Zexi Huang (黄泽熙)
PhD Candidate in Computer Science

Welcome, my name is Zexi Huang. I am a PhD candidate at the Department of Computer Science, University of California, Santa Barbara (UCSB), starting from Fall 2018. Before joining UCSB, I received my Bachelor's Degree in Computer Science and Technology with the highest honor at Yingcai Honors College (elite school of top 5% undergraduates), University of Electronic Science and Technology of China (UESTC).

 

My research interests span the analysis of social, informational, and biological networks with machine learning and data mining techniques. At UCSB, I work in Dynamic Networks: Analysis and Modeling (Dynamo) Lab, with Prof. Ambuj Singh as my advisor. My current research topic is representation learning for information-rich graphs. Previously, I had multiple applied science internships at Books Tech, Amazon, where I leveraged graph-based machine learning techniques to solve large-scale industry problems including fraud detection, inventory management, and content discovery. I have also interned at Computational Intelligence Lab, Nanyang Technological University, working with Prof. Sinno Jialin Pan on transfer learning framework for community detection in multiplex networks. As an undergraduate, I was a research assistant at Data Mining Lab, UESTC, with Prof. Junming Shao as my advisor. I developed dynamics-based algorithms for detecting overlapping communities at that time.

News

Oct. 2022

Our work on global counterfactual explanation of graph neural networks was accepted to WSDM'23!

Oct. 2022

I will serve as the Registration Chair for KDD 2023!

Sep. 2022

I finished another applied science internship at Amazon with two exciting projects!

Jun. 2022

I passed my PhD proposal on Learning Representations for Information-rich Graphs!
Education

Sep. 2018 - Exp. Apr. 2023

College of Engineering, University of California, Santa Barbara
Doctor of Philosophy in Computer Science (expected) and Master of Science in Computer Science (awarded)
  • Course GPA: 4.0/4.0. UCSB Computer Science Outstanding Scholar Fellow (top 4 out 63 PhD students).
  • Advisor: Prof. Ambuj Singh. Thesis topic: Learning Representations for Information-rich Graphs

Sep. 2014 - Jun. 2018

Yingcai Honors College, University of Electronic Science and Technology of China
Bachelor of Engineering in Computer Science and Technology (Honors)
  • GPA: 3.96/4.0, Avg. Score: 92.79/100 (1/90 in freshman year, 1/87 in sophomore year, 1/93 in junior year).
  • Advisors: Prof. Junming Shao and Prof. Sinno Jialin Pan. Thesis: Transfer Learning for Community Detection.

Feb. 2016 - Jun. 2016

College of Electrical and Computer Engineering, National Chiao Tung University
Exchange Student
  • GPA:4.0/4.0, Avg. Score: 96.44/100, Straight A+.
Work Experience

Jun. 2022 - Sep. 2022

Applied Scientist Intern, Books Tech, Amazon
Stochastic Inventory Management for Print-On-Demand and Graph-based Text Classification for Content Intelligence
  • Developed a stochastic inventory management model based on dynamic programming for Amazon’s Print-On-Demand business, optimizing the ordering strategy for 42.4M units of books and realizing an annual saving of $10.4M.
  • Designed and implemented a graph-based NLP model for long text embedding and classification using graph neural networks, leading to superior performance to state-of-the-art transformer-based models for Kindle book contents.

Jun. 2021 - Sep. 2021

Applied Scientist Intern, Books Tech, Amazon
Graph-based Fraud Detection in Kindle Direct Publishing
  • Designed graph-based machine learning models for fraud detection based on multi-modal signals in Kindle Direct Publishing.
  • Implemented the models in an end-to-end fashion and deployed them to graphs with millions of nodes and billions of edges in production.
  • Validation results show that the models can surface fraud rings undetected by existing processes with an estimated annual value of $2.4M.

Jun. 2020 - Sep. 2020

Applied Scientist Intern, Books Tech, Amazon
Graph-based Fraud Detection in Kindle Direct Publishing
  • Proposed to augment existing fraud detection methods with graph-based machine learning models for Kindle Direct Publishing.
  • Designed and implemented various heuristics and an embedding framework for attributed heterogeneous multiplex networks.
  • The models are deployed into production and results show that they surface up to 15 times more fraud compared to the existing processes.

Sep. 2017 - Feb. 2018

Research Intern, Computational Intelligence Lab, Nanyang Technological University
Transfer Learning for Community Detection in Multiplex Networks
  • Proposed to refine community detection results in some layers with transferred knowledge from other layers in multiplex networks.
  • Designed a representation-based community detection framework and implemented it with an extended symmetric NMF approach.
  • Our algorithm outperforms other representation-based community detection algorithms, especially when the target layer is noisy.
Publications

[Under review] Zexi Huang, Mert Kosan, Arlei Silva, Ambuj Singh. Link Prediction without Graph Neural Networks. Under review, 2022. [Code]

[Preprint] Wei Ye, Zexi Huang, Yunqi Hong, Ambuj Singh. Graph Neural Diffusion Networks for Semi-supervised Learning. arXiv preprint arXiv:2201.09698, 2022. [Code]

[WSDM-2023] Zexi Huang*, Mert Kosan*, Sourav Medya, Sayan Ranu, Ambuj Singh. Global Counterfactual Explainer for Graph Neural Networks. ACM International Conference on Web Search and Data Mining, 2023. (*: equal contribution) [Code]

[WSDM-2022] Zexi Huang, Arlei Silva, Ambuj Singh. POLE: Polarized Embedding for Signed Networks. ACM International Conference on Web Search and Data Mining, 2022. [Code] [Poster] [Slides] [Talk]

[KDD-2021] Zexi Huang, Arlei Silva, Ambuj Singh. A Broader Picture of Random-walk Based Graph Embedding. ACM SIGKDD Conference on Knowledge Discovery & Data Mining, 2021. [Code] [Poster] [Slides] [Talk] [Talk (in Chinese)]

Research Experience

Apr. 2021 - Present

Multiscale Community Detection Based on Pointwise Mutual Information
Advisor: Prof. Ambuj Singh, Collaborator: Manu Kondapaneni, Arlei Silva
  • Developing a stability optimization algorithm for multiscale community detection based on the pointwise mutual information similarity.
  • Preliminary experiments show that the proposed algorithm can uncover the natural scales for different communities in the graph.

May. 2021 - Sep. 2022

Link Prediction without Graph Neural Networks
Advisor: Prof. Ambuj Singh, Collaborators: Mert Kosan, Arlei Silva
  • Scrutinized the training and evaluation of link prediction methods and identify their limitations in handling class imbalance.
  • Proposed a novel topology-centric framework that combines graph learning, topological heuristics, and an N-pair loss for link prediction.
  • Results showed that the proposed method is 145% more accurate and trains/infers 11/6,000 times faster than the state-of-the-art methods.

Oct. 2021 - Aug. 2022

Global Counterfactual Explanation for Graph Neural Networks
Advisor: Prof. Ambuj Singh, Collaborators: Mert Kosan, Sourav Medya, Sayan Ranu
  • Formulated the novel problem of global counterfactual reasoning/explanation of graph neural networks for graph classification.
  • Proposed GCFExplainer, the first global explainer powered by vertex-reinforced random walks on an edit map with a greedy summary.
  • Results showed that GCFExplainer not only provides crucial high-level insights but also outperforms existing methods in recourse quality.

Oct. 2020 - Aug. 2021

Signed Embedding for Polarized Graphs
Advisor: Prof. Ambuj Singh, Collaborator: Arlei Silva
  • Designed a novel polarization measure for signed graphs and showed that existing methods fail in polarized signed link prediction.
  • Proposed a polarized embedding algorithm that captures both topological and signed similarity jointly via signed autocovariance.
  • Extensive experiments showed that the proposed model outperforms state-of-the-art methods by up to one order of magnitude.

Apr. 2020 - Oct. 2021

Multiscale Graph Convolution via Neural Diffusions
Advisor: Prof. Ambuj Singh, Collaborator: Wei Ye, Yunqi Hong
  • Interpreted the layer-wise propagation rule of GCN from the perspective of power iteration and analyzed its converging process.
  • Designed a novel GCN architecture that learns to aggregate multiscale information based on graph diffusions with a neural network.
  • Illustrated the effectiveness and efficiency of the proposed model by extensive comparative studies with state-of-the-art methods.

Sep. 2018 - Feb. 2021

Graph Representation Learning Based on Random-walks
Advisor: Prof. Ambuj Singh, Collaborator: Arlei Silva
  • Presented a unified view of embedding, covering different random-walk processes, similarity metrics, and embedding algorithms.
  • Showed both theoretical and empirical evidence of the superiority of the novel autocovariance embedding in link prediction.
  • Illustrated ways to exploit the multiscale nature of random-walk similarity to further optimize embedding performance.

Jan. 2020 - Aug. 2020

Prospect Theory for Group Decision Making Dynamics
Advisor: Prof. Ambuj Singh, Collaborator: Mert Kosan
  • Extended the Prospect Theory to model group-level risky decision making dynamics with an interpersonal influence system.
  • Results on two human-subject experiments show that the group behavior shifts towards consensus and is explained by the influence.

Jul. 2016 - Aug. 2017

Overlapping Community Detection Based on Game Theory-incorporated Label Propagation Dynamics
Advisor: Prof. Junming Shao
  • Proposed an intuitive algorithm for fast overlapping community detection in networks.
  • Introduced gain and loss functions from game theory to model the intrinsic dynamics between nodes.
  • Results are comparable to state-of-the-art algorithms.
Teaching Experience

2020 - 2021

Lead Teaching Assistant
Department of Computer Science, UCSB

Fall 2019

Teaching Assistant, CS 130A: Data Structures and Algorithms
Department of Computer Science, UCSB

Spring 2019

Co-designer and Instructor, Machine Learning Workshops
LMU/UCSB Junior Nanotech Network PhD Student Exchange and Symposium

Winter 2019

Teaching Assistant, CS 8: Introduction to Computer Science
Department of Computer Science, UCSB

Fall 2018

Teaching Assistant, CS 174A/174N: Fundamentals of Database Systems
Department of Computer Science, UCSB
Honors & Awards

Feb. 2022

WSDM NSF Travel Award
$300

Sep. 2020

UCSB Computer Science Lead Teaching Assistant Fellowship
$3,225

Sep. 2018

Computer Science Outstanding Scholar Fellowship at UCSB
6%, top four out of 63 admitted PhD students, $10,000

Sep. 2018

Computer Science Academic Excellence Fellowship at UCSB
$2,000

Jun. 2018

Outstanding Bachelor Thesis Award of UESTC
10%

Jun. 2018

Tang Lixin Scholarship for Studying Abroad
$1,453

Dec. 2017

The Most Outstanding Students Award of UESTC
0.2%, top 10 out of 5,000 seniors, $2,180

Dec. 2017

Honorary Graduate of Sichuan Province
1%, top one in Yingcai Honors College

Dec. 2017

Honorary Graduate of UESTC
10%, top twelve in Yingcai Honors College

Oct. 2017

National Scholarship of China
0.2%, top two in Yingcai Honors College, $1,162

May. 2017

National English Competition for College Students of China 2017
0.1%, Special Prize in National Final (Level C, for Non-English professionals)

Dec. 2016

People's Scholarship of UESTC
4%, Special Class, top four in Yingcai Honors College, $436

Dec. 2015

Tang Lixin Scholarship of Yingcai Honors College
1%, First Prize, top one in Yingcai Honors College, $4,359

Dec. 2015

Tang Lixin Scholarship of UESTC
0.2%, $1,453 per year until graduation

Dec. 2015

The 7th Chinese Mathematics Competitions
0.05%, 1st out of 1819 selected candidates in Sichuan Province
Academic Services
Registration Chair
  • KDD’23
Program Committee
  • AAAI’23
  • KDD’22
  • SDM’22
Reviewer
  • NeurIPS’22
  • ICLR’22
  • TIST’22
  • TKDD’21-22
  • KDD’20-21
  • WebConf’20-21
Representative
  • Graduate Affairs Committee, Department of Computer Science, UCSB
General Judge
  • SB Hacks V, VII, VIII Hackathon
Skills
Programming
  • Python, Matlab, R, Java, C/C++, SQL, Cypher, LaTeX, Verilog, ARM
Languages
  • Chinese (native), English (fluent), German (basic)

Last updated on October 18, 2022