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I am a second-year master student at the Machine Learning Department at Carnegie Mellon University, where I work under the direction of Prof. Eric P. Xing. I also work very closely with Prof. Zhiting Hu. I'm interested in research on Machine Learning and Natural Language Processing. Prior to joining CMU, I worked as a data scientist in the industry after interning at Baidu Research, supervised by Prof. Hui Xiong. I did my undergrad at Columbia University, where I double-majored in Mathematics-Statistics and Computer Science. |
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Mingkai Deng*, Bowen Tan*, Zhengzhong Liu, Eric P. Xing, Zhiting Hu EMNLP 2021 (Oral) Slides / Blog / arXiv / PyPI / GitHub A general framework that helps solve the difficulty of evaluating natural language generation (NLG) with a single unified operation. Inspired evaluation metrics improve over SOTA metrics for diverse NLG tasks. Our metrics are available as library on PyPI and GitHub |
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Mingkai Deng*, Guanglei Du*, Xinjiang Lu, Jingbo Zhou, Jing Sun, Yiming Zhang Submitted to AAAI 2020 during internship at Baidu Combine structured point-of-interest (POI, e.g., parking lots and buildings) data with unstructured satellite image data to automatically identify and draw the boundaries of urban areas-of-interest (e.g., residential areas and schools). |
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Mingkai Deng, Biqing Qiu, Yanda Chen, Iddo Drori Submitted to EMNLP 2019 PDF / GitHub Formulate the Transformer model as an ordinary differential equation (ODE) and perform forward-backward operations using an ODE solver. The resulting continuous-depth model has fewer parameters, converges faster, and shows promising performance in empirical experiments. |
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Mingkai Deng, Jerry Shi, Yvonne Zhou ASA DataFest Fall 2018 (Best Insights Award) Slides / GitHub A data-driven narrative of NYC gentrification patterns over time and their second-order impact on the city's inhabitants. Construct significant leading predictors using resources from NYC OpenData. |
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The source code of this website is adapted from here |