Rasteh Nili, Data Scientist · Bioinformatician · Data Analyst · Computational Biologist

About

• 6 years of experience in data analysis and analytics (bioinformatics and biometrics) • Work experience with time series and high-throughput data • Well-developed understanding of molecular biology, bioinformatics, statistical analysis, and analytics • Fluent in python and R programming with proven ability to learn new software fast • Proficient in Data processing, manipulation, visualization, and modeling using Machine learning and Deep learning approaches • MSc in bioinformatics from Concordia (Concordia Award of Excellence) • “The Transcriptional Portrait of Zinc Cluster Transcription Factors in Candida Albicans: A Network Approach to Capture the Complicated Co-Dependencies and Regulatory Relationships” TECHNICAL SKILLS Languages: Python, R, SQL, NoSQL, and Bash script Technologies: Unix, Git, and BigQuery Software packages: Scikitlearn, Tkinter, Keras, TensorFlow, pyMongo, python Dash, Dash Bio, explainerdashboard, SHAP, PDPbox, lime, LMFIT, subprocess, multiprocessing, difflib, SMOGN, OpenCV, Pillow, Scipy, spectrochempy, Biopython, primer3, ViennaRNA, UNAFold, NCBI API, BWA, STAR, SAMtools, Picard, GATK, HTSeq, Trimamatic, FastQC, MEME Suite, HOMER, PHYLIP, ClustalW2, InterProScan, Cytoscape, ggplot2, ComplexHeatmap, DESeq2, GSEABase, GOstats

Experience

Galenvs

Oct 2020 – Sep 2022

Montreal

Bioinformatic Specialist

• Create pipelines to pre-process data, balance, and engineer features for machine learning • Statistical analysis and visualization to detect clusters, outliers, confounding factors, and collinearity using DBSCAN, Gaussian Mixture, tSNE, and collinearity heatmap • Explain models using feature interaction, feature importance, PDP plots, and Shapley value • Developed Machine learning models (e.g. XGBoost regression, Random Forest, and Focal Loss) • Develop Mathematical models (e.g. Sigmoid, Voight, Lorentzian, and Gaussian) for IR analysis and deconvolution • Optimize speed, performance, and organization of software using multiple processing and design patterns (e.g. singleton and factory) • Obtain and curate genetic data from public databases (e.g. GSAID, IRD, and NCBI API) • Designed Toehold-mediated strand displacement, LAMP, primers, and molecular beacons • Generate interactive dashboards for data visualization, manipulation, and analysis • Generate, maintain and implement non-structural databases into GUI pipelines that would be interactively modified and updated through dashboards ACHIEVEMENTS: • Complete multi-page dashboards for analyzing Spectroscopy and Bioanalyzer Data • Complete multipage dashboard for identification and integration of Buffer recipes into the database • Generated multipage dashboard for designing primers and probes • Completed Specific Oligo design that is under patent review

RE-AK technology inc.

Mar 2019 – Apr 2020

Montreal

Data Scientist

• Create pipelines to pre-process, manipulate, and select features for machine learning algorithms • Statistically analyze and visualization data to explore distribution (e.g. Gaussian, F ratio, Poisson, negative binomial, and Chi-squared), and model hidden patterns in data • Develop mathematical models such as hidden semi-Markov model (HSMM), Bayesian network and exponential distribution to scale, classify and extract information on conditional dependencies • Develop traditional machine learning algorithms using approaches such as; PCA, LDA, Kmean, Knn, and Gradient boosting • Generate simulation, GUI, and computer software to test the performance of developed models and debug the code • Integrate and deploy models into the main engine for data processing and collaborate with the backend team • Provide excellently written documentation of reports and codes to inform the supervisor about the project’s progress ACHIEVEMENTS: Complete all analysis and analytics required for the company’s patent

WinAIML

Oct 2019 – Mar 2020

Montreal

Research Assistant

• Select appropriate datasets from public databases to develop deep learning algorithms • Study previous works on sMRI and transform existing prototypes • Extract high-quality images using data representation • Develop deep learning pipes such as tensorData, Multilayer Perceptron, and convolutional 3D ACHIEVEMENTS: Generate a deep learning model to detect schizophrenia from sMRI

Concordia University

Sep 2016 – Jan 2019

Montreal

Master’s Thesis

• Create, build and optimize bioinformatics tools and pipeline for RNAseq pre-processing • Collect, manage, evaluate and present large and complex sequencing datasets • Study sources and impacts of specific issues (e.g. missing values, batch effects) in experimental protocols and find creative solutions to minimize technical artifacts and increase accuracy • Statistically analyze, explore and visualize data using heatmap, volcano plot, and Venn diagram • Explore evolutionary relationships using multiple sequence alignment and phylogenetic tree • Analysis of transcriptional rewiring using phylogenetic footprinting, motif discovery, hierarchical clustering, and network analysis • Document report and interpret results collaborators to define new directions and experiments ACHIEVEMENTS: $15,000 Concordia Award of Excellence for two years

Education

McGill

May 2019 – Jan 2020

Graduate Diploma (GDIP), Data Science and Machine Learning

Concordia University

Sep 2016 – Jan 2019

Master’s Thesis, Bioinformatics

Concordia University

Oct 2014 – May 2016

Graduate Diploma (GDIP), Biotechnology and Genomics

University of the West of England

Sep 2007 – May 2010

BSc, Honor, Biotechnology

Azad University of Central Tehran

Sep 2000 – Jan 2005

Associate Degree, Pure Mathematics

Rasteh Nili

Data Scientist · Bioinformatician · Data Analyst · Computational Biologist

Skills

Languages