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Welcome New MCL Member Alek Yegazarian

We are very happy to welcome a new MCL member, Alek Yegazarian. Here is a quick interview with Alek:

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

My name is Alek Yegazarian, I am a recent Master of Science Electrical Engineering graduate from the University of Southern California, earning the Ming Hsieh Department of Electrical and Computer Engineering academic achievement award and the 2025 Viterbi Master’s 1 student commencement speaker honor. My research background and interests lie at the intersection of computational imaging, computer vision, and robotics. Outside of engineering and academia, I am a magician member of the Academy of Magical Arts at the Magic Castle in Hollywood, enjoy playing piano, and stay active by practicing taekwondo.2. What is your impression of MCL and USC?

I am so excited and grateful to be a part of MCL. Dr. Kuo’s passion and enthusiasm for his students and research is inspiring. I especially enjoy the lab environment, as everyone is very friendly and supportive. I am truly happy and excited every time I come to campus for research! Shout out to Wei Wang and Yixing Wu for being so welcoming, attentive, and helpful during these first few weeks working with them.

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

During this summer, I am supporting research on Green U-Shaped Learning (GUSL) for super-resolution and denoising tasks. I hope to learn and contribute as much as I can to MCL and Green Learning, while simultaneously getting to know my fellow lab members and getting a taste of life as a researcher in academia.

By |June 22nd, 2025|News|Comments Off on Welcome New MCL Member Alek Yegazarian|

Welcome New MCL Member Jimmy Xiao

We are very happy to welcome a new MCL member, Jimmy Xiao. Here is a quick interview with Jimmy:

1. Could you briefly introduce yourself and your research interests?My name is Jimmy Xiao, and I’m a rising senior undergraduate student double majoring in Economics and Computer Science at USC. I joined MCL as a summer intern. My research interests include machine learning and computer vision. Outside of academics, I enjoy swimming and golfing as a way to stay balanced and energized.

2. What is your impression of MCL and USC?

MCL is an intellectually vibrant and supportive group where people are willing to exchange ideas, offer guidance, and work together toward solving challenging problems. I enjoy interacting with people at MCL and am excited about the chance to collaborate and contribute to meaningful research in the future. USC is dynamic and opportunity-rich, and is always filled with enthusiasm.3. What is your future expectation and plan in MCL?

I look forward to deepening my engagement with MCL by continuing to participate in and contribute to challenging and intriguing research at MCL. I hope to learn from experienced researchers in MCL and continue to develop my technical and analytical skills. I also would like to interact with individuals in MCL and make lasting connections. 

By |June 15th, 2025|News|Comments Off on Welcome New MCL Member Jimmy Xiao|

Welcome New MCL Member Kevin Lim

We are very happy to welcome a new MCL member, Kevin Lim. Here is a quick interview with Kevin:

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

My name is Kevin Lim, and I am a second year master’s student studying Computer Science. I’ve explored a few subfields within the field of AI but I’m most interested in deep learning and computer vision. I found Professor Kuo’s work in green learning to be very interesting which is part of my motivation to apply for an internship at MCL.

2. What is your impression of MCL and USC?

I think USC is a place with many opportunities for ambitious people. Hard-working and passionate individuals thrive here and this serves as an inspiration for others as well. I’ve spoken to a few people at MCL already and they are very friendly, and I appreciate Professor Kuo’s leadership and guidance.

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

I hope to gain a better understanding of research trends in the image processing field, as I’ve already started to do while preparing for the Mediatek research project. I also hope to build strong connections with members of the lab.

By |June 8th, 2025|News|Comments Off on Welcome New MCL Member Kevin Lim|

Welcome New MCL Member Qi Cao

We are very happy to welcome a new MCL member, Qi Cao. Here is a quick interview with Qi:

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

My name is Qi Cao, and I’m currently a PhD student in the ECE department at USC. I joined the MCL at the end of 2024. My research interests span machine learning, computer vision, and time-series analysis. Since joining MCL, I’ve been eager to contribute to ongoing projects, collaborate with lab members who have diverse technical backgrounds, and continue refining my skills through both hands-on experiments and regular discussions.

2. What is your impression of MCL and USC?

MCL comes across as a supportive, close-knit lab where the collaborative atmosphere helps make challenging projects feel manageable. USC strikes me as the kind of campus that buzzes with energy—entrepreneurial, diverse, and always open to new perspectives.

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

I’m looking forward to collaborating closely with everyone in MCL,  sharing ideas during group meetings, and learning from the lab’s diverse expertise. I hope to contribute actively to interesting projects by participating in regular discussions and offering support whenever it’s needed. I aim to both grow my own skills and help the whole team move our research forward.

By |June 1st, 2025|News|Comments Off on Welcome New MCL Member Qi Cao|

MCL Research on Image Classification

Image classification is a key task in computer vision, typically driven by high-performing deep learning methods. However, these methods are criticized for lacking transparency. As an alternative, Green Learning offers more interpretable models, though their performance is not as strong as that of deep learning.

We propose a novel Green Learning framework that enhances performance by integrating label supervision directly into the feature extraction process. A key component of our approach is the LDA filter block, which is a feedforward mechanism that uses Linear Discriminant Analysis (LDA) to create convolution filters without the need for backpropagation. Additionally, we present spectral LNT, a new variant of the least-squares normal transform (LNT) that takes advantage of the spatial-spectral structure of feature maps by applying localized linear combinations of features. Together, our methods create a label-guided, interpretable feature extraction pipeline.

By |May 25th, 2025|News|Comments Off on MCL Research on Image Classification|

Congratulations to Wei Wang for Passing Her Defense!

Congratulations to Wei Wang for passing her defense today. Wei’s thesis title is “Explainable and Lightweight Machine Learning Models for Image Super-Resolution and Denoising.” Her Dissertation Committee includes Jay Kuo (Chair), Antonio Ortega, and Jernej Barbic (Outside Member). The Committee members were pleased with Wei’s thesis work. Here is a summary of her thesis:Image signal processors convert camera sensor data into images via digital image processing operations. In recent years, we have witnessed an increased interest in using Deep Learning to enhance the ISP raw images for improved quality. The main challenge is the regularization of this ill-posed problem.

Practically, the trade-off between quality improvement and the computational complexity is the key issue of widely applying the existing state-of-the-art methods. In this dissertation defense talk, we propose effective and efficient techniques for the typical low-level image enhancement problem, image super-resolution (SR) based Green Learning. First, we propose LSR and LSR++, a light-weight super-resolution green learning method with feedforward design without backpropagation, and its advanced version with significantly smaller computational complexity. Second, we develop Green U-Shaped Learning (GUSL) method for super-resolution with multi-level coarse-to-fine semantic estimation and effective conditional residual prediction. Third, we introduce Green U-Shaped Learning method to medical computed tomography (CT) images for low-dose CT image denoising enhancement to demonstrate its generalization capability. All above, these methods contribute to advanced efficiency with competitiveperformance, mathematical transparency, and robust generalizability in low-level computer vision problems.

Wei generously shared her experiences in the MCL Lab with us: It has been a long journey. First and foremost, I would like to extend my sincere thanks to my Ph.D. advisor, Professor C.-C. Jay Kuo, for his outstanding mentorship and consistent support throughout my doctoral journey. His dedication to research, strong work ethic, and sense [...]

By |May 18th, 2025|News|Comments Off on Congratulations to Wei Wang for Passing Her Defense!|
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    Congratulations to Qingyang Zhou  for Passing His Defense!

Congratulations to Qingyang Zhou  for Passing His Defense!

Congratulations to Qingyang Zhou on successfully defending his dissertation today! His thesis, titled “Advanced Techniques for Point Cloud Quality Assessment, Surface Reconstruction, and Coding,” was reviewed by his committee—chaired by Jay Kuo, with Antonio Ortega as a member and Stefanos Nikolaidis serving as the outside examiner. The committee praised the rigor and excellence of Qingyang’s research. Here is a summary of his thesis:Point clouds (PCs) play a critical role in 3D vision and graphics applications such as AR/VR, digital preservation, and medical imaging. However, their large data volume, vulnerability to quality degradation, and the need for surface reconstruction from sparse, noisy points pose long-standing challenges. In this work, we address these issues through three research thrusts: compression, quality assessment, and surface reconstruction. First, we propose GPCGC, a low-complexity geometry compression method that leverages vector quantization and block-level rate-distortion modeling to achieve fast encoding/decoding with competitive performance. Second, we introduce GPQA, an interpretable, saliency-guided quality assessment framework that projects local 3D structures into 2D patches and uses a green machine learning model to predict perceptual quality under both full- and no-reference settings. Third, we develop two surface reconstruction methods: GPSR, an unsupervised diffusion-based approach, and LPSR, a supervised variant under the green learning paradigm. Together, these contributions advance the efficiency, interpretability, and robustness of the point cloud processing pipeline, enabling practical deployment in resource-constrained environments.He generously shared his experiences in the MCL Lab with us:My PhD journey at the University of Southern California’s Media Communications Lab (MCL) has been one of the most rewarding chapters of my life. Under the guidance of Professor C.-C. Jay Kuo, and with the generous support of MCL alumni and lab members, I not only deepened my technical knowledge but [...]

By |May 11th, 2025|News|Comments Off on Congratulations to Qingyang Zhou  for Passing His Defense!|

MCL Research on Prostate Segmentation

Automatic segmentation of the prostate is a crucial step in the computer-aideddiagnosis of prostate cancer and in treatment planning. Current methods for prostatesegmentation primarily rely on deep learning models with neural networks. However,these models tend to be large and lack transparency, which is essential forphysicians. We proposed a new data-driven 3D prostate segmentation method onMRI named Green U-shaped Learning (GUSL). Different from deep learning basedmethods, GUSL employs a feed-forward system that utilizes successive subspacelearning (SSL).To keep enough detailed information on a dataset with a large image size, wepropose a cascading model in two stages, as shown in Figure 1: (1) segmentation ondownsampled images and (2) segmentation on the cropped patches. GUSL consistsof three main modules, as shown in Figure 2: representation learning, featurelearning, and residual correction. All modules are applied at multiple levels withvarying resolutions. We achieve fine-to-coarse unsupervised representation learning

using cascaded VoxelHop units, as well as coarse-to-fine segmentation throughfeature learning and residual correction. GUSL maintains a very competitive standingperformance-wise with other DL baseline models and keeps a smaller model sizeand less complexity, with transparency for doctors.

By |May 4th, 2025|News|Comments Off on MCL Research on Prostate Segmentation|

MCL Research on Green Image Super-resolution

Single image super-resolution (SISR) is an intensively studied topic in image processing. It aims at recovering a high-resolution (HR) image from its low-resolution (LR) counterpart. SISR finds wide real-world applications such as remote sensing, medical imaging, and biometric identification. Besides, it attracts attention due to its connection with other tasks (e.g., image registration, compression, and synthesis). 

The main challenge of SISR is the ill-pose issue. We recently have been developing a solution by providing reasonable performance and effectively reduced complexity. We propose a green U-shape method to progressively enhance the LR images from global structure to local details with increasing spatial sizes and conditional residual estimation. 

By |April 27th, 2025|News|Comments Off on MCL Research on Green Image Super-resolution|

MCL Research on Nuclei Segmentation

Nuclei Segmentation is a key step in understanding the distribution, size, and shape of nuclei in the underlying tissue. Traditionally, pathologists view histology slides under the microscope to analyze the nuclear structure. However, this process is time-consuming and is prone to inter-reader variability. An AI-based segmentation algorithm can aid pathologists in cancer detection and prognosis, and help speed up the cancer screening procedure. 

While there are several deep learning methods addressing this problem, we propose a Green Nuclei Segmentation algorithm that uses a simple, reliable, and modular approach to delineate nuclei in a histopathology slide. The Green U-shaped Learning(GUSL) is a 4 level pipeline that involves three main modules: representation learning using PixelHop, feature selection using RFT, and supervised learning using XGBoost Regressor. The different levels help look at the histopathology image at multiple resolutions, while we attempt to segment the nuclei in a coarse to fine manner. At each level, we aim to correct the previous layer’s predictions through residue correction. While this model gives good results, we can further improve the performance by refining the boundary regions to yield precise nuclei contours. 

By |April 20th, 2025|News|Comments Off on MCL Research on Nuclei Segmentation|