جستجوی استاد راهنمای دانشگاهی

Sarita V Adveمشاهده پروفایل

استاد

• Computer Architecture
• Extended Reality Systems
• Heterogeneous Computing
• +۴ مورد دیگر

Sarita V Adve is the Richard T. Cheng Professor of Computer Science at the University of Illinois at Urbana-Champaign, where she conducts research spanning hardware, programming languages, operating systems, and applications with a focus on domain-specific systems. Her work bridges theoretical foundations and practical implementations, particularly in extended reality and heterogeneous computing. Her educational background includes a Ph.D. and M.S. in Computer Science from the University of Wisconsin-Madison (1993, 1989) and a B.Tech in Electrical Engineering from the Indian Institute of Technology Bombay (1987). Prior to joining Illinois, she served on the faculty at Rice University from 1993 to 1999. Adve's research centers on generalizable and scalable specialization for domain-specific systems, with current emphasis on extended reality (XR) systems including virtual, augmented, and mixed reality. She chairs the ILLIXR consortium to democratize XR research and developed the first fully open-source XR system (ILLIXR). Her foundational contributions include memory consistency models for C++ and Java programming languages, the Spandex coherence framework for heterogeneous systems, and software-driven approaches for hardware reliability. Her work spans hardware reliability (SWAT and RAMP projects), power management (GRACE system), and instruction-level parallelism. Recent publications reveal a strong focus on energy-efficient XR systems, hardware-software co-design for AI workloads, and resilience analysis. Her team explores rendering offload, visual-inertial odometry optimization, and compositional error injection frameworks, often targeting tradeoffs between energy, latency, and accuracy in mobile and edge environments. Fellow of the American Academy of Arts and Sciences Fellow of the ACM and IEEE ACM/IEEE-CS Ken Kennedy Award Anita Borg Institute Woman of Vision in Innovation Award ACM SIGARCH Maurice Wilkes Award Alfred P. Sloan Research Fellowship UIUC University Scholar University of Illinois Campus Award for Excellence in Graduate Student Mentoring Adve actively mentors students and has received multiple teaching awards. She co-founded the CARES movement to address discrimination in CS research events and chairs CS@Illinois CARES. Her service includes leadership roles in ACM SIGARCH (2015-2019), DARPA/ISAT study group, ACM Council, and Computing Research Association. She has secured significant funding including DARPA initiatives and Google Faculty Research Awards. She leads the ILLIXR consortium and has established collaborative research programs such as the $8.3M DARPA Joint University Microelectronics Program. Her lab focuses on open-source XR development, heterogeneous system architectures, and reliability-aware designs, with strong industry and government partnerships.

Timothy Roscoeمشاهده پروفایل

استاد

• Operating Systems
• Networking
• Distributed Systems
• +۳ مورد دیگر

Prof. Dr. Timothy Roscoe is a Full Professor in the Department of Computer Science at ETH Zurich. His research focuses on operating systems, networking, and distributed systems. He was inducted as an ACM Fellow in 2014 for contributions to these fields. Previously, he worked at Intel Research Berkeley, UC Berkeley (Adjunct Professor), and Sprint Labs. His work includes foundational contributions to systems like PlanetLab, P2 Declarative Networking, and the Nemesis OS. Roscoe holds a PhD from the University of Cambridge and has led projects in cloud computing, wide-area network measurement, and secure systems design. His current research emphasizes hardware-software co-design, formal verification of systems, and scalable memory architectures. Education: PhD in Computer Science, University of Cambridge (1995) Key Roles: Principal Researcher at Intel Berkeley Lab, Visiting Researcher at National ICT Australia Research Highlights: Contributions to distributed systems (PlanetLab), declarative networking (P2), and secure microkernels (seL4 integration). Current projects address challenges in disaggregated memory systems, coherent interconnects, and trustworthy embedded systems. Awards: ACM Fellow (2014), numerous grants and industry collaborations. Labs/Teams: Systems Group at ETH Zurich, focusing on OS design, hardware architectures, and formal methods.

Kyle Jamiesonمشاهده پروفایل

استاد

• Wireless Networks
• 5G Technology
• Quantum Computing in Communications
• +۳ مورد دیگر

Dr. Kyle Jamieson is a Professor of Computer Science at Princeton University, leading the Princeton Advanced Wireless Systems (PAWS) lab within the Department of Computer Science. He is also Affiliated Faculty in the Department of Electrical and Computer Engineering. His research focuses on wireless networking systems, 5G architecture, IoT networks, and quantum computing applications in wireless communication. He has pioneered work in reconfigurable intelligent surfaces, MIMO detection algorithms, and metamaterials for millimeter-wave networks. Dr. Jamieson has developed courses such as COS 597S: Recent Advances in Wireless Networks (graduate seminar), COS 463: Wireless Networks , and COS 418: Distributed Systems . His teaching emphasizes interdisciplinary approaches to networking challenges, including physical-layer design, computational structures for wireless processing, and cross-layer optimization. His lab’s research spans smart surfaces for 5G networks, quantum annealing for MIMO processing, and edge computing for live video analytics. Recent work includes deploying reconfigurable metamaterials for enhanced mmWave networks and developing tools like NR-Scope for 5G telemetry. While no awards are listed in the provided text, his contributions to wireless systems have advanced both academic and industrial applications in areas such as network resilience, IoT scalability, and quantum-enabled wireless processing. Dr. Jamieson’s advising focuses on graduate and undergraduate students working in wireless systems, though specific advisee names are not provided. His lab collaborates on projects like Wall-Street for roadside networking and Spider for multi-hop mmWave video analytics. External collaborations include work with Microsoft Research and guest lecturing roles at Berkeley. His research bridges theoretical foundations with practical implementations, often addressing real-world challenges in wireless infrastructure and next-generation communication systems.

Tulika Mitraمشاهده پروفایل

استاد

• Hardware-Software Co-design
• Real-Time Embedded Systems
• Energy-Efficient Computing
• +۷ مورد دیگر

Professor Tulika Mitra is the Dean of the School of Computing and Vice Provost (Special Projects) at the National University of Singapore (NUS). She holds the Provost’s Chair Professor in the Department of Computer Science and has been instrumental in shaping academic policies and strategic initiatives at NUS since joining in 2001. PhD in Computer Science, Stony Brook University (2000) M.E. in Computer Science, Indian Institute of Science (1997) B.E. in Computer Science, Jadavpur University (1995) Her research focuses on hardware-software co-design for energy-efficient computing systems, particularly in real-time embedded systems, heterogeneous architectures, and AI accelerators. She leads major research programs such as the NRF Competitive Research Programme on Low-Power Edge Accelerators and the MOE Tier-3 Programme on Green AI , collaborating with industry leaders like ARM, AMD, and Meta. Her recent publications highlight innovations in CGRA optimization , sparse attention mechanisms , photonic-digital hybrid architectures , and low-power ML inference . These works often integrate compiler techniques, architectural design, and real-time constraints for edge computing applications. Scientific Awards : ESWEEK Test-of-Time Award (2022), ACM SIGDA Distinguished Service Award, IEEE CEDA Outstanding Service Recognition Award, Teaching Excellence Award (2006), and multiple best paper recognitions. Education Leadership : Spearheaded the Computer Engineering (CEG) Programme at NUS, a joint initiative between Engineering and Computing. As a mentor , she has supervised over 25 PhD students , many now in prominent academic or industrial roles. Her research group eCO Lab focuses on embedded computing challenges, while her grant collaborations include projects on 5G base stations, reconfigurable architectures, and IoT-optimized SoCs.

Debjit Palمشاهده پروفایل

پژوهشگر ارشد

• Machine Learning for Electronic Design Automation (EDA)
• System-on-Chip (SoC) Verification
• Hardware Verification
• +۶ مورد دیگر

Debjit Pal is a Post-Doctoral Associate at the School of Electrical and Computer Engineering, Cornell University, and a member of the Computer Systems Laboratory. His research focuses on machine learning techniques for hardware verification, SoC validation, and FPGA optimization. Education: Ph.D. in Computer Engineering (University of Illinois at Urbana-Champaign, 2019) M.S. in Computer Science (IIT Kharagpur, 2012) B.E. in Electronics Engineering (Jadavpur University, 2008) Research Interests: Machine Learning for Electronic Design Automation (EDA) System-on-Chip (SoC) Verification Edge Intelligence as a Service Compiler Optimizations for Reconfigurable and High-Performance Computing Scientific Awards: IEEE CEDA Student Research Award (2016) Best Paper Nomination (ICCAD 2015, DAC 2018, ASP-DAC 2019) E. J. McCluskey Best Doctoral Thesis Competition Semi-Finalist (2020) Travel Grants for ICCAD/DAC/ASPDAC (2018-2019) Professional Roles: Technical Program Committee Member (DAC, VLSID), Reviewer (IEEE TVLSI, DATE, ICCAD). Collaborates with researchers like Zhiru Zhang and Shobha Vasudevan.

• Machine Learning in Electronics
• Electrostatic Discharge (ESD) Protection
• Circuit Reliability
• +۳ مورد دیگر

Elyse Rosenbaum is the Melvin and Anne Louise Hassebrock Professor in Electrical and Computer Engineering at the University of Illinois at Urbana-Champaign. She also serves as the Acting Associate Dean for Research at the Grainger College of Engineering. She is the director of the NSF-supported Center for Advanced Electronics through Machine Learning (CAEML), a collaboration between the University of Illinois, North Carolina State University, and Penn State University. Education: Ph.D. in Electrical Engineering, University of California, Berkeley, 1992 M.S. in Electrical Engineering, Stanford University B.S. in Electrical Engineering, Cornell University (with distinction) Research Interests: Her research focuses on machine learning applications in electronics, ESD-robust high-speed I/O circuit design, compact modeling, behavioral modeling of circuits, and CDM-ESD protection for advanced packaging technologies. Scientific Awards: IEEE Fellow for contributions to electrostatic discharge reliability of integrated circuits Best Student Paper Award, IEDM Outstanding and Best Paper Awards, EOS/ESD Symposium Technical Excellence Award, SRC NSF CAREER Award IBM Faculty Award ESD Association’s Industry Pioneer Recognition Award Advising and Grants: She supervises graduate and undergraduate researchers, primarily focusing on those with strong academic records and relevant experience. Her work is supported by NSF and other prominent organizations. Labs and Teams: She leads the CAEML center, which aims to apply machine learning to optimize microelectronic circuits and systems, enhancing design automation and reliability.

• Neuromorphic Engineering
• Neural Networks
• Edge Computing
• +۳ مورد دیگر

Shih-Chii Liu holds the rank of Privatdozent (Associate Professor) in the Department of Information Technology and Electrical Engineering at ETH Zürich. He is affiliated with the Institute of Neuroinformatics , a joint institute between the University of Zurich and ETH Zurich. His research focuses on neuromorphic engineering, bio-inspired neural hardware, and edge computing systems, emphasizing energy-efficient algorithms and sensor technologies. Key research areas include neuromorphic sensors for real-time data processing, sparsity-aware neural networks, and adaptive computing architectures for edge devices. His work spans applications such as speech enhancement, wearable health monitoring, and bio-inspired keyword spotting systems. He leads the Sensors Research Group, which develops neuromorphic systems integrating novel sensors, spiking neural networks, and low-power hardware accelerators. Recent projects include the DeltaKWS low-power keyword spotting IC, EFLOP computational cost metrics for spiking networks, and NeuroBench benchmarking frameworks for neuromorphic systems. His contributions emphasize bridging biological neural principles with practical engineering solutions for IoT and embedded systems. Liu teaches courses such as Neuromorphic Engineering I and collaborates on cross-disciplinary projects involving neuroprosthetics, smart wearables, and multimodal sensor fusion. His work is characterized by hardware-software co-design approaches to tackle challenges in real-time, low-latency, and energy-constrained computing environments.

Nicola Nicoliciمشاهده پروفایل

استاد

• Digital Integrated Circuit Design
• Hardware Testing
• Post-Silicon Validation
• +۷ مورد دیگر

Nicola Nicolici is a Professor in the Department of Electrical and Computer Engineering at McMaster University. His research focuses on methods and algorithms for the design of digital integrated circuits and systems, with significant contributions in manufacturing test, post-silicon validation and debug. His work has expanded to include embedded systems, low-energy computing, and custom hardware-accelerated computing systems. Professor Nicolici's research interests span multiple areas of digital system design and validation. His early work focused on manufacturing test methodologies and power-aware testing strategies for integrated circuits. More recently, he has made significant contributions to post-silicon validation techniques, including constrained-random stimuli generation, trace signal selection, and bit-flip detection. His research has evolved to address emerging challenges in embedded computing systems, low-energy design, and specialized hardware acceleration for various applications including deep neural networks and signal processing. His recent publications reveal a strong trend toward hardware acceleration for specialized computing tasks. The research spans matrix multiplication algorithms (Strassen and Karatsuba), memory system optimization (DDR5 calibration), FPGA-based radar processing, and neural network acceleration. His work consistently bridges theoretical algorithm development with practical hardware implementation considerations, particularly focusing on precision analysis, fault tolerance, and energy efficiency. The research demonstrates a clear progression from traditional digital circuit testing to more complex system-level validation and acceleration techniques. Professor Nicolici has been actively involved in teaching courses related to system-on-chip design and test, digital systems, and embedded systems. His teaching portfolio includes advanced courses such as System-on-Chip (SOC) Design and Test and Digital Systems Design , reflecting his expertise in the field. While specific grant information isn't detailed in the provided text, his extensive publication record suggests ongoing research funding support. His research has contributed significantly to the fields of digital circuit testing, post-silicon validation, and hardware acceleration. The work has practical applications in semiconductor manufacturing, embedded systems design, and specialized computing architectures. His recent focus on neural network acceleration and memory system optimization reflects the evolving landscape of computer architecture research.

David Wentzlaffمشاهده پروفایل

استاد

• Computer Architecture
• Green Computing
• Cloud Computing
• +۶ مورد دیگر

David Wentzlaff is a Professor of Electrical and Computer Engineering at Princeton University, with associated faculty roles in Computer Science and the High Meadows Environmental Institute (HMEI). He leads research in computing architecture, green computing, and sustainable system design. As Director of Undergraduate Studies, he shapes educational programs in his field. Education: Ph.D., Electrical Engineering, MIT (2012) M.S., Electrical Engineering and Computer Science, MIT (2002) B.S., Electrical Engineering, University of Illinois at Urbana-Champaign (2000) Research Focus: Future Computing Systems: Designing manycore architectures, cloud computing infrastructure, and chiplet-based systems for exascale computing. Sustainability: Developing energy-efficient hardware, recyclable computing systems, and eco-friendly decommissioning strategies. Hardware-Software Co-Design: Exploring FPGA integration, in-memory computing, and parallel processing frameworks. Advising & Grants: Advises 8 current graduate students, focusing on topics like chiplet design, neural acceleration, and sustainable computing. Recipient of NSF grants for projects like OpenPiton (open-source manycore research platform) and CAREER awards for energy-efficient architectures. Labs & Collaborations: Leads the Wentzlaff Research Group at Princeton. Develops open-source frameworks like PRGA (FPGA prototyping) and OpenPiton (manycore processor).

• Machine Learning
• Database Systems
• Artificial Intelligence
• +۳ مورد دیگر

Christopher Re is a Professor in the Department of Computer Science at Stanford University, affiliated with the Stanford AI Lab, Machine Learning Group, and Center for Research on Foundation Models. His research focuses on the intersection of machine learning, database systems, and scientific computing, with applications in humanitarian efforts, scientific discovery (e.g., extrasolar neutrinos, DNA foundation model Evo), and industry partnerships with companies like Apple and Google. He has been recognized with prestigious awards, including the MacArthur Foundation Fellowship and multiple test-of-time awards. His work emphasizes advancing thermal materials, phase-change memory, and ultrafast electron microscopy technologies. Re's research contributions span database theory, systems, and machine learning, with best papers at PODS 2012, SIGMOD 2014, and ICML 2016. His lab’s innovations have been incorporated into products globally, and he actively invests in technology startups. Key projects include developing thermal interface materials for 3D integrated circuits and exploring energy-efficient neuro-inspired memory systems. His awards reflect sustained excellence: NeurIPS 2020 and PODS 2022 test-of-time awards, along with recent accolades for student-led initiatives at MIDL 2022 and ICLR22. Re’s interdisciplinary approach bridges academia and industry, driving both scientific and humanitarian impact.

Thuy T. Leمشاهده پروفایل

استاد

• Digital System Design
• Computer Architecture
• Hardware Accelerators
• +۶ مورد دیگر

Thuy T. Le is a Professor of Electrical Engineering at San Jose State University's College of Engineering. With a distinguished career spanning several decades, he teaches graduate and undergraduate courses in digital system design, computer architecture, microprocessor systems, and related fields. His academic journey began with earning B.S., M.S., and Ph.D. degrees from the University of California, Berkeley. Professor Le's research interests encompass a broad spectrum of cutting-edge technological domains. His primary focus areas include System-on-Chip (SoC) and Embedded System Design, Hardware Accelerators for complex algorithms, Quantum Computing, implementation of Probability theory and Monte Carlo simulation, and radiation effects on electronic devices and systems. His work bridges traditional electrical engineering with emerging computational paradigms, demonstrating a consistent ability to adapt to evolving technological landscapes while maintaining strong foundations in core engineering principles. Analysis of Professor Le's publication record reveals a consistent trajectory from nuclear reactor physics and computational methods toward modern hardware acceleration and quantum computing. His early work focused on nuclear reactor simulation and radiation shielding, then evolved to parallel computing and distributed systems, and has recently centered on hardware acceleration for complex algorithms, quantum computing applications, and AI hardware. This progression demonstrates his ability to transition between major technological paradigms while maintaining expertise in computational methods and hardware implementation. Professor Le has demonstrated significant leadership in professional service, having served as keynote speaker, general chair, technical program chair, session chair, reviewer, and committee member for numerous international conferences. His service extends beyond academia through his role as Co-Founder and Advisor of the Vietnamese Strategic Ventures Network and Chairman of the Board of the United States–Vietnam Foundation. In his educational role, Professor Le has made substantial contributions to engineering curriculum development and assessment. He has taught a wide range of courses including EE271 (Advanced Digital System Design), EE210, EE250, and various project/thesis courses. His research advising spans digital system design, ASIC, SOC, and hardware accelerators. He has also collaborated with local companies on projects related to high-performance system architectures, parallel algorithms, digital arithmetic, and System-on-Chip verification.

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.

Charles Gillanمشاهده پروفایل

مدرس ارشد

• High Performance Computing (HPC)
• Artificial Intelligence (AI) in Healthcare
• Quantum Computing
• +۴ مورد دیگر

Charles Gillan is a Senior Lecturer at Queen's University Belfast's School of Electronics, Electrical Engineering and Computer Science, affiliated with the High Performance and Distributed Computing department and the Institute of Electronics, Communications & Information Technology. His research bridges HPC systems, AI applications in healthcare, and computational physics. Key projects include managing ICU patient care via neural networks, exascale-ready mathematical packages, and edge computing architectures. Research interests focus on high-performance computing (HPC), quantum computing, real-time data analytics, and electron-molecule scattering simulations. Notable contributions include developing microserver architectures for edge analytics and advancing AI-driven clinical decision support systems. Gillan has collaborated on interdisciplinary projects like food authenticity testing using spectroscopy and improving ventilator management in intensive care units. Publications span AI in healthcare, HPC system design, and computational methods for physics problems. He has secured funding for initiatives such as the KTP partnership with Foods Connected Ltd and the HANDHELD olfactory detection project. Gillan's work emphasizes practical applications of advanced computing across healthcare, engineering, and cybersecurity domains.

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.

Kanad Basuمشاهده پروفایل

دانشیار

• Hardware Security
• System Security
• Hardware Reliability
• +۱۰ مورد دیگر

Kanad Basu is an Associate Professor in the Department of Electrical, Computer, and Systems Engineering at The University of Texas at Dallas, Jonsson School of Engineering and Computer Science. He leads the Trustworthy and Intelligent Embedded Systems (TIES) lab, focusing on hardware security, reliability, and emerging computing paradigms. His research spans AI hardware, quantum computing, functional safety, and hardware-based security validation. Research Interests: His work emphasizes improving the trustworthiness of modern hardware systems. Key areas include hardware security (e.g., side-channel analysis, hardware trojans), functional safety in AI accelerators, quantum computing security and verification, and post-silicon validation techniques. He combines formal methods, machine learning, and hardware design to address vulnerabilities in SoCs, DNN accelerators, and quantum systems. Publication Trends: Recent publications (2023–2025) show a strong focus on interdisciplinary research, integrating AI/ML with hardware security, quantum computing, and functional safety. There is a growing emphasis on using large language models for assertion generation, symbolic execution for hardware fuzzing, and graph neural networks for quantum circuit analysis. His work frequently appears in top venues like DAC, DATE, HOST, ISVLSI, and IEEE journals. Scientific Awards: NSF CAREER Award, 2025 IEEE Top Picks in Test and Reliability, 2024 and 2023 Multiple Hack@DAC Prizes (2nd and 3rd) Best Paper Award at VLSI Design 2011 Assistant Professor Award at UTD Jonsson School, 2024 Nominated for Blavatnik Awards for Young Scientists, 2019 Advising and Grants: Dr. Basu has mentored numerous PhD, MS, and undergraduate students, many of whom have published in top-tier venues. He leads the TIES lab, which has received significant recognition, including the NSF CAREER Award. He actively collaborates across disciplines, advising students on topics ranging from quantum computing to AI hardware and functional safety. His lab produces high-impact research with real-world applications in automotive, cloud, and embedded systems. Labs and Teams: He leads the Trustworthy and Intelligent Embedded Systems (TIES) lab at UT Dallas, which fosters innovation in hardware security and reliability. The lab has produced award-winning work, including second prize at HACK@DAC 2025. He also serves on technical committees for IEEE DATE and HOST, and acts as Hardware Hacking Chair for IEEE HOST, indicating strong leadership in the hardware security community.