Kang Ann Arbor, MI | [email protected] www-personal.umich.edu/∼kangliu | www.linkedin.com/in/kang-liu

EDUCATION University of Michigan, Ann Arbor Ann Arbor, MI PhD, Control Systems, Electrical Engineering and Computer Science, GPA: 4.0/4.0 Sept. 2016 - Present Professional Development Chair of Tau Beta Pi Honor Society, Activity Chair of ECE Graduate Student Council Harbin Institute of Technology Harbin, CN Bachelor of Science, Mechanical Engineering, GPA: 3.91/4.00 Sept. 2012 - June 2016 Chiang Overseas Fellowship (Hong ), Summer Research Grant by University of California, Los Angeles

WORK EXPERIENCE American Express Company (New York Office) July - Aug. 2020 Manager Intern, Machine Learning Research Group, Credit & Fraud Risk Unit · Used machine learning algorithms to detect the probability of customers being business owners and transactions with business purposes to improve credit line assignment. Model accuracy improved by 15% compared to the existing one. · Integrated the above metrics into existing production models, improved default prediction accuracy by 0.2%. · Researched on the impact of COVID-19 to US and international business owners, set project goals to incorporate effects of market shutdown. Developed strategies to clean-up datasets and recalibrate target variables for training. · Technical knowledge and skills involved included XGBoost, RNN, Python, SQL, cloud computing, Linux and git. PROJECT Thesis: The (α, β) Precision Theory for Production System Monitoring and Improvement Sept. 2017 - Present · Calculated the number of measurements required to obtain estimates of machine parameters, machine effciency and system throughput with a specified precision level, developed upper bound on the data collection time. · First of its kind to address the optimal number of measurements problem in production operation research. · Delivered a presentation at the 2019 International Federation of Automatic Control Conference in Berlin, Germany. · Concluded the results in 1 peer reviewed conference paper and 2 journal papers. Chinese Ancient Painting Coloring Using Generative Neural Networks Jan. - Apr. 2019 · Implemented Deep Convolutional Generative Adversarial Network (DCGAN), and Wasserstein Generative Adversarial Network (WGAN) on MNIST and CIFAR-10 to color ancient Chinese painting sketches. · Compared the two implementations, DCGAN generates colored images with lower identification error rate.

A Vehicle Routing Problem (VRP) Variant Solved by Sequential Optimization Aug. - Sept. 2018 · Solved a VRP, with stochastic customer demand and chance constraints, using stochastic sequential optimization. · Delivered a presentation at the 2019 American Control Conference. Results published on IEEE conference proceedings.

PUBLICATION AS FIRST AUTHOR/LEADING AUTHOR

K. Liu, N. , I. Kolmanvosky, A. Girard, “A Vehicle Routing Problem with Dynamic Demands and Restricted Failures Solved Using Stochastic Predictive Control,” Proceedings of the IEEE 2019 American Control Conference, Philadelphia. [IEEE][ResearchGate] P. Alavian, Y. Eun, K. Liu (Leading), S. M. Meerkov and L. (Alphabetical Order), “The (α, β)-Precise Estimates of MTBF and MTTR: Definitions, Calculations, and Induced Effect on Machine Efficiency Evaluation,” Proceeding of the IFAC Manufacturing Modelling, Management and Control - 9th MIM 2019, Berlin, Germany, IFAC- PapersOnLine 52.13 (2019): 1004-1009. [IFAC][ResearchGate] P. Alavian, Y. Eun, K. Liu (Leading), S. M. Meerkov and L. Zhang (Alphabetical Order), “The (α, β)-Precise Estimates of MTBF and MTTR: Definition, Calculation, and Observation Time,” IEEE Transactions on Automation Science and Engineering. [IEEE][ResearchGate] P. Alavian, Y. Eun, K. Liu (Leading), S. M. Meerkov and L. Zhang (Alphabetical Order), “Precision of Machine Efficiency and System Throughput Estimates Based on (α, β)-Precise Estimates of MTBF and MTTR,” submitted to International Journal of Production Research.