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Dr. Ke (Cory) Wang serves as Assistant Professor at the University of North Carolina at Charlotte in the Department of Electrical and Computer Engineering, where he conducts research in computer architecture and parallel systems. Ph.D. in Computer Engineering (2022), George Washington University M.Sc. in Electrical Engineering (2015), Worcester Polytechnic Institute B.Sc. in Computer Science (2013), Peking University His research focuses on machine learning-enabled computer architecture with emphasis on network-on-chip design , domain-specific accelerators , and cross-layer optimization for manycore systems. Specific areas include anomaly detection, graph neural network acceleration, and fault-tolerant communication frameworks. Recent publications demonstrate increasing integration of deep learning techniques with traditional architecture design, particularly for graph convolutional networks and heterogeneous systems . His work addresses multi-objective optimization across performance , energy efficiency , and security dimensions . Key scientific recognitions include: National Science Foundation award (CSR: Small: Cross-layer Design, 2023) National Science Foundation award (CRII: SHF: Flexible Design Framework, 2023) Dr. Wang leads the Intelligent Computer Architecture & Systems Laboratory , which develops advanced frameworks for network-on-chip optimization and AI-driven system design.

• Computer Architecture
• Interconnection Networks
• Machine Learning
• +8 more

Carliss Y. Baldwin is the William L. White Professor of Business Administration at the Harvard Business School. She specializes in corporate finance and real options theory, with a focus on the financial consequences of modular design in hardware and software systems and their impact on economic institutions. Education: MIT (BSc in Economics, 1972), Harvard Business School (MBA, DBA) Her research explores how modular architectures enable flexibility, scalability, and innovation in the computer industry, as detailed in her co-authored book Design Rules: The Power of Modularity . She also investigates the interplay between design principles and economic value creation, emphasizing the role of organizations in implementing and commercializing designs. Baldwin has served on Harvard University's joint Ph.D. program in Information Technology and Management and currently teaches courses like Acquisitions & Alliances at the MBA level. Beyond academia, she contributes to the Climate Neutral Network and Madeira School as a director and trustee, respectively.

• Corporate Finance
• Real Options Theory
• Modular Design
• +2 more

Dr. Viktor Melnyk serves as an Assistant Professor in the Department of Applied Computer Science at the Institute of Mathematics, Informatics and Landscape Architecture within the Faculty of Natural Sciences and Technology at John Paul II Catholic University of Lublin. His academic activities span teaching, research, and extensive student supervision across multiple years. His research interests focus on cybersecurity, cryptography, and FPGA-based systems, with particular expertise in wireless network security (especially IEEE 802.15.4), hardware acceleration, and machine learning applications in electronic design. His work bridges theoretical computer science with practical security implementations, evident in his numerous publications and student thesis topics. Analysis of Dr. Melnyk's recent publications reveals a consistent focus on hardware security implementations, particularly for low-rate wireless networks and reconfigurable computing systems. His research trajectory shows increasing integration of machine learning techniques with traditional security protocols, reflecting broader trends in the field. The publications demonstrate both theoretical depth and practical implementation focus, with many papers addressing specific hardware implementations. Dr. Melnyk has served as an Independent External Expert for evaluating grant proposals in the European COST Open Call competition, indicating professional recognition of his expertise. His contributions to the academic community extend to reviewing numerous scientific articles across various domains including biomedical data processing, cyber-physical systems, and cloud computing. His academic advising is exceptionally active, with documentation of supervising over 100 diploma theses from 2015 through 2025 across both Bachelor's and Master's levels. The thesis topics consistently align with his research interests, covering cybersecurity, cryptography, network security, and hardware implementations. He teaches courses including 'Operating Systems,' 'Network Data Protection Technologies,' and 'Multimedia Systems,' with teaching materials adapted for remote delivery during the pandemic period.

• Cybersecurity
• Cryptography
• Information Security
• +7 more

Scott Mahlke is a Professor and Associate Chair in the Department of Electrical Engineering and Computer Science at the University of Michigan's College of Engineering. He is affiliated with both the Advanced Computer Architecture Laboratory and the Software Systems Laboratory. Dr. Mahlke joined the University of Michigan in 2001 after completing his Ph.D. at the University of Illinois and working at HP Laboratories. Ph.D., University of Illinois Former Researcher, HP Laboratories Dr. Mahlke's research spans compilers, computer architecture, and high-level synthesis, with particular focus on overcoming challenges in performance, power consumption, and reliability for next-generation computer systems. His work integrates hardware and software co-design approaches to address fundamental limitations in modern computing platforms. His research has evolved from traditional compiler and architecture topics toward increasingly incorporating machine learning acceleration, autonomous systems, and reliability engineering. Analysis of his recent publications (2021-2025) reveals a strong trend toward hardware-software co-design for emerging workloads, particularly in autonomous systems, neural network acceleration, and reliability-aware computing. His work demonstrates consistent innovation in bridging compiler technology with architectural innovations to solve real-world performance and efficiency challenges. Dr. Mahlke has received significant recognition for his contributions to the field: National Science Foundation CAREER Award (2003) for "Compiler-Directed Synthesis of Application Specific Processors" Morris Wellman Faculty Development Assistant Professor appointment (2004) ISCA Most Influential Paper Award (2006) for the 1991 paper "IMPACT: An Architectural Framework for Multiple Instruction Issue Processors" Young Alumni Award from the University of Illinois ECE Department (2007) As an educator, Dr. Mahlke has taught core computer systems courses including EECS 370 (Introduction to Computer Organization), EECS 483 (Compiler Construction), and EECS 583 (Advanced Compilers) since joining Michigan. His teaching philosophy follows Yale Patt's 10 commandments for teaching, emphasizing understanding over memorization, genuine respect for students, and taking responsibility for course content. He has received mixed but generally positive student evaluations, with students noting both his deep subject matter expertise and areas for improvement in lecture delivery. Dr. Mahlke maintains active research leadership through his affiliations with the Advanced Computer Architecture Laboratory and Software Systems Laboratory, where his team continues to explore innovative approaches to compiler and architecture challenges in modern computing systems.

• Compilers
• Computer Architecture
• High-Level Synthesis
• +5 more

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
• +3 more

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
• +4 more

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
• +6 more

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
• +3 more

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)
• +7 more

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
• +4 more

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
• +2 more

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
• +3 more

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
• +3 more

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
• +3 more

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
• +5 more