Welcome to Roy R. Lederman's homepage.
In 2015-2018 I was 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.
Interests and Recent work
- Mathematics of data science
- The combination of inverse problems and unsupervised learning
- Applied harmonics analysis
- Numerical analysis and signal processing: the truncated Fourier transform, prolate functions, the Laplace transform, decaying signals
- Empirical geometry of data: unsupervised learning, manifold learning, diffusion maps, multi-sensor problems
- Structural biology and cryo-EM: inverse problems and unsupervised learning, applications 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-electron microscopy (cryo-EM) is a method for imaging molecules without crystallization. The Nobel Prize in Chemistry 2017 was awarded to Jacques Dubochet, Joachim Frank and Richard Henderson "for the development of cryo-electron microscopy, which both simplifies and improves the imaging of biomolecules." 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 paper and this paper.
This is one of several approaches that I am developing for the heterogeneity problem in cryo-EM, and for other aspects of cryo-EM. For more information on my work in cryo-EM, see project page.
Numerical Analysis and Signal Processing
The Truncated Fourier Transform and its eigenfunctions, Prolate Spheroidal Wave Functions (PSWF) and Generalized Prolate Spheroidal Functions (GPSF) (also known as Slepian Functions) are frequently encountered in mathematics, physics, signal processing, optics and other areas. Surprisingly, very few resources and code for the numerical computation of GPSFs and their eigenvalues are publicly available. Our sample implementation and associated paper are available at http://github.com/lederman/prol. The code also contains an experimental "open-source proof," which is code for analytical proofs of some of the results that appear in this paper.
The Laplace Transform and Grunbaum Functions
The 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.
Bounds on Transforms
Lower bounds on the truncated Fourier transform and truncated Laplace transform: see paper.
Geometry of Data
Alternating 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 paper.
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 paper about the properties of DNA and sequencing.
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: paper.
Additional Computational Biology Algorithms
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 paper and ways of using this property in reads with many "indels," in this paper.
Homopolymer Length Filters
Homopolymer length filters eliminate the mapping problem caused by homopolymer length errors (ionTorrent/454). See paper.
Non-Unique Games Over Compact Groups and Orientation Estimation in Cryo-Em Technical Report Forthcoming
A Bias-Variance Decomposition for Bayesian Deep Learning Conference
Diffusion operators for multimodal data analysis Book Chapter
Inverse Problems, 2019.
Learning by Coincidence: Siamese Networks and Common Variable Learning Journal Article
Pattern Recognition, 2018.
Nucleic Acids Research, 2017.
Lower Bounds for Truncated Fourier and Laplace Transforms Journal Article
Integral Equations and Operator Theory, 87 (4), pp. 529-543, 2017.
Dynamical sampling with random noise Conference
2017 International Conference on Sampling Theory and Applications (SampTA) IEEE, 2017.
Applied and Computational Harmonic Analysis, 2016.
SIAM Journal on Numerical Analysis, 54 (2), pp. 665–687, 2016.
Applied and Computational Harmonic Analysis, 2015.
SIAM Journal on Numerical Analysis, 53 (3), pp. 1214-1235, 2015.
YALE/DCS (1506), 2015.
2015 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), IEEE IEEE, 2015, ISBN: 978-1-4673-6997-8.
Yale University, 2014.
YALE/DCS (1477), 2013.
A random-permutations-based approach to fast read alignment Journal Article
BMC bioinformatics, 14 (5), pp. S8, 2013, (RECOMB-seq 2013).
YALE/DCS (1474), 2013.
YALEU/DCS (1470), 2012.
Homopolymer Length Filters Miscellaneous
|S&DS262/S&DS562 : Computational Tools for Data Science||Yale, Spring 2020|
|S&DS663 : Computational Mathematics for Data Science||Yale, Fall 2019|
|S&DS676 : Signal Processing for Data Science||Yale, Spring 2019|
|S&DS663 : Computational Mathematics for Data Science||Yale, Fall 2018|
|MATH555 / AMTH555 : Elements of Mathematical Machine Learning||Yale, Spring 2015|
|MATH 112 : Calculus of Functions of One Variable I||Yale, Spring 2015|