How I started the journey? - Part I.2

I wasn’t strong enough, but I was truly determined.

My expectation was that, after the doctoral study, I would be a person KNOWING A LOT! But, more of being an intellectual who knows what he doesn’t know and how to study the UNKNOWN.

It turned out … I had a nearly impossible goal. No one would be able to tell exactly how to study the UNKNOWN without knowing it already. The more I studied, the more I thought I barely knew anything. The vastness of uncovered truths, principles and knowledge was far more profound than I could’ve ever imagined.

😎 Being a Ph.D., you need to get comfortable of being an “idiot”. It is very normal for you to learn enough only to find out you barely knowing anything. A good side effect, however, is that no one would be able to talk over your head easily!

My education background prior to Ph.D. was an M.S. in Industrial Engineering, mostly focused on the applied side of Operations Research. After the master program, I started working in the tech industry. It was around my 2nd year of being a data scientist when I started thinking of a Ph.D. seriously.

By the time I came up with this crazy idea, I’ve already worked on solving some of the most complex problems in the field. I was amazed by the intricacy of the natural beauty of problem solving. Meanwhile, I was also deeply puzzled by and confused about the complexity of the problem solving techniques in general.

Then, Google DeepMind released AlphaGo and beat Lee Sedol in March of 2016!

It really caught my eyes, and I thought Reinforcement Learning could be THE ultimate source of power in solving some of the most complex problems!

📖 If you know a little bit about RL, you probably know there were two almost identical yet seemingly separate tracks: one was the computer science version primarily led by Dr. Richard Sutton, University of Alberta, and the other was the operations research version, called Approximate Dynamic Programming (aka. ADP) led by Dr. Warren Powell, Princeton University.

Apparently, I didn’t land in the field of RL. Like I said, it was quite hard to navigate through a Ph.D. journey without some surprises and uncertainties. To make my story more dramatic, I quitted working with my first advisor and went with another IN THE END OF MY 3RD YEAR!

I will talk about some of those necessary struggles in other parts.

Back to the story…

The idea of doing a Ph.D. kept me up at night that I eventually stopped overthinking and pushed myself one step further: to figure out HOW!

I was fortunate enough to keep in touch with my advisor from the master program at Lehigh. The professor educated me a lot on what I would need to make it happen. Among many things, I knew securing the funding was the most important part of it.

🤔 I wouldn’t do it without funding. Education is expensive. Getting it for free is sweet, paying for it out-of-pocket is really a bad idea. In my years at Lehigh, a self-funded engineering Ph.D. would need to pay 💵108K for the tuition. I would need to pay a discounted price of 💵70K only because I already had a master degree. There are already so many things that worry every Ph.D. student, full-time or part-time. But, you would also worry about the actual ROI in dollar, and many things would be influenced by it.

It was generally true that any top level professors would not like the idea of funding a part-time Ph.D. student. With many not-necessarily-more-capable-but-definitely-having-more-time applicants applying their apprenticeship every year from all over the world, the incentives to spend their funding on someone like us was fairly low.

Taking my department at Lehigh as an example. When they considered my application, they knew I would be the first part-time person for many decades in the history of the department. If I were the admission committee, I would worry if this guy could ever be able to finish the program.

😎 However nasty the process will be, it shouldn’t concern you much as long as you can secure the spot. The outcome matters more than how others think of you during the process.

The story of me getting into the program was not dramatic. My employer agreed to cover 100% of tuition and other expenses … while continued to pay me as a full-time data scientist. I got the chair and my advisor from the master program rooting for me, and others were somehow convinced. That’s about how I got into the program. Honestly, I think having my own funding and a familiar face were probably why I got accepted.

😮‍💨 You almost always need to have a solid proof of external funding before any of good programs can make an admission decision. They are not as crazy as you are. Raising a Ph.D. is very expensive and highly risky.

😮‍💨 In my years at Lehigh, a fair estimate of carrying a full-time engineering student to the end was around 💵200K. Part-time students were a bit cheaper, as the only expense would be the tuition. Sadly, most top schools/programs/professors believed their risks of seeing you fail would cost much more than the savings from stipend.

😮‍💨 Most employers’ funding comes with some form of repayment agreements. Either you stay with them for X months/years after the program or let them collect every penny of the funding from you. It sounds terrible, but I still think it is way better than self-funding. I still stay with my job at the moment of writing this part, and I am about to finish my term very soon.

😮‍💨 Everything has an exception. My advisor paid a small chunk of the tuition. But, it came with three necessary conditions: i) my advisor/program had some extra funding, ii) I deserved an award , and iii) I ASKED. I think i) and iii) matter more than ii), and I wish I could just ask much much earlier. I would start from day 1 to get some professors pay me if I could do it again.
Zheng Shi
Zheng Shi
Team Lead, Data Science | Ph.D., Machine Learning & Optimization

Deep Learning, Machine Learning, Optimization Algorithms, and Data Science.