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

Projects

A (very incomplete) selection of some of the topics I work on (past and present). More pages under construction.

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All Deep Learning Theory Optimization Matrix Factorization

Structured Matrix Factorization

Learning Matrix Factorizations with Structured Factors

Neural Network Loss Landscapes

Characterizing the loss surface of neural network training problems.

Understanding Dropout

Understanding the effects of Dropout regularization when training neural networks.

Recent Publications

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

Christina Baek, Ziyang Wu, Kwan Ho Ryan Chan, Tianjiao Ding, Yi Ma, Benjamin D Haeffele
2022 IEEE Conference on Computer Vision and Pattern Recognition

Efficient Maximal Coding Rate Reduction by Variational Forms

Paris V Giampouras, Benjamin D Haeffele, René Vidal
2022 International Conference on Learning Representations

Implicit Bias of Projected Subgradient Method Gives Provable Robust Recovery of Subspaces of Unknown Codimension

Aditya Chattopadhyay, Stewart Slocum, Benjamin D Haeffele, Rene Vidal, Donald Geman
2022 arXiv preprint arXiv:2207.00938

Interpretable by Design: Learning Predictors by Composing Interpretable Queries

Gregory N McKay, Anisha Oommen, Carolina Pacheco, Mason T Chen, Stuart C Ray, René Vidal, Benjamin D Haeffele, Nicholas J Durr
2022 arXiv preprint arXiv:2203.09999

Lens free holographic imaging for urinary tract infection screening

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

Understanding Doubly Stochastic Clustering

Benjamin D Haeffele, Chong You, René Vidal
2021 International Conference on Learning Representations

A Critique of Self-Expressive Deep Subspace Clustering

See all publications