Logesh Kumar Umapathi

Logesh Kumar Umapathi

Machine Learning Consultant

BLACKBOX.AI

Logesh Kumar Umapathi is a Machine learning Engineer at Blackbox.ai. His work focuses on building agentic systems and models that help automate software development and improve developer productivity. He has led the development of state-of-the-art software engineering agents, and his research has been cited by leading ML labs including OpenAI , Meta and Microsoft . His interests include Code generation LLMs , Synthetic data generation with LLMs and alignment of code LLMs to Human preferences. 

In recent years, large language models (LLMs) have redefined what machines can do with text. But language alone is not enough when the goal is true intelligence — grounded, embodied, and interactive. In this session, the speaker will share his ongoing journey from working with LLMs, Language agents and natural language processing to diving deep into the world of reinforcement learning and robotics.

Logesh will walk through how the intuitions developed in NLP & LLMs — translate (or don't) into embodied learning systems. He will explore some of the key concepts for making the transition, and his practical learning and struggles of building and training a robotic arm ( LeRobot and So-100). Of course, including a live demo featuring my robotic arms.

Whether you're a curious NLP expert or an RL enthusiast seeking cross-domain insights, this session offers practical wisdom, reflections, and guidance to navigate your next leap.

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Managing and scaling ML workloads have never been a bigger challenge in the past. Data scientists are looking for collaboration, building, training, and re-iterating thousands of AI experiments. On the flip side ML engineers are looking for distributed training, artifact management, and automated deployment for high performance

Read More

Managing and scaling ML workloads have never been a bigger challenge in the past. Data scientists are looking for collaboration, building, training, and re-iterating thousands of AI experiments. On the flip side ML engineers are looking for distributed training, artifact management, and automated deployment for high performance

Read More
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