I am a Master's student in Computer Science at Columbia University , specializing in Machine Learning . My focus lies at the intersection of Mechanistic Interpretability, Generative Modeling, and MLOps.
I am passionate about building robust, interpretable AI systems and optimizing model deployment on cloud infrastructure.
I am currently working on advanced research involving LLMs and Diffusion Models:
- Uncertainty Quantification in LLMs (IBM & Columbia): Utilizing mechanistic interpretability to isolate "entropy neurons" in Llama 3.1 8B, successfully capturing 90% of incorrect generations via activation thresholding .
- Manifold-Aware Diffusion Models: Developing extensions to DDPMs using anisotropic noise injection and local manifold estimation (k-NN/PCA) to improve sample complexity on non-Euclidean datasets .
- Efficient Fine-Tuning: Implemented a pipeline combining GRPO, LoRA, and 4-bit quantization to fine-tune 7B parameter models on single-GPU hardware .
- Medical VQA: Created a multimodal contrastive learning system for radiology image-question pairs, utilizing noise injection to improve robustness by 8.8% .
Machine Learning / MLOps Intern | LTIMindTree
- Enhanced large-scale RAG frameworks by extending AWS Bedrock deployments to support multimodal inputs .
- Deployed computer vision systems for container ID detection, cutting deployment costs by 80% .
Software Engineer | The Hartford
- Prototyped 3 LLM-based applications for document analysis, improving claims processing efficiency by 40% .
- Delivered 30+ features for insurance underwriting systems in Java and implemented CI/CD pipelines .
- Languages: Python, C++, Java, SQL
- ML Frameworks: PyTorch, TensorFlow, Hugging Face, CUDA
- Cloud & MLOps: AWS (Solutions Architect Associate), Kubernetes, AWS CDK
- Domains: Computer Vision, NLP, Mechanistic Interpretability, Diffusion Models