May 2024 – May 2025 · 1 yr · Baltimore, Maryland, United States

• Worked at the Center for Digital Health and Artificial Intelligence (CDHAI) to develop Foundation Models for Medical Message Design, Diabetes Prediction and Management.

Apr 2023 – Jan 2024 · 9 mos · United States · Remote

• Worked in the SPT Capabilities team in Global Currency Solutions on projects including work on field tools (ICE and RISE) and Data Lab that are sourced via Cornerstone data, acquired and processed from the gold servers using Apache Hive and PySpark.

Sep 2021 – Jan 2022 · 4 mos · Bengaluru, Karnataka, India

• Worked on the General Behaviour Model of their Hybrid Inference Engine, to analyze user behaviour from connected car data, which is then processed to provide app and contact recommendations.

Dec 2020 – Feb 2021 · 2 mos · Bangalore Urban, Karnataka, India

• Performed Normalization and Speckle Noise Removal in Synthetic Aperture Radar (SAR) images using Neuro-fuzzy and Wavelet Transform techniques.

May 2020 – Aug 2020 · 3 mos

• Worked as a student developer for the FrameNet Brasil Organization on my project- New Frame-Based Image and Video Annotation Pipeline for the FNBr Web Annotation Tool.

May 2020 – Jul 2020 · 2 mos · Bengaluru, Karnataka

• Provided a contactless payment solution using VISA APIs through a multi-platform mobile app.

Feb 2020 – Jun 2020 · 4 mos · Bangalore, India

• Secured CVE vulnerabilities in open-source components on containers such as Docker, Kubernetes, etc.

Dec 2019 – Jul 2020 · 7 mos · Bengaluru Area, India

• Worked on an On-Device AI application of voice recognition, to segment and cluster different voice samples from online multiparty meetings through experiments performed with an audio fingerprinting approach and a deep learning pipeline of autoencoders, RNNs and self organizing maps implemented in tensorflow.js

May 2019 – Jul 2019 · 2 mos · Bangalore, India

• Worked as a summer research fellow under Prof. Shalabh Bhatnagar in the Stochastic Systems Lab at the Computer Science and Automation department. Proposed a Multi-Agent Reinforcement Learning solution for demand and supply management in Microgrids, using two DQN networks for scheduling of jobs (ADL Network) and trading energy (ET network) through a Dynamic Pricing scheme.