Krishna is a machine learning engineer who is curious and passionate about applied deep learning in computer vision, NLP, and reinforcement learning. He has four years of experience with machine learning, including having been a part of the analytics division of JP Morgan Chase & Co. He is a great communicator and enthusiastic developer.
AI, ML & LLM
Machine Learning
Deep Neural Networks
Deep Learning
LLM Agents
Large Language Models (LLMs)
LLM
Generative AI
Generative Artificial Intelligence (GenAI)
Designed, developed, and deployed a temporal GNN (Graph-LSTM, contrastive learning) for creating dynamic user embeddings, enhancing downstream ML model performance, and eliminating feature engineering time.
Developed and deployed reusable SageMaker model explainability reporting pipelines (SHAP, PDP), standardizing validation processes and reducing model debugging time by 60%, increasing stakeholder trust.
Designed a modular, reusable self-supervised learning pipeline (self-distillation), improving user tagging performance in cold-start settings.
Built customized on-disk graph back end using custom Feature Store, GraphStore implementation in PyG to scale the GNN training on huge data (500M edges & 50M nodes).
Mentored data scientists through pair-programming, peer reviews, and hiring participation.
A fully convolutional network (FCN-32s) trained to semantically segment forest scene images with RGB and nir_color input images. The project was developed to help unmanned drones in smooth navigation. The model is trained and tested on still images of forest scenes. Used Intel Edison and Microsoft Kinect for POC and prototype creation.
Worked on a smart medical network for Intel ESDC 2016, Shanghai. The project aimed to create an ecosystem of a medical network that stores patients' clinical and real-time data for smoother and quicker diagnosis in an emergency.
