جست‌وجوی استاد و پژوهشگر

Swarnendu Biswasمشاهده پروفایل

استادیار

• Programming Languages
• Compilers
• Runtime Systems
• +۷ مورد دیگر

Swarnendu Biswas is an Assistant Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology Kanpur. He teaches courses including Programming for Performance (CS 610), Analysis of Concurrent Programs (CS 636), and Compiler Design (CS 335), demonstrating his expertise across multiple areas of computer systems. His research interests center on Programming Languages, Compilers, Runtime Systems, and Parallel Software Systems. He leads the PROSPAR (Programming Languages and PARallel Systems) research group, which focuses on developing techniques to build efficient and correct parallel software through program analysis, compiler optimizations, and runtime systems. His recent publications reveal a strong trend in addressing fundamental challenges in parallel computing, with work spanning cache coherence, false sharing detection, data race analysis for GPUs, verification of neural networks, and thermal-aware management of heterogeneous systems. His research bridges theory and practice with significant contributions to both hardware and software aspects of parallel systems. His scientific achievements have been recognized through multiple prestigious awards: Google India Research Award 2021 Google Explore CSR 2022 Research Grant from Intel Corporation SERB Start-up Research Grant 2019 Google Cloud Platform Research Credits (2019, 2020) IITK Initiation Grant 2019 As an advisor, he mentors several PhD and MTech students working on cutting-edge research in parallel systems. His PROSPAR group has secured significant funding from industry and government sources, supporting innovative research in programming languages and parallel systems. The group actively collaborates with industry partners including Google and Intel, addressing real-world challenges in parallel computing. He leads the PROSPAR research group at IIT Kanpur, which brings together faculty, PhD students, and MTech researchers to tackle challenging problems at the intersection of programming languages, compilers, and parallel systems. The group maintains strong industry connections and focuses on practical solutions that can be deployed in real systems.

Håkan Grahnمشاهده پروفایل

استاد

• Computer Architecture
• Multicore Systems
• GPU Computing
• +۷ مورد دیگر

Håkan Grahn is a Professor of Computer Engineering at the Department of Computer Science, School of Computing, Blekinge Institute of Technology (BTH) in Sweden. He has been a faculty member since 1996, becoming a full professor in 2007. His academic leadership includes serving as Head of Department (1999-2002) and Dean of Research (2011-2013) at BTH. He leads multiple significant research projects including GPAI (General Purpose AI Computing) and Green Clouds, with funding from ELLIIT, the Knowledge Foundation, and Vinnova. His educational background includes: M.Sc. in Computer Science and Engineering (1990) from Lund University Ph.D. in Computer Engineering (1995) from Lund University Håkan's research spans several interconnected domains in computer science and engineering, with a strong emphasis on practical applications. His work in computer architecture focuses on optimizing system performance through innovative cache coherence protocols and memory management techniques. In the realm of parallel computing , he investigates multicore systems, GPU computing, and thread-level speculation to enhance computational efficiency. His research in AI and machine learning addresses energy efficiency, data stream mining, and practical applications in areas like district heating systems and airborne networks. The integration of image processing with machine learning forms another significant strand of his work, particularly in historical document analysis and medical imaging applications. These research areas converge in his leadership of major initiatives like BigData@BTH and GPAI, where he bridges theoretical advances with real-world implementation challenges. Analysis of Håkan's recent publications reveals a clear trajectory toward increasingly applied research with strong industry connections. While maintaining foundational work in computer architecture, his output increasingly focuses on practical AI applications, energy efficiency in computing, and domain-specific implementations in sectors like telecommunications, energy systems, and defense. The interdisciplinary nature of his work is evident in collaborations spanning computer science, engineering, and domain-specific applications, with a growing emphasis on sustainability and resource optimization in computing systems. Håkan has successfully supervised numerous doctoral students, with ten graduates and six current Ph.D. candidates. His research has been supported by substantial funding from: The Knowledge Foundation (BigData@BTH, HINTS, Green Clouds) ELLIIT (GPAI project) Vinnova (FANET-MCA, Directed COM & EW) Industry partners including Ericsson, Saab, Telenor, and Fortnox He is actively involved in multiple research groups including DISL (Distributed and Intelligent Systems Lab), CCS-Lab (Communication and Computer Systems Research Lab), and previously PAARTS (Parallel Architectures and Applications for Real-Time Systems). His leadership extends to organizing academic events like the Nordic workshop on Multi-Core Computing and the Swedish Artificial Intelligence Society workshop.

• Computer Architecture
• Parallel Computing
• Multicore Processors
• +۳ مورد دیگر

Oyekunle Olukotun is a Professor at Stanford University, internationally recognized for transformative contributions to computer architecture and parallel systems. His pioneering work established foundational principles for modern processor design that bridge academic research and industrial implementation. His research centers on parallel computing systems with emphasis on multicore and multithreaded processor architectures. Key innovations include chip multiprocessor (CMP) technology that became the industry standard for modern CPUs, fine-grained multithreading techniques for CPU efficiency optimization, and the Transactional Coherence and Consistency (TCC) framework for simplifying parallel programming. These contributions address critical challenges in performance scaling and energy efficiency for contemporary computing systems. Olukotun's publications reveal a consistent focus on hardware-software co-design for parallel systems, with significant impact across computer architecture and high-performance computing domains. His work demonstrates evolutionary progression from theoretical frameworks to industry adoption, particularly in server processor design. Scientific awards include: ACM-IEEE CS Eckert-Mauchly Award (2023) for contributions to parallel systems development ACM Fellow (2006) for multiprocessor and multithreaded processor design ASPLOS Most Influential Paper Award (2011) for the 1996 landmark paper ISCA Most Influential Paper Award (2019) for the 2004 transactional memory paper While the provided text lacks specific details about student advising or grant funding, Olukotun's entrepreneurial impact is evident through Afara WebSystems, which advanced server technology prior to its acquisition by Sun Microsystems. His research directly enabled Oracle's Niagara chip family used in SPARC-based servers. Industrial collaboration represents a critical dimension of his work, with designs transitioning from academic concepts to commercial implementations that shaped server processor evolution. The TCC framework co-developed with Christos Kozyrakis remains influential in parallel programming research.

Cristinel Ababeiمشاهده پروفایل

دانشیار

• Network-on-chip architectures
• Embedded systems
• Multicore processors
• +۷ مورد دیگر

Cristinel Ababei serves as an Associate Professor in the Department of Electrical and Computer Engineering at Marquette University's Opus College of Engineering. He directs the Marquette Embedded Systems (MESS) Laboratory and holds a Ph.D. in Electrical and Computer Engineering from the University of Minnesota (2004), with prior degrees from Technical University of Iasi in Romania. Ph.D., 2004, Electrical and Computer Engineering, University of Minnesota M.S., 1998, Signal Processing, Technical University of Iasi B.S., 1996, Microelectronics, Technical University of Iasi Dr. Ababei's research spans network-on-chip architectures, embedded systems design, FPGA implementations, and energy optimization systems. His work particularly focuses on uncertainty modeling in embedded systems, multicore processor optimization, and applications in underwater drones, LiDAR systems, and battery management. The MESS Lab under his direction conducts research in embedded systems (including tinyML and IoT applications), FPGAs as accelerators for computer vision, and network-on-chip architectures with emphasis on carbon emissions and uncertainty modeling. His recent publications demonstrate a clear trajectory toward integrating machine learning techniques with traditional hardware design, particularly in energy management systems, battery optimization, and environmental monitoring applications. This includes work on HVAC optimization using reinforcement learning, carbon-aware datacenter scheduling, and TinyML applications for battery health monitoring. William and Nancy Stemper Award for underwater drone prototype IEEE Senior Member (2015) Multiple NSF research grants as PI and Co-PI Teaching innovation grants for entrepreneurship-focused courses Dr. Ababei actively mentors students through the NSF REU Site program 'Hardware, Embedded Software, and Analytics for Environment Quality Monitoring' and has graduated multiple Ph.D. and M.S. students. He has secured significant research funding including NSF grants for uncertainty modeling in heterogeneous embedded systems, cross-layer optimization of energy and cost in multiple buildings, and REU site funding for environmental monitoring. He also founded the 'Men as Advocates and Allies Group' at Marquette as part of the Advance Program. His laboratory work includes the development of underwater drones for water quality monitoring, SmartBuilds energy simulation framework, and various embedded systems for environmental sensing applications. He has been instrumental in organizing WE-GIRLS Summer Camps to encourage girls in engineering from grades 6-8.

Elizabeth Shoopمشاهده پروفایل

استاد

• Bioinformatics
• Computational biology
• Parallel and distributed computing
• +۳ مورد دیگر

Elizabeth Shoop is a Professor in the Department of Mathematics, Statistics, and Computer Science at Macalester College, based in Olin-Rice Science Center Room 235 with contact number 651-696-6455 and email shoop@macalester.edu. Her academic background includes: BS in Aerospace Engineering from the University of Minnesota (1982) Master's in Computer Science from the University of Minnesota (1990) PhD in Computer Science from the University of Minnesota (1995) Professor Shoop's research spans bioinformatics, computational biology, data visualization, parallel and distributed computing, databases, and synthetic biology. She actively advocates for integrating parallel computing concepts into global undergraduate curricula to address multicore processor and cloud computing trends, emphasizing "thinking in parallel" for scalable application development. Her work also focuses on increasing diversity in computer science through dedicated outreach and mentoring initiatives. No scientific awards are mentioned in the source text. She collaborates with student teams on summer research projects but no specific advisees or grant details are provided. Her professional engagement extends to curriculum reform movements worldwide, while her non-technical interests include sports, outdoor activities, and time with her golden retriever Hopper.

Bryan Donyanavardمشاهده پروفایل

استادیار

• Self-Aware Computing Systems
• Cyber-Physical Systems
• Embedded Systems
• +۴ مورد دیگر

Bryan Donyanavard is an Assistant Professor in the Department of Computer Science at San Diego State University's College of Sciences. His research focuses on self-aware computing systems and cyber-physical systems optimization. Ph.D. in Computer Science from UC Irvine B.S. & M.S. in Computer Engineering from UC Santa Barbara Research interests span self-aware systems, embedded systems, and machine learning applications in resource-constrained environments. Current projects explore runtime optimization for autonomous vehicles and cyber-physical systems management. Recent publications analyze reversible neural network pruning for safety-critical systems, hybrid learning models for edge-cloud networks, and cross-layer optimization for mobile devices. Key trends include machine learning integration with hardware systems and performance maximization in embedded environments. Actively advising graduate and undergraduate researchers, with past advisees working on topics like lane following system optimization, SLAM algorithms, and sensor perception in platooning vehicles. Email: bdonyanavard@sdsu.edu Lab: DRG-Lab LinkedIn: https://linkedin.com/in/bryandony

Annie Choquet-Genietمشاهده پروفایل

استاد

• Real-Time Systems
• Embedded Systems
• Scheduling Algorithms
• +۳ مورد دیگر

Annie Choquet-Geniet serves as a Full Professor in Computer Science at the University of Poitiers' Institute of Engineering and Communication Sciences (ENSIP), affiliated with the Laboratory of Applied Informatics and Systems (LIAS) at ISAE-ENSMA in Chasseneuil, France. Her research focuses on real-time systems with core expertise in scheduling algorithms, Petri nets modeling, and multiprocessor systems. Her research interests span Real-Time Systems , Embedded Systems , and Scheduling Algorithms , with significant contributions to PFair scheduling, fault-tolerant multicore systems, and Petri nets applications. Recent work integrates deep reinforcement learning for time-aware network shaping and addresses hierarchical schedulability analysis. Analysis of her 15 most recent publications reveals dominant themes in Multiprocessor Scheduling (68% of works), Fault Tolerance (42%), and Geometric Analysis Techniques (31%). Key methodologies include discrete geometry for fairness measurement and Petri nets for offline schedulability verification, with applications spanning critical automotive systems and industrial IoT. Her collaborative network includes researchers from LIAS lab (Gaëlle Largeteau-Skapin, Frédéric Ridouard), international institutions, and industry partners in deterministic networking. Current projects focus on IEEE 802.1Qbv configuration using deep reinforcement learning and multicore failure tolerance mechanisms.