How we use Self-Supervised Learning in Personalised Medicine
Capitalising on exceedingly large and real-time physiological data in the absence of sufficient labels — Operating in the realm of physiological and behavioural data has never been more fruitful, nor more challenging. As a consequence of the widespread adoption of wearables, coupled with the ability to decode behaviour information from apps/wearables, users effectively generate data continuously.
Towards generally applicable machine learning. — The goal of machine learning is to extrapolate past the training set. To what extent, however, can we extrapolate past the training domain — that is the distribution that generated the training set? In our applications, we need to employ algorithms that work across many hospitals in geographically distinct regions…
Bayesian Reinforcement Learning & other nonlinear Probabilistic Graphs
How to encode arbitrary system dynamics & still achieve generalization. — Probabilistic programming is a powerful approach that combines Bayesian inference & programmatic logic to model real-world systems. The premise is simple: State Space: Define some (arbitrary) model that encapsulates the mechanics of a system. Arbitrary in that the model can be nonlinear & include any sequence of deterministic & stochastic…
How Uber uses machine learning to achieve hyper-growth in sales.
Personalized Marketing How do we extract meaningful information from (potential) clients' data to drive personalized marketing strategies that improve client acquisition/revenue by an order of magnitude? All (good) technology company now faces the unique challenge of distilling information from a plethora of data. Consumer tech products are able to collect…
Intelligent Hypothesis Generating
How to use modular data architectures to efficiently generate & test hypotheses. — Exciting problems tend to have some hierarchical depth of complexity. Intelligent problem solving, then, requires mechanisms to abstract to a suitable level of complexity, given the current requirements & available resources. I’m a data scientist so in my case, this applies to testing theories captured in data — however, think…
Applications of Machine Learning in Material Science & Chemical Engineering
Chemical reactions and phase transformations underpin phenomena ranging from cosmological processes, to the emergence of life on Earth, to modern technologies and are therefore of tremendous interest for both basic and applied sciences. Here I provide a review of recent advancements in the intersection between computational mathematics, material science &…
100X Improvement in Efficiency of Atomic Chemical Compound Analysis with Deep Learning
Applications of Deep Learning in Material Science & Chemical Engineering — #PaperReview Applications of Electron Microscopy Electron microscopy — a method of generated nanoscale and mesoscale imagining by capturing how electrons interact with materials — is becoming increasingly powerful. EM hosts a series of important applications: Life Sciences Microbiology: providing insight into the microbiology of bacteria, viruses & other cells.
Come as you are,
Go when you please.
Should I wonder how & when you might leave?
Or focus on how you differ from me?
Or try to change you, meticulously?
Forget all of that, & thank you, for now
The laughs. & quarrels. The jokes you allow
Analysis of Mixed Duration Passive Equity Portfolios
Investment Thesis — Can individual investors expect high yield on short term equity portfolios? Problem Specification Suppose you want to build a sound investment portfolio. Suppose, further, that you’ve settled on a long-term strategy. A problem arises: How should short term funds be handled? Short-term investment requirements are often disjoint from their long-term counterparts. Short…