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Contributor

Biography
Maya Gokhale is Distinguished Member of Technical Staff at the Lawrence Livermore National Laboratory, USA. Her career spans research conducted in academia, industry, and National Laboratories. Maya received a Ph.D. in Computer Science from University of Pennsylvania. Her current research interests include data intensive heterogeneous architectures and reconfigurable computing. Maya is co-recipient of an R&D 100 award for a C-to-FPGA compiler, co-recipient of four patents related to memory architectures for embedded processors, reconfigurable computing architectures, and cybersecurity, and co-author of more than one hundred forty technical publications.

Maya is on the editorial board of IEEE Access and an associate editor of IEEE Micro. She is a co-recipient of the National Intelligence Community Award, is a member of Phi Beta Kappa, and is an IEEE Fellow.
Presentations
Posters
Research Posters
10am - 5pm Tuesday, 14 November 2023 DEF Concourse
Artificial Intelligence/Machine Learning
Architecture and Networks
Heterogeneous Computing
I/O and File Systems
Performance Measurement, Modeling, and Tools
Post-Moore Computing
Programming Frameworks and System Software
Quantum Computing
TP
XO/EX
Workshop
11:19am - 11:35am Sunday, 12 November 2023 505
Accelerators
Edge Computing
Heterogeneous Computing
W
Paper
4:30pm - 5pm Wednesday, 15 November 2023 301-302-303
Cloud Computing
Distributed Computing
Data Movement and Memory
Performance Measurement, Modeling, and Tools
TP
Workshop
2pm - 2:01pm Monday, 13 November 2023 601
Applications
Architecture and Networks
Data Movement and Memory
Heterogeneous Computing
I/O and File Systems
Large Scale Systems
Middleware and System Software
Performance Measurement, Modeling, and Tools
Performance Optimization
W
Chair of Sessions
Workshop
2pm - 5:30pm Monday, 13 November 2023 601
Applications
Architecture and Networks
Data Movement and Memory
Heterogeneous Computing
I/O and File Systems
Large Scale Systems
Middleware and System Software
Performance Measurement, Modeling, and Tools
Performance Optimization
W