two sigma phd fellowship application

[hyunjimoon]

using research proposal for 2sigma fellowship cld
Angie_Moon_Probabilistic Programming for Equitable Startup Capitalization.pdf
Angie_Moon_resume.pdf

[hyunjimoon]

Students’ research must be relevant to one of the disciplines listed below:

• Statistics – Topics of interest include: statistical learning, statistical inference, Bayesian statistics, optimization, time series analysis, high dimensional statistics, non-parametric statistics.
• Computer Science – Topics of interest include: machine learning & artificial intelligence, deep learning, reinforcement learning, natural language processing, computer vision, robotics, computational biology
• Physics –Topics of interest include: applications of machine learning, simulation and big data techniques to physics, including particle physics, astrophysics, condensed matter.
• Applied Mathematics – Topics of interest include: linear / nonlinear optimization, numerical analysis, financial mathematics, operations research, probability,  differential equations, signal processing.
• Finance/Econometrics – Topics of interest include: machine learning & empirical asset pricing, game theory, market microstructures, agent based models, behavioral finance, quantitative finance, volatility modeling and forecast, optimal execution, portfolio optimization, derivatives modeling, high frequency econometrics, theoretical asset pricing dynamic equilibrium models, stochastic models, option pricing, credit risk modeling, asset covariance estimation, inference, high-dimensional data, financial optimization-optimal execution, portfolio optimization, optimal hedging.

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Awardee Name	Research Area
Daniel Soriano	Statistics, causal inference, observational studies 7
Jason Altschuler	optimization, probability, machine learning, mathematics of data science, optimal transport 9
Jimmy Butler	Not specified
Saachi Jain	Machine Learning, Robustness, Distribution Shift 5
Kristy Choi	Machine learning with limited labeled supervision; developing techniques for better adaptation and controllability in deep generative models. 12
William Merrill	Formal methods in computational linguistics and deep learning, focusing on language models and their capabilities 1
Suproteem Sarkar	Economics and Computer Science 4
Xiang Lisa Li	Computational Linguistics 3
Huy Tuan Pham	Combinatorics, probability, extremal combinatorics, theoretical computer science 10
Zhexiao Lin	Causal inference, Econometrics, Rank- and graph-based methods, Nonparametric statistics, Deep learning 2
Miyabi Ishihara	Causal inference methods in complex observational studies, focusing on environmental hazards and property values 11
Ellianna Abrahams	Building data-driven methods for incorporating scientific intuition into computer vision algorithms; interdisciplinary work in astronomy and statistics8
Haixiang Zhang	Applied Mathematics, Optimization 6

Citations:
[1] https://stanfordsciencefellows.stanford.edu/people/huy-tuan-pham
[2] https://scholar.google.com/citations?hl=en&user=xCFZMjYAAAAJ
[3] https://scholar.google.com/citations?hl=en&user=n28Kb3gAAAAJ
[4] https://www.ias.edu/scholars/huy-tuan-pham
[5] https://jasonaltschuler.github.io
[6] https://www.twosigma.com/articles/meet-the-two-sigma-fellows-ellianna-abrahams/
[7] http://kristychoi.com
[8] https://statistics.berkeley.edu/people/dan-soriano
[9] https://scholar.google.com/citations?hl=en&user=yc4KxT0AAAAJ