Byungkon (Byung) Kang

Biography

I got my BSc degree in Computer Science from the University of Texas at Austin at 2006. My PhD work was done at KAIST until 2013. After that, I joined Samsung Advanced Institute of Technology as a research staff member, working on medical image analysis and natural language understanding. In 2016, I left SAIT for a research professor position at Ajou University before finally joining SUNY Korea in 2019. 

Research Summary

My research interest spans the area of Artificial Intelligence. In the past, I had been involved in projects that deal with reinforcement learning and stochastic planning algorithms. Through time, my topic gradually shifted over other sub-fields such as unsupervised learning, graph mining, and image processing, although I have managed to remain within the boundary of AI. My current research interest lies in the broad area of representation learning including, but not limited to, multimodal embedding, distribution learning, and feature representation for domain generalization. I love talking about CS research in general, so please feel free to come talk to me, regardless of the alignment between our research areas.

Courses

• Data structures (Fall'20, Fall'21, Fall'22, Spring'23, Fall'23, Spring'24, Fall'24, Spring'25, Fall'25)

• Machine learning (Fall'19, Fall'20, Fall'21, Spring'23, Spring'24, Fall'24, Spring'25(G), Fall'25)

• Artificial intelligence (Spring'20, Spring'21, Spring'22, Fall'23)

• Computer science principles (Spring'22, Fall'22)

• Introduction to web design and programming (Spring'21)

Notice

I am looking for RAs. Possible areas of research include, but are not limited to: machine learning, natural language processing, and vision.

Successful  candidates are expected to: 

• Be well-versed in (or at least willing to learn) mathematics and programming,

• Conduct individual/group projects resulting in publishable research papers, and

• Participate in periodic group discussions pertaining to on-going researches.

Exceptional undergraduate interns are also welcome (see below).

If you're interested, please send me an email to arrange for a discussion.

** This offer is valid only to students of SUNY Korea. Prospective students should first be admitted to the university before contacting me.  I do not accept solicitations for admission - I have neither the authority, nor the intention to admit students without proper credentials.

Note to undergraduates looking for research opportunities.

The following are the conditions that I require from research participants from now on:

• You MUST have basic knowledge of the topics that you wish to work on. It's normally advised that you complete either CSE352 or CSE353 with a reasonably good grade, but the exact requirements can be discussed.  I can waive this requirement if you have graduate-level proficiency in mathematics and good programming skills.

• If you're seeking credit for CSE487, then that CSE487 must be the only research course you're taking. I want you to stay focused.

• Also with CSE487: You shouldn't be taking more than two (2) CS courses besides CSE487.

• If you want to work as a paid research intern during the break, then I don't want you working elsewhere. 

Don't get me wrong: Research is loads of fun. But I don't want you to confuse 'fun' with 'easy-going'.

Note on letters of recommendation.

Starting 2024 Fall, I will only write letters of recommendation for those who have actively participated in research with me. 'Active participation' means you have successfully submitted a paper to a conference/journal (regardless of acceptance) in my company. In the past, there have been students who would pretend to engage in research just long enough to get a recommendation from me, after which withdrawing from commitment. 

Publications (Peer-reviewed)

Conferences

• Joonkyu Han, Dennis Wong, Zhoulai Fu, and Byungkon Kang. "AuthZit: Personalized Visual-Spatial and Loci-Tagging Fallback  Authentication", In proceedings of IEEE PRDC 2024.

• Daye Eun and Byungkon Kang. "Accurate Embedding-based Log Determinant Optimization", In proceedings of ACM CIKM 2024.

• Joonkyu Han, Dennis Wong, and Byungkon Kang. "AuthZit: Multi-Modal Authentication with Visual-Spatial and Text Secrets", In proceedings of ACNS 2023 (Poster).

• Jeongmin Yoo, Yeeun Sohn, Yooha Bae, and Byungkon Kang. "Clean Bottle: A reverse vending machine that detects clean PET bottles with machine learning", In proceedings of ICIAE 2022.

• Joonkyu Han, Byungkon Kang, and Dennis Wong. "HWAuth: Handwriting-Based Socially-Inclusive Authentication", In proceedings of SIGGRAPH Asia 2021 (Poster).

• Namhyuk Ahn, Byungkon Kang, and Kyung-Ah Sohn, “Fast, Accurate, and Lightweight Super-Resolution with Cascading Residual Network”, In proceedings of ECCV 2018.

• Seungwon Shin, Dongkyu Kim, Homin Park, Byungkon Kang, and Kyung-Ah Sohn, “Finding Compact Class Sets for Korean Font Image Classification”, In proceedings of ACPR 2017.

• Namhyuk Ahn, Byungkon Kang, and Kyung-Ah Sohn, “Image Distortion Detection using Convolutional Neural Networks”, In proceedings of ACPR 2017.

• Byungkon Kang and Kyung-Ah Sohn, “Embedding Senses via Dictionary Bootstrapping”, In proceedings of UAI 2017.

• Paul K.J. Park, Kyoobin Lee, Jun Haeng Lee, Byungkon Kang, Chang-Woo Shin, Jooyeon Woo, Jun-Seok Kim, Yunjae Seo, Sungho Kim, Saber Moradi, Ogan Gurel, and Hyunsurk Ryu “Computationally Efficient, Real-time Motion Recognition based on Bio-inspired Visual and Cognitive Processing”, In proceedings of ICIP 2015.

• Byungkon Kang “Fast Determinantal Point Process Sampling with Application to Clustering”, In proceedings of NIPS 2013.

• Ruo Ando, Kang Byung, Youki Kadobayashi, “Log Analysis in Cloud Computing Environment using Automated Reasoning”, In proceedings of ICONIP 2010.

Journals

• Hyo Jong Chung, Byungkon Kang, and Yoon Seok Yang. " N-DriverMotion: Driver motion learning and prediction using an event-based camera and directly trained spiking neural networks on Loihi 2", IEEE Open Journal of Vehicular Technology, 2024.

• Hyunwook Koh, Won Gu, Hyo Jung Jang, Byungho Lee, and Byungkon Kang. "MiSurv: An integrative web cloud platform for user-friendly microbiome data analysis with survival responses", Microbiology Spectrum, 2023.

• Hyojung Jang, Hyunwook Koh, Won Gu, and Byungkon Kang. “Integrative web cloud computing and analytics using MiPair for design-based comparative analysis with paired microbiome data”, Scientific Reports, 2022.

• Won Gu, Jeongsup Moon, Crispen Chisina, Byungkon Kang, Taesung Park, and Hyunwook Koh. "MiCloud: A unified web platform for comprehensive microbiome data analysis", PLoS ONE, 2022. 

• Namhyuk Ahn, Byungkon Kang, Kyung-Ah Sohn, "Efficient Deep Neural Network for Photo-realistic Image Super-resolution", Pattern Recognition, 2022.

• Byungkon Kang, Jisang Yoon, Ha Young Kim, Sung Jin Jo, Yourim Lee, Hye Jin Kam, "Deep-learning based automated terminology mapping in OMOP-CDM", Journal of the American Medical Informatics Association, 2021

• Garam Lee, Byungkon Kang, Kwangsik Nho, Kyung-Ah Sohn, Dokyoon Kim, “MildInt: Deep learning-based multimodal longitudinal data integration framework”, Frontiers in Genetics, 2019

• Garam Lee, Kwangsik Nho, Byungkon Kang, Kyung-Ah Sohn, Dokyoon Kim, and Alzheimers Disease Neuroimaging Initiative, “Predicting Alzheimers disease progression using multi-modal deep learning approach”, Scientific Reports, 2018.

• Habtamu Minassie Aycheh, Joon-Kyung Seong, Jeong-Hyeon Shin, Duk L. Na, Byungkon Kang, Sang Won Seo, Kyung-Ah Sohn, “Biological Brain Age Prediction using Cortical Thickness Data: A Large Scale Cohort Study”, Frontiers in Aging Neuroscience, 2018.

• Byung Kon Kang and Kee-Eung Kim, “Exploiting Symmetries for Single- and Multi-agent Partially Observable Stochastic Domains”, Artificial Intelligence, 2012.

Workshops

• Eun-Ju Park, Hoyoung Kim, Seonghwan Jeong, ByungKon Kang and YoungMin Kwon, "Keyword-based vehicle retrieval", In proceedings of CVPR Workshops 2021.

• Homin Park, Byungkon Kang, Arnout Van Messem, Wesley De Neve, "3-D deep learning-based item classication for beltconveyors targeting packaging and logistics", In proceedings of ICPR International Workshop on Industrial Machine Learning, 2020.

• Namhyuk Ahn, Byungkon Kang, and Kyung-Ah Sohn, “Image Super-resolution via Progressive Cascading Residual Network”, In proceedings of CVPR Workshop (NTIRE) 2018.

• Byungkon Kang, and Kyomin Jung, “Robust and Efficient Nearest Neighbor Search in Large Data Sets”, NIPS Workshop on Big Learning (BigLearn) 2012.

• Byung Kang, Woosang Lim, and Kyomin Jung, “Scalable Kernel k-Means via Centroid Approximation”, NIPS Workshop on Big Learning (BigLearn) 2011.

• Byung Kon Kang, Kee-Eung Kim, Wook Jung, and Jin-Soo Kim, “Reinforcement Learning for Optimizing Flash Memory Cache Management”, USENIX Workshop on Tackling Computer Systems Problems with Machine Learning Techniques (SysML) 2008. (Invited poster)