Aaditya V. Rangan
Courant Institute of Mathematical Sciences, New York University,
251 Mercer Street, New York, NY 10012.
Phone: (212) 998-3303, email: rangan (at) cims (dot) nyu (dot) edu
webpage: http://www.cims.nyu.edu/~rangan/
Room: 1101 WWH
This semester I'm serving as the director of the master's program here at NYU.
If you have any questions, or would like to chat, please let me know online:
Google Calendar
and you can stop by my office (1101 WWH).
Research Interests: Applications of numerical-analysis and scientific-computing to the biological sciences.
CV (last updated 2025/01/05)
List of publications (last updated 2025/01/05)
Cryo-Electron Microscopy (Cryo-EM)
Recently I've been exploring computational strategies for use in cryo-em.
Link to paper [spectral sensitivity analysis for the single-particle-reconstruction problem]
Link to paper [robust ab-initio reconstruction with small data-sets]
Link to paper [radial compression for alignment]
Link to paper [factorization of the translation operator]
These tools (and more) are currently available at (Link to github ).
A more user-friendly version (in Python) is hosted at (Link to github ).
Bioinformatics
I've also been applying a 'loop-counting' algorithm for biclustering genomic data. I believe the results are rather promising.
Link to paper [analysis of genotyped data: Bipolar Disorder]
Link to paper [analysis of gene-expression data: Alzheimer's Disease] with additional benchmarking in the Appendix.
Link to paper [analysis of genotyped data: Alzheimer's Disease]
Here is a brief explanation of what the loop-counting algorithm actually does, as well as a tutorial which presents an example of this algorithm applied to a standard data set:
Link to paper
(tutorial on github)
A much more detailed explanation of this loop-counting algorithm is given in:
(tutorial in pdf)
(Supplementary Material).
A comparison of loop-counting with other popular clustering algorithms (i.e., U-MAP and Louvain clustering) is discussed here.
This comparison (taken from the Appendix linked above) demonstrates the advantages of loop-counting as applied to single-cell RNA-sequencing data.
A significantly more efficient implementation of this algorithm (written in C) is available at:
lakcluster_ver18 on github
This implementation also includes several subroutines which perform binary vector-vector, matrix-vector and matrix-matrix operations.
I've also worked on simple strategies for fitting time-series data with stochastic-differential-equations (SDEs), allowing for certain kinds of causal inference (Link to github).
This method is described in more detail here: Link to paper
251 Mercer Street, New York, NY 10012.
Phone: (212) 998-3303, email: rangan (at) cims (dot) nyu (dot) edu
webpage: http://www.cims.nyu.edu/~rangan/
Room: 1101 WWH
This semester I'm serving as the director of the master's program here at NYU.
If you have any questions, or would like to chat, please let me know online:
Google Calendar
and you can stop by my office (1101 WWH).
Research Interests: Applications of numerical-analysis and scientific-computing to the biological sciences.
CV (last updated 2025/01/05)
List of publications (last updated 2025/01/05)
Cryo-Electron Microscopy (Cryo-EM)
Recently I've been exploring computational strategies for use in cryo-em.
Link to paper [spectral sensitivity analysis for the single-particle-reconstruction problem]
Link to paper [robust ab-initio reconstruction with small data-sets]
Link to paper [radial compression for alignment]
Link to paper [factorization of the translation operator]
These tools (and more) are currently available at (Link to github ).
A more user-friendly version (in Python) is hosted at (Link to github ).
Bioinformatics
I've also been applying a 'loop-counting' algorithm for biclustering genomic data. I believe the results are rather promising.
Link to paper [analysis of genotyped data: Bipolar Disorder]
Link to paper [analysis of gene-expression data: Alzheimer's Disease] with additional benchmarking in the Appendix.
Link to paper [analysis of genotyped data: Alzheimer's Disease]
Here is a brief explanation of what the loop-counting algorithm actually does, as well as a tutorial which presents an example of this algorithm applied to a standard data set:
Link to paper
(tutorial on github)
A much more detailed explanation of this loop-counting algorithm is given in:
(tutorial in pdf)
(Supplementary Material).
A comparison of loop-counting with other popular clustering algorithms (i.e., U-MAP and Louvain clustering) is discussed here.
This comparison (taken from the Appendix linked above) demonstrates the advantages of loop-counting as applied to single-cell RNA-sequencing data.
A significantly more efficient implementation of this algorithm (written in C) is available at:
lakcluster_ver18 on github
This implementation also includes several subroutines which perform binary vector-vector, matrix-vector and matrix-matrix operations.
I've also worked on simple strategies for fitting time-series data with stochastic-differential-equations (SDEs), allowing for certain kinds of causal inference (Link to github).
This method is described in more detail here: Link to paper