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A background in Physical Science and limited coding experience did not stop Shudhanshu Kumar Yadav from exploring Artificial Intelligence (AI) and Data Science. The IIT Madras BS student gradually built his technical skills, moving from learning programming to working on machine learning projects and exploring specialised AI research.
His efforts have opened up several opportunities. Yadav secured a paid summer research internship at Dhirubhai Ambani University (formerly DA-IICT) in Gandhinagar and received internship offers from Jamia Millia Islamia and IIM Bangalore. He was also selected for the Matsuo & Iwasawa Laboratory at the University of Tokyo and GCI World 2026.
Currently pursuing a BS in Data Science and Applications at IIT Madras and an MSc in Artificial Intelligence and Data Science at the Central University of Andhra Pradesh, Yadav is exploring how emerging technologies can be applied to real-world problems.
Yadav's academic journey began with a bachelor's degree in Physical Science from the University of Delhi. While his studies provided a foundation in mathematics, physics and chemistry, programming, machine learning and AI were relatively unfamiliar areas.
โI had studied Physical Science, and areas like programming, Data Science, and Artificial Intelligence were completely new to me,โ he said.
The IIT Madras BS programme provided a structured learning environment in which he could develop his programming knowledge and gradually apply theoretical concepts to practical problems.
He began exploring data analysis, machine learning and AI-based applications, including a crop yield prediction system developed during his internship with Infosys Springboard.
The project involved analysing rainfall, temperature, humidity and soil nutrient parameters to predict crop yields. His work included data preprocessing, feature engineering and exploratory data analysis, alongside machine learning models such as XGBoost and Random Forest. He also used SHAP to examine model predictions and understand the factors influencing them.
โAs I continued learning, I gradually became more confident in applying what I learned to real-world problems,โ he said.
Yadav's growing technical interests led him to a summer research internship at Dhirubhai Ambani University in Gandhinagar, where he worked under the guidance of Professor Ankit Vijayvargiya.
During the eight-week internship, he explored the use of Large Language Models (LLMs) for Human Activity Recognition using smartphone inertial measurement unit (IMU) signals.
Human Activity Recognition involves identifying activities using sensor data collected from devices such as smartphones and wearables. His research examined how LLM-based reasoning could be explored in this area, combining sensor signal processing and machine learning with technologies such as Retrieval-Augmented Generation (RAG), QLoRA fine-tuning and LangGraph-based multi-agent systems.
The project exposed him to specialised research questions at the intersection of machine learning and generative AI. It also complemented his independent work on AI applications, backend systems, APIs and data-driven software.
Yadav is particularly interested in Large Language Models, AI agents and the development of applications that combine these technologies to solve practical problems.
For Yadav, the transition has involved more than learning programming or developing machine learning models. โComing from a non-technical background, I feel the biggest change has been the confidence I have developed along the way,โ he said.
Beyond academics, he served as president of the Vivekananda Study Circle at Deen Dayal Upadhyaya College, University of Delhi, leading a student body of more than 80 members. He also served as a group leader at Saranda House in the IIT Madras BS programme, gaining experience in teamwork and coordination.
Yadav is now exploring opportunities in Generative AI, LLM applications, machine learning and AI research, with a focus on building practical AI-powered solutions. His journey highlights how consistent learning and hands-on experience can help students enter emerging technology fields, even from a different academic background.
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