I’m a former research mathematician seeking employment as a data scientist. I’m very versatile in my ability to apply what I know: I understand ideas at a high level of generality, which enables me to recognize non-obvious ways of applying them to powerful effect.
I’m looking for a job using statistics and machine learning in conjunction with real world contextual information to deliver high impact results. My interests include model selection, engineering features that are robustly predictive across contexts, and brainstorming with colleagues to identify how to best serve a company’s data needs.
Machine learning: I’ve made extensive use of linear models for regression and classification, Bayesian hierarchical models, principal component analysis and cross-validation. I’ve also experimented with random forests and collaborative filtering, and am familiar with their strengths and limitations. I rediscovered many of these methods in rudimentary form before learning about them. The process of doing so gave me a deep understanding of the algorithms.
General analytics: I worked as a research analyst at GiveWell vetting cost-effectiveness estimates of global health interventions (example). I’ve done economic analyses in various capacities, including a thorough review of the evidence for and against majoring in economics increasing earnings (link) that I wrote at Cognito Mentoring to advise high school students.
Communication: I have a strong interest in communicating to technical ideas to less technical audiences. I’ve been deeply involved in math education in many capacities: high school and college instruction, online courses, personal tutoring and expository writing.
Speed Dating Project (Source ): I analyzed a public speed dating data set to predict participants’ decisions. I gradually shifted focus to understanding what the data tells us about human diversity. Following this line of thought ultimately facilitated the creation of a better predictive model, while having relevance extending beyond the context of speed dating.
• Constructed a Bayesian hierarchical modeling of individual preferences.
• Derived predictive features using revealed preferences, collaborative filtering, and PCA.
• Discovered and quantified substantial and statistical robust variation between individuals with respect to attractiveness / personality trait tradeoffs.
• Used PCA to identify demographic clusters with unusually high preferences for attractiveness and for intelligence.
• Determined the extent to which the group consensus on somebody’s attractiveness predicted individuals’ perceptions.
• Questions, Answers & comments
• Voting & Tagging
• Filtering questions by vote count, author, voter, and tag.
• Editing objects in place
• Auto-complete search
I investigated a problem adjacent to a famous discovery by Srinivasa Ramanujan: “If n is 4 more than a multiple of 5, the number of partitions of n is divisible by 5.” I proved that a class of superficially similar patterns doesn’t exist. The proof uses the theory of modular forms (mod p).