+ Led a team to develop a stochastic variational bayesian inference and supervised machine learning infrastructure for various classification tasks, using scalable ETL processes to integrate structured data (HIPAA Compliance), successfully bayesian-search trained billions of parameters at 3.1X optimized compressed RAM (2 Terabytes/node) and achieved ≥87% prediction accuracy+ Launched and developed several distributed solutions for the clients using Neural ODE training and paralleled Neural architecture search on QuadroP4000 GPUs and X86 Xeon servers (>40 TFLOPs, O(1) memory O(E) time), achieved 4X speedup and deployed at heterogeneous VM & Container infrastructures + Built software pipelines for deep neural network architecture search and CNN training, fed data via CUDA GPUDirect RDMA, then trained them on proprietary Keras, Tensorflow, and TFX built, achieving 1.5X throughput speedup

With Denti.AI through Med.Tips Technology Inc.

IT and Business Intelligence services for private organizations: + Creating a Machine Learning framework and infrastructure specifically for healthcare providers. The primary emphasis are on areas like predictive analysis, treatment optimization, patient monitoring and follow-up care+ DDoS attack and bandwidth saturation penetration tests (SYN, ICMP flood & Anycast exploitation)+ Natural language processing and sentimental analysis of various social media data+ Financial data-mining-based decision support for private companies+ M&A tech and financial due diligence on pre-series B tech startups+ Local environmental regulatory compliance review and filing review for construction projects

Online-to-offline healthcare therapeutic music service, covering licensing, composition and production: www.eigentunes.com

+ Treatment planning study of different respiratory gating thresholds for gated SBRT breast cancer patients+ Clinical management of breast cancer treatment outcome using 3D-CT and 4D-CT dose calculations in radiotherapy.