2-6300, bldg10, NIH
frashidi AT iu DOT edu

I am a predoctoral visiting fellow at the National Cancer Institute (NCI) working in Cancer Data Science Laboratory (CDSL) under supervision of Dr. Cenk Sahinalp. I am also a Ph.D. candidate studying Computer Science at the Indiana University Bloomington (IUB) since 2017. My main research focus is on Tumor Heterogeneity and Cancer Evolution modeling using Single-cell sequencing data. I completed my Master’s thesis at the Sharif University of Technology where I worked on learning of Alternative Splicing problem, jointly supervised by Dr. Abolfazl Motahari and Dr. Hamid Rabiee. I received my Bachelor’s degree from Amirkabir University of Technology (Tehran Polytechnic) in Iran.

Publications:

	P7 Profiles of expressed mutations in single cells reveal subclonal expansion patterns and therapeutic impact of intratumor heterogeneity bioRxiv Full text Poster Webpage Code Presentation
	P6 Epigenomic tumor evolution modeling with single-cell methylation data profiling bioRxiv Full text
	P5 Review on Studying the History of Tumor Evolution from Single-Cell Sequencing Data by Exploring the Space of Binary Matrices Journal of Computational Biology Full text
	P4 A Transcriptionally Distinct Subpopulation of Healthy Acinar Cells Exhibit Features of Pancreatic Progenitors and PDAC Cancer Research Full text Supplementary
	P3 PhISCS-BnB: a fast branch and bound algorithm for the perfect tumor phylogeny reconstruction problem Bioinformatics ISMB 2020 Proceedings Full text Supplementary Code Presentation
	P2 Clonal Evolution and Heterogeneity of Osimertinib Acquired Resistance Mechanisms in EGFR Mutant Lung Cancer Cell Reports Medicine Full text Supplementary
	P1 PhISCS: a combinatorial approach for subperfect tumor phylogeny reconstruction via integrative use of single-cell and bulk sequencing data Genome Research Full text Supplementary Code Presentation

Software:

	S6 scPhylo-tools: a python toolkit for single-cell tumor phylogenetic analysis Webpage Code Star Fork
	S5 Trisicell: a scalable tumor phylogeny reconstruction and validation tool using single-cell data Webpage Code Publication Star Fork
	S4 PhISCS: a tool for sub-perfect tumor phylogeny reconstruction via integrative use of single-cell and bulk sequencing data Code Publication Star Fork
	S3 PhISCS-BnB: a fast tool for reconstructing the perfect tumor phylogeny using single-ceqll data via branch and bound algorithm Code Publication Star Fork
	S2 HUNTRESS: a provably fast intratumor heterogeneity inference from single-cell sequencing data Code Star Fork
	S1 Kaggle Solutions: a searchable list of almost all available solutions and ideas shared by top performers in the past Kaggle competitions Webpage Code Star Fork