Vamsi K. Ithapu



Peer-reviewed Conferences

[12]   V. K. Ithapu
Decoding the Deep: Exploring Class Hierarchies of Deep Representations using Multiresolution
Matrix Factorization
, Explainable Computer Vision Workshop (ECVW), CVPR 2017 [PDF] [Oral Presentation]

[11]   H. Hao, Y. Zhang, V. K. Ithapu, S. C. Johnson, G. Wahba, V. Singh
When can Multi-site Datasets be Pooled for Regression? Hypothesis Tests, l2 consistency and
Neuroscience Applications
, International Conference on Machine Learning (ICML), 2017 [PDF]

[10]   V. K. Ithapu , R. Kondor, S. C. Johnson, V. Singh
The Incremental Multiresolution Matrix Factorization Algorithm,
Computer Vision and Pattern Recognition (CVPR), 2017 [PDF]

[9]   V. K. Ithapu , S. Ravi, V. Singh
On the interplay of network structure and gradient convergence in deep learning,
54th Allerton Conference on Communication, Control and Computing, 2016 [PDF]

[8]   H. Hao, V. K. Ithapu, S. Ravi, V. Singh, G. Wahba, S. C. Johnson
Hypothesis Testing in Unsupervised Domain Adaptation with Applications in Alzheimer's Disease,
Neural Information Processing Systems (NIPS), 2016 [PDF]

[7]   S. Ravi, V. K. Ithapu, S. C. Johnson, V. Singh
Experimental Design on a Budget for Sparse Linear Models and Applications,
International Conference on Machine Learning (ICML), 2016 [PDF]

[6]   L. Mukherjee, S. Ravi, V. K. Ithapu, T. Holmes, V. Singh
An NMF perspective on Binary Hashing,
International Conference on Conputer Vision (ICCV), 2015 [PDF]

[5]   V. K. Ithapu , S. Ravi, V. Singh
Convergence of gradient based pre-training in Denoising autoencoders,
arxiv:1502.03537 [PDF]

[4]   S. J. Hwang, M. Collins, S. Ravi, V. K. Ithapu, N. Adluru, S. C. Johnson, V. Singh
A Projection free method for Generalized Eigenvalue Problem with a nonsmooth Regularizer,
International Conference on Conputer Vision (ICCV), 2015 [PDF]

[3]   V. K. Ithapu, V. Singh, O. Okonkwo, S. C. Johnson
Randomized denoising autoencoders for smaller and efficient imaging based AD clinical trials,
Medical Image Computing and Computer Assisted Intervention (MICCAI), 2014 [PDF]

[2]   V. K. Ithapu*, C. Hinrichs*, Q. Sun, S. C. Johnson, V. Singh
Speeding up Permutation Testing in Neuroimaging,
Advances in Neural Information Processing Systems (NIPS), 2013 [PDF]
* : Hinrichs and Ithapu contributed equally [Oral Spotlight]

[1]   J. Xu, V. K. Ithapu, L. Mukherjee, J. Rehg, V. Singh,
GOSUS: Grassmannian Online Subspace Updates with Structured-sparsity,
International Conference on Computer Vision (ICCV), 2013 [PDF]

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Journal Manuscripts

[5]   F. Gutierrez-Barragan, V. K. Ithapu , C. Hinrichs, C. Maumet, S. C. Johnson, T. E. Nichols, V. Singh and the ADNI
Accelerating Permutation Testing in Voxel-wise Analysis through Subspace Tracking: A new plugin for SnPM,
Neuroimage, 2017 [PDF]

[4]   V. K. Ithapu , S. Ravi, V. Singh
On architectural choices in deep learning: From network structure to gradient convergence and parameter estimation,
submitted (arXiv:1702.08670) [PDF]

[3]   N. N. Kumar, M. Gautam, J. J. Lochhead, D. J. Wolack, V. K. Ithapu, V. Singh, R. G. Thorne
Relative vascular permeability and vascularity across different regions of the rat nasal mucosa: implications for nasal physiology and drug delivery,
Nature Scientific Reports, 2016 [PDF]

[2]   V. K. Ithapu, V. Singh, O. C. Okonkwo, R. J. Chappell, N. M. Dowling, S. C. Johnson,
Imaging based enrichment criteria using deep learning algorithms for efficient clinical trials in MCI,
Alzheimer's and Dementia 2015 [PDF]

[1]   V. K. Ithapu, V. Singh, C. Lindner, B. Austin, C. Hinrichs, C. Carlsson, B. Bendlin, S. C. Johnson,
Extracting and summarizing white matter hyperintensities using supervised segmentation methods in Alzheimer's disease risk and aging studies,
Human Brain Mapping 2013 [PDF]

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Abstract Submissions

[11]   V. K. Ithapu
Decoding deep networks,
Midwest Machine Learning Symposium (MMLS), 2017

[10]   T. Vo, V. K. Ithapu, V. Singh, M. Newton
Multiple Hypothesis Testing with Graph-Associated Data,
Center for Predictive Computational Phenotyping (CPCP) Retreat, 2017

[9]   V. K. Ithapu, R. Kondor, S. C. Johnson, V. Singh
Generalizing Statistical Leverage Scores using Incremental Multiresolution Matrix Factorization,
Center for Predictive Computational Phenotyping (CPCP) Retreat, 2017

[8]   V. K. Ithapu, L. Clark, V. Singh, R. Koscik, S. C. Johnson,
Deductive Mode Finding: Tracing Back Cognitive Decline in Biomarker Positive Middle-Aged Adults,
Alzheimer's Association International Conference (AAIC), 2017

[7]   H. Zhou, V. K. Ithapu, S. Ravi, V. Singh, S. C. Johnson, G. Wahba, R. L. Koscik, S. Asthana, C. M. Carlsson, K. Blennow, H. Zetterberg,
Statistical Algorithms for Harmonizing Biomarker Distributions Across Different Cohorts, Sites and Assays: Applications to CSF Measurements,
Alzheimer's Association International Conference (AAIC), 2017

[6]   S. Ravi, V. K. Ithapu, V. Singh, R. Koscik, S. C. Johnson,
Machine Learning Algorithms for Experiment Design in High Dimensional Longitudinal Cohort Studies: Implications for Clinical Trials,
Alzheimer's Association International Conference (AAIC), 2017

[5]   H. Zhou, S. Ravi, V. K. Ithapu, S. C. Johnson, G. Wahba, V. Singh
Hypothesis Testing in Unsupervised Domain Adaptation with Applications in Neuroscience,
Center for Predictive Computational Phenotyping (CPCP) Retreat, 2016

[4]   T. Vo, V. K. Ithapu, V. Singh, M. Newton
Graph partitioning: mixtures for modeling and clustering graph-associated data,
Center for Predictive Computational Phenotyping (CPCP) Retreat, 2016

[3]   V. K. Ithapu, V. Singh, O. Okonkwo, S. C. Johnson
A predictive multi-modal imaging marker for designing efficient and robust AD clinical trials,
Clinical Trials on Alzheimer's Disease (CTAD), 2014 [Oral Communication]

[2]   V. K. Ithapu, V. Singh, O. Okonkwo, R. J. Chappell, S. C. Johnson
A predictive multimodal imaging marker for efficient sample enrichment in AD clinical trials,
Alzheimer's Association International Conference (AAIC), 2014

[1]   V. K. Ithapu, V. Singh, B. Austin, C. Hinrichs, C. Carlsson, B. Bendlin, S. C. Johnson,
Extracting white matter hyperintensities in Alzheimer's disease risk and aging studies using supervised segmentation methods,
Alzheimer's Association International Conference (AAIC), 2013

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Book Chapters

[1]   V. K. Ithapu, V. Singh, S. C. Johnson,
Randomized deep learning methods for clinical trial enrichment and design in Alzheimer's disease,
Deep Learning for Medical Image Analysis (1st Edition) ISBN: 9780128104088; Chapter 15
[Elsevier Link] [Amazon Link] [Google Books Link]

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Patents

[2]   V. K. Ithapu, V. Singh, S. C. Johnson, O. C. Okonkwo
Medical Imaging System Providing Disease Prognosis
US Patent 20160073969, 2016 [URL]

[1]   V. K. Ithapu, A. K. Mishra,
Cooperative Multi-Monostatic Synthetic Aperture Radar, Patent Number: 499/kol/2010

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Selected Talks

[4]   Machine Learning methods for enriching clinical trials in Preclinical Alzheimer's Disease,
Mayo Symposium on the BRAIN Initiative, 2017 [Slides]

[3]   On the interplay of network structure and gradient convergence in deep learning,
Allerton Conference on Communications, Control and Computing (ALLERTON), 2016 [Slides]

[2]   A predictive multi-modal imaging marker for designing efficient and robust AD clinical trials,
Clinical Trials on Alzheimer's Disease (CTAD), 2014 [Slides]

[1]   Speeding up Permutation Testing in Neuroimaging,
Advances in Neural Information Processing Systems (NIPS), 2013 [Slides (Longer Version)]

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For publications before 2011 (during my bachelors), please see here [Prior to 2011]