Research

I study learning from data with structures, motivated by applications in healthcare and social science. Specifically, my research focuses on high-dimensional data that are supported on structures such as graphs/networks, low-dimensional manifolds, or sparsity, and explores how our knowledge about those structures can be exploited to solve challenging inference problems more accurately and quickly.

Mathematically, I am interested in developing algorithms for nonconvex optimization with provable performance guarantees using data geometry, for which I leverage signal processing, machine learning, optimization, information theory, network science, spectral graph theory, and (high-dimensional) statistics.

I am interested in data science (statistical signal processing and machine learning) with

• high-dimensional data supported on graph structures (networks)
• high-dimensional data with low-dimensional underlying structure (subspaces, manifold)
• machine learning applied to clinical time series data
• science of science and data science for social good
• nonconvex optimization; multitask learning; optimal transport

Publications

In author list, * denotes equal contribution, and † indicates alphabetical ordering.

Data Theory and Methods

Graph-structured data

Graph regularization for trend filtering (applied to transportation data), matrix factorization (applied to remote sensing), and federated multi-task learning (applied to census data).

• H Lee, AL Bertozzi, J Kovačević and Y Chi, Privacy-Preserving Federated Multi-task Linear Regression: A One-Shot Linear Mixing Approach Inspired by Graph Regularization, ICASSP, 2022.

• J Qin, H Lee, J Chi, L Drumetz, J Chanussot, Y Lou and AL Bertozzi, Blind Hyperspectral Unmixing Based on Graph Total Variation Regularization, IEEE Transactions on Geoscience and Remote Sensing, 2021. Related work presented at WHISPERS.

• R Varma*, H Lee*, J Kovačević and Y Chi, Vector-Valued Graph Trend Filtering With Non-Convex Penalties, IEEE Transactions on Signal and Information Processing over Networks, 2020. Partial results presented at ICASSP.

Data supported on low-dimensional structure

• A Lee†, H Lee†, JA Perea†, N Schonsheck† and M Weinstein†, \(O(k)\)-Equivariant Dimensionality Reduction on Stiefel Manifolds, Preprint, 2023.

• H Lee, Multitask Principal Components Analysis (PCA), Chapter 4.5 in Better Inference with Graph Regularization (CMU PhD Thesis), 2021.

Optimal transport

• A Blob Method for Dynamic Optimal Transport with State and Control Constraints. Ongoing work with Katy Craig and Karthik Elamvazhuthi.

Computational Social Science Applications

Science of science

Network analysis and natural language processing to bring together traditionally disjointed scientific fields in diverse intelligence (including cognitive science and ML/AI).

• M Krenn [and 15 others, including H Lee†], Forecasting the Future of Artificial Intelligence with Machine Learning-based Link Prediction in an Exponentially Growing Knowledge Network, Nature Machine Intelligence, 2023.

• H Lee, R Sonthalia and JG Foster, Dynamic Embedding-based Methods for Link Prediction in Machine Learning Semantic Network, IEEE BigData, 2021.

• L Cheng, JG Foster and H Lee, A Simple, Interpretable Method to Identify Surprising Topic Shifts in Scientific Fields, Frontiers in Research Metrics and Analytics, 2022.

• L Cheng*, G Ganesan*, W He*, D Silverston*, H Lee and JG Foster, Joint Content-Context Analysis of Scientific Publications: Identifying Opportunities for Collaboration in Cognitive Science, AI for Science: Mind the Gaps (NeurIPS Workshop), 2021.

Data science for social good

• Power analysis of a statistical test to quantify gerrymandering. Ongoing work with Ranthony AC Edmonds, Susan Glenn, and Soledad Villar.

• Branching models for gender imbalance in math. Ongoing work with Heather Zinn Brooks, Phil Chodrow, Anna Haensch, Mason Porter, and Juan Restrepo.

Healthcare Applications

Speech and audio

Deep learning for non-semantic speech and audio signals including coughing and sneezing.

• H Lee and A Saeed, Distilled Non-Semantic Speech Embeddings with Binary Neural Networks for Low-Resource Devices, Preprint, 2022.

• H Lee, A Saeed and AL Bertozzi, Active Learning of Non-semantic Speech Tasks with Pretrained Models, ICASSP, 2023.

Sleep health

Pediatric sleep scoring.

• H Lee and A Saeed, Automatic Sleep Scoring from Large-scale Multi-channel Pediatric EEG, Learning from Time Series for Health (NeurIPS Workshop), 2022.

• H Lee, B Li, S DeForte, ML Splaingard, Y Huang, Y Chi and SL Linwood, A Large Collection of Real-world Pediatric Sleep Studies, Nature Scientific Data, 2022.

Cardiology

Risk model from long-term EEG signals.

• H Lee, Building improved risk stratification models for patients post non ST-segment elevation acute coronary syndrome using ambulatory ECG data, MIT Master’s Thesis, 2017.