Sang-Hwa Oh

About

Sang-Hwa Oh is an interdisciplinary researcher, teacher, and consultant specializing in societal- and individual-level wellbeing through innovative communications and media strategies. Her research investigates emerging media effects, the dissemination of health and risk misinformation, and how digital technologies can be leveraged to promote health prevention and the public good. Her work addresses the role of media and emerging communication technologies in shaping public understanding of urgent health and social issues, influencing behaviors at both individual and policy levels, strategies to mitigate misinformation, and how brands can enhance communication efforts to promote public good through transparency, trust, and emotional engagement. Dr. Oh has a background in mass communications, public health, and social welfare, with academic degrees from Ewha Womans University, Sogang University, and the University of South Carolina. She has served on editorial boards of prominent journals and her research has been published in esteemed outlets such as Health Communication, Risk Analysis, and the International Journal of Communication. Her interdisciplinary approach combines insights from communication, media effects, public relations, public health, psychology, sociology, and computational analysis to provide a comprehensive view of digital media effects and misinformation.

Research topics

• Computer science
• Artificial intelligence
• Mathematics
• Algorithm
• Machine learning

Selected publications

• Foundation model for mass spectrometry proteomics

  ArXiv.org · 2025-05-16 · 2 citations

  preprintOpen access

  Mass spectrometry is the dominant technology in the field of proteomics, enabling high-throughput analysis of the protein content of complex biological samples. Due to the complexity of the instrumentation and resulting data, sophisticated computational methods are required for the processing and interpretation of acquired mass spectra. Machine learning has shown great promise to improve the analysis of mass spectrometry data, with numerous purpose-built methods for improving specific steps in t…

  PublisherOA PDFDOI

• OpenThoughts: Data Recipes for Reasoning Models

  ArXiv.org · 2025-06-04 · 1 citations

  preprintOpen access

  Reasoning models have made rapid progress on many benchmarks involving math, code, and science. Yet, there are still many open questions about the best training recipes for reasoning since state-of-the-art models often rely on proprietary datasets with little to no public information available. To address this, the goal of the OpenThoughts project is to create open-source datasets for training reasoning models. After initial explorations, our OpenThoughts2-1M dataset led to OpenThinker2-32B, the…

  PublisherOA PDFDOI

• Economic and Environmental Performance Improvements Based on S-100 Hydrographic Information

  Sensors and Materials · 2025-02-28 · 1 citations

  articleOpen access
  PublisherOA PDFDOI

• Secure Stateful Aggregation: A Practical Protocol with Applications in Differentially-Private Federated Learning

  arXiv (Cornell University) · 2024-10-15 · 1 citations

  preprintOpen access

  Recent advances in differentially private federated learning (DPFL) algorithms have found that using correlated noise across the rounds of federated learning (DP-FTRL) yields provably and empirically better accuracy than using independent noise (DP-SGD). While DP-SGD is well-suited to federated learning with a single untrusted central server using lightweight secure aggregation protocols, secure aggregation is not conducive to implementing modern DP-FTRL techniques without assuming a trusted cen…

  PublisherOA PDFDOI

• S4S: Solving for a Diffusion Model Solver

  ArXiv.org · 2025-02-24

  preprintOpen accessSenior author

  Diffusion models (DMs) create samples from a data distribution by starting from random noise and iteratively solving a reverse-time ordinary differential equation (ODE). Because each step in the iterative solution requires an expensive neural function evaluation (NFE), there has been significant interest in approximately solving these diffusion ODEs with only a few NFEs without modifying the underlying model. However, in the few NFE regime, we observe that tracking the true ODE evolution is fund…

  PublisherOA PDFDOI

Recent grants

• CIF: RI: Small: Information-theoretic measures of dependencies and novel sample-based estimators

  NSF · $450k · 2019–2022

• EAGER: A Graphical Approach for Choice Modeling

  NSF · $88k · 2015–2015

• TWC: Small: Fundamental Limits in Differential Privacy

  NSF · $495k · 2015–2019

Frequent coauthors

• Pramod Viswanath

  88 shared

• Sreeram Kannan

  49 shared

• Peter Kairouz

  38 shared

• Yihan Jiang

  University of Florida

  31 shared

• Kiran Koshy Thekumparampil

  26 shared

• Ashish Khetan

  25 shared

• Hyeji Kim

  25 shared

• Giulia Fanti

  25 shared

Labs

• Charles H. Sandage Department of AdvertisingPI

Education

• Ph.D., Mass Communications/Public Health

  University of South Carolina

• M.A., Mass Communications

  Sogang University

• B.A., Social Welfare/Mass Communications

  Ewha Womans University

Awards & honors

• Inaugural Teri Thompson Outstanding Article Award for best a…
• JWY Research Award, Department of Advertising, University of…
• Best Research Paper, 2nd place, Global Colloquium, Korea Adv…
• Rainbow Top Research Paper Award, Korea Health Communication…
• Red Raider Public Relations Research Award, International Pu…

Similar researchers at University of Illinois Urbana-Champaign

• Peter Sheldonh-95
• Daniel Simonsh-87
• Steve Hallh-40