Mohammad Rostami

USC Department of Computer Science

USC Information Sciences Institute

Office: ISI 938

rostamim [AT]


I am a faculty member at the USC Computer Science department and a Research Lead at the USC Information Sciences Institute. I obtained my PhD from the Univeristy of Pennsylvania, where my adviser was Eric Eaton. Before that, I obtained my master's degree from the University of Waterloo, advised by Zhou Wang, and my undergrad degrees from Sharif Univeristy of Technology.


Despite significant progress in AI, as a result of re-emergence of deep learning, natural inteligence can still outperform AI in terms of learning efficiency and robustness, in a wide range of problems. Transfer learning is a primary research focus in the machine learning community to close the gap between natural inteligence and AI. My research focus is transfer learning in general. More specifically, I am interested in learning in data-scarce regimes to improve the speed, efficiency, and robustness of machine learning . My research interest includes:
Lifelong/Continual Learning
Lifelong learning is the ability of learning efficiently from a stream of data through benefiting from past experiences, and without forgetting the past learned knowledge.
Domain Adaptation
Domain adaptation relaxes the need for data annotation in a "target domain" through transferring knowledge from a relevant "source domain", where annotated data is accessible.
Low-Shot Leaning, including Zero-Shot and Few-Shot Learning
The goal in low-shot learning is acquiring the ability to recognize instances from novel classes using a small number of training instances from these classes.
Applications of transfer learning techniques
In application areas such as medicine and remote sensing, using data-driven deep learning is not feasible. Transfer learning can help to broaden the outreach of deep learning.


UPenn Joseph D'16 and Rosaline Wolf Best PhD Dissertation Award
IJCAI Distinguished Student Paper Award
University of Waterloo Outstanding Graduate Achievement Award
Keston Research Award
ISI Exploratory Research Award


Most publications are listed on Google Scholar.

Prospective Students

Please check the following if you are interested in joining my group:

PhD Applicants:
I understand that many applicants would like to know more about the PhD program before the application deadline but I apologize in advance that due to a large number of PhD application inquiries, I cannot respond to all emails. If you are interested in working with me, please mention my name on your PhD application. I will check your profile after our deadline when the application pool is ready. I may respond to a small number of emails if I feel that you have checked my publication well and consciously have narrowed down your research interests. You can also email me after the application deadline to let me know your application is complete.
MSc Students:
If you are already a master's student at USC and are interested in doing your thesis under my supervision, please check my recent papers and contact me for further discussion. Fruitful theses are normally the result of at least two semesters of continual effort.
USC Undergraduate Students:
Please contact me if you have already passed 2-3 courses on machine learning, data science, and artifcial inteligence.
Volunteer Remote Students:
Remote collaborations have become common recently and I have received inquiries for this purpose. You can contact me for volunteer-based collaboration. I will try to define a research project for you but please note that remote collaboration requires significant motivation on your end.


Fall 2021: Applied Natural Language Processing (CSCI 544)
Fall 2020: Building Knolwedge Graphs (DSCI 558)


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