in hardware-software co-design for next-generation computing systems.

Peer NeubertView profile

Professor

• Autonomous Robotics
• Artificial Intelligence
• Computer Vision
• +5 more

Peer Neubert is a Professor at the University of Koblenz , appointed in October 2022, and previously a senior researcher (Dr.-Ing.) in the Automation Technology group at TU Chemnitz . His work lies at the intersection of autonomous robotics , computer vision , and machine learning , with particular expertise in place recognition in changing environments and hyperdimensional computing . His research spans: Vector Symbolic Architectures and hyperdimensional representations for robotics, Visual place recognition robust to seasonal and illumination changes, Neurologically inspired navigation models (e.g., grid-cell representations), Deep learned and hand-crafted image descriptors for mobile robot localization, Efficient graph optimization and SLAM techniques. Since 2010 he has published extensively in RSS , ICRA , CVPR , IEEE RA-L , and other premier venues, with a clear trend toward unsupervised, memory-efficient, and biologically inspired solutions. In 2022 he was nominated for the Heinz Maier-Leibnitz Prize by the Deutsche Forschungsgemeinschaft. No advisees or direct contact details are provided in the source material.

• Machine Learning
• Artificial Intelligence
• Computer Science
• +3 more

Paul Clarke is a PhD Research Fellow at the Department of Information and Communication Technology, University of Agder (UiA). His research focuses on advanced machine learning techniques, particularly Tsetlin Machines and hyperdimensional computing. Research interests include: Machine Learning Algorithms Computational Intelligence Data Science and Neural Networks His recent work explores hyperdimensional vectors for enhancing Tsetlin Machine performance, presented at the 2024 International Symposium on the Tsetlin Machine (ISTM).

• Wireless Communications
• 5G/6G Networks
• Machine Learning Applications in Networks
• +7 more

Sergey Andreev is a Professor at the Faculty of Information Technology and Communication Sciences, Tampere University, where he holds a position in the Department of Electrical Engineering. His research focuses on advanced wireless communication systems, particularly in 5G/6G networks, mmWave technologies, UAV-based networks, and machine learning-driven network optimization. He is actively involved in projects addressing energy efficiency, resource allocation in heterogeneous networks, and the integration of non-terrestrial networks (NTNs) with terrestrial infrastructure. His work emphasizes practical applications such as extended reality (XR) communication, industrial IoT, and reliable service continuity in challenging environments. Andreev's recent publications highlight breakthroughs in federated learning for edge computing, delay-aware scheduling in integrated access and backhaul (IAB) systems, and the deployment of UAVs for enhancing urban network coverage. His contributions span theoretical frameworks to experimental validations, leveraging tools like ray tracing, digital twins, and reinforcement learning. He has also explored the interplay between user behavior and network performance in immersive applications, emphasizing real-world scalability and sustainability. His research often combines interdisciplinary approaches, such as merging machine learning with traditional signal processing techniques to tackle complex network challenges. While no specific awards are listed, his prolific publication record underscores his influence in the field. He has advised numerous projects but no formal student names are provided in the texts. His labs and teams likely focus on cutting-edge 6G research, though specific lab names are not mentioned.

FERNANDO RAFAEL PARDO SECOView profile

Professor

• Electronics Engineering
• Computer Engineering
• Signal Processing
• +3 more

Fernando Rafael Pardo Seco is a Professor at the Department of Electronics and Computing, affiliated with the Higher Technical School of Engineering at the University of Santiago de Compostela. He earned his PhD in 2008 with a thesis titled Software-hardware FPGA-Based System for the solution of the 3D heat equation applications on the non-destructive evaluation of minefields , supervised by Dr. Diego Cabello Ferrer and Dr. Marco Balsi. His research focuses on FPGA-based systems, CMOS architectures, thermal modeling, image processing, and non-destructive evaluation techniques. His academic contributions span engineering education innovation, including methodologies like cooperative learning and e-learning integration. His technological work emphasizes embedded systems, hardware acceleration for numerical simulations, and safety-critical applications such as landmine detection through thermal analysis. Recent publications highlight advancements in hyperdimensional computing architectures, 3D image processing, and thermal simulation tools. His work bridges hardware-software co-design principles with real-world applications in geophysics, security systems, and industrial engineering education.