Scanning electron micrograph of Mycobacterium tuberculosis, the pathogen that causes TB (CDC).
Scanning electron micrograph of Mycobacterium tuberculosis, the pathogen that causes TB (CDC).


My research interests lie primarily in the mathematical and computational modeling of infectious diseases, both on the molecular level (using approaches from computational and systems biology) as well as on the population level (using approaches from epidemiology and biostatistics). I am particularlly interested in the interactions between science, medicine and policy as they relate to improving patient outcomes, especially in low-income, low-resource settings.

I hold a PhD in Applied Mathematics from MIT and a BSc in Mathematics and Computer Science from McGill University. My doctoral work focused on metabolic models of tuberculosis (TB). I was a postdoctoral fellow in Ted Cohen's group from 2012 to 2015 before joining the School of Computing Science at Simon Fraser University as an Assistant Professor. I currently lead a group of 12 people focusing on the genomics of infectious diseases, and I am also involved with the ReSeqTB Consortium and the Omics Data Sciences Institute .

Between my PhD and my postdoctoral fellowship I worked as a computational biologist at Pfizer, developing methods for interpreting gene expression, genetic and metabolomic data using a large network of known biological relationships. I also spent some time working at the Massachusetts General Hospital and the Clinton Health Access Initiative.


I am actively looking for excellent, highly motivated postdoctoral fellows (see the ad on the ODSI website). If you are interested in working on machine learning for drug resistance please email me!

Read more about the latest project we received funding for: SFU-led team fights drug-resistant tuberculosis with AI

In February 2016 I was selected as an Alfred P. Sloan Research Fellow in Computational and Evolutionary Molecular Biology. See the announcement on the Simon Fraser University website.

Q & A with Leonid Chindelevitch: Can computer models predict pandemics?


As an applied mathematician I use a variety of mathematical methods to address one of today's most pressing problems: infectious disease epidemics. I am pursuing a cross-disciplinary approach to the study of infectious disease that incorporates computational biology, phylogenetics, epidemiology, and economic game theory.

Computational biology: analyzing metabolic networks, with applications to M. tuberculosis.

My doctoral research involved the modeling of the metabolism of TB. I developed an algorithmic pipeline called MetaMerge which allowed me to reconcile differences in format, nomenclature, and annotation between two models of TB metabolism. MONGOOSE, another doctoral project of mine, is a tool for analyzing metabolic network models in exact arithmetic, resulting in consistent, reproducible predictions.

Epidemiology: compartmental modeling of the tuberculosis-HIV/AIDS co-epidemic.

As part of my postdoctoral research I developed a compartmental model that accounts for the joint natural history of TB and HIV. I calibrated this model to data available for South Africa and used it to investigate the effects of various interventions aimed at reducing the burden of these diseases in order to help policy-makers select the most impactful and cost-effective one among many alternatives.

Phylogenetics: analyzing prevalence and transmission patterns of mixed tuberculosis infection.

I am developing a method for elucidating the genetic forces at play in the TB epidemics in South Africa and South America by using the TB strains' MIRU-VNTR fingerprints. My method integrates uncertainties about the strain composition of a sample and the phylogenetic relationship between the strains into a robust algorithmic framework to identify patients with mixed infections or mutating strains.

Economic game theory: modeling the alignment of incentives between public and private sector.

I am currently pursuing a collaboration examining the economic incentive structures for the interaction between public and private medical sectors in India. Using a principal-agent paradigm and incorporating the stochasticity of the health outcomes allows us to combine a population-level stochastic model of the course of an infectious disease epidemic with a game-theoretic model of dynamic contract negotiation.



Speeding up Dualization in the Fredman-Khachiyan Algorithm B. (HTML)

Nafiseh Sedaghat, Tamon Stephen, Leonid Chindelevitch.

Proceedings of the 17th International Symposium on Experimental Algorithms (SEA 2018), Volume 6, pages 1-13  (2018)

On the Rank-Distance Median of 3 Permutations. (HTML)

Leonid Chindelevitch, João Paulo Pereira Zanetti and João Meidanis.

BMC Bioinformatics, Volume 19, Number 6, Page 43  (2018)

Relatedness of the Incidence Decay with Exponential Adjustment (IDEA) Model, “Farrs Law” and Compartmental Difference Equation SIR Models. (HTML)

Mauricio Santillana, Ashleigh Tuite, Tahmina Nasserie, Paul Fine, David Champredon, Leonid Chindelevitch, Jonathan Dushoff, David Fisman.

Infectious Disease Modelling, Volume 3, Pages 1-12  (2018)

MentaLiST - A fast MLST caller for large MLST schemes. (HTML)

Pedro Feijão, Hua-Ting Yao, Dan Fornika, Jennifer Gardy, William Hsiao, Cedric Chauve, Leonid Chindelevitch.

Microbial Genomics, 10 January  (2018)


A standardised method for interpreting the association between mutations and phenotypic drug-resistance in Mycobacterium tuberculosis. (HTML)

Paolo  Miotto, Belay Tessema, Elisa Tagliani, Leonid Chindelevitch, [30 other authors], Tim Rodwell.

European Respiratory Journal, 50(6)  (2017)

On the Rank-Distance Median of 3 Permutations. (HTML)

Leonid Chindelevitch and João Meidanis.

Proceedings of RECOMB Comparative Genomics; Lecture Notes in Computer Science, Volume 10562, pp. 256-276  (2017)

Polyclonal Pulmonary Tuberculosis Infections and Risk for Multidrug Resistance, Lima, Peru. (HTML)

Ruvandhi Nathavitharana, Cynthia Shi, Leonid Chindelevitch, Roger Calderon, Zibiao Zhang, Jerome Galea et al.

Emerging Infectious Diseases, 23 (11)  (2017)

Eyes on Bhopal, Three Decades Later: Vision screening results in a cohort of Bhopal gas disaster survivors. (HTML)

Premnandhini Satgunam, Leonid Chindelevitch.

Current Science, 112(10)  (2017)


Within-Host Heterogeneity of Mycobacterium tuberculosis Infection Is Associated With Poor Early Treatment Response: A Prospective Cohort Study (HTML)

Ted Cohen, Leonid Chindelevitch, Reshma Misra, Maria Kempner, Jerome Galea et al.

Journal of Infectious Diseases, 213 (11): 1796-1799  (2016)

ClassTR: classifying within-host heterogeneity based on tandem repeats with application to Mycobacterium tuberculosis infections. (HTML)

Leonid Chindelevitch, Caroline Colijn, Prashini Moodley, Douglas Wilson, Ted Cohen.

PLoS Computational Biology, 12(2):e1004475  (2016)


"Do genome-scale models need exact solvers or clearer standards?" (HTML)

Leonid Chindelevitch, Jason Trigg, Aviv Regev, Bonnie Berger.

Molecular Systems Biology, 11(10):830 (2015)

Evaluating the potential impact of enhancing HIV treatment and tuberculosis control programmes on the burden of tuberculosis (HTML)

Leonid Chindelevitch, Nicolas Menzies, Carel Pretorius, John Stover, Joshua Salomon, Ted Cohen.

Journal of the Royal Society Interface, 12:106 (2015)


An exact arithmetic toolbox for a consistent and reproducible structural analysis of metabolic network models (HTML)

Leonid Chindelevitch, Jason Trigg, Aviv Regev, Bonnie Berger.

Nature Communications, 5:4893 (2014)

Health benefits, costs, and cost-effectiveness of earlier eligibility for adult antiretroviral therapy and expanded treatment coverage: a combined analysis of 12 mathematical models (HTML)

Jeffrey Eaton, Nicolas Menzies, John Stover, Valentina Cambiano, Leonid Chindelevitch et al.

The Lancet Global Health, Volume 2, Issue 1, January 2014, Pages e23-e34

The potential effects of changing HIV treatment policy on TB outcomes in South Africa: results from three TB-HIV transmission models (HTML)

Carel Pretorius, Nick Menzies, Leonid Chindelevitch, Ted Cohen, Anne Cori et al.

AIDS (2014) January Supplement (1) S25-34


Optimizing a global alignment of protein interaction networks (HTML)

Chindelevitch, Leonid; Ma, Cheng-Yu; Liao, Chung-Shou; Berger, Bonnie.

Bioinformatics, Volume 29, Issue 21, 2013, Pages 2765-2773


Causal reasoning on biological networks: interpreting transcriptional changes (HTML)

Chindelevitch, Leonid; Ziemek, Daniel; Enayetallah, Ahmed; Randhawa, Ranjit; Sidders, Ben; Brockel, Christoph; Huang, Enoch S.

Bioinformatics, Volume 28, Issue 8, 2012, Pages 1114-1121

MetaMerge: scaling up genome-scale metabolic reconstructions with application to Mycobacterium tuberculosis (PDF)

Chindelevitch, Leonid; Stanley, Sarah; Hung, Deborah; Regev, Aviv; Berger, Bonnie.

Genome Biology, Volume 3, Issue 1, 2012, Page R6

Assessing statistical significance in causal graphs (HTML)

Chindelevitch, Leonid; Loh, Po-Ru; Enayetallah, Ahmed; Berger, Bonnie; Ziemek, Daniel.

BMC Bioinformatics, Volume 3, Issue 1, 2012, Page 35

Correlation set analysis: detecting active regulators in disease populations using prior causal knowledge (HTML)

Huang, Chia-Ling; Lamb, John; Chindelevitch, Leonid; Kostrowicki, Jarek; Guinney, Justin; DeLisi, Charles; Ziemek, Daniel.

BMC Bioinformatics, Volume 3, Issue 1, 2012, Page 46


Causal reasoning on biological networks: Interpreting transcriptional changes (PDF)

Chindelevitch, Leonid; Ziemek, Daniel; Enayetallah, Ahmed; Randhawa, Ranjit; Sidders, Ben; Brockel, Christoph; Huang, Enoch.

Research in Computational Molecular Biology, 2011, Pages 34-37

Metabolic network analysis demystified (PDF)

Chindelevitch, Leonid; Regev, Aviv; Berger, Bonnie.

Research in Computational Molecular Biology, 2011, Pages 31-33


Local optimization for global alignment of protein interaction networks (PDF)

Chindelevitch, Leonid; Liao, Chung-Shou; Berger, Bonnie.

Pacific Symposium on Biocomputing, Volume 15, 2010, Pages 123-132


Inverting the Viterbi algorithm: an abstract framework for structure design (PDF)

Schnall-Levin, Michael; Chindelevitch, Leonid; Berger, Bonnie.

Proceedings of the 25th International Conference on Machine Learning, 2008, Pages 904-911


Error analysis and preconditioning for an enhanced DtN-FE algorithm for exterior scattering problems (HTML)

Chindelevitch, Leonid; Nicholls, David P; Nigam, Nilima.

Journal of Computational and Applied Mathematics, Volume 2004, Issue 2, 2007, Pages 493-504


On the inference of parsimonious indel evolutionary scenarios (HTML)

Chindelevitch, Leonid; Li, Zhentao; Blais, Eric; Blanchette, Mathieu.

Journal of Bioinformatics And Computational Biology, Volume 4, Issue 3, 2006, Pages 721-744


MetaMerge: combining and reconciling metabolic network model reconstructions

MetaMerge is a package for combining different metabolic network models of the same organism by reconciling the differences in annotation, coverage and model specification. We successfully used it to combine two existing metabolic models of TB. MetaMerge is available for download at

MONGOOSE: exact arithmetic-based analysis of metabolic network models

MONGOOSE (MetabOlic Network GrOwth Optimization Solved Exactly) is a package for structural analysis and refinement of constraint-based metabolic networks. Unlike other existing software, MONGOOSE uses exact rational arithmetic, which makes its results certifiably accurate. MONGOOSE is available for download at

ClassTR: classifying complex TB infections

ClassTR is a program that takes microsatellite (molecular fingerprinting) data of a collection of TB strains and for each strain containing loci with more than one variant, determines whether it's more likely to be the result of microevolution within the host or multiple infections. ClassTR is available for download as an R package at PLoS Computational Biology.

MongooseGUI: graphical user interface for MONGOOSE.

MONGOOSE (MetabOlic Network GrOwth Optimization Solved Exactly) is a package for structural analysis and refinement of constraint-based metabolic networks. Unlike other existing software, MONGOOSE uses exact rational arithmetic, which makes its results certifiably accurate. MongooseGUI is available for download on GitHub.

TreeCentrality: computes tree shape statistics.

TreeCentrality is a package for computing network science statistics on (rooted or unrooted) phylogenetic trees in linear time and space. In addition, this package can compute the spectra of the adjacency, Laplacian, and distance matrices as well as a number of classical topology statistics. TreeCentrality is available for download on GitHub.

MentaLiST: finds MLST types from WGS data

MentaLiST is a fast MLST caller for large MLST schemes. MLST (multi-locus sequence typing) is a classic technique for genotyping bacteria, widely applied for pathogen outbreak surveillance. MentaLiST is based on a k-mer counting algorithm and written in the Julia language, specifically designed and implemented to handle large typing schemes. MentaLiST is available for download on GitHub.

StackTB - classifier of TB lineages.

StackTB - A novel method for predicting the lineage of a tuberculosis strain from its copy number variants from MIRU-VNTR data. StackTB is a machine learning-based method that outperforms the current state-of-the-art tools despite using a substantially smaller training dataset. StackTB is available for download via Web interface

DivMLST - detects MLST diversity from WGS data.

DivMLST is a cubic algorithm for the generalized rank median of three genomes. This package deconvolutes the Diversity of Within-host Pathogen Strain in a MLST Framework. DivMLST is available for download at github MLST.

PRINCE - computes copy numbers from WGS data.

PRINCE is a Machiavellian Approach for Tandem Repeat Copy Number Approximation. PRINCE estimates Variable Number Tandem Repeats (VNTR) copy number from raw next generation sequencing (NGS) data. PRINCE is available for download at github PythonPRINCE.

Lab Members

Our lab is a friendly, dynamic group of people, ranging from first-year undergraduates to senior postdocs. We have biweekly meetings and regular social events. Please email me ( if you are interested in joining our lab!

  • Maryam Hayati

    PhD student

  • Nafiseh Sedaghat

    PhD student

  • Hooman Zabeti

    PhD student

  • Mohsen Katebi

    MSc student

  • Reza Mirsaskarshahi

    MSc student

  • Jianwei Li

    MSc student

  • Julius Booth

    MSc student

  • Alex Sweeten

    MSc student

  • Brian Lee

    Undergraduate student

  • Matthew Nguyen

    Undergraduate student

  • Johnathan Wong

    Undergraduate student

  • Einar Gabbasov

    Undergraduate student

Former Lab Members

  • Nithum Thain

    Postdoctoral Fellow

    Now: Google Research Fellow

  • Pedro Feijão

    Postdoctoral fellow

    Now: Computational Scientist at Contextual Genomics

  • Fang Zhang

    MSc student

  • Ali Pazoki

    MSc student

    Now: PhD student at UCLA

  • Guo Liang Gan

    MSc student

    Now: Machine learning researcher at 1QBit

  • Etienne Lasalle

    Undergraduate student

    Now: MSc Student at ENS-Cachan

  • Hua-Ting Yao

    Undergraduate student

    Now: MSc Student at Polytechnique

  • Christopher Le

    Undergraduate student

    Now: Software Development Engineer at Amazon

  • Emre Erhan

    Undergraduate student

    Now: MSc student at UBC

  • Jack Seary

    Undergraduate student

    Now: Software Developer

  • Nathan Nastili

    Undergraduate student

    Now: Graduate Student at Cornell

  • Abby Leung

    Undergraduate student

    Now: student at McGill

  • Joseph Lucero

    Undergraduate student

    Now: MSc student at SFU

  • Elijah Willie

    Undergraduate student

    Now: MSc student at UBC

  • Sean La

    Undergraduate student

    Now: MSc student at UBC

Former Visitors

  • Omar Castillo

    PhD student, U. Bielefeld

  • Nicole Althermeler

    PhD student, U. Bielefeld

  • Parham Ghasemloo

    Undergraduate student, Sharif University

  • Sriram Prithvi

    Undergraduate student, IIT Guwahati


TASC 1 9425
8888 University Drive,
Burnaby, BC, V5A 1S6

+1 (778) 782-4973

Office Location: