Vision and multimodal AI including,

• Incremental learning

• Transfer learning

• Explanability in deeplearning

• Data driven prediction

LG AI Research, Seoul, Korea (Apr. 2022 ~ present)

• Medical AI

  • Multi-modal learning

  • Biomarker discovery

  • Prognosis predicition

LG AI Research, Toronto, ON, Canada(Feb. 2019 ~ Mar. 2022)

• Enterprise AI, Vision inspection

  • AutoML & Reinforcement learning

  • Leaning theory for lifelong & transfer learning

  • Explainable AI(XAI)

  • Data driven prediction

• Academy-Industry collaborative projects

LG CNS, Seoul, Korea (Jul. 2018 ~ Jan. 2019)

• AI research for Inspection & Automation in Smart Factory

Center for Integration of Advanced Medicine Life Science Innovative Technologies(CAMIT), Kyushu University, Fukuoka, Japan (Jun. 2016 ~ Jun. 2018)

• Deeplearning in medicine

• Surgical Navigation, research and development

• 3D visualization of organs

• Measuring of anatomic structures and movements

• Machine learning for medical application

Institute of Intelligent Surgical Technology, Hanyang University, Seoul, Korea (Sep. 2014 ~ May 2016)

• Medical software (viewing, rendering, processing medical images)

• Surgical Navigation

• System Integration

Ph.D, Biomedical Engineering, Hanyang University, Seoul, Korea (2015)

Bs, Mechanical Engineering, Hanyang University, Seoul, Korea (2007)

3rd Prize in AutoCV1 of AutoDL challenge (Jul. 2019)

• Team base_1

• https://autodl.chalearn.org/

4th place in the Adversarial Vision Challenge (Robust Model Track) in NIPS2018 (Dec. 2018)

• Team LAIR

• https://www.crowdai.org/challenges/nips-2018-adversarial-vision-challenge-robust-model-track/leaderboards

Excellent Paper in Korea Software Congress(KSC) 2018 (Dec. 2018)

• Paper: Comparing regularization methods for generalization of neural network against noises including adversarial attack (적대적 노이즈를 포함한 교란된 데이터에 대항하여 분류기의 일반화 성능을 확보하기 위한 정규화 방법)

Press reported: "3-modality fusion imaging may illuminate surgical guidance" (Apr. 2017)

• https://www.healthimaging.com/image-category/molecular-imaging/3-modality-fusion-imaging-may-illuminate-surgical-guidance

Scholarship for post doctoral fellowship by Japan Society for the Promotion of Science (Jul. 2016 ~ May 2018)

Journals

1. Zhang, Z., Jeong, Y., Jang, J., & Lee, C. G. (2022). A Pattern-driven Stochastic Degradation Model for the Prediction of Remaining Useful Life of Rechargeable Batteries. IEEE Transactions on Industrial Informatics, 18(12), 8586-8594

2. Lee, C., Jang, J., Lee, S., Kim, Y. S., Jo, H. J., & Kim, Y. (2020). Classification of femur fracture in pelvic X-ray images using meta-learned deep neural network. Scientific reports, 10(1), 1-12.

3. Na, Y. J., Jang, J. S., Lee, K. H., Yoon, Y. J., Chung, M. S., & Han, S. H. (2019). Thyroid cartilage loci and hyoid bone analysis using a video fluoroscopic swallowing study (VFSS). Medicine, 98(30).

4. Lee, C., Kim, Y., Kim, Y. S., & Jang, J. (2019). Automatic disease annotation from radiology reports using artificial intelligence implemented by a recurrent neural network. American Journal of Roentgenology, 212(4), 734-740.

5. Lee, C., Jang, J., Kim, H. W., Kim, Y. S., & Kim, Y. (2019). Three-dimensional analysis of acetabular orientation using a semi-automated algorithm. Computer Assisted Surgery, 24(1), 18-25.

6. Park, S., Jang, J., Kim, J., Kim, Y. S., & Kim, C. (2017). Real-time triple-modal photoacoustic, ultrasound, and magnetic resonance fusion imaging of humans. IEEE transactions on medical imaging, 36(9), 1912-1921.

7. Jang, J., Kim, H. W., So, B. R., & Kim, Y. S. (2015). Experimental study on restricting the robotic end-effector inside a lesion for safe telesurgery. Minimally Invasive Therapy & Allied Technologies, 24(6), 317-325.

8. Jang, J., Kim, H. W., & Kim, Y. S. (2014). Construction and verification of a safety region for brain tumor removal with a telesurgical robot system. Minimally Invasive Therapy & Allied Technologies, 23(6), 333-340.

Conference Proceedings

1. Lee, H. R., Sreenivasan, R. A., Jeong, Y., Jang, J., Shim, D., & Lee, C. G. Multi-policy Grounding and Ensemble Policy Learning for Transfer Learning with Dynamics Mismatch. In IJCAI-ECAI 2022

2. Sattarzadeh, S., Sudhakar, M., Plataniotis, K. N., Jang, J., Jeong, Y., & Kim, H. (2021, June). Integrated grad-CAM: Sensitivity-aware visual explanation of deep convolutional networks via integrated gradient-based scoring. In ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 1775-1779). IEEE.

3. Sudhakar, M., Sattarzadeh, S., Plataniotis, K. N., Jang, J., Jeong, Y., & Kim, H. (2021, June). Ada-SISE: adaptive semantic input sampling for efficient explanation of convolutional neural networks. In ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 1715-1719). IEEE.

4. Shim, D., Mai, Z., Jeong, J., Sanner, S., Kim, H., & Jang, J. (2020, February). Online Class-Incremental Continual Learning with Adversarial Shapley Value. In AAAI-21

5. Sattarzadeh, S., Sudhakar, M., Lem, A., Mehryar, S., Plataniotis, K. N., Jang, J., ... & Bae, K. (2020, February). Explaining Convolutional Neural Networks through Attribution-Based Input Sampling and Block-Wise Feature Aggregation. In AAAI-21

6. Park, S., Jang, J., Kim, J., Kim, Y. S., & Kim, C. (2017, April). Photoacoustic image-guided navigation system for surgery (Conference Presentation). In Photons Plus Ultrasound: Imaging and Sensing 2017 (Vol. 10064, p. 100640E). International Society for Optics and Photonics.

7. Jang, J., Kim, H. W., & Kim, Y. S. (2014, November). Co-segmentation of inter-subject brain magnetic resonance images. In 2014 11th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI) (pp. 80-84). IEEE.

8. Jang, J., & Kim, Y. S. (2013, October). Safety management algorithm for telesurgical robot system for brain tumor surgery. In IEEE ISR 2013 (pp. 1-2). IEEE.

Workshops

1. Yoon, J., Kim, K., & Jang, J. (2019, October). Propagated Perturbation of Adversarial Attack for well-known CNNs: Empirical Study and its Explanation. In 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW) (pp. 4226-4234). IEEE.

2. Jang, J. (2018, November) How to generate a robust image classifier against an adversarial attack: From the experience of NIPS 2018 Adversarial Vision Challenge. In NVIDIA AI Conference Seoul 2018. https://www.nvidia.com/ko-kr/ai-conference/old-agenda/