Dongfang Liu Headshot

Dongfang Liu

Assistant Professor

Department of Computer Engineering
Kate Gleason College of Engineering

Dongfang Liu

Assistant Professor

Department of Computer Engineering
Kate Gleason College of Engineering

Bio

Dr. Dongfang Liu presently holds the Assistant Professor within the Department of Computer Engineering at Rochester Institute of Technology (RIT). His academic journey boasts the attainment of a Ph.D. degree from Purdue University, which has laid a robust foundation for his subsequent research ventures.

Dr. Liu's scholarly pursuits revolve around the convergence of vision-language intelligence and embodied AI, guided by a broader mission to cultivate versatile AI systems that efficaciously tackle pressing societal dilemmas. His scholarly endeavors have garnered substantial acclaim, evident in the endorsement of his research endeavors by the prestigious National Science Foundation (NSF), particularly within the realm of robot intelligence.

Throughout his academic trajectory, Dr. Liu has made significant contributions to his field, a fact underscored by his prolific publication portfolio. His research discoveries have graced prominent conferences that serve as vital platforms for disseminating advancements in artificial intelligence and robotics. Distinguished conferences encompassing his publication record encompass CVPR, ECCV, ICCV, ICLR, NIPS, 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. Commencing in 2023, he has taken on the role of an Area Chair at CVPR, and he also serves as a senior program committee for AAAI and IJCAI, thereby assuming a central role in shaping the 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), Multimedia Tools and Applications (MTAP), and ACM Journal on Autonomous Transportation (JATS).

Currently Teaching

CMPE-679
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
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
1 - 6 Credits
Doctoral-level research by the candidate on an appropriate topic as arranged between the candidate and the research advisor.
IMGS-891
0 Credits
Continuation of Thesis

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