● Conducted comprehensive literature reviews to remain updated on the latest advancements in LLMs, automated program repair, and associated optimization techniques.● Fine-tuned Large Language Models (LLMs) such as Codellama, PLBART, Refact, CodeGen, and CodeT5 for Automated Program Repair (APR) tasks, employing techniques including PEFT (Parameter Efficient Fine-Tuning), LoRA (Low-Rank Adaptation), and int-8 optimization.● Implemented and optimized LLMs using state-of-the-art methodologies, ensuring compatibility with resource-constrained environments through various optimization methods like Gradient Checkpointing, int-8 optimization, etc.● Utilized SLURM to manage and distribute computing tasks across multiple GPUs, optimizing model training and experimentation efficiency.

● Collaborated closely under the guidance of supervisors to develop expertise in Real-Time Neural Style Transfer on Videos.● Successfully navigated significant challenges, including conducting extensive literature reviews, implementing state-of-the-art methods, and enhancing them to ensure temporal consistency across consecutive frames in live camera feed videos during style transfer.● Acquired comprehensive knowledge of research areas such as Deep Learning Optimization Techniques, including Knowledge Distillation and Quantization. Utilized libraries such as TensorRT and Pytorch-JIT to accelerate inference time while performing style transfer.● Investigated the impact of Automatic Mixed Precision on the training and inference speeds and GPU utilization of PyTorch models for different class of GPUs

● Engaged in daily activities that encompassed researching established Computer Vision architectures and exploring avenues for their enhancement.● Conducted research focused on image classification and segmentation tasks within the medical domain, specifically addressing areas such as Brain Tumor Segmentation and Glaucoma Classification.● Furthermore, I developed a test GUI using PyQt5 for our glaucoma classification task, enabling input image analysis and delivering conclusive judgments, along with heat maps showcasing the diagnosis.● Assumed a mentorship role during summer and winter internship periods, providing guidance and support to junior interns within the organization.● Undertook a notable final project involving the utilization of Generative Adversarial Networks for facial inpainting, aimed at reconstructing partially damaged faces.