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PhD Student
Max Planck Institute for Security and Privacy (MPI-SP)
Data Science for HumanityΒ
#CompSocSci π¦ #AI4Good π #GenAI π€
#AI4Science π¬ #ProteinDesign π§¬Β
Email: bryan.wijaya (at) mpi-sp.org
π (Twitter)Β |Β BlueskyΒ |Β Instagram
I am a PhD student at the Data Science for Humanity Group of MPI-SP, advised by Professor Meeyoung Cha. My research focuses on applied artificial intelligence (AI) for scientific and societal impact, spanning computational social science, AI for social good, representation learning, and generative modeling. I am interested in building data-driven, scalable, and explainable AI systems to understand human behavior and support decision-making in real-world settings, with applications across procurement systems, public health, healthcare policy, and online safety. Within AI for science, I am interested in the generative modeling of protein structures for antigen-conditioned antibody design. Previously, I received my MS in Computer Science and BS in Electrical Engineering from KAIST in 2026 and 2024, respectively.
Feel free to say hi and connect with me anytime!
π Education
PhD in Computer Science, MPI-SP, 2026βpresent
Advisor: Prof. Meeyoung ChaΒ
MS in Computer Science, KAIST, 2024β2026
Advisors: Prof. Tae-Kyun Kim, Prof. Ho Min Kim β Supervisor: Prof. Meeyoung ChaΒ
Thesis: End-to-End Design of Target-Conditioned Antibody Sequences and Structures Using Geometric Deep Learning with Matching and Diffusion Models
BS in Electrical Engineering, KAIST, 2020β2024
AI Special Designated Major, Minor in Chemical and Biomolecular Engineering (Summa Cum Laude)
π Research Statement
My research interests lie at the intersection of applied artificial intelligence, data science, social systems, and life sciences, with the goal of developing data-driven AI systems that are scalable, technically rigorous, and societally meaningful. My work can be broadly organized into the following themes:
Computational Social Science and AI for Social Good
I study how AI and data-driven methods can be used to understand societal dynamics and support positive social impact. My current interests include developing explainable models to identify key determinants of womenβs health, as well as multimodal AI pipelines for anomaly detection with applications to support anti-corruption efforts in procurement systems, decision-making, and content moderation. Related prior work includes an analysis of the effects of air quality on subjective well-being in Korea and a longitudinal study identifying factors influencing nurse turnover to support workforce well-being.
Generative AI for Science: Protein and Antibody Design
Under this theme, I focus on generative models for antibody sequenceβstructure co-design, with a particular interest in diffusion-based approaches for antigen-conditioned design. This line of work informs my broader interest in structured generation and scientific modeling. In this area, I have authored a review on AI-driven methods for antibody design and optimization, contributed to a survey on interpretable machine learning for protein science, and evaluated protein structure reconstruction methods for antibody structures.
Machine Learning Fundamentals and Representation Learning
This theme broadly investigates critical topics in machine and deep learning, such as privacy-preserving machine learning, data-efficient training, as well as representations in modern architectures, e.g., large language models (LLMs) and vision-language models (VLMs). A work related to this topic is the use of generative methods to produce synthetic data for data-free knowledge distillation.Β
π Publications
*Equal contribution
Journal Papers
L. F. Vecchietti, M. Lee, B. Hangeldiyev, B. N. Wijaya, H. Jung, H. Park, T.-K. Kim, M. Cha, and H. M. Kim, "Interpretable Machine Learning for Protein Science: Structure, Function, and Interactions," ACM Comput. Surv. (accepted) β [Paper]
B. N. Wijaya, Y. Park, J. H. Jeung, and K. Lee, "Exploring the relationship between air quality and happiness in South Korea using artificial neural networks," Environ. Impact Assess. Rev. 117, 108135 (2026) β [Paper] [Code]
L. F. Vecchietti*, B. N. Wijaya*, A. Armanuly, B. Hangeldiyev, H. Jung, S. Lee, M. Cha, and H. M. Kim, "Artificial intelligence-driven computational methods for antibody design and optimization," mAbs 17(1), 2528902 (2025) β [Paper]
S. K. Nandanwar, S. B. Borkar, B. N. Wijaya, E. Bayaraa, L. L. A. Piad, Q. Jin, C. S. Tiara, H. J. Kim, and N. H. Tarte, "Synthesis and characterization of novel chelation-free Zn(II)-azole complexes: Evaluation of antibacterial, antioxidant, and DNA binding activity," Indian J. Chem., Sect A 59(5), pp. 589β597 (2020) β [Paper]Β
S. K. Nandanwar, S. B. Borkar, B. N. Wijaya, J. H. Cho, N. H. Tarte, and H. J. Kim, "Cobalt(II) Benzazole Derivative Complexes: Synthesis, Characterization, Antibacterial and Synergistic Activity," ChemistrySelect 5(11), pp. 3471β3476 (2020) β [Paper]
Conference Papers
B. N. Wijaya, L. F. Vecchietti, A. Cho, S. Han, M. Cha, and C. Cha, "Longitudinal Identification of Critical Factors in Nurse Turnover for Supporting Workforce Well-Being," in Proc. HCI Korea 2026, Hongcheon, Korea, pp. xxxβxxx (2026) β [Paper] [Slides]
B. A. Tolera*, B. N. Wijaya*, and M. R. C. Mahim, "Leveraging Synthetic Data for Data-Free Knowledge Distillation," in Proc. KIISE Korea Software Congress 2024, Yeosu, Korea, pp. 692β694 (2024) β [Paper] [Poster] [Slides] [Code]
B. N. Wijaya, L. F. Vecchietti, M. Cha, and H. M. Kim, "Evaluating Antibody Structure Reconstruction with an SE(3)-Equivariant Graph Neural Network," in Proc. KIISE Korea Software Congress 2023, Busan, Korea, pp. 1681β1683 (2023) β [Paper] [Poster]
π§Έ Posters and Talks
The presenter(s) are underlined.Β *Equal contribution
Unveiling the Pregnancy and Health Trends of Nurses in South Korea: Analyzing and Modeling the Korea Nursesβ Health Study
(B. N. Wijaya, L. F. Vecchietti, S. Han, A. Cho, M. Cha, and C. Cha)
Lightning Talk at CASA & RC Trust Summer School 2025, RUB, Bochum, Germany, 2025.06
Poster at the MPI-SP Institute Retreat 2025, Bad Breisig, Germany, 2025.06
Towards low-resource culture-aware harmful meme detection
(B. N. Wijaya*, L. F. Vecchietti*, C. Y. Low*, W. Dong, A. Zhunis, and M. Cha)
Poster at the AI & Data for Social Good Forum, Yonsei University, Seoul, Korea, 2024.05
π Selected Experience
PhD Student, MPI-SP, 2026.03βpresent
Student Intern, UNDESA, 2025.06βpresent
Graduate Research Assistant, KAIST, 2024.02β2026.02
Guest Researcher and Intern, MPI-SP, 2024.02β2026.02
Project Member, Daewoong Pharmaceutical Co., Ltd., 2022.05β2024.12
Visiting Student and Intern, Institute for Basic Science, 2023.07β2024.09Β
Undergraduate Research Assistant and Intern, KAIST, 2021.06β2024.02
Project Member and Intern, Daewoong Pharmaceutical Co., Ltd., 2021.05β2021.08
Extra: TMI corner
I was a chemistry enthusiast, but everything changed when the Fire Nation attacked.Β
I am an ISTP, a mala and mint chocolate enthusiast, and I love my sugar gliders.
My hobbies are traveling π°, photography πΈ, cooking π₯, singing π€, visiting art museums ποΈ, and binge-watching πΏ
As a Chinese Indonesian, I also go by Zhixiong Huang (ι»ζΊι), but ζδΈζδΈζ π₯²
We might have crossed paths in one of these conferences/workshops: KIISE KSC 2023, ICML 2024, SICSS-Korea 2024, AOTULE 2024, WSDM 2025,Β CASA & RC Trust Summer School 2025, CIKM 2025, HCI Korea 2026
