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Princut-Auto
Princut-Auto
In this project, we built an annotation software for 3D nuclei image segmentation.
In this project, we built an annotation software for 3D nuclei image segmentation.
Zheng, Wei, Boyu Lyu, Xuelong Mi, James Cheng Peng, Yizhi Wang, Mengfan Wang, Zuolin Cheng, Shuoxuan Qiao, Zeyuan Hou, and Guoqiang Yu. "Princut-Auto: An Unsupervised 3D Cell Detection Tool for Embryonic Data." In 2024 IEEE International Symposium on Biomedical Imaging (ISBI), pp. 1-5. IEEE, 2024.
NIS3D
NIS3D
In this project, we built an annotated benchmark for dense 3D nuclei image segmentation. The data is from zebrafish embryo.
In this project, we built an annotated benchmark for dense 3D nuclei image segmentation. The data is from zebrafish embryo.
Zheng, Wei, Cheng Peng, Zeyuan Hou, Boyu Lyu, Mengfan Wang, Xuelong Mi, Shuoxuan Qiao, Yinan Wan, and Guoqiang Yu. "NIS3D: a completely annotated benchmark for dense 3D nuclei image segmentation." Advances in neural information processing systems 36 (2023): 4741-4752.
Spatial-temporal analysis on COVID-19 vaccination campaign
Spatial-temporal analysis on COVID-19 vaccination campaign
In this project, we used Presto SQL to query terabytes of mobility data during the COVID-19 pandemic from the locational database of Cuebiq. We analyzed more than 15M users' exposure to ads vs. vaccination rates, and then leveraged Bayesian spatial-temporal modeling and concluded the campaign boosted weekly vaccination rates by 2.2%.
In this project, we used Presto SQL to query terabytes of mobility data during the COVID-19 pandemic from the locational database of Cuebiq. We analyzed more than 15M users' exposure to ads vs. vaccination rates, and then leveraged Bayesian spatial-temporal modeling and concluded the campaign boosted weekly vaccination rates by 2.2%.
Li, Qingfeng, James Cheng Peng, Diwakar Mohan, Brennan Lake, Alex Ruiz Euler, Brian Weir, Lena Kan, Cui Yang, and Alain Labrique. "Using location intelligence to evaluate the COVID-19 vaccination campaign in the United States: spatiotemporal big data analysis." JMIR Public Health and Surveillance 9 (2023): e39166.
Reinforcement learning in sports scheduling
Reinforcement learning in sports scheduling
In this project, we designed a reinforcement learning environment to solve the single round-robin tournament scheduling problem. The article can be found at
In this project, we designed a reinforcement learning environment to solve the single round-robin tournament scheduling problem. The article can be found at
J. C. Peng, A. D. Clark and A. Dahbura, "Introducing Human Corrective Multi-Team SRR Sports Scheduling via Reinforcement Learning," 2021 IEEE Symposium Series on Computational Intelligence (SSCI), 2021, pp. 1-7, doi: 10.1109/SSCI50451.2021.9660171.
Approximation to the optimal strategy in the Mozart Cafe problem by simultaneous perturbation stochastic approximation
Approximation to the optimal strategy in the Mozart Cafe problem by simultaneous perturbation stochastic approximation
This is the my master thesis project in Johns Hopkins University. In this project, I designed an approximation algorithm to search for the optimal strategy in the general Mozart Cafe problem. The manuscript is attached.
This is the my master thesis project in Johns Hopkins University. In this project, I designed an approximation algorithm to search for the optimal strategy in the general Mozart Cafe problem. The manuscript is attached.
Thesis.pdfMy duties in this project include:
My duties in this project include:
Designed novel k-Markovian parameterization technique as generic representation for any symmetric rendezvous search strategy in Mozart-Café problem
Developed Monte Carlo simulation programs by Python to estimate expected rendezvous time for any symmetric rendezvous search strategy parameterized by our k-Markovian technique
Utilized Simultaneous Perturbation Stochastic Approximation (SPSA) algorithm on parameter optimization in k-Markovian modeling and reproduced optimal strategies in small size cases
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