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