How did you get interested in mathematics and problem solving?
Since my childhood, I have consistently had a great passion for mathematics. This deep-rooted interest was motivating that whenever my father taught mathematics to my older sister; I joined them to learn and solve my sister’s exercises for myself.
Can you tell us a bit about your research as a PhD student? How have you been applying this in industry?
My research focused on developing models and approaches, leading to a better operation of large-scale systems with real-time optimization. My main interest is changing the myopic view into a systematic view and focus on the global perspective rather than short-term outcomes.
I have been applying different methods, but with the same mindset, in various areas and industries, including:
- Scheduled the movements of vehicles at factories (Robert Bosch LLC);
- Improved the planning of mega-projects in the oil and gas industries (Project Production Institute);
- Upgraded the forecast and consequently the decision making by incorporating global view to the uncertainties (Lyft);
- Developed new methods that improve planning in the construction industry (Project Production Systems Laboratory).
How do you think your research interests might change over the next 5-10 years?
I believe the next big things are quantum computing and blockchain. Quantum computing will improve our computational power by an order of magnitude, and blockchain will get more attention in the next few years as the value of privacy will become more recognized. These two will change almost every aspect of our field and bring so many exciting research topics, and I believe many other researchers and I will be working on those topics 5-10 years from now.
What part of industry is most interesting to you right now?
Tech companies, as they have the most data and therefore provide more opportunity to have an impactful contribution to people’s everyday life.
What is the most exciting thing about the field of IEOR right now?
While machines are capable of helping us in so many ways, they cannot create models that require creativity and intuition. Leveraging the data through innovative mathematical modeling is so exciting and so much needed in today’s industry.
What are some challenges coming up for the field?
I believe the most important one is fairness. The estimated amount of data in the world is 10^21 bytes. This number is much more significant than we can imagine. If we store 100 bytes in each grain of sand on the earth, we cannot fit all of the available data. As a result, every day, we rely more on machines and algorithms to process the data for us, and it is essential to make sure those algorithms are fair.
Do you have any advice for future PhD students? What might have you done differently if you had a chance to start over?
I would say try not to limit yourself to one topic, one industry, or one research area. Working on cross-disciplinary issues allows you to have a meaningful contribution to real-world problems.