Technical leader and researcher with a decade of experience in High Performance Computing (HPC) and five years in Machine Learning.
In my current role, I am leveraging HPC to scale Deep Learning algorithms. I've also engineered large-scale Generative ML algorithms for the analysis and generation of source code, resulting in the creation of the IBM Watson Code Assistant.
Previously, I evaluated and improved Linux OS performance and scalability for IBM's Summit and Sierra supercomputers, the world's fastest in 2018. I also led the development of PNNL’s GEMS, a high performance, massively multithreaded graph database, which resulted in the spin-off of Trovares, a startup offering highly scalable cloud-based graph analytics.
I have taught a graduate course on how to leverage HPC for Machine Learning, and I have authored 24 academic publications in international conferences and journals. Additionally, I have contributed to 17 patents.