Partial Monotonicity for Submodular Maximization with a Knapsack Constraint
None
CV
Education
- Ph.D in Computer Science and Engineering, Nanyang Technological University, Aug. 2023 - Present
- M.S. in Computational Mathematics, Beijing Normal University, Sep. 2021 - Jun. 2023 (Early Graduation with Honor)
- B.S. in Mathematics and Applied Mathematics, Beijing Normal University, Sep. 2016 - Jul.2020
- Summer Scholar in Math and Economics, Duke University, Jul. 2018 – Aug. 2018
- Exchange Student in Mathematics, Rutgers University, Jan. 2019 – May. 2019
Work experience
- Jun. 2019 – Sep. 2019: Data Analyst Intern
- LiCaiMoFang, Beijing KouDaiCaiFu In-Tech. Co. Ltd.
- Duties includes:
- Analyzed time series of user purchase behavior and visualization;
- Predicted price index trend in stock market using multi-dimensional macro-economic data under the framework of ‘xgboost’ (a supervised learning algorithm);
- Implemented macro-economic index model to compute risk-premium return;
- Implemented multi-factor model using ‘Fama-Macbeth Regression’ to compute risk-premium return;
- Supervisor: Yongan Ma
- Aug. 2020 – Jan. 2021: Research Assistant
- ISCAS – Institute of Software Chinese Academy of Sciences
- Duties included:
- Deployed state-of-art fuzzing tools like Vuzzer, Memlock, AFL and analyzed their performance;
- Run large-scale fuzzing jobs on some open-source softwares like base64, uniq, asn1c, etc and submit possible vulnerability report to NVD;
- Designed and implemented large-scale knowledge graph model Vulgraph, using state-of-art graph database Neo4j and logistic regression model to make prediction;
- Designed and implemented an automated fuzzing platform Autofuzz, including front-end page design, data-flow model and Restful API design;
- Acquired Two Software Patents for Autofuzz project;
- Supervisor: Jingzheng Wu
- Feb. 2021 – Sep. 2021: Software Development Engineer
- Baidu Inc.
- Duties included:
- Large-scale (a few Terabytes) log pipeline modeling using Hadoop;
- Backend development support for vertical search project Aladdin;
- Building coupon recommendation system for impending service center in Baidu App;
- Supervisor: Aowei Li
Skills
- Environments/Tools: Linux, Git, Docker, Vim, SSH
- Programming languages: C/C++, Python, SQL, bash, Java, tcl, PHP
- Databases: MySQL, PostgreSQL, sqlite3, Neo4j, MongoDB, Redis
- Frontend: HTML/CSS/Javascript, Node.js, React
- Languages: Mandarin (native), English (TOFEL 108, GRE 325 + 4.0), Spanish
- Hobbies: badminton, basketball, swimming
Publications
SMuCo: Reinforcement Learning for Visual Control via Sequential Multi-view Total Correlation
Cheng, T., Dong, H., Wang, L., Qiao, B., Lin, Q., Rajmohan, S., & Moscibroda, T. (15--19 Jul 2024). SMuCo: Reinforcement Learning for Visual Control via Sequential Multi-view Total Correlation. In N. Kiyavash & J. M. Mooij (Eds), Proceedings of the Fortieth Conference on Uncertainty in Artificial Intelligence (pp. 698–717). PMLR.
Multi-Agent Reinforcement Learning with Shared Policy for Cloud Quota Management Problem
Tong Cheng, Hang Dong, Lu Wang, Bo Qiao, Si Qin, Qingwei Lin, Dongmei Zhang, Saravan Rajmohan, and Thomas Moscibroda. 2023. Multi-Agent Reinforcement Learning with Shared Policy for Cloud Quota Management Problem. In Companion Proceedings of the ACM Web Conference 2023 (WWW '23 Companion). Association for Computing Machinery, New York, NY, USA, 391–395. https://doi.org/10.1145/3543873.3584634
Talks
SMuCo: Reinforcement Learning for Visual Control via Sequential Multi-view Total Correlation
Talk at Universitat Pompeu Fabra, Barcelona, Spain, Universitat Pompeu Fabra, Barcelona, Spain
Multi-Agent Reinforcement Learning with Shared Policy for Cloud Quota Management Problem
Talk at New York, NY, USA, New York, NY, USA
Teaching
Service and leadership
- Reviewers for UAI 2024, UAI 2025, UAI 2026, AISTATS 2026