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Avinash Gopa

Avinash Gopa

CEO & Research Scientist in Agricultural Statistics and AI

Ahmedabad, Gujarat, India
researchgate+1
Joined March 2026
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ICAR Agricultural Statisticians
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Early Career Ag Researchers
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Agricultural Data Scientists
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Summary

Applied researcher in agricultural statistics focused on forecasting: His publications and ResearchGate profile show sustained work on time-series forecasting, combining statistical and deep-learning methods to address volatility in agricultural commodity prices and crop yield prediction. researchgate+2
Transition from academic research to applied AI entrepreneurship: Timeline and records indicate a Ph.D. (ICAR-IASRI 2020–2024), a project/scientist role at NIOH (2024–2025), followed by leadership of Avyagraha Research and Analytics LLP beginning in 2025. rocketreach+2
Founder/leader of training and R&D initiatives in AI and analytics: Avyagraha's materials and event listings show he leads multi-domain AI training programs and provides research consultancy, analytics, and training services across agriculture, healthcare, finance, and IT. scribd+2
Recognized early-career contributions and academic awards: His ResearchGate profile lists honors such as NPTEL Discipline Star 2024 and the Prof. M.N. Deshpande Memorial Award for the Young Statistician (2024), indicating academic recognition during/after doctoral work. researchgate

Work

Education

Projects

Writing

Price Forecasting of TOP (Tomato, Onion and Potato) Commodities using Hidden Markov-based Deep Learning Approach

November 8, 2024

Applies a Hidden Markov–based deep learning approach to forecast prices of perishable TOP commodities, aiming to help growers and stakeholders make informed production and marketing decisions.

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Hidden Markov guided Deep Learning models for forecasting highly volatile agricultural commodity prices

Presents a framework combining Hidden Markov models with deep learning approaches to improve forecasting of highly volatile agricultural commodity prices, addressing non-stationarity and volatility in time series data.

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