جست‌وجوی استاد و پژوهشگر

Jonathan M. Bakerمشاهده پروفایل

استادیار

• Quantum Computing
• Quantum Computer Architecture
• Quantum Error Correction
• +۴ مورد دیگر

Jonathan M. Baker is an Assistant Professor in the Department of Electrical and Computer Engineering at The University of Texas at Austin, holding the Advanced Micro Devices Chair in Computer Engineering. His research centers on quantum computer architecture with emphasis on practical quantum error correction implementation across the quantum computing stack. His educational background includes a Ph.D. in Computer Science from the University of Chicago (advised by Fred Chong) and dual B.S. degrees in Mathematics and Chemistry and Computer Science from the University of Notre Dame. Baker's research spans quantum compilation, logic synthesis, multi-radix architectures, and error mitigation for both near-term and fault-tolerant quantum systems. His work addresses critical challenges in quantum hardware-software co-design, with particular focus on optimizing quantum circuits for real-world hardware constraints and noise characteristics. Current projects emphasize qudit-based computing, neutral atom architectures, and efficient error correction implementations. His publication record shows strong focus on quantum architecture innovations, with recent work exploring qudit advantages, modular chiplet designs, and dynamic noise adaptation. Key trends include hardware-aware compilation techniques, communication optimization across quantum systems, and practical approaches to fault tolerance. Best Paper Award Runner Up, MICRO 2020 IEEE Micro Top Pick, 2020 (Virtualized Logical Qubits) IEEE Micro Top Pick, 2020 (Extending Frontier with Qutrits) IEEE Micro Top Pick, 2021 (Emerging Technologies) Best Poster Award, MICRO 2018 Baker actively mentors graduate students in quantum computing architecture research and serves on conference review committees including MICRO and ASPLOS. His teaching includes specialized quantum systems courses at UT Austin and online EdX modules covering quantum computation fundamentals and architecture. He collaborates with the Duke Quantum Center and maintains strong industry connections through the AMD Chair position, focusing on bridging academic research with practical quantum computing implementations.

Henry Hoffmannمشاهده پروفایل

استاد

• Self-aware & Adaptive Computing
• Computer Systems
• Computer Architecture
• +۶ مورد دیگر

Henry Hoffmann is a Professor and Liew Family Chair in the Department of Computer Science at the University of Chicago. His research focuses on self-aware computing systems that adapt to meet goals like power efficiency, performance, and security. He leads the SEEC project and has contributed to advancements in computer architecture, embedded systems, and quantum computing. Hoffmann received the PECASE (2019), DOE Early Career Award (2015), and was inducted into the Samsung Hall of Fame for discovering vulnerabilities in SmartTVs. He holds a PhD from MIT (2013) and has co-founded Config Dynamics (2019). His work bridges control theory, machine learning, and traditional computer systems to create adaptive solutions for modern computing challenges. Education: PhD in Electrical Engineering and Computer Science from MIT (2013), SM (2003), and B.S. (1999) with highest honors from UNC Chapel Hill. Professional experience includes roles at Tilera Corporation and MIT Lincoln Laboratory. Research Interests: Self-aware systems, adaptive resource management, quantum computing optimization, and cybersecurity. His SEEC framework enables systems to autonomously adapt to constraints like energy and performance. Recent work explores applying adaptive techniques to AI/ML models for energy-efficient inference and security. Awards: Over $19M in research funding, 100+ publications, and leadership roles in NSF Expedition EPiQC (quantum computing). Named Chair of UChicago CS Department (2023-2024). Labs/Teams: Systems Group, EPiQC (quantum computing), and CERES (unstoppable computing systems). Current students include Jerry Ding and Ryien Hosseini. Notable alumni include Yi Ding (now faculty at Purdue) and Nikita Mishra.

• High-Performance Computing
• Reconfigurable Computing
• FPGA Acceleration
• +۷ مورد دیگر

Carlos Alvarez Martinez is a faculty member at the Universitat Politècnica de Catalunya (UPC), affiliated with the Department of Computer Architecture at the Barcelona School of Informatics (FIB). He is a key member of the Programming Models (PM) research group and collaborates closely with the Barcelona Supercomputing Center (BSC). His research focuses on high-performance computing, FPGA acceleration, task-based programming models like OmpSs, and hardware-software co-design for heterogeneous systems. His research interests center on advancing parallel computing through innovative programming models and hardware acceleration. He investigates efficient task scheduling, resource management in multicore and FPGA-based systems, and runtime support for dataflow models. His work enables high-performance execution of complex applications in domains such as scientific computing and cyber-physical systems. He actively contributes to European initiatives like TEXTAROSSA and AXIOM, aiming to develop next-generation exascale supercomputing technologies. The trend in his recent publications shows a strong focus on leveraging FPGAs for HPC, optimizing SpMV operations, improving task scheduling with hardware support, and developing frameworks for multi-FPGA clusters. His work consistently bridges theoretical models with practical implementations, emphasizing performance, scalability, and energy efficiency in heterogeneous computing environments. Scientific Awards: Premi UPC al Compromís Social 2019 Premi Disseny per al Reciclatge 2013 Alvarez Martinez has advised or collaborated with several doctoral students, including Jaume Bosch, Xubin Tan, and Fahimeh Yazdanpanah. He has been involved in numerous competitive R&D projects, often related to high-performance computing and parallel programming models. His work includes significant contributions to educational innovation, particularly in active learning methodologies and formative assessment using interactive systems. He leads and participates in research labs and teams focused on programming models and computer architecture, notably the PM group at UPC/BSC. These teams develop runtime systems, compilers, and hardware accelerators to push the boundaries of parallel computing efficiency and programmability.

Heiner Litzمشاهده پروفایل

دانشیار

• Computer Architecture
• Computer Systems
• Hardware-Software Interface
• +۳ مورد دیگر

Heiner Litz is an Associate Professor in the Computer Science & Engineering Department at UC Santa Cruz's Baskin School of Engineering. He holds the Kumar Malavalli Endowed Chair of Storage Systems Research and serves as Director of the Center for Research in Storage Systems (CRSS). He is also a member of UCSC's Hardware Systems Collective (HSC). Litz earned his PhD from Mannheim University and previously held positions at MIT, Google, and Stanford University. His research focuses on computer architecture and systems optimization , specifically improving hardware-software interfaces for data center workloads. Key areas include microarchitectural mechanisms (branch prediction, prefetching, cache design), profile-guided optimizations, and storage/disaggregated memory systems. His work bridges compiler techniques and hardware efficiency for emerging cloud applications. Recent publications emphasize profile-guided optimization across microarchitecture layers, storage scalability, and resource allocation in distributed systems. Trends include hardware-software co-design for data centers, RDMA-based protocols, and real-time control systems. Awards & Honors: Kumar Malavalli Endowed Chair of Storage Systems Research Best Paper Award at MICRO (2022) IEEE Micro Top Picks (2019, 2023) Best Paper Awards at ICPP (2008) and ARC (2009) Advising & Grants: He currently advises 11 PhD students and has graduated 8 MS/PhD students. Research is supported by NSF, Intel, Samsung, Google, Meta, Nutanix, HPE, ARM, Marvell, Cerabyte, Western Digital, and Broadcom. Labs & Teams: Directs CRSS, focusing on storage systems innovation, and collaborates with HSC on hardware-software integration projects.

Xiaoming Liمشاهده پروفایل

دانشیار

• Compiler Optimization
• Machine Learning for Code Generation
• Hardware-Software Interaction
• +۵ مورد دیگر

Xiaoming Li is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Delaware , focusing on compiler optimization, GPU computing, and hardware-software interaction. His work bridges machine learning with code generation to enhance program efficiency. B.S. and M.E. from Nanjing University (1998, 2001) Ph.D. in Computer Science from University of Illinois at Urbana-Champaign (2006) Research interests include: Compiler optimizations for static and dynamic code transformation Machine learning-driven code generation techniques FFT algorithms for sparse and hybrid systems Non-traditional compilers for SAT solvers and virtual machines GPU acceleration for large-scale computational problems His publications span 15 years , emphasizing: FFT optimization across GPU/CPU architectures Compiler techniques for heterogeneous systems Adaptive scheduling and error resilience Integration of empirical and model-driven approaches Notable awards: NSF CAREER Award (2008) Best Paper Award at ADAPT Workshop (2013) Advising highlights: Current students: Ryan Taylor, Sha Li, Shuo Chen, Yuanfang Chen, Chao Yang, Chaoyu Chen Graduates: Liang Gu (FFT Libraries), Jakob Siegel (GPGPU Frameworks), Murat Bolat (Context-Aware Compilation)

Kimia Zamiri Azarمشاهده پروفایل

استادیار

• Hardware Security
• Verification Formal Methods
• Advanced VLSI Testing
• +۳ مورد دیگر

Kimia Zamiri Azar serves as an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Central Florida, focusing on hardware security and verification methodologies. Her research bridges theoretical formal methods with practical security implementations in semiconductor design and testing. Her educational background includes: Ph.D. in Electrical and Computer Engineering, George Mason University (2021) Postdoctoral Research, University of Florida Dr. Azar's research spans hardware security with emphasis on system-level verification, VLSI design-for-trust, and advanced IC testing. She pioneers techniques in logic locking, secure heterogeneous integration, and IC supply chain security, developing frameworks for authenticated encryption in Systems-in-Package and runtime security monitoring. Her work integrates formal verification with innovative testing methodologies to address hardware trust challenges across the semiconductor lifecycle. Analysis of her recent publications reveals two dominant trends: (1) Application of large language models (LLMs) to hardware design tasks including high-level synthesis code generation and RTL optimization, and (2) Advancement of secure heterogeneous integration techniques for System-in-Package architectures with focus on counterfeit prevention and split-test security protocols. These directions address critical gaps in hardware trustworthiness amid increasingly complex semiconductor supply chains. Her scientific contributions have earned significant recognition: Best Paper Award at ICCAD 2019 Best Paper Award at ISVLSI 2020 Best Paper Award at ICCAD 2020 Best Paper Award at IEEE DCAS 2020 Best Paper Award at HOST 2022 Best Paper Award at DATE 2023 Dr. Azar secures substantial research funding from premier agencies including NSF, SRC, DARPA, AFRL, DoD (NG), and Microsemi. Her grants support projects spanning hardware security validation frameworks, secure heterogeneous integration, and AI-augmented verification methodologies. She actively mentors students in her research group, guiding publications in top venues like IEEE D&T, IEEE TC, and DAC while fostering industry-academic collaborations. Her work directly impacts semiconductor security standards through patented innovations and open-source verification tools. As an active IEEE and ACM member, she contributes to community advancement through conference organization (HOST, DATE), journal editorial roles, and workshop leadership on hardware security standards. Her research group collaborates with semiconductor industry leaders to translate theoretical security frameworks into practical design-for-trust methodologies for next-generation integrated circuits.

Nikos Hardavellasمشاهده پروفایل

استاد

• Computer Architecture
• Quantum Computing Systems
• Parallel Systems
• +۴ مورد دیگر

Nikos Hardavellas is a Professor of Computer Science and Electrical and Computer Engineering at Northwestern University, affiliated with the McCormick School of Engineering. He leads the Parallel Architecture Group at Northwestern (PARAG@N), focusing on energy-efficient parallel computing and quantum systems. His research spans quantum computing systems, fault-tolerant quantum error management, memory-centric architectures, and photonics-based interconnects. Education: Ph.D. Computer Science, Carnegie Mellon University (2009) M.S. Computer Science, Carnegie Mellon University (2006) M.S. Computer Science, University of Rochester (1997) B.S. Computer Science, University of Crete (1995) Research Interests: Quantum system software stack and error mitigation Memory-centric computing and programmable memory systems Energy-efficient architectures and dark silicon Photonics and optical interconnects Parallel systems and compiler-hardware co-design Key Contributions: Developed SupermarQ, a scalable quantum benchmark suite Pioneered optical cache hierarchies (Pho$) and energy-proportional photonic networks Advanced compiler-driven virtual memory systems (CARAT) and MPI autotuning (ACCLAiM) Awards & Honors: NSF CAREER Award (2015) Future CRA Leader (2024) Best Paper Awards at HPCA (2022) and ISLPED (2021 nomination) Test-of-Time Award at EDBT (2019) Grants & Service: Secured $4.8M in research funding from NSF, industry partners, and university initiatives Executive Committee member of Northwestern’s INQUIRE Institute for Quantum Research General Co-chair of IEEE/ACM MICRO 2022 Extensive service on departmental committees and thesis advisory boards Labs & Teams: Directs PARAG@N, collaborating on quantum computing, photonics, and energy-efficient architectures. Engages with industry partners like AMD, Intel, and Synopsys.

David Andrewsمشاهده پروفایل

استاد

• Embedded Systems
• Computer Architecture
• Reconfigurable Computing
• +۵ مورد دیگر

David Andrews is a Professor in the Department of Computer Science and Computer Engineering at the University of Arkansas College of Engineering. He holds the Mullins Endowed Chair of Computer Engineering and directs research through the Computer Systems Design Laboratory (CSDL). His work bridges hardware and software systems with a focus on practical implementation. His educational background includes: Ph.D. in Computer Engineering from Syracuse University Computer Engineer Degree from Syracuse University M.S.E.E. from University of Missouri-Columbia B.S.E.E. from University of Missouri-Columbia Andrews' research centers on embedded systems architectures from a holistic systems perspective, examining interactions between programming languages, runtime systems, and hardware components. His work spans reconfigurable computing, FPGA-based acceleration, and hybrid CPU/FPGA systems. A key contribution is the HybridThreads (hthreads) platform, which abstracts hardware/software boundaries to enable thread-based programming for heterogeneous systems. Recent publications demonstrate his focus on accelerating machine learning workloads on FPGAs, particularly transformer models and attention mechanisms, while addressing resource scheduling and real-time constraints. His publication trends reveal a consistent evolution from foundational work in parallel and distributed embedded systems toward specialized hardware acceleration for modern AI workloads. The research increasingly focuses on memory-centric architectures, computational overlays, and practical implementations for real-time applications across diverse domains including cultural heritage documentation and cybersecurity. As director of the Computer Systems Design Laboratory, Andrews leads interdisciplinary research in real-time embedded systems, reconfigurable computing, multiprocessor systems on chip, and hardware/software co-design. The lab integrates knowledge into undergraduate and graduate curricula covering digital design, computer organization, embedded systems, and systems modeling. CSDL supports a collaborative environment with undergraduate, master's, and PhD students working alongside visiting researchers from global institutions.

Sandhya Dwarkadasمشاهده پروفایل

استاد

• Parallel and distributed computing
• Computer architecture
• Compiler/Runtime-Architecture Interaction
• +۵ مورد دیگر

Sandhya Dwarkadas is the Walter N. Munster Professor and Chair of the Department of Computer Science at the University of Virginia. Her research focuses on the intersection of computer hardware and software, particularly in parallel computing, computer architecture, and compiler/runtime-architecture interaction. She holds dual roles as department chair and active researcher, balancing leadership with contributions to energy-efficient and reconfigurable computing systems. Education: B.Tech. in Electrical Engineering, Indian Institute of Technology (1986) M.S. and Ph.D. in Electrical and Computer Engineering, Rice University (1989, 1993) Research interests emphasize parallel and distributed computing architectures, with a focus on energy efficiency, cache coherence, and security in multicore systems. Recent work explores mitigating side-channel attacks via innovations like TimeCache and RollingCache. Her publications span 30+ years, addressing both foundational and applied challenges in computer systems. Awards highlight her impact: ACM and IEEE Fellowships (2018/2017), University of Rochester’s Hajim Award (2020), and AAAS Fellowship (2024). She actively mentors students through courses like CS 6190 and leads interdisciplinary projects like TriForce. Labs/Teams: Her work is anchored in the University of Virginia’s Computer Science Department, collaborating across academia and industry to advance next-generation computing systems.

Jürgen Citoمشاهده پروفایل

دانشیار

• Software Engineering
• Explainable AI
• Performance Engineering
• +۳ مورد دیگر

Jürgen Cito is an Associate Professor with tenure at Vienna University of Technology (TU Wien), specializing in software engineering, explainable AI, and performance engineering. He leads research at the IPA Lab (as indicated by his personal website) and maintains a visiting researcher position at Google. His academic journey began with joining TU Wien as an Assistant Professor in Spring 2020, with promotion to Associate Professor announced in April 2024. His research interests span multiple critical areas of modern software development, with particular focus on developer experience, program comprehension, and the intersection of AI with software engineering practices. His work bridges theoretical foundations with practical industrial applications, as evidenced by collaborations with major technology companies. Analysis of his recent publications reveals a strong emphasis on practical tools and methodologies that enhance software quality, performance, and security. His research trajectory shows increasing focus on explainable AI techniques applied to software engineering problems, performance prediction from source code, and automated security testing approaches that leverage large language models. best teaching award for distance learning for Web Engineering (2020) Cito actively contributes to the software engineering community through numerous conference committee roles, including program committee positions at ASE, ICSE, ESEC/FSE, and other major venues. His lab appears to focus on developer tools, program analysis, and AI-assisted software engineering, with connections to both academic and industrial research environments.

Gustavo Adolfo Junipero Rodriguez-Riveraمشاهده پروفایل

عضو هیئت علمی

• Operating Systems
• Computer Networks
• Memory Management
• +۱۱ مورد دیگر

Gustavo Rodriguez-Rivera is an Associate Teaching Professor in the Department of Computer Science at Purdue University, part of the School of Science. He joined the department in 2000 and holds a Ph.D. in Computer Science from Purdue University (1998), an M.S. in Electrical Engineering from ITESM Campus Monterrey (1990), and a B.S. in Electrical Engineering from the same institution (1985). His research focuses on Operating Systems, Computer Networks, Memory Management, Embedded Systems, Real-Time Systems, and broader areas like Numerical Analysis and Artificial Intelligence. Dr. Rodriguez-Rivera has received multiple teaching awards, including the ACM Faculty Award in Computer Science (2020, 2023) and Best Teacher in the School of Science (2014, 2016). His work spans software engineering education, structural health monitoring for wind turbines, and memory management algorithms. Notable publications include studies on garbage collection techniques, real-time project tracking in programming courses, and vibro-acoustic modulation for turbine blade inspection. He has advised numerous projects and contributed to grants such as the NSF-funded work on wind turbine blade monitoring. His academic contributions also include developing secure programming course modules and tools for interactive debugging systems.

Kate Smithمشاهده پروفایل

استادیار

• Quantum Computing
• Computer Architecture
• Quantum Compilation
• +۵ مورد دیگر

Kate Smith is an Assistant Professor of Computer Science at Northwestern University, affiliated with the McCormick School of Engineering. She holds a PhD in Electrical Engineering from Southern Methodist University (SMU), along with MS and BS degrees in Electrical Engineering and Mathematics from the same institution. Her research focuses on quantum computing, specifically in system architecture, optimized compilation, error mitigation, and security. Prior to joining Northwestern in 2024, she worked at Infleqtion managing the Superstaq compiler team and as a postdoctoral scholar at the University of Chicago under the CQE/IBM program. She has contributed to over 25 peer-reviewed publications and served on technical committees for major conferences like MICRO, ISCA, and DAC. Education: SMU (PhD 2019, MS 2015, BS in EE/Math 2014). Professional experience includes roles at EPFL (Switzerland), Texas Instruments, and the Darwin Deason Institute for Cyber Security. Her honors include the 2022 HPCA Best Paper Award, MIT EECS Rising Star (2021), and the IEEE TC-MVL Early Career Award (2021). Research interests span quantum compilation, distributed systems, qudit processing, and quantum security. She co-organized the 2023 CCC Workshop on Next Steps in Quantum Computing and chaired the 2022 ISMVL conference. Her work emphasizes bridging hardware-software gaps to enable scalable quantum systems. Grants and collaborations include the EPiQC group at the University of Chicago and projects funded by the Swiss NSF. Teaching experience includes courses on quantum computing fundamentals and digital design at SMU, University of Chicago, and adjunct roles.

Xiaoguang Wangمشاهده پروفایل

استادیار

• Systems and Software Security
• Heterogeneous CPU Architectures
• Secure Software Systems Design
• +۴ مورد دیگر

Xiaoguang Wang is an Assistant Professor in the Department of Computer Science at the University of Illinois Chicago. His research spans systems and software security, focusing on heterogeneous CPU architectures, secure software systems, and virtualization-based security frameworks. He actively mentors PhD and Master’s students and offers funded research opportunities for UIC students. University of Illinois Chicago, Department of Computer Science Research: Systems & Software Security, Heterogeneous Architectures, Virtualization His work includes projects like sMVX (multi-variant execution), Dapper (live program rewriting), and DynaCut (dynamic program customization). Recent publications address cross-architecture process migration, Linux kernel security, and using large language models (LLMs) for software security. He teaches advanced courses such as CS 487: Building Secure Computer Systems and CS 594/561: Adv. Linux Kernel Programming , emphasizing hands-on kernel development and security techniques. Grants from the U.S. Office of Naval Research and NSF support his work on secure systems and cross-architecture security solutions.

Mirela Alistarمشاهده پروفایل

استادیار

• Cyber-Physical Systems
• Biochips
• Digital Microfluidics
• +۷ مورد دیگر

Mirela Alistar is an Assistant Professor at the ATLAS Institute and the Department of Computer Science at the University of Colorado Boulder. She leads the Living Matter Lab , focusing on cyber-physical systems based on biochips to revolutionize healthcare diagnostics. Her interdisciplinary work bridges computer science, engineering, biotechnology, and bioart. Education : PhD in Embedded Systems Engineering (2010-2014, Technical University of Denmark), Postdoc in Human-Computer Interaction (2015-2018, Hasso Plattner Institute). Her research advances digital microfluidics and fault-tolerant biochips , enabling at-home diagnostic tools like OpenDrop . She also explores bioart through installations such as Semina Aeternitatis and Perfume Distillation Machine . She co-founded >top , a Berlin-based art & science project space, and advises startups digi.bio and bold.health . Her UIST'16 Honorable Mention Award highlights her innovative contributions. The Living Matter Lab under her leadership investigates interactive biodesign , combining technical precision with creative exploration of living systems.

• High-performance Computing
• Energy-efficient Computing
• Machine Learning
• +۳ مورد دیگر

Dr. Ngoc Nha Vi Tran is an Associate Professor of Computer Science at UiT The Arctic University of Norway. She holds a PhD from UiT and was a visiting scholar at Rutgers University, USA. Her research focuses on high-performance and energy-efficient computing, machine learning, and bioinformatics. She is a member of the NORA.startup Steering Group and leads the Arctic Green Computing Group. Education: PhD in Computer Science (UiT), M.Sc. in Software Engineering via Erasmus Mundus (Blekinge Institute of Technology, Sweden & Technical University of Kaiserslautern, Germany). Research interests include energy-efficient algorithms, bioinformatics tools (e.g., vCOMBAT), and applications of machine learning in healthcare and robotics. She teaches courses such as INF-2200 Computer Architecture, INF-2900 Software Engineering, and INF-2202 Concurrent Programming. Her work spans computational models for antibiotic target-binding, runtime energy optimization (REOH framework), and power models for embedded systems (RTHpower/ICE). She contributed to the EXCESS project on energy-efficient computing systems. Labs/Teams: Arctic Green Computing Group, EXCESS consortium.