I am a data scientist with a background in software engineering and a passion for AI, machine learning, and data-driven solutions. My expertise spans across predictive modeling, data visualization, cloud computing, and AI optimization, with a focus on healthcare analytics, business intelligence, and financial reporting.
MS in Information Science, University of Pittsburgh
(2022-2024)
GPA: 3.8
BE in Computer Science & Engineering, Visvesvaraya Technological University
(2016 – 2020)
GPA: 3.2
Programming & Development: Python, R, JavaScript, Flask, Angular, HTML/CSS, Tailwind CSS
Machine Learning & AI: Generative AI, NLP, Deep Learning, BERT, Bayesian Models, SVM, DNN, KNN, Prompt Engineering
Data & Cloud: SQL, MongoDB, Elasticsearch, AWS SageMaker, Linux, AWS, Azure
Data Visualization: Excel, Power BI, Tableau
Data Scientist
University of Pittsburgh School of Medicine
June 2024 – Present
Pittsburgh, USA
Data Analyst
University of Pittsburgh
Sept 2023 – April 2024
Pittsburgh, USA
Senior Software Engineer
Capgemini
Oct 2020 – Aug 2024
Bangalore, India
Software Engineer Intern
Capgemini
Jan 2020 – May 2020
Bangalore, India
Women Tech-makers Ambassador
Jul 2023 - Present
Student Information Technology subcommittee Member
University of Pittsburgh Student Affairs
Jan 2023 - Present
Student Mentor
CGI
Jan 2023 - Jun 2023
Campus Business Manager
PrepBytes
Jul 2020 - Sep 2020
Event Coordinator
Computer Society of India
Feb 2018
Salesforce Certified AI Specialist
Issued Oct 2024
Salesforce Certified AI Associate
Issued Nov 2023
Salesforce Certified Associate
Issued Jul 2023
Developed a digital health platform with HTML/CSS/JS, Flask, JWT authentication, MongoDB, and ChatGPT API, leading to a 30% increase in user engagement.
Created a document retrieval system using BERT, Angular, and Tailwind CSS, with a backend powered by Flask, Celery, Redis, and Elasticsearch for efficient real-time data interaction.
Trained multiple ML models (Bayesian, SVM, DNN, KNN) to predict corrosion rates, reducing incidents by 20% through data-driven insights.
Enhanced classification accuracy by 15% using Epsilon Differential Privacy, leveraging Python libraries like Pandas, Scikit-learn, Pytorch, TensorFlow, and Keras.
Comprehensive Study of Differentially Private Deep Learning Mechanism
NATIONAL CONFERENCE ON EMERGING TRENDS IN ENGINEERING SCIENCE FOR FUTURE TECHNOLOGY
Aug 1, 2020
Conference
1st National Conference on Emerging Trends in Engineering Science for Future Technology (NCETESFT-2020)
James G. Williams Scholarship Fund
School of Computing and Information 04/2024
Second Place, She-Innovates Hackathon
School of Computing and Information 02/2023
Hey everyone!
I've been getting a lot of DMs for guidance, so decided to take action on it.
I'm excited to help folks out and give back to the community via Topmate. Feel free to reach out if you have any questions or just want to say hi!
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