Machine Learning Data Scientist

Suruchi Sharma

510-***-**** San Jose, CA (Open to Relocation)

*****************@*****.*** linkedin.com/in/suruchi97sharma github.com/suruchi1997 DATA SCIENTIST AI ENGINEER MACHINE LEARNING ENGINEER AI RESEARCH SCIENTIST Proficient and highly motivated professional with a Master’s degree in Artificial Intelligence and a Bachelor’s degree in Computer Engineering, bringing a robust skill set in AI, Data Science and Software Engineering spaces. Demonstrated capability in leading innovative research and development projects and solving research problems at San Jose State University, with a focus on pioneering control solutions using PyTorch, and deep learning techniques. Noteworthy contributions to application development, including involvement in tool development for data comparison and predictive analysis during tenure at Accenture. Recognized for academic excellence such as RSCA Fellowship and publications in esteemed conferences and journals.

EDUCATION

Master of Science, Artificial Intelligence (STEM) San Jose State University 3.8/4.0 Dec 2023 Bachelor’s in Computer Science (STEM) K.J. Somaiya Institute of Technology 3.85/4.0 May 2019 SKILLS

Libraries: PyTorch NumPy TensorFlow Pandas Media Pipe Opencv Matplotlib scikit-learn Methodologies and Techniques: AI ML DL VAE GAN Transformers NLP Data Mining MLFlow Programming Languages: C Java Python R SQL queries Relational Databases: MySQL

Web technologies: React.js Spring Boot REST HTML CSS Javascript Cloud Computing Technologies: AWS (AWS EC2 S3) Data Visualization Tool: Tensorboards

Math Concepts: Statistics Linear Algebra Calculus Probability PROFESSIONAL EXPERIENCE

AI Engineer AI Research and Developer (R&D) San Jose State University San Jose, CA May 2022 - Present

● Implemented Controllability Constrained Deep Neural Network based Controllers using PyTorch and Deep Learning techniques

● Trained Controllability Constrained and Standard Models using CUDA GPU over HPC clusters.

● Performed qualitative and quantitative analysis comparing Controllability Constrained models to Standard Controllers, demonstrating a 9% reduction in control cost for pendulum and 5% reduction for CartPole environment Application Development Analyst Software Engineer Accenture Aug 2019 - Jun 2021

● Collaborated in a team project developing a Data Comparator tool, leveraging object-oriented programming techniques to enable automation of the process of data comparison and accelerating by 80%

● Managed XML and JSON data for seamless data comparison

● Seamlessly integrated tool with MySQL database, enabling real-time updates for enhanced data accuracy

● Designed a Spring backend with restful web services consisting of logic to populate the database

● Implemented UI using React JS for user-friendly data presentation and rest APIs interactions Application Development Analyst Software Engineer Accenture Aug 2019 - Jun 2021

● Performed data processing, utilized the data for training and evaluation.

● Utilized data visualization techniques to extract out key features for data analysis

● Developed predictive models using Machine Learning algorithms (Logistic Regression, Random Forest, and SVM) for loan eligibility prediction, achieving an average accuracy rate of 70% ARTIFICIAL INTELLIGENCE & DATA SCIENCE PROJECTS

AI GYM Trainer Aug 2022 - Dec 2022

● Implemented AI based GYM Trainer that used computer vision tools Mediapipe and YOLO for Pose Detection, neural networks based framework to calculate total repetitions and critical angles to compute effective repetitions Song release year prediction Aug 2021- Dec 2021

● Developed machine learning model to extract insights from a large data set of 0.5 million instances and 90 features, accurately predicting release year of the song Covid 19’s Varied Impact: Aug 2021- Dec 2021

● Performed Exploratory Data Analysis in RStudio to study the impact of Covid 19 across various US States PUBLICATION RECORD

Controllability Constrained Deep Network Models for Enhanced Control of Dynamical Systems IEEE American Control Conference 2024

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