Aishwarya M.
Bioinformatician II at Icahn School of Medicine at Mount Sinai
Bioinformatics | Data Analytics | Biostatistics | Multi-omics
5+ years of experience in Bioinformatics Data Analytics using Supervised and Unsupervised Data Mining approaches. Actively looking for opportunities in Immunology, Cancer research and Genomics research involving NGS pipeline development and Multi-omics data analysis. Passionate about Computational, Statistical and Data Mining approaches for High-throughput biomedical data processing and analytics.

Skills:
• R, Python, Nextflow, Shell scripting, SQL, SAS, MATLAB, TABLEAU
• R Markdown, Jupyter notebook, R Shiny, Dash, Data Visualization packages in R and Python

Github:
https://github.com/am794
Icahn School of Medicine at Mount Sinai
https://www.linkedin.com/company/icahnmountsinai
• Providing bioinformatics support in translational research projects
• NGS data processing and pipeline development  
• Single cell data processing from 10X platform - 10X GEX, 10X ATAC, citeseq, Spatial Transcriptomics, Multiomic data
• Analyzing high-throughput data using custom scripts in shell, Python and R
• Downstream Analysis - Differential Expression analysis; Differential Methylation analysis; Functional Analysis
• Working on Visual analytics for bioinformatics data using RShiny. Reporting detailed results using RMarkdown/Jupyter notebooks
New York, New York, United States
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Bioinformatician II
J. Craig Venter Institute
https://www.linkedin.com/company/j-craig-venter-institute
• Provided Flow and Mass Cytometry data analytical support in collaborative immunology research projects
• Applied computational and data driven approaches for immune cell surface phenotyping
• Used supervised and unsupervised data mining approaches for analyzing the data
• Performed statistical modeling for cross-sectional and longitudinal data using linear regression, GLM, mixed effects models, GLMM, ANOVA
• Experienced in static and interactive data visualization in R and Python; Data Manipulation using SQL.
• Performed downstream statistical analysis of RNA-seq data using packages like DESeq2, edgeR, limma for differential gene expression analysis
• Performed pathway analysis and gene set enrichment analysis
La Jolla, California, United States
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Bioinformatics Analyst
University at Buffalo-State University of New York, NY State Center of Excellence in Bioinformatics
• Worked on evaluation of genome wide supervised cis-regulatory module prediction program implementing various statistical methods for scoring schemes. 
• Implemented machine learning techniques to learn parameters from the training sets of Drosophila Melanogaster to predict the CRMs in test sets obtained from other species. 
• Tools and software used :  Bedtools, R, Python
Research Assistant
University at Buffalo, The State University of New York, Department of Biostatistics
• Worked on Model based Clustering approaches especially in High-dimensional data (with n << p case) using packages on R.
• Dimension reduction, regularization, subspace clustering approaches in model-based clustering for NCI-60 cancer dataset and other simulated datasets using R packages.
• Compared the accuracy using similarity measures for clusters.
Research Assistant
Synteny Life Sciences Pvt. Life sciences
https://www.linkedin.com/company/synteny-life-sciences
• Implemented Protein homology modeling, Docking and Mutational studies for c-Kit, PDGFRA proteins and Tyrosine kinase inhibitor ligands.
• Applied QSAR properties and De-novo drug designing using bioinformatics approaches.
• Used Accelrys discovery studio, MOE, Schrodinger, auto dock.
• Explored and retrieved the data from PubChem, Protein bank in NCBI, PDB databases.
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Research Intern
Master’s Degree
Bioinformatics and Biostatistics
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University at Buffalo
https://www.linkedin.com/school/university-at-buffalo's-school-of-public-health-and-health-professions/
Bachelor of Technology - BTech
Biotechnology
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Sreenidhi Institute of Science and Technology
https://www.linkedin.com/school/sreenidhi-snist/
The four vinca alkaloids, Vinblastine (VBL), vinorelbine (VRL), vincristine (VCR) and vindesine (VDS) work by inhibiting the ability of cancer cells to divide by disrupting the microtubule functioning, causing metaphase arrest. This study involved docking studies of alpha-beta subunits of tubulin with the four vinca alkaloids and comparison of their binding affinities.
INSILICO ANALYSIS OF VINCA ALKALOIDS AS ANTIMITOTIC AND ANTI-MICROTUBULE AGENTS: A POTENTIAL CURE FOR CANCER
INTERNATIONAL JOURNAL OF  PHARMACEUTICAL SCIENCES AND RESEARCH
http://ijpsr.com/bft-article/insilico-analysis-of-vinca-alkaloids-as-antimitotic-and-anti-microtubule-agents-a-potential-cure-for-cancer/
Received gold medal in the Department of Biotechnology for standing first in academics through 2011-15
SNIST
SNIST-ISTE GOLD MEDAL
Received an award in academics through 2014-15
SNIST
Merit certificate
Received a merit certificate for the academic year 2013-14
SNIST
Academic award
Analysis of Variance
STA504
Bioinformatics and Computational Biology
BCH519
Bioinformatics and Genome Analysis
BIO 500
Introduction to Statistical inference
STA502
Public health population well being
CHB550
Regression Analysis
STA503
Statistical Computing
STA511
Statistical Data Mining: Supervised learning
STA545
Statistical Data Mining: Unsupervised learning
STA546
Statistical Genomics
STA509
Statistics for Bioinformatics
STA525
Biochemistry
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Bioinformatics
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Cell Biology
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Computational Biology
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Genetics and Molecular Biology
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Immunology
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Categorical data analysis
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Longitudinal data analysis
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Model-based clustering approaches for high-dimensional data
Developed and compared Bayesian network models for determining if a new applicant is a good credit risk or a bad credit risk.
Bank Credit Risk Analysis With Bayesian Network Decision Tool:
https://uk.linkedin.com/in/eleanor-groves-528000174
Kingston upon Thames
Eleanor Groves
Health Data in Practice PhD Programme Manager at Queen Mary University of London
https://www.linkedin.com/in/lauren-hsu-155a282a
Cambridge, MA
Lauren Hsu
PhD Candidate in Biostatistics @ Harvard
https://www.linkedin.com/in/lindsey-rateau
Greater Boston
Lindsey Rateau
Statistical Programmer II at Biostatistics and Epidemiology Data Analytics Center (BEDAC)
https://gr.linkedin.com/in/giorgos-maragkopoulos-316196198
Aryiroúpolis
Giorgos Maragkopoulos
Highly motivated Data Scientist with a passion in the area of time series forecasting
https://uk.linkedin.com/in/ahmad-zyoud-68744229
Cambridge
Ahmad Zyoud
Bioinformatician at European Bioinformatics Institute | EMBL-EBI
https://www.linkedin.com/in/sanjavickovic
New York, NY
Sanja V.
Tech enthusiast | Assistant Professor Columbia University | Core Faculty NYGC | Wallenberg Academy Fellow Uppsala University
https://www.linkedin.com/in/abby-sloan-47528a179
Greater Boston
Abby Sloan
Manager of Biostatistics at Pfizer
https://uk.linkedin.com/in/joseph-ellaway
Mulbarton
Joseph Ellaway
Bioinformatician at European Bioinformatics Institute | EMBL-EBI
https://www.linkedin.com/in/jieqiong-dai-phd-a1997135
Washington DC-Baltimore Area
Jieqiong Dai, PhD
Senior Bioinformatics Scientist in Clinical Research and Application
https://www.linkedin.com/in/david-requena-0547b714a
New York City Metropolitan Area
David Requena
Scientist with expertise in Bioinformatics, MultiOmics, Immunoinformatics and Spatial Transcriptomics for Cancer Research
https://www.linkedin.com/in/xingyu-li-748ab13b
Cambridge, MA
Xingyu Li
Senior Manager, Biostatistics at Alnylam Pharmaceuticals
https://www.linkedin.com/in/chongshutonychen
Cambridge, MA
Chongshu (Tony) Chen
Manager Biostatistics at Alnylam Pharmaceuticals
https://www.linkedin.com/in/tyler-waite-956061122
San Francisco Bay Area
Tyler Waite
Scientific Researcher at Genentech
https://www.linkedin.com/in/xiaoyu-lu-19451b153
Indianapolis, IN
XIAOYU LU
Single Cell Sequencing/Spatial Transcriptomics Co-Op at Merck
https://www.linkedin.com/in/chenhao-li-1b695b99
Cambridge, MA
Chenhao Li
Research Fellow at Broad Institute of MIT and Harvard
https://uk.linkedin.com/in/maisey-davidson-294359117
United Kingdom
Maisey Davidson
Bioinformatician at Bactobio
https://www.linkedin.com/in/thuyngo1
Greater Boston
Thuy Ngo
Recruiter at Catalent Pharma Solutions
https://www.linkedin.com/in/shainalu
United States
Shaina Lu
Bioinformatics Scientist at Eagle Genomics
https://www.linkedin.com/in/prachitiprabhu
San Francisco Bay Area
Prachiti P. Prabhu
Bioinformatics Software Engineer at Genentech
Shwetha Hara Sridhar
      
          



            
              
        
              
  

    
        "Hard work always pays off", is the phrase that comes to mind when I think about Aishwarya. I’ve had the pleasure of knowing Aishwarya for 7 years, during which we worked on several RNAseq and ChIPseq Analysis projects together. Above all, I was impressed with Aishwarya’s ability to grasp any concept in a short span of time. She enjoys learning more and is always the best at what she does. She is a hard worker and has been a gold medalist during her bachelors program. She has always been actively involved in multiple levels of research projects. Aishwarya, would be a true asset for any positions requiring statistical analysis, bioinformatics and genomics analysis knowledge and skills and comes with my heartfelt recommendation.
Liked by Aishwarya M.
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Thank you FRAXA and the Pierce Family Fragile X Foundation for supporting our exciting research! #research #thankyou #innovation #community…
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Congratulations to Viome Life Sciences team for another breakthrough research. Gut microbiome activity predicts the risk of type 2 diabetes and…
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I am honored to announce that I have completed my 10 week externship as a member of the Clinical Assistant Program at Memorial Sloan Kettering Cancer…
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Bengaluru
Aishwarya M
Talent acquisition specialist at Ian Martin group
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Chennai
Aishwarya M
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Palghat
Aishwarya M
(Qt l Cpp l HMI) at QuEST Global Engineering Services , Pvt Ltd
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Tamil Nadu, India
Aishwarya M
Budding Content Creator
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Bengaluru
Aishwarya M
Data Analyst at KPMG Global Services | Microsoft Certified Data Engineer
Machine Learning and Data Science
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NGS (Next Generation Sequencing)
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Biostatistics
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Data Mining, Statistics, Big Data, Data Visualization, AI, Machine Learning, and Data Science
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Bioinformatics & Computational Biology (B&CB) : The emerging science
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