About Me

I am a passionate, intelligent, hard working, curious machine learning scientist and engineer. I absolutely love exploring and understanding data. I find a deep, fulfilling satisfaction from using data and machine learning to build excellent, valuable products. I have extensive experience in natural language processing; specifically within the areas of knowledge acquisition & representation, semantic compositionality, reasoning, and inference. My professional and academic experience is a blend of research and software engineering, using functional programming principles to quickly write correct modeling code.

What Have I Done?

Professionally, I have spent the last half decade mostly working in early-stage startups, infusing machine learning into products. In working at early stage companies, I have gained a broad business experience, with insights into marketing, sales, operations, and valuable lessons on team building. Technically, I have gained tremendous experience in writing production-grade software. More than once I have been tasked with leading development of an initial product idea, determining how to make an MVP with clever algorithms and nuggets of machine learning, making all of the software necessary to deploy into a production environment, and continuing to improve the product with ever-more-refined machine learning as data flows in from users.

Academically, I pursued research in information extraction and automatic knowledge graph construction. In my master’s thesis work (http://goo.gl/DzMr6c), I developed a novel algorithm for extracting semantic relationships from multiple sentences; all other state-of-the-art work at the time only dealt with the single-sentence relation extraction problem. I combined linear SVMs with n-gram language features and used a scalable probabilistic first-order logic system to find maximally consistent facts across a web-scale text corpus. This line of research stemmed from my work with the Never Ending Language Learner (NELL) group at Carnegie Mellon University (Mitchel et. al., “Never-Ending Learning”, AAAI 2015).

How Do I Want to Work?

I yearn for an environment that prioritizes technical excellence without sacrificing social graces. I have made my profession my main intellectual outlet. It is crucial for me to have a workplace where I grow and stretch the abilities of my mind. I personally find machine learning algorithms among the hardest to implement correctly: the challenge of getting it perfectly right drives me. However, I am done with interacting with “brilliant jerks.” In my experience, I have never found the toxicity to be worth it: individuals who choose not to treat others with compassion and invest in their relationship skills lead to unfulfilling, unproductive, broken teams. In a similar vein, I need to work with others that continuously seek balance in their greater life. My own desires to grow extend beyond my intellectual pursuits and I need to work with others who, at the very least, empathize and understand my position.

How Do I Want to Grow?

I want to become a world-class machine learning practitioner: I want to hone my talent for constructing learning problems and making algorithms. Growth is a crucial core concept for me. I try to always learn and continue to improve & find new problem domains: what I want least in a position is to stagnate and become dull. While I have improved my engineering skills tremendously by working in small, dedicated start-ups, I am keenly interested in pursuing opportunities of developing new ML models and crafting novel algorithms. Additionally, as I greatly enjoy sharing knowledge, I want to be able to publish my work and have the opportunity to give conference presentations. In my pursuit, I hope to make tools, products, services, and algorithms that solve interesting problems and help real people in their own life pursuits.