Non-linearity and local optimum mindset

Time flies. It’s been 7 months since I started to work as a full-time Data Scientist. It sometimes feels much shorter than 7 months: imaging neurons in the laboratory as a graduate student and walking on the stairs in front of Alma mater on campus was just like yesterday. It sometimes feels much longer than that: working is so vastly different from academic study, and I’ve learned so much on so many aspects of data science within this short-long period of time, that my mind and understanding of the industry and the world hardly resembles the graduate student me.

Here I am summarizing a few lessons I learned from work.

Not ready yet…Go!

I officially graduated from Columbia last Tuesday after my PhD thesis defense, and started to work full time immediately in the following week. This is the first week of my transition from academia to industry, and I already experience some culture shock in time management and prioritizing. Learning on the job is not only about the technical details, but also about the pattern of thinking.





1. 前言


May the Force be with you

My internship at Tapad has officially come to an end last week, after I gave a final presentation of my project (see the intro video below). It’s been a very memorable and rewarding summer. I not only learned about the latest technological development and application of machine learning and big data, but also got to experience the industrial work style and start-up culture.

Take the audience on a journey

During my final round interview at Tapad, the VP of Data Science noticed my academic background in neuroscience, and we started to discuss how neuroscience research could contribute to online advertising. Later in the interview, he asked “Have you heard of reinforcement learning (RL)?” And I said no. He explained this reward-guided learning paradigm, which immediately reminded me of my undergraduate research on aversive learning in fruit flies. After the interview, he emailed me the link to RL Courses on YouTube taught by David Silver, the leading scientist of AlphaGo.

Iterative learning

In my very first meeting with my mentor at Tapad, after an introduction to DeviceGraph and AdTech 101, we started to discuss the project I would be working on during my internship. The first week was very intense and my mentor understood my struggle in the face of information overload. He showed me his system of organizing information by writing down his incremental knowledge about certain technology as well as his spark of ideas. I adapted his system and developed my own archive to track my learning process and ideas using Google Docs (the picture above).  Now, 5 weeks have passed and I have continuously updated the documents as I learn: the more I know, the more I realize I do not know, and the more I want to learn. Learning is an iterative process.