جستجوی استاد راهنمای دانشگاهی

Dr. Tiantai Deng is a Lecturer in Electronics and Digital Systems at the School of Electrical and Electronic Engineering , University of Sheffield (since 2021). His industrial background includes a senior research engineer role at HiSilicon/Huawei , where he focused on hardware architecture design for CNN, GEMM, and image/video processing on FPGAs/ASICs. Education: BEng, MSc, PhD Research interests span FPGA-based hardware acceleration , sparse processing architecture for CNN/GEMM, number system design , approximation computing , and high-level design environments . His work integrates algorithm-hardware co-optimization for efficiency in AI and mathematical computing. Recent publications emphasize neurodynamic systems for opinion modeling, parallel processing elements for ODE/AI acceleration, and low-power FPGA implementations for clustering/modulation classification. Earlier work addressed combustion dynamics and image processing pipelines. Contact: t.deng@sheffield.ac.uk | Office: G108, Sir Frederick Mappin Building, Sheffield S1 3JD | ORCID 0000-0003-4507-5746

• FPGA-based Hardware Acceleration
• Sparse Processing Architecture for CNN and GEMM
• Number System and Approximation Computing
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Professor İsmail Serdar Özoğuz is a distinguished academic at Istanbul Technical University , affiliated with the Department of Electronics and Communication Engineering . Holding the title of Professor since 2009, he has contributed extensively to analog circuit design, neural network applications, and wireless communication systems. Ph.D. in Electronics and Communications Engineering (1995) Department Head (2020-present) Vice Dean (2017-2020) Research Interests: His work spans Electronics , Analog Design , Circuits and Systems Theory , with recent focus on: Neural network-based filter optimization Memristor emulator circuits Spintronic devices for wireless and memory applications Intelligent optimization in RF designs Article Trends: Recent publications highlight AI integration in engineering challenges, power efficiency in wireless systems, and hardware-software co-optimization . His work bridges theoretical models (e.g., fractional-order neural networks) with practical implementations (e.g., GaN amplifiers). Scientific Awards: GEBIP Award (TUBA), 2002 Mustafa Parlar Foundation Research Incentive, 2003 TUBITAK Incentive Award, 2004 Grants & Projects: As Principal Investigator, he has led initiatives on: Spintronic devices for wireless/memory/analog uses Passive combiners in HF transmitters High-power GaN amplifier development Fractional-order neural network models

• Electronics
• Analog Design
• Circuits and Systems Theory
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Tao Li is an Assistant Professor at the Purdue Polytechnic Institute of Purdue University, specializing in security and privacy challenges for networked and mobile systems. His work bridges AI/machine learning with practical applications in indoor localization, cloud/edge computing, and mobile crowdsourcing.

• Security and Privacy
• Networked Systems
• Mobile Systems
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Dr.-Ing. Andreas Hoffmann is affiliated with RWTH Aachen University as a researcher at the Chair of Software for Systems on Silicon. His work focuses on the intersection of software development and silicon-based systems. Research Interests: Design of system-on-chip architectures Hardware-software co-design methodologies Embedded systems programming Very large-scale integration (VLSI) design approaches Computer architecture optimization for silicon systems Application-specific integrated circuit (ASIC) software development

• Software Engineering
• Embedded Systems
• Computer Architecture
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Dr. Gul N. Khan is a Professor in the Department of Electrical, Computer and Biomedical Engineering at Toronto Metropolitan University (formerly Ryerson University). He has held academic positions at the University of Saskatchewan, Nanyang Technological University, RMIT University, and Quaid-i-Azam University. His career spans over three decades with a focus on embedded systems , network-on-chip (NoC) , and heterogeneous computing . Education: Ph.D. (Imperial College, 1989), M.Sc. (Syracuse University, 1982), B.Sc. (UET Lahore, 1979) Dr. Khan’s research interests include hardware-software co-design , CPU-GPU systems , fault-tolerant computing , and smart RFID systems . His work has led to over 125 refereed publications and three US patents. His recent publications highlight advancements in GPU auto-tuning , NoC synthesis , and digital time interpolators . Despite being listed in a Google Scholar block with unrelated public health topics, these appear to be errors, as his core expertise remains in computer engineering. Dr. Khan has supervised numerous graduate projects in embedded systems and SoC design . He served as Program Director for Computer Engineering from 2004–2015 and leads the Microsystems Research Lab at Toronto Metropolitan University.

• Embedded Systems
• Hardware/Software Co-design
• Network-on-Chip (NoC)
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Yuvraj Agarwal is a Professor at Carnegie Mellon University's School of Computer Science, where he founded and directs the SYNERGY Lab. He previously served as Executive Director of the NSF Expeditions in Variability (2010-2013) and was affiliated with UCSD's Microelectronic Embedded Systems Lab (MESL) and Systems and Networking Group (SysNet). Research Themes : Systems & Networking, Embedded Systems, Mobile Computing, with focus on energy efficiency and privacy. Leadership : Director of SYNERGY Lab, Co-PI in NSF CoDec expedition, Brick Consortium member. His research bridges hardware-software systems with societal impact, including smart building energy optimization using occupancy sensing, mobile privacy tools like ProtectMyPrivacy, and IoT security labeling frameworks . Recent work includes the Computational Decarbonization NSF expedition ($12M over 5 years) to reduce carbon footprints in societal infrastructure. Scientific Awards : 2024: Promoted to Full Professor 2012: UCSD Outstanding Faculty Award for Sustainability 2016: Google Faculty Research Award Advising : Guided 11 PhD students to completion (now in academia/startups) and mentored 10+ MS/undergraduate researchers. Current advisees include 4 PhD students in Carnegie Mellon's Societal Computing program. Grants : NSF Expeditions in Variability (2010-2013), NSF #1564009 (2016), NSF #1526237 (2015), NSF #1513957 (2015), DARPA BRANDEIS program (2015), and Google research funding (2015). Labs & Collaborations : SYNERGY Lab at CMU focusing on IoT and smart campus systems. Key collaborations with the Brick Consortium (Johnson Controls, Schneider Electric), Microsoft Research, Intel Research, and academic institutions including UCSD and UMass Amherst.

• Systems and Networking
• Embedded Systems
• Mobile Computing
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Hamed Ghavamnia is a Security Researcher at Bloomberg, where he focuses on software and systems security. Previously, he served as an Assistant Professor at the School of Computing, University of Connecticut (2023–2024). He holds a PhD in Computer Science from Stony Brook University (under Michalis Polychronakis), an M.S. in Computer Engineering from Sharif University of Technology (Network Security focus), and a B.E. in Software Engineering from University of Isfahan. His research interests center on system/software security, particularly attack surface reduction through program analysis and memory-safe languages. Key projects include Confine (system call policy generation for containers), Temporal Specialization (system call filtering based on execution phases), and LeakLess (data protection in serverless platforms). He has contributed to seminal works on configuration-driven security (C2C), memory protection (xMP), and automated policy inference. His work bridges static/dynamic analysis, program specialization, and security hardening. Notable outcomes include reducing attack surfaces via system call filtering and debloating techniques, with applications in cloud and containerized environments.

• System/Software Security
• Attack Surface Reduction
• Memory Safe Languages
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Sam Amiri is a Lecturer in Microelectronics / Embedded Electronics at Loughborough University. He holds a BSc in Software Engineering (2007, Iran), M.Sc. in Embedded Systems (2010, Masaryk University), and Ph.D. in Electrical/Electronic Engineering (2014, Masaryk University). Prior to his current role, he worked as a Researcher at Queen’s University Belfast (2015-2017) and the University of Bristol (2017-2018). His research focuses on hardware design, embedded systems, signal/image processing, and FPGA-based solutions for real-time systems. Key research areas include intrusion detection systems for vehicular networks using binarized neural networks, RISC-V processor optimization for neural networks, and FPGA-based acceleration of CNN models. His work also explores reliability engineering in wind turbine drivetrains and heterogeneous computing architectures combining FPGAs with CPUs. Publications span topics like embedded cybersecurity, low-power edge computing, and hardware-software co-design for real-time applications. His recent work emphasizes lightweight neural networks on constrained hardware and fault tolerance in aerospace systems. No specific awards or grants are listed in the provided information.

• Embedded Systems
• Hardware Design
• Signal Processing
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Michael D. Smith is the John H. Finley, Jr. Professor of Engineering and Applied Sciences and Distinguished Service Professor at Harvard University. He previously served as Dean of the Faculty of Arts and Sciences (FAS) for 11 years, leading Harvard's largest school with a focus on undergraduate education, faculty development, and interdisciplinary research. He holds a Ph.D. from Stanford University (1993), a Master’s from Worcester Polytechnic Institute, and a Bachelor’s from Princeton University. His academic career is complemented by industry experience, including co-founding Liquid Machines (acquired by Check Point in 2010), and advisory roles with venture capital firms like 1984 Ventures and GSV Ventures. Research interests span computer science education innovation, technology’s societal impact, and hardware/software systems. He pioneered work on voltage noise mitigation in processors, cybersecurity solutions, and online education platforms like edX. Awards include the NSF Young Investigator Award (1993), Alpha Iota Teaching Prize, and W.E.B. Du Bois Medal. Currently, he advises startups and educational initiatives such as CS Forward and Kira Learning, while developing new in-person and online courses. His leadership roles include Board member at The Peddie School and former edX Board member (2012–2018).

• Computer Science
• Education Technology
• Data Security
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Bradley Denby is an Assistant Professor in the Department of Aerospace & Ocean Engineering at Virginia Tech, holding the Marty and Anna Irvine AOE Faculty Fellow position. He specializes in cyber-physical systems, computational nanosatellite constellations, and machine learning for autonomy, with a focus on orbital edge computing. Denby leads the Starbelt Lab, focusing on advanced air mobility and satellite systems research. Education: PhD in Electrical and Computer Engineering, Carnegie Mellon University (20XX) MS in Computer Engineering, Air Force Institute of Technology (AFIT) BS in Physics, Southern Illinois University Research Interests: Denby’s work bridges computer science and aerospace engineering, addressing challenges in computational satellite systems, edge computing in space, and autonomous decision-making. His projects include batteryless satellites (Tartan Artibeus) and high-coverage nanosatellite constellations (EagleEye). He explores orbital edge computing frameworks to enable real-time data processing directly on satellites. Recent Research Trends: His publications emphasize scalable nanosatellite constellations, overcoming computational bottlenecks in space, and integrating machine learning for on-orbit inference. Themes include energy-efficient systems, inter-satellite communication, and mission-critical autonomy. Awards: Marty and Anna Irvine AOE Faculty Fellow (Virginia Tech) Teaching & Labs: Instructs courses on space engineering, satellite design, and advanced air mobility. Runs the Starbelt Lab (website/GitHub linked).

• Cyber-physical systems
• Computational nanosatellite constellations
• Machine learning for autonomy
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Minseok Kwon is a Professor and Associate Chair in the Department of Computer Science at Rochester Institute of Technology (RIT), part of the Golisano College of Computing and Information Sciences. He holds a Ph.D. and M.S. from Purdue University and a B.S. from Seoul National University. His research focuses on network systems and protocols (including programmable networks, P4 software testing, SDN, and in-network security) as well as machine learning systems (especially workload prediction). He is a member of the RIT Center for Cybersecurity. Professional Contributions Technical committee member for Computer Communications Journal and multiple IEEE conference program committees (e.g., HPCC 2021, ICC 2021-2021). Publicity Chair for ICNP 2008 and NPSec 2005. Research Highlights Recent work emphasizes programmable data planes for green data centers, security in wireless server networks, and efficient packet processing using P4. Notable projects include P4Kube (in-network Kubernetes load balancing) and CuVPP (filter-based routing optimization). His publications span topics from network latency measurement tutorials to cutting-edge SDN implementations. Awards & Recognition Recipient of the Best Paper Award at IEEE Cluster 2020 for CuVPP . Teaching & Advising Current courses include CSCI-261 (Algorithms), CSCI-788 (MS Project), and supervising graduate co-op placements. His advising focuses on advanced network systems and cloud computing.

• Computer Networks
• Distributed Computing
• Network Security
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Shan Lu is a Professor in the Department of Computer Science at the University of Chicago. He is affiliated with the UChicago Systems Group and holds a faculty position at the Crerar Library. His research focuses on software systems, reliability, and program analysis, with an emphasis on improving software correctness and efficiency through automated tools and methodologies. Lu earned his Ph.D. from the University of Illinois, Urbana-Champaign in 2008, under the guidance of Yuanyuan Zhou. His work spans multiple areas, including concurrency bug detection, performance optimization, and machine learning integration in software systems. His research interests include developing automated tools for detecting and fixing software bugs, optimizing database-backed web applications, and enhancing the reliability of distributed systems. Recent work highlights include innovations in large language model serving, retry bug detection, and hybrid data plane optimizations. Lu has been recognized with prestigious awards, including the SOSP Best Paper Award (2019), OSDI Best Paper Award (2022), and ACM Distinguished Member status. He actively contributes to program committees, including roles such as Vice Chair of the ACM Publications Board and Chair of ACM SIGOPS. His advising spans over 20 students, many of whom have secured notable positions at top institutions and companies like Google, Facebook, and LinkedIn. Lu’s research has also led to impactful tools like SkyWay, Yak, and DCatch, addressing critical challenges in software systems.

• Computer Systems
• Software Reliability
• Program Analysis
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Milos Prvulovic is a Professor in the School of Computer Science at Georgia Institute of Technology's College of Computing. His research focuses on computer architecture, hardware security, and physical side channels, particularly leveraging electromagnetic emissions for program monitoring, malware detection, and secure execution. He has published extensively in top-tier conferences like HPCA, MICRO, and IEEE Transactions series. His work has been recognized with awards including ACM Senior Member (2009) and multiple best paper awards. Teaching includes courses such as High Performance Computer Architecture (OMS CS 6290/CS 4290), Processor Design (CS 3220), and advanced topics like Reliability & Security in Computer Architecture (CS 7292/8804). His research lab explores innovative solutions for embedded system security, IoT protection, and side-channel vulnerabilities. Key contributions include the EMSim tool for electromagnetic side-channel simulation, REMOTE malware detection framework, and the EDDIE anomaly detection system. Collaborations with Alenka Zajic and others highlight interdisciplinary work in hardware-software security interfaces.

• Computer Architecture
• Hardware Security
• Side Channels
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Billy Moses is an Assistant Professor in the Department of Computer Science at the University of Illinois Urbana-Champaign (UIUC), with affiliate roles in Electrical and Computer Engineering (courtesy) and the Coordinated Science Library. He holds a PhD and dual S.B. degrees in Electrical Engineering and Computer Science from MIT (2023, 2017), as well as an S.B. in Physics from MIT (2017). His research focuses on compilers, parallel computing, and compiler-driven optimization techniques for high-performance systems. Moses has pioneered work on the Tapir framework for fork-join parallelism, the MLIR compiler infrastructure, and Enzyme for automatic differentiation. Moses' research spans compiler design, GPU acceleration, and AI-driven compiler optimization. Notable contributions include the Polygeist compiler for C-to-MLIR transformation, the Autophase reinforcement learning system for HLS phase ordering, and the Enzyme framework for GPU kernel differentiation. His work emphasizes practical compiler solutions for parallelism, performance portability, and end-to-end code generation in domains like deep learning and scientific computing. Awards: 2024 SIGHPC Doctoral Dissertation Award Courses Taught: CS 598 APE (Advanced Performance Engineering) His recent projects include compiler-based approaches to GPU-to-CPU transpilation, performance portability in heterogeneous systems, and AI-driven compiler decision-making. Moses collaborates with industry and academic partners on advancing compiler technologies for exascale computing and machine learning acceleration.

• Compilers
• Parallel Computing
• Automatic Differentiation
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Martin Sjölund is an Associate Professor at Linköping University's Department of Computer and Information Science (IDA), part of the Software and Systems (SAS) division. His work focuses on software engineering and cyber-physical systems, with a specialization in Modelica compiler development and open-source simulation tools. He contributes to the OpenModelica project, advancing compiler frameworks, integration with Julia, and standardization efforts. Research interests include compiler design, formal methods, and domain-specific languages. Recent work emphasizes modular compiler architectures, structural variability handling, and interoperability between Modelica and other systems like Julia and Python. He has co-authored over 30 peer-reviewed publications since 2017, with a focus on compiler optimization, co-simulation, and educational applications of Modelica. As part of the SAS division, he collaborates with researchers like Lena Buffoni, Adrian Pop, and Peter Fritzson on projects funded by the MODPROD Center and other initiatives. His contributions to open-source tools have been recognized through conference proceedings and industry partnerships.

• Software Engineering
• Cyber-Physical Systems
• Modelica Compiler Development
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