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Senior Principal Engineer R&D (Machine Learning) at Baxter
I am a result oriented, growth driven data scientist working in Deep learning. Working in healthcare domain to bring the state-of-the-art techniques to improve healthcare outcomes of patients. I am passionate in using my product development, software engineering skills to save and sustain lives. I have implemented different machine learning techniques and its applications in Vision care, Natural Language Processing, Computer Vision - Artificial Intelligence. worked on developing Data Analytics for Big Data applications to solve the real world problems.
Cornell University
Product Management and Product Innovation, Business Administration and Management, General
August 1, 2023 – December 1, 2024
Indiana University Bloomington
Master's degree, Data Science
January 1, 2015 – January 1, 2017
Acharya Nagarjuna University
Bachelors in Information Technology, Computer Science
N/A – Present
Baxter International Inc.
Senior Principal Engineer
September 1, 2025 – Present
Baxter International Inc.
Principal Engineer R&D
January 1, 2023 – September 1, 2025
Baxter International Inc.
Senior Machine Learning Engineer
January 1, 2022 – December 1, 2022
Hillrom
Senior Machine Learning Engineer R&D
October 1, 2020 – January 1, 2022
New York, United States
Hillrom
Machine Learning Engineer - R&D
January 1, 2019 – September 1, 2020
New York, United States
Welch Allyn
Machine Learning Engineer
January 1, 2018 – January 1, 2019
New York, United States · On-site
Indiana University Bloomington
Natural Language Processing - Deep Learning
October 1, 2017 – January 1, 2018
Bloomington, Indiana Area
Indiana University Bloomington
Lead Associate Instructor - Advanced Python
May 1, 2017 – July 1, 2017
Indiana University Bloomington
Graduate Research Assistant
January 1, 2016 – April 1, 2017
Indiana University Bloomington
Associate Instructor, Artificial Intelligence
August 1, 2015 – April 1, 2016
Computer Sciences Corporation (CSC)
Senior Software Engineer
July 1, 2015 – July 1, 2015
India · On-site
Computer Sciences Corporation (CSC)
Software Engineer
July 1, 2015 – July 1, 2015
India · On-site
Face Expression Recognition
May 1, 2017 – July 1, 2017
• Implemented and trained a Convolutional Neural Network, Deep Neural Network to detect the facial expression. • used Numpy and Tensorflow over google cloud to classify the facial expression of a given image. • Designed a real-time face expression detection of an Image which moves into the region of the Camera using HAAR and classification using the model trained using CNN. • Implemented motion detection of humans on a real time surveillance camera.
Image Feature Extraction and Classification
April 1, 2017 – Present
• Extract features of the given image as follows: a) Haar Features b) Bag of Visual Words c) Deep Learning Features • Classification of the given image using Support Vector Machine algorithm.
Image Matching Using RANSAC
March 1, 2017 – Present
• Generation of summary vectors for 128-dimensional SIFT features to improve running time. • Implementation of RANSAC algorithm to estimate homography between given images. • Detection of Inliers and Outliers between matching correspondence points between the images.
Learning Analytics and Student Flow Analysis
August 1, 2016 – April 1, 2017
• Automated the data extraction , cleaning and transformation from the university SIS database system and created a data pipeline using the Pandas, Numpy. • Applied the Google Geo-Coding over processed data from the data pipeline to analyze and visualize the students addresses for a targeted marketing campaign for future Indiana University student admissions. • Applied an HMM and apriori market basket analysis in “R” over the current and past student’s data to find the course co-occurrence to understand the patterns and redefine the course offerings at IU. • Used the hierarchical clustering and Louvain Method for community detection (a graph based method) to detect the communities and student flow among various courses in a 3-year-old program at Indiana University.
Bayesian Data Analysis
August 1, 2016 – November 1, 2016
Bayesian Data Analysis • Derived the MLE and MAP for various distributions using the conjugate priors. • Implemented the Gibbs Sampling for data analysis • Implemented the Hierarchal clustering using bayesian data analysis aprach.
Empowering Instructors in Learning Management Systems
May 1, 2016 – July 1, 2016
• Designed an Interactive Heat Map Analytics Dashboard over student learning behavior web logs using statistical and NLP models.
Credit Card Defaulters - PCA and RandomForest
March 1, 2016 – April 1, 2016
• Used Principal Component Analysis and Random-forest to find the credit card defaulters with 3% increased accuracy using a prior. • Cleaned the data and did the Exploratory Analysis. • Did feature engineering from the EDA done and added a new custom prior feature to improve the performance. • Used PCA to remove the irrelevant features • Applied the Random Forest Algorithm to decide whether a customer can be a defaulter soon.
Student Program Planning with Career Information
January 1, 2016 – July 1, 2017
• Proposed a text and graph based recommendation algorithm that suggests the education opportunities those meet market needs. • Scraped the LinkedIn, MOOC and Residential courses of different universities and cleaned using NLTK package of Python NLP. • Implemented a Random Walk graph mining algorithm, applied topic modeling and TF- IDF to further reduce the noise in knowledge graph. • Created a custom text based search and page rank algorithm using Lucene, Java Beans and Maven. •• iConference paper : https://www.conftool.com/iConference2017/index.php?page=browseSessions&print=head&form_session=265
Movie Recommendation Systems
November 1, 2015 – Present
• Designed, Implemented recommendations system on MovieLens Database • Suggest movies to a user by inferring the user's movie rating after finding top similar users. • Evaluated the performance of the system using 5-fold Cross Validation. • Tested on various distance metrics
Analysis of baseball data for performance measure and prediction
November 1, 2015 – December 1, 2015
• Predicted the player’s performance based on batting, pitching and fielding for draft selections using R and Python. • The main aim of the project was to analyze the baseball dataset to understand the team performance and predict their performance in the near future. • Analyzed the baseball data and visualize the performance of each individual over various performance metrics like batting, pitching and fielding performances. • Evaluate the contribution of each player to the team. • Predict the future performance of the players.
Predictive models for analysis of Breast Cancer
October 1, 2015 – Present
• Implemented k-means clustering algorithm using JAVA on “Wisconsin Breast Cancer data” and built classification model to classify the breast cancer data. • Implemented 10-fold cross validation to optimize the model and achieved an accuracy of 95% in classifying data. • Analyzed the data by building classification models using Decision trees, Naïve Bayes techniques, and KNN algorithms.
Robotics software engineer
Udacity
June 24, 2026 – Present
Learning NoSQL Databases
Lynda.com
June 24, 2026 – Present
Machine Learning - Coursera by Dr. Andrew Ng
Coursera
June 24, 2026 – Present
Cultural Fit Analysis
The candidate's diverse project portfolio, ranging from academic research to industry R&D in healthcare, demonstrates adaptability and a broad interest in applying ML across different domains. Their progression through various ML Engineer roles to Principal Engineer at Baxter International Inc. aligns with a growth-oriented mindset. The academic background and teaching experience suggest a collaborative and knowledge-sharing inclination. The target role of ML Engineer aligns well with their core competencies and career trajectory.
Soft Skills & Operational Fit
The candidate's experience as a Lead Associate Instructor and Principal Engineer suggests strong leadership, mentoring, and project management capabilities. Their work in R&D indicates an ability to innovate and solve complex problems. The project descriptions, while detailed, could benefit from more explicit articulation of problem-solving approaches and team collaboration aspects to fully assess operational fit.