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<!DOCTYPE html>
<html lang="en">
<head>
<title>Liuyi Yao</title>
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<a
href="papers/CV-liuyi.pdf"
target="_blank"><font size="3">[CV]</font></a>
<a
href="https://www.linkedin.com/in/liuyi-yao-93b775115/"
target="_blank">
<font size="3" >[Linkedin]</font></a>
<p>Email: yly287738 AT alibaba-inc.com
</p>
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</div>
<div class="col-lg-7">
<div class="hero-text">
<h3>Liuyi Yao</h3>
<p> I'm currently a staff in Alibaba DAMO Academy.
I obtained my PhD degree in <a href="", target="_blank">Department of Computer Science and Engineering</a>
in University at Buffalo under the advisory of Prof. <a href =
"https://engineering.virginia.edu/faculty/aidong-zhang" target="_blank"> Aidong Zhang</a> and Prof. <a href =
"https://engineering.purdue.edu/~jinggao/" target="_blank"> Jing Gao</a> in 2020. Before that, I got my B.S. degree in statistics from <a href="https://www.nju.edu.cn/EN/7f/77/c7136a163703/page.htm" target="_blank">Nanjing University</a> in 2015. </p>
<p>I have board interests in Data Mining and Machine Learning. I am particularly interested in Causal Inference, Federated Learning and Temporal Data Analysis. </p>
</div>
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<!-- <h2>General Info</h2> -->
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<li><span>Address</span>341 Davis Hall, Buffalo, NY 14260 </li>
<li><span>E-mail</span>[email protected]</li>
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<h2> Publication <a
href="https://scholar.google.com/citations?hl=en&user=0c5is-gAAAAJ"
target="_blank"><font size="4">[Google Scholar] </font></a>
<a
href="https://dblp.org/pers/hd/y/Yao:Liuyi"
target="_blank"><font size="4">[DBLP]</font></a></h2>
<h6> 2022 </h6>
<ul>
<li>Mengdi Huai, Tianhang Zheng, Chenglin Miao, <bb>Liuyi Yao</bb>, Aidong Zhang, <tl>On the Robustness of Metric Learning: An Adversarial Perspective</tl>, <pc>ACM Transactions on Knowledge Discovery from Data </pc>(<bb>TKDD</bb>). [ <a href="https://dl.acm.org/doi/pdf/10.1145/3502726"
target="_blank" > pdf </a> ] </li>
<li>Xuanying Chen, Zhining Liu, Li Yu, <bb>Liuyi Yao</bb>, Wenpeng Zhang, Yi Dong, Lihong Gu, Xiaodong Zeng, Yize Tan, Jinjie Gu, <tl>Imbalance-Aware Uplift Modeling for Observational Data</tl>. <pc>Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence</pc>, (<bb>AAAI 2022</bb>). [ <a href="https://www.aaai.org/AAAI22Papers/AAAI-8295.ChenX.pdf" target="_blank" > pdf </a> ]</li>
</ul>
<h6> 2021 </h6>
<ul>
<li><bb>Liuyi Yao</bb>, Yaliang Li, Sheng Li, Mengdi Huai, Jing Gao, Aidong Zhang, <tl>SCI: Subspace Learning Based Counterfactual Inference for Individual Treatment Effect Estimation</tl>, <pc>Proceedings of the 30th ACM International Conference on Information & Knowledge Management, </pc>(<bb>CIKM 2021</bb>). </li>
<li>Siqing Li, <bb>Liuyi Yao</bb>, Shanlei Mu, Wayne Xin Zhao, Yaliang Li, Tonglei Guo, Bolin Ding, Ji-Rong Wen, and Mengdi Huai, Aidong Zhang, <tl>Debiasing Learning based Cross-domain Recommendation</tl>. <pc>Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining</pc>, (<bb>KDD 2021</bb>). </li>
<li><bb>Liuyi Yao</bb>, Zijun Yao, Jianying Hu, Jing Gao, Zhaonan Sun, <tl>Deep Staging: An Interpretable Deep Learning Framework for Disease Staging</tl>. <pc>2021 IEEE 9th International Conference on Healthcare Informatics</pc>, (<bb>ICHI 2021</bb>). </li>
<li><bb>Liuyi Yao</bb>, Zhixuan Chu, Sheng Li, Yaliang Li, Jing Gao, Aidong Zhang. <tl>A Survey on Causal Inference</tl>. <pc>ACM Transactions on Knowledge Discovery from Data </pc>(<bb>TKDD</bb>)
</li>
</ul>
<h6> 2020 </h6>
<ul>
<li>Mengdi Huai, Jianhui Sun, Renqin Cai, <bb>Liuyi Yao</bb>, Aidong Zhang, <tl>Malicious attacks against deep reinforcement learning interpretations</tl>. <pc>Proceedings of the 26th ACM SIGKDD Conference on Knowledge Discovery & Data Mining</pc>, (<bb>KDD 2020</bb>, <pc style="color:red">Best Paper Runner-up</pc>). </li>
<li>Sheng Li, <bb>Liuyi Yao</bb>, Yaliang Li, Jing Gao and Aidong Zhang, <tl>Representation Learning for Causal Inference</tl>, <pc>the Thirty-Fourth AAAI Conference on Artificial Intelligence, Tutorial, </pc>(<bb>AAAI 2020</bb>). [ <a href="http://cobweb.cs.uga.edu/~shengli/Docs/AAAI-20-Causal-Inference-Tutorial.pdf"
target="_blank" > slides </a> ] </li>
<li>Jinduo Liu, Junzhong Ji, Guangxu Xun, <bb>Liuyi Yao</bb>, and Mengdi Huai, Aidong Zhang, <tl>EC-GAN: Inferring Brain Effective Connectivity via Generative Adversarial Networks</tl>. <pc>Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence</pc>, (<bb>AAAI 2020</bb>). </li>
</ul>
<h6> 2019 </h6>
<ul>
<li>Jinduo Liu, Junzhong Ji, <bb>Liuyi Yao</bb>, Aidong Zhang, <tl>Estimating Brain Effective Connectivity in fMRI Data by Non-stationary Dynamic Bayesian Networks</tl>. <pc>IEEE International Conference on Bioinformatics and Biomedicine</pc>,
(<bb>BIBM 2019</bb>).
</li>
<li><bb>Liuyi Yao</bb>, Sheng Li, Yaliang Li, Mengdi Huai, Jing Gao, and Aidong Zhang. <tl>ACE: Adaptively Similarity-preserved Representation Learning for Individual Treatment Effect Estimation</tl>.
<pc>Proceedings of the 19th IEEE International Conference on Data Mining</pc> (<bb>ICDM 2019</bb>).
</li>
<li><bb>Liuyi Yao</bb>, Sheng Li, Yaliang Li, Hongfei Xue, Jinggao, and Aidong Zhang. <tl>On the Estimation of Treatment Effect with Text Covariates</tl>. <pc>Proceedings of the Twenty-Seventh International Joint Conference on
Artificial Intelligence</pc> (<bb>IJCAI 2019</bb>).
</li>
<li>Mengdi Huai, Hongfei Xue, Chenglin Miao, <bb>Liuyi Yao</bb>, Lu Su, Changyou Chen, and Aidong Zhang. <tl>Deep Metric Learning: The Generalization Analysis and an Adaptive Algorithm</tl>. <pc>Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence</pc> (<bb>IJCAI 2019</bb>).
</li>
<li><bb>Liuyi Yao</bb>, Yaliang Li, Yezheng Li, Hengtong Zhang, Mengdi Huai, Jing Gao, Aidong Zhang. <tl>DTEC: Distance Transformation Based Early Time Series Classication</tl>. <pc>Proceedings of the 2019 SIAM International Conference on Data Mining</pc> (<bb>SDM 2019</bb>).
</li>
<li>Suo Qiuling, <bb>Yao Liuyi</bb>,Xun Guangxu, Sun Jianhui, Zhang Aidong. <tl>Recurrent Imputation for Multivariate Time Series with Missing Values</tl>. <pc>2019 IEEE International Conference on Healthcare Informatics</pc> (<bb>ICHI 2019</bb>).
</li>
</ul>
<h6> 2018 </h6>
<ul>
<li> <bb> Liuyi Yao</bb>, Sheng Li, Yaliang Li, Mengdi Huai, Jing Gao, and Aidong Zhang. <tl>Representation Learning for
Treatment Effect Estimation from Observational Data</tl>. <pc>Advances in Neural Information Processing Systems 31</pc> (<bb>NeurIPS 2018</bb>). [
<a
href="https://github.com/Osier-Yi/SITE" target="_blank" > Code
</a> ] [ <a href="http://www.acsu.buffalo.edu/~liuyiyao/pdf/nips18poster.pdf"
target="_blank" > Poster </a> ] </li>
<li><bb>Liuyi Yao</bb>, Lu Su, Qi Li, Yaliang Li, Fenglong Ma, Jing Gao and Aidong Zhang. <tl>Online Truth Discovery on Time Series Data</tl>. <pc>Proceedings of the 2018 SIAM International Conference on Data Mining</pc> (<bb>SDM 2018</bb>).
</li>
</ul>
</div>
</div>
<div class="container">
<div class="publication">
<h2>Preprint</h2>
<ul>
<li><bb>Liuyi Yao</bb>, Dawei Gao, Zhen Wang, Yuexiang Xie, Weirui Kuang, Daoyuan Chen, Haohui Wang, Chenhe Dong, Bolin Ding, Yaliang Li. <tl>A Benchmark for Federated Hetero-Task Learning</tl>. <pc>ArXiv preprint arXiv:2206.03436.</pc>
</li>
<li>Zhen Wang, Weirui Kuang, Yuexiang Xie, <bb>Liuyi Yao</bb>, Yaliang Li, Bolin Ding, Jingren Zhou. <tl>FederatedScope-GNN: Towards a Unified, Comprehensive and Efficient Package for Federated Graph Learning</tl>. <pc>ArXiv preprint arXiv:2204.05562.</pc>
</li>
<li>Yuexiang Xie, Zhen Wang, Daoyuan Chen, Dawei Gao, <bb>Liuyi Yao</bb>, Weirui Kuang, Yaliang Li, Bolin Ding, Jingren Zhou. <tl>FederatedScope: A Comprehensive and Flexible Federated Learning Platform via Message Passing</tl>. <pc>ArXiv preprint arXiv:2204.05011.</pc>
</li>
<li>Yaliang Li, <bb>Liuyi Yao</bb>, Nan Du, Jing Gao, Qi Li, Chuishi Meng, Chenwei Zhang, Wei Fan. <tl>Finding Similar Medical Questions from Question Answering Websites</tl>. <pc>ArXiv preprint arXiv:1810.05983.</pc>
</li>
</ul>
</div>
</div>
<div class="container">
<div class="publication">
<h2>Work Experience</h2>
<p>
Research Intern, May 2019 - Aug. 2019, IBM Thomas J. Watson Research Center, Yorktown Heights.
</p>
<p>
Staff, Oct 2020 - present, Alibaba DAMO Academy.
</p>
</div>
</div>
<!-- <div class="container">
<h2>Professional Activity </h2>
<p>I have attended the NII Shonan School
on <a href="https://shonan.nii.ac.jp/seminars/103" target="_blank" >"Semantics of Effects, Resources, and
Applications" </a> in Shonan Village near Tokyo, Japan,
2017. </p>
<p> I have presented my poster <a
href="https://popl18.sigplan.org/profile/weihaoqu" target="_blank"> "Relational Cost Analysis
with State"</a> within the Student Research Competition-track at
POPL, LA, CA 2018 </p>
<p>I was invited to give a talk "Relational Cost
Analysis for functional-imperative Programs" within <a
href="http://eglpls2019.cs.cornell.edu/program.html"target="_blank" > EGLPLS </a>
at Cornell University in Ithaca, NY, 2019. </p>
<p>I presented my work on "Relational Cost
Analysis for functional-imperative Programs" in the form of
20 minutes <a
href="https://www.youtube.com/watch?v=ZH8n3Rh5i4s&list=PLyrlk8Xaylp4EuohGj90SbJOcmEwrS-ZO&index=18"
target="_blank">talk </a>at ICFP, Berlin, Germany, 2019 </p>
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