I'm a funded Robotics master's student at Carnegie Mellon University (CMU) Robotics Institute - School of Computer Science with Computer Vision (CV), Deep Learning (DL) & Machine Learning (ML) depth and C++ Software Engineer Intern experience at JBT Corp. My unwavering commitment to solving complex real-world Artificial Intelligence (AI) challenges, coupled with a strong desire to democratize affordable and accessible technology, drives career aspirations in my speciality upon graduation in May 2024. Given my strong academic background, prestigious accolades, 3+ years of extensive practical experience developing industry-relevant solutions in diverse CV, DL and ML global ventures, with over 8 publications, combined with my demonstrated track record of soft skills, I am an excellent candidate for a relevant position.

At CMU, I am cultivating expertise in creating practical AI solutions that align seamlessly with business principles while effectively serving client needs. My experience spans 3 diverse labs, coordinated with Pitt and UPMC physicians, sponsored by DoD, NSF and DARPA, emphasizing 3 industry-scale AI challenges: 1. Transparency, deployability and resource limitations for AI adoption: Heuristics-guided explainable AI; 2. Precision inadequacy in automated robotic interventions: Physics-informed generative AI; 3. Vast unlabelled and unstructured training data: Contrastive unsupervised representation learning. Moreover, through course projects, I explored shuffled position embeddings in ResNet-ViT and zero-shot visual place recognition with foundation models like DINO. Summer of 2023, interning at JBT, collaborating with ifm, Oppent, and OSU, I integrated an O3R camera with autonomous vehicle navigation-vision C++ stack on edge VPU, enabling obstacle detection, generating requirements, reducing stack lag and streamlining verification & validation protocols while leveraging agile practices, version control systems and CI/CD pipelines.

Before CMU, I made the most of the COVID-19 lockdown by capitalizing on the flexibility of my undergrad online classes and engaging in numerous global collaborations aimed at enriching my skillset. I managed multiple concurrent projects, effectively scaling my skillset while transferring knowledge from one project to another. This iterative process allowed me to continually learn from each project, making improvements and building on previous experiences. This includes: UWaterloo, NUS, ICL and CUHK (exchange study) exposed me to designing industrial automation software: A clinical diagnosis and experimental analysis app to aid ophthalmologists and CV-DL frameworks to tackle automation delays in medical imaging (Graph networks) & latency in computer-assisted surgery (Paced curriculum learning with knowledge distillation).

My introduction to CV, DL & ML was at IIT-M, building sketch query 3D CAD model CNN retrieval systems and honed my skillset at Origin Health Pte. Ltd., prototyping in-house Python GUI and fetal screening CV-algorithms that piqued VCs. Moreover, I garnered this passion firstly initially, devising a DL-based EMG-controlled CPM machine, as an undergraduate instrumentation & control engineer at my university robotics club.

These experiences made me industry-ready, equipped with the organizational ethos and AI expertise in signal, image, video and volume data.

• 💬 Ask me about: Anything tech
• 📫 How to reach me: Drop a mail
• ⚡ Fun fact: Most people pronounce my first name wrong. Give it a try 🙃
• ♟ Hobbies: Chess, Athletics and Music