جست‌وجوی استاد و پژوهشگر

Geoffrey Mainlandمشاهده پروفایل

دانشیار

• Programming Languages
• Systems
• High-level Programming Languages
• +۱۲ مورد دیگر

Geoffrey Mainland is an Associate Professor in the Department of Computer Science at the College of Computing and Informatics, Drexel University. His research bridges programming languages and systems, with a focus on enabling high-level, efficient programming of specialized hardware such as GPUs, FPGAs, and software-defined radios. He has developed domain-specific languages and runtime systems to simplify and optimize non-general-purpose computation. PhD in Computer Science, Harvard University, 2011 AB in Physics, Harvard University, Magna Cum Laude, 2000 His research interests include high-level programming languages, runtime support for specialized devices, functional programming (particularly in Haskell), stream fusion, SIMD vectorization, and domain-specific languages for systems programming. He investigates how to make it easier to exploit the performance of GPUs, sensor networks, and FPGAs without sacrificing abstraction or safety. The recent publications highlight a consistent theme: enhancing performance in functional languages through language design, compiler optimizations, and runtime systems. Key areas include SIMD support in Haskell, GPU programming via Nikola and MetaHaskell, staged programming for sensor networks (Flask), and domain-specific languages like Ziria for software-defined radio. These works demonstrate a strong focus on bridging the gap between high-level abstractions and low-level efficiency. Geoffrey Mainland has not been mentioned as receiving any scientific awards in the provided material. He advises current PhD student Dresden Feitzinger and has previously advised Mahshid Shahmohammadian, now at Intel. His research has been supported through academic and possibly federal funding, though specific grants are not detailed. His work is implemented in open-source projects hosted on GitHub, including contributions to the Haskell vector library and the development of Ziria and DragonRadio. He leads a research group focused on programming systems, with active projects in software-defined radio (Ziria, DragonRadio), high-performance numerics in Haskell (HSpiral), and language support for heterogeneous computing. His team develops tools that enable researchers and developers to write efficient, correct code for specialized hardware using high-level functional abstractions.

• Cloud Computing
• Serverless Computing
• Distributed Systems
• +۵ مورد دیگر

Ass.-Prof. Dr. Sashko Ristov is an Assistant Professor at the Department of Computer Science, University of Innsbruck. His research focuses on serverless computing, distributed systems, and workflow orchestration, with applications in cloud computing, healthcare informatics, and high-performance computing. He leads research on federated serverless infrastructures, resilient function choreographies, and digital twins in construction engineering. His work addresses challenges in cross-cloud resource management, workflow scheduling, and anomaly detection in microservices. Recent contributions include frameworks like StoreLess for federated storage orchestration and CODE for cross-platform serverless deployment. Research trends in his publications emphasize bi-objective optimization for batch workflows, GPU-accelerated document processing, and disaster-resilient cloud systems. He actively participates in workflows community summits to advance scientific workflow standards and interoperability. Dr. Ristov has collaborated on projects like the Montage workflow analysis and ECG monitoring systems using serverless architectures. His work bridges theoretical computing models with practical implementations in healthcare and civil engineering domains.

Ki Hwan Yumمشاهده پروفایل

مدرس ارشد

• Computer Architecture
• Parallel/Distributed Systems
• Computer Networks
• +۵ مورد دیگر

Ki Hwan Yum is a Senior Lecturer in the Department of Computer Science and Engineering at Texas A&M University, affiliated with the College of Engineering. His research focuses on computer architecture, parallel/distributed systems, and energy-efficient computing. He holds a Ph.D. from The Pennsylvania State University (2002), M.S. from Pohang University of Science and Technology (1994), and B.S. in Mathematics from Seoul National University (1989). His research interests include computer networks, cluster computing, fault-tolerant systems, and wireless sensor networks. Notable contributions include work on QoS-aware clusters, on-chip network design, and power-efficient architectures. He has been recognized with the Best Graduate Research Assistant Award (2002) from Penn State University. Recent publications emphasize innovations in network-on-chip (NoC) architectures, energy-efficient designs, and distributed deep learning systems. His work bridges theoretical models with practical implementations, addressing challenges in scalability, latency, and power consumption in modern computing systems. Awards include the 2002 Best Graduate Research Assistant Award. His research has been published in top-tier journals like IEEE Transactions on Computers and Journal of Parallel and Distributed Systems. He advises no current students, but his work has influenced graduate research in computer architecture and distributed systems.

Ali Manzakمشاهده پروفایل

عضو هیئت علمی

• Low Power VLSI Design
• Embedded Systems
• Mixed-Signal IC Design
• +۳ مورد دیگر

Ali Manzak is an Assistant Teaching Professor in the Department of Electrical and Computer Engineering at Florida State University (FSU). He holds a M.Sc. in Control Systems from the University of Colorado Boulder and a Ph.D. in VLSI Systems from Arizona State University. Prior to academia, he worked as a digital IC design engineer at Lattice Semiconductor in California. His research focuses on low-power VLSI design, embedded systems, mixed-signal IC design, and hardware implementation of machine learning algorithms. He actively contributes to academia through teaching and lab supervision, designing ICs for both educational and research purposes. Education: Ph.D., Electrical Engineering (VLSI Systems), Arizona State University M.Sc., Electrical Engineering (Control Systems), University of Colorado Boulder Research Interests: Dr. Manzak’s work emphasizes energy-efficient VLSI circuits, emerging nanotechnologies like carbon nanotube FETs, and applying machine learning to hardware systems. His publications span IEEE journals and conferences, addressing topics such as power-aware cache design, low-power latch optimization, and AI-driven solutions for electromagnetic problems. Teaching & Labs: He instructs courses like EEL 3705/L, EEL 3927, and EEL 4742/L, and maintains hands-on IC design experience across multiple platforms. His labs integrate industry practices with academic research, fostering practical engineering skills.

Christopher Greggمشاهده پروفایل

دانشیار

• Computer Architecture
• General Purpose Computing on GPUs
• Computer Science Pedagogy
• +۱ مورد دیگر

Christopher Gregg is an Associate Professor (Teaching) of Computer Science at Stanford University. He holds a Ph.D. in Computer Engineering from the University of Virginia (2012), an M.Ed. in Education (Physics) from Harvard University (2002), and a B.S. in Electrical Engineering from Johns Hopkins University (1994). His research interests span computer architecture, particularly general-purpose computing on GPUs, and the pedagogy of computer science education. Before joining Stanford, he taught at Tufts University and high schools in Massachusetts and California. He has served in the U.S. Navy for seven years and remains a Commander in the Navy Reserves, focusing on Information Warfare / Cryptology.

• Computer Architecture
• GPU Architecture
• Power and Performance Modeling
• +۵ مورد دیگر

Sohan Lal is a postdoctoral researcher at the Technical University of Berlin (TU Berlin), focusing on advanced modeling and runtime support for large-scale HPC clusters under a DFG-funded project. His PhD in Computer Engineering from TU Berlin (2019) explored power modeling and architectural techniques for energy-efficient GPUs. He contributed to EU-funded LPGPU projects on low-power GPU computing, leading tasks and collaborating across consortium members. Previously, he lectured at Shri Mata Vaishno Devi University and worked as an IT specialist in the Government of India. Education: PhD in Computer Engineering, TU Berlin (2019) Masters in Computer Science, IIT Delhi (2011) Bachelor in Computer Science and Engineering, GCET Jammu (2003) His research interests span GPU architecture, power/performance modeling, memory systems, and applied machine learning. Notable contributions include techniques like Selective Lossy Compression (SLC) for GPUs and entropy encoding-based memory compression (E²MC). He received HiPEAC travel/grants and was an ACM SRC semifinalist (2018). Grants & Collaborations: HiPEAC Collaboration Grant for joint work with TU/e DFG-funded postdoctoral research He actively teaches advanced computer architectures and multicore systems at TU Berlin, reflecting his passion for education developed during his early teaching career.

• Computer Architecture
• High Performance Computing
• GPU Programming
• +۵ مورد دیگر

Josie Esteban Rodriguez Condia is a Fixed-term Assistant Professor in the Department of Control and Computer Engineering (DAUIN) at Politecnico di Torino. She is a member of the CARS@PoliTO Interdepartmental Center - Center for Automotive Research and Sustainable Mobility and serves as an invited member of both the College of Electronic, Telecommunications, and Physics Engineering and the College of Computer, Film, and Mechatronics Engineering. Her research focuses on computer architecture reliability, particularly in GPU and AI accelerator systems. Key areas include functional testing, general purpose graphics processing units (GPGPUs), hardware accelerators, hardware architecture, and parallel processing. Her work addresses critical challenges in reliability assessment of AI-based automotive systems, self-test libraries for tensor cores, and hardening techniques for neural networks on GPUs. Her recent publications demonstrate a strong trend toward reliability engineering for AI hardware, with particular emphasis on automotive applications and GPU-based neural network implementations. The research spans fault injection methodologies, error modeling, and architectural solutions to enhance system resilience against soft errors and permanent faults. Dr. Rodriguez Condia actively supervises PhD students including Gustavo Vilar De Farias, Giuseppe Esposito, and Robert Alexander Limas Sierra, all working on reliability evaluation and enhancement of neural networks. She is a member of the PNRR Research Group for the National Center for HPC, Big Data and Quantum Computing (2022-2025). She teaches multiple courses including GPU Programming and High Performance Computing for both Computer Engineering and Quantum Engineering programs. Her editorial work includes serving as Guest Editor for APPLIED SCIENCES in 2024.

Emrah Dönmezمشاهده پروفایل

دانشیار

• Robotics
• Machine Learning
• Computer Vision
• +۳ مورد دیگر

Emrah Dönmez is an Associate Professor at the Software Engineering Department of Bandırma Onyedi Eylül University's Faculty of Engineering and Natural Sciences. He previously held academic roles at institutions like Malatya Turgut Özal University and Inönü University. His research focuses on robotics, AI applications in logistics, cybersecurity, and medical imaging. He has led projects on autonomous robots, smart logistics, and cybersecurity systems. Education: PhD and research assistantships at Istanbul Technical University and Bingöl University. His academic contributions include co-authoring a book on accounting standards and publishing extensively in IEEE journals and conferences. He teaches courses on artificial intelligence, robotics, and logistics systems. Research highlights include developing vision-based mobile robot control systems, deep learning models for medical diagnostics, and cybersecurity frameworks. He has led over 10 projects, including smart pathology lab systems and autonomous robot navigation. Grants: TÜBİTAK-funded projects on multi-robot control and smart logistics infrastructure. Labs: Robotics and Computer Engineering labs at Bandırma University. Advising: Supervised master's theses on plant disease classification and robotics.

Stefan Engblomمشاهده پروفایل

استاد

• Scientific Computing
• Numerical Methods
• Fast Multipole Methods
• +۳ مورد دیگر

Stefan Engblom is a Professor in Scientific Computing at Uppsala University, Department of Information Technology. His research focuses on developing computational methods and software for scientific applications, with particular expertise in numerical methods, fast algorithms, and simulation of complex systems. He is affiliated with the Scientific Computing division within the Information Technology department at Uppsala University. Engblom's research spans several key areas including fast multipole methods for efficient computation of long-range interactions, stochastic simulation of reaction-diffusion processes in complex geometries, computational epidemiology for modeling disease spread, and fluid dynamics simulations. His work combines theoretical development with practical implementation, resulting in several widely used software packages. His publications reveal a consistent focus on developing efficient numerical algorithms that address computational challenges in various scientific domains. The research shows a progression from fundamental algorithm development (fast multipole methods) to applications in biology (reaction-diffusion systems) and public health (epidemic modeling), demonstrating the versatility and applicability of his computational approaches. Engblom maintains active software development through several research codes including SimInf for epidemic modeling, URDME for reaction-diffusion processes, FMM2D/FMM3D for fast multipole methods, and other specialized tools for fluid dynamics and fiber simulations. His stenglib provides general-purpose Matlab libraries used in his research and available to the broader scientific community.

Andrew Nereمشاهده پروفایل

استادیار

• Machine Learning
• Computer Architecture
• Embedded Systems
• +۳ مورد دیگر

Andrew Nere serves as Assistant Professor of Computer Science in the Math & Computer Science Department at Western Colorado University, teaching courses including Introduction to Web Design, Computer Science I, and Software Entrepreneurship since joining the faculty in Fall 2022. His industry background includes co-founding Thalchemy (2013), a startup specializing in embedded machine learning for wearable and environmental sensor applications, alongside prior internships at IBM and Qualcomm. His educational credentials feature: PhD in Electrical Engineering from University of Wisconsin-Madison (2013) MS in Electrical Engineering from University of Wisconsin-Madison (2010) B.S. in Computer Engineering from St. Cloud State University (2007) Nere's research centers on hardware-software co-design for efficient AI deployment, with dual focus on neuromorphic computing architectures and practical embedded systems implementation. He bridges theoretical neuroscience with engineering solutions, particularly for resource-constrained environments like fitness trackers and environmental sensors where computational efficiency is paramount. His work emphasizes translating academic research into real-world applications rather than pure theoretical exploration. Analysis of his 2010-2013 publications reveals consistent innovation in brain-inspired computing hardware, with recurring themes of GPU-accelerated neural simulations, specialized cache architectures for AI workloads, and energy-efficient neuromorphic designs. The research demonstrates strong interdisciplinary collaboration across computer architecture, neuroscience, and machine learning communities, frequently targeting hardware acceleration for cognitive computing tasks. Scientific recognition includes: Best Paper Nomination at IEEE International Symposium on Workload Characterization (2012) Best Paper in Track at International Parallel and Distributed Processing Symposium (2011) Nere brings substantial industry experience to academia, having served as university collaborator on the DARPA/IBM SyNAPSE project modeling brain functionality in computing systems. His startup Thalchemy exemplifies his commitment to applied research translation, while his current Software Entrepreneurship course provides students with practical business development frameworks for technology ventures. He actively leverages Gunnison Valley's natural environment for both recreation and potential research applications, noting particular interest in environmental sensing opportunities afforded by the region's wilderness proximity and diverse ecosystems.

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.

Beomjoon Kimمشاهده پروفایل

دانشیار

• Robotics
• Artificial Intelligence
• Humanoid Robotics
• +۳ مورد دیگر

Beomjoon Kim is an Associate Professor at the Graduate School of AI at Korea Advanced Institute of Science and Technology (KAIST). He directs the Humanoid Generalization (HuGe) lab, which focuses on creating general-purpose humanoids capable of efficient decision-making in complex environments. Education: Ph.D. in Computer Science from MIT CSAIL M.Sc. in Computer Science from McGill University B.Math in Computer Science and Statistics from University of Waterloo Professor Kim's research spans multiple areas of robotics and artificial intelligence, with a particular focus on humanoid robotics, task and motion planning, and robot learning. His work aims to bridge the gap between high-level task planning and low-level motion control, enabling robots to operate effectively in complex, real-world environments. He has made significant contributions to the field of geometric task and motion planning, developing novel algorithms that improve the efficiency and effectiveness of robot decision-making processes. Analysis of Professor Kim's recent publications reveals a strong focus on humanoid robotics, with particular emphasis on motion planning, object manipulation, and learning-based approaches. His work increasingly integrates deep learning techniques with traditional robotics algorithms, demonstrating a trend toward more data-driven approaches in robotics research. Many of his papers address the challenge of generalization in robotics, seeking to develop systems that can handle novel objects and environments without extensive retraining. Scientific Awards: Best Cognitive Robotics Paper award at ICRA 2017 Oral presentation (top 6% of accepted papers) at AAAI 2020 Oral presentation (top 6% of accepted papers) at AAAI 2019 Oral presentation (top 6% of accepted papers) at AAAI 2018 Spotlight presentation (top 3.5% of accepted papers) at NeurIPS 2018 Spotlight presentation (top 4% of accepted papers) at NeurIPS 2013 Plenary talk (top 12% of accepted papers) at CoRL 2020 Professor Kim actively mentors students at various levels, currently advising multiple Ph.D. and Master's students in the HuGe lab. His research is supported by grants that enable his team to pursue ambitious projects in humanoid robotics and AI. Through collaborations with institutions like MIT and industry partners, his work has significant impact on both academic research and practical applications of robotics technology. The HuGe lab, under Professor Kim's direction, has established itself as a leading research group in humanoid robotics. The lab maintains strong connections with other research institutions and regularly publishes in top-tier robotics and AI conferences. Current research directions include developing more efficient motion planning algorithms, improving robot manipulation capabilities, and exploring the integration of large language models with robotic systems.