Tejas Ravishankar

• Developed a novel framework leveraging pretrained image transformers for video understanding tasks.
• Utilized the NCut algorithm and Optical Flow to segregate dynamic foreground tokens from static backgrounds.
• Treated video sequences as weighted graphs for robust temporal dynamics understanding, leveraging a graph transformer to maintain object continuity across frames.
• Validated the framework using HMDB51 and AViD datasets, achieving ~9% lower accuracy than state-of-the-art while having just 1% of the trainable parameters.

• Developed a web application using Angular, ASP.NET, and MongoDB, which was integrated with various APIs to enable users to stream and favorite videos.
• Demonstrated proficiency in collaborative teamwork, employing version control, meticulous documentation, and comprehensive testing practices to ensure the successful development and maintenance of the project.

• Enhanced low-light environments in real time video feeds using Cycle GANs and AutoEncoder-based models.
• Experimented with image encoding in a memory buffer to reduce video frame transfer time over the network.
• Prototype developed was under 1 megabyte and inference was in the order of 0.001 seconds per frame.
• Adhered to ML Ops principles for efficient model deployment, monitoring, and continuous improvement with a focus on scalability, reliability, and reproducibility.

• Designed a computationally efficient methodology for segmentation of narrow river streams in satellite imagery.
• Utilized a combination of image processing methods - thresholding and gamma correction - and an encoder-decoder architecture constructed using depthwise separable convolutions.
• Achieved higher accuracies than state-of-the-art models, whilst being 1/3rd the size.

• Developing a low-code, drag drop platform to construct and automate complex agentic workflows.
• Built a custom multi-agent state machine with human in the loop, step-based debugging and logging.
• Developed 10+ software integrations, architected efficient React components to reduce client-side latency by 30% and created a real-time UI with websocket integration.
• Architected a cloud platform provisioning secure Firecracker microVMs to users executing arbitrary code.

Tejas Ravishankar, Trisha C. Anil, Ujjwal Verma, Manohara MM Pai and Radhika Pai