جست‌وجوی استاد و پژوهشگر

• Computer Architecture
• Parallel Programming
• Compilers
• +۶ مورد دیگر

Martha A. Kim is an Associate Professor in the Department of Computer Science at Columbia University's School of Engineering and Applied Science. She serves as a Member of the Data Science Institute, Center Co-Chair of the Center for Computing Systems for Data-Driven Science, and Chair of the Computer Engineering Program. Professor Kim's research focuses on computer architecture with particular emphasis on the hardware-software interface. Her work develops tools and designs that help computer systems operate efficiently and intuitively, including database accelerators for energy-efficient queries, techniques for optimizing parallel software to better utilize hardware resources, and improving the versatility of specialized hardware accelerators. She leads the ARCADE Lab where she conducts research in computer architecture, parallel programming, compilers, and low-power computing. Her publication record shows consistent output at top-tier conferences including ASPLOS, MICRO, MEMOCODE, ISLPED, DAC, DaMoN, CC, and CGO, with recent work spanning video transcoding in the cloud, pipelined dataflow circuits, thermal monitoring, and database acceleration. Professor Kim has received several prestigious awards: 2013 Rodriguez Family Award 2015 Edward and Carole Kim Faculty Involvement Award 2013 NSF CAREER award 2016 Anita Borg Early Career Award She currently advises three doctoral students (Martha Barker, Thomas Repetti, and Andrea Lottarini) and has previously graduated PhD students Melanie Kambadur (2016) and Lisa Wu (2014). Her research is supported by C-FAR, DARPA, Google, Intel, and NSF. Professor Kim teaches Fundamentals of Computer Systems, Computer Architecture, and Principles and Practice of Parallel Programming courses at Columbia University. The ARCADE Lab provides opportunities for students to work on cutting-edge research in hardware-software interface optimization, with applications in energy efficiency, database acceleration, and parallel computing systems.

• Artificial Intelligence
• Edge AI
• Sensors
• +۷ مورد دیگر

Amirreza Yousefzadeh is an Assistant Professor specializing in computer architecture design for embedded systems. His research focuses on hardware acceleration for artificial intelligence, particularly in energy-efficient neuromorphic computing and edge AI applications. Research Interests Neuromorphic computing architectures Event-driven AI hardware Sparsity exploitation in neural networks Embedded vision systems Digital circuit design for AI Research Trends Recent work (2024-2025) demonstrates expertise in spiking neural networks (SNNs), activation sparsity, and hardware-software co-design for neuromorphic processors. Key areas include object detection, energy efficiency optimization, and digital implementations of synaptic delays. Technical Contributions Developed SENMap for multi-objective data-flow mapping Created SENSIM simulator for multi-core neuromorphic systems Investigated 3D stacking for memory-dominated architectures Explored temporal sparsity in event-based processing

Stefano Markidisمشاهده پروفایل

استاد

• High-Performance Computing
• Heterogeneous Systems
• Quantum Computing
• +۳ مورد دیگر

Stefano Markidis is a Professor of Computer Science specializing in high-performance computing systems at KTH Royal Institute of Technology in Sweden. He works in the Division of Computational Science and Technology, focusing on supercomputers, quantum computers, and computational methods for scientific simulations. His research spans multiple domains including plasma physics, computational fluid dynamics, and quantum computing. Markidis holds an MS degree from Politecnico di Torino and a PhD in Nuclear Engineering from the University of Illinois at Urbana-Champaign. Prior to joining KTH, he was a graduate research assistant at Los Alamos National Laboratory and Lawrence Berkeley National Laboratory, followed by a postdoc at KU Leuven. His academic journey reflects a strong foundation in both engineering and computational science. His primary research interests include High-Performance Computing , Heterogeneous Systems , and Quantum Computing . Markidis develops computational methods for plasma physics, particle-in-cell simulations, and fluid dynamics. His work bridges theoretical physics and practical computing, with applications in space physics, fusion energy, and materials science. He is particularly known for contributions to parallel computing, GPU acceleration, and the development of scalable simulation frameworks like Neko for computational fluid dynamics. His research increasingly integrates machine learning techniques with traditional numerical methods. Analysis of Markidis' recent publications reveals a strong focus on quantum-classical hybrid computing, advanced particle-in-cell methods, and high-fidelity computational fluid dynamics. His work demonstrates expertise in programming models for heterogeneous architectures including GPUs and quantum processors, with growing emphasis on AI-enhanced scientific computing. R&D100 award (2005) for the CartaBlanca project R&D100 award (2017) for the SHIELDS project Markidis teaches multiple courses at KTH including Applied GPU Programming, Quantum Computing for Computer Scientists, and High-performance Computing for Computational Scientists. He has supervised numerous degree projects across various specializations in computer science and electrical engineering. His research has been supported by various grants related to high-performance computing and quantum technologies, with applications spanning from space physics to medical treatments. Markidis leads research in computational science with a focus on developing frameworks like Neko for extreme-scale computational fluid dynamics. His team works on integrating traditional HPC methods with emerging technologies including quantum computing and AI, contributing to advancements in scientific simulation across multiple disciplines.

• Power Electronics
• Semiconductor Devices
• Analog Circuit Design
• +۳ مورد دیگر

Prof. Dr. Ertugrul Sönmez serves as Professor in the Faculty of Engineering at Reutlingen University, leading the Semiconductor Circuit Technology Lab. His research focuses on power electronics innovation through semiconductor circuit design, analog systems, and advanced converter topologies. Contact details include email ertugrul.soenmez@reutlingen-university.de and office location in Building 4, Room 213. His work centers on power conversion systems with emphasis on GaN-based technologies , wireless power transfer , and active damping techniques . Key interests include delta-sigma modulation for multilevel converters, dV/dt filter design for motor drives, and high-frequency operation using wide-bandgap semiconductors. Research addresses critical industry challenges in electric vehicle charging, renewable energy integration, and industrial motor control systems. Recent publications (2022-2024) reveal strong focus on modular converter architectures and stability enhancement techniques. His work bridges theoretical control concepts with practical implementation, frequently appearing in top industry conferences like PCIM Europe. Collaborative projects with colleagues such as Gernot Schullerus demonstrate cross-disciplinary approaches to power electronics challenges. Through the Semiconductor Circuit Technology Lab, Prof. Sönmez provides hands-on research opportunities in semiconductor characterization and circuit synthesis. The lab supports student projects including bachelor's/master's theses and industry-collaborative initiatives like ArduSmartPilot. His consistent publication output indicates active supervision of graduate research in power electronics design and implementation.

• Computational Electromagnetics
• Electrical Machines
• Finite Element Analysis
• +۴ مورد دیگر

Professor Andrzej Demenko is a distinguished academic at Poznań University of Technology, where he holds a position in the Institute of Electrical Engineering and Industrial Electronics within the Faculty of Automatic Control, Robotics and Electrical Engineering. He serves as Chairman of the Committee on Electrical Engineering of the Polish Academy of Sciences, demonstrating his significant standing in the national academic community. His career spans several decades, beginning with his doctoral dissertation in 1975 on modeling magnetic circuits of electrical machines using potential field analyzers. Professor Demenko's research focuses on computational electromagnetics, electrical machine analysis and design, finite element methods, and power system safety. His work particularly emphasizes permanent magnet synchronous motors, field-circuit coupling approaches, and electromagnetic phenomena in nonlinear circuits. He has made significant contributions to understanding squirrel cage design in motors, temperature effects on motor performance, and the development of numerical methods for electromagnetic field calculations. Analysis of his recent publications (2015-2024) reveals a continued focus on advanced electrical machine design, particularly permanent magnet synchronous motors, with increasing attention to optimization techniques, thermal effects, and control strategies. His work consistently combines theoretical developments with experimental validation, demonstrating practical relevance alongside theoretical rigor. Professor Demenko has supervised five doctoral dissertations to completion and has reviewed numerous others, indicating his commitment to academic mentorship. His publication record is substantial, with 65 articles, 61 book chapters, and 11 books or edited volumes spanning several decades of research. As an active researcher, Professor Demenko continues to contribute to the field through ongoing projects, editorial work for international conferences like the Electromagnetic Phenomena in Nonlinear Circuits (EPNC) symposium series, and collaborations with researchers worldwide. His work bridges theoretical developments in computational electromagnetics with practical applications in electrical machine design and power system safety.

Klaus von Gleissenthallمشاهده پروفایل

استادیار

• Programming Languages
• Security
• Systems
• +۳ مورد دیگر

Klaus von Gleissenthall is a tenured Assistant Professor in Computer Science at Vrije Universiteit Amsterdam, affiliated with the Theory Group and VUSec security lab. He holds a joint appointment with CWI's Computer Security group. Previously, he was a post-doc at UCSD and completed his PhD at TUM under a Microsoft Research scholarship. His research integrates programming languages , security , and systems to develop formally verified, low-overhead solutions for hardware/software correctness. Key focus areas include: Side-channel attack mitigation via leakage contracts Refinement-type systems for hardware verification Byzantine fault tolerance in distributed systems Publications demonstrate strong emphasis on hardware security (45% of recent papers), formal methods (30%), and distributed systems (25%), with consistent appearances in top-tier venues (S&P, CCS, OOPSLA). Awards & Honors: ERC Starting Grant (€1.5M, 2024) Intel Hardware Security Award Honorable Mention (2020, 2024) Distinguished Paper Awards: CCS'23, OOPSLA'23, POPL'21 He advises four PhD students and two post-docs, supported by his ERC grant. Current projects include refinement types for hardware and pre-silicon leak detection. His lab collaborates with VUSec and CWI, focusing on scalable verification tools like LLVM Blade and methodologies for constant-time execution guarantees.

Minxue Panمشاهده پروفایل

استاد

• Software Modelling and Verification
• Software Analysis and Testing
• Cyber-Physical Systems
• +۵ مورد دیگر

Minxue Pan is a Professor and PhD supervisor at the Software Institute, State Key Laboratory for Novel Software Technology, Nanjing University, China. His research focuses on the dependability of complex software systems, with expertise spanning software modeling and verification, software analysis and testing, cyber-physical systems, mobile computing, and intelligent software engineering. Ph.D. in Computer Science and Technology from Nanjing University (2014), supervised by Prof. Xuandong Li B.Sc. from Nanjing University Studied at UC Berkeley's Department of Electrical Engineering and Computer Sciences (2009-2010) under Prof. Edward A. Lee Professor Pan's research interests center on improving software dependability through innovative approaches to modeling, analysis, and testing. His work spans traditional software systems, mobile applications (particularly Android), cyber-physical systems, and the application of AI techniques to software engineering problems. He has made significant contributions to GUI testing, vulnerability detection, and deep learning applications in software engineering. His recent publications (2024-2025) demonstrate a strong focus on applying advanced machine learning techniques to software testing and security challenges. Key trends include leveraging large language models for test migration, enhancing fault localization with graph learning and contrastive learning, developing specialized frameworks for Android security analysis, and improving test efficacy through GUI and functional equivalence. His work consistently targets real-world industrial settings and addresses practical challenges in mobile and complex software systems. ISSTA 2020 Distinguished Paper Award ICSE 2025 Best Artifact Award Professor Pan actively advises graduate students and has developed several notable tools including Q-testing (reinforcement-learning based Android testing), ISDChecker (model checking for interrupt-driven systems), PREFEST (preference-wise testing for Android), Sketchoid (GUI code search), and PI-REC (hand-drawn draft conversion). His research is supported by extensive publication records in top-tier software engineering venues including ASE, ICSE, FSE, ISSTA, and TOSEM. He teaches undergraduate courses in Advanced Programming with C++, Software System Design, and Software Construction, as well as graduate courses in Advanced Software Design. His laboratory work focuses on developing practical solutions for real-world software dependability challenges through the State Key Laboratory for Novel Software Technology.