Away from my own work
Books, conversations and explanations I’ve found useful.
A clear primer on causal thinking: why it is different from correlation, and why it matters for science that aims to intervene. It also gives perspective on how recent the formal mathematical language of causation is — and why that history still shapes how we argue about evidence.
A historical biography of cancer that makes modern oncology feel less like a snapshot and more like an evolving story. It helps explain why certain clinical conventions exist, and how older assumptions still echo in present-day treatment decisions.
A guide to reading medical evidence with a skeptical, patient-centered lens — especially in oncology. It highlights how incentives can diverge from patient benefit, and offers practical instincts for spotting over-interpretation in trials and headlines.
A global-health perspective on tuberculosis that pulls you out of a Western-centric frame. It’s a reminder of how common and deadly TB still is, and how social structure and access to care shape what we call “progress” in medicine.
Exceptionally clear visual explanations of core machine learning concepts, with a strong emphasis on intuition over jargon. Great for building a mental model you can actually use when choosing methods and debugging results.
A friendly, visual introduction to neural networks and deep learning that removes unnecessary mystique. It’s especially useful for translating buzzwords into concrete mental pictures and implementation-level understanding.
A narrative exploration of the cell as the fundamental unit of biology, and how cell-based thinking reshaped modern medicine. It connects the history of ideas to the practical reality of therapies, diagnostics, and the limits of reductionism.
A deep dive into dual-system thinking, cognitive biases, and how humans evaluate evidence under uncertainty. It’s highly relevant to scientific reasoning — especially when interpreting noisy data, p-values, and compelling stories.
Short primers from leaders in microbiome and probiotic research, explained directly by the experts. A good way to stay anchored in mechanisms, definitions, and what the evidence actually supports.
Focuses on the creative, non-linear side of science — how ideas actually form before they become polished papers. It’s an honest look at exploration, taste, and the messy path from curiosity to a research question.
Examines how we know whether medical interventions truly help patients, with a critical lens on industry-sponsored clinical trials. The discussions are practical and evidence-first, with attention to real-world decision-making.
Dr. Kellen Cavagnero walks through complex topics in inflammation together with domain experts in a clear, digestible way. Great for learning immunology by following concrete papers and specific biological questions.
Deep dives into medical evidence, oncology trials, and how to critically read the literature. The podcast is currently paused, but the back catalogue is still a useful reference for evidence hygiene.
Conversations around reproducible workflows, data pipelines, and best practices in modern computational biology. Especially useful if you care about operationalizing analysis: versioning, containers, and portability.
Discussions on data science, statistics, reproducibility, and tooling with a strong applied perspective. It’s a good bridge between method details and how teams actually ship analysis.
Long-form conversations and essays on science, technology, and policy aimed at thoughtful scientific audiences. A nice complement to paper-reading when you want broader context and synthesis.
Comprehensive lecture-style series covering metagenomics, RNA-seq, R, pathway analysis, and more. I come back to it when I want a structured, training-oriented walkthrough rather than a quick tutorial.
Training resources and talks on bioinformatics infrastructure, reproducibility, and best practices. Particularly valuable for the operational side of bioinformatics: tooling, platforms, and community standards.
Intuitive math explanations with exceptional visual clarity, great for building deep intuition. Useful when you want to actually understand linear algebra, probability, and dynamical ideas behind models.
One of the clearest places to build intuition for statistics, machine learning, and RNA-seq analysis without getting lost in jargon. Especially useful when you want to understand what a method is doing before you worry about its implementation details.
Clear, practical machine learning and deep learning lessons that emphasize intuition and implementation. Good for bridging the gap between theory and code without drowning in formalism.
Friendly tutorials on modern web development and programming fundamentals. Great for picking up a framework quickly and building the muscle memory to ship small, clean projects.
Fast, high-signal overviews of modern developer tools and frameworks; great for quick orientation. I use it to get the lay of the land before diving into docs.
Applied bioinformatics tutorials and walkthroughs, often focused on practical workflows. Helpful when you want to see a tool used end-to-end and understand the typical failure modes.
Educational content focused on deep learning concepts and learning resources. A useful supplement when you want a different angle on core ideas and how to study them effectively.
MIT-level introductions to AI and deep learning, blending theory, intuition, and practical examples. Great when you want a course-like arc that still stays grounded in implementation details.
Open-source microbiome research content, PhD defenses, and excellent Anvi’o tutorials. Ideal for learning by watching real analyses and hearing the reasoning behind decisions.
Bioinformatics workflows, tool walkthroughs, and practical how-to content. Useful for quickly sanity-checking a pipeline step or seeing a common tool used in context.