News

We are so happy to welcome a new undergraduate member of MCL, Alex Ding! Here is an interview with Alex:

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

My name is Yulong (Alex) Ding and I am a third-year undergraduate student at USC with a major in Electrical Engineering. I plan to further pursue studies in the field of EE at graduate level after I complete my bachelor’s degree and I find the application of machine learning and CNNs in data and image processing at MCL as an exciting area for further exploration in the future. At the same time, I would like to gain insight into the process of conducting research in the academic field through valuable experiences at MCL.

2. What is your impression about MCL and USC?

Having attended the weekly seminars held by Professor Kuo and the MCL community, I felt inspired by the various topics of research presented as well as the passion shared by members of MCL. The potential opportunities surrounding data processing and communication that one may explore at MCL are fascinating. Furthermore, everyone in the MCL community, including both current students and alumni, are very supportive, building upon the well-known Trojan network connecting across all career paths.

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

My current goal at MCL is to acquire knowledge in data processing and machine learning, as well as gain experience in the process of conducting research through involvement in the lab. The valuable experiences at MCL will serve as important preparation for my future pursuit in studies and research at graduate level, and ultimately for potential career opportunities in the relevant field.

It is well known that CNNs-based methods have weaknesses in terms of efficiency, scalability, and robustness. CNNs-based methods require large computational efforts and are not scalable to the change of object class numbers and the dataset size. Furthermore, these CNN models are not robust to small perturbations due to their excess dependence on the end-to-end optimization methodology. The Saak (Subspace approximation with augmented kernels) transform [1] is proposed to provide the possible solution to overcome these shortcomings.

The Saak transform consists of two new ingredients on top of traditional CNNs. They are: subspace approximation and kernel augmentation. The Saak transform allows both forward and inverse transforms so that it can be used for image analysis as well as synthesis (or generation). One can derive a family of joint spatial-spectral representations between two extremes – the full spatial-domain representation and the full spectral-domain representation using multi-stage Saak transforms. Being different with CNNs, all transform kernels in multi-stage Saak transforms are computed by one-pass feedforward process. Neither data labels nor backpropagation is needed for kernel computation.

Currently, we have successfully developed Saak transform approaches [2] to solve the handwritten digits recognition problem. This new approach has several advantages such as higher efficiency than the lossless Saak transform, scalability against the variation of training data size and object class numbers and robustness against noisy images. In the near future, we would like to apply the Saak transform approach to the general object classification problem with more challenging datasets such as CIFAR-10, CIFAR-100, and ImageNet.

Reference

[1]  C-C Jay Kuo and Yueru Chen, “On Data-driven Saak Transform,” arXiv preprint arXiv:1710.04176, 2017.

[2] Chen, Y., Xu, Z., Cai, S., Lang, Y., & Kuo, C. C. J. (2017). A Saak Transform Approach to Efficient, Scalable [...]

We would like to say congratulations to Dr. Eddy Wu for graduating from USC/MCL. He is going to join a start-up company in Seattle in 2018 February. He prepared a message about his job hunting experience to share with all MCL members, Alums and friends. We wish him the very best in his career at Magvii Research.

After graduating from MCL, I’ve decided to join Megvii Research USA as a senior research scientist. Megvii is a fast-growing unicorn startup company based in Beijing, China focusing on AI technologies. It is especially famous for its face recognition technology (known as Face++). Megvii research USA is a new branch located at Redmond WA, and is directed by Dr. Jue Wang (http://www.juew.org/). The whole Megvii research is led by Dr. Jian Sun (http://www.jiansun.org/).

Joining a startup company is always risky, but I have a strong desire becoming part of Megvii for several reasons. First, I like fast-paced environments, and I enjoy the challenges of bringing latest computer vision and deep learning technologies to real-world applications. Secondly, Megvii has shown great successes in certain AI fields with their talented teams, and signs have shown that China may have potential to lead in AI development in the foreseeable future. Another reason is that my new role in Megvii also has a great match with my researches in MCL as well as my 6-year industrial experiences prior to my PhD study.

Job hunting is an exhausted process. I applied for, and interviewed with different types companies including big name ones, industrial research labs, and startup companies in different stages. I only targeted computer vision or deep learning roles, with different types (engineer/scientist) and levels. Although I got interview invitations from many companies, eventually [...]

We are so happy to welcome a new Ph.D. member of MCL, Sibo Song. Here is an interview with  Sibo :
1. Could you briefly introduce yourself and your research interests?

My name is Sibo Song. I am currently a Ph.D. student at Singapore University of Technology and Design (SUTD) and I received my Bachelor’s degree from Zhejiang University(ZJU) in Automation. My research interests include activity recognition, multi-modal data analysis, and deep learning.

2. What is your impression about MCL and USC?

I know MCL from Prof. Kuo when he visited Singapore and presented his recent works on Saak transform. Through the talking, I knew that MCL is one big family of many intelligent people who are willing to offer their help and enthusiastic about doing research. I just can’t wait to join MCL and meet them all. 

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

During the exchange, I plan to receive research training and investigate Saak transform on various applications such as generative model and adversarial attacks in image and video processing. Also, I hope that I can make more friends here in MCL and learn together with them.
 

We are so happy to welcome a new Master’s member of MCL, Zhaolei Xiao. Here is an interview with Zhaolei.

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

My name is Zhaolei Xiao, come from the capital city of China, Beijing. I’m focusing on signal and image processing and this is my last semester in master’s program in USC. I like dealing with multimedia data: compress files, denoise a signal, and solve inpainting problem, etc., all of them give me a lot of fun. Moreover, after acquiring knowledge in machine learning field, like convolutional neural networks, building classifiers, I find it a new world to explore.

2. What is your impression about MCL and USC?

USC is the home of the Signal and Image Processing Institute (SIPI) and a leader over 40 years which is one of the reasons why I chose here to study. But not only that, professors are knowledgeable and helpful also in career, classmates are friendly and enthusiastic. I enjoy talking with them about academic and life.

I know MCL from Prof. Kuo who is my professor of the course Multimedia Data Compression that MCL likes a family, everyone helps each other and get progress together. I’m looking forward to making friends with all of them and doing cooperative research. It must be very pleasant.

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

Since I’m interested in compression and machine learning, Prof. Kuo’s project of joint compression and understanding catch my eyes. I treasure this opportunity very much and want to go deep into this application.