Master of Science, Artificial Intelligence

Katz School of Science and Health, Yeshiva University

Aug 2023 - Dec 2024 | GPA: 3.7
  • Developed expertise in Machine Learning, NLP, Data Acquisition, and Digital Asset Management (DAM).
  • Conducted research in AI-driven decision-making, implementing advanced neural networks and transfer learning techniques.
  • Worked on LLMs & Deep Learning models for intelligent automation, improving efficiency in data-driven solutions.
  • Notable Projects: Dog Hip Analysis using Deep Learning & LLMs, WiFi Sensing for Elderly Monitoring using RSSI.

Diploma in Data Science

International Institute of Information Technology Bangalore, Bangalore

Oct 2022 - Jun 2023
  • Specialized in Statistical Analysis, Predictive Modeling, Deep Learning, and AI Ethics, with hands-on experience in Python and R.
  • Built and optimized Machine Learning models for classification, regression, and anomaly detection using TensorFlow, Scikit-Learn, and Matplotlib.
  • Gained proficiency in EDA, feature engineering, and cloud-based AI deployments, handling large-scale datasets efficiently.
  • Skills: Python, R, TensorFlow, Sci-Kit Learn, Matplotlib, Seaborn.
  • Notable Projects: IMdb-SQL-Analytics, lead Scoring Case Study, and Telecom-Customer-Churn-Prediction

B.Tech, Computer Science and Engineering

KIIT College of Engineering

Aug 2015 - Aug 2019 | CGPA: 7.8
  • Gained strong foundational knowledge in Computer Science, Data Structures, Algorithms, and Advanced Software Development.
  • Hands-on experience in C, C++, Advanced Java, SQL, and Full-Stack Web Development.
  • Developed a Breast Cancer Prediction System as a major project, applying Machine Learning algorithms such as Random Forest, Logistic Regression, and SVM.
  • Focused on software engineering principles, database management, and cloud-based applications, integrating AI solutions into healthcare analytics.
  • Notable Project: Breast Cancer Prediction: Machine Learning for Early Detection – Developed an ML-powered predictive system leveraging clinical datasets to assist early-stage breast cancer diagnosis, utilizing evaluation metrics like Accuracy, Precision, Recall, and F1-Score to enhance medical decision-making.