knowledge and learning
KLLAB News
- Paper accepted in IJF: Exploring the social influence of Kaggle virtual community on the M5 competition
- The dqr paper is published in the Journal of Business & Economic Statistics
- New Paper: Bayesian forecast combination using time-varying features
- Paper accepted in IJF: Exploring the representativeness of the M5 competition data
- The FFORMPP paper is accepted in the International Journal of Forecasting
- Meet our KLLAB members on ISF 2021
- Feng Li will be presenting at the ISBA 2021 meeting
- The dlsa paper is accepted in the Journal of Computational and Graphical Statistics
- New Paper: Forecasting reconciliation with a top-down alignment of independent level forecasts
- New Paper: Exploring the social influence of Kaggle virtual community on the M5 competition
Welcome to Dr. Yanfei Kang and Feng Li’s Lab — KLLAB (pronounced as [col·lab], meaning collaborating). A lab for knowledge and learning.
The initiative of KLLAB is to bring collaborations between Dr. Yanfei Kang‘s institution Beihang University and Dr. Feng Li‘s institution Central University of Finance and Economics. KLLAB is not only a lab named after Dr. Kang and Li’s initials but also stands for knowledge and learning for the people in our lab.
Our KLLAB started as a joint meetup between Beihang University and Central University of Finance and Economics in earlier 2016. In 2020, the KLLAB has reached 20 members.
We focus on finding and solving interesting problems in forecasting, statistical computing, and distributed learning.
Our KLLAB has invited collaborators to visit the lab every year, and we also organize focused workshops with a specific theme. Our research network reaches Australia, UK, Sweden, the US, and other countries. Please checkout our collaboration network on this page.
We welcome university students to join us from all levels, from undergraduates to Ph.D. The KLLAB also sends the best students to the world’s leading universities.
Dr. Yanfei Kang and Dr. Feng Li are also offering undergraduate and graduate-level courses in statistical computing [K][L], Bayesian analysis[K][L], distributed statistical computing [L], and data science [L] every year.