جستجوی استاد راهنمای دانشگاهی

• Brain-inspired Circuit Design
• Artificial Intelligence Acceleration
• Spiking Neural Networks
• +۳ مورد دیگر

Jason Eshraghian is an Assistant Professor at the Department of Electrical and Computer Engineering, University of California, Santa Cruz. He leads the UCSC Neuromorphic Computing Group, focusing on brain-inspired circuits for AI acceleration and spiking neural networks. His work bridges biological principles with hardware implementation to solve computational challenges in AI efficiency. Assistant Professor, Electrical and Computer Engineering UCSC Neuromorphic Computing Group leader Research intersections: Neuromorphic engineering, AI hardware, spiking networks Research Interests: Dr. Eshraghian's work centers on neuromorphic computing and spiking neural networks for AI acceleration. His lab explores Hardware-software co-design for ultra-low-power systems Memristor-based neural architectures Event-driven medical diagnostics Spiking language models (e.g., SpikeGPT) Closed-loop neurostimulation Article Trends: Recent publications emphasize Scalable neuromorphic architectures for AI (2025: Ising machines, FPGA implementation) Medical applications including cytometry and brain-computer interfaces Energy-efficient designs for language models and tracking systems Hybrid attention mechanisms and temporal learning frameworks

Eva Darulovaمشاهده پروفایل

دانشیار

• Programming Languages
• Software Verification
• Program Synthesis
• +۳ مورد دیگر

Eva Darulova is an Associate Professor at the Department of Information Technology, Uppsala University, and Adjunct Faculty at Max Planck Institute for Software Systems (MPI-SWS). Previously held tenure-track faculty at MPI-SWS and completed her PhD at École Polytechnique Fédérale de Lausanne (EPFL) under Viktor Kuncak. Current research: Applications in numerical/embedded domains, program synthesis, software verification, approximate computing Key projects: Blossom (fuzzing), Lassie (HOL4 tactics), Pine (floating-point loops), LTLTalk (robot instruction), Incarnate (3D printing), Icing (compiler semantics), Flipper (NLP interface), FloVer (error checking), Daisy (accuracy analysis), Rosa (real compiler) Teaching: Program Design (2021/22), Program Synthesis (2021), Program Analysis (multiple years), Advanced Program Analysis (2019), Static Program Analysis (2017), Approximate Computing seminar (2016) Service: Artifact evaluation chair (ASPLOS'22), PC member (ECOOP'22, ESEC/FSE'22, EMSOFT'21, PLDI'21) Research Trends: Focus on numerical computing verification, approximate computing frameworks, and program synthesis techniques. Publications span TACAS, EMSOFT, ISSTA, POPL, CAV, FMCAD, TOPLAS, with emphasis on floating-point error analysis, mixed-precision optimization, and compiler verification. Scientific Recognition: Nominated for EAPLS best paper award (TACAS'21). Co-advisor for Anastasiia Izycheva and mentor for multiple MSc/BSc students including Mustafa Hafidi, Joachim Bard, Nikita Zyuzin, Youcef Merah, Safya Alzayat, Anastasiia Izycheva. Collaborations: Works with researchers at EPFL, MPI-SWS, University of Saarland, University of Washington, and Saarbrücken/Kaiserslautern institutions.

Sorin Lernerمشاهده پروفایل

استاد

• Programming Languages
• Program Verification
• Security and Privacy
• +۳ مورد دیگر

Sorin Lerner is a Professor and Chair of the Department of Computer Science and Engineering at the University of California, San Diego (UCSD). His research spans programming languages, program verification, security/privacy, and human-computer interaction (HCI). He actively seeks graduate students and postdocs to join his research group. Education: PhD in Computer Science, University of Washington (2006) Research Interests: Sorin’s work focuses on applying programming language techniques to program verification, security, and live programming environments. His projects include the Rango system for retrieval-augmented proving, Projection Boxes for live programming visualization, and Radar for concurrent program analysis. He also explores gamification in education through Proof Games and Code Spells. Awards: Recipient of the PLDI 2003 Best Paper Award ICSE 2025 Distinguished Paper Award for Rango Teaching: Sorin teaches courses on programming languages (CSE 130, CSE 230), compilers (CSE 231), and specialized topics in automated theorem proving (CSE 291). He emphasizes live programming and interactive tools to enhance learning. Lab & Collaborations: Leads the PL@UCSD research group, collaborating with institutions like Microsoft, Google, and The University of Texas at San Antonio. Projects often involve cross-disciplinary efforts in software reliability, formal methods, and educational technology.

Gilbert Bernsteinمشاهده پروفایل

استادیار

• Human-Centered Computing
• Interaction with the Physical World
• Software & Hardware Systems
• +۱۰ مورد دیگر

Gilbert Bernstein is an Assistant Professor at the University of Washington in the Computer Science & Engineering department within the Paul G. Allen School of Computer Science & Engineering . He specializes in Computer Graphics and Programming Languages , with a focus on high-performance domain-specific languages (DSLs). Postdoctoral scholar at UC Berkeley and MIT with Jonathan Ragan-Kelley PhD from Stanford University under Pat Hanrahan His research spans Human-Centered Computing , Interaction with the Physical World , and Software & Hardware Systems , including projects in: Compiler design for knitting machines using knot theory Differentiable rendering of neural signed distance fields Hardware DSLs for accelerator programming Geometric pattern completion for quilting Responsive web retargeting His recent publications demonstrate expertise in combining Mathematical Modeling with Language Design for applications in Graphics , Simulation , and Fabrication .

Filippo Sanfilippoمشاهده پروفایل

استاد

• Robotics
• Artificial Intelligence
• Human-Robot Interaction
• +۷ مورد دیگر

Filippo Sanfilippo is a Professor at the Department of Engineering Sciences, University of Agder (UiA), Grimstad, Norway. He holds a PhD in Engineering Cybernetics from NTNU, specializing in robotic control systems. His research focuses on robotics, wearables, human-robot collaboration, AI, and control theory, with emphasis on applications in healthcare, Industry 5.0, and emergency response. He leads projects like SAKO (Service Robots in Municipal Health Services) and the EMERALD initiative (3D printing biomimetic mechatronic systems). He chairs IEEE Norway Section and the IEEE Robotics & Automation Joint Chapter, and serves as Treasurer of NAIS (Norwegian AI Association). His research groups include Artificial Intelligence, Biomechatronics, and Collaborative Robotics. He coordinates activities in teaching robotics at bachelor/master levels and supervises PhD students. Over 70+ publications span journals like IEEE Transactions and conferences like HICSS and ICRA. Key contributions include VR/AR integration in surgical training, federated learning for edge devices, and adaptive control for flexible robotic joints. He actively promotes interdisciplinary research through digital twins, human-robot teaming frameworks, and ethical AI applications.

Adam Chlipalaمشاهده پروفایل

استاد

• Programming Languages
• Formal Verification
• Compiler Verification
• +۲ مورد دیگر

Adam Chlipala is the Arthur J. Conner Professor of Computer Science at MIT, with appointments in the Computer Science and Artificial Intelligence Laboratory (CSAIL) and the Department of Electrical Engineering and Computer Science. His research integrates programming languages and formal methods to develop verified computer systems. Education: PhD in Computer Science, University of California, Berkeley MS in Computer Science, University of California, Berkeley BS in Computer Science, Carnegie Mellon University Research Focus: Dr. Chlipala's work spans: Verified compilers and programming tools Cryptographic protocol implementation Hardware description languages Proof automation in theorem provers High-performance parallel computing His current projects focus on rethinking abstractions for parallel systems with end-to-end formal verification. Teaching: He has created and teaches courses on verified software engineering (6.S057), programming fundamentals (6.1010), and formal reasoning about programs (6.5120). His educational materials include textbooks on Certified Programming with Dependent Types and Formal Reasoning About Programs. Awards and Honors: His research has been recognized with the German IT Security Award, Humies Gold Award for evolutionary computation, and multiple teaching prizes including the Jamieson Prize. He is an ACM Distinguished Member and NSF CAREER awardee. Research Teams: Dr. Chlipala leads the Programming Languages & Verification Group at MIT CSAIL. His startup Nectry develops no-code enterprise software based on the Ur/Web language and UPO proof system.

• Embedded Systems
• IoT Security
• Digital System Design
• +۳ مورد دیگر

Paulino Ruiz de Clavijo Vázquez is a Professor in the Department of Electronic Technology at the University of Seville. His primary research focuses on embedded systems, IoT security, digital system design, FPGA implementation, and hardware security. He leads and collaborates on numerous research projects, including advanced IoT boot systems, secure embedded communication, and high-precision timing synchronization for IoT devices. Key projects include the IRIS secure boot framework for IoT and the E-LUKS hardware encryption solution. His work spans both theoretical contributions (e.g., novel trigonometric computation algorithms) and applied hardware-software co-design (e.g., NanoFS file system). He has advised doctoral theses such as 'LILA: Low-level IoT Lightweight Applications.' His research also involves FPGA-based systems for real-time applications, including time servers and FFT modules. Projects like HAVOC (hardware audio codec) and HardTIME (high-precision timing) highlight his focus on practical hardware implementations. He has contributed to over 50 peer-reviewed publications, conference presentations, and patents related to embedded systems, digital circuit design, and IoT security solutions.

• Computer Vision
• Deep Learning
• Neural Network Acceleration
• +۵ مورد دیگر

Kaisheng Ma is a Professor in the Department of Computer Science and Technology at Tsinghua University's School of Information Science and Technology. With an extensive publication record spanning from 2013 to 2025, he has established himself as a leading researcher in computer vision, deep learning, and computer architecture. His work bridges theoretical advances with practical applications in neural network acceleration and hardware-software co-design. Professor Ma's research interests focus on advancing the frontiers of computer vision and deep learning systems. His work in knowledge distillation has significantly improved the efficiency of neural networks, while his contributions to hardware acceleration have enabled more powerful AI systems. His recent research demonstrates a growing emphasis on 3D vision, embodied AI, and the intersection of language and vision systems. He has pioneered techniques in frequency domain analysis for image processing and developed novel approaches for neural network acceleration through hardware-software co-design. Analysis of Professor Ma's recent publications (2023-2025) reveals a strong research trajectory with increasing focus on multimodal learning, 3D scene understanding, and efficient neural network deployment. His work spans both theoretical advances in machine learning (particularly knowledge distillation and continual learning) and practical hardware implementations for AI acceleration. The publications show consistent collaboration with a core group of researchers while expanding into new areas like brain-computer interfaces and language-grounded spatial reasoning. Professor Ma has successfully mentored numerous PhD students who have become active contributors to the field, including Linfeng Zhang, Runpei Dong, and Zekun Qi. His research has been supported by significant grants that enable cutting-edge work in neural network acceleration and 3D vision systems. His students have published in top-tier conferences including CVPR, NeurIPS, and ICCV, demonstrating the strength of his research group. Professor Ma leads a research group focused on the intersection of computer vision, deep learning, and computer architecture. His lab investigates efficient neural network deployment, hardware acceleration techniques, and novel computer vision algorithms. The team maintains strong collaborations with industry partners working on AI hardware and has developed several innovative approaches to neural network acceleration and efficient model deployment.

Visvesh S Satheمشاهده پروفایل

دانشیار

• Energy-Efficient Computing
• Implantable Electronics
• Power Management Systems
• +۵ مورد دیگر

Visvesh S. Sathe is an Associate Professor in the School of Electrical and Computer Engineering at Georgia Institute of Technology. Previously, he served as Associate Professor at the University of Washington (2013-2022) and held roles at AMD (2007-2013), where he contributed to energy-efficient microprocessor design. His research focuses on energy-efficient computing, implantable electronics, and closed-loop neural interfaces. He leads the Processing Systems Lab (PSyLab), exploring computational techniques for hardware optimization in digital and mixed-signal systems. Education: B.Tech from IIT Bombay; M.S. and Ph.D. from the University of Michigan, Ann Arbor. Research Interests: Energy-efficient IC design, power management architectures, bi-directional neural interfaces, adaptive baseband processing, and SoC optimization. His work emphasizes run-time control of clocking, voltage regulation, and thermal management. Key Awards: NSF CAREER Award (2019), Intel Outstanding Researcher Award (2021), and IEEE Distinguished Lecturer (2021-2022). Notable contributions include the first resonant-clocked production processor and adaptive clocking for supply droop mitigation. Advisees/Grants: While specific grant details aren't provided, his lab’s work is funded by NSF and industry partnerships. His research spans from circuit-level innovations to system-level integration, with applications in biomedical and high-performance computing. Labs/Teams: PSyLab at Georgia Tech drives interdisciplinary projects in energy-efficient systems and neural interfaces, collaborating with industry and academic partners globally.

Barton P. Millerمشاهده پروفایل

استاد

• Binary Code Analysis
• Software Security
• Distributed Systems
• +۵ مورد دیگر

Barton P. Miller is a Vilas Distinguished Achievement Professor and the Amar & Balinder Sohi Professor of Computer Sciences at the University of Wisconsin, Madison, where he has made significant contributions to computer science research and education. As a faculty member in the Computer Sciences Department within the College of Engineering, he directs major research initiatives including the Paradyn Tools project and leads cybersecurity efforts as Chief Scientist of the DHS-funded Software Assurance Marketplace (SWAMP) research center. Miller received his B.A. degree from the University of California, San Diego in 1977, and M.S. and Ph.D. degrees in Computer Science from the University of California, Berkeley in 1980 and 1984. His educational background laid the foundation for his pioneering work in software testing and analysis. His research spans multiple critical areas of computer science, with a primary focus on binary code analysis and instrumentation , software security , and distributed and parallel program performance . Miller is particularly renowned for founding the field of Fuzz random software testing in 1988 and dynamic binary code instrumentation in 1992. His work bridges theoretical computer science with practical applications, addressing real-world challenges in high-performance computing systems, cybersecurity, and scalable distributed systems. His research has direct applications in national security through his work with DHS and TrustedCI, the NSF Cybersecurity Center of Excellence. Miller's publications reveal a consistent focus on binary analysis tools, security vulnerability detection, and performance optimization techniques. His recent work shows increasing emphasis on ransomware analysis, GPU performance optimization, and practical security tool development for developers. The trajectory of his research demonstrates how foundational work in binary instrumentation has evolved to address contemporary cybersecurity challenges while maintaining relevance to high-performance computing environments. Among his notable honors, Miller is a Fellow of the ACM and recipient of the prestigious Jean-Claude Laprie Award in Dependable Computing , an R&D 100 Award , and multiple best paper awards including at HPDC 2009 and CCGrid 2018. His contributions to the field have been recognized through distinguished lectureships at institutions including Ben Gurion University and IBM T.J. Watson Research Center. As an educator, Miller has mentored numerous students and taught courses spanning operating systems, software security, and distributed systems. He has received teaching awards and developed educational resources including free and open software security training materials. His work with TrustedCI has focused on cybersecurity for scientific communities, helping researchers secure their computational infrastructure while maintaining research productivity. Miller also co-directs the MIST software vulnerability assessment project in collaboration with colleagues at the Autonomous University of Barcelona. Miller directs the Paradyn Tools project, which investigates program scalability and binary program analysis technologies for use in HPC, systems design, and cyber-security. His work extends to practical applications through collaborations with government agencies including DHS and the U.S. Secret Service Electronic Crimes Task Force. His research group has developed tools like Dyninst, MRNet, and the Wisconsin Safety Analyzer that have become foundational in their respective domains.

Hussam Amrouchمشاهده پروفایل

استاد

• AI Hardware Acceleration
• Emerging Nanoelectronics
• Cryogenic Circuits
• +۴ مورد دیگر

Prof. Hussam Amrouch is a Full Professor of AI Processor Design at the Technical University of Munich (TUM), leading the TUM School of Computation, Information and Technology. His research focuses on ultra-efficient embodied AI, reliable designs in emerging technologies, and cryogenic circuits for quantum computing. He holds a Dr.-Ing. from Karlsruhe Institute of Technology (2015, Summa cum Laude) and previously led the "Dependable Hardware" group at KIT and the Chair of Semiconductor Test and Reliability at University of Stuttgart. He is affiliated with Munich Quantum Valley (MQV) and Munich Institute of Robotics and Machine Intelligence (MIRMI). Research interests include ferroelectric FETs, in-memory computing, cryogenic electronics, and neuromorphic systems. Key achievements include 10× HiPEAC Paper Awards, 3× DAC/DATE best paper nominations, and pioneering work on FeFET-based AI accelerators. His work bridges nanoelectronics with AI, addressing challenges in energy efficiency, reliability, and quantum integration. Publications span cutting-edge topics like cryogenic FinFETs, hyperdimensional computing, and monolithic 3D integration. He has developed novel testing methodologies, self-aware silicon systems, and energy-efficient architectures for edge-AI. Current projects explore cryogenic circuit design, radiation-resistant FeFETs, and carbon-efficient 3D neural networks. His awards reflect contributions to high-performance and embedded architectures. Research groups under his leadership focus on device-level innovations and system-level integration of emerging technologies. He actively contributes to interdisciplinary initiatives at MQV and MIRMI, advancing quantum computing and AI hardware frontiers.

• Hardware Design
• FPGA Programming
• Parallel Computing
• +۵ مورد دیگر

Satnam Singh is a Professor at Newcastle University's School of Electrical and Electronic Engineering, UK. With a research career spanning over three decades from 1989 to present, Singh has established himself as a leading expert in hardware design, FPGA programming, and parallel computing systems. His research interests focus on hardware-software co-design , reconfigurable computing , and functional programming applications for hardware design. Singh has pioneered work in using functional languages like Haskell for hardware description and verification, particularly through his contributions to the Lava hardware description language. His work bridges theoretical computer science with practical hardware implementation challenges. Analysis of his publication history reveals a clear evolution from early work on formal verification and FPGA design in the 1990s, through substantial contributions to parallel programming models in the 2000s, to more recent applications of machine learning techniques in diverse domains including cheminformatics and sensory systems. His 2022-2025 publications demonstrate continued innovation in specialized processor programming, AI applications for olfactory systems, and health hazard classification using deep learning. Singh has maintained extensive collaborations throughout his career, notably with Krishna R. Pattipati (15 joint publications), Anuradha Kodali (8 publications), and David J. Greaves (5 publications), reflecting his ability to bridge theoretical computer science with practical engineering applications. His work spans multiple prestigious venues including FPGA, FCCM, ICFP, and IEEE Transactions on Systems, Man, and Cybernetics, demonstrating both theoretical depth and practical impact across computer architecture, programming languages, and applied machine learning domains.

Borivoje Nikolicمشاهده پروفایل

استاد

• Integrated Circuits (VLSI)
• Computer Architecture
• Signal Processing
• +۳ مورد دیگر

Borivoje Nikolic is a Professor in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley. He holds a Ph.D. from UC Davis (1999) and academic degrees from the University of Belgrade (Dipl.Ing. 1992, M.Sc. 1994). His research focuses on integrated circuits, VLSI design, and energy-efficient electronics, with contributions to analog/digital circuit design, signal processing algorithms, and wireless communication systems. Notable roles include Technical Program Chair for the 2022 Symposium on VLSI Technology and Circuits, and General Chair for the 2024 IEEE Symposium on VLSI Technology and Circuits. He was an IEEE Distinguished Lecturer (2014–2015). His work has led to over 100 publications in top-tier venues like ISSCC, IEEE Journal of Solid-State Circuits, and IEEE Transactions on Circuits and Systems. Research interests span: VLSI Design Automation and Generators Low-Power and Energy-Efficient Circuits Wireless Communication Systems and MIMO Architectures Robotics and Heterogeneous SoC Design His publications highlight innovations in clock generators, data converters, and hardware-software co-design for AI/ML acceleration. Awards include the NSF CAREER Award (2003), IEEE Fellow (2017), and the 2024 SSCS Innovative Education Award.

Klaus Hofmannمشاهده پروفایل

استاد

• Analog and Digital Integrated Circuits
• High-Voltage Generation Systems
• Sensor Integration in Robust Environments
• +۵ مورد دیگر

Prof. Klaus Hofmann is a Professor at the Technical University of Darmstadt, leading the Department of Electrical Engineering and Information Technology. His research focuses on integrated circuits, high-voltage systems, and sensor-integrating machine elements, with applications in Industry 4.0 and robust environments. Previously, he held senior engineering roles at Siemens HL, Infineon Technologies AG, and Qimonda AG, specializing in semiconductor product development and advanced technology. Education: Holds a doctorate in engineering from TH Darmstadt (1997), with earlier studies at Ruhr University Bochum (Germany), Purdue University (USA), and the University of Cooperative Education Stuttgart. His academic journey includes training in electrical engineering and communications systems. Research Interests: Dr. Hofmann's work spans analog/digital circuit design, reconfigurable electronics, and sensor integration in mechanical systems. He explores emerging technologies like memristive devices and oscillator-based computing for optimization problems. His projects emphasize practical applications, such as energy-efficient sensor platforms and sustainable microelectronics education. Recent Work: Key contributions include studies on Germanium FETs, adaptive differential amplifiers, and sensor-integrating bolts for multi-axial force measurement. He collaborates on initiatives like the SENSOTERIC project to advance smart sensors and promotes STEM education through innovative teaching methods. Labs & Teams: Leads the Integrated Electronic Systems (IES) Group at TU Darmstadt, focusing on interdisciplinary research in circuits, systems, and emerging technologies.

Ramon Canal Corretgerمشاهده پروفایل

استاد

• Microarchitecture
• Processor Design
• Memory Design
• +۱۵ مورد دیگر

Ramon Canal Corretger is a Full Professor in the Department of Computer Architecture at the Faculty of Informatics of Barcelona (FIB), Universitat Politècnica de Catalunya (UPC). He previously served as Vice Dean of Postgraduate Studies at FIB and leads the VirtuOS (Virtualization and Operating Systems) research group. His work bridges computer architecture, hardware security, and system-level reliability. Doctorate from UPC, co-supervised at the University of Wisconsin-Madison Sabbaticals at Harvard University (2006–2007) and the University of Cyprus (2019–2020) Active leadership in EU-funded projects such as Vitamin-V (Horizon Europe) His research focuses on microarchitecture, processor and memory design, reliability under variability, and security at the hardware-software interface. He explores low-power multicore architectures, virtualization optimizations, and secure RISC-V-based systems. His recent work integrates AI for intrusion detection and privacy-preserving federated learning in fog computing environments. The most recent publications demonstrate a strong trend toward security, reliability, and trustworthy computing , particularly in RISC-V ecosystems and cloud/edge infrastructures. There is increasing emphasis on hardware-software co-design , attack detection via performance monitoring , and energy-efficient secure accelerators using emerging technologies like neuromorphic and photonic computing. Scientific Awards: HiPEAC Paper Awards (2017, 2010) IEEE Senior Member (2016) Fulbright Award (2006) IBM Faculty Award (2000) Best Student Paper at HPCA-6 (2000) Multiple teaching excellence recognitions from UPC and AQU Catalunya First Prize in Epson Foundation Rosina Ribalta Award (2001) He has advised several PhD students including Manish Rana, Zoran Jaksic, and Shrikanth Ganapathy, many of whom received honors such as the Intel Doctoral Student Programme recognition. His research is supported by competitive grants from the Spanish government, EU Horizon programs, and industry collaborations. He is actively involved in the design of secure, reliable, and efficient computing systems for future cloud and embedded applications. He leads the VirtuOS research group, which focuses on virtualization, operating systems, and hardware-software interface optimization. The group contributes to open-source RISC-V initiatives and participates in large-scale European R&D projects targeting trustworthy computing infrastructures.