News

Dr. Jonghye Woo, an MCL alumnus, was recently promoted to Assistant Professor at Harvard Medical School. Congratulations!

Dr. Woo graduated from MCL in 2009. He became a research associate at Cedars-Sinai Medical Center in 2010 and joined University of Maryland and John Hopkins University as post-doc in 2012. After that he became a faculty member at MGH/Harvard. His research interests lie in medical imaging, particularly multimodal MRI analysis of speech, cardiac/tongue motion modeling and analysis, etc.

Talking about his career path, he thinks it was deeply influenced by his PhD journey, during which he was advised by Prof. Kuo and met several well-known people in the field of medical imaging. Recalling the days in MCL, he felt thankful that he joined the large MCL group, where he interacted a lot with his fellows from various sub-groups. Those experiences are helpful because communication skills are crucial to start career in academia.

Dr. Woo also gave several suggestions to those who want to pursue success in academia. To become a researcher, one needs to be highly motivated and always keeps a clear goal. Humbleness is another important characteristic, which makes one open to different ideas and collaboration with other researchers. The last characteristic he emphasized is persistence. Research is challenging and persistence helps one endure the inevitable struggling.

MCL has a new PhD student, Ye Wang, in Fall 2015. Let’s give him a warm welcome!

Ye received M.S. degree from Peking University and B.S. degree from Xi’an Jiaotong University in 2014 and 2011, respectively. He joined MCL to pursue his PhD degree in Fall 2015. We had a briefly interview with him.

1. Could you briefly introduce yourself? (Previous research experience, project experience, research interest and expertise)

My previous research field focuses on semiconductor fabrication and test, especially in GaN enhancement mode MOSFET. In 2012 I had a wonderful opportunity to participate in a 3-year project in ‘National Science and Technology Major Project 02’. In the meantime, I published two IEEE journal papers and three top conference papers as well as an invited conference paper.

Since arriving at USC, I have heard more and more about computer vision and machine learning, which are always my keen interest that I haven’t got chances to learn before. Therefore I really appreciate Prof. Kuo for giving me this opportunity to study in MCL and explore more in this exciting area.

2.What’s your first impression of USC and MCL?

USC has a beautiful campus, and Trojans are like a big family with great diversity. Since MCL is a very large group, I was curious about how Prof. Kuo could guide so many students.

However, when I became a member of this group, I finally understand the way Prof. Kuo inspires the students and how they all work together closely and efficiently. Moreover, their passion for research, self-discipline and sound knowledge in this field make me feel really lucky to join MCL.

3. What’s your future expectation for MCL?

To tell the truth, I am completely new to computer vision and machine learning. I feel really [...]

The segmentation research in our lab made a significant breakthrough these days. The paper “Robust Image Segmentation Using Contour-guided Color Palettes” by Xiang Fu, Chien-Yi Wang, Chen Chen, Changhu Wang and C.-C. Jay Kuo was accepted by ICCV 2015. In this paper, the contour-guided color palette (CCP) is proposed to efficiently integrate contour and color cues of an image. To find representative colors of an image, color samples along long contours between regions, similar in spirit to machine learning methodology that focus on samples near decision boundaries, are collected to achieve an image-dependent color palette. This color palette provides a preliminary segmentation in the spatial domain, which is further fine-tuned by post-processing techniques such as leakage avoidance, fake boundary removal, and small region mergence. While CCP offers an acceptable standalone segmentation result, it can be further integrated into the framework of layered spectral segmentation to produce a more robust segmentation.

For more details, please refer to our paper that will be published soon. The latest code of this paper can be downloaded here. It is written in MATLAB, and has been tested under 64-bit Windows, Linux, and Mac OSX.

Congratulations to Sanjay Purushotham and Pang-Chang (Brian) Lan, who passed their defense last week! Following are their thesis abstracts and they also shared their PhD experiences.
Thesis: Advanced Machine Learning Techniques for Video, Social, and Biomedical Data Analytics (Sanjay Purushotham)
In this thesis, advanced machine learning techniques are developed to tackle challenging problems arising in three Big Data application domains. They are: 1) partial near-duplicate content copy detection and alignment for the multimedia (video) application, 2) personalized single user and group recommender systems for the social media data application, and 3) sparse learning models for identification of discriminative feature interactions for gene expression prediction and cancer stage classification for the biomedical data application. Novel and suitable machine learning algorithms and models are designed to meet the nature of the data in each specific application domain. 

Thesis: Secure Wireless Communications with Side Information: Secrecy Analysis and Unitary Modulation Realization (Brian Lan)
In wireless communication systems such as LTE, information security is protected by symmetric cryptography which assumes that the user and the base station shares the same secret key. But before the setup of the symmetric cryptosystems, the key sharing and authentication processes are vulnerable to eavesdropping. This is referred to as the secure initiation problem. Our work proposes to use physical layer techniques to achieve perfect secrecy without using cryptography, so the secure initiation problem can be solved. In particular, we bring up the concept of having only channel state information (CSI) at the transmitter side but not at the receiver nor the eavesdropper. This concept will be shown beneficial in secrecy comparing to the conventional assumption of full CSI at all terminals. A practical scheme, unitary modulation, is then proposed to exploit this concept. [...]

MCL has a new Ph.D student, Yuhang Song in Fall 2015. Let’s give him a warm welcome!

Yuhang received B.S. degree from the EE Department of Tsinghua University in Fall, 2015. He decided to join USC MCL to pursue his Ph.D degree beginning from this Fall semester. We had a brief interview with him.

What is your first impression of USC and MCL?

USC is a beautiful campus with high cultural diversity, and MCL is a big group with friendly mates. It really impressed me a lot during the first group meeting, where team members talked about their current work. Their enthusiasm about research and technology led a strong influence on me.

Could you briefly introduce yourself? (Previous research experience, project experience, research interest and expertise)

I had a couple of research experience about multiple fields, including pedestrian action categorization, image retrieval, structural light coding and time series analysis.  After my attendance of this lab, I want to keep an open mind on a variety of topics of computer vision and machine learning, and to dive into certain interesting topic after several tries. 

What’s your future expectation for MCL?

As I’m new here, I hope to learn more about the ongoing projects and state-of -the-art technology in the lab. After that, I hope to devote myself to developing my research abilities, as well as communication skills. It will be a great journey in my life during this period in USC and MCL, and I’ll do my best to be a good team member and keep good relationship with my colleagues.