Research Outcomes & Achievements

Samrat Raj Sharma is the author of the published research paper “Decoding Toxicity: Unveiling Hidden Molecular Patterns with Graph Neural Networks”, which presents an interpretable deep-learning framework for molecular toxicity prediction using advanced GNN architectures. During his ML Research Bootcamp Internship under a Lead Researcher at IIT Madras (Visiting Faculty, University of California), he worked at the intersection of computational toxicology, cheminformatics, and graph representation learning. His contributions spanned end-to-end data processing from SDF chemical structures, molecular fingerprint generation (ECFP/Morgan), and construction of atom–bond graph networks, to implementing GCN and GAT models for multi-endpoint toxicity classification using Tox21 datasets. He further explored attention-based interpretability to identify structural toxicophores driving endpoints such as NR-AR, SR-MMP, and NR-AhR, evaluating models through ROC-AUC, PR curves, and multi-label frameworks. This research strengthened his expertise in bioinformatics, explainable AI, and deep learning for drug-discovery applications, while providing hands-on proficiency with RDKit, PyTorch Geometric, and scikit-learn in real-world molecular modelling tasks.

Bhagyashree Birje achieved a major milestone by having her research accepted for poster presentation at IIChE–CHEMCON 2024, demonstrating that dedicated young researchers can also reach national academic platforms early in their journey. During her Data Science and AI – ML Research Bootcamp Internship under the Lead Researcher and Founder, R&D at IIT Madras, she conducted advanced work on graph-based and convolutional neural architectures for protein thermostability prediction. She built rigorous data-processing pipelines, model-comparison frameworks, and actively contributed to methodological discussions in collaborative seminars. Her research maturity, computational depth, and scholarly engagement throughout the internship stand as a strong example of what motivated learners can accomplish with perseverance and curiosity.

During her internship on Decoding Songs of Ethiopia (Part I and II), Titas engaged deeply with the field of ethnomusicology and translation studies. She translated multiple Ethiopian folk songs performed during agricultural rituals, communal ceremonies, and cultural events, gaining valuable insight into Ethiopia’s social and musical traditions. Through this work, she strengthened her translation abilities, cultural interpretation skills, and academic writing competencies. Additionally, by proofreading and editing research papers authored by Ethiopian students, she enhanced her analytical rigour and methodological understanding within cross-cultural research contexts.

Krishna Joshi’s major accomplishment during her internship was producing a detailed research analysis on how social media marketing shapes brand loyalty: demonstrating that motivated students can also create impactful, industry-ready work. She independently examined how digital campaigns influence brand awareness and consumer engagement in the e-commerce sector. Her contributions included analysing marketing strategies, reviewing platform engagement data, and identifying clear behavioural patterns in audience response. This experience strengthened her understanding of digital marketing effectiveness and sharpened her ability to derive insights that inform strong brand communication strategies.

During her research internship under the Senior Scientist at ICMR in the Antimicrobial Resistance (AMR) program, Deepashree Chanda contributed to evaluating expert perspectives on AMR priorities and policy interventions using structured Delphi-based approaches. She gained hands-on experience in data organisation, thematic and qualitative assessment, and synthesising expert inputs for evidence-informed decision-making. Her work strengthened her understanding of national AMR challenges, surveillance gaps, and the policymaking process in public health research.

Two of Sinchana’s research projects were accepted for presentation at ICDDA 2025, marking a key achievement of her Data Analytics Internship and showing how internship work can contribute to international academic platforms. Her contributions spanned data preprocessing, model development using Prophet, LSTM, Random Forest, and XGBoost, as well as synthetic data generation through VAEs and Copulas. She also supported feature extraction for protein datasets and behavioural analytics, alongside assisting in research documentation and conference preparation. Through these roles, she strengthened her core competencies in data analytics, machine learning, and research communication.

Megha Jain’s central contribution to the project involved conducting a systematic survey analysis across participating schools, generating foundational data for examining student motivation patterns. She was responsible for organising, cleaning, and interpreting quantitative and qualitative responses, allowing the research team to identify meaningful trends within school learning environments. In addition, she contributed scholarly insights on the application of psychological frameworks, particularly growth-mindset theory, to educational practice. Her internship strengthened her methodological competencies in field-based data collection, research design, and evidence interpretation, while deepening her understanding of how mindset-oriented interventions can be operationalised within school systems to enhance student outcomes.

Saiyantany Choudhury’s key contribution to the Neuropsychology Research Internship was delivering structured presentations for Professors at Symbiosis International University, Pune, where she synthesised complex data on neuropsychological tendencies among adolescents into clear, academic insights. She worked extensively with raw behavioural datasets, organising, cleaning, and refining the information to ensure analytical accuracy and readiness for deeper interpretation. Her work also involved regular analytical discussions to refine methodological choices and define appropriate parameters for the study. Through this experience, she developed strong competencies in neuropsychological data handling, research planning, and scholarly communication, contributing significantly to the project’s analytical rigour.

Aprajita Rai successfully produced a comprehensive research report during her Child Psychology Research Internship, demonstrating that dedicated students can also achieve impactful scholarly work early in their careers. While interning under the Assistant Professor at the University of Kashmir, Srinagar, she conducted a focused study on the social media behaviour of sadfishing, exploring its psychological roots such as validation-seeking, loneliness, and self-presentation patterns. She further evaluated its broader implications for emotional well-being and examined the ethical challenges surrounding digital emotional expression. By proposing evidence-based intervention strategies, her work showcases strong analytical thinking and contributes meaningfully to ongoing conversations in child and adolescent psychology.

Deepsikha M J’s principal achievement in the Antimicrobial Resistance Research Internship was her analytical contribution to the ICMR Delphi study, where she worked on interpreting expert consensus to inform national AMR strategies. She managed and organised complex datasets, ensuring that qualitative and quantitative insights were accurately captured for policy-oriented analysis. Her work involved synthesising expert inputs, evaluating thematic patterns, and contributing to evidence maps that support public health decision-making. Through this experience, she developed a strong foundation in the Delphi methodology, qualitative coding, and the practical interface between research evidence and health policy formulation.