About CI2P Lab

Computational Intelligence Lab at University of Jinan, advancing research in Machine Learning, Pattern Recognition, and Medical Image Analysis

University of Jinan, Shandong Province, China 🇨🇳

Lab Director

Professor Sijie Niu

Prof. Sijie Niu

Professor & Doctoral Supervisor

PhD, Pattern RecognitionACM Jinan Rising StarYouth Innovation Team Leader

Computational Intelligence Lab (CI2P)

School of Information Science and Engineering

No. 336, Nanxinzhuang West Road, Shizhong District
Jinan, Shandong Province, 250022, China

Academic Background

Professor Sijie Niu is a distinguished researcher and educator at the University of Jinan, where he serves as a Professor and Doctoral Supervisor in the School of Information Science and Engineering. He received his PhD in Pattern Recognition and Intelligent Systems from Nanjing University of Science and Technology in 2016.

In October 2014, he was awarded a full scholarship from the China Scholarship Council to conduct research at Stanford University. From December 2019 to January 2021, he completed a postdoctoral fellowship at the University of North Carolina at Chapel Hill under the supervision of Professor Dinggang Shen.

Professor Niu is the leader of the Shandong Province Higher Education Youth Innovation Team and a Haiyou Industry Leading Talent. He has been honored with the ACM Jinan Rising Star Award and has received the Outstanding Doctoral Dissertation Award from both Jinan and Nanjing University of Science and Technology.

Research Interests

Machine Learning

Weakly supervised, semi-supervised, and transfer learning

Pattern Recognition

Visual recognition models with minimal supervision

Medical Image Analysis

Retinal imaging, lesion segmentation, diagnosis

Remote Sensing

Hyperspectral image interpretation and analysis

Notable Achievements

59+

Published Papers

18 as first/corresponding author

Top 1%

ESI Highly Cited Paper

5 consecutive years (2017-2021)

7+

Research Projects

Including NSFC and provincial grants

Join Our Research Team

We welcome motivated students and researchers interested in machine learning and artificial intelligence.