I am an Artificial Intelligence undergraduate at NITK (CGPA: 9.59) passionate about building impactful solutions with Deep Learning and Computer Vision. I have hands-on experience in time-series forecasting from my internship at Deutsche Bank and generative models from my research on multimodal medical image fusion. My works have been published at an international conference and submitted to a top-tier conference.

Languages: Python, Java, C++
AI/ML: PyTorch, TensorFlow, Keras, Scikit-learn, Pandas, NumPy, OpenCV
Tools: Git, GitHub, VS Code, Google Colab, Kaggle

• Technology Intern @ Deutsche Bank: Built an AI-driven deal market analysis tool to forecast trends using LSTM and Transformer models.
• Research Intern @ HALE Lab, NITK: Developed a WGAN framework in PyTorch for high-fidelity fusion of MRI & PET scans.
• Project - AI Exam Proctor: Engineered a real-time (2.7 fps) proctoring system using LSTMs, YOLO, and Deepface, achieving 91% accuracy. Paper submitted to EAAI 2026. arXiv Preprint
• Project - Software Reliability: Published a framework using ML and Genetic Algorithms to achieve 90% defect prediction accuracy. Paper
• Achievements: Won a prize at ETH India 24, ranked 1st of 50+ teams in the IEEE NITK Kaggle Cup, and served as Chairperson for an AI/ML student group.