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Mingxi Cheng
A 4th year Ph.D. candidate. (Update: I successfully defended my thesis on May 17th, 2022!)
I received my B.S. degree from Beijing University of Posts and Telecommunications (BUPT), China in 2016 and M.S. degree from Duke University in 2018. I am currently a 4th year Ph.D. candidate at the University of Southern California (USC), under the supervision of Prof. Nazarian and Prof. Bogdan. My research interests include deep learning, trust frameworks, natural language processing (rumor, fake news, misinformation detection), and artificial intelligence.
Recent News
Virus Minority Report: USC Viterbi researchers show how we might one day develop vaccines for viruses that haven’t even evolved yet.
Research & Publications
[Updated on April 2022, more to appear ;) ]
NLP - Rumor/Misinformation/Fake News Analysis/Detection/Prediction - GAN/VAE/Deep Learning/Complex Network
[2021] Cheng, Mingxi, et al. "Deciphering the laws of social network-transcendent COVID-19 misinformation dynamics and implications for combating misinformation phenomena." Scientific Reports 11.1 (2021): 1-14.
[2021] Cheng, Mingxi, et al. "A COVID-19 rumor dataset." Frontiers in Psychology 12 (2021)." [Github link]
[2021] Cheng, Mingxi, Yizhi Li, Shahin Nazarian, and Paul Bogdan. "From rumor to genetic mutation detection with explanations: a GAN approach." Scientific Reports 11, no. 1 (2021): 1-14.
[2020] Cheng, Mingxi, Shahin Nazarian, and Paul Bogdan. "VRoC: Variational Autoencoder-aided Multi-task Rumor Classifier Based on Text." In Proceedings of The Web Conference 2020, pp. 2892-2898. 2020. [Github link]
Trust in AI - Cyber-Physical-Human Systems/MultiAgent Systems/Neural Networks
[2021] Cheng, Mingxi, et al. "Trust-aware Control for Intelligent Transportation Systems." 2021 IEEE Intelligent Vehicles Symposium (IV). IEEE, 2021.
[2021] Cheng, Mingxi, Chenzhong Yin, Junyao Zhang, Shahin Nazarian, Jyotirmoy Deshmukh, and Paul Bogdan. "A General Trust Framework for Multi-Agent Systems." In Proc. of the 20th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2021), Online, May 3–7, 2021, IFAAMAS, 9 pages. [Video available][Github]
[2020] Cheng, Mingxi, Shahin Nazarian, and Paul Bogdan. "There is hope after all: Quantifying opinion and trustworthiness in neural networks." Frontiers in Artificial Intelligence 3 (2020): 54.
DRL - Deep Reinforcement Learning
[2019] Cheng, Mingxi, Ji Li, Paul Bogdan, and Shahin Nazarian. "Resource and Quality of Service-Aware Task Scheduling for Warehouse-Scale Data Centers: A Hierarchical and Hybrid Online Deep Reinforcement Learning-Based Framework." IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2019.
[2018] Cheng, Mingxi, Ji Li, and Shahin Nazarian. "DRL-cloud: deep reinforcement learning-based resource provisioning and task scheduling for cloud service providers." Proceedings of the 23rd Asia and South Pacific Design Automation Conference. IEEE Press, 2018.
Others - Hardware
[2018] Mehta, Raghav, Yuyang Huang, Mingxi Cheng, Shrey Bagga, Nishant Mathur, Ji Li, Jeffrey Draper, and Shahin Nazarian. " High-performance training of deep neural networks using pipelined hardware acceleration and distributed memory." Quality Electronic Design (ISQED), 2018 19th International Symposium on. IEEE, 2018.
More About ME
Github: https://github.com/cmxxx?tab=repositories
LinkedIn: https://www.linkedin.com/in/mingxi-c-06a46a15b/
Google Scholar: https://scholar.google.com/citations?user=-Kp2g58AAAAJ&hl=en&oi=ao