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Rocktim Jyoti Das
I am a first year Computer Science PhD student at University of Maryland, College Park working with Prof. Dinesh Manocha . My research focuses on developing physically grounded algorithms for robotic navigation and manipulation, with an emphasis on high-fidelity simulation and the estimation of material and mechanical properties.
Prior to joining my PhD, I was a Research Associate II at MBZUAI in Abu Dhabi working on Robotic Manipulation and Reasoning under the supervison of Prof. Ivan Laptev and Prof. Preslav Nakov. I worked on developing intelligent agents that reason, plan and interact with the environment to solve complex tasks using the world knowledge acquired using internet scale data.
I completed my undergraduate studies at the Indian Institute of Technology, Delhi, which set the foundation for my academic journey. I had the incredible opportunity to spend a year at the DAIR Lab, IIT Delhi as a Research Assistant under the supervision of Prof. Mausam. As a researcher at DAIR lab, I focused on Task-Oriented Dialog Systems and Conversation based Medical Diagnosis.
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Selected Research
* denotes joint first authors
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BLAZER: Bootstrapping LLM-based Manipulation Agents with Zero-Shot Data Generation
Rocktim Jyoti Das*,
Harsh Singh*,
Diana Turmakhan,
Mohammad Abdullah Sohail,
Mingfei Han,
Preslav Nakov,
Fabio Pizzati,
Ivan Laptev,
ArXiv, 2025
🌐 Website
📄 arXiv
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MALMM: Multi-Agent Large Language Models for Zero-Shot Robotics Manipulation
Harsh Singh*,
Rocktim Jyoti Das*,
Mingfei Han,
Preslav Nakov,
Ivan Laptev,
accepted to IROS, 2025
🌐 Website
📄 arXiv
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Synergizing In-context Learning with Hints for End-to-end Task-oriented Dialog Systems
Vishal Vivek Saley,
Rocktim Jyoti Das,
Dinesh Raghu,
Mausam,
accepted in EMNLP main, 2024
📄 arXiv
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Beyond Size: How Gradients Shape Pruning Decisions in Large Language Models
Rocktim Jyoti Das
Mingjie Sun,
Liqun Ma,
Zhiqiang Shen,
Arxiv, 2023
📄 arXiv
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EXAMS-V: A Multi-Discipline Multilingual Multimodal Exam Benchmark for Evaluating Vision Language Models
Rocktim Jyoti Das,
Simeon Emilov Hristov,
Haonan Li,
Dimitar Dimitrov,
Ivan Koychev,
Preslav Nakov,
accepted in ACL main, 2024
🌐 Website
📄 arXiv
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Exploring Distributional Shifts in Large Language Models for Code Analysis
Shushan Arakelyan,
Rocktim Jyoti Das,
Yi Mao,
Xiang Ren,
accepted in EMNLP main, 2023
📄 arXiv
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Open-Sourced Project
Nanda is the leading open-sourced Hindi bilingual model for generation and reasoning, surpassing current models on safety benchmarks. Although our model is optimized for Devanagari Hindi, it also seamlessly supports Romanized and code-mixed Hindi, making it highly versatile for real-world applications. Check out the paper for more details about training, evaluation!
📄 Paper link
Give our model a try!
Download LLaMA-3-Nanda-10B weights from Hugging Face and see its capabilities in action:
💾 Model weights
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