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Raghav Nautiyal

Berkeley, CA, USA

Hi! I'm Raghav, a Junior at Berkeley studying EECS and a researcher at Berkeley Artifical Intelligence Research (BAIR). I’m super interested in speech and multimodal ML, model evaluation, and agentic systems. Previously, I've worked on post-training and synthetic data at Sarvam AI, and LLM agents for enterprise data analysis at Skan AI. Outside of work, I lift weights, run, and train boxing.

Research

StrumStart visualization

StrumStart: A Dataset of 1-on-1 Guitar Lessons for Speech-Music Co-reasoning (Ongoing)

Introduces a dataset and benchmark for speech-music co-reasoning in large audio language models (LALMs), collected from real 1-on-1 guitar lessons. Experiments showed 30-40% performance degradation on co-reasoning tasks, highlighting gaps in current multimodal reasoning.

EMG-to-Speech experiment visualization

Towards EMG-to-Speech with a Necklace Form Factor [Interspeech 2024]

Co-authored Towards EMG-to-Speech with a Necklace Form Factor, investigating silent speech decoding using a wearable EMG neckband. Results demonstrate high-accuracy (92.7%) speech classification and strong correlations between EMG and self-supervised speech representations. Read here.

Deception game visualization

Speech To Speech Deception Games (Ongoing)

Working on speech-to-speech deception games, training spoken language models to play social deduction games like Carrot in a Box. The project studies reasoning, persuasion, and deception through spoken dialogue and paralinguistic cues rather than text alone.

Climate projects visualization

Climate Modeling with Machine Learning and Causal Inference [Lawrence Berkeley National Laboratory]

Applied causal inference to large-scale climate data to identify lagged drivers of extreme US precipitation, spanning anthropogenic and natural factors, with experiments run on the Perlmutter supercomputer at Berkeley Lab. Spring, 2024

Built end-to-end ML pipelines to predict methane emissions across US coastal wetlands, training Random Forest and MLP models on eddy covariance and chamber data to achieve R² up to 0.78. Check out the poster. Fall, 2024

Industry Experience

Autonomous LLM Agents for Enterprise Data Analysis @ Skan AI

Spring & Summer, 2025

Designed, built, and productionized an autonomous LLM-based data analyst, taking the system from initial concept to deployment. The agent explores enterprise datasets, iteratively reasons via code execution and custom tools, and surfaces actionable insights. Deployed as a core production feature for 8 Fortune 500 customers, reducing insight generation time from weeks to minutes.

Post-Training and Synthetic Data Generation @ Sarvam AI

Summer, 2024

Worked on post-training LLMs for financial data extraction, fine-tuning an open-source LLaMA model with PEFT + LoRA to convert unstructured ledgers into structured JSON for automated reporting. Trained model on multi-GPU clusters using DeepSpeed and Slurm, achieving a 30% accuracy improvement over regex baselines and an F1 score of 0.89 for a production customer.

Designed and built a synthetic data pipeline with LLaMA 3 70B to compensate for the lack of high-quality Indian conversational data, generating 100k+ realistic, single-turn examples grounded in code-mixed speech and region-specific entities for ASR training.

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