Ammar Ul Hassan

Ammar Ul Hassan

AI Engineer at DeltaX

I am an AI Engineer at DeltaX.AI, focusing on building Advanced Driver Assistance Systems (ADAS), including Driver Monitoring Systems (DMS), Occupancy Monitoring Systems (OMS) and Exterior Monitoring Systems (EMS) for autonomous driving.

I recieved Ph.D. in Computer Science at Soongsil University, Seoul, South Korea, advised by Prof. Jaeyoung Choi. I received B.S. in Software Engineering and M.S. in Computer Science, at International Islamic University and Soongsil University respectively. I was funded by International Graduate Research scholarship by Soongsil University for my M.S. and Ph.D. studies.

During my Ph.D. I published in some top tier journals including few in Expert Systems with Applications (ESWA) journal, currently ranked number 1 in the Google Scholar h-index ranking for Artificial Intelligence publications. I also presented one of my research papers at the AI for Content Creation (AI4CC) workshop at CVPR 2022.

My research interests in computer vision span generative modeling, object detection, semantic segmentation, and depth estimation. As an AI professional, I specialize in crafting lightweight deep neural network architectures for EdgeAI devices. My expertise extends to applications such as Advanced Driver Assistance Systems (ADAS), encompassing Driver Monitoring Systems (DMS), Occupancy Monitoring Systems (OMS), and Exterior Monitoring Systems (EMS) for autonomous driving.

Published Research

Learning font-style space using style-guided discriminator for few-shot font generation
Ammar Ul Hassan, Irfanullah Memon, and Jaeyoung Choi
Expert Systems With Applications (ESWA 2023)
paper

Exploiting mixing regularization for truly unsupervised font synthesis
Ammar Ul Hassan, Hyunsoo Lee, and Jaeyoung Choi
Pattern Recognition and Letters (PR-Letters 2023)
paper

Real-time high quality font generation with Conditional Font GAN
Ammar Ul Hassan, Irfanullah Memon, and Jaeyoung Choi
Expert Systems With Applications (ESWA 2022)
paper

FontNet: Closing the gap to font designer performance in font synthesis
Ammar Ul Hassan, Jaeyoung Choi
AI for Content Creation Workshop (CVPR 2022)
paper

Unpaired font family synthesis using conditional generative adversarial networks
Ammar Ul Hassan, Hammad Ahmed, and Jaeyoung Choi
Knowledge-Based Systems (KBS 2021)
paper

SKFont: skeleton-driven Korean font generator with conditional deep adversarial networks
Debbie Honghee Ko, Ammar Ul Hassan, Jungjae Suk, and Jaeyoung Choi
International Journal on Document Analysis and Recognition (IJDAR 2021)
Code | paper

SkelGAN: A Font Image Skeletonization Method
Debbie Honghee Ko, Ammar Ul Hassan, Saima Majeed, and Jaeyoung Choi
Journal of Information Processing Systems (JIPS 2021)
Code | paper

Teaching

5041345801 at Soongsil University Seoul, South Korea. Deep learning programming. Fall 2021. Head TA.

Talks

  • GANs-Tutorial at Korean Society Of Computational Science And Engineering 2020 (KSCSE)
  • Talk on Font Generation trends using Machine Learning at AMGCC 2020 . link

CV

Here’s my CV

Portfolio

Here’s my Portfolio