View source for Liang Zhao

{{Infobox academic
| name               = Liang Zhao
| image              =
| birth_date         =
| birth_place        =
| nationality        =
| occupation         = [[Computer scientist]] and academic
| title              =
| awards             = [[National Science Foundation CAREER Award|NSF Career Award]]
| website            =
| education          = Ph.D. [[Computer Science]]<br>M.S. [[Control Theory]] and [[Control Engineering]]<br>B.S. [[Automation]]
| alma_mater         = [[Virginia Polytechnic Institute and State University]]<br>[[Northeastern University]]
| thesis_title       = Spatio-temporal Event Detection and Forecasting in Social Media
| thesis_url         = https://vtechworks.lib.vt.edu/server/api/core/bitstreams/a6d646f3-a6df-4951-8b2d-603c8d490b07/content
| thesis_year        = 2016
| workplaces         = [[Emory University]]
}}
'''Liang Zhao''' is a [[computer scientist]] and academic. He is an associate professor in the Department of Computer Science at [[Emory University]].<ref>{{cite web|url=https://computerscience.emory.edu/people/index.html|title=FACULTY}}</ref>
Zhao's research focuses on [[data mining]], [[machine learning]], and [[artificial intelligence]], with particular interests in [[deep learning]] on graphs, societal event prediction, interpretable machine learning, [[Multimodal learning|multi-modal machine learning]], [[Generative artificial intelligence|generative AI]], and distributed deep learning.<ref>{{cite web|url=https://cina.gmu.edu/people/liang-zhao-2/|title=Liang Zhao EMORY UNIVERSITY Assistant Professor}}</ref> His book titled ''Graph Neural Networks: Foundations, Frontiers, and Applications'' has been published by [[Springer Publishing|Springer]]. He published articles in journals and conferences, some of which have won Best Paper Awards.<ref>{{cite web|url=https://icdm.zhonghuapu.com/Awards/BestPapers.shtml|title=IEEE ICDM Best Paper Awards}}</ref>
Zhao received the Oracle for Research Grant Award, Cisco Faculty Research Award, Amazon Research Award<ref name=aaa>{{cite web|url=https://www.amazon.science/research-awards/program-updates/aws-machine-learning-research-awards-recipients-announced|title=2020 Q1/Q2 AWS Machine Learning Research Awards recipients announced}}</ref> and Meta Research Award.<ref name=ppp>{{cite web|url=https://research.facebook.com/blog/2022/9/announcing-the-winners-of-the-2022-ai-system-hardwaresoftware-codesign-request-for-proposals/|title=Announcing the winners of the 2022 AI System Hardware/Software Codesign request for proposals}}</ref> He also won the Jeffress Trust Award for deep [[generative model]]s for biomedical research, and the [[National Science Foundation CAREER Award|NSF Career Award]] for his research on explainable and interactive AI for spatial and [[Graph database|graph data]].<ref name=lll>{{cite web|url=https://www.nsf.gov/awardsearch/showAward?AWD_ID=1942594&HistoricalAwards=false|title=CAREER: Spatial Network Deep Generative Modeling, Transformation, and Interpretation}}</ref>
Zhao was a Computing Innovation Fellow Mentor for the [[Computing Community Consortium]]<ref>{{cite web|url=https://cifellows2021.org/2021-class/|title=2021 Class of CIFellows}}</ref> and is an [[Institute of Electrical and Electronics Engineers|IEEE]] Senior Member.<ref name=laqq>{{cite web|url=https://ieeexplore.ieee.org/author/37085520757|title=Liang Zhao - IEEE}}</ref>
==Education==
Zhao earned his Bachelor of Science in [[Automation]] and then obtained a Master of Science in [[Control Theory]] and [[Control Engineering]] from [[Northeastern University]] graduating from there in 2012. He completed his Ph.D. in [[Computer Science]] at [[Virginia Polytechnic Institute and State University]] in 2016.<ref>{{cite web|url=https://cina.gmu.edu/people/liang-zhao/|title=Liang Zhao GEORGE MASON UNIVERSITY}}</ref>
==Career==
Zhao began his career as an assistant professor in the Departments of Information Science and Technology<ref>{{cite web|url=https://volgenau.gmu.edu/news/2019-11/phd-student-wins-award-international-conference-data-mining|title=PhD student wins award at international conference on data mining}}</ref> and Computer Science at [[George Mason University]] in 2016, the same year he was named as one of the Top 20 Rising Stars in Data Mining by [[Microsoft Academic Search]].<ref>{{cite web|url=https://dac.cs.vt.edu/2016/11/09/liang-zhao-named-one-top-20-new-stars-data-mining/|title=Liang Zhao named one of Top 20 New Stars in Data Mining}}</ref> He served as an assistant professor in the Department of Computer Science at Emory University from 2020 to 2023 and has been serving as an associate professor since then.<ref>{{cite web|url=https://cs.emory.edu/~lzhao41/|title=Liang Zhao}}</ref>
==Research==
Zhao has focused his research on advancing data mining, machine learning and AI techniques, particularly their applications to critical real-world problems.<ref>{{cite web|url=https://sanghani.cs.vt.edu/person/liang-zhao/|title=Liang Zhao - Sanghani Center for Artificial Intelligence and Data Analytics}}</ref> His interests encompass intelligent learning strategies, scalable optimization methods, and modeling text data to develop solutions for open and critical real-world issues through research.<ref name=laqq/>
===Generative AI on complex data===
Zhao has conducted research on complex data deep modeling, focusing on spatial, temporal, networked, textual, and heterogeneous types. He advanced new graph neural networks and inference strategies to encode complex data into their components, enabling independent and combined learning of their embeddings.<ref>{{cite web|url=https://proceedings.neurips.cc/paper/2021/hash/12e35d9186dd72fe62fd039385890b9c-Abstract.html|title=Representation Learning on Spatial Networks}}</ref> To contribute to the deep graph learning research domain, he published ''Graph Neural Networks: Foundations, Frontiers, and Applications'' with Jian Pei, Peng Cui, and Lingfei Wu which covered a range of topics in deep learning on graphs.<ref>{{cite web|url=https://rmit.alma.exlibrisgroup.com/discovery/fulldisplay?docid=alma9922067437201341&context=L&vid=61RMIT_INST:RMITU&lang=en&search_scope=EverythingNOTresearch&adaptor=Local%20Search%20Engine&tab=AllNOTresearch&query=sub,exact,%20Domains%20,AND&mode=advanced&offset=0|title=Graph Neural Networks: Foundations, Frontiers, and Applications}}</ref>
Zhao jointly characterized the distributions of temporal and network aspects using new techniques in temporal random walk generation and end-to-end walk assembly.<ref>{{cite web|url=https://dl.acm.org/doi/abs/10.1145/3442381.3449818|title=TG-GAN: Continuous-time Temporal Graph Deep Generative Models with Time-Validity Constraints}}</ref> His contributions include creating deep generative models for spatial networks that identify the interplay between spatial and network factors, as well as factors related solely to spatial or network information.<ref>{{cite web|url=https://dl.acm.org/doi/abs/10.1145/3447548.3467394|title=Deep Generative Models for Spatial Networks}}</ref> Further enhancing deep graph transformation, which generated target graphs conditioned on source graphs, he used applications such as molecule structure optimization and circuit obfuscation with his work on deep generative models for graphs extending into several directions, including property-controllable complex data generation and design.<ref>{{cite web|url=https://arxiv.org/abs/2310.07683|title=Controllable Data Generation Via Iterative Data-Property Mutual Mappings}}</ref> While addressing the critical need for unique datasets and model evaluation strategies in deep generative models, he released benchmark dataset repositories such as GraphGT at NeurIPS 2021,<ref>{{cite web|url=https://openreview.net/forum?id=NYgt9vcdyjm|title=GraphGT: Machine Learning Datasets for Graph Generation and Transformation}}</ref> along with review papers on the method categorization and standardization.<ref>{{cite web|url=https://graph-neural-networks.github.io/static/file/chapter12.pdf|title=Graph Neural Networks: Graph Transformation}}</ref>
===Collaborative machine learning strategies===
Zhao focused on developing models for learning and predicting across both known and unknown tasks. His research introduced directions in spatial multi-task learning, balancing the trade-offs between task relations and differences, as well as spatial correlation and heterogeneity.<ref>{{cite web|url=https://dl.acm.org/doi/abs/10.1145/2783258.2783377|title=Multi-Task Learning for Spatio-Temporal Event Forecasting}}</ref> Beyond traditional approaches, he extended multi-task learning frameworks into domain generalization scenarios, allowing models trained on known tasks to generalize to unseen tasks across different locations and times.<ref>{{cite web|url=https://dl.acm.org/doi/abs/10.1145/3534678.3539442|title=Saliency-Regularized Deep Multi-Task Learning}}</ref><ref>{{cite web|url=https://arxiv.org/abs/2205.10664|title=Temporal Domain Generalization with Drift-Aware Dynamic Neural Networks}}</ref> He then conducted research on continual learning integrated temporal considerations to manage the persistence and adaptability of a model's memory across old and new tasks, drawing insights from neuroscience principles of memory retention to achieve high accuracy with low computational costs.<ref>{{cite web|url=https://www.sciencedirect.com/science/article/abs/pii/S0893608021003166|title=Schematic memory persistence and transience for efficient and robust continual learning}}</ref>
===Human-AI interaction and alignment===
Zhao investigated explainable AI, particularly AI reasoning and its correction through human guidance, to improve medical imaging for disease diagnosis by developing techniques, benchmarks, and evaluation scenarios in collaboration with radiologists and clinicians. He focused on proposing methods that narrow the distributional gaps between humans and AI's explanations in complex data such as 2D/3D images, graphs, and spatiotemporal data<ref>{{cite web|url=https://dl.acm.org/doi/abs/10.1145/3534678.3539419|title=RES: A Robust Framework for Guiding Visual Explanation}}</ref><ref>{{cite web|url=https://arxiv.org/abs/2403.10831|title=DUE: Dynamic Uncertainty-Aware Explanation Supervision via 3D Imputation}}</ref><ref>{{cite web|url=https://ieeexplore.ieee.org/abstract/document/9679041/|title=GNES: Learning to Explain Graph Neural Networks}}</ref> which can benefit both training and promotion of AI models.<ref>{{cite web|url=https://arxiv.org/abs/2310.08420|title=Visual Attention Prompted Prediction and Learning}}</ref> Additionally, his team developed and deployed user interface systems that enables the online interaction between human and AI.<ref>{{cite web|url=https://dl.acm.org/doi/abs/10.1145/3555590|title=Aligning Eyes between Humans and Deep Neural Network through Interactive Attention Alignment}}</ref>
==Awards and honors==
*2020 – NSF CAREER Award, [[National Science Foundation]]<ref name=lll/>
*2020 – Amazon Research Award, Amazon Science<ref name=aaa/>
*2022 – Meta Research Award, [[Meta Platforms|Meta Platform]]<ref name=ppp/>
==Bibliography==
===Selected books===
*''Graph Neural Networks: Foundations, Frontiers, and Applications'' (2022) ISBN 978–9811660566
===Selected articles===
*Zhao, L. (2021). Event prediction in the big data era: A systematic survey. ACM Computing Surveys (CSUR), 54(5), 1-37.
*Ling, C., Zhao, X., Lu, J., Deng, C., Zheng, C., Wang, J., ... & Zhao, L. (2023). Domain specialization as the key to make large language models disruptive: A comprehensive survey. arXiv preprint arXiv:2305.18703.
*Zhao, L., Sun, Q., Ye, J., Chen, F., Lu, C. T., & Ramakrishnan, N. (2015). Multi-task learning for spatio-temporal event forecasting. In Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining (pp.&nbsp;1503–1512).
*Chai, Z., Chen, Y., Anwar, A., Zhao, L., Cheng, Y., & Rangwala, H. (2021). FedAT: A high-performance and communication-efficient federated learning system with asynchronous tiers. In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (pp.&nbsp;1–16).
*Ling, C., Jiang, J., Wang, J., Thai, M. T., Xue, R., Song, J., ... & Zhao, L. (2023). Deep graph representation learning and optimization for influence maximization. In International Conference on Machine Learning (pp.&nbsp;21350–21361). PMLR.
==References==
{{reflist}}
{{authority control}}
[[Category:American computer scientists]]
[[Category:American academics]]
[[Category:Emory University faculty]]
[[Category:Virginia Tech alumni]]
[[Category:Northeastern University alumni]]
[[Category:Year of birth missing (living people)]]
[[Category:Living people]]