I am an aspiring AI/ML Researcher driven by a deep fascination with understanding complex systems, from Deep Learning architectures to the intricate workings of the human brain. My research interests are centered around Deep Learning and Large Language Models (LLMs), with a particular focus on mechanistic interpretability and drawing inspiration from connectomics. With a background in Computer Science and Engineering, I am eager to contribute to cutting-edge research and am actively seeking AI/ML research internships to further my understanding and make impactful contributions to the field.
- Mechanistic Interpretability: Passionate about understanding the inner workings of complex AI systems and biological neural networks. I believe that truly understanding how these systems arrive at their outputs is crucial for advancing AI and ensuring its safety and reliability.
- Human Brain & Connectomics: Captivated by the design and architecture of the human brain and inspired by advancements in connectomics. I am exploring how insights from neuroscience and the human connectome can inform and inspire the next generation of AI architectures, potentially leading to breakthroughs in artificial neural networks.
- Generative Models and Architectural Complexity: Fascinated by the architectural innovations in generative models like Stable Diffusion, particularly their use of VAEs and UNet. I am eager to contribute to the development and understanding of these increasingly intricate AI systems.
- Deep Learning Architectures: Developing and understanding novel deep learning architectures, including ResNet, LSTM, Bi-LSTM, and UNet, to improve model performance in various domains such as computer vision and natural language processing.
- Large Language Models (LLMs): Exploring the capabilities and limitations of LLMs, with a focus on language understanding, generation, and fine-tuning for specific applications.
- B.Tech in Computer Science & Engineering.
- Completed the Machine Learning Specialization on Coursera, strengthening my knowledge of core machine learning concepts and methodologies.
- ECG Signal Classification: Developing a deep learning model for classifying ECG signals into four categories and comparing its performance with transformer-based approaches to advance healthcare technology.
- AI-Powered Coronary Artery Stenosis Detection System [In Progress]: Developing a system using state-of-the-art deep learning models and image processing techniques to segment coronary arteries and detect stenosis, contributing to the study and diagnosis of Coronary Artery Disease (CAD).
- LLM Semantic Equivalence Research [In Progress]: Independently researching how LLMs process semantically equivalent prompts across languages and code-switched languages (Hinglish), investigating whether they understand semantic equivalence in a human-like manner.
- Intern at a leading AI research organization like Google DeepMind to gain hands-on experience in solving real-world problems through advanced AI techniques and contribute to research in areas like mechanistic interpretability and brain-inspired AI.
- Continue publishing research in the areas of Deep Learning, Natural Language Processing, and Mechanistic Interpretability to contribute to the broader AI community.
- Deep Learning Specialization from DeepLearning.AI, focusing on advanced deep learning concepts and methodologies.
- Implementing Attention in Transformers: Concept and Code, a short course by DeepLearning.AI, to gain practical experience with transformer architectures and attention mechanisms.
- Enhancing my proficiency in Data Structures and Algorithms (DSA) to solve complex problems efficiently.
- Deepening my understanding of Connectomics and Neuroscience to inspire novel AI approaches.
- Programming Languages: Python, TensorFlow, PyTorch, Keras
- Research Tools: Jupyter, LaTeX, Git
- Libraries/Frameworks: NumPy, SciPy, OpenCV, Hugging Face
I am actively seeking research intern roles where I can apply my knowledge of Deep Learning and Machine Learning, particularly in areas related to mechanistic interpretability, brain-inspired AI, and generative models. If you are working on exciting research or have internship opportunities, I would love to connect!