Associate Research Scientist

Johns Hopkins Univeristy

Ben Haeffele

I am a research faculty member in the Johns Hopkins Mathematical Institute for Data Science (MINDS) and Center for Imaging Science (CIS). My current research is broadly on developing theory and algorithms for processing high-dimensional data at the intersection of machine learning, optimization, and computer vision. In addition to basic research in data science I also work on a variety of applications in medicine, microscopy, and computational imaging.

Education

  • PhD in Biomedical Engineering

    Johns Hopkins University

  • BS in Electrical Engineering

    Georgia Institute of Technology

Research Interests

Machine Learning

Deep learning theory | Sparse and low-rank methods | Matrix/tensor factorizations | Subspace clustering | Generative models | Non-linear/manifold clustering | Physics constrained learning

Computer Vision

Image/Video segmentation | Image classification | Compressed sensing | Object detection | Multi-object tracking

Optimization

Non-convex optimization | Low-rank problems | Convex relaxation/lifting methods

Microscopy

Lens-free imaging | Holography | Two-photon imaging

Biomedical Applications

Image analysis for microscopy | Hematology microscopy | Infection detection and monitoring | Digital pathology

Recent Publications

Juan Cervino, Luiz FO Chamon, Benjamin David Haeffele, Rene Vidal, Alejandro Ribeiro
2023 International Conference on Machine Learning

Learning globally smooth functions on manifolds

Tianjiao Ding, Shengbang Tong, Kwan Ho Ryan Chan, Xili Dai, Yi Ma, Benjamin D Haeffele
2023 IEEE International Conference on Computer Vision

Unsupervised manifold linearizing and clustering

Aditya Chattopadhyay, Kwan Ho Ryan Chan, Benjamin D Haeffele, Donald Geman, René Vidal
2023 International Conference on Learning Representations

Variational information pursuit for interpretable predictions

Yaodong Yu, Sam Buchanan, Druv Pai, Tianzhe Chu, Ziyang Wu, Shengbang Tong, Benjamin D Haeffele, Yi Ma
2023 arXiv preprint arXiv:2306.01129

White-Box Transformers via Sparse Rate Reduction

Tianzhe Chu, Shengbang Tong, Tianjiao Ding, Xili Dai, Benjamin David Haeffele, Rene Vidal, Yi Ma
2023 arXiv preprint arXiv:2306.05272

Image Clustering via the Principle of Rate Reduction in the Age of Pretrained Models

Tianjiao Ding, Derek Lim, René Vidal, Benjamin D Haeffele
2022 International Conference on Machine Learning

Understanding Doubly Stochastic Clustering

Carolina Pacheco, Gregory N McKay, Anisha Oommen, Nicholas J Durr, René Vidal, Benjamin D Haeffele
2022 Optics Express

Adaptive sparse reconstruction for lensless digital holography via PSF estimation and phase retrieval

See all publications