جستجوی استاد راهنمای دانشگاهی

Marco Donatoمشاهده پروفایل

استادیار

• Hardware for Machine Learning
• Non-Volatile Memories
• SoC Design
• +۵ مورد دیگر

Marco Donato is an Assistant Professor in both the Department of Electrical and Computer Engineering and the Department of Computer Science at Tufts University. He leads the TECS Lab (Testchip, Embedded Computing Systems) focused on hardware design for emerging applications. Prior to joining Tufts, he was a postdoctoral fellow at Harvard University's John A. Paulson School of Engineering and Applied Sciences. Dr. Donato received his academic training from prestigious institutions: Ph.D. in Electrical Sciences and Computer Engineering from Brown University (2016) M.Sc. in Electrical Engineering from Università di Roma La Sapienza, Rome, Italy (2010) B.Sc. in Electrical Engineering from Università di Roma La Sapienza, Rome, Italy (2008) Dr. Donato's research primarily focuses on designing reliable and energy-efficient hardware systems leveraging emerging technologies. His work centers on co-design methodologies for building specialized architectures for machine learning applications that utilize dense, fault-prone embedded non-volatile memories. He investigates noise modeling and reliability aspects of next-generation memory technologies, with particular emphasis on how these can be effectively integrated into system-on-chip (SoC) designs for edge computing and IoT applications. His research bridges the gap between circuit-level design and system-level architecture to create holistic solutions for hardware acceleration of machine learning workloads. Analysis of Dr. Donato's publication record reveals a strong focus on hardware acceleration for machine learning, particularly through innovative memory system designs. His work spans multiple domains including non-volatile memory technologies, energy-efficient circuit design, and flexible SoC architectures. A notable trend is his exploration of how emerging memory technologies can be leveraged to create more efficient implementations of deep neural networks, with particular attention to the trade-offs between reliability, density, and energy consumption. His research often involves full-stack approaches that consider everything from device physics to system architecture. Dr. Donato is actively involved in mentoring and has indicated he is "looking for Ph.D. students." His work has been supported by significant research grants that have enabled the fabrication of multiple test chips, as evidenced by his extensive publication record in top-tier venues including IEEE Journal of Solid-State Circuits, ISSCC, and MICRO. He leads the TECS Lab at Tufts University, which focuses on testchip development, embedded computing systems, and hardware acceleration. The lab appears to maintain connections with researchers at Harvard University and other institutions, reflecting Dr. Donato's collaborative approach to research. The lab's work emphasizes practical, real-world implementations of novel hardware concepts through actual silicon fabrication, which is relatively rare in academic settings.

Sanchuan Chenمشاهده پروفایل

استادیار

• Machine Learning Security
• Trusted Execution Environment
• Software Security
• +۱ مورد دیگر

Sanchuan Chen is an Assistant Professor in the Department of Computer Science and Software Engineering at Auburn University's College of Engineering. His research focuses on Machine Learning Security, Trusted Execution Environments, Software Security, and Programming Languages. He holds a Ph.D. from The Ohio State University, an M.E. from the Chinese Academy of Sciences, and a B.E. from the University of Science and Technology of China. His work addresses critical challenges in secure computing, including enclave vulnerabilities, speculative execution attacks, and data privacy preservation. Key research contributions include defenses against SGX enclave leaks (e.g., SGXpectre attacks), data flow tracking techniques, and hardening strategies for binary code. His publications span topics like side-channel mitigation, speculative execution exploits, and privacy-preserving data publishing. Chen collaborates on hardware-software co-design solutions to enhance system security without compromising performance.

• Nanoelectronic Circuit Design
• Neuromorphic Computing
• Hardware Security
• +۴ مورد دیگر

Garrett Rose is a Professor and Department Head in the Min H. Kao Department of Electrical Engineering and Computer Science at the University of Tennessee, Knoxville (UTK). He holds a B.S. in Computer Engineering from Virginia Tech (2001), and M.S. and Ph.D. in Electrical Engineering from the University of Virginia (2003/2006). Prior to UTK, he served as Assistant Professor at NYU Polytechnic (2006–2011) and Senior Electronics Engineer at the Air Force Research Lab (2011–2014). His research focuses on nanoelectronic circuit design, neuromorphic computing, hardware security, and memristor-based systems. He leads the SENECA Research Group and the TENNLab initiative, exploring applications in neuromorphic architectures, hardware security primitives (e.g., PUF devices), and device modeling. Recent work emphasizes memristor-driven neuromorphic systems, secure FPGA designs, and in-memory computing. Grants include projects on neuromorphic target detection and nanotechnology-based security solutions. Rose actively mentors students and collaborates on co-design methodologies for real-world neuromorphic applications. Education: Ph.D. Electrical Engineering, University of Virginia, 2006 M.S. Electrical Engineering, University of Virginia, 2003 B.S. Computer Engineering, Virginia Tech, 2001 Research Interests: Dr. Rose’s work spans neuromorphic hardware design, including memristor-based neural networks and spiking systems. He investigates hardware security through nanoscale devices like memristors for PUFs and side-channel resistant circuits. His team develops novel memristor models and explores applications in reconfigurable computing and energy-efficient architectures. Recent efforts focus on neuromorphic vision systems, robotic navigation, and neuromorphic processors with co-design frameworks. Grants & Projects: "Ground-roaming autonomous neuromorphic targeter" (2020) "Secure Backup and Restore for IoT using Nanotechnology" (2020) "Physically Unclonable Reconfigurable Computing System (PURCS)" (2020)

• Cyber Defence
• Reverse Engineering
• Malware Analysis
• +۵ مورد دیگر

Geir Olav Dyrkolbotn is an Associate Professor at NTNU's Center for Cyber and Information Security (CCIS) and a Major in the Norwegian Armed Forces, serving at the Norwegian Defence Cyber Academy (NDCA). He leads the NTNU Malware Lab and the cyber defence research group at CCIS. He holds a PhD in Information Security from Gjøvik University College and a MSc in Computer Science from NTNU. With over 25 years in the military, his work focuses on tactical communication systems, defensive cyber operations, and operational security. His research emphasizes cyber defence, reverse engineering, malware analysis, side-channel attacks, and machine learning applications. Education: PhD in Information Security, Gjøvik University College (HiG) MSc in Computer Science, NTNU Research Interests: Geir Olav's work bridges theoretical cybersecurity research and practical military applications. He explores innovative methods for hardware reverse engineering, malware detection/classification using low-level features, and forensic acquisition techniques. His contributions include analyzing USB power delivery vulnerabilities, NTFS cluster allocation behavior, and secure chip exploitation for digital forensics. Teaching: Courses include IIKG6500/IMT4213 Cyber-taktikk, IMT4214 Cyber-etterretning, IIKG6501 Cyber Intelligence, and IMT4116 Malware Analysis & Reversing. Labs & Teams: Heads the NTNU Malware Lab and leads the cyber defence research group at CCIS, collaborating on projects like the Digital Forensic Acquisition Kill Chain and hardware security vulnerability assessments.

Tønnes Nygaardمشاهده پروفایل

دانشیار

• Robotics
• Evolutionary Algorithms
• Morphological Adaptation
• +۳ مورد دیگر

Tønnes Nygaard is an Associate Professor at the Department of Technology Systems, University of Oslo, affiliated with the Faculty of Mathematics and Natural Sciences. His research focuses on evolutionary robotics, morphological adaptation, and embodied artificial intelligence. He leads projects like COCOMO (Co-evolution of Control and Morphologies) and works extensively with the DyRET (Dynamic Robot for Embodied Testing) platform. Key research interests include robot control systems, adaptive morphology design, and real-world implementation of evolutionary algorithms. His work bridges theoretical computer science with practical robotics applications, emphasizing hardware-software co-evolution and embodied cognition principles. Publications span topics like morphological adaptation in quadruped robots, semi-supervised learning for terrain classification, and overcoming convergence issues in multi-objective evolutionary algorithms. Nygaard collaborates internationally and contributes to both academic journals and conferences in robotics and AI. No scientific awards are explicitly listed, though his impactful contributions to real-world evolutionary robotics suggest potential recognition pending explicit mentions. Advising and grant activities are central to his role, though specific student names or grant amounts are not detailed in the provided texts. Labs/Teams: Core contributor to the DyRET project and affiliated with the Section for Autonomous Systems and Sensor Technologies at UiO.

Amanda Prorokمشاهده پروفایل

استاد

• Multi-Agent Systems
• Robotics
• Collective Intelligence
• +۶ مورد دیگر

Professor Amanda Prorok leads the Prorok Lab at the University of Cambridge's Department of Computer Science and Technology, focusing on multi-agent and multi-robot systems. Her work integrates machine learning, planning, and control to coordinate intelligent agents in shared environments, with applications in transport, environmental monitoring, and search-and-rescue. She is a Fellow of Pembroke College and holds editorial roles at IEEE Robotics and Automation Letters and Autonomous Robots. Education: Ph.D., EPFL (Switzerland); Postdoctoral Research, University of Pennsylvania (USA). Research Interests: The lab pioneers methods like differentiable communication between learning agents and develops decentralized algorithms for navigation, coverage, and coordination. Key themes include neural diversity in collective learning, resilient swarm systems, and environment-aware control. Notable Achievements: ERC Starting Grant, Amazon Research Award, EPSRC New Investigator Award ABB Prize for Best Thesis in Computer Science (EPFL) Teaching: Leads Computing for Collective Intelligence (MPhil/Part III) modules. Lab & Infrastructure: The Prorok Lab operates the Cambridge RoboMaster platform and develops testbeds for connected vehicles and robot swarms. Recent work emphasizes scalable reinforcement learning and graph neural networks for decentralized decision-making.

• Biomedical Engineering
• Tissue Engineering
• 3D Printing
• +۵ مورد دیگر

Neil Lin is an Assistant Professor in the Department of Mechanical and Aerospace Engineering at the University of California, Los Angeles (UCLA), with a joint appointment in Bioengineering. His research focuses on developing 3D-printed tissues that replicate the structure, mechanics, and functionality of human organs, with applications in drug screening and regenerative medicine. Lin leads the Lin Lab - Living Soft Material Engineering , advancing soft and living material engineering through interdisciplinary approaches. Lin holds a Ph.D. and M.S. in Engineering from Cornell University (2016 and 2013) and a B.S. in Engineering from National Tsing Hua University, Taiwan (2008). His work spans biomaterials mechanics, quantitative imaging, and AI-driven biological analysis. Research interests include the structure and dynamics of soft biomaterials, image-based force measurements, and quantitative imaging techniques for material characterization. Recent work explores cell morphology regulation, AI applications in microscopy, and mechanical heterogeneity in live tissues. Lin’s publications highlight advancements in cell trapping, AI-based image translation, and prostate cancer metabolism modeling. He has received prestigious awards, including the Young Investigator Award (2022), Hellman Fellowship, and NIH Maximizing Investigators' Research Award (2022). His lab integrates engineering and biology to address challenges in tissue engineering, with ongoing projects on 3D kidney models, drug response assays, and AI-driven phenotyping of senescent cells.

• Scientific Computing
• GPU Computing
• Signal Processing
• +۵ مورد دیگر

Professor Wes Armour is a Professor of Scientific Computing at the University of Oxford and serves as the Associate Head of Department for Research in the Department of Engineering Science. He previously directed the Oxford e-Research Centre, an interdisciplinary research center within the Engineering Science Department. With over £31 million secured as PI or Co-I, his work spans supercomputing, signal processing, machine learning, computational fluid dynamics, and protein crystallography. Professor Armour's research focuses on extracting science from data through fundamental challenges in modeling, simulation, and data processing. His work draws from numerical analysis, signal processing, and machine learning to develop technologies enabling future scientific discoveries, particularly for the Square Kilometre Array (SKA) telescope. Key interests include GPU computing, high performance computing, and machine learning applications across diverse domains from radio astronomy to finance. As Director and Principal Investigator of JADE and JADE2, a 700-GPU machine, he established the UK's first national High Performance Computer facility dedicated to advancing Artificial Intelligence and Machine Learning. His research group has pioneered GPU applications across multiple fields, including Square Kilometre Array data processing, protein crystallography, and graphene simulations. Current projects span energy-efficient machine learning, stock price prediction in finance, prime number prediction in cryptography, and multi-modal CCTV data analysis. Professor Armour has been instrumental in developing real-time signal processing techniques for astronomical observations, including the ARTEMIS system for millisecond radio transient detection. His publications demonstrate consistent innovation in GPU-accelerated computing dating back to early work in 2008 on accelerating conjugate gradient routines for electron transport in graphene.

• Biomedical Instrumentation
• Medical Technology
• Clinical Engineering
• +۳ مورد دیگر

Jan Olav Høgetveit is an Associate Professor in the Department of Physics at the University of Oslo (UiO), within the Faculty of Mathematics and Natural Sciences. He also serves as Head of Research & Development in the Department of Biomedical and Clinical Engineering at Rikshospitalet, Norway’s national hospital. His work bridges physics and clinical practice, focusing on medical instrumentation. Education: Bachelor of Electronic Engineering (Technical Cybernetics), Oslo University College, 1993 Master of Electronics, University of Oslo (Department of Physics), 1997 Ph.D. in Physics (Technology Applications for Medical Devices), University of Oslo, 2008 Research Interests: Høgetveit specializes in biomedical instrumentation and clinical engineering, particularly in surgical technology and wireless communication impacts on medical devices. His work addresses challenges like real-time physiological monitoring during heart-lung machine use, non-invasive blood glucose detection, and bioimpedance-based viability assessment of organs. He emphasizes interdisciplinary collaboration between engineering and medicine to enhance patient safety and surgical outcomes. Scientific Contributions: His research trends span bioimpedance applications in ischemia/reperfusion injury, machine learning for surgical decision support, and electrosurgery safety. He has explored ventilator optimization during pandemics and implant-related thermal risks. Contributions highlight both hardware development (e.g., optically isolated current sources) and software innovations (e.g., neural networks for viability prediction). Awards: No scientific awards explicitly mentioned in the text. Advising & Grants: Høgetveit received a 1998–2001 research council scholarship. As Head of R&D since 2001, he oversees translational projects. No formal advisees/students listed, though he collaborates extensively with teams on device development and clinical trials. Labs & Teams: Affiliated with UiO’s Department of Physics and Rikshospitalet’s Biomedical and Clinical Engineering department. Active in the Electronics research group at UiO. Engages with multidisciplinary teams addressing surgical instrumentation and physiological monitoring challenges.

Nicola Calabrettaمشاهده پروفایل

استاد

• Optical Communication Systems
• Photonics
• Telecommunications Engineering
• +۶ مورد دیگر

Nicola Calabretta is a Full Professor in Electro-Optical Communication Systems and Senior Research Fellow at Eindhoven University of Technology (TU/e). His work focuses on smart optical networks, high-speed electronics, FPGA implementations for scheduling algorithms, and photonic integrated circuits. He holds a PhD from TU/e (2004) and previously conducted research at DTU Fotonik and the Sant'Anna School of Advanced Studies. His expertise spans optical signal processing, multi-level modulation formats, and applications in data center and metro networks. Key research areas include optical switching architectures (e.g., SOA-based switches), WDM systems, and low-latency interconnect networks. He has led projects like ADAPTOR (resource optimization), SmartTWO (future telecom technologies), and 5G-MOBIX (cross-border mobility). His courses include 'Optical Fibre Communication Technology' and 'Optical Interconnection Networks.' Collaborations involve institutions globally, with recent work emphasizing photonic integration for neural networks, ultra-fast switching, and edge computing. His contributions align with UN SDGs through sustainable telecom infrastructure advancements.

Chao Tianمشاهده پروفایل

دانشیار

• Information Theory
• Distributed Data Storage
• Coding Theory
• +۶ مورد دیگر

Dr. Chao Tian is an Associate Professor in the Department of Electrical and Computer Engineering at Texas A&M University, part of the College of Engineering. He holds a Ph.D. from Cornell University (2005) and a B.E. from Tsinghua University (2000). His research focuses on information theory, distributed storage systems, coding theory, and machine learning applications, including large language models (LLMs) and privacy-preserving techniques like steganography and private information retrieval (PIR). He has received notable awards, including the 2017 IEEE Jack Wolf ISIT Best Student Paper Award and the 2014 IEEE ComSoc DSTC Best Paper Award. Dr. Tian’s work bridges theoretical foundations and practical applications, such as optimizing storage codes for distributed systems, developing secure PIR protocols, and exploring LLMs in energy and steganographic contexts. His group has pioneered methods for variable-order Markov chain modeling with transformers and designed quantization techniques for perceptual quality. Recent activities include a faculty development leave at MIT/Harvard and delivering distinguished lectures globally on AI and information theory. He advises students on cutting-edge topics like LLM-based steganography and diffusion models. His publications span journals like IEEE Transactions on Information Theory and conferences such as NeurIPS and ISIT, with a focus on coding theory, privacy, and machine learning. He has also contributed to open-source tools like the CAI toolbox for investigating information-theoretic limits. Current projects include optimizing cryptocurrency mining in energy markets and advancing federated learning with adversarial robustness.

Marcus Botacinمشاهده پروفایل

استادیار

• Malware Analysis
• Hardware-Assisted Security
• Antivirus Technology
• +۴ مورد دیگر

Marcus Botacin is an Assistant Professor in the Department of Computer Science & Engineering at Texas A&M University (TAMU), USA. Previously, he held positions as Visiting Assistant Professor (2022-2024) and Lecturer at the Federal University of Paraná (Brazil). His research focuses on malware analysis, hardware-assisted security, and antivirus technology. He earned his Ph.D. in Computer Science from UFPR (2021), M.Sc. from UNICAMP (2017), and B.Sc. in Computer Engineering from UNICAMP (2015). Education: Ph.D., Computer Science, Federal University of Paraná, Brazil (2021) M.Sc., Computer Science, University of Campinas, Brazil (2017) B.Sc., Computer Engineering, University of Campinas, Brazil (2015) Research Interests: Botacin's work addresses challenges in malware detection (static/dynamic methods, sandboxing), hardware-enhanced security mechanisms (e.g., branch monitoring), and antivirus internals. He emphasizes practical solutions and ethical considerations in cybersecurity, advocating for culturally aware threat models (e.g., Brazilian financial malware studies). Publications & Trends: His recent work explores adversarial ML attacks on malware detectors, HPC-based detection frameworks, and automated malware generation risks. He frequently publishes in top venues like ACM CCS, USENIX Security, and IEEE TDSC. Awards: Top-3 Best Ph.D. Thesis in Computer Security (Brazilian Computer Society, 2022) Best PhD Thesis Award from UFPR (2022) MLSec Evasion Challenge 1st place (2021/2020) Advising & Grants: Supervises 15+ students at TAMU and leads NSF-funded projects (e.g., $523K grant for HPC malware detection frameworks). Serves on 21+ conference program committees and reviews for over 50 journals. Lab & Projects: Develops Corvus (public malware analysis sandbox) and explores hardware-accelerated AV solutions like Terminator and HEAVEN.

Vivek Sarkarمشاهده پروفایل

استاد

• Parallel Computing
• Programming Languages
• Compilers
• +۵ مورد دیگر

Vivek Sarkar is the John P. Imlay, Jr. Dean of the College of Computing at Georgia Institute of Technology and a professor in the School of Computer Science. He leads the Habanero Extreme Scale Software Research Laboratory, focusing on parallel computing, programming languages, compilers, and runtime systems. Previously, he was a Professor and Chair of Computer Science at Rice University and held senior roles at IBM Research, where he contributed to projects like the X10 programming language and the Jikes Research Virtual Machine. Research Interests: His work spans parallel computing software, including programming languages (e.g., X10, Habanero-Java), compiler optimizations, runtime systems, and debugging tools for high-performance systems. He emphasizes scalability and correctness in distributed and heterogeneous environments. Awards & Affiliations: ACM Fellow (2008), IEEE Fellow, Ken Kennedy Award (2011), member of the US Department of Energy’s ASCAC, and former IBM Academy of Technology member. He chairs the Center for Research into Novel Computing Hierarchies (CRNCH) at Georgia Tech. Grants & Students: His research is supported by NSF grants. He advises students in parallel computing, with openings for researchers interested in his lab’s work on asynchronous systems, graph processing, and quantum-classical programming. Labs & Projects: Habanero Lab, CRNCH, and collaborations on Chapel runtime systems, actor-based programming models, and exascale computing challenges.

Berk Sunarمشاهده پروفایل

استاد

• Applied Cryptography
• Cybersecurity
• Microarchitectural Security
• +۵ مورد دیگر

Berk Sunar is a Professor of Electrical & Computer Engineering and the founder of the Vernam Applied Cryptography and Cybersecurity Laboratory at Worcester Polytechnic Institute (WPI). He joined WPI in 2000 after holding postdoctoral and research roles at Oregon State University (OSU) and Trust Inc. His work focuses on applied cryptography, microarchitectural security, AI security, post-quantum cryptography, and homomorphic encryption. Sunar received his BSc from Middle East Technical University (1995) and PhD from Oregon State University (1998). Research interests include vulnerabilities in hardware (e.g., Rowhammer, TPM-FAIL), side-channel attacks, and cryptographic implementations. Notable contributions include discovering flaws in Intel CPUs and TPM chips affecting billions of devices, as well as developing defenses like cuHE (GPU-accelerated homomorphic encryption). Publications highlight breakthroughs in transient execution attacks (e.g., LVI, RIDL), post-quantum signature schemes (Dilithium), and cloud security (Firecracker VMM vulnerabilities). Awards include NSF CAREER (2002) and IBM Pat Goldberg Best Paper (2007). Advised over 30 graduate students, many of whom hold senior roles in academia and industry. Current research addresses AI security, quantum-resistant algorithms, and automated attack detection via machine learning. The Vernam Lab remains a hub for cybersecurity innovation.

• Security
• Intrusion Detection
• Digital Provenance
• +۷ مورد دیگر

Thomas Pasquier is an Assistant Professor in the Department of Computer Science at the University of British Columbia, where he is affiliated with both the UBC Security & Privacy Group and the Systopia Lab. His research focuses on designing and implementing computer systems that are inherently observable and transparent, with particular emphasis on security, privacy, and system accountability. Dr. Pasquier earned his PhD in Computer Science from the University of Cambridge in 2016, following an MPhil in Advanced Computer Science from the same institution in 2012. His educational background also includes a Diplôme d'Ingénieur from Institut Supérieur d'Electronique de Paris and a Diplôme Universitaire de Technologie from Conservatoire National des Arts et Métiers. His primary research interests span Security, Intrusion Detection, Digital Provenance, Operating Systems, Distributed Systems, Data Protection, and Privacy. His work specifically addresses the design of systems with built-in observability and transparency mechanisms, focusing on provenance-based security solutions. Analysis of his recent publications reveals a consistent focus on provenance-based intrusion detection systems, with significant contributions in making these systems more practical, usable, and robust. His research also extends to eBPF technology in the Linux kernel, exploring security applications and performance optimizations. Amazon Research Award 2023 for Building Robust Provenance-based Intrusion Detection Incredible Instructor Award Dr. Pasquier has advised numerous graduate students at both UBC and the University of Bristol, where he previously held an Assistant Professor position. His students have gone on to careers at major technology companies including Amazon, Salesforce, Huawei, and Oracle Labs. He has served on program committees for prestigious conferences including ACM ASPLOS, EuroSys, USENIX Security, and ACM CCS. His research is conducted within the Systopia Lab at UBC, which focuses on systems research broadly construed, with particular emphasis on security, privacy, and performance optimization. The lab collaborates with industry partners including Amazon through the Amazon Research Awards program.