Publications (24)
Automated Discovery and Optimization of 3D Topological Photonic Crystals
Samuel Kim, Thomas Christensen, Steven G. Johnson +1
Topological photonic crystals have received considerable attention for their ability to manipulate and guide light in unique ways. They are typically designed by hand based on care…
Toward estimating personal well-being using voice
Samuel Kim, Namhee Kwon, Henry O'Connell
Estimating personal well-being draws increasing attention particularly from healthcare and pharmaceutical industries. We propose an approach to estimate personal well-being in term…
Wide-Angle, Multiplexed Backscatter Communications Using a Dynamic Metasurface-Backed Luneburg Lens
Samuel Kim, Tim Sleasman, Avrami Rakovsky +2
Backscatter communications is attractive for its low power requirements due to the lack of actively radiating components; however, commonly used devices are typically limited in ra…
Reactor Antineutrino Spectral "Bump": Cumulative Fission Yields of Irradiated U-235 and Pu-239 Measured by HPGe Gamma-Ray Spectroscopy
Samuel Kim, C. J. Martoff, Michael Dion +1
Recent measurements of the reactor antineutrino emission show that there exists a spectral excess (the "bump") in the 5-7 MeV region when compared to the Huber-Muller prediction ba…
Deep Learning for Bayesian Optimization of Scientific Problems with High-Dimensional Structure
Samuel Kim, Peter Y. Lu, Charlotte Loh +3
Bayesian optimization (BO) is a popular paradigm for global optimization of expensive black-box functions, but there are many domains where the function is not completely a black-b…
Integration of Neural Network-Based Symbolic Regression in Deep Learning for Scientific Discovery
Samuel Kim, Peter Y. Lu, Srijon Mukherjee +4
Symbolic regression is a powerful technique that can discover analytical equations that describe data, which can lead to explainable models and generalizability outside of the trai…
Predicting Development of Chronic Obstructive Pulmonary Disease and its Risk Factor Analysis
Soojin Lee, Ingu Sean Lee, Samuel Kim
Chronic Obstructive Pulmonary Disease (COPD) is an irreversible airway obstruction with a high societal burden. Although smoking is known to be the biggest risk factor, additional…
Surrogate- and invariance-boosted contrastive learning for data-scarce applications in science
Charlotte Loh, Thomas Christensen, Rumen Dangovski +2
Deep learning techniques have been increasingly applied to the natural sciences, e.g., for property prediction and optimization or material discovery. A fundamental ingredient of s…
VisAnatomy: An SVG Chart Corpus with Fine-Grained Semantic Labels
Chen Chen, Hannah K. Bako, Peihong Yu +10
Chart corpora, which comprise data visualizations and their semantic labels, are crucial for advancing visualization research. However, the labels in most existing corpora are high…
Data augmentation method for modeling health records with applications to clopidogrel treatment failure detection
Sunwoong Choi, Samuel Kim
We present a novel data augmentation method to address the challenge of data scarcity in modeling longitudinal patterns in Electronic Health Records (EHR) of patients using natural…
Detection and prediction of clopidogrel treatment failures using longitudinal structured electronic health records
Samuel Kim, In Gu Sean Lee, Mijeong Irene Ban +1
We propose machine learning algorithms to automatically detect and predict clopidogrel treatment failure using longitudinal structured electronic health records (EHR). By drawing a…
Interpretable Depression Detection from Social Media Text Using LLM-Derived Embeddings
Samuel Kim, Oghenemaro Imieye, Yunting Yin
Accurate and interpretable detection of depressive language in social media can support early identification of mental health conditions and inform timely interventions. In this pa…
Leveraging Federated Learning for Automatic Detection of Clopidogrel Treatment Failures
Samuel Kim, Min Sang Kim
The effectiveness of clopidogrel, a widely used antiplatelet medication, varies significantly among individuals, necessitating the development of precise predictive models to optim…
Why Do Students Drop Out? University Dropout Prediction and Associated Factor Analysis Using Machine Learning Techniques
Sean Kim, Eliot Yoo, Samuel Kim
Graduation and dropout rates have always been a serious consideration for educational institutions and students. High dropout rates negatively impact both the lives of individual s…
Work Smarter Not Harder: Simple Imitation Learning with CS-PIBT Outperforms Large Scale Imitation Learning for MAPF
Rishi Veerapaneni, Arthur Jakobsson, Kevin Ren +3
Multi-Agent Path Finding (MAPF) is the problem of effectively finding efficient collision-free paths for a group of agents in a shared workspace. The MAPF community has largely foc…
Deep Learning and Symbolic Regression for Discovering Parametric Equations
Michael Zhang, Samuel Kim, Peter Y. Lu +1
Symbolic regression is a machine learning technique that can learn the governing formulas of data and thus has the potential to transform scientific discovery. However, symbolic re…
Multimodal Foundation Models for Material Property Prediction and Discovery
Viggo Moro, Charlotte Loh, Rumen Dangovski +7
Artificial intelligence is transforming computational materials science, improving the prediction of material properties, and accelerating the discovery of novel materials. Recentl…
Automatic prediction of mortality in patients with mental illness using electronic health records
Sean Kim, Samuel Kim
Mental disorders impact the lives of millions of people globally, not only impeding their day-to-day lives but also markedly reducing life expectancy. This paper addresses the pers…
Extracting Interpretable Physical Parameters from Spatiotemporal Systems using Unsupervised Learning
Peter Y. Lu, Samuel Kim, Marin SoljaÄiÄ
Experimental data is often affected by uncontrolled variables that make analysis and interpretation difficult. For spatiotemporal systems, this problem is further exacerbated by th…
Predicting Students' Exam Scores Using Physiological Signals
Willie Kang, Sean Kim, Eliot Yoo +1
While acute stress has been shown to have both positive and negative effects on performance, not much is known about the impacts of stress on students grades during examinations. T…
OccamNet: A Fast Neural Model for Symbolic Regression at Scale
Owen Dugan, Rumen Dangovski, Allan Costa +4
Neural networks' expressiveness comes at the cost of complex, black-box models that often extrapolate poorly beyond the domain of the training dataset, conflicting with the goal of…
Multi-Site Clinical Federated Learning using Recursive and Attentive Models and NVFlare
Won Joon Yun, Samuel Kim, Joongheon Kim
The prodigious growth of digital health data has precipitated a mounting interest in harnessing machine learning methodologies, such as natural language processing (NLP), to scruti…
Enhanced strain coupling of nitrogen vacancy spins to nanoscale diamond cantilevers
Srujan Meesala, Young-Ik Sohn, Haig A. Atikian +4
Nitrogen vacancy (NV) centers can couple to confined phonons in diamond mechanical resonators via the effect of lattice strain on their energy levels. Access to the strong spin-pho…
Word Definitions from Large Language Models
Bach Pham, JuiHsuan Wong, Samuel Kim +2
Dictionary definitions are historically the arbitrator of what words mean, but this primacy has come under threat by recent progress in NLP, including word embeddings and generativ…