Dr. Chandravesh Chaudhari

Assistant Professor | Multimodal AI Researcher

Multimodal Machine Learning

Empowering business through data-driven intelligence.

I build production-ready machine learning systems for finance, decision intelligence, and research automation. I combine rigorous research methods with practical engineering to deliver reproducible, scalable systems used in experiments and production...

Email: chandraveshchaudhari@gmail.com

Bangalore, India

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Dr. Chandravesh Chaudhari — profile

Research Interests

Decision-level and meta-fusion for multimodal learning
Multimodal AutoML for tabular, temporal, and text data
Local LLM integration and RAG pipelines for research automation
Temporal alignment and multi-scale modeling for financial time-series
Representation learning for heterogeneous tabular data
Information-theoretic analysis of fusion and ensembles
Hybrid feature selection with LLM-guided priors
Tabular foundation models and scalable structured-data models
Efficiency, compression, and edge-deployable multimodal systems

Books

Programming for Machine Learning and Business

Programming for Machine Learning and Business

This interactive book is designed to help learners, researchers, and professionals bridge the gap between Python programming, machine learning fundamentals, and real-world business applications.

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Machine Learning for Business

Machine Learning for Business

Machine Learning for Business is an interactive and practical learning hub that connects business strategy with modern machine learning techniques.

Open Book

Projects

Financial Variable Generation

Financial Variable Generation

Generate financial ratios and derived fundamental-analysis variables from extracted financial statement data.

BMMA: Multimodal AutoML

BMMA: Multimodal AutoML

Multimodal AutoML framework that orchestrates modality adapters, search, and robust evaluation for tabular, time-series, text, and image data.

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Hybrid Feature Selection & Importance

Hybrid Feature Selection & Importance

Toolkit for hybrid subset selection and importance ranking for explainable ML across high-dimensional datasets.

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Financial Variable Generation

Financial Variable Generation

Automated generation of financial indicators, corporate-event-aware features, and backtest-ready time-series feature pipelines.

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LitSynth: Literature Synthesis / Research Management System

LitSynth: Literature Synthesis / Research Management System

RAG + agentic flows and citation-graph intelligence for literature triage and automated synthesis of systematic reviews. Also maintained as a research management system for provenance-enabled workflows.

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Resume and Portfolio Website

Resume and Portfolio Website

Generator for rich resume content and portfolio pages with exportable PDF/HTML resume focused on applied-scientist roles.

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InstantGrade: Automated Evaluator

InstantGrade: Automated Evaluator

Automated evaluation system for notebooks and Excel assignments with rubric-driven feedback generation.

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JupyterBook + JupyterLite Template (v2)

JupyterBook + JupyterLite Template (v2)

Template combining JupyterBook with JupyterLite for interactive educational books with CI-ready builds and launchers.

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