Meet our alumni: Tristan Fletcher
Dr Tristan Fletcher is an entrepreneur and a supporter of Friends of Computer Science, the department's partnership programme for industry and alumni.
What's your current job title?
CEO and co-founder at ChAI. The company's AI platform provides market intelligence, price forecasting and insurance for buyers and sellers of raw materials (metals, energies, plastics and agricultural products).
At UCL Computer Science, I supervise PhD and MSc students in machine learning and finance.
What did you study at UCL Computer Science and during which years?
PhD in Machine Learning from 2008 to 2011.
Looking back, what stands out as a highlight or memorable experience from your time here?
I was extremely fortunate to study with incredibly bright people who were a pleasure to work with. I did my PhD with John Shawe-Taylor, Professor of Computational Statistics and Machine Learning and former Head of the Department and Professor David Barber, who is now the Director of the UCL Centre for Artificial Intelligence.
The students in my cohort around this time were all doing research in machine learning 14 to 15 years ago, so we were way ahead of the curve. To have that network now is fantastic.
What's it like supervising the students?
John and I have probably co-supervised 30 to 40 students since 2011 and the students we’ve worked with over that time have been inspiring. Machine learning has gone bonkers, in many different directions, and the students are so excited to be part of this field.
It also keeps me on my toes. I have to be perceived as an expert, so I need a working knowledge of whatever techniques they have decided to pursue and therefore have a constant reason to stay up to date.
Have you hired any UCL students that you have supervised?
Yes, I have hired four UCL alumni - all of whom have both been incredibly high calibre and a pleasure to work with. One of the big constraints for AI companies in the UK is getting access to good people. UCL produces really, really good people who are intelligent, motivated and have a great work ethic.
Tell us about your career journey since completing your PhD at UCL. Can you highlight one particular achievement from that journey?
I've set up a company that has operated for over six years, employed more than 40 people in that time and is helping the world economy become more stable. So, feeling that you are doing something useful with the skills that you are passionate about is an achievement. I feel proud of that.
What's on the horizon for you and ChAI?
ChAI forecasts commodity prices using machine learning techniques, adapting the same kind of skills and datasets that I used working for banks and hedge funds. We've brought this forecasting capability to companies that make the products we enjoy as consumers, such as cars and biscuits.
What advice would you give to an organisation or individual considering joining Friends of UCL Computer Science?
As an entrepreneur, I take advantage of the opportunities to network. UCL has enormous credibility globally. It's taken incredibly seriously in major companies but also among the startup community. This is the institution that spawned DeepMind!
What advice do you have for fellow alumni who are starting out on their career path?
Take a creative approach to your career. Keep your options open and don't settle on one path too early.
I started off as a manufacturing engineer, then a management consultant in manufacturing engineering, where I first encountered AI. I thought, I want to get into this!
I jumped into being a trader, literally shouting "buy" and "sell" on the trading floor (but more getting shouted at), and tried to incorporate machine learning into that. I did my PhD at the same time. I drifted further into investment banking and hedge funds.
Then, I pivoted and did a postdoc in cardiovascular research using machine learning techniques but with much more tangible, human outcomes. Eventually, I got into what I am doing now.
The most professionally satisfied people I know are, on the whole, those who have found their way empirically, through trial and error and often those who are doing something entrepreneurial.
