I am a PhD candidate in Computer Science at the Johns Hopkins University, researching in the Hopkins Storage Systems Lab. My primary advisor is Randal Burns, PhD. I obtained an MSE in Computer Science (2015) and an MSE in Engineering Management (2013), both from Hopkins.
I was awarded both the Hopkins Computer Science Graduate (Paul V. Renoff) Fellowship, and the
UPE Special Recognition award in 2014.
In 2017 I received the UPE Academic Achievement award and the Best Presentation award at High-Performance Parallel and Distributed Computing (HPDC) Conference.
I love to work on highly-scalable tools (generally in C++ and Python) for data science and machine learning applications.
I enjoy backend web-service building and strongly believe in the web-service paradigm for user interaction with tools.
I also enjoy studying the effect of advancements in computing on business and their co-evolution as computing becomes ubiquitous in all industries.
Developing C++ and Python framework for Cognitive Memory explainable AI graph engine and database.2017 - Present
Develop MPI based parallel/distributed matrix-sketching-infused machine learning routines, including novel clustering techniques.May. 2016
Design and analysis of a Medium Access Control protocol for Ultra-Wide Band (UWB) embedded units in MATLAB then C.Summer 2009
Parallel & Distributed Computing
Development of a parallelized, NUMA-architecture aware k-means. We utilize methods such as informed thread binding, NUMA-aware task scheduling and other memory access latency reduction techniques. We achieve a 10-100 X speedup when compared with commercial products such as Spark's MLlib, Turi (Formerly Dato, GraphLab) and H2O.
BrainLab CI (BLCI), a continuous integration environment for collaborative, community experiments with data-quality controls and full provenance. Users can run code within a pipeline which may have code or data dependenciesds. All side effects of the new code are propagated to both code and data artifacts within a repo.
I teach a Seminar in Emerging Technologies for the MSEM program. We discuss, evaluate and develop concrete business plans for emerging and disruptive technologies. We also invite several CEOs and owners of businesses to speak.
I teach an Excel and Python bootcamp for the MSEM program. Students learn (i) essential (advanced) skills in MS Excel, (ii) fundamental programming skills in Python to allow students to reason about how softare is developed.
I have been a teaching assistant for Parallel Programming several times. We tackle topics and projects using OpenMP, Java Threads, Hadoop!/MapReduce, Spark, Message Passing Interface (MPI) and GPU programming via CUDA.
I received a full scholarship to compete at NCAA D1 level for Morgan State University as an undergraduate.