Ph.D. Candidate

Penn State University

Biography

Hello! My name is Hangzhi Guo (郭杭之 | Birkhoff). I am a Ph.D. candidate in the College of Information Sciences and Technology (IST) at Penn State University advised by Dr. Amulya Yadav. I graduated from Wenzhou-Kean University majoring Computer Science.

I am broadly interested in human-centered machine learning and AI for Social Good. My current focus is to develop actionable and robust Explainable Machine Learning models (e.g., CounterNet, RoCourseNet). I am also devoted to applying techniques in machine learning and human-computer interaction to help under-served groups (including low-resource farmers, maternal mothers, children with autism). Media coverage of these works includes ThePrint.in, News Medical Life Sciences, and Penn State News.

In 2022, I founded and organized the Penn State CSRAI Young Achievers Symposium.

News

📢 Unveiling ReLax v0.2 🎉 - A JAX-based recourse explanation library designed for efficiency and scalability [ GitHub] [ Twitter].

Interests

  • Explainable AI
  • AI for Social Good
  • Human-centered Machine Learning
  • Deep Learning

Education

  • Ph.D. in Informatics, 2020 - Present

    Pennsylvania State University

  • BSc in Computer Science, 2016 - 2020

    Wenzhou-Kean University

Publications

See Google Scholar for all publications.

Selected Papers

Hangzhi Guo, Feiran Jia, Jinghui Chen, Anna Squicciarini, Amulya Yadav (2023). RoCourseNet: Robust Training of a Prediction Aware Recourse Model. Proceedings of the 32nd ACM International Conference on Information and Knowledge Management (CIKM ’23).

PDF DOI Arxiv

Hangzhi Guo, Thanh H. Nguyen, Amulya Yadav (2023). CounterNet: End-to-End Training of Prediction Aware Counterfactual Explanations. Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD ’23).

PDF Code Slides Poster Twitter Workshop DOI

 Best Paper Runner-up Award @ICML-21 Workshop on Recourse

Wenbo Zhang, Hangzhi Guo, Prerna Ranganathan, Jay Patel, Sathyanath Rajasekharan, Nidhi Danayak, Manan Gupta, Amulya Yadav (2023). A Continual Pre-training Approach to Tele-Triaging Preganant Women in Kenya. Proceedings of the AAAI Conference on Artificial Intelligence.

PDF DOI Tweet

 Deployed @ Jacaranda Health

Hangzhi Guo, Lizhen Zhu, Ting-Hao ‘Kenneth’ Huang (2022). Are GANs Biased? Evaluating GAN-Generated Facial Images via Crowdsourcing. Neurips 2022 Workshop on Human Evaluation of Generative Models.

PDF Workshop

Hangzhi Guo, Alexander Woodruff, Amulya Yadav (2020). Improving Lives of Indebted Farmers using Deep Learning: Predicting Agricultural Produce Prices using Convolutional Neural Networks. Thirty-Fourth AAAI Conference.

PDF AAAI Print.in News Medical Life Sciences PSU News Workshop

See all publications

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