Student Seminar – Spring 2020

Schedule for Spring 2020


Seminars are on Wednesdays
Time: 12:00pm – 1:00pm
Location: Room 1025, 1255 Amsterdam Avenue
Contacts: Yuling Yao, Owen Ward

Information for speakers: For information about schedule, direction, equipment, reimbursement and hotel, please click here.


Phd student town hall


Wenda Zhou ”An Introduction to Statistical Computing”


Wayne Lee (Columbia) ‘Stats and Data Science Industry’



Venkat Venkatasubramanian (Columbia,  Chemical Engineering Department)

“The Promise of Artificial Intelligence in Chemical Engineering: Is it here, finally?”


Artificial intelligence (AI) started off with great promise in the early 1980s, spurred by the success of the expert system paradigm in certain applications. This prompted a flurry of research activities in chemical engineering in the mid-1980s. However, as the ensuing three decades showed, AI didn’t quite live up to its promise in chemical engineering.

So, what went wrong with AI?

In this talk, I will review the different phases of AI in chemical engineering over the last 35 years, providing some background and explanation to this question. I will also argue that this time it is different – I believe the time for AI in chemical engineering, and in other domains, has arrived, finally. There are many applications that are ready to yield quick successes in this new data science phase of AI. I will highlight recent work in materials design and in process operations as examples of exciting progress. However, the really interesting and intellectually challenging problems lie in developing such conceptual frameworks as hybrid models, mechanism-based causal explanations, domain-specific knowledge discovery engines, and analytical theories of emergence. These breakthroughs would require going beyond purely data-centric machine learning, despite all the current excitement, and leveraging other knowledge representation and reasoning methods from the earlier phases of AI. They would require a proper integration of symbolic reasoning with data-driven processing. I will discuss these challenges and opportunities going forward.

Xiao Cen (Columbia Business School)
“Environmental Disasters and Entrepreneurial Career Choices.”
This study investigates how environmental disasters affect entrepreneurial career choices, based on U.S. Census individual-level employment data, deed records, and geographic information system (GIS) data. Using floods as natural disaster events, a regression discontinuity analysis shows that residents who were actually inundated are 7% less prone to entrepreneurial careers relative to their neighbors just outside the boundary, all within a 0.1-mile-wide band. A broader comparison yields the same inference between high- and low-elevation areas within the same county, and a comparison between owners and renters attribute the difference to the impaired housing wealth due to the floods. The effect is more profound in the propensity to become startup employees than founders. The career distortion leads to a long-run income loss comparable to the direct flood damage.

Breck Baldwin (Stan)

“Hacking Stan, Bayesian Inference vs Deep Learning, Bayes Rule via

Galton box and Biased Coins.”

This talk is a collection of topics that hopefully has something for

everyone. I will cover:

– Tips and tricks on how to run Stan programs, everyone’s favorite

Bayesian modeling language.

– Discuss the differences of Bayesian AI and the current dominant

paradigm of deep learning approaches.

– Report my progress on aerodynamically inspired biased coins.

– Show a design for a Galton box style demonstration for Bayes rule that

has been accepted for display at MoMath–the NYC math museum.




Charles Margossian and Owen Ward (Columbia) “Github, collaboration and websites.”


Spring Break