Used advanced computer vision techniques and frameworks (PyTorch) to find a scale-invariant, Markovian state space representation of cooking. Synthesized a massive dataset with thousands of multimodal data points augmented with auto-generated labels via LLMs, trained a state-of-the-art visual classifier for food using transfer learning and contrastive learning techniques. Developed GPU and RAM optimized code to train model given limited compute; adapted real-world data (blurry and disorganized unlabeled video footage, incomprehensible machine-generated cooking logs, etc.) to structured formats using data augmentation and preprocessing techniques. Developed novel model architectures incorporating CNN and transformer-based components. Discussed ideas with CloudChef engineers (top IIT graduates) and implemented the solutions independently.

Worked as research assistant at Yale (Computer) Vision Laboratory under Prof. Alex Wong; was involved in a PyTorch project involving tracking a robot performing an anastomosis surgery using convolutional neural architectures.

Interned at Appian (a multibillion-dollar public cloud-computing and enterprise software company) directly for founder Marc Wilson. Conducted interviews and leveraged tools such as Salesforce to develop a data-driven executive engagement program for the company. Participated in company-critical meetings at Appian and performed financial analysis on key Appian accounts. Designed a program that is now fully implemented across the entire 2,500-employee company and has led to a new Office of Executive Engagement. Received offer to return to employment at Appian.