# Boyan Duan

I am a Data Scientist in Google. Before that, I just got my Ph.D. degree in the Department of Statistics & Data Science at Carnegie Mellon University. I was very fortunate to be advised by professor Aaditya Ramdas and professor Larry Wasserman. We worked on developing interactive methodologies that leverage machine and human intelligence for various hypothesis testing problems. Here is my thesis.

Here is my Curriculum Vitae.

# Education

- Ph.D. in Statistics, 2021
- Carnegie Mellon University, Pittsburgh, USA

- B.S. in Statistics, 2016
- University of Science and Technology of China, Hefei

- Intern in Computer Science, 2015
- University of Birmingham, UK

# Research Interests

Human-in-the-loop interactive testing

Multiple testing and nonparametric testing

Causal inference: identifications under heterogeneous treatment effect

Reproducibility in science and technology

# Papers and submissions

- Interactive Martingale Tests for the Global Null (EJS, 2020) arxiv code
**Boyan Duan, Aaditya Ramdas, Sivaraman Balakrishnan, Larry Wasserman**- Global null testing is a classical problem going back about a century to Fisher’s and Stouffer’s combination tests. We present simple martingale analogs of these classical tests, which are applicable in two distinct settings: (a) the online setting in which there is a possibly infinite sequence of p-values, and (b) the batch setting, where one uses prior knowledge to preorder the hypotheses. Built on the martingale analogs, we use a recent idea of “masking” p-values to develop a novel interactive test for the global null. It can take advantage of covariates and repeated user guidance to create a data-adaptive ordering that achieves higher detection power against structured alternatives.

- Familywise error rate control by interactive unmasking (ICML, 2020) arxiv code talk
**Boyan Duan, Aaditya Ramdas, Larry Wasserman**- We propose a method for multiple hypothesis testing with familywise error rate (FWER) control, called the i-FWER test. Most testing methods are predefined algorithms that do not allow modifications after observing the data. However, in practice, analysts tend to choose a promising algorithm after observing the data; unfortunately, this violates the validity of the conclusion. The i-FWER test allows much flexibility: a human (or a computer program acting on the human’s behalf) may adaptively guide the algorithm in a data-dependent manner. We prove that our test controls FWER if the analysts adhere to a particular protocol of “masking” and “unmasking”. We demonstrate via numerical experiments the power of our test under structured non-nulls and then explore new forms of masking.

- Which Wilcoxon should we use? An interactive rank test and other alternatives (In submission) arxiv code
**Boyan Duan, Aaditya Ramdas, Larry Wasserman**- Classical nonparametric tests to compare multiple samples, such as the Wilcoxon test, are often based on the ranks of observations. We design an interactive rank test called i-Wilcoxon—an analyst is allowed to adaptively guide the algorithm using observed outcomes, covariates, working models and prior knowledge—that guarantees type-I error control using martingales. Numerical experiments demonstrate the advantage of (an automated version of) our algorithm under heterogeneous treatment effects. The i-Wilcoxon test is first proposed for two-sample comparison with unpaired data, and then extended to paired data, multi-sample comparison, and sequential settings, thus also extending the Kruskal-Wallis and Friedman tests. As alternatives, we numerically investigate (non-interactive) covariance-adjusted variants of the Wilcoxon test, and provide practical recommendations based on the anticipated population properties of the treatment effects.

- Interactive identification of individuals with positive treatment effect while controlling false discoveries arxiv code talk
**Boyan Duan, Larry Wasserman, Aaditya Ramdas**- Out of the participants in a randomized experiment with anticipated heterogeneous treatment effects, is it possible to identify which ones have a positive treatment effect, even though each has only taken either treatment or control but not both? While subgroup analysis has received attention, claims about individual participants are more challenging. We frame the problem in terms of multiple hypothesis testing: we think of each individual as a null hypothesis (the potential outcomes are equal, for example) and aim to identify individuals for whom the null is false (the treatment potential outcome stochastically dominates the control, for example). We develop a novel algorithm that identifies such a subset, with nonasymptotic control of the false discovery rate (FDR). Our algorithm allows for interaction — a human data scientist (or a computer program acting on the human’s behalf) may adaptively guide the algorithm in a data-dependent manner to gain high identification power. We also propose several extensions: (a) relaxing the null to nonpositive effects, (b) moving from unpaired to paired samples, and (c) subgroup identification. We demonstrate via numerical experiments and theoretical analysis that the proposed method has valid FDR control in finite samples and reasonably high identification power.

# Previous Projects

Although not related to my current research interests, I did some projects in previous years.

- Perception of security over time in the Democratic Republic of Congo
**with Robin Mejia, Anjali Mazumder, Patrick Vinck, Phuong Pham**- Over the past decades, there has been continuous armed conflict and economic and political instability in the Democratic Republic of the Congo (DRC). Despite the effort made by the Congolese government to rebuild the country and the ongoing United Nations peacekeeping mission, there has been little improvement in terms of peace and justice. In discussions, Vinck noted that conflicts in Congo are often described as ethnic conflicts, despite a lack of formal study to assess this description. To examine the evolution of the conflict, we study the changing pattern of the population’s perception of security and ethnic relations and uncover regions and subpopulations with different trends.

- The automatic generation of semantic environment maps from robot sensor data
**with Lars Kunze**- During my undergraduate internship, I participated in an international research project on robotics for long-term autonomy in mobile robots involving six European universities, called STRANDS. We use simple machine learning algorithms to interpret sensor data from the robots, such as identifying objects, recognizing the space to move freely, classifying the types of rooms, etc. It was an exciting experience for me to get involved with research on robotics for the first time.

# Teaching Assistant

In-class tutorials, office hours, and grading for the following classes in Carnegie Mellon University:

- 36-218 Probability Theory for Computer Scientists (Head TA)
- 36-708 Statistical Methods in Machine Learning (Ph.D. Level)
- 36-705 Intermediate Statistics (Ph.D. Level)
- 46-927 Statistical Machine Learning II
- 36-410 Introduction to Probability Modeling
- 36-401 Modern Regression
- 36-217 Probability Theory and Random Processes
- 36-225 Introduction to Probability Theory