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<!DOCTYPE html>
<html lang="en">
<head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
<title>Wanrong Zhang</title>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width">
<link href="./files/styles.css" rel="stylesheet" type="text/css" media="all">
<link href="./files/fonts.css" rel="stylesheet" type="text/css">
</head>
<body>
<aside id="introduction" class="bodywidth clear">
<div id="introleft">
<h2>Wanrong <span class="blue">Zhang</span></h2>
<br>
<br>
<p>
Postdoctoral Fellow in Computer Science <br>
Harvard University <br>
email: imwanrongz [at] gmail {dot} com <br>
<a href="https://scholar.google.com/citations?user=y8s4ok0AAAAJ&hl=en"><img src="./files/scholar.png" height="30" id="gsicon"></a>
<br>
<br>
</p>
</div>
<blockquote id="introquote">
<div align="right">
<img src="./files/wanrongzz.jpeg" height="300">
</div>
</blockquote>
</aside>
<div id="maincontent" class="bodywidth clear">
<div id="aboutleft">
<p> I am a <a href="https://cifellows2021.org/2021-class/" target="_blank"> Computing Innovation Fellow</a> at <a href="https://www.seas.harvard.edu" target="_blank">Harvard John A. Paulson School Of Engineering And Applied Sciences</a>, hosted by
<a href="https://salil.seas.harvard.edu" target="_blank"> Salil Vadhan</a>.
I received my Ph.D. from the
<a href="https://www.isye.gatech.edu" target="_blank">H. Milton Stewart School of
Industrial and Systems Engineering</a> at Georgia Tech, where I was advised by
<a href="https://pwp.gatech.edu/rachel-cummings/" target="_blank"> Rachel Cummings</a> and <a href="https://www2.isye.gatech.edu/~ymei/" target="_blank"> Yajun Mei</a>.
Prior to Georgia Tech, I received my B.S. in Statistics at
<a href="http://www.vt.edu/" target="_blank">Peking University</a>. In Summer 2019, I interned at Microsoft Research Cambridge, working with
<a href="https://people.eng.unimelb.edu.au/oohrimenko/" target="_blank"> Olya Ohrimenko</a> and <a href="https://www.microsoft.com/en-us/research/people/shtople/" target="_blank"> Shruti Tople</a>. In summer 2020, I worked with <a href="https://www.microsoft.com/en-us/research/people/nasingal/" target="_blank"> Nalin Singal</a> ,
<a href="https://www.microsoft.com/en-us/research/people/rsim/" target="_blank"> Robert Sim</a>, <a href="https://www.microsoft.com/en-us/research/people/jakul/" target="_blank"> Jana Kulkarni</a> and Priyanka Kulkarni
at Microsoft Research AI.
</p>
<p>
My research has been focused on data privacy, in particular, in differential privacy, including (1) developing the theoretical foundations, (2) designing privacy-preserving algorithms for machine learning models and statistical analysis tools, and (3) adapting existing tools to solve domain-specific questions. In addition, I am interested in (4) broader privacy concerns, including understanding privacy vulnerabilities and proposing solutions.
Currently, I’m broadly interested in responsible AI.
</p>
<br>
<h3>Conference Papers</h3>
Note: The convention in TCS is to list authors in alphabetical order. (* indicates primary author)
<p></p>
<p>
<pubtitle> Membership Inference Attacks and Privacy in Topic Modeling </pubtitle> <br>
Nico Manzonelli, Wanrong Zhang, Salil Vadhan<br>
In submission. 2023.
</p>
<p>
<pubtitle> Safeguarding Data in Multimodal AI: A Differentially Private Approach to CLIP Training </pubtitle> <br>
Alyssa Huang, Peihan Liu, Ryumei Nakada, Linjun Zhang, Wanrong Zhang<br>
In submission. 2023.
</p>
<p>
<pubtitle> Concurrent Composition for Interactive Differential Privacy with Adaptive Privacy-Loss Parameters </pubtitle> <br>
Samuel Haney, Michael Shoemate, Grace Tian, Salil Vadhan, Andrew Vyrros, Vicki Xu*, Wanrong Zhang* (Alphabetical order)<br>
<i> CCS </i>. 2023.
[<a href="https://arxiv.org/abs/2309.05901" target="_blank">paper</a>]<br>
<b>CCS 2023 Distinguished Paper Award</b>
</p>
<p>
<pubtitle> Continual Release of Differentially Private Synthetic Data </pubtitle> <br>
Mark Bun, Marco Gaboardi, Marcel Neuhoeffer, Wanrong Zhang (Alphabetical order)<br>
<i> PODS </i>. 2024.
[<a href="https://arxiv.org/pdf/2306.07884" target="_blank">paper</a>]<br>
</p>
<p>
<pubtitle> DP-Fast MH: Private, Fast, and Accurate Metropolis-Hastings for Large-Scale Bayesian Inference </pubtitle> <br>
Wanrong Zhang, Ruqi Zhang<br>
<i> ICML </i>. 2023.
[<a href="http://arxiv.org/abs/2303.06171" target="_blank">paper</a>]<br>
</p>
<p>
<pubtitle> Concurrent Composition Theorems for Differential Privacy </pubtitle> <br>
Salil Vadhan, Wanrong Zhang* (Alphabetical order)<br>
<i> STOC </i>. 2023.
[<a href="https://arxiv.org/abs/2207.08335" target="_blank">paper</a>]<br>
Poster presentation in <a href="https://tpdp.journalprivacyconfidentiality.org/2022/" target="_blank">TPDP @ICML</a>. 2022.<br>
</p>
<p>
<pubtitle> Private Sequential Hypothesis Testing for Statisticians: Privacy, Error Rates, and Sample Size </pubtitle> <br>
Wanrong Zhang, Yajun Mei, Rachel Cummings <br>
<i> AISTATS</i>. 2022.
[<a href="https://arxiv.org/abs/2204.04597" target="_blank">paper</a>]<br>
Poster presentation in <a href="https://tpdp.journalprivacyconfidentiality.org/2021/" target="_blank">TPDP @ICML</a>. 2021.<br>
</p>
<p>
<pubtitle> Attribute Privacy: Framework and Mechanisms </pubtitle> <br>
Wanrong Zhang, Olga Ohrimenko, Rachel Cummings <br>
<i> ACM FAccT</i>. 2022.
[<a href="https://arxiv.org/pdf/2009.04013" target="_blank">paper</a>]<br>
Presentation at <a href="https://responsiblecomputing.org/forc-2021/" target="_blank">FORC</a>. 2021, non-archival track. <br>
Poster presentation in <a href="https://tpdp.journalprivacyconfidentiality.org/2020/" target="_blank">TPDP @CCS</a>. 2020.<br>
</p>
<p>
<pubtitle> Leakage of Dataset Properties in Multi-Party Machine Learning </pubtitle> <br>
Wanrong Zhang, Shruti Tople, Olga Ohrimenko <br>
<i> USENIX Security Symposium</i>. 2021.
[<a href="https://arxiv.org/abs/2006.07267" target="_blank">paper</a>]<br>
</p>
<p>
<pubtitle> PAPRIKA: Private Online False Discovery Rate Control </pubtitle> <br>
Wanrong Zhang, Gautam Kamath, Rachel Cummings <br>
<i> ICML</i>. 2021.
[<a href="https://arxiv.org/abs/2002.12321" target="_blank">paper</a>][<a href="https://github.com/wanrongz/PAPRIKA" target="_blank">code</a>]<br>
Presentation at <a href="https://responsiblecomputing.org/forc-2021/" target="_blank">FORC</a>. 2021, non-archival track. <br>
Poster presentation in <a href="https://tpdp.journalprivacyconfidentiality.org/2020/" target="_blank">TPDP @CCS</a>. 2020.<br>
</p>
<p>
<pubtitle> Privately Detecting Changes in Unknown Distributions </pubtitle> <br>
Rachel Cummings, Sara Krehbiel, Yuliia Lut, Wanrong Zhang* (Alphabetical order) <br>
<i> ICML</i>. 2020.
[<a href="https://arxiv.org/abs/1910.01327" target="_blank">paper</a>]<br>
Poster presentation in <a href="https://tpdp.journalprivacyconfidentiality.org/2019/" target="_blank">TPDP @CCS</a>. 2019.<br>
</p>
<p>
<pubtitle> Differentially Private Change-point Detection </pubtitle> <br>
Rachel Cummings, Sara Krehbiel, Yajun Mei, Rui Tuo, Wanrong Zhang* (Alphabetical order) <br>
<i>NeurIPS</i>. 2018.
[<a href="http://papers.nips.cc/paper/8280-differentially-private-change-point-detection" target="_blank">paper</a>]<br>
Poster presentation in <a href="https://tpdp.journalprivacyconfidentiality.org/2018/" target="_blank">TPDP @CCS</a>. 2018.<br>
</p>
<br>
<h3>Journal Papers</h3>
<p>
<pubtitle> A standardised differential privacy framework for epidemiological modelling with mobile phone data </pubtitle> <br>
Merveille Koissi Savi, Akash Yavad, Wanrong Zhang, Navin Vembar, Andrew Schroeder, Satchit Balsari, Caroline Buckee, Salil Vadhan, N. Kishore <br>
PLOS Digital Health. 2023.
[<a href="https://www.medrxiv.org/content/medrxiv/early/2023/03/23/2023.03.16.23287382.full.pdf" target="_blank">paper</a>]
</p>
<p>
<pubtitle> Single and Multiple Change-Point Detection with Differential Privacy </pubtitle> <br>
Wanrong Zhang, Sara Krehbiel, Rui Tuo, Yajun Mei, Rachel Cummings <br>
<i>JMLR</i>. 2021.
[<a href="http://pwp.gatech.edu/rachel-cummings/wp-content/uploads/sites/679/2019/10/MultipleChangepointDP.pdf" target="_blank">paper</a>]
</p>
<p>
<pubtitle> Bandit Change-Point Detection for Real-Time Monitoring High-Dimensional Data Under Sampling Control </pubtitle> <br>
Wanrong Zhang, Yajun Mei <br>
<i> Technometrics</i>. 2022.
[<a href="https://arxiv.org/abs/2009.11891" target="_blank">paper</a>]<br>
Poster presentation in <a href="https://math.gsu.edu/yichuan/2019Workshop/" target="_blank">The 7th Workshop on Biostatistics and Bioinformatics</a>. 2019. <br>
Best Student Poster Awards for the ASA Georgia Chapter.<br>
</p>
<br>
<h3>Workshop Papers</h3>
<p>
<pubtitle> Training Private and Efficient Language Models with Synthetic Data from LLMs </pubtitle> <br>
Da Yu, Arturs Backurs, Sivakanth Gopi, Huseyin Inan, Janardhan Kulkarni, Zinan Lin, Chulin Xie, Huishuai Zhang, Wanrong Zhang <br>
<i>Socially Responsible Language Modelling Research (SoLaR)</i> 2023.
[<a href="https://arxiv.org/pdf/2304.06929.pdf" target="_blank">paper</a>]
</p>
<br>
<h3>White Paper</h3>
<p>
<pubtitle> Challenges towards the Next Frontier in Privacy </pubtitle> <br>
Rachel Cummings, Damien Desfontaines, David Evans, Roxana Geambasu, Matthew Jagielski, Yangsibo Huang, Peter Kairouz, Gautam Kamath, Sewoong Oh, Olga Ohrimenko, Nicolas Papernot, Ryan Rogers, Milan Shen, Shuang Song, Weijie Su, Andreas Terzis, Abhradeep Thakurta, Sergei Vassilvitskii, Yu-Xiang Wang, Li Xiong, Sergey Yekhanin, Da Yu, Huanyu Zhang, Wanrong Zhang <br>
2023.
[<a href="https://arxiv.org/pdf/2304.06929.pdf" target="_blank">paper</a>]
</p>
<br>
<h3>Talks</h3>
<p>
<pubtitle> Joint Statistical Meetings, Toronto, </pubtitle> August 2023. <br>
Continual Release of Differentially Private Synthetic Data
</p>
<p>
<pubtitle> Theory and Practice of Differential Privacy Workshop, Boston, </pubtitle> September 2023. (Invited keynote Talk) <br>
Composition Theorems for Interactive Differential Privacy
</p>
<p>
<pubtitle> STOC, Orlando, </pubtitle> June 2023. <br>
Concurrent Composition Theorems for Differential Privacy
</p>
<p>
<pubtitle> CATT 2022 Global Analytics Conference, UT Austin, </pubtitle> November 2022. <br>
Composition Theorems for Interactive Differential Privacy
</p>
<p>
<pubtitle> Societal Considerations and Applications Workshop, Simons Institute for the Theory of Computing, </pubtitle> November 2022. <br>
Concurrent Composition Theorems for Differential Privacy
[<a href="https://simons.berkeley.edu/talks/concurrent-composition-theorems-all-standard-variants-differential-privacy" target="_blank">video</a>]
</p>
<p>
<pubtitle> Google Privacy Seminar, </pubtitle> August 2022. <br>
Concurrent Composition Theorems for Differential Privacy
</p>
<p>
<pubtitle> ICSA Applied Statistics Symposium, </pubtitle> June 2022. <br>
Differentially Private Approaches for Streaming Data Analysis
</p>
<p>
<pubtitle> INFORMS ICS, </pubtitle> Tampa, January 2022. <br>
Private Sequential Hypothesis Testing for Statisticians: Privacy, Error Rates, and Sample Size
</p>
<p>
<pubtitle> USENIX Security Symposium, </pubtitle> August 2021. <br>
Leakage of Dataset Properties in Multi-Party Machine Learning
</p>
<p>
<pubtitle> ICML, </pubtitle> July 2021. <br>
PAPRIKA: Private Online False Discovery Rate Control
[<a href="https://icml.cc/media/icml-2021/Slides/8425.pdf" target="_blank">slides</a>]
</p>
<p>
<pubtitle> FORC, </pubtitle> June 2021. <br>
Attribute Privacy: Framework and Mechanisms <br>
PAPRIKA: Private Online False Discovery Rate Control
</p>
<p>
<pubtitle> Microsoft Research, </pubtitle> February 2021. <br>
Privacy-Preserving Statistical Tools: Differential Privacy and Beyond
</p>
<p>
<pubtitle> CDAC Rising Stars in Data Science, </pubtitle> January 2021. <br>
PAPRIKA: Private Online False Discovery Rate Control
</p>
<p>
<pubtitle> DP Lunch Seminar, Boston University, </pubtitle> December 2020. <br>
Differentially Private Change-point Detection
</p>
<p>
<pubtitle> INFORMS, </pubtitle> November, 2020. <br>
Attribute Privacy: Framework and Mechanisms
</p>
<p>
<pubtitle> INFORMS, </pubtitle> November, 2020. <br>
Bandit Change-Point Detection for Real-Time Monitoring High-Dimensional Data Under Sampling Control
</p>
<p>
<pubtitle> ICML, </pubtitle> July, 2020. <br>
Privately Detecting Changes in Unknown Distributions
[<a href="https://icml.cc/media/Slides/icml/2020/virtual(no-parent)-16-13-00UTC-5854-privately_detec.pdf" target="_blank">slides</a>]
</p>
<p>
<pubtitle> Cybersecurity Lecture Series, Georgia Tech, </pubtitle> March, 2020.<br>
Differentially Private Change-point Detection
[<a href="https://drive.google.com/file/d/1IKpLgNCy-W1Lt7Gmq2P3pFme6w1Xnx7T/view" target="_blank">slides</a>]
</p>
<p>
<pubtitle> INFORMS, Seattle, </pubtitle> October, 2019. <br>
Differentially Private Change-point Detection
</p>
<br>
<h3>Service</h3>
<p>
<br> I am currently co-organizing <a href="https://bostondataprivacy.github.io/talks.html" target="_blank">the Boston-area data privacy seminar series</a>.
<p>
<br>I have been (or will be) on the program committee (i.e., a reviewer) for conferences: NeurIPS20, AAAI21, ICLR21, AISTATS21, ICML21, NeurIPS21, ICLR22, ICML22, FAccT22, COLT22, ISIT22, FAccT23, COLT23 ; and workshops: TPDP20, TPDP21, TPDP22; and journals: Journal of Applied Statistics, Statistica Sinica, Journal of Machine Learning Research, Transactions on Machine Learning Research, Computers&Security.
</p>
<br>
<h3>Teaching</h3>
<p>
<br> <a href="https://opendp.github.io/cs208/spring2022/" target="_blank">CS 208: Applied Privacy for Data Science</a>, Spring 2022
<p>
<pubtitle>Assistantships</pubtitle>
<br>ISyE 6412: Theoretical Statistics, Fall 2019
<br>ISyE 6669: Deterministic Optimization, Fall 2018
<br>ISyE 4031: Regression and Forecasting, Spring 2018
<br>ISyE 3039: Methods Quality Improvement, Summer 2017
<br>ISyE 2028: Basic Statistical Methods, Spring 2017
<br>ISyE 3770: Statistics and Applications, Fall 2016
<p>
<br>
<br>
</div>
</div>
</body></html>