Machine Learning Computer Engineering

Yukta Dandekar

+1-571-***-**** *****@**.*** www.linkedin.com/in/yuktadandekar www.github.com/yukta28 Blacksburg, VA EDUCATION

Master of Science, Computer Engineering August 2023- May 2025 Virginia Tech, Bradley Department of Electrical and Computer Engineering – Blacksburg, VA GPA:3.7/4 Coursework: Applications of Machine Learning, Advanced Machine Learning, Deep Learning, Computer Vision, Information Retrival Bachelor of Engineering, Computer Engineering August 2019 - May 2023 University of Mumbai – Mumbai, India GPA: 9.5/10

PROFESSIONAL EXPERIENCE

Graduate Teaching Assistant (ECE 5464: Applications Of ML)January 2025 – Current Virginia Tech Blacksburg, VA

•Led ML coursework on model interpretability, feature selection, and optimization, boosting student performance by 20% and advancing research on hyperparameter tuning methods.

•Integrated automated grading pipelines for ML assignments using Python, compressing evaluation time by 30% and ensuring scalability for 50+ students.

•Designed interactive Jupyter Notebook tutorials on TensorFlow, PyTorch and scikit-learn, increasing student engagement by 40% and reinforcing industry-relevant ML techniques.

Machine Learning Engineer Intern May 2024 – August 2024 IGEMA India Pvt Ltd Pune, IN

•Engineered anomaly detection models for streaming sensor data using unsupervised learning (Isolation Forest, DBSCAN), refining fault detection accuracy by 35%.

•Fine-tuned feature extraction and dimensionality reduction pipelines in Python & SQL, lowering preprocessing latency by 40% for 10M+ sensor records.

•Productionized ML models on AWS Lambda with optimized inference pipelines, cutting cloud compute costs by 25% and enhancing real-time accuracy.

Jr. Software Development Engineer May 2022 – Aug 2023 Wian Tech Pvt Ltd Mumbai, IN

•Built a deep learning-based recommendation system (Transformer-based & collaborative filtering), increasing user engagement by 30% through personalized content suggestions.

•Refactored model training pipelines with TensorFlow and PyTorch, decreasing training time by 50% through parallel processing and GPU acceleration.

•Implemented MLOps best practices by automating ML model versioning, CI/CD pipelines, and containerized deployments using Docker and Kubernetes, improving deployment efficiency by 40%. PROJECT EXPERIENCE

AI-Powered Pothole Detection System Using YOLOv5 & Geospatial Data for Smart Road Monitoring June 2022 – April 2023

•Constructed a customer-first CNN-based pothole detection solution leveraging deep learning frameworks and YOLOv5, resulting in a 40% improvement in complaint processing efficiency. Implemented scalable data pipelines for high-throughput image processing.

•Optimized image verification and GPS extraction using Python libraries like Pillow and EXIF, elevating data quality and integrity by achieving 95% accuracy in geospatial mapping and complaint processing.

•Leveraged SQL for complex joins in high-throughput data pipelines to enhance data quality and optimize geospatial accuracy in mapping pothole complaints.

Breast Cancer Survival Prediction Using Machine Learning & Ensemble Models January 2022 – February 2022

•Designed an end-to-end pipeline for breast cancer survival prediction (Random Forest, XGBoost, SVM), achieving 92% accuracy and reducing misclassification by 18%.

•Fine-tuned model training with Optuna and feature selection with SHAP, shortening training time by 35%, minimizing dimensionality by 25%, and cutting compute costs by 20%.

•Deployed real-time inference on AWS EC2 with SageMaker for versioning and TensorFlow Serving, trimming inference latency by 50% and deployment overhead by 40%.

SKILLS

Programming Languages: Python, Java, Golang, SQL, C++, .NET, C# Machine Learning & AI: Supervised & Unsupervised Learning, Deep Learning (CNN, RNN, LSTMs, Transformers), Anomaly Detection, Feature Engineering, Hyperparameter Tuning (Optuna), Model Deployment (TensorFlow Serving, AWS SageMaker) MLOps & Deployment: CI/CD for ML (Jenkins, GitHub Actions), Model Versioning, Docker, Kubernetes, FastAPI, MLflow, Airflow Software Development & System Design: RESTful APIs, Microservices Architecture, Distributed Systems, Load Balancing, Caching Strategies (Redis, Nginx), Performance Optimization Cloud & DevOps: AWS (EC2, Lambda, S3, SageMaker), GCP, Kubernetes, Terraform, Serverless Architectures, API Development Data Engineering & Databases: Apache Spark, Kafka, ETL Pipelines, SQL Optimization, NoSQL (MongoDB, Redis, Cassandra)

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