Hello! I an aspiring scientist working in Cambridge MA.
Below, you'll find an assortment of notes and presentation slides that I made over the course of my independent study and work. Most of them are about foundational ideas that lie at the intersection of the life and the mathematical sciences. I write these notes in order to deeply understand the core ideas that animate these two areas of knowledge. I am distilling them here for future reference.
While I have every intention of completing each set of notes, most of what you'll see here will remain incomplete for a very long time. If you find any bugs (errors, or hard to understand sections) feel free to open a PR with the intended fix, or shoot me an e-mail. Finally, If it isn't obvious by now, this blog format was heavily inspired by Frank McSherry's awesome blog
A distillation of some of the most elegant ideas in computer science and mathematics. The collection is currently skewed towards ideas that showcase cool ways to design algorithms and data structures. Some of the links are to notes hosted on notion. Such links have been marked with an asterisk. Finally, notes that evolved into full fledged libraries or binaries have the appropriate badges next to them.
- Common Algorithmic Patterns *
-
<O(n), O(1)>RMQ in Rust 🌟 ✨ - Pattern Matching & String Indexing
- The Bottom-up Splay Tree *
- The Bottom up Splay Tree
- Word Level Parallelism
- Graphs: All Foundational Methods
- Streaming Algorithms: Sampling and Sketching 🌟 ✨
- Discrete Optimization
- The Lazy Binomial Heap
- Specialized Containers for Integers
- The Multiplicative Weights Algorithm
- The Deferred Acceptance Algorithm
- Laplacian Dynamics on General Graphs
- Network Structure from Rich but Noisy Data
- Retroactive Data Structures
Is it possible to use ideas, tools, and techniques developed to understand the structure and function of biological black boxes to develop a general framework for understanding and interpreting trained Neural Networks (aka computational black boxes)?
- Current Neural Network Interpretation Methods
- The Network as an Organism
- Neural Network Training: Cues from Evolution
- The Genetics of Neural Networks
- A new perspective on entropy*
- An Algebra of Neural Network Traits
Biological Systems are
compositional. They are made up of components that combine to form new components. This compositionality exists at multiple levels — proteins are composed of amino acids, tissues are composed of individual cells, e.t.c. Given this observation, one wonders if there exist rules that govern biological compositionality. That is, if there exists a grammar, or, an algebra, of biological systems.
- Does New Physics Lurk inside Living Matter?
- Ratcheting the Evolution of Multicellularity
- A new perspective on entropy*
- Bioelectric Signalling: Reprogrammable Circuits underlying embryogenesis, regeneration, and cancer
- Basal Cognition + Evolution of Multicellularity + RL/ML = ?
- Models in Biology: Perspectives from Gunawardena et. al
- Developmental Bioelectricity, Multicellularity, and Scale-free Cognition
- Elementary Nervous Systems
- Evolutionary Transitions in Learning and Cognition
- Active Perception during Angiogenesis
- Re-afference and the origin of the self in early nervous system evolution
- Endogenous Bioelectric Signalling Networks
- Top Down models in Biology
- Current Best Practices in scRNAseq Analysis
- RNA Velocity of Single Cells
- Life, Logic, and Information
- An Algebra and Calculus of Living Systems
- System Level Diseases: A Framework for Understanding Complex Diseases
- Systems Biology of Neuro-degenerative Diseases
- Gene Delivery into Cells and Tissues
A distillation of various techniques that I've found quite useful when learning new things.