• Computational Biology Group leader. Experience building a team, supervising direct reports, and supporting their professional growth (up to 3 reports from Sr Research Associate to Sr Scientist levels).• Single-cell analysis of in vitro (iMG) and patient data (AD and PD), datasets integration, and identification of resilient microglia subpopulations via the intersection of expression and deeply phenotyped data.• CRISPR pooled and PerturbSeq data analysis for the Functional Genomics team.• Working on external collaborations with academia partners to evaluate the effect of MS4A antibodies in various systems (organoids, xenotransplanted mice with human microglia).• Development of an in-house Data Portal for data visualization and analysis of internal data across various types, coordinating the work of 2 external software engineers.• Supporting current programs with omics analysis (RNAseq, scRNAseq, proteomics, and lipidomics).

• Bioinformatics leader for AL044/MS4A program. Processed 20+ in-house bulk RNAseq datasets, meta-analysis across studies, contributed critical data for IND filing, wrote several Research Technical Reports for the Business Development package, and participated as a key computational expert in discussions with Pharma partners.• Supported Immuno-Oncology with novel targets through TCGA data analysis (GWAS on patient survival and immune cell fraction data for multiple cancer types). Led working group meetings with the IO team and presented results to the Research Leadership Team and Scientific Advisory Board. By the results of this work the new IO program was formed in Q3 2022.• Processing of public datasets in support of Alector programs. Prepared and submitted applications for 10+ datasets (Synapse, EGA, NIAGADS), processed genotype data (liftOver, imputation), variant calling from RNAseq (GATK, DeepVariant), trans-eQTL effects of risk/protective alleles across different cell types and brain regions, coordinated work of several team members.• Providing and coordinating computational support for Alector Research across various departments (Genomics, Biology, Biomarkers, Preclinical) and various programs (ALS biomarkers via TargetALS, proteomics analysis of GLP and DRF tox studies).

• Established comprehensive in-house bulk RNAseq processing pipeline. Processed multiple bulk RNAseq datasets across different species (Human, Cyno, Mouse), different cell types (macrophages, microglia, bulk brain, iPSC, THP1), and in support of multiple programs. Created request tracking system to organize data details, reports, status updates. Presented and discussed results with Project Teams. Formatted and uploaded data to the Data Portal to make them available for Research.• Developed scRNAseq visualization tool (ShinyApp deployed on Alector server) to allow scientists explore and interact with the data.• Proactively initiated establishment of best practices within Bioinformatics group. Organized and led group meetings, facilitated Alector GitHub roll out, shared bulk RNAseq pipeline and led training session, created TDG SharePoint site for genomics analysis requests tracking.

• Supported Neuroscience Therapeutic Area with computational analysis. Evaluation and internalization of large Alzheimer’s Disease cohort data (AMP-AD), RNAseq data processing, splicing analysis of the putative targets. Role of APOE in microglia biology: gene imputation of macrophage dataset, differential expression and network analysis.• Provided computational support for Oncology Research Team in target identification. Performed data analysis of the genome- wide CRISPR and RNAi screening datasets, applied machine learning modeling to generate ~2M of tumor dependences on molecular features (comprehensive Cancer Dependency Map) that can be mined for novel cancer targets. Identified candidate gene list with focal cytotoxic profiles (high demand cancer targets).• Contributed to the advancement of siRNA therapies. Helped with designing strategies for improved siRNA selectivity: off-target identification with in silico prediction tools, RNAseq data analysis, effect of chemical modification, enrichment analysis of mismatch off-targets. Participated and presented at multiple cross-functional discussions.

• Developed variant reprioritization pipeline that utilizes GWAS data, advanced functional annotation, network analysis, unsupervised composite scoring method, and weighted evidence approach. The pipeline could be used for gene candidate prioritization for both target discovery and biomarkers of drug-induced toxicities.
• Contributed to the development of high impact drug screening in vitro assays for potential hepatic steatosis toxicity. Suggested gene candidates. Collaborated with various groups across the organization on designing experimental validation procedure.
• Helped to design the proposal on the use of liver 3D chip technology for validation of putative hepatic steatosis biomarkers that was awarded $100K by Organovo (see link below).
• Cleaned and organized internal datasets of target liability assessment reports, mapped putative liability to MedDRA terms. Performed exploratory data analysis of preclinical in vivo data and clinical trials data.
• Actively engaged in outreach with external partners in industry and academia.

Advisor: Carlos Bustamante• Lead Machine Learning Working Group at the Clinical Genome Resource (ClinGen) consortium, a $25M NIH-funded project aimed to catalog the disease-association of common, rare and novel variants identified through genome-scale testing. • Merged and cleaned variant data from 6 diverse data sources, that led to collection of ~2,000 CFTR variants.• Developed pipelines for NGS data analysis, including open source software (PolyPhen-2, SIFT, CADD, FoldX, etc.) and custom tools for extraction of features from the protein 3D structure, DSSP and AAindex datasets.• Developed machine learning method for pathogenicity prediction of variants that give 16% increase in accuracy over widely used tools like PolyPhen-2 and CADD, and 7% increase over the best-performing tool MutPred.

Advisor: Arieh Warshel, Nobel Laureate in Chemistry 2013!• Developed and implemented new energy terms in Coarse Grain method important for simulation of membrane proteins• Debugged and supported developed methods• Modeled translocon-assisted protein insertion processes• Published results in 5 papers (including 3 first author PNAS papers)• Calculated absolute binding free energy of GPCR inhibitors• Practiced Java and C++ programming with emphasis on advanced programming techniques

Taught the lab sections for:CHEM300 “Analytical Chemistry”, CHEM105b “General Chemistry”, CHEM203 “Chemistry in life: AIDS drug discovery and development”.

Advisor: Sergey Nuzhdin• Participated in bioinformatics project in Prof. S. Nuzhdin's lab: calculated correlation between transcription factor binding and gene expression data sets• Published results in Trends in Genetics

Advisor: Ian Haworth• Participated in team work designing a new inhibitor for histone deacetylase• Participated in drug design project in Prof. I. Haworth's lab: calculated scoring functions for a set of glucose-regulated protein inhibitors

Advisor: Alexander Kukushkin• Completed fundamental courses in physics and mathematics• Applied quantum-chemical methods to study structure and function of the photosynthetic oxygen evolving complex• Published results in 1 book chapter and 1 journal article

Taught the lab section titled “Calculation of simple molecules by semiempirical methods of quantum chemistry” for the undergraduate students

Taught science (math, physics, chemistry, biology) to high school students

Advisor: Yegor Yegorov• Participated in the development of telomerase activity measurement as a potential new method for early cancer diagnostics