We apply machine learning on interesting interdisciplinary problems, such as molecule generation in Chemistry, inverse design in Material Science, ocean soundscape analysis, biomedical hypothesis generation, and bioinformatics.
- Yujun Zhou, Jingdong Yang, Yue Huang, Kehan Guo, Zoe Emory, Bikram Ghosh, Amita Bedar, Sujay Shekar, Zhenwen Liang, Pin-Yu Chen, Tian Gao, Werner Geyer, Nuno Moniz, Nitesh V. Chawla, and Xiangliang Zhang. Benchmarking Large Language Models on Safety Issues in Scientific Labs. Nature Machine Intelligence, Jan 2026. https://doi.org/10.1038/s42256-025-01152-1 Highlighted by New Scientist magazine, and Science News.
- Yue Huang, Zhengzhe Jiang, Xiaonan Luo, Kehan Guo, Haomin Zhuang, Yujun Zhou, Zhengqing Yuan, Xiaoqi Sun, Jules Schleinitz, Yanbo Wang, Shuhao Zhang, Mihir Surve, Nitesh V Chawla, Olaf Wiest, Xiangliang Zhang. ChemOrch: Empowering LLMs with Chemical Intelligence via Groundbreaking Synthetic Instructions. Accepted by NeurIPS 2025 (Accepted 5290 (24.52%) papers out of 21575 submissions).
- Taicheng Guo, Changsheng Ma, Kehan Guo, Xiuying Chen, Bozhao Nan, Shichao Pei, Nitesh Chawla, Olaf Wiest, and Xiangliang Zhang. ReactionTeam: Teaming Experts for Divergent Thinking Beyond Typical Reaction Patterns. Accepted as a regular paper for the 2025 IEEE International Conference on Big Data. (acceptance rate: 18.7%, 140 out of 746 submissions).
- Yingshuai Ji, Haomin Zhuang, Matthew Toole, James McKenzie, Xiaolong Liu, and Xiangliang Zhang. Quasiparticle Interference Kernel Extraction with Variational Autoencoders via Latent Alignment. Accepted as a regular paper for the 2025 IEEE International Conference on Big Data. (acceptance rate: 18.7%, 140 out of 746 submissions).
- Yuan Yan, Yimu Yang, Yinchang Ma, Kadin Reed, Shengzhi Li, Shichao Pei, Zhenwen Liang, Xixiang Zhang, Yi Wan, Xiangliang Zhang, Rongyu Lin. Machine learning for 2D material–based devices. Materials Science and Engineering: R: Reports, Volume 166, 2025.
- Kehan Guo, Zhen Liu, Zhichun Guo, Bozhao Nan, Olexandr Isayev, Nitesh Chawla, Olaf Wiest and Xiangliang Zhang. Proto-Yield: An Uncertainty-Aware Prototype Network for Yield Prediction in Real-world Chemical Reactions. Accepted by CIKM 2025 (full paper). (Acceptance Rate 27%, 443 out of 1,627 full paper submissions)
- Yili Shen, Yijun Tian, Cheng-Wei Ju, Olaf Wiest and Xiangliang Zhang. Towards Few-shot Chemical Reaction Outcome Prediction. Accepted by CIKM 2025 (full paper). (Acceptance Rate 27%, 443 out of 1,627 full paper submissions)
- Kehan Guo, Yili Shen, Gisela Abigail Gonzalez-Montiel, Yue Huang, Yujun Zhou, Mihir Surve, Zhichun Guo, Prayel Das, Nitesh V Chawla, Olaf Wiest, Xiangliang Zhang. Artificial Intelligence in Spectroscopy: Advancing Chemistry from Prediction to Generation and Beyond. IJCAI 2025 (the 34th International Joint Conference on Artificial Intelligence) Survey Track (52/265).
- Angus Keto, Taicheng Guo, Nils Gönnheimer, Xiangliang Zhang, Elizabeth H. Krenske, and Olaf Wiest. Improving Reaction Prediction through Chemically Aware Transfer Learning. Digital Discovery, 2025, DOI: 10.1039/D4DD00412D.
- Xiuying Chen, Tairan Wang, Taicheng Guo, Kehan Guo, Juexiao Zhou, Haoyang Li, Zirui Song, Xin Gao & Xiangliang Zhang. Unveiling the power of language models in chemical research question answering. Communications Chemistry volume 8, Article number: 4 (2025)
- Ying Wu, Waqas Ahmed, Haicheng Cao, Changqing Xu, Mohamed Farhat, Muhammad Amin, Xiaohang Li, and Xiangliang Zhang. Machine learning assisted plasmonic metascreen for enhanced broadband absorption in ultra-thin silicon films. Light: Science & Applications. 14, 42 (2025)
- Kehan Guo, Bozhao Nan, Yujun Zhou, Taicheng Guo, Zhichun Guo, Mihir Surve, Zhenwen Liang, Nitesh V Chawla, Olaf Wiest, Xiangliang Zhang. Can LLMs Solve Molecule Puzzles? A Multimodal Benchmark for Molecular Structure Elucidation. Accepted by NeurIPS 2024 Datasets and Benchmarks Track as a spotlight. (acceptance rate 25.3% , from 1820 valid paper submissions). Link to data
- Angus Keto, Taicheng Guo, Morgan Underdue, Thijs Stuyver, Connor W. Coley, Xiangliang Zhang, Elizabeth H. Krenske, Olaf Wiest. Data-Efficient, Chemistry-Aware Machine Learning Predictions of Diels–Alder Reaction Outcomes. Journal of the American Chemical Society (JACS). Vol 146/Issue 23.
- Xiaobao Huang, Mihir Surve, Yuhan Liu, Tengfei Luo, Olaf Wiest, Xiangliang Zhang and Nitesh Chawla. Application of Large Language Models in Chemistry Reaction Data Extraction and Cleaning. Accepted at the 33rd ACM International Conference on Information and Knowledge Management (CIKM 2024) for the Short Research Papers Track.
- Jiayuan Chen, Kehan Guo, Zhen Liu, Olexandr Isayev, Xiangliang Zhang. Uncertainty-aware Yield Prediction with Multimodal Molecular Features. The 38th AAAI Conference on Artificial Intelligence (AAAI 2024). (Acceptance rate of 23.75%, 2342/9862(reviewed), out of 12100 submission).
- Yihong Ma, Xiaobao Huang, Bozhao Nan, Nuno Moniz, Xiangliang Zhang, Olaf Wiest, Nitesh V Chawla. Are we making much progress? Revisiting chemical reaction yield prediction from an imbalanced regression perspective. Accepted by TheWebConf2024 (short paper).
- Changsheng Ma, Taicheng Guo, Qiang Yang, Xiuying Chen, Xin Gao, Shangsong Liang, Nitesh Chawla, Xiangliang Zhang. A Property-Guided Diffusion Model for Generating Molecular Graphs. IEEE ICASSP 2024.
- Taicheng Guo, Kehan Guo, Bozhao Nan, Zhenwen Liang, Zhichun Guo, Nitesh V. Chawla, Olaf Wiest, Xiangliang Zhang. What can large language models do in chemistry? A comprehensive benchmark on eight tasks. Accepted by NeurIPS 2023 Datasets and Benchmarks track. (Acceptance rate 32.7%, out of 987 submission). arXiv version
- Zhichun Guo, Kehan Guo, Bozhao Nan, Yijun Tian, Roshni Iyer, Yihong Ma, Olaf Wiest, Xiangliang Zhang, Wei Wang, Chuxu Zhang, Nitesh Chawla. Graph-based Molecular Representation Learning. The 32nd International Joint Conference on Artificial Intelligence (IJCAI-23). 19th-25th August 2023. Macao, S.A.R. (Survey) arXiv
- Hongxiao Wang; Gang Huang; Zhuo Zhao; Liang Cheng; Anna Juncker-Jensen; Máté Levente Nagy; Xin Lu; Xiangliang Zhang; Danny Z. Chen. CCF-GNN: A Unified Model Aggregating Appearance, Microenvironment, and Topology for Pathology Image Classification, in IEEE Transactions on Medical Imaging, vol. 42, no. 11, pp. 3179-3193, Nov. 2023, doi: 10.1109/TMI.2023.3249343.
- Waqas W. Ahmed, Mohamed Farhat, Kestutis Staliunas, Xiangliang Zhang, Ying Wu. Machine learning for knowledge acquisition and accelerated inverse-design for non-Hermitian systems. Nature Communications Physics. Volume 6, Article number: 2 (2023).
- Waqas Ahmed, Mohamed Farhat, Pai-Yen Chen, Xiangliang Zhang, and Ying Wu. A Generative Deep Learning Approach for Shape Recognition of Arbitrary Objects from Phaseless Acoustic. To appear in Advanced Intelligent Systems. 20 January 2023.
- A. Kaidarova, N. R. Geraldi, R. P. Wilson, J. Kosel, M. G. Meekan, V. M. Eguíluz, M. M. Hussain, A. Shamim, H. Liao, M. Srivastava, S. S. Saha, M. S. Strano, X. Zhang, B. S. Ooi, M. Holton, L. W. Hopkins, X. Jin, X. Gong, F. Quintana, A. Tovasarov, A. Tasmagambetova, and C. M. Duarte. Wearable sensors for monitoring marine environments and their inhabitants. Nature Biotechnology, 2023 Jun 26. doi: 10.1038/s41587-023-01827-3.
- Rongyu Lin, Zhiyuan Liu, Peng Han, Ronghui Lin, Yi Lu, Haicheng Cao, Xiao Tang, Chuanju Wang, Vishal Khandelwal, Xiangliang Zhang and Xiaohang Li. A machine learning study on superlattice electron blocking layer design for AlGaN deep ultraviolet light-emitting diodes using the stacked XGBoost/LightGBM algorithm. Journal of Materials Chemistry C. 2022,10, 17602-17610
- Changsheng Ma, Qiang Yang, Xin Gao, Xiangliang Zhang. DEMO: Disentangled Molecular Graph Generation via an Invertible Flow Model. A full paper by CIKM 2022.
- Chongsheng Zhang, Bin Wang, Ke Chen, Ruixing Zong, Bofeng Mo, Yi Men, George Almpanidis, Shanxiong Chen, Xiangliang Zhang. Data-Driven Oracle Bone Rejoining: A Dataset and Practical Self-Supervised Learning Scheme. KDD 2022 Applied Data Science Track. August 14-18, 2022, in Washington, DC. (Acceptance Rate: 195/753 = 26%)
- Peng Han, Peilin Zhao, Chan Lu, Junzhou Huang, Jiaxiang Wu, Shuo Shang, Bin Yao, Xiangliang Zhang. GNN-Retro: Retrosynthetic planning with Graph Neural Networks. The Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI 2022) (acceptance rate = 1349/9020 = 15%)
- Adnan Qamar, Sarah Kerdi, Najat Amin, Xiangliang Zhang, Johannes Vrouwenvelder, Noreddine Ghaffour. A deep neural networks framework for in-situ biofilm thickness detection and hydrodynamics tracing for filtration systems. Separation and Purification Technology. Volume 301, 15 November 2022.
- Changsheng Ma, Xiangliang Zhang. GF-VAE: A Flow-based Variational Autoencoder for Molecule Generation. A full paper at the CIKM 2021 conference (Acceptance rate = 271/1251 = 21.7%).
- Rongyu Lin, Peng Han, Yue Wang, Ronghui Lin, Yi Lu, Zhiyuan Liu, Xiangliang Zhang, Xiaohang Li. Low resistance asymmetric III-nitride tunnel junctions designed by machine learning. Nanomaterial. 2021, 11(10), 2466
- Duarte, Carlos M.; Chapuis, Lucille; Collin, Shaun P.; Costa, Daniel P.; Devassy, Reny P.; Eguiluz, Victor M.; Erbe, Christine; Gordon, Timothy A. C.; Halpern, Benjamin S.; Harding, Harry R.; Havlik, Michelle N.; Meekan, Mark; Merchant, Nathan D.; Miksis-Olds, Jennifer L.; Parsons, Miles; Predragovic, Milica; Radford, Andrew N.; Radford, Craig A.; Simpson, Stephen D.; Slabbekoorn, Hans; Staaterman, Erica; Van Opzeeland, Ilse C.; Winderen, Jana; Zhang, Xiangliang; Juanes, Francis. The soundscape of the Anthropocene ocean. Science. Vol 371, Issue 6529. 05 February 2021.
Underwater Noise Pollution is Disrupting Ocean Life – But We Can Fix It.
In the Media [Time Magazine] [Reuters] [Yahoo! News] [Gulf News] [BBC News]
- Waqas W. Ahmed, Mohamed Farhat, Xiangliang Zhang, Ying Wu. Deterministic and probabilistic deep learning models for inverse design of broadband acoustic cloak. Regular Article in Physical Review Research. 3 (1), 013142. 2021
- Guoxian Yu, Yeqian Yang, Yangyang Yan, Maozu Guo, Xiangliang Zhang, Jun Wang, “DeepIDA: predicting isoform-disease associations by data fusion and deep neural networks”. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2021.
- Ziying Yang, Guoxian Yu, Maozu Guo, Jiantao Yu, Xiangliang Zhang and Jun Wang. CDPath: Cooperative driver pathways discovery using integer linear programming and Markov clustering. IEEE/ACM Transactions on Computational Biology and Bioinformatics, . Jul-Aug 2021;18(4):1384-1395.
- Yingwen Zhao, Jun Wang, Maozu Guo, Xiangliang Zhang and Guoxian Yu. Cross-Species Protein Function Prediction with Asynchronous-Random Walk. IEEE/ACM Transactions on Computational Biology and Bioinformatics. Jul-Aug 2021;18(4):1439-1450.
- Uchenna Akujuobi, Jun Chen, Mohamed Elhoseiny, Michael Spranger, and Xiangliang Zhang. Temporal Positive-unlabeled Learning for Biomedical Hypothesis Generation via Risk Estimation. Neural Information Processing Systems (NeurIPS 2020). Virtual-only Conference. (Acceptance rate = 1900 /9454 = 20%).
- Uchenna Akujuobi, Michael Spranger, Sucheendra K Palaniappan, Xiangliang Zhang, “T-PAIR: Temporal Node-pair Embedding for Automatic Biomedical Hypothesis Generation”. IEEE Transactions on Knowledge and Data Engineering (TKDE), 2020 [DOI].
- Cecilia Martin, Stephen Parkes, Qiannan Zhang, Xiangliang Zhang, Matthew McCabe, Carlos M. Duarte: Use of Unmanned Aerial Vehicles for Efficient Beach Litter Monitoring. Marine Pollution Bulletin. Jun;131(Pt A):662-673, 2018