جست‌وجوی استاد و پژوهشگر

Emilie Dressaireمشاهده پروفایل

استادیار

• Fluid Dynamics
• Complex Fluids
• Bio-inspired Engineering
• +۳ مورد دیگر

Emilie Dressaire is an Assistant Professor in the Department of Mechanical Engineering at the University of California, Santa Barbara (UCSB). Her research focuses on fluid mechanics at small scales and complex fluids, with an emphasis on bio-inspired engineering solutions for fluid flow control and monitoring. She holds a PhD in Mechanical Engineering from Harvard University, an MS in Biophysics from the University of Paris, and a BS in Physics and Chemical Engineering from ESPCI. Her work spans interdisciplinary applications, including fluid-driven fractures, microfluidics, filtration systems, and biological fluid dynamics. Notable contributions include studies on clogging mechanisms in microfluidic arrays, pulsatile flow dynamics, and the design of bio-inspired filtration systems. She has received the Regents' Junior Faculty Fellowship from UCSB's Academic Personnel Office. Her research integrates experimental and computational approaches to understand phenomena such as capillary interactions, viscoelastic fluid behavior, and flow instabilities. Recent projects explore fluid dynamics in porous media, fungal networks, and the mechanics of droplet impacts on flexible fibers. Education: PhD, Mechanical Engineering, Harvard University MS, Biophysics, University of Paris BS, Physics and Chemical Engineering, ESPCI Awards: Regents' Junior Faculty Fellowship, UCSB Academic Personnel Office Advising & Grants: While no specific students or grants are listed, her research aligns with NSF-funded themes in fluid mechanics and bio-inspired engineering. Labs/Teams: Active in UCSB's Mechanical Engineering research groups focused on microfluidics and soft matter physics.

Behrooz Parhamiمشاهده پروفایل

استاد

• Computer Arithmetic
• Parallel Processing
• Fault Tolerance
• +۴ مورد دیگر

Behrooz Parhami is a Distinguished Professor in the Department of Electrical and Computer Engineering at the University of California, Santa Barbara, where he has been a faculty member since 1988. He previously served as Associate Dean for Academic Personnel in the College of Engineering from 2009-2012 and as Vice Chairman of the ECE Department from 1990-1992. Before joining UCSB, he was a professor at Sharif University of Technology in Tehran, Iran from 1974-1988. He received his PhD in Computer Science from UCLA in 1973. Professor Parhami's research focuses on computer arithmetic, parallel processing, and fault-tolerant computing. In computer arithmetic, he pioneered generalized signed-digit number systems as a unified framework for redundant representations. His work in parallel processing includes contributions to database processors, interconnection networks, and scalable architectures. In fault tolerance, he developed systematic data-driven methodologies for reliable hardware and software systems. He has also made significant contributions to adapting computer technology for Persian language computing. His scholarly output demonstrates consistent focus on fundamental computing principles. Recent publications show continued exploration of unconventional number systems, reliability analysis, and specialized computing architectures. His work bridges traditional computer architecture with emerging technologies including atomic-scale computing, neuromorphic systems, and novel memory technologies. His research maintains relevance through connections to both theoretical foundations and practical implementations. IEEE Life Fellow Fellow of Institution of Engineering and Technology Chartered Fellow of British Computer Society IEEE Centennial Medal (1984) Top-cited article award from Journal of Parallel and Distributed Computing (2010) IET Circuits, Devices & Systems Premium Achievement Award (2009) Sharif University Technology Association's Dr. Amin Lifetime Achievement Award (2024) Professor Parhami has graduated four PhD and numerous MS students. His teaching spans both undergraduate and graduate levels, with recent courses including ECE 1B (Puzzling Problems in Computer Engineering), ECE 252B (Computer Arithmetic), ECE 254B (Parallel Processing), and ECE 257A (Fault Tolerant Computing). He has authored six textbooks that have been widely adopted internationally, including works on parallel processing, computer arithmetic, and computer architecture. His consulting activities focus on high-performance digital system design and intellectual property issues. He is actively involved in promoting gender equity through UCSB's Men Advocating for Gender Equity (MAGE) group, which he currently chairs.

Daoru Hanمشاهده پروفایل

دانشیار

• Plasma Dynamics
• Space Propulsion
• Computational Plasma Physics
• +۵ مورد دیگر

Dr. Daoru (Frank) Han is an Associate Professor in the Department of Mechanical and Aerospace Engineering at Missouri University of Science and Technology, where he directs the Gas & Plasma Dynamics Laboratory. His research focuses on plasma aerospace applications, space propulsion systems, and computational plasma physics, with a strong emphasis on lunar surface engineering and high-performance computing for large-scale simulations. Dr. Han holds a Ph.D. in Astronautical Engineering from the University of Southern California (2015), an M.S. in Aerospace Engineering from Missouri S&T (2011), and a B.S. in Aeronautical Propulsion from Nanjing University of Aeronautics and Astronautics (2009). His work spans plasma-material interactions, regolith beneficiation, and in-situ resource utilization for lunar exploration. His recent research trends prioritize kinetic plasma modeling of lunar surface charging, electrostatic sieving for regolith processing, and development of advanced simulation tools like PIFE-PIC for plasma dynamics. He has contributed to ground testing facilities replicating space environments and explored innovative methods for lunar aluminum production via molten salt electrolysis. While no scientific awards are explicitly listed, his extensive publication record highlights contributions to plasma physics and space engineering. Advising and grant details are not provided in the text, but his lab focuses on advancing plasma-based technologies for space exploration challenges such as dust mitigation and resource utilization.

Arun Natarajanمشاهده پروفایل

استاد

• Millimeter-Wave Circuits
• Integrated Circuits
• High-Speed Wireless Communication
• +۴ مورد دیگر

Arun Natarajan is a Professor in the Electrical Engineering and Computer Science department at Oregon State University . He leads the High-Speed Integrated Circuits Lab , focusing on millimeter-wave and sub-millimeter-wave integrated circuits for wireless communication and imaging applications. Education: Ph.D., Electrical Engineering, California Institute of Technology (2007) M.S., Electrical Engineering, Caltech (2003) B.Tech., Electrical Engineering, Indian Institute of Technology Madras (2001) His research bridges cutting-edge circuit design with practical applications in high-speed wireless systems, radar imaging, and energy-efficient computing. Current projects involve millimeter-wave phased arrays , self-healing circuits , and compute-in-memory architectures . His work has been recognized with the NSF CAREER Award and DARPA Young Faculty Award . Recent publications highlight advancements in analog correlators , full-duplex receivers , and wideband systems , with applications in 5G/6G communication, radar, and optical networks. He previously worked at IBM T.J. Watson Research Center (2007-2012) and serves on the technical program committee of the IEEE RFIC conference.

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.

David Temperleyمشاهده پروفایل

استاد

• Music Cognition
• Computational Modeling
• Music Theory
• +۲ مورد دیگر

David Temperley is a Professor of Music Theory at the Eastman School of Music , University of Rochester. His work bridges computational modeling of music cognition and language research . PhD in Music Theory, Columbia University Postdoctoral Fellowship, Ohio State University Temperley's research focuses on metric perception, key perception, harmonic analysis , and corpus analysis of musical styles. His notable publications include The Cognition of Basic Musical Structures (MIT, 2001) and The Musical Language of Rock (Oxford, 2018). Recent computational studies explore syncopation patterns , melodic efficiency , and information density in musical contexts. Scientific Awards : Society for Music Theory Emerging Scholar Award (2001) for his first book Temperley's compositions— piano works, chamber music , and songs —blend classical forms with rock influences . Upcoming performances include premieres at Eastman and collaborations with artists like Peter Kurau and Juliana Athayde.

Kishwar Ahmedمشاهده پروفایل

استادیار

• High Performance Computing
• Cyber-Physical Systems
• Parallel Computing
• +۳ مورد دیگر

Kishwar Ahmed is an Assistant Professor in the Department of Electrical Engineering and Computer Science at the University of Toledo . Her research focuses on High Performance Computing (HPC) and Cyber-Physical Systems , with emphasis on energy-efficient modeling, resource allocation, and scalable simulation frameworks. Her recent work explores trends in parallel computing education , edge device optimization , and power-aware HPC systems . She has secured a $600K NSF grant for collaborative research with NMSU. Scientific Awards NSF CRII Award Service Roles TPC Member, HiPC 2024 Workshop Organizer, EduHiPC 2023-2024 Program Committee Member, ACM SIGSIM PADS 2019-2024 Journal Reviewer (IEEE TCC, ACM TOMACS, etc.)

Edgar Solomonikمشاهده پروفایل

دانشیار

• Tensor computations
• Numerical optimization
• Parallel and high performance computing
• +۵ مورد دیگر

Edgar Solomonik is an Associate Professor at the University of Illinois at Urbana-Champaign, affiliated with the Siebel School of Computing and Data Science. His research focuses on high performance computing, parallel algorithms, and tensor computations. He earned a PhD from UC Berkeley (2014) and a BS from UIUC (2010). Notable contributions include the Cyclops Tensor Framework and work on communication-avoiding algorithms. Awards include the NSF CAREER Award (2020) and the Jack Dongarra Early Career Award (2024). He teaches courses on parallel numerical algorithms, tensor computations, and numerical analysis. Current research explores quantum computing, graph algorithms, and efficient tensor operations for scientific computing. Education: PhD, UC Berkeley (2014); BS, University of Illinois (2010) Research interests span numerical linear algebra, parallel programming systems, and quantum algorithms. Key projects include optimizing tensor contractions for quantum chemistry simulations and developing scalable algorithms for distributed-memory systems. His work has been published in top venues like SISC, SIAM Review, and SC conferences.

• Social Network Analysis
• Epidemic Modeling
• Knowledge Base Systems
• +۴ مورد دیگر

Eunice E Santos serves as a Professor at the School of Information Sciences, University of Illinois, where she leads interdisciplinary research bridging computer science and public health systems. Her work focuses on computational modeling of complex social phenomena with direct applications to epidemic response and healthcare policy. Her research program centers on Social Network Analysis and Epidemic Modeling, with specialized expertise in Knowledge Base Systems, Parallel Algorithms, and Agent Based Modeling. She develops frameworks that integrate socio-behavioral dynamics into disease transmission models, particularly examining how community structures influence information spread during pandemics and medical guideline adoption in physician networks. Recent projects emphasize culturally infused modeling approaches to address disparities in healthcare dissemination. Analysis of her 89+ publications reveals a strong trajectory toward unified computational frameworks for socio-technical systems, with increasing emphasis on real-world public health applications since 2020. Her work demonstrates consistent methodological innovation in large-scale network analysis and influence prediction algorithms. Her scientific recognition includes: IEEE Fellow (2023) for contributions to social network analysis and epidemic modeling Dr. Santos actively engages with policy communities, as evidenced by her 2020 expert commentary on pandemic behavior prediction that reached 57 Mendeley readers and was covered by 5 news outlets. She co-organizes the IEEE ParSocial workshop series, fostering collaboration between parallel computing and computational social science researchers.

John L. Hennessyمشاهده پروفایل

استاد

• Computer Architecture
• RISC Technology
• Wireless Communication
• +۳ مورد دیگر

John L. Hennessy is a renowned computer scientist and former President of Stanford University (2000–2016). As a professor of Electrical Engineering and Computer Science since 1977, he pioneered RISC architecture and co-founded MIPS Computer Systems and Atheros Communications. He currently serves as the Shriram Family Director of the Knight-Hennessy Scholars program and chairs Alphabet’s board. His leadership revitalized Stanford’s financial aid and arts programs, and his research contributions include seminal work in computer architecture and parallel processing. Education: Bachelor’s in Electrical Engineering from Villanova University (1973) Master’s and Ph.D. in Computer Science from Stony Brook University (1975–1977) Hennessy’s research interests span computer architecture, RISC technology, and high-performance computing. He co-authored landmark textbooks Computer Architecture: A Quantitative Approach and Computer Organization and Design , which are industry standards. His leadership roles include Dean of Engineering, Provost, and President at Stanford, and board memberships at Alphabet and the Gordon and Betty Moore Foundation. His articles and books focus on multiprocessor systems, cache coherence, and hardware-software co-design, reflecting his deep impact on both academia and industry. Key awards include the ACM Turing Prize (2017), IEEE Medal of Honor (2012), and the Vannevar Bush Award (2024). Grants & Leadership: Launched Knight-Hennessy Scholars, the world’s largest graduate fellowship program Directed Stanford’s growth in interdisciplinary research and accessibility initiatives Hennessy’s legacy combines technical innovation with transformative leadership in higher education.

Huy Voمشاهده پروفایل

دانشیار

• Big Data Systems
• Data Science
• High-Performance Computing
• +۳ مورد دیگر

Huy Vo is an Associate Professor in the Department of Computer Science at the City College of New York (CCNY), part of the City University of New York (CUNY) system. His research focuses on Big Data Systems, Data Science, High-Performance Computing, Large-Scale Visualization, and Scalable Display Building. He is affiliated with CCNY's School of Engineering and maintains an active website at https://www-cs.ccny.cuny.edu/~hvo/ . His work spans urban analytics, visualization technologies, and machine learning applications. Notable contributions include analyzing global urban road networks using GNNs, developing semiconductor-specific large language models (SemiKong), and optimizing fleet management for ride-pooling in NYC. He also explores public health technologies, such as asthma self-management apps and pandemic policy impact visualizations. Vo's research often integrates interdisciplinary approaches, combining geospatial data, computational modeling, and real-time systems. His recent work highlights trends in scalable computing, AR visualization, and data-driven decision-making in urban environments. No scientific awards are explicitly mentioned in the provided text. His advising and grants sections remain unspecified, but his extensive publication record indicates active research collaborations and grant-funded projects. The SAT-Hub project exemplifies his focus on smart urban infrastructure, while his work on visualization frameworks like Unitypic underscores innovations in interactive 3D systems.

Aydin Buluçمشاهده پروفایل

دانشیار مدعو

• Parallel Computing
• High Performance Graph Analysis
• Machine Learning
• +۴ مورد دیگر

Aydin Buluç is a Senior Scientist at Lawrence Berkeley National Laboratory (LBNL) and an Adjunct Associate Professor of EECS at UC Berkeley. His work focuses on parallel computing, high-performance graph analysis, machine learning, sparse computations, and computational biology. He leads the Sparsitute DOE center and directs the PASSION Lab, emphasizing scalable algorithms and their applications in genomics and data science. Education: PhD in Computer Science from UC Santa Barbara (2010); BS in Computer Science and Engineering from Sabanci University, Turkey (2005). Research: Develops parallel algorithms for graph analysis, sparse linear algebra, and communication-avoiding techniques. Key projects include HipMCL for metagenome analysis and GraphBLAS for combinatorial computing. Awards: DOE Early Career Award (2013), IEEE TCSC Early Career Award (2015), ACM Gordon Bell Prize Finalist (2022). Teaching: Co-teaches CS267 (Applications of Parallel Computers) at UC Berkeley, emphasizing practical high-performance computing. Professional Roles: Editor of ACM Transactions on Parallel Computing, PC member for major conferences (SPAA, IPDPS), and leader in ExaGraph and DOE initiatives.

Katherine A. Yelickمشاهده پروفایل

استاد

• High-Performance Computing
• Programming Systems
• Parallel Algorithms
• +۴ مورد دیگر

Katherine A. Yelick is the Robert S. Pepper Distinguished Professor of Electrical Engineering and Computer Sciences at the University of California, Berkeley, and Vice Chancellor for Research at UC Berkeley. She holds a joint appointment as Senior Faculty Scientist at Lawrence Berkeley National Laboratory, where she previously served as Associate Laboratory Director for Computing Sciences (2010-2019) and Director of NERSC (2008-2012). Her research spans high-performance computing, programming systems, parallel algorithms, and computational genomics, with leadership roles in projects like ExaBiome and Bebop. Ph.D. in EECS from MIT (1991) M.S. in EECS from MIT (1985) B.S. in EECS from MIT (1982) Yelick specializes in parallel programming languages , automatic performance tuning , and exascale computing , co-inventing Unified Parallel C (UPC) and Titanium. Her recent work focuses on genomic data analysis , GPU-accelerated algorithms , and communication-avoiding techniques for large-scale scientific computing. Her publications reveal trends in bioinformatics , GPU parallelism , and exascale-ready frameworks , with keywords spanning computational biology, algorithms, and cloud computing. Awards include the ACM-IEEE Ken Kennedy Award (2015), National Academy of Engineering (2017), and AAAS Fellowship (2018). Leadership : ExaBiome (exascale microbiome analysis), Bebop (optimization group) Mentorship : Co-advising Alok Tripathy and Reginald Frank As a key figure in Lawrence Berkeley National Laboratory 's Computing Sciences division, she oversees NERSC, ESnet, and CRD, driving interdisciplinary applications in climate, fusion, and biomedical research.

Isaac Soninمشاهده پروفایل

استاد

• Probability Theory
• Stochastic Control
• Markov Chains
• +۵ مورد دیگر

Isaac Sonin is a Professor in the Department of Mathematics and Statistics at the University of North Carolina at Charlotte. His research bridges Probability Theory , Stochastic Control , and Game Theory with applications in economics, operations research, and finance. University of North Carolina at Charlotte (1991-present) Moscow State University (PhD in Probability Theory) His work focuses on optimal stopping of Markov chains , Gittins index theory , and stochastic control , producing foundational results like the Decomposition-Separation Theorem for nonhomogeneous Markov chains. Recent publications explore variants of Bayesian games (locks/bombs/testing models) and parallel computing for Markov chains , reflecting interdisciplinary impact. His 2020s research includes papers on compound interest formulas and financial clearing models , connecting theoretical probability to practical economic problems. Earlier contributions to multi-armed bandit problems and Nash equilibrium analysis (1990 book) established him as a leader in stochastic decision-making.

Daniel J. Fellemanمشاهده پروفایل

استاد

• Neuroscience
• Neuroanatomy
• Visual Processing
• +۴ مورد دیگر

Dr. Daniel J. Felleman is a Professor at The University of Texas Health Science Center at Houston, specializing in the organization, function, and plasticity of the primate cerebral cortex. His research focuses on visual processing mechanisms in cortical areas V2, V4, and posterior inferotemporal cortex, with particular emphasis on color and form discrimination. 713.500.5629 Research Interests: The laboratory employs optical recording of intrinsic cortical signals and neuroanatomical tracers to map functional modules in primate visual cortex. Key findings include the segregation and integration of color and form inputs in V4, and developmental changes in prefrontal cortex following neonatal temporal lobe damage. Neurodevelopmental changes in prefrontal cortex after medial temporal lobe damage 3D computer graphics analysis of cortical circuitry Scientific Contributions: Spanning four decades, his work has established foundational understanding of cortical organization through publications in Nature , Science , and Cerebral Cortex . Recent studies (2014-2024) explore orientation pinwheels in V4 and mutual information analysis in stripe compartments. Technological Innovations: Developed wireless neural interface systems for primate research (2015), enabling unconstrained behavioral studies. Pioneered difference-image techniques to identify activated cortical regions through chromatic and luminance stimulation.