hi ,
I'm QUANG PHAM .

Ph.D. Student in AI

download CV download Portfolio

about me

Pham Tran Minh Quang
Ph.D. student at Media System lab, SKKU.

Medical image analysis using deep learning. Apply CNN, and GAN to medical image generation. Apply LSTM, and GNN to disease prediction.
GAN model for face aging tasks.
Diffusion for image editing.
Large Language Model for generative agents.

email

binquangbk@gmail.com

Link

GitHub

Scholar

Kaggle

Linkedin

Blog

profile_image

education

SEP 2012 - APR 2017

Bachelor's of computer science

Ho Chi Minh City University of Technology

HCM City, Vietnam

Honors Program in Computer Science.
GPA: 8.74/10 - Rank 4/330

SEP 2018 - PRESENT

Combined Program of computer science

Sungkyunkwan university

Korea

Current GPA: 4.47/4.5
Media System lab.
(Expected Graduation August 2023)

skills

Research

  • Deep Learning: CNN, GAN, LSTM, NLP, GNN, Diffusion.
  • Libraries: Pytorch, Tensorflow, OpenCV.
  • Programming languages: Python, C++, Java.
  • Other: AWS EC2.

LANGUAGES

  • English: IELTS 6.5.

experience

Media System Lab, SKKU, Korea — Researcher

SEP 2018 - PRESENT


Working on medical image analysis using deep learning. Apply CNN, and GAN to medical image generation. Apply LSTM, and GNN to disease prediction.
Working on GAN model for face aging tasks.
Diffusion and LLM.

Daewoong Foundation, Korea — Participant

JAN 2021 - JUL 2021


Daewoong Ai & Big Data Global Scholarship Program: Denovo Drug Design (Graph Network, RNN, LSTM, GAN).

VinaDigital Co., Ho Chi Minh City, Viet Nam —Intern

JUL 2015 - SEP 2015


Working on a food recommender system for hotels. (ANN, Association rule).

Bach Khoa University, Ho Chi Minh City, Viet Nam —Student Researcher

JUL 2016 - APR 2017


Prediction of romantic relationships via interactions on the social network. (Graph theory, text classification, and machine learning models).

Projects

MuMo-GAN

MuMo-GAN: predict future fundus images for AMD patients. (GAN, Segmentation)
Lights

Multi-modal framework for predicting future visual field. (CNN, LSTM)
Lights

SS-FaceGAN: semi-supervised model for face aging. (GAN, StyleGAN)
Lights

Interact between semantic information for image editing. On-going project. (Diffusion)
Lights

Generative environment with Large Language Models. On-going project. (LLMs)

publications

Journal

  1. Quang T. M. Pham, SangIl Ahn, Jitae Shin, and Su Jeong Song, "Generating future fundus images for early age-related macular degeneration based on generative adversarial networks," Computer Methods and Programs in Biomedicine, Volume 216, Apr. 2022.
  2. Quang T. M. Pham, J. C. Han, D. Y. Park and J. Shin, "Multimodal Deep Learning Model of Predicting Future Visual Field for Glaucoma Patients," in IEEE Access, vol. 11, pp. 19049-19058, 2023, doi: 10.1109/ACCESS.2023.3248065.
  3. SangIl Ahn, Quang T. M. Pham, Jitae Shin, and Su Jeong Song, "Future Image Synthesis for Diabetic Retinopathy Based on Lesion Occurrence Probability," Special Issue "Deep Learning for Medical Images: Challenges and Solutions", Electronics 2021, 10, 726. https://doi.org/10.3390/electronics10060726, Mar. 19, 2021.
  4. Quang T. M. Pham, SangIl Ahn, Su Jeong Song, and Jitae Shin, "Automatic Drusen Segmentation for Age-Related Macular Degeneration in Fundus Images Using Deep Learning," Electronics 2020, 9(10), 1617; https://doi.org/10.3390/electronics9101617 - 01 Oct 2020, Oct. 01, 2020.
  5. Quang T. M. Pham, Janghoon Yang, and Jitae Shin, "Semi-supervised FaceGAN for face-age progression and regression with synthesized paired images," Electronics 2020, Vol 9, No. 4, 603, Apr. 2020.

Conference

  1. Quang T. M. Pham, Han, J.C., Shin, J. (2022). Visual Field Prediction with Missing and Noisy Data Based on Distance-Based Loss. In: Zamzmi, G., Antani, S., Bagci, U., Linguraru, M.G., Rajaraman, S., Xue, Z. (eds) Medical Image Learning with Limited and Noisy Data. MILLanD 2022.
  2. Quang T. M. Pham and J. Shin, "Generative Adversarial Networks for Retinal Image Enhancement with Pathological Information," 2021 15th International Conference on Ubiquitous Information Management and Communication (IMCOM), Seoul, Korea (South), 2021, pp. 1-4, doi: 10.1109/IMCOM51814.2021.9377363.
  3. Quang T.M. Pham, Han, J.C., Shin, J, "Predicting the visual field of glaucoma patients with contrastive learning [p1-73]," IPIU 2023, Feb. 08, 2023.

Contact Me

Email: binquangbk@gmail.com
Linkedin: https://www.linkedin.com/in/pham-quang-skku/