جست‌وجوی استاد و پژوهشگر

Shawki M. Areibiمشاهده پروفایل

استاد

• VLSI Physical Design Automation
• Reconfigurable Computing Systems
• Hardware/Software Co-Design for Embedded Systems
• +۷ مورد دیگر

Shawki M. Areibi is a Professor and Area Head of Engineering Systems and Computing in the School of Engineering at the University of Guelph. His research focuses on VLSI Physical Design Automation, Reconfigurable Computing Systems, and Hardware/Software Co-design for Embedded Systems. He leads efforts in developing advanced algorithms for CAD tools, FPGA design, and machine learning applications. His work addresses challenges in VLSI layout optimization, parallel processing, and embedded systems design. Affiliations: AI Affiliated Faculty, Area Heads, Computer Engineering, Engineering Systems and Computing Research. Research Interests: VLSI Circuit Layout, Reconfigurable Computing, Machine Learning, and FPGA-based Accelerators. His research integrates meta-heuristics like Genetic Algorithms and Tabu Search to solve complex optimization problems. He has contributed to hardware acceleration frameworks for machine learning algorithms and embedded systems, with applications in domains like signal processing and data mining. His recent work includes congestion-estimation models for modern FPGAs and analytic placement tools for ultra-scale architectures. Publications span VLSI design, reconfigurable computing, and machine learning, emphasizing algorithmic innovation and hardware-software co-design. His students have explored topics ranging from FPGA placement to domain adaptation in remote sensing. Grants and Advising: Advises graduate and undergraduate students on projects involving FPGA acceleration, machine learning, and embedded systems. His labs focus on developing next-generation CAD tools and hardware accelerators.

• Testability
• Built-in Self Test (BIST)
• Digital Circuit Testing
• +۳ مورد دیگر

Professor Jacob Savir is a distinguished academic in the Department of Electrical and Computer Engineering at the New Jersey Institute of Technology . He specializes in testability, built-in self-test (BIST), and fault detection for digital and analog circuits. His work focuses on improving integrated circuit testing methodologies, including BIST pretesting, defect level analysis, and reliability optimization in safety-critical systems. His research interests span BIST architectures, delay fault analysis, analog circuit testing, and power-constrained test synthesis . He has contributed extensively to the development of testing algorithms for embedded memories, scan designs, and fault diagnosis in complex systems. Notable contributions include studies on the impact of BIST pretesting on IC defect levels and yield optimization. His work bridges theoretical advancements with practical applications in aerospace and industrial electronics. Despite no awards listed, his substantial citation count (1,897) and h-index (22) reflect his influential contributions to the field. Prof. Savir has collaborated on 114 research outputs since 1977, with a focus on enhancing circuit testability and reliability across domains like FPGA-based systems and embedded memory diagnostics. His research emphasizes both academic rigor and industrial relevance, addressing challenges in modern VLSI design and testing.

Zebo Pengمشاهده پروفایل

استاد

• Embedded Systems
• Electronic Design Automation
• SoC Testing
• +۶ مورد دیگر

Zebo Peng is a Professor and Deputy Head of Department at Linköping University's Department of Computer and Information Science (IDA), leading the Software and Systems (SAS) division. His research focuses on embedded systems design, electronic design automation, SoC testing, and real-time systems with emphasis on fault tolerance and hardware/software co-design. He has contributed to projects like the ASTECC initiative, funded by the Swedish Foundation for Strategic Research, addressing adaptive software in edge-cloud continuum systems. Key research interests include cyber-physical systems security, time-sensitive networking (TSN), and optimization techniques using genetic algorithms. Recent work explores thermal-aware design for reliability, security-aware scheduling, and stability guarantees in control systems. His publications span journals like IEEE TPDS and ACM TECS, alongside conference contributions on topics like resource management and fault detection in distributed systems. Prof. Peng collaborates extensively within the SAS division, which bridges academic and industrial research in software engineering and computer systems. His team's projects address challenges in real-time systems, embedded security, and parallel computing architectures.

Brandon A. Jonesمشاهده پروفایل

دانشیار

• Space Situational Awareness
• Satellite Navigation
• Orbit Uncertainty Propagation
• +۵ مورد دیگر

Brandon A. Jones is an Associate Professor in the Department of Aerospace Engineering and Engineering Mechanics at the University of Texas at Austin. He holds the Charles Elmer Rowe Fellowship in Engineering and leads the Texas Spacecraft Laboratory (TSL) and the Controls, Autonomy, Estimation, and Learning for Uncertain Systems (CAELUS) Laboratory. His research focuses on space situational awareness, spacecraft navigation, and uncertainty quantification, with applications to orbital mechanics, multi-target tracking, and autonomous systems. Dr. Jones received his Ph.D. in Aerospace Engineering from the University of Colorado Boulder and has held roles at NASA Johnson Space Center and as a Research Assistant Professor. He is an Associate Fellow of the AIAA and former chair of the American Astronautical Society's Space Surveillance Technical Committee. His work includes NASA-funded projects like the SCOPE-1 CubeSat mission for terrain-relative navigation and the Crater-based Navigation and Timing (CNT) system for lunar missions. Key research areas include: Multi-source information fusion for space object tracking Machine learning for crater detection and autonomous navigation Uncertainty propagation in cislunar and highly perturbed orbits Event-based sensor systems for harsh-lighting environments Recent achievements include the 2023 W. A. 'Tex' Moncrief Grand Challenge Award and leadership in collaborative projects with NASA, JPL, and academia. His labs emphasize student-driven CubeSat missions and cutting-edge algorithms for space domain awareness.

• Embedded Systems
• Computer Engineering
• Electronics
• +۶ مورد دیگر

Prof. Akash Kumar is a Professor at the Chair of Embedded Systems at Ruhr University Bochum, Germany. He previously held professorships at TU Dresden (2015–2024) and the National University of Singapore (NUS; 2011–2015). His research focuses on design automation of embedded systems, reliability optimization, and approximate computing, with a strong emphasis on FPGA and emerging technologies. He leads projects such as Lean-MICS (DFG-funded) and SecuREFET-II, addressing cross-layer reliability and secure circuits. Education: PhD in Multimedia Multiprocessor Systems from Eindhoven University of Technology (TUe) and NUS (2005–2009), Master of Technological Design (Embedded Systems) from NUS (2003–2004), and B.Eng (Computer Engineering) from NUS (1999–2002, First Class Honours). Research interests span embedded systems, reconfigurable architectures, and hardware-software co-design. His work includes optimizing energy efficiency, fault tolerance, and cross-layer approximation techniques. Recent publications highlight advancements in FPGA-based accelerators, machine learning optimizations, and mixed-criticality systems. Active in grants and leadership, Kumar is Principal Investigator on multiple DFG and industry-funded projects, emphasizing collaborative research in distributed computing and approximate architectures. His contributions bridge theory and practice, with applications in edge AI, IoT, and cybersecurity.

• Machine Learning
• Bayesian Optimization
• Gaussian Processes
• +۴ مورد دیگر

Ti John is a Research Fellow at Aalto University's Department of Computer Science within the School of Science. He is affiliated with Professor Marttinen's research group and the Probabilistic Machine Learning group led by Professor Samuel Kaski. His work connects with the Finnish Center for Artificial Intelligence (FCAI) and the Helsinki Institute for Information Technology (HIIT). Dr. John's research focuses on machine learning, particularly Bayesian optimization, Gaussian processes, and point process models. His work spans theoretical developments in neural processes and practical applications in healthcare analytics and large language models. He has made significant contributions to equivariant neural processes, causal mediation analysis in healthcare, and interpretability of additive models. His publication record shows consistent output with 17 publications between 2021-2024, including multiple papers at top AI conferences like NeurIPS, ICML, and ICLR. His research demonstrates strong interdisciplinary connections between statistical modeling, artificial intelligence, and healthcare applications. Active reviewer for NeurIPS, ICLR, AISTATS Reviewer for Journal of Machine Learning Research Member of Finnish Center for Artificial Intelligence project Dr. John has been actively contributing to the machine learning community through peer review and conference participation, demonstrating expertise across multiple subfields of artificial intelligence and statistical modeling.

Adrian Perrigمشاهده پروفایل

استاد

• Network Security
• Cybersecurity
• Distributed Systems
• +۳ مورد دیگر

Adrian Perrig is a Full Professor at the Department of Computer Science at ETH Zürich. He leads research in network security, distributed systems, and internet architecture, focusing on projects like the SCION secure internet architecture and its commercialization through Anapaya Systems. His work emphasizes secure communication, denial-of-service defense, and public key infrastructure (PKI) innovations. Affiliations: ETH Zürich, Institute for Information Security Key Contributions: SCION, SAGE, RHINE, F-PKI Research interests include path-aware networks, cryptographic protocols, and resilient systems. His publications span over 295 results since 2005, with notable awards including the Best Paper Award (CoNEXT 2021) and ANRP 2023. He has contributed to foundational work in secure routing, DNS security, and GPU attestation. Scientific awards include Best Paper Awards at CoNEXT and ACM SIGCOMM, as well as recognition for applied networking research. His work bridges academia and industry, addressing challenges in global network security and scalability.

Natalia Berloffمشاهده پروفایل

استاد

• Applied Mathematics
• Theoretical Physics
• Quantum Computing
• +۶ مورد دیگر

Professor Natalia Berloff is a Professor of Applied Mathematics at the University of Cambridge's Department of Applied Mathematics and Theoretical Physics (DAMTP), where she has been a faculty member since 2002. She is also a Fellow of Jesus College, Cambridge. From 2013 to 2016, she served as Professor, Dean of Faculty, and Director of the Photonics and Quantum Materials Program at Skoltech. Previously, she held positions at the University of California, Los Angeles, including UC President's Research Fellow (1997-1999) and PIC Assistant Professor (1999-2002). Her research focuses on quantum fluids, physics-inspired computing, and non-equilibrium quantum systems. Key areas include coherence in quantum systems, superfluidity, Bose-Einstein condensates, and classical/quantum simulators. Her work bridges applied mathematics and theoretical physics, with applications in optical computing and quantum technologies. Recent studies emphasize Ising machines, photonic networks, and analog computing solutions for optimization problems. Her publications span over two decades, with recent trends in analog optical computing, gain-based systems, and quantum annealing. She leads the Quantum Fluids group, exploring novel computational paradigms using quantum fluids and polariton condensates. Her contributions have advanced interdisciplinary fields like quantum simulation and photonic-based AI. Education: Doctorate in Applied Mathematics (details not explicitly stated but implied via career progression). Grants/Awards: No specific grants or awards listed, though her leadership roles imply significant external funding. Labs/Teams: Leads the Quantum Fluids group and the Physics-inspired Computing team at DAMTP.

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.

Yun Zhangمشاهده پروفایل

استاد

• Remote Sensing
• Image Processing
• Computer Vision
• +۴ مورد دیگر

Dr. Yun Zhang is a Professor and Canada Research Chair in the Department of Geodesy and Geomatics Engineering at the University of New Brunswick. He holds a PhD from the Free University of Berlin and has pioneered research in remote sensing, image processing, and computer vision since 2000. His patented technologies are licensed to global companies including PCI Geomatics and DigitalGlobe. Research Focus: Optical/radar image processing, digital photogrammetry, AI applications in geomatics, and sensor fusion for UAV systems. His work enables advanced geospatial analysis across environmental, urban, and defense sectors. Distinctions: First Giuseppe Inghilleri Award (ISPRS 2012) NSERC Synergy Innovation Award from Governor General of Canada (2011) ASPRS Talbert Abrams Grand Award (2005) Featured in CFI 20th Anniversary Book for breakthrough innovations Technology Impact: Solutions deployed by NASA, USGS, Google Earth, and DND Canada across five continents. Recognized among top 9 Canadian research achievements in AUTM's global case studies alongside MIT and Stanford innovations.

Azadeh Davoodiمشاهده پروفایل

استاد

• Electronic Design Automation (EDA)
• Integrated Circuit Design
• Machine Learning in Hardware
• +۵ مورد دیگر

Azadeh Davoodi is a Vilas Distinguished Achievement Professor and Associate Chair of Undergraduate Studies in the Department of Electrical and Computer Engineering at the University of Wisconsin-Madison. Her research focuses on Electronic Design Automation (EDA), integrated circuit debug, and machine learning applications in VLSI design. She holds editorial roles in journals like IEEE TCAD and ACM TRETS, and has chaired major conferences such as ISPD 2015 and served on technical program committees for DAC, ICCAD, and others. Education: PhD in Electrical Engineering, University of Maryland-College Park (2006) Research Interests: Machine learning for VLSI chip design VLSI design automation for machine learning IC-CAD for emerging nanotechnologies Hardware security Recent Research Trends: Her work bridges machine learning and hardware design, with publications on neural network optimization, distributed inference, and explainable AI for circuit design. She emphasizes energy-efficient CNNs, latency reduction in edge computing, and security in split manufacturing. Awards: 2025 DATE Best Paper Candidate 2024 Vilas Distinguished Achievement Professor 2015 ACM Best Paper Award 2011 NSF CAREER Award Service and Grants: Leads NSF-funded projects on explainable ML for CAD and holds grants for distributed neural network synthesis. Her service includes roles as IEEE HKN member and editorial board positions. Labs/Teams: Engages in interdisciplinary research teams at UW-Madison, focusing on EDA innovation and hardware-software co-design.

• Natural Language Processing
• Large Language Models
• Soft Robotics
• +۴ مورد دیگر

Peiyi Wang is an Assistant Professor at Peking University's School of Electronics Engineering and Computer Science, Institute for Artificial Intelligence. With strong research output spanning both natural language processing and robotics, Wang maintains significant collaborations with Southern University of Science and Technology and National University of Singapore, particularly in soft robotics research with Professor Cecilia Laschi. Additionally, Wang is actively involved with DeepSeek-AI, contributing to several major language model initiatives including DeepSeek-R1 and DeepSeek-V2. Peking University, School of EECS, Institute for Artificial Intelligence (Primary) Southern University of Science and Technology (Collaborative) National University of Singapore (Collaborative) DeepSeek-AI Research Organization Dr. Wang's research spans two primary domains with significant intersection points. In natural language processing, Wang focuses on large language model reasoning capabilities, mathematical verification, uncertainty estimation, and preference alignment. The robotics work centers on soft robotics, particularly origami-inspired designs, strain-based modeling, and control systems for continuum manipulators. These domains converge in Wang's work on vision-language models, embodied AI, and multimodal reasoning systems. Recent work demonstrates particular innovation in mathematical reasoning verification (Math-Shepherd), soft robotic control systems, and red teaming frameworks for language model safety. Wang's publication record shows remarkable productivity, with over 40 publications between 2021-2025 across top-tier venues including ACL, EMNLP, CVPR, and IEEE Transactions on Robotics. The work demonstrates consistent progression from foundational NLP tasks to increasingly sophisticated multimodal and reasoning systems. The most recent publications (2024-2025) show particular emphasis on mathematical reasoning verification, soft robotics control, and language model safety evaluation. While specific awards aren't documented in the provided materials, Wang's work has clearly gained significant recognition through acceptance at top-tier conferences and collaborations with leading researchers in both NLP and robotics fields. Wang's research demonstrates strong interdisciplinary connections, bridging theoretical NLP work with practical robotics applications. The work with DeepSeek-AI suggests active industry collaboration while maintaining strong academic research output. Current research directions appear focused on improving language model reasoning reliability while developing novel soft robotic systems that can interact safely and effectively with complex environments.

• FPGA Design
• VLSI Systems
• Logic Synthesis
• +۷ مورد دیگر

Pierre-Emmanuel Gaillardon is a Professor in the Department of Electrical & Computer Engineering and Adjunct Professor in the School of Computing at the University of Utah. He holds a joint appointment since July 2024, having previously served as Assistant Professor (2016–2019) and Adjunct Assistant Professor in Computing (2016–2019). His research focuses on FPGA design, VLSI systems, nanoelectronics, and hardware security. He leads projects in emerging devices like TIGFETs, compute-in-memory architectures, and radiation-hardened FPGA fabrics. Teaching includes courses on Digital VLSI Design, Embedded Systems Design, and thesis supervision. He has secured grants from NSF, DARPA, and industry partners totaling over $10M, addressing topics like FPGA redaction, neuromorphic systems, and environmental sensors. Notable awards include the NSF CAREER Award (2018) and IEEE Senior Member elevation (2016). He actively serves on IEEE committees for nanoelectronics and EDA tools, contributing to standards like OpenFPGA.

Rohan Tabishمشاهده پروفایل

استادیار

• Real-Time Systems
• Embedded Systems
• Multicore Processors
• +۵ مورد دیگر

Rohan Tabish is an Assistant Professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign (UIUC), specializing in real-time systems, embedded systems, and cybersecurity. His work focuses on developing predictable and secure software frameworks for multi-core and heterogeneous architectures. Education: Ph.D. in Computer Science and Engineering Master's in Computer Science and Engineering B.Sc. in Electrical Engineering with Telecommunications specialization Research Interests: Real-Time Task Scheduling Fault Tolerance in Embedded Systems Inter-Core Communication Frameworks Memory Bandwidth Management Cyber-Physical Systems Scratchpad-Centric Operating Systems Awards: Outstanding Paper & Best Paper Award (RTSS 2020) Outstanding Paper & Best Student Paper Award (RTSS 2020) Best Presentation Award (RTAS 2016) Nominated for Best Paper Award (ECRTS 2019) Teaching: CS 431: Embedded Systems (Instructor, 2015-2018) CS 424: Real-Time Systems (Instructor, 2019) CS 438: Communication Networks (TA, 2019) Labs/Teams: Active member of the Real-Time Systems Lab (RTSL) at UIUC, focusing on safety-critical embedded systems and real-time software frameworks.

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.