Ranjit Raut

Hi. I’m Ranjit Raut, a scholar and practitioner of artificial intelligence. I endeavor at the intersection of computer science, mathematics, cognitive science, physics, and engineering, with a focus on researching and building artificial intelligence systems that address real-world challenges and deliver solutions.

On the community side, I worked as Principal Coordinator (AI CONVERGENCE) at Kathmandu University Artificial Intelligence Club (KUAIC), one of Nepal’s largest student-led AI initiatives; served as a Microsoft Learn Student Ambassador (MLSA) at Microsoft Learn; and maintained an active open-source presence across DigitalOcean Hacktoberfest, GoogleCloudReady, Google Crowdsource, Mozilla, Monkeytype, GeeksforGeeks, GitHub, and Google Summer of Code. On the research side, I worked as a Research Intern at the Information and Language Processing Research Lab (ILPRL), Kathmandu University. Previously, I served as a Research Assistant at the Artificial Intelligence and High Performance Computing (AIHPC) Lab, Kathmandu University, a student-led research laboratory for artificial intelligence & high-performance computing, and also as a Research Fellow & Publication Lead at the Artificial Intelligence and Smart System Research (AISSR) Lab, Kathmandu University. On the production side, I’ve also led and contributed to teams at HomoDeTerra, Quotesnaut, SurfTheKnowledge, and Moxcie.

I research, explore, experiment, and innovate across machine learning, neural networks, and deep learning; supervised learning, unsupervised learning, self-supervised learning, semi-supervised learning, reinforcement learning, & meta-learning; computer vision, natural language processing, & robotics; small and large unimodal, multimodal, and reasoning models; generative ai, ai agents, retrieval-augmented generation, and agentic ai; graph deep learning and geometric deep learning; spatial ai, deep generative models, and anomaly detection; cloud computing, parallel processing, distributed computing, and quantum computing; speech and video processing, recognition, and synthesis; embodied ai, physical ai, and robot learning; foundational models, state-space models, and world models; objective-driven ai, physics-informed neural networks, energy-based model, and quantum machine learning & quantum ai; automl, responsible ai & explainable ai; space computing & space ai.