publications

2023

1. MultiQG-TI: Towards Question Generation from Multi-modal Sources EDU NLP

   Zichao Wang, Richard G. Baraniuk
   

   Workshop on Innovative Use of NLP for Building Educational Applications (BEA), 2023
2. Interpretable Math Word Problem Solution Generation Via Step-by-step Planning EDU NLP

   Mengxue Zhang, Zichao Wang, Zhichao Yang, Weiqi Feng, Andrew Lan
   

   Annual Meeting of the Association for Computational Linguistics (ACL), 2023
3. Improving Reading Comprehension Question Generation with Data Augmentation and Overgenerate-and-Rank EDU NLP

   Nischal Ashok Kumar, Nigel Fernandez, Zichao Wang, Andrew Lan
   

   Workshop on Innovative Use of NLP for Building Educational Applications (BEA), 2023
4. MANER: Mask Augmented Named Entity Recognition for Extreme Low-Resource Languages NLP

   Shashank Sonkar, Zichao Wang, Richard G. Baraniuk
   

   Workshop on Simple and Efficient Natural Language Processing (SustaiNLP), 2023
5. Retrieval-based Controllable Molecule Generation ML

   Zichao Wang*, Weili Nie*, Zhuoran Qiao, Chaowei Xiao, Richard G. Baraniuk, Anima Anandkumar
   (*=equal contribution)

   International Conference on Learning Representations (ICLR), 2023
6. CausalEdu: a real-world education dataset for temporal causal discovery and inference EDU

   Wenbo Gong, Digory Smith, Zichao Wang*, Craig Barton, Simon Woodhead, Nick Pawlowski, Joel Jennings, Cheng Zhang
   

   Causal Learning and Reasoning (CLeaR), 2023

2022

1. Open-Ended Knowledge Tracing EDU NLP

   Naiming Liu*, Zichao Wang*, Richard G. Baraniuk, Andrew Lan (*=equal contribution)

   Conference on Empirical Methods in Natural Language Processing (EMNLP), 2022
2. Towards Human-like Educational Question Generation with Large Language Models EDU NLP

   Zichao Wang*, Jakob Valdez*, Debshila Basu Mallick, Richard G. Baraniuk (*=equal contribution)

   Conference on Artificial Intelligence in Education (AIED), 2022
3. Automated Scoring for Reading Comprehension via In-context BERT Tuning EDU NLP

   Nigel Fernandez, Aritra Ghosh, Naiming Liu, Zichao Wang, Benoît Choffin, Richard G. Baraniuk, Andrew Lan

   Conference on Artificial Intelligence in Education (AIED), 2022
4. DeepHull: Fast Convex Hull Approximation in High Dimensions ML

   Randall Balestriero*, Zichao Wang*, Richard G. Baraniuk (*=equal contribution)

   International Conference on Acoustics, Speech, & Signal Processing (ICASSP), 2022

2021

1. Mathematical Formula Representation via Tree Embeddings NLP

   Zichao Wang, Mengxue Zhang, Andrew S. Lan, and Richard G. Baraniuk

   IEEE nternational Conference on Big Data (IEEE BigData), 2021
2. Math Word Problem Generation with Mathematical Consistency and Problem Context Constraints NLP

   Zichao Wang, Andrew S. Lan, and Richard G. Baraniuk

   Conference on Empirical Methods in Natural Language Processing (EMNLP), 2021
3. Math Operation Embeddings for Open-ended Solution Analysis and Feedback EDU

   Mengxue Zhang, Zichao Wang, Richard G. Baraniuk, and Andrew S. Lan

   International Conference on Educational Data Mining (EDM), 2021
4. Towards Bloom’s Taxonomy Classification Without Any Labels EDU

   Zichao Wang, Kyle Manning, Debshila Basu Mallick, and Richard G. Baraniuk

   Conference on Artificial Intelligence in Education (AIED), 2021
5. The Recurrent Neural Tangent Kernel ML

   Sina Alemohammad, Zichao Wang, Randall Balestriero, and Richard G. Baraniuk

   International Conference on Learning Representations (ICLR), 2021
6. Wearing a MASK: Compressed Representations of Variable-Length Sequences Using Recurrent Neural Tangent Kernels ML

   Sina Alemohammad, Hossein Babaei, Randall Balestriero, Matt Y. Cheung, Ahmed Imtiaz Humayun, Daniel LeJeune, Naiming Liu, Lorenzo Luzi, Jasper Tan, Zichao Wang, and Richard G. Baraniuk

   International Conference on Acoustics, Speech, & Signal Processing (ICASSP), 2021
7. Large-Scale Educational Question Analysis with Partial Variational Auto-encoders EDU ML

   Zichao Wang, Sebastian Tschiatschek, Simon Woodhead, José Miguel Hernández-Lobato, Simon P. Jones, Richard G. Baraniuk, and Cheng Zhang

   Symposium on Educational Advances in Artificial Intelligence (AAAI-EAAI), 2021

2020

1. Scalable Neural Tangent Kernel of Recurrent Architectures ML

   Sina Alemohammad, Randall Balestriero, Zichao Wang, and Richard G. Baraniuk

   arXiv preprint, 2020
2. Diagnostic Questions: The NeurIPS 2020 Education Challenge EDU

   Zichao Wang, Angus Lamb, Evgeny Saveliev, Pashmina Cameron, Yordan Zaykov, José Miguel Hernández-Lobato, Richard E. Turner, Richard G. Baraniuk, Craig Barton, Simon P. Jones, and Cheng Zhang

   arXiv preprint, 2020
3. VarFA: A Variational Factor Analysis Framework For Efficient Bayesian Learning Analytics EDU ML

   Zichao Wang, Yi Gu, Andrew S. Lan, and Richard G. Baraniuk

   International Conference on Educational Data Mining (EDM), 2020

2019

1. A Max Affine Spline Perspective of Recurrent Neural Networks ML

   Zichao Wang, Randall Balestriero, and Richard G. Baraniuk

   International Conference on Learning Representations (ICLR), 2019
2. A Meta-Learning Augmented Bidirectional Transformer Model for Automatic Short Answer Grading EDU NLP

   Zichao Wang, Andrew S. Lan, Andrew E. Waters, Phillip J. Grimaldi, and Richard G. Baraniuk

   International Conference on Educational Data Mining (EDM), 2019

2018

1. QG-Net: A Data-driven Question Generation Model for Educational Content EDU NLP

   Zichao Wang, Andrew S. Lan, Weili Nie, Andrew E. Waters, Phillip J. Grimaldi, and Richard G. Baraniuk

   ACM Conference on Learning at Scale (L@S), 2018

2017

1. A Latent Factor Model for Instructor Content Preference Analysis EDU ML

   Zichao Wang, Andrew S. Lan, Phillip J. Grimaldi, and Richard G. Baraniuk

   International Conference on Educational Data Mining (EDM), 2017

2016

1. Constrained Multi-channel Linear Prediction for Adaptive Speech Dereverberation ML

   Ante Jukić, Zichao Wang, Toon Waterschoot, Timo Gerkmann, and Simon Doclo

   IEEE International Workshop on Acoustic Signal Enhancement (IWAENC), 2016