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Columbia University Post-doctoral Position

Columbia University Post-doctoral Position

The Department of Industrial Engineering and Operations Research (IEOR) at Columbia University in New York City invites applications for a post-doctoral position. The primary responsibility of the candidate is to research on novel methodologies for the analysis of robo-advising systems. The objective of this research is to leverage and extend existing techniques from the machine learning and statistical literature to design automated investment systems that exhibit superior performance to human-only and machine-only driven systems. The position is funded by JP Morgan, and the candidate will work closely with Prof. Agostino Capponi and with the artificial intelligence team at JP Morgan, with ample opportunities to leverage their technology and in-house data.

Machines have the ability to process hard information, i.e., to make complex reasoning based on the gigantic amounts of market information. However, they can only approximate human behavior up to a quantitative model. In contrast, humans can assess complex environments and make judgments based on a holistic perspective that incorporates soft information sources. Humans? internalized risk preferences and objectives, however, are difficult to quantify and communicate to a machine. It is thus central to design a mechanism through which the machine progressively learns and acts according to the investor that it serves. Investors need to trust that the machine understands the dynamics of their risk preferences, their objectives, and their market beliefs, before delegating higher autonomy to the machine.

The candidate will have the unique opportunity to work on a cutting edge framework that departs from the current approaches currently used in the wealth management industry. These approaches are typically based on a one-shot interaction: the investor communicates once and for all his risk attitude or objectives to the machine, and the latter executes autonomously the investment. Unlike these approaches, in the proposed approach, the machine actively elicits information about the investor?s risk preference. Through the establishment of a communication protocol, the human-machine system selects portfolio instruments and strategies that uniquely reflect the investor?s current taste for risk and reward.

Candidates must have a PhD degree in Computer Science, Statistics, Electrical Engineering, Operations Research, or Applied Mathematics. Candidates are expected to be familiar with reinforcement learning, active learning, collaborative filtering, game theory, computational economics, convex and stochastic optimization. Experience with programming languages such as Python is highly desirable.

The candidate will also benefit from interactions with various faculty from the IEOR Department, the school of Engineering and Applied Sciences, the Business School, and the School of International and Public Affairs. This is a one-year position with possibility of renewal depending on the progress achieved.

Candidates should submit electronically to Prof. Agostino Capponi (ac3827@columbia.edu) the following: curriculum-vitae, a copy of their official degree transcript, and a representative research paper. The applicants should also arrange to have at least one letter of recommendation submitted electronically to the same address. The position will remain open until filled and applications will be reviewed as they are submitted.

Applicants can consult www.ieor.columbia.edu for more information about the department.

Columbia University is an Equal Opportunity/Affirmative Action employer---Race/Gender/Disability/Veteran.

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