My journey to IBM:
I joined IBM Research in 2008, after 6 years as a Research Scientist at The George Washington University working on advances in accelerated computing. At IBM research I've been part of the Communications and Computation Subsystems Group specializing on accelerated computing where I've worked on a number of advnaced processors such as BlueGeneQ, FPGA based processor simulators, Optical Network reliability, 3D Stacked FPGA, and more.
What I do:
I specialize in AI (Machine Learning and Deep Learning), Analytics (BigData and Complex Analytics), Accelerators (FPGA, GPU, DSPs, ASICs etc), HPC and Parallel Computing (Compilers, UPC, MPI, etc), and Architecture (Processor, Memory, and Network design). My most recent work has been focused primarily on advances in Deep Learning.
My work has allowed me to contribute to multiple fields including Financial, Insurance, Telecommunications, Energy, Utilities, Media, Public, Federal, Distribution, and Security research and development. I find problem solving to be one of my most enjoyable endeavors. I have been fortunate enough to experience the entire stack, from custom chip design all the way up to fully deployed commercial solutions.
I received my Doctor of Science in Computer Engineering from the George Washington University in Washington DC, and my Bachelor of Science Degrees in both Computer Engineering and Electrical Engineering from Drexel University in Philadelphia.
Research interest topics:
Machine Learning, Deep Learning, AI, Advanced Analytics, Business Intelligence, Signal Intelligence, Statistical Modeling, Big Data, Signaling QoS, Cloud Computing, Parallel Filesystems, High Performance Storage, 3D Stacking, Mobility, Application Hardware Accelerators, HPC Messaging, Grid Computing, Parallel Computing, Performance Analysis and Application Acceleration, Hardware Design, Kernel Development, Embedded Computing, Processor Design, Verification, Compiler Design, High-speed Network design, Memory Architecture Design