Find Academic Supervisors

Billy BrumleyView profile

Professor

• System Security
• Cryptography Engineering
• Side-Channel Analysis
• +3 more

Billy Brumley is a Professor in the Department of Cybersecurity at Rochester Institute of Technology (RIT), holding the Kevin O’Sullivan Endowed Professorship in Cybersecurity. He serves as Director of Research at the Golisano College of Computing and Information Sciences (GCI). His research focuses on system security, cryptography engineering, and side-channel analysis. Brumley earned his Sc.D. from Aalto University (2012) and previously held a Professorship at Tampere University (Finland) for a decade. He is a 2018 ERC Starting Grant Laureate and former Staff Engineer at Qualcomm's Product Security Initiative. Education: Sc.D., Aalto University (2012) Key Roles: Director of Research (GCI), Endowed Professor Brumley’s research addresses vulnerabilities in cryptographic implementations and open-source software security, particularly OpenSSL. His work includes side-channel attack analysis, microarchitectural timing exploits, and countermeasure development. Recent projects include investigating child sexual abuse material dissemination on the Tor network and advancing post-quantum cryptography standards. He teaches courses such as Side Channel Analysis and Open Source Software Security, emphasizing practical mitigation strategies and vulnerability lifecycle management. Awards include the ERC Starting Grant and recognition as an Endowed Professor. Grants: ERC Starting Grant (2018) Labs/Teams: Leads cybersecurity research initiatives at RIT’s ESL Global Cybersecurity Institute

Federico CorradiView profile

Assistant Professor

• Neuromorphic Computing
• Spiking Neural Networks
• Energy-Efficient Computing
• +7 more

Federico Corradi is an Assistant Professor in the Electrical Engineering Department at Eindhoven University of Technology (TU/e), where he leads the Neuromorphic Edge Computing Systems Lab. He also holds the position of EAISI Foundational Assistant Professor at the Eindhoven Artificial Intelligence Systems Institute. His research focuses on neuromorphic computing and engineering, spanning from efficient computational models to novel microelectronic architectures for deep learning and brain-inspired algorithms. Dr. Corradi's academic background includes a Ph.D. in Neuroinformatics from the University of Zurich and an international Ph.D. from the ETH Neuroscience Centre Zurich (2015). Prior to joining TU/e in 2022, he worked at IMEC in the Netherlands (2018-2022) where he started a group focused on neuromorphic IC design. Before that, he was with Inilabs, a spin-off from the Institute of Neuroinformatics, developing event-based cameras and neuromorphic processors (2015-2018). His research interests center around understanding natural neural computation principles to develop energy-efficient sensing and computing technologies. Key areas include: Neuromorphic computing and engineering Spiking neural networks Event-based vision systems Energy-efficient microelectronic architectures Applications in robotics, machine vision, and biomedical signal analysis Dr. Corradi's recent publications demonstrate a strong focus on practical implementations of neuromorphic systems for edge applications. His work shows consistent innovation in translating theoretical neural models into efficient hardware implementations, with particular emphasis on handling temporal data and developing brain-realistic computational models. The research spans multiple domains including radar signal processing, optical computing, and error correction systems. He serves as an active review editor for Frontiers in Neuromorphic Engineering, IEEE, and IOP Science journals, and participates in technical program committees for several machine learning and neuromorphic conferences including ICTOPEN, AICAS, AIAI, ICONS, NEWCAS, DSD, and EUROMICRO. His work has received significant media attention with coverage from multiple news outlets and social media mentions. Dr. Corradi leads the Neuromorphic Edge Computing Systems Lab, which bridges theoretical neuroscience with practical engineering challenges, creating novel solutions for real-world problems. The lab's research has practical applications in robotics, autonomous systems, and biomedical technologies, reflecting his commitment to developing energy-efficient computing solutions that can operate effectively at the edge.

Maarten J. MolendijkView profile

Researcher

• Machine Learning
• Analog-to-Digital Converters
• Energy Efficiency
• +3 more

Maarten J. Molendijk is a Researcher and PhD candidate at the Department of Electrical Engineering , Eindhoven University of Technology (TU/e), affiliated with the Integrated Circuits group and Resource Efficient Electronics Lab . He holds BSc (2019) and MSc (2022) degrees in Electrical Engineering from TU/e. Since 2022, he has been employed at NXP Semiconductors while pursuing his PhD research focusing on applying machine learning to correct analog impairments in Analog-to-Digital Converters (ADCs). His research interests include Energy Efficiency , Deep Neural Networks , and Network Inference , with a focus on hardware-software co-design for neural network accelerators. Key contributions include energy-efficient NN SoCs (e.g., BrainTTA) and mixed-precision inference acceleration techniques. Maarten’s work contributes to UN Sustainable Development Goals related to Industry, Innovation, and Infrastructure (SDG 9) and Affordable and Clean Energy (SDG 7). His publications span topics like neuromorphic architectures, compiler optimization, and benchmarking of embedded systems.

Zhong ShaoView profile

Professor

• Programming Languages
• Formal Methods
• Operating Systems
• +5 more

Zhong Shao is the Thomas L. Kempner Professor of Computer Science at Yale University, leading the FLINT research group. His work focuses on certified software systems, formal methods, and secure system design. He is known for pioneering research in certified assembly programming (CAP) and the development of the CertiKOS verified operating system kernel. Research Interests: Formal semantics, concurrency and coordination, certified compilation, language-based security, and verified system software. Recent Contributions: Pioneered deep specifications for layered system software, developed the VeriML proof automation framework, and advanced formal verification for distributed systems and smart contracts. Current efforts include verified blockchain systems and compositional verification techniques for concurrent programs. Teaching: Currently teaches CS430 Formal Semantics in Spring 2025 and has previously taught courses on compilers, operating systems, and programming languages. Labs/Teams: Director of the FLINT Group, collaborating with researchers from Princeton, UPenn, and MIT on NSF Expedition projects in deep specifications. Involved in systems seminars like APLAR and SPAM.

• Embedded System Design
• Unmanned Aerial Systems
• Logic Synthesis
• +2 more

Venkatesan Muthukumar is a Professor and Graduate Coordinator in the Department of Electrical and Computer Engineering at the University of Nevada, Las Vegas. His research expertise spans embedded system design, system-on-chip architectures, logic synthesis, and high-level synthesis. He specializes in developing adaptive guidance, navigation, and control algorithms for Unmanned Aerial Vehicles (UAVs), including onboard multi-sensor fusion and hardware acceleration techniques. Professor Muthukumar's research focuses on: Embedded control design using multi-core processors Hardware-in-the-loop testing of GNC algorithms UAV flight control optimization Real-time systems for aerial navigation His recent publications demonstrate a strong focus on educational interventions in STEM, computer vision applications in environmental science, and hardware optimization for AI systems. The research spans educational technology, agricultural sensing, and aerospace control systems, showcasing interdisciplinary collaborations. Professor Muthukumar leads the UAV research group at UNLV, developing novel flight control systems and sensor integration platforms for autonomous aerial vehicles. His lab collaborates with aerospace industry partners on next-generation UAV technologies.

• Computer Architecture
• Memory Systems
• Reconfigurable Computing
• +6 more

Ronald Dreslinski is a Professor in the Department of Computer Science and Engineering at the University of Michigan, College of Engineering. His work focuses on computer architecture, memory systems, and reconfigurable computing, with applications in machine learning, wireless communication, and autonomous systems. Research spans hybrid memory systems, fault-tolerant networks, and low-power processors Key technologies include PIM (Processing-in-Memory), systolic arrays, and Galois field accelerators His publications highlight trends in adapting reconfigurable architectures for sparse data, optimizing wireless communication decoders, and integrating neural networks with traditional circuit design. He mentors PhD students through weekly 1-on-1 meetings, project-specific sessions, and group presentations. The department emphasizes broadening participation in computing and student support through community initiatives.

Christophe DubachView profile

Associate Professor

• Data-Parallel Language Design
• Hardware Design Space Exploration
• Heterogeneous Computing
• +4 more

Christophe Dubach is an Associate Professor at McGill University, jointly appointed in the Department of Electrical and Computer Engineering (ECE) and the School of Computer Science (CS). He holds a Canada CIFAR AI Chair at Mila. Previously, he was a Reader (Associate Professor) at the University of Edinburgh until 2019. His research focuses on data-parallel language design, high-level code generation for parallel hardware (GPUs/FPGAs), hardware design space exploration, and applying machine learning to these areas. He has advised numerous PhD and MSc students on topics like FPGA-based high-level synthesis, neural networks, and compiler optimization. Education: PhD in Informatics (2009, University of Edinburgh), MSc in Computer Science (2005, EPFL, Switzerland). Teaching includes courses on compiler design, computer organization, and high-level synthesis. His projects include Lift (a functional IR for GPU code generation) and High-Level Synthesis of Neural Networks on FPGAs. Collaborators include Michel Steuwer, Lu Li, and others in the fields of compilers, machine learning, and parallel computing.

Christopher BattenView profile

Professor

• Computer Architecture
• Electronic Design Automation (EDA)
• VLSI Design
• +7 more

Christopher Batten is a Professor of Electrical and Computer Engineering at Cornell University, affiliated with the Computer Science department. He leads the Batten Research Group within the Computer Systems Laboratory (CSL), focusing on computer architecture, electronic design automation, and VLSI systems. His work spans programmable accelerators, interconnection networks, and agile chip design methodologies. Batten holds a PhD from MIT, an M.Phil. from the University of Cambridge, and a B.S. from the University of Virginia. He has held visiting roles at UC Berkeley and NVIDIA. Research Interests: Computer Architecture: Accelerators, interconnection networks, and emerging technologies VLSI Design: Agile methodologies, chip prototyping, and physical design Hardware-Software Co-Design: Parallel programming frameworks and productivity tools Recent Work: Focuses on optical interconnects, 3D integration, and AI-driven hardware design. Led projects like Cornell Custom Silicon Systems (C2S2), which taped out multiple chips in SkyWater 130nm. Collaborates with industry partners like NVIDIA and Meta. Awards: Recognized with the ACM/IEEE MICRO Hall of Fame, NSF CAREER Award, and multiple teaching accolades. His group emphasizes student-led chip projects and open-source frameworks like PyMTL3. Grants & Sponsors: Supported by NSF, DARPA, AFOSR, and industrial partners including Intel, NVIDIA, and Xilinx. Current efforts include NSF CSSI projects improving gem5 and NSF Panorama for pangenomics.

Sajad Saeedi GharahbolaghView profile

Assistant Professor

• Robotics
• Simultaneous Localization and Mapping (SLAM)
• Focal-plane Sensor-processor Arrays (FPSP)
• +6 more

Sajad Saeedi Gharahbolagh is an Assistant Professor in the Department of Mechanical, Industrial, and Mechatronics Engineering at Toronto Metropolitan University and an Honorary Research Fellow at Imperial College London's Department of Computing. His research spans robotics, SLAM, focal-plane sensor-processor arrays (FPSP), and deep learning for autonomous systems. Education : PhD in Electrical and Computer Engineering (2014) from the University of New Brunswick. Prior Roles : Dyson Research Fellow (2018-2019) at Imperial College London; Postdoctoral Fellow at University of New Brunswick (2014); R&D Engineer at 2G Robotics (2015). His research focuses on Simultaneous Localization and Mapping (SLAM) for single/multi-robot systems Focal-plane Sensor-Processor Arrays (FPSP) for high-speed, low-power vision processing Autonomous aerial/underwater robotics Deep learning integration with traditional robotics algorithms Control systems for heterogeneous robotic platforms His work addresses challenges in computational efficiency, robustness in GPS-denied environments, and real-time multi-sensor data fusion. Recent publications highlight advancements in Distributed NeRF for collaborative mapping MR.CAP multi-robot control/planning BIT-VIO visual-inertial odometry WiFi-based geometric mapping FPSP-optimized CNNs PathBench benchmarking framework Scientific Awards : Dyson Research Fellowship (2018-2019) Best Robotics Paper (CRV 2021) Best Student Presentation (IROS 2023) Research Team : PhD Students: Christopher Kolios, Navid Zarrabi, Messiah Esfahani, Ishaan Mehta, Mahboubeh Asadi, Jack Saunders MASc Students: Georgia Jovanovic, Hussein Ali Jaafar, Austin Vuong, Roni Sherman, Matthew Lisondra, Glenn Shimoda, Ali Babaei, Robel Efrem, Messiah Ataey, Christopher Kolios, Nikolas Kourtzanidis Laboratory Facilities : Robotics and Computer Vision Lab (RCVL) with Vicon motion capture system 14 TurtleBot 3 platforms (Waffle Pi/Burger variants) Germicidal UVC-equipped G-Robots Jetbots with onboard GPU processing OpenMANIPULATOR robotic arms

• Security Systems and Networking
• Mobile Systems
• Robotics and Automation
• +3 more

Shih-Chun You is a Researcher at the Department of Computer Science and Technology, University of Cambridge. Their research focuses on cryptographic hardware security, particularly side-channel attacks and vulnerability analysis in cryptographic implementations. They specialize in template attacks targeting permutation-based ciphers like ASCON, Keccak, and SHA-3, with a focus on embedded systems and 32/8-bit devices. Research interests include hardware security, cryptographic algorithm implementations, and improving resistance against physical attacks. Their work bridges theoretical cryptography and practical embedded device security. Publications emphasize novel attack methodologies and their implications on cryptographic system design. No awards are explicitly mentioned in the provided texts.

Dolly SapraView profile

Researcher

• Machine Learning
• Edge Computing
• Embedded Systems
• +8 more

Dolly Sapra is a researcher at the University of Amsterdam , affiliated with the Department of Computer Science under the Faculty of Science . Her work focuses on adaptive deep learning, secure neural inference, energy-efficient computing, and fault-aware systems. She received the IEEE/ACM CASES '24 Outstanding Reviewer Award . Her research spans Machine Learning , Edge Computing , and Embedded Systems , with a focus on Model elasticity for CNNs Privacy-preserving edge intelligence Power-efficient inference Transformer optimization Climate-aware computing . The 15 most recent articles highlight her leadership in adaptive neural architectures , secure multi-party inference , and sustainable computing , with applications in embedded systems and real-time environments .

• Internet of Things (IoT)
• Quantum Computing
• Wireless Sensor Networks (WSN)
• +4 more

Filippo Gandino is an Associate Professor at Politecnico di Torino, affiliated with the Department of Automatic Control and Computer Science (DAUIN) and the Interdepartmental Centre PIC4SeR for Service Robotics. He is a member of the College of Biomedical Engineering and the College of Computer, Cinema and Mechatronics Engineering. His research focuses on Internet of Things (IoT), Quantum Computing, and Wireless Sensor Networks (WSN), with a particular emphasis on pervasive computing and healthcare applications. He has been recognized with the Best Paper Award from the 6th International Conference of Broadband and Wireless Computing, Communication and Applications (BWCCA-2011). Professor Gandino teaches courses in Algorithms and Object-Oriented Programming, Computer Science, and Electronic Calculators across multiple engineering programs, including Aerospace, Cinema, and Computer Engineering. His academic contributions include supervising PhD student Pietro Chiavassa, whose thesis addresses security and reliability in pervasive computing. His work spans interdisciplinary projects, such as IoT-based environmental monitoring systems and quantum computing optimization algorithms. His research explores cutting-edge areas like secure intermittent computing, particulate matter measurement techniques, and quantum PCI planning. He actively contributes to IEEE conferences and journals, focusing on sensor networks, cybersecurity, and distributed systems. His lab, CAD - Electronic CAD & Reliability Group (DAUIN), drives innovations in electronic design and reliability engineering.

Gerd von CöllnView profile

Professor

• Energy-Efficient Systems
• Embedded Systems
• Cyber-Physical Systems
• +5 more

Prof. Gerd von Cölln is a Professor of Computer Architecture at the School of Technology, Electrical Engineering and Computer Science, University of Applied Sciences Emden/Leer. His primary focus is on energy-efficient and autonomous systems, particularly in embedded and cyber-physical systems. He leads research in energy-autonomous sensors, low-power electronics, and IoT applications. Education details are not explicitly provided in the text, but his academic role suggests advanced qualifications in electrical engineering or computer science. His research projects include Sichere Offshore-Operationen (SOOP), Energieautarke Sensorsysteme (ID3AS), and LoRaWAN-based sensors (ELVIS), demonstrating expertise in industrial automation, sensor networks, and energy harvesting. Publications emphasize sensor design, wireless communication protocols, and optimization algorithms for complex environments. While no awards are listed, his extensive project portfolio highlights contributions to applied engineering research. Advising details are unavailable, but his role implies supervision of technical projects involving students and industry partners. Prof. von Cölln collaborates on labs and teams focused on maritime automation, industrial IoT, and embedded systems, with projects addressing challenges like harsh environmental conditions and energy sustainability.

Jonathan CookView profile

Professor

• Software Process
• Reliable Component-Based Systems
• Software Engineering Environments
• +5 more

Jonathan Cook is a Professor in the Department of Computer Science at New Mexico State University (NMSU). He specializes in software process improvement, reliable component-based systems, and high-performance computing (HPC) performance analysis. His work includes developing tools and methodologies for monitoring HPC applications, optimizing memory systems, and validating proxy applications against real-world software. Education: Cook holds a Ph.D. in Computer Science from the University of Colorado at Boulder (1996), an M.S. in Computer Engineering from Case Western Reserve University (1991), and a B.S. in Computer Engineering from the same institution (1988). Research Interests: His expertise spans HPC performance analysis, runtime monitoring systems, compiler optimization, and software engineering environments. He focuses on improving the reliability and efficiency of large software systems through empirical methods and tool development. Recent Work Trends: His articles emphasize HPC monitoring (e.g., heartbeat data analysis, proxy application validation), novel memory architectures (scratchpad memory, 3D PIM NoCs), and compiler-driven performance optimization. His work bridges theoretical models with practical deployment in production environments. Grants and Advising: While specific grants or student advisees are not listed, his research actively involves experimental validation through collaborative projects with institutions like the Exascale Computing Project (ECP). Labs/Teams: His contributions align with NMSU’s broader efforts in HPC and software engineering, though no specific lab names are mentioned in the provided text.

Sourav DuttaView profile

Associate Professor

• High-performance Computing
• Hardware/Software Co-design
• Machine Learning
• +3 more

Sourav Dutta is an Associate Professor in the Department of Computer Science at Ramapo College of New Jersey, part of the School of Theoretical and Applied Science (TAS). He joined the institution in 2017. His research focuses on high-performance computing, hardware/software co-design, and machine learning. Dutta holds a PhD in Computer Engineering from Southern Illinois University Carbondale. His educational background includes advanced studies in computer engineering, with a specialization in parallel computing systems. His work bridges theoretical computer science with practical applications in distributed systems, resource optimization, and intelligent algorithms. Key research areas include multithreaded systems performance analysis, collaborative recommendation systems, and bioinformatics applications using big data techniques. His publications span venues like IEEE Transactions on Parallel and Distributed Systems, IEEE Big Data, and ACM Transactions on Parallel Computing. Dutta teaches courses such as Computer Science I and II, Data Structures and Algorithms, Operating Systems, and Big Data Computing. His contributions to the field include innovations in concurrent system design, IoT resource allocation, and energy-efficient computing architectures.