User:Nelis Jecan1912/sandbox

Mi Zhang is an Associate Professor in the Departments of Electrical and Computer Engineering, Computer Science and Engineering, and Biomedical Engineering at Michigan State University, where he is the director of the Machine Learning Systems (MLSys) Lab. He works at the intersection of systems and AI with a particular focus on Edge AI.

Early life and education
Born in Beijing, Mi Zhang received his B.S. in Electrical Engineering from Peking University in China. He received his Ph.D. in Computer Engineering and M.S. in both Electrical Engineering and Computer Science from University of Southern California. Before joining Michigan State University, he was a Postdoctoral Associate at Cornell University.

Honors and awards

 * 2021 - ACM SenSys Best Paper Award
 * 2020 - Facebook Faculty Research Award
 * 2020 - NeurIPS Workshop on Scalability, Privacy, and Security in Federated Learning Best Paper Award.
 * 2020 - MSU Innovation of the Year Award
 * 2019 - Amazon AWS Machine Learning Research Award
 * 2019 - NeurIPS Google MicroNet Challenge (CIFAR-100 Track) 4th Place Winner
 * 2018 - IEEE CNS Best Paper Award
 * 2017 - NSF Hearables Challenge Third Place Winner
 * 2016 - NSF CRII Award
 * 2016 - NIH Pill Image Recognition Challenge First Place Winner
 * 2015 - ACM UbiComp Best Paper Award Honorable Mention

Selected publications

 * Yu Zheng, Zhi Zhang, Shen Yan, and Mi Zhang. "Deep AutoAugment". International Conference on Learning Representations (ICLR), (2022).
 * Xiao Zeng, Ming Yan, and Mi Zhang. "Mercury: Efficient On-Device Distributed DNN Training via Stochastic Importance Sampling". ACM Conference on Embedded Networked Sensor Systems (SenSys), (2021).
 * Chenning Li, Hanqing Guo, Shuai Tong, Xiao Zeng, Zhichao Cao, Mi Zhang, Qiben Yan, Li Xiao, Jiliang Wang, and Yunhao Liu. "NELoRa: Towards Ultra-low SNR LoRa Communication with Neural-enhanced Demodulation". ACM Conference on Embedded Networked Sensor Systems (SenSys), (2021).
 * Shen Yan, Kaiqiang Song, Fei Liu, and Mi Zhang. "CATE: Computation-aware Neural Architecture Encoding with Transformers". International Conference on Machine Learning (ICML), (2021).
 * Shen Yan, Yu Zheng, Wei Ao, Xiao Zeng, and Mi Zhang. "Does Unsupervised Architecture Representation Learning Help Neural Architecture Search?". Conference on Neural Information Processing Systems (NeurIPS), (2020).
 * Chaoyang He, Songze Li, Jinhyun So, Xiao Zeng, Mi Zhang, Hongyi Wang, Xiaoyang Wang, Praneeth Vepakomma, Abhishek Singh, Hang Qiu, Li Shen, Peilin Zhao, Yan Kang, Yang Liu, Ramesh Raskar, Qiang Yang, Murali Annavaram, Salman Avestimehr. "FedML: A Research Library and Benchmark for Federated Machine Learning". Conference on Neural Information Processing Systems (NeurIPS) Federated Learning Workshop, (2020).
 * Xiao Zeng, Biyi Fang, Haichen Shen, and Mi Zhang. "Distream: Scaling Live Video Analytics with Workload-Adaptive Distributed Edge Intelligence". ACM Conference on Embedded Networked Sensor Systems (SenSys), (2020).
 * Biyi Fang, Xiao Zeng, and Mi Zhang. "NestDNN: Resource-Aware Multi-Tenant On-Device Deep Learning for Continuous Mobile Vision". ACM International Conference on Mobile Computing and Networking (MobiCom), (2018).
 * Xiao Zeng, Kai Cao, and Mi Zhang. "MobileDeepPill: A Small-Footprint Mobile Deep Learning System for Recognizing Unconstrained Pill Images". ACM International Conference on Mobile Systems, Applications, and Services (MobiSys), (2017).
 * Biyi Fang, Jillian Co, and Mi Zhang. "DeepASL: Enabling Ubiquitous and Non-Intrusive Word and Sentence-Level Sign Language Translation". ACM Conference on Embedded Networked Sensor Systems (SenSys), (2017).
 * David C. Mohr, Mi Zhang, and Stephen M. Schueller. "Personal Sensing: Understanding Mental Health Using Ubiquitous Sensors and Machine Learning". Annual Review of Clinical Psychology (ARCP), Volume 13, Pages 23-47, (2017).
 * Biyi Fang, Nicholas D. Lane, Mi Zhang, Aidan Boran, and Fahim Kawsar.. "BodyScan: Enabling Radio-based Sensing on Wearable Devices for Contactless Activity and Vital Sign Monitoring". ACM International Conference on Mobile Systems, Applications, and Services (MobiSys), (2016).
 * Sohrob Saeb, Mi Zhang, Christopher J. Karr, Stephen M. Schueller, Marya E. Corden, Konrad P. Kording, and David C. Mohr. "Mobile Phone Sensor Correlates of Depressive Symptom Severity in Daily-Life Behavior: An Exploratory Study". Journal of Medical Internet Research (JMIR), Volume 17, Issue 7, Pages e175, (2022).