An open knowledge hub bridging theoretical research and practical application in AI for Medicine. The skills usually learned in a MS or PhD — how to read, write, present, and publish an AI paper — distilled into a free, self-paced curriculum available to anyone.
From foundational ML concepts to clinical data access, regulatory awareness, and deployment constraints — we cover what papers don't.
You don't need a lab or a supervisor to learn how to write a publishable paper. This curriculum makes that knowledge free and accessible to all.
India's healthcare AI market is on a steep growth curve. We cover its startups, researchers, datasets, and institutions from the inside.
Work through these articles in order, or jump to whatever you need. Each article is standalone but links to the others.
If you're a B.Tech student, industry engineer, or anyone curious about AI research, Track 1 gives you everything to start a project and publish your first paper — from zero.
Identify a gap, verify novelty, and define a research question worth pursuing.
Baselines, experiments, and the right metrics for imbalanced clinical data.
LaTeX, references, conference deadlines, and submitting your first paper.
Interviews, mentors, travel funding, postdoc and industry research roles.
Using Parkinson's disease subtyping as a case study: CITI training, data access, clinical motivation frameworks, ML challenges with patient data, and writing for clinical journals — every step covered.
Read the guideA strategic guide to proving impact and winning the research job offer — from postdoc applications to AI industry roles.
Read the guideThe full research lifecycle — motivation, problem ideation, literature survey, problem verification — from someone who's done it for 8 years.
Read the guideSix traits of a genuinely good mentor, and the warning signs that a relationship will cost you years of your life.
Read the guideA 6-month playbook with a worked medical NLP example — shared tasks, the right conferences, and a weekly schedule that works.
Read the guideFrom IIT Kharagpur to L3S Research Center during a pandemic — the search, the interview, the visa, and what I'd do differently.
Read the storyEverything here is grounded in first-hand research experience across India, Germany, and the US.
Kalyani Government Engineering College, West Bengal. First paper, first hackathon (Smart India Hackathon 2017), first taste of real research problems.
MS Research in CS at IIT Kharagpur. Published at WebSci 2019 (Boston). Began PhD. Full guide to the MS Research application →
Moved to Hannover during COVID. Clinical AI research on Parkinson's disease subtyping. How I got there →
Research internships in India. Published Parkinson's paper in Frontiers in AI (IF: 4.7). Presented at IJCAI 2024.
Continuing research in AI and clinical medicine. This site documents everything learned across the full journey.
Clinical datasets, Indian startups, Parkinson's research, LLMs in radiology.
LaTeX, references, replication to publication, shared tasks.
Python, NLP, imbalanced data, EDA in R, end-to-end projects.
IIT Kharagpur MS, mentor selection, travel funding, Germany research roles.
Hugging Face Hub, MIMIC-CXR, CheXpert, PPMI, UK Biobank, FIRE — every major platform and domain-specific collection with access instructions.
Open the guideCORE-ranked month-by-month table covering NLP, ML, and medical AI venues — with MICCAI, AMIA, ML4H, and all major CS conferences.
Check deadlinesAnnotated reading list covering classical ML, deep learning, NLP, IR, and medical AI — with honest notes on who each book is for, and free PDF links.
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Join researchers, students, and engineers from IITs, ISI, and industry labs across India.