Conference

• INFORMS Annual Meeting 2021 Virtual. A Stochastic Alternating Balance k-Means Algorithm for Fair Clustering. (presentation)
• MOPTA 2021: Lehigh University, Bethlehem, PA. A Stochastic Alternating Balance k-Means Algorithm for Fair Clustering. (presentation and session chair of “Fairness in ML and optimization”)
• INFORMS Annual Meeting 2020 Virtual. Accuracy and Fairness Trade-offs in Machine Learning: A Stochastic Multi-Objective Approach. (presentation)
• INFORMS Annual Meeting 2019: Seattle, WA. The Stochastic Multi-Gradient Algorithm for Multi-Objective Optimization and its Application to Supervised Machine Learning. (presentation)
• MOPTA 2019: Lehigh University, Bethlehem, PA. The Stochastic Multi-Gradient Algorithm for Multi-Objective Optimization and its Application to Supervised Machine Learning. (presentation)
• 12th Annual Machine Learning Symposium: New York, NY. (attendance)
• DIMACS/MOPTA 2018: Lehigh University, Bethlehem, PA. (summer school and conference attendance)
• MOPTA 2017: Lehigh University, Bethlehem, PA. (attendance)

Paper Referees

• Efficient and Sparse Neural Networks by Pruning Weights in a Multiobjective Learning Approach. arXiv preprint arXiv:2008.13590.
• Inexact Derivative-Free Optimization for Bilevel Learning. arXiv preprint arXiv:2006.12674.
• Cocchi G, Liuzzi G, Lucidi S, Sciandrone M. On the convergence of steepest descent methods for multiobjective optimization. Computational Optimization and Applications. 2020 May 5:1-27.

Internship

Amazon Alexa Local Information Team (Remote, base at San Francisco)

Applied Scientist Intern Sep. 13 – Dec. 31, 2021

• Minimizing perplexity and LM score for the Geo-GDM dynamic language model.

Autodesk AutoCAD Machine Learning Team (Remote, base at San Francisco)

Machine Learning Engineering Intern Jun. 1st – Aug. 20, 2021

• Command sequences pattern analysis: N-gram analysis, word embedding model in NLP.
• Geometry block recommendations: geometry/graph embedding, convolutional neural network.

SAS Institute Analytics R&D / Scientific Computing Group (Virtual)

Graduate Intern May 19 – Aug 7, 2020

• Improve Hidden Markov Models using Black-Box optimization solver instead of the default multi-start mode (SAS CASL)
• Propose three promising tuning configurations and conducted numerical experiments for RS-AR models (discovering hidden stock market states)
• Saved 50-90% computation time in finding the best model and parameters estimation while achieving the same or less AIC errors
• More details about numerical results and analysis were published as a SAS OR Blog. Check out here

Sinotrans Air Transportation Development Co, Ltd., Beijing, China

Data Analyst Jul. 2014 – Sep. 2014

• Optimize the workflow of the Export department at North branch
• Collected and visualized operation data from the CA/BGS cargo consignment centers (MySQL, High charts)
• Conducted data analysis and lean improvement to identify bottlenecks and reduce idle time
  

Online courses taken

I have taken several interesting courses on Coursera, especially in Deep Learning Specialization offered by DeepLearning.AI. Here are the list of courses!

• Sequence Models, Feb 2022.
• Convolutional Neural Networks, July 2021.
• Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization, Mar 2021.
• Neural Network and Deep Learning, Jan 2021.
• Artificial Intelligence Data Fairness and Bias, Nov 2020.