Viktor Petukhov, PhD
Education, degreesand work experience
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Work Experience
Position
Date
Place
Projects
Independent AI Consultant
Electronic Data Interchange department Projects
09/2021 → 11/2021

— Advising Phystech Ventures on computational biology projects

— Improving machine learning algorithms for Viaduct.ai

Team Lead, Data Science — Cleverbots
A data science outsource start-up
05/2018 → 10/2019
Moscow, Russia

— Sales forecast using Bayesian time-series modeling

— Prediction of personal marketing offers for pharmacy customers

— Forecasting of client churn

— Factory production process modeling and optimization

— People recommendation system for improvement of networking during events

Algorithm Developer — Epam Systems
Enterprise Software Development, Design & Consulting
09/2015 → 04/2017
St. Petersburg, Russia

— Development of algorithms for whole-genome de-novo assembly and structural variations search using optical maps (languages: Perl, Python, C++)

— Implementation of different curve-fitting algorithms for dose-response curve analysis (languages: Java, R)

— Statistical analysis for groups comparison in drug discovery (languages: SAS, R)

Education
Degrees
Date
University
Projects
PhD, Biostatistics and Bioinformatics
06/2019 → 12/2021
University Of Copenhagen

Principal supervisor: Konstantin Khodosevich, PhD, University Of Copenhagen

Primary co-supervisor: Peter Kharchenko, PhD, Harvard Medical School

Thesis: «Case-control analysis of single-cell RNA-seq studies»

Master, Applied Informatics and Computer Science

Master, Applied Informatics and Computer Science
09/2015 → 06/2017
St. Petersburg Polytechnic University

Supervisor: Peter Kharchenko, PhD, Harvard Medical School

Thesis: «Accurate estimation of molecular counts in droplet- based single-cell

RNA- seq experiments». GPA: 3.6.

Bachelor, Applied Mathematics And Computer Science
09/2011 → 06/2015
South Ural State University
My open source projects
Computational biology
dropEst
low-level pre-processing of scRNA-seq fastq files for accurate estimation of gene expression matrices
Genome Biology, 2018
C++
pagoda2
processing and analysis of individual scRNA-seq samples
R
conos
joint analysis of scRNA-seq collections
Nature Methods, 2019
R
cacoa
case-control analysis of scRNA-seq experiments
bioRxiv, 2022
R
Baysor
Bayesian cell segmentation in single-molecule protocols for spatially-resolved transcriptomics
Nature Biotech, 2021
Julia
CellAnnotatoR
marker-based annotationof scRNA-seq data
R
Comparison of single-molecule transcriptomic protocols
It was made at 2019 and is outdated now
Python
vpscituls
my helper functions for single-cell analysis
R
Machine learning
Enforcing spatial structure on word2vec
Application of ideas from Interpretable Neuron Structuring with Graph Spectral Regularization during my participation in the AI Safety Camp
Python
Piecewise-linear approximation
of 1D time-series using splines on stock data
Python
General
ggrastr
rasterize individual layers for ggplot2
R
dataorganizer
a research compendium package for scientific projects in R
R
MailDay Extension for Thunderbird
implementation of the Maxim Dorofeev’s idea on how to motivate yourself to proces old emails.
JS
My publications
See Scholar for more info
2022
Reference-based cell type matching of spatial transcriptomics data.
Zhang, Y., Miller, J. A., Park, J., Lelieveldt, B. P. F., Aevermann, B. D., Biancalani, T., Comiter, C., Langseth, C. M., Long, B. R., Petukhov, V., Scalia, G., Vaishnav, E. D., Zhao, Y., Lein, E. S., & Scheuermann, R. H.
Expression profile of synaptic vesicle glycoprotein 2A, B, and C paralogues in temporal neocortex tissue from patients with temporal lobe epilepsy (TLE).
Pazarlar, B. A, Aripaka, S. S., Petukhov, V., Pinborg, L. H., Khodosevich, K., & Mikkelsen, J. D
Case-control analysis of single-cell RNA-seq studies.
Petukhov, V., Igolkina, A. A., Rydbirk, R., Mei, S., Christoffersen, L. B, Khodosevich, K., & Kharchenko, P. V.
2021
Cell segmentation in imaging-based spatial transcriptomics.
Petukhov, V., Xu, R. J, Soldatov, R. A., Cadinu, P., Khodosevich, K., Moffitt, J. R., & Kharchenko, P. V.
2020
Selective vulnerability of supragranular layer neurons in schizophrenia.
Batiuk, M. Y., Tyler, T., Mei, S., Rydbirk, R., Petukhov, V., Sedmak, D., Frank, E., Feher, V., Habek, N., Hu, Q., Igolkina, A. A., Roszik, L., Pfisterer, U., Petanjek, Z., Adorjan, I., Kharchenko, P. V., & Khodosevich, K.
Identification of epilepsy-associated neuronal subtypes and gene expression underlying epileptogenesis.
Pfisterer, U., Petukhov, V., Demharter, S., Meichsner, J., Thompson, J. J, Batiuk, M. Y., Martinez, A. A., Vasistha, N. A., Thakur, A., Mikkelsen, J. D, Adorjan, I., Pinborg, L. H., Pers, T. H., von Engelhardt, J., Kharchenko, P. V., & Khodosevich, K.
2019
Joint analysis of heterogeneous single-cell RNA-seq dataset collections.
Barkas, N., Petukhov, V., Nikolaeva, D. D., Lozinsky, Y., Demharter, S., Khodosevich, K., & Kharchenko, P. V.
2018
RNA velocity of single cells.
Manno, G. L., Soldatov, R. A., Zeisel, A., Braun, E., Hochgerner, H., Petukhov, V., Lidschreiber, K., Kastriti, M. E., Lönnerberg, P., Furlan, A., Fan, J., Borm, L. E., Liu, Z., van Bruggen, D., Guo, J., He, X., Barker, R. A., Sundström, E., Castelo-Branco, G., Cramer, P., Adameyko, I., Linnarsson, S., & Kharchenko, P. V.
2017
dropEst: pipeline for accurate estimation of molecular counts in droplet-based single-cell RNA-seq experiments.
Petukhov, V., Guo, J., Baryawno, N., Severe, N., Scadden, D. T., Samsonova, M. G., & Kharchenko, P. V.
Share your contacts to discuss your ideas and needs
If there is no specific request, but you are interested in how machine learning can be useful to you — feel free to write too
viktor.petukhov@pm.me