Welcome to Roy R. Lederman's homepage.

I am a postdoc in the Program in Applied and Computational Mathematics at Princeton University, working with Amit Singer.

In 2014-2015 I was a Gibbs Assistant Professor in the Applied Mathematics Program at Yale University, where I also got my PhD, working with Vladimir Rokhlin and Raphy Coifman. I have a BSc in physics and a BSc in electrical engineering from Tel-Aviv University.

Research


 

 

 

 

Interests and Recent work

  • Mathematics of data science
  • Numerical analysis: signal processing, the Laplace transform, decaying signals
  • Empirical geometry of data: manifold learning, diffusion maps, multi-sensor problems, unordered datasets
  • Cryo-EM: application of representation theory, numerical analysis, and data organization to imaging of molecules
  • Computational biology: fast search algorithms, statistics of DNA, sequencing, organization of biological data

Cryo-EM

Cryo-electron microscopy (cryo-EM) is a method for imaging molecules without crystallization. It has been named “Method of the Year 2015” by Nature Methods.

I work on various problems of alignment, classification and signal processing that are motivated by application in cryo-EM with many other applications. I am particularly interested in heterogeneity, i.e. imaging of mixtures of different types of molecules.

I work on “hyper-molecules” which represent heterogeneous molecules as higher-dimension objects. The movie below is an example of a reconstruction of a continuously heterogeneous object, using the approach described in this report.

I also work on a representation theory approach to simultaneous alignment and classification (this work is not related to the movie below), with applications in the heterogeneity problem in cryo-EM.

For more information on my work in cryo-EM, see project page.


No, this is not a dancing cat. See project page.

Numerical Analysis and Signal Processing

Function06The Laplace transform is frequently encountered in mathematics, physics, engineering and other areas. However, the spectral properties of the Laplace transform tend to complicate its numerical treatment; therefore, the closely related "Truncated" Laplace Transforms are often used in applications.

The numerical and analytical properties of the Truncated Laplace Transform are discussed in this report (dissertation), this paper (part I) and this paper (part II).

Geometry of Data

Alternating Diffusion SimulationAlternating Diffusion, a method for recovering the common variable in multi-sensor experiments, is discussed in this paper, this technical report and this project webpage.
A different approach to the common variable recovery problem, which also constructs representations that are invariable to unknown transformations, is discussed in this technical report.

What's going on? Why is everything spinning? See project webpage,
this paper and in this report.

This experiment has nothing to do with the cryo-EM experiment above. Rotating animals are a very convenient visualization.
 

Computational Biology

Random Permutations Based Alignment

I have developed randomized algorithms for sequencing of DNA and RNA. Paper: "A Random-Permutations-Based Approach to Fast Read Alignment" (RECOMB-SEQ 2013). Also see this report on the properties of sequencing.      

Additional Application: Assembly. The algorithm is also used to construct approximate overlap graphs. These graph are used for fast assembly. Unlike other algorithms, this algorithm allows errors in the reads, so no error-correction is necessary prior to the construction of the graph. See: technical report.
   

Additional Computational Biology Algorithms

Long-Range "Independence" The repetitive nature of DNA strings is one of the challenges in read alignment. When one examines longer substrings of DNA, they appear less repetitive, or more unique; permutations-based algorithms benefit from this property. We describe a way of measuring the property in this report and ways of using this property in reads with many "indels," in this report.

Homopolymer Length Filters Homopolymer length filters eliminate the mapping problem caused by homopolymer length errors (ionTorrent/454). A technical report is available here.
     

More information about my work in computational biology is available at http://roy.lederman.name/compbio/ .

Papers and Technical Reports

 

 

 

 

Papers and Technical Reports

2017

Lederman, Roy R; Singer, Amit

Continuously heterogeneous hyper-objects in cryo-EM and 3-D movies of many temporal dimensions Technical Report

2017.

Links | BibTeX | Tags: Cryo-EM, Data Science, Geometry of Data, Harmonic Analysis, Heterogeneity, Machine Learning, Multi Reference Alignment, Optimization, Signal Processing, Structural Biology, Unsupervised Learning

Stanton, Kelly P; Jin, Jiaqi; Lederman, Roy R; Weissman, Sherman M; Kluger, Yuval

Ritornello: high fidelity control-free chromatin immunoprecipitation peak calling Journal Article

Nucleic Acids Research, 2017.

Links | BibTeX | Tags: ChIP-seq, Computational Biology, DNA, Medicine, Numerical Analysis, Sequencing, Signal Processing, Statistics, Unsupervised Learning

Lederman, Roy R; Steinerberger, Stefan

Lower Bounds for Truncated Fourier and Laplace Transforms Journal Article

Integral Equations and Operator Theory, 87 (4), pp. 529-543, 2017.

Links | BibTeX | Tags: Harmonic Analysis, Laplace Transform, Numerical Analysis, Signal Processing, SVD, Truncated Laplace Transform

Aldroubi, Akram; Huang, Longxiu; Krishtal, Ilya; Lederman, Roy R

Dynamical sampling with random noise Conference

2017 International Conference on Sampling Theory and Applications (SampTA) IEEE, 2017.

Links | BibTeX | Tags: Dynamical Sampling, Harmonic Analysis, Numerical Analysis, Signal Processing

Shaham, Uri; Lederman, Roy R

Learning by Coincidence: Siamese Networks and Common Variable Learning Journal Article Forthcoming

Pattern Recognition, Forthcoming.

BibTeX | Tags: Alternating Diffusion, Data Science, Deep Networks, Geometry of Data, Machine Learning, Multiview, Optimization, Siamese Networks, Unsupervised Learning

2016

Lederman, Roy R; Singer, Amit

A Representation Theory Perspective on Simultaneous Alignment and Classification Technical Report

2016.

Links | BibTeX | Tags: Cryo-EM, Data Science, Geometry of Data, Harmonic Analysis, Heterogeneity, Multi Reference Alignment, Non-Unique-Games, Numerical Analysis, Optimization, Representation Theory, Structural Biology, Unsupervised Learning

Lederman, Roy R; Rokhlin, Vladimir

On the Analytical and Numerical Properties of the Truncated Laplace Transform - Part II Journal Article

SIAM Journal on Numerical Analysis, 54 (2), pp. 665–687, 2016.

Links | BibTeX | Tags: Harmonic Analysis, Laplace Transform, Numerical Analysis, Signal Processing, SVD, Truncated Laplace Transform

2015

Lederman, Roy R; Talmon, Ronen

Learning the geometry of common latent variables using alternating-diffusion Journal Article

Applied and Computational Harmonic Analysis, 2015.

Links | BibTeX | Tags: Alternating Diffusion, Data Science, Geometry of Data, Harmonic Analysis, Machine Learning, Manifold Learning, Multiview, Signal Processing, Unsupervised Learning

Lederman, Roy R; Rokhlin, Vladimir

On the Analytical and Numerical Properties of the Truncated Laplace Transform - I Journal Article

SIAM Journal on Numerical Analysis, 53 (3), pp. 1214-1235, 2015.

Links | BibTeX | Tags: Harmonic Analysis, Laplace Transform, Numerical Analysis, Signal Processing, SVD, Truncated Laplace Transform

Shaham, Uri; Lederman, Roy R

Common Variable Discovery and Invariant Representation Learning using Artificial Neural Networks Technical Report

YALE/DCS (1506), 2015.

Links | BibTeX | Tags: Alternating Diffusion, Data Science, Deep Networks, Geometry of Data, Machine Learning, Multiview, Siamese Networks, Unsupervised Learning

Lederman, Roy R; Talmon, Ronen; Wu, Hau-tieng; Lo, Yu-Lun; Coifman, Ronald R

Alternating diffusion for common manifold learning with application to sleep stage assessment Conference

2015 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), IEEE IEEE, 2015, ISBN: 978-1-4673-6997-8.

Links | BibTeX | Tags: Alternating Diffusion, Data Science, Geometry of Data, Laplace Transform, Machine Learning, Medicine, Multiview, Unsupervised Learning

2014

Lederman, Roy R

On the Analytical and Numerical Properties of the Truncated Laplace Transform PhD Thesis

Yale University, 2014.

Links | BibTeX | Tags: Harmonic Analysis, Laplace Transform, Numerical Analysis, Signal Processing, SVD, Truncated Laplace Transform

2013

Lederman, Roy R

Using the Long Range “Independence” in DNA: Coupled-Seeds and Pre-Alignment Filters Technical Report

YALE/DCS (1477), 2013.

Links | BibTeX | Tags: Alignment (DNA), Data Science, DNA, Fast Algorithms, Fast Search, Geometry of Data, Sequencing

Lederman, Roy R

A random-permutations-based approach to fast read alignment Journal Article

BMC bioinformatics, 14 (5), pp. S8, 2013, (RECOMB-seq 2013).

Abstract | Links | BibTeX | Tags: Alignment (DNA), Computational Biology, Data Science, DNA, Fast Algorithms, Fast Search, Geometry of Data, Laplace Transform, Randomized Algorithms, Sequencing

Lederman, Roy R

A permutations-based algorithm for fast alignment of long paired-end reads Technical Report

YALE/DCS (1474), 2013.

Links | BibTeX | Tags: Alignment (DNA), Data Science, DNA, Fast Algorithms, Fast Search, Geometry of Data, Randomized Algorithms, Sequencing

Lederman, Roy R

A Note about the Resolution-Length Characteristics of DNA Technical Report

YALE/DCS (1473), 2013.

Links | BibTeX | Tags: Computational Biology, Data Science, DNA, Geometry of Data, Randomized Algorithms, Sequencing, Signal Processing, Statistics

2012

Lederman, Roy R

Building approximate overlap graphs for DNA assembly using random-permutations-based search Technical Report

YALEU/DCS (1470), 2012.

Links | BibTeX | Tags: Computational Biology, Data Science, De Novo Assembly, DNA, Fast Algorithms, Fast Search, Geometry of Data, Sequencing

Lederman, Roy R

Homopolymer Length Filters Technical Report

YALE/DCS (1465), 2012.

Links | BibTeX | Tags: Computational Biology, Data Science, DNA, Fast Algorithms, Geometry of Data, Sequencing, Signal Processing

Teaching

 

 

 

 

Select Teaching

MATH555 / AMTH555 : Elements of Mathematical Machine Learning Yale, Spring 2015
MATH 112 : Calculus of Functions of One Variable I Yale, Spring 2015
AMTH 160 : The Structure of Networks – TA (Instructor: R.R. Coifman) Yale, Spring 2014
AMTH 160 : The Structure of Networks – TA (Instructor: R.R. Coifman) Yale, Spring 2013
AMTH 561 / CPSC 662 : Spectral Graph Theory – TA (Instructor: D.A. Spielman) Yale, Fall 2012
CPSC 365 : Design and Analysis of Algorithms – TA (Instructor: D.A. Spielman) Yale, Spring 2012
CPCS 445/545 : Introduction to Data Mining – TA (Instructor: V. Rokhlin) Yale, Fall 2011