Abhishek Jain Email & Phone Number

Palo Alto, California, United States

Developing Machine Learning / Data Science projects in the text and time-series domain to make SRE processes more efficient. Working exclusively in python. Projects:- Advancing observability with Root-Cause Analysis: Utilizing graph machine learning algorithms (GraphSAGE Neural Net), with Transformer encoder/decoder models (BERT, GPT) as well as unsupervised text clustering techniques (KMeans, HDBSCAN, UMAP), to develop SRE incident root-cause analysis applications. - RAG Chatbot: In-house chatbot end-to-end chatbot application for JPMC internal data/systems using the Retrieval Augmented Generation (RAG) pipeline. Responsibilities include creating data pipelines, developing fine-tuned Retriever and Re-Ranker, as well as validation testing for the LLM Reader. - AWS Simulations framework: Developed an AWS simulation framework for mocking realistic behavior of several AWS services, for unit testing and application simulation in JPMC.

Corning, New York, United States

Deep Learning for Gorilla Glass: Developed and deployed a custom ML solution, using Convolutional Neural Networks (CNN), for defect classification on 20+ Gorilla Glass inspection lines. Acascaded ML system is developed, with classical image processing techniques employed to catchcritical corner cases. ML Modeling is done using the Tensorflow-Python API, and deploymentdone by building a C# wrapper on the model files to communicate with the vendor equipmentusing TCP/IP protocols.Machine Health Estimation for Optical Fiber : Developed an ML Predictive Maintenance systemto estimate machine health and forecast failure times for optical fiber manufacturing equipment,increasing overall uptime by 3+ months. Multivariate Bayesian linear regression is used to gen-erate predictions and confidence intervals for machine health, which are visualized in PowerBIdashboards for plant personnel.Constrained Optimization for Ceramic Filters: Implemented and deployed Model PredictiveControl (MPC) algorithms for process control of extruder systems, resulting in reduced processdeviations by approximately 7%.

Raleigh-Durham, North Carolina Area

Conducted research focused on sparse optimal control methods for wide-area control in power systems. Frequency-based selective modal damping for power network oscillations allowed an improvement of stability by 5% over existing methods in the literature. Published papers in Control Engineering Practice and presented work at various technical conferences such as the American Controls Conference.

Corning, Ny

Development of a pre-built deep learning classification model AlexNet to separate defective parts. Deployment of a predictive process controller with Matlab optimization toolbox.

Corning, Ny

Assisted senior developers with PLC ladder logic, and emulating RSLogix PLC control blocks in the Matlab environment using Stateflow and Simulink.

Fort Collins, Colorado Area

Development of a distributed controls framework for a power system case study using off-the-shelf commercial devices including OrionLX from NovaTech, and Opal-RT real-time simulation platform.

Fort Collins, Colorado Area

Integrated a substation controller - OrionLX from NovaTech LLC, to the real-time computing hardware Opal-RT, through a TCP/IP connection through Modbus communication protocol.

Doctor Of Philosophy (Ph.D.), Optimization And Control Theory, Gpa: 3.75

Master Of Science (M.S.), Electrical And Computer Engineering, Gpa: 3.7

Bachelor’S Degree, Electrical, Electronics And Communications Engineering, First Class