Dongfang Liu Headshot

Dongfang Liu

Assistant Professor

Department of Computer Engineering
Kate Gleason College of Engineering
dongfang.liu@rit.edu

Dongfang Liu

Assistant Professor

Department of Computer Engineering
Kate Gleason College of Engineering

Bio

Dr. Dongfang Liu is presently an Assistant Professor in the Department of Computer Engineering at the Rochester Institute of Technology (RIT). He obtained his Ph.D. from Purdue University in 2021. Dr. Liu's scholarly pursuits revolve around artificial general intelligence (AGI), guided by a broader mission to cultivate versatile AI systems that effectively tackle pressing societal dilemmas. His scholarly endeavors have garnered substantial acclaim, as evidenced by the endorsement of his research by the National Science Foundation (NSF). Throughout his academic trajectory, Dr. Liu has made significant contributions to the AI field, a fact underscored by his prolific publication record. His work has been published in distinguished conferences, including CVPR, ECCV, ICCV, ICLR, NeurIPS, ICML, AAAI, IJCAI, ACL, EMNLP, WWW, IROS, among several others.

Beyond his influential research, Dr. Liu actively fosters engagement within the academic community, assuming pivotal roles in prominent organizations. Since 2023, he has served as an Area Chair at CVPR and as a senior program committee member for AAAI and IJCAI, thereby playing a central role in shaping scholarly discourse within these domains. Furthermore, his expertise is sought after as an associate editor for esteemed journals, including IEEE Transactions on Circuits and Systems for Video Technology (TCSVT), Neurocomputing, Multimedia Tools and Applications (MTAP), and ACM Journal on Autonomous Transportation (JATS).

dongfang.liu@rit.edu
Personal Links
Personal Website
Areas of Expertise
Artificial Intelligence
Vision-language intelligence

Currently Teaching

CMPE-679
Deep Learning
3 Credits
Deep learning has been revolutionizing the fields of object detection, classification, speech recognition, natural language processing, action recognition, scene understanding, and general pattern recognition. In some cases, results are on par with and even surpass the abilities of humans. Activity in this space is pervasive, ranging from academic institutions to small startups to large corporations. This course emphasizes convolutional neural networks (CNNs) and recurrent neural networks (RNNs), but additionally covers reinforcement learning and generative adversarial networks. In addition to achieving a comprehensive theoretical understanding, students will understand current state-of-the-art methods, and get hands-on experience at training custom models using popular deep learning frameworks.
CMPE-789
Special Topics
3 Credits
Graduate level topics and subject areas that are not among the courses typically offered are provided under the title of Special Topics. Such courses are offered in a normal format; that is, regularly scheduled class sessions with an instructor.
IMGS-890
Research & Thesis
1 - 6 Credits
Doctoral-level research by the candidate on an appropriate topic as arranged between the candidate and the research advisor.
IMGS-891
Continuation of Thesis
0 Credits
Continuation of Thesis

In the News

More News