Results-driven Electrical Engineering student with expertise in AI/ML and deep learning, backed by hands-on research experience at premier institutions. Proven track record of developing scalable ML systems with 85%+ accuracy and conducting impactful academic research. Strong foundation in computer vision, fairness in AI, and signal processing with publications in progress. Seeking challenging opportunities in AI/ML engineering roles.
Developing state-of-the-art CNN architectures for automated skin disease classification on 10K+ medical images
Achieved 85%+ test accuracy through advanced data augmentation, transfer learning, and ensemble methods
Implementing novel regularization techniques and hyperparameter optimization for improved generalization
Collaborating on research paper submission to top-tier ML conference (manuscript in preparation)
Designed and implemented Successive Cancellation (SC) and SCF polar decoders with CRC integration
Conducted extensive AWGN channel simulations analyzing BER performance across multiple code configurations
Optimized encoding/decoding algorithms achieving 40% speed improvement while maintaining low error rates
Contributing to academic publication on advanced error correction techniques in wireless communications
Engineered end-to-end multi-player tracking system combining YOLOv8 object detection with DeepSORT tracking
Fine-tuned cutting-edge transformer models (Swin, ViT) on ISIC 2018 and SD-198 dermatology datasets
GPA: N/A