جست‌وجوی استاد و پژوهشگر

• Compiler Optimization for High Performance Computing
• Optimization of Sparse/Dense Matrix/Tensor Computations
• Scalable Machine Learning
• +۱ مورد دیگر

P. (Saday) Sadayappan is a Professor in the School of Computing at the University of Utah. He serves as a lead researcher in high-performance computing, with a focus on compiler optimization and algorithm-architecture co-design. His current projects include NIH SBIR Phase 2 funding for large-scale image analysis and NSF grants for tensor applications and cyber-infrastructure for AI. Research Interests : Compiler Optimization for High Performance Computing Optimization of Sparse/Dense Matrix/Tensor Computations Scalable Machine Learning Algorithm-Architecture Co-Design Optimization Research Trends in Publications : His work emphasizes optimizing computational workflows for emerging hardware architectures, with a focus on accelerating machine learning and scientific computing through compiler-level innovations. Recent trends include co-design for CNNs, sparse matrix optimizations, and distributed algorithms. Scientific Awards : ACM SIGPLAN Most Influential PLDI Paper Award (2018) Grants & Projects : NSF (2022–2027): Comprehensive Framework for Tensor Applications NSF AI Institute ICICLE (2021–2026): Cyber-infrastructure for environmental AI NIH SBIR (2023–2025): Next-gen machine learning for image analysis Labs & Teams : Collaborates with institutions like Ohio State University and RNET Technologies on projects involving parallel computing, sparse algorithms, and compiler design.

• Machine Automation
• Autonomous Mobile Working Machines
• Heavy-Duty Robotics
• +۴ مورد دیگر

Professor Jouni Mattila is a leading academic in Machine Automation at Tampere University's Faculty of Engineering and Natural Sciences, affiliated with the Automation Technology and Mechanical Engineering department. He is part of the IHA-Innovative Hydraulics and Automation research group. His expertise spans autonomous mobile working machines, nonlinear control engineering, and safety-critical systems like those in the ITER project. He holds a Technical Editor role in ASME/IEEE Transaction on Mechatronics (2015-2020). Research interests include real-world autonomous systems, whole-body motion control for rough-terrain robots, energy-efficient actuators, and teleoperation systems. His work integrates advanced control theory, AI, and robotics for heavy-industry applications. Recent publications focus on robust control frameworks, LiDAR-inertial SLAM navigation, and fault-tolerant systems for mobile robots. Publications highlight advancements in hydraulic/electromechanical actuator systems, visual-inertial feedback control, and energy-efficient robotics. Awards/recognitions are not explicitly listed, but his contributions are evident through collaborations with Finnish industry and big science projects. Advising focuses on MSc and Dr (Tech) students in robotics and automation, with a mission to bridge academia and industry for high-tech innovation. Labs/teams include the Intelligent Hydraulics and Automation (IHA) group, emphasizing practical R&D in cleantech and heavy-duty robotics. Ongoing projects address challenges in autonomous rock-breaking systems, exoskeleton control, and energy-efficient robotic actuators.

Maozhen Liمشاهده پروفایل

استاد

• High Performance Computing
• Big Data Analytics
• Artificial Intelligence
• +۱۳ مورد دیگر

Maozhen Li is a Professor in the Department of Electronic and Electrical Engineering at Brunel University of London , within the College of Engineering, Design and Physical Sciences . He serves as the Vice-Dean of the NCUT Transnational Education (TNE) programme, overseeing a joint school with North China University of Technology. He has been at Brunel since 2002, progressing from Lecturer to Professor in 2013. Education: PhD, Institute of Software, Chinese Academy of Sciences (1997) Postdoctoral Research, School of Computer Science and Informatics, Cardiff University (1999–2002) His primary research interests lie in high performance computing, big data analytics, and artificial intelligence, with applications in smart grids, smart manufacturing, and cybersecurity. He focuses on developing interpretable, robust, and lightweight AI models, including work in causal AI, parallel machine learning, and edge computing. His research integrates advanced techniques such as deep learning, reinforcement learning, and blockchain for real-world system optimization. An analysis of his recent publications reveals a strong and consistent research trajectory in AI-driven solutions for environmental monitoring (e.g., PM2.5 prediction), industrial defect detection, IoT security, and intelligent transportation. His work frequently combines deep learning with graph-based modeling and federated or reinforcement learning, emphasizing scalability, efficiency, and robustness in distributed and edge environments. Scientific Awards and Recognition: Fellow of the Institution of Engineering and Technology (IET) Fellow of the British Computer Society (BCS) Shortlisted for the Computing UK BIG DATA EXCELLENCE AWARDS 2018 in the category of Most Innovative Big Data Solution Maozhen Li has successfully supervised 25 PhD students and examined over 30 PhD theses externally. He has secured significant research funding from EPSRC, the European Union (Horizon 2020), Innovate UK, and the Royal Society , with projects including Z-BRE4K, IoRL, and TDX-ASSIST. He serves as an Associate Editor for journals such as the Journal of Cloud Computing and the International Journal of Grid and High Performance Computing . Research Groups and Teams: He is affiliated with the Intelligent Engineering Frameworks (IEF) research group at Brunel, contributing to collaborative efforts in AI, IoT, and smart systems. His leadership in transnational education also fosters international research collaboration between Brunel and Chinese institutions.

Zaman Noorمشاهده پروفایل

مدرس ارشد

• Distributed Systems
• Large Scale Computation
• Big Data Analytics
• +۷ مورد دیگر

Zaman Noor is a Senior Lecturer in the Department of Computer Science and Engineering at The University of Texas at Arlington (UTA), where he has been serving since September 2022. He previously held an Adjunct Professor role at the same institution from January to August 2022 and was a Lecturer at Port City International University from 2015 to 2016. PhD in Computer Science, The University of Texas at Arlington, 2021 BS in Computer Science, Chittagong University of Engineering, 2019 His research focuses on Distributed Systems , Large Scale Computation , and Big Data Analytics , with an emphasis on optimizing array-based programming models for distributed environments such as Spark SQL. He explores compiler techniques for translating high-level array operations into efficient distributed queries, enabling scalable data analytics. Zaman Noor's publications reveal a strong trend in bridging programming languages with database systems, particularly through the translation of array-based loops and graph programs into optimized SQL-based distributed execution plans. His work intersects computer science, database systems, and high-performance computing, targeting applications in cloud computing, big data management, and machine learning infrastructure. He has received recognition for his research, including the Best Paper Award at IEEE BigData Congress in 2018 . Best Paper Award, IEEE BigData Congress, July 6, 2018 Zaman Noor advises master's students, including Priyank Gupta, and serves on thesis committees. He is actively involved in teaching and curriculum development, offering courses in distributed systems, cloud computing, and algorithms. He also contributes to academic service as a faculty advisor for the Google Developer Student Club and The Cornerstone, and as a member of the Publicity Committee. He leads and participates in educational initiatives and student mentorship, particularly in cloud computing and big data technologies, and supports student research through thesis supervision and committee roles. Zaman Noor is affiliated with research and teaching teams focused on data-intensive systems and distributed computing. His GitHub profile indicates engagement with open-source machine learning frameworks, including contributions to TensorFlow-related repositories, reflecting his interest in practical implementations of large-scale computation.

Michael J. Sandelمشاهده پروفایل

استاد

• Political Philosophy
• Ethics
• Democracy
• +۶ مورد دیگر

Michael J. Sandel is a Professor of Government at Harvard University, renowned for his work in political philosophy and public ethics. His scholarship explores justice, democracy, market morality, and the ethical implications of biotechnology, reaching global audiences through his televised course Justice and BBC series The Public Philosopher . B.A., Brandeis University (1975) D.Phil., Oxford University (1981) as a Rhodes Scholar His research interrogates the moral limits of markets, genetic engineering, and the erosion of civic virtue in modern democracies. Notable works include What Money Can’t Buy (2012), which critiques market society, and The Case Against Perfection (2007), addressing biotechnology’s ethical challenges. Princess of Asturias Award in Social Sciences (2018) Member, American Academy of Arts and Sciences Rhodes Scholarship As a public intellectual, Sandel has lectured globally at venues like St. Paul’s Cathedral and Seoul’s Olympic Stadium, engaging debates on corruption, climate ethics, and intergenerational justice. His Tech Ethics course examines AI and social media’s moral dimensions.

Dukka KCمشاهده پروفایل

استاد مدعو

• Bioinformatics
• Data Science
• Machine Learning
• +۴ مورد دیگر

Dukka KC is an Adjunct Professor in the Department of Computer Science at Michigan Technological University and a member of the Institute of Computing and Cybersystems (ICC). His research focuses on computational data science with applications in bioinformatics, computational biology, and health informatics, particularly leveraging machine learning and high-performance computing to develop predictive tools for protein and nucleic acid modifications. Ph.D., Informatics, Kyoto University, 2006 M.Inf., Informatics, Kyoto University, 2003 B.Eng., Computer Science, Kyoto University, 2001 Research interests include: Developing GPU-accelerated bioinformatics tools (e.g., GPU-I-TASSER) Predicting post-translational modification sites using deep learning (e.g., DeepNGlyPred, DeepRMethylSite) Machine learning approaches for malonylation, succinylation, and sulfenylation site prediction High-throughput analysis of next-generation sequencing data Interdisciplinary projects in biometrics, cybersecurity, and disaster prediction Recent publications highlight a strong trend in applying deep learning to protein structure and function prediction, GPU-parallelization for computational efficiency, and machine learning for both biological and cybersecurity applications. The lab also emphasizes cross-domain collaborations and the development of scalable bioinformatics workflows. Grants and funding include projects like the President's Convergence Science Initiative (PI, $300K), NSF III grants for protein function prediction ($111K), and multi-institutional collaborations on biometric test-beds and synthetic biology research. The KC Lab at Michigan Tech specializes in integrating computational data science with molecular biology, focusing on protein/RNA/DNA modification site prediction and contributing to large-scale proteome analysis through machine learning-driven pipelines.

Ruben Martinsمشاهده پروفایل

استادیار

• Constraint Programming
• Program Synthesis
• Formal Verification
• +۳ مورد دیگر

Ruben Martins is an Assistant Professor at Carnegie Mellon University's School of Computer Science and serves as the program director of the Master of Science in Computer Science (MSCS) . His research focuses on the intersection of constraint programming, program synthesis, analysis, and verification, with recent work aiming to make formal methods tools more accessible through automated reasoning. Ruben earned his Ph.D. with honors from the Technical University of Lisbon, Portugal (2013) , followed by postdoctoral research at the University of Oxford (2014-2015) and UT Austin (2015-2017) . Research Interests : Ruben's work bridges constraint programming and program synthesis , with applications in software verification , optimization , and automated reasoning . He has developed award-winning tools like Open-WBO , a modular MaxSAT solver that has won gold medals in international competitions. His publications span top-tier venues such as POPL , PLDI , FSE , SAT , and CP , often addressing real-world challenges from program analysis to network security. Scientific Awards include: Distinguished Paper Award at PLDI 2018 Distinguished Paper Award at FSE 2021 Distinguished Paper Award at SAT 2022 Gold medals for Open-WBO in MaxSAT competitions Teaching & Advising : Ruben mentors Ph.D., Master’s, and undergraduate students in research projects related to program synthesis, formal methods, and constraint solving. He teaches courses such as Bug Catching: Automated Program Verification and Advanced Topics in Logic: Automated Reasoning and Satisfiability , emphasizing hands-on experience with tools like Why3. His advising spans topics from AI-driven program repair to network protocol verification , fostering collaboration across disciplines.

Alexandra Fedorovaمشاهده پروفایل

استاد

• Systems Software
• Memory and Storage Management
• Accelerator-Centric Computing
• +۴ مورد دیگر

Dr. Alexandra Fedorova is a Professor in the Department of Electrical and Computer Engineering at the University of British Columbia (UBC), with an Associate Member role in the Computer Science department. She leads the Systopia systems research group, focusing on system software design, memory/storage management, and accelerator-centric computing. Her work emphasizes performance optimization, energy efficiency, and hardware-software co-design. She holds a PhD from Harvard University (2006), where she researched operating system scheduling under Margo Seltzer. Prior to UBC, she was an Associate Professor at Simon Fraser University (2006–2015). Fedorova is a recipient of the Alfred P. Sloan Research Fellowship and the Anita Borg Early Career Award. She consults for MongoDB's storage engine team and collaborates with industry on storage and performance challenges. Her research spans tools like Non-sequitur for program trace visualization, studies on storage-class memory (e.g., Optane), and frameworks for GPU acceleration. Recent efforts include Sunstone (spatial accelerator scheduling) and ExtMem (application-aware memory management). Her work bridges low-level systems with high-performance computing needs. Key contributions include optimizing NUMA systems, improving storage engine performance, and exploring processing-in-memory architectures. Fedorova’s projects often involve open-source collaboration, reflected in her GitHub repositories such as vividperf and perf-logging , which support performance analysis tools.

Kurt Keutzerمشاهده پروفایل

استاد

• Artificial Intelligence
• Deep Learning
• Computer Vision
• +۶ مورد دیگر

Kurt Keutzer is a Professor in the Department of Electrical Engineering and Computer Science at the University of California, Berkeley, and a key member of the Berkeley AI Research Lab (BAIR). He holds a Ph.D. in Computer Science from Indiana University (1984) and was previously Chief Technical Officer at Synopsys, Inc. His research focuses on systems issues in deep learning, particularly for computer vision, speech recognition, NLP, and finance. He has published over 250 refereed articles and six books, and is a highly cited author in hardware and design automation. Keutzer has received multiple IEEE Fellowships, DAC awards, and best paper accolades at conferences like Embedded Vision Workshop and ICPP. Educations: 1984, PhD, Computer Science, Indiana University Kurt Keutzer's research interests span Artificial Intelligence , Computer Architecture , and Scientific Computing , with a focus on computational efficiency in AI systems. His work explores hardware-aware neural architecture search, domain adaptation, and quantization techniques to optimize models from edge to cloud. Recent publications highlight advancements in vision transformers , LLM inference efficiency , and autonomous driving . He also contributes to multimodal AI and self-supervised learning frameworks. Scientific Awards: Institute of Electrical & Electronics Engineers (IEEE) Fellow (1996) DAC's Most Influential Paper Award (2023) Top Ten Cited Author and Paper at DAC Best Paper Awards at Embedded Vision Workshop and ICPP Kurt Keutzer has advised numerous Ph.D. and Master’s students, including Forrest Iandola (co-founder of DeepScale), Sheng Shen, and Michael Murphy. His research teams have pioneered hardware-efficient deep learning solutions like SqueezeNet and FireCaffe. Current projects include optimizing large language models (LLMs) for edge deployment and advancing 3D reconstruction for autonomous vehicles. He is also involved in diffusion models , sparse attention mechanisms , and multi-agent coordination for complex tasks.

• Soft Robotics
• Microfluidics
• Nanofabrication
• +۳ مورد دیگر

Ramses Martinez is an Assistant Professor in the Department of Industrial Engineering and Biomedical Engineering at Purdue University . He holds a B.A. in Applied Physics from Universidad Autonoma de Madrid (2004) and a Ph.D. in Physics and Materials Science from the Spanish National Research Council (CSIC) in 2009. Prior to joining Purdue, he conducted postdoctoral research in the lab of Prof. George M. Whitesides at Harvard University, focusing on nanofabrication, microfluidics, and soft robotics. Education B.A. in Applied Physics, Universidad Autonoma de Madrid (2004) Ph.D. in Physics and Materials Science, Spanish National Research Council (CSIC) (2009) His research bridges soft robotics , flexible electronics , and nanofabrication , with a focus on creating self-powered e-textiles , omniphobic paper-based devices , and programmable mechanical metamaterials . His work has led to over 25 publications and 9 patents, emphasizing practical applications in health monitoring and industrial automation . Notable projects include waterproof electronic decals for biofluid monitoring, smart bandages for chronic wound detection, and laser nanoforming methods for scalable metallic structures. His research has been recognized through the Fulbright Fellowship and the Marie Curie IOF Grant .

Shad Roundyمشاهده پروفایل

دانشیار

• Energy Harvesting
• Wireless Power Transfer
• MEMS
• +۵ مورد دیگر

Shad Roundy is an Associate Professor at the University of Utah , holding dual appointments in the Department of Mechanical Engineering and Department of Electrical and Computer Engineering within the College of Engineering . He directs the Integrated Self-Powered Sensing Lab , focusing on energy harvesting, wireless power transfer, and ubiquitous sensing systems. PhD in Mechanical Engineering, University of California, Berkeley (2003) Senior Lecturer, Australian National University (postdoctoral) His research bridges mechanical design , smart materials , and sensor networks , with significant contributions to: Self-powered biomedical implants Wearable health-monitoring devices Underground agricultural sensor networks MEMS sensor development His 15 most recent publications demonstrate interdisciplinary work spanning wireless power transfer for soil sensors, flexoelectric materials, and disease severity assessment via wearables. Articles emphasize energy efficiency , novel transducer designs , and environmental sensor applications . Scientific recognition includes: NSF CAREER Award for magnetoelectric implant powering Associate Editor, Smart Materials and Structures and International Journal of Precision Engineering and Manufacturing-Green Technology Research funded by National Science Foundation , NASA , and industry partners through the ASSIST Engineering Research Center . Publications highlight collaborations across biomedical, agricultural, and wearable technology domains.

Gary Grewalمشاهده پروفایل

دانشیار

• Computer-Aided Design (CAD) Tools
• Field Programmable Gate Arrays (FPGAs)
• Machine Learning
• +۵ مورد دیگر

Gary Grewal is an Associate Professor at the School of Computer Science , University of Guelph. His research focuses on developing intelligent Computer-Aided Design (CAD) tools for Field Programmable Gate Arrays (FPGAs) , integrating classical optimization techniques with machine learning and deep learning to address challenges in placement and routing for heterogeneous devices. He has received the Michal Servit Award (2017, 2018) for outstanding FPGA research and the University of Guelph Faculty Association Distinguished Professor Award for Excellence in Teaching (2017) . Grewal has held NSERC Discovery Grants annually from 1999 to 2023. Co-founder of the Guelph FPGA CAD Group Key collaborator with institutions like Ryerson University , University of Toronto , and University of British Columbia His work extends to health technology through the IronTracker mobile app , developed with Andrew Hamilton-Wright and students (A. D'Angelo, J. Carter, F. Liu, R. Pattison) to manage Hereditary Hemochromatosis (HHC) . The app, available in four languages and adopted in 100+ countries, was recognized at Parliament Hill and the Ontario Legislature. Scientific Awards : Michal Servit Award (2018) Michal Servit Award (2017) Distinguished Professor Award for Teaching (2017) NSERC Discovery Grants (1999-2023) His recent publications highlight trends in machine learning for FPGA CAD , including reinforcement learning for partitioning, deep learning for congestion estimation, and adaptive algorithms for placement. Grewal remains active in teaching courses like Discrete Optimization (CIS*6070) and Digital Systems I (CIS*3120).

Dan Moldovanمشاهده پروفایل

استاد

• Natural Language Processing
• Artificial Intelligence
• Question Answering Systems
• +۸ مورد دیگر

Dan Moldovan is a Professor in the Department of Computer Science at The University of Texas at Dallas, within the Erik Jonsson School of Engineering and Computer Science. He leads the InterVoice Research Center in the Human Language Technology Research Institute and has played a pivotal role in advancing question answering technologies, achieving top performance in multiple TREC evaluations (TREC-8 to TREC-13). Education: Ph.D. in Electrical Engineering and Computer Science, Columbia University (1978) M.S. in Electrical Engineering and Computer Science, Columbia University (1974) Diploma Engineer in Electrical Engineering, Polytechnic Institute of Bucharest (1969) His research centers on Natural Language Processing and Artificial Intelligence , with major contributions in Question Answering Systems , Knowledge Acquisition from Text (KAT) , Word Sense Disambiguation , and Semantic Indexing and Retrieval . He has developed systems to enrich WordNet with domain knowledge and transform it into a logical knowledge base. His work also explores distributed and parallel processing for scalable NLP algorithms. The recent publications (2005–2007) highlight his focus on semantic reasoning, multilingual QA, validation of answers using logic provers like COGEX, and the semantics of noun compounds. These works reflect a strong trend toward deep semantic understanding, contextual reasoning, and integration of logic in NLP systems. Scientific Awards: Best performance in TREC-8 (1999), TREC-9 (2000), TREC-11 (2002), TREC-12 (2003), and TREC-13 (2004) QA competitions Dan Moldovan has advised numerous research projects and led major initiatives such as Extended WordNet and the development of PowerAnswer. He has secured significant research support through appointments and leadership roles across institutions. His work in distributed NLP and parallel processing of rule-based systems reflects a long-standing interest in scalable AI architectures. He has directed several key research labs: InterVoice Research Center, UTD Parallel and Distributed Computer Systems Laboratory, University of Southern California Parallel Knowledge Processing Laboratory, University of Southern California

Poman Soمشاهده پروفایل

دانشیار

• Multiscale Modeling
• Biomedical Engineering
• Microwave CAD
• +۵ مورد دیگر

Poman So is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Victoria, Canada. He is a Professional Engineer (P.Eng) registered in British Columbia and holds memberships in IEEE (Senior Member), ACES, and CMBES. Dr. So co-founded Faustus Scientific Corporation, creator of MEFiSTo electromagnetic field modeling software. Prior to joining UVic in 1998, he worked as senior scientific staff at MDA in Quebec. Education: Ph.D. in Electrical Engineering (University of Victoria) M.A.Sc. and B.A.Sc. in Electrical Engineering (University of Ottawa) B.Sc. (University of Toronto) Research Interests: Focuses on multiscale electromagnetic field modeling, biomedical electromagnetics and instrumentation, time-domain microwave circuit analysis, and open-source computational electromagnetics (CEM) tools. His work integrates GPU-based parallel computing and meta-material applications in microwave CAD. Professional Activities: Administers the Talbot Memorial Fund Awards, established to honor engineering students based on academic excellence, community service, and leadership. Since 2007, the fund has awarded two $2500 scholarships annually. Teaching: Instructs courses including ECE 216 (Electricity and Magnetism), ECE 335 (Biosensors and Instrumentation), and ECE 521 (Computational Electromagnetics). Labs/Teams: Leads research in biomedical engineering instrumentation and microwave circuit design through Faustus Scientific’s MEFiSTo platform, emphasizing open-source software development.

Mateo Valero Cortésمشاهده پروفایل

استاد

• Computer Architecture
• High-Performance Computing
• Parallel Computing
• +۳ مورد دیگر

Mateo Valero Cortés is a renowned Professor of Computer Architecture at the Polytechnic University of Catalonia and Director of the Barcelona Supercomputing Center (BSC). He has held academic and leadership roles since 1974, advancing high-performance computing (HPC) and computer architecture research. His work includes pioneering contributions to vector architectures, multithreading, and instruction-level parallelism. Education includes a Telecommunications Engineering degree from the Polytechnic University of Madrid (1974) and a PhD in Telecommunications Engineering from the Polytechnic University of Catalonia (1980). His research spans over 700 publications, focusing on HPC systems, parallel computing, and supercomputing infrastructure. Key research interests include vector processing, super-scalar processors, and task-based programming models. Recent work emphasizes scalable architectures for exascale computing and energy-efficient hardware-software co-design. Notable achievements include the Eckert-Mauchly Prize (highest in computer architecture), Seymour Cray Award, and Charles Babbage Prize. He has led initiatives like the Spanish Supercomputing Network (RES) and PRACE (European HPC partnership). Academic affiliations include the Royal Academy of Engineering of Spain, ACM Fellow, and IEEE Fellow. He has received 13 honorary doctorates and awards such as Mexico’s Order of the Aztec Eagle. Current projects include the Mont-Blanc HPC prototype and ERC-funded research on multi-core chip design. His BSC oversees over 300 researchers and manages MareNostrum supercomputers.