News

We are so glad to welcome our new MCL member, Shuiyang Mao! Here is a short interview with Shuiyang:

1. Could you briefly introduce yourself and your research interests?

Hi, I’m Shuiyang Mao, Fernando, a Year Three Ph.D. student (entering my fourth year) from DSP Lab, The Chinese University of Hong Kong (CUHK). I grew up in a coastal city called Ningbo located in the eastern China. In 2013, I received my bachelor’s degree in physics from Xiamen University, China. I was working as a hardware engineer on signal processing in R&D Department, Ningbo Zhendong Optoelectronics CO.,LTD. from Jul. 2013 to Jun. 2015. I then joined Micro Sensor Group, Chinese Academy of Sciences, working as a research assistant on chemical gas pattern recognition from Jul. 2015 to Jan. 2016. I started my Ph.D. study in CUHK in Aug. 2016. Since then, I was mainly focusing on speech signal processing and machine learning. My research interest covers deep learning, speech emotion detection, automatic speech recognition and related fields.

2. What is your impression about MCL and USC?

I am impressed by the diversity of the USC campus and perfect sun shine of California. I have met a bunch of amazing and smart people here. The MCL is a big warm family and Prof. Jay Kuo is super nice and enthusiastic. I like the regular project meeting very much, where we can share our experience and learn from prof. Kuo’s knowledge and wisdom. The weekly seminar is also very impressive, from which I have seen several quality works and sharings that inspire me a lot.

3. What is your future expectation and plan in MCL?

I cherish this precious opportunity to have exposure of doing research in a different environment and learn [...]

We are so glad to welcome our new MCL member, Vaishnavi Krishnamurthy! Here is a short interview with Vaishnavi:
1.    Could you briefly introduce yourself and your research interests?
My name is Vaishnavi Krishnamurthy and I am a graduate student at USC pursuing Masters in Electrical engineering with focus on image processing and machine learning. I hail from a city called Bengaluru which is located in the southern part of India. I completed my Bachelors degree in Electronics and Communication Engineering at Rashtreeya Vidyalay College of Engineering, Bengaluru. Post my undergrad studies and prior to joining USC, I worked as a Senior Executive at Bharti Airtel, Gurugram in Networks for a year. 
Broadly, my research interest lies in solving the current challenges in image processing using deep learning approach. I developed interest in this field when I was working on a biomedical imaging project that aimed at brain tissue segmentation for tumor detection. Currently, I am continuing my research on semantic image segmentation at the MCL lab in USC.
2.    What is your impression about MCL and USC?
In my initial days at USC, I felt that the coursework was challenging and it took a while for me to cope with the fast paced curriculum. The student life at USC has taught me that consistency is the key to achieve excellence in any given field. For a toddler in research like me, MCL lab has provided an extremely conducive environment to be in. Working under the guidance of Prof. Jay Kuo and his extremely motivated and helpful bunch of PhD students has been an amazing experience and I wish to utilize this opportunity to learn more and hone my research skills.
3.    What is [...]

We are so happy to welcome our new MCL member, Pranav Kadam! Here is a short interview with Pranav.

1. Could you briefly introduce yourself and your research interests?
My name is Pranav Kadam and am a graduate student at USC pursuing Master’s degree in Electrical Engineering. I grew up in a city called Pune which is in the western part of India. Prior to joining USC in Fall 2018, I completed my Bachelor’s degree in Electronics and Telecommunications Engineering from Savitribai Phule Pune University. In my undergraduate coursework, I found topics like image processing and neural networks quite interesting. I did some projects in these fields and read a lot a regarding recent developments then, such as CNNs. I am continuing my research and specialization in Computer Vision and Machine Learning at USC.

2. What is your impression about MCL and USC?
USC is a great urban campus with excellent educational and research facilities. A lot of focus is given on mastering the fundamental topics and concepts for a long-term career in the field of interest. In addition, Los Angeles is a large metropolis and there are plenty of options for entertainment, food and sightseeing, all very nearby campus, making it an ideal city to be in during graduate studies. MCL is a team of talented and passionate students headed by Prof. Kuo who is very experienced, insightful and supportive. Lab activities like research meetings and weekly seminars ensure that everyone is aware of each other’s work and there is a good bonding among members.

3. What is your future expectation and plan in MCL?
I would like to work under the guidance of Prof. Kuo and develop the necessary skills to be a successful researcher. [...]

We are so glad to welcome our new MCL member, Yifan Wang! Here is a short interview with Yifan:

1. Could you briefly introduce yourself and your research interests?

My name is Yifan Wang, a graduate student in Department of Electrical and Engineering of USC. I received my bachelor’s degree in electrical engineering from Fudan University. When I was an undergraduate student, I learnt courses from multi-fields including digital and analog circuit, computer architecture etc. Among these, I found my interests lies in vision fields which is of vital importance to both human and machine.

2. What is your impression about MCL and USC?

I have been USC for two semesters. It is a really nice place for studying, since there are few entertainment sites outside school along with the super good sun shine prohibit me from going outside regularly. MCL is a large warn family where I met lots of talent people. It would be nice to make new friends and learn new things in MCL.

3. What is your future expectation and plan in MCL?

I would like to learn more theories and mathematics related to computer vision which would be the foundation of my future research. Besides, I would like to gain more practice in deep learning fields which are highly hardware relied.

With the rise of visualization, animation and autonomous driving applications, the demand for 3D point cloud analysis and understanding has rapidly increased. Point Cloud is a kind of data obtained from lidar scanning which contains abundant 3D information. ModelNet40 is a point cloud dataset contains 40 classes of objects. In this project, we use ModelNet40 dataset for the analysis and evaluation of point cloud classification. Many of the recent works focus on developing end to end algorithm like other convolutional neural networks for images. However, object and scene understanding with Convolutional Neural Networks (CNNs) on 3D volumetric data is still limited due to its high memory requirement and computational cost. For some simple tasks like classification, this method is too much.

An interpretable CNN design based on the feedforward (FF) methodology [1] without any backpropagation (BP) was recently proposed by the Media Communications Lab at USC. The classification baseline is composed by four Saab units, each unit contains KNN query, space grouping and Saab transform, and between units we use farthest sampling to improve efficiency. We are still working on it to improve as much as possible. Our goal is to catch up with the state-of-the-art results and show that FF design is powerful and useful. The advantages of the FF design methodology are multiple folds. It is completely interpretable. It demands much less training complexity and training data. Furthermore, it can be generalized to weakly supervised or unsupervised learning scenarios in a straightforward manner. The latter is extremely important in real world application scenarios since data labeling is very tedious and expensive.

The advantages of the FF design methodology are multiple folds. It is completely interpretable. It demands much less training complexity and training data. [...]