Sandeep Acharya
St. Louis,MO
Summary
A computational biologist with 5.5 years of experience in large-scale genomic data analysis, built on a strong foundation in statistics and classical machine learning. Transitioned into genomics from a purely mathematical background, quickly adapting to the field and successfully applying quantitative expertise to address complex genomic problems. Led and managed a diverse group of scientists, resulting in a first-author publication of a novel computational method with 12 co-authors in Human Genetics, and another first-author manuscript currently in preparation with 7 co-authors. Additionally, collaborated with esteemed scientists across multiple U.S. universities, with work published in Aging Cell, JACC: Basic to Translational Science, and The Journals of Gerontology.
Overview
7
7
years of professional experience
Work History
Ph.D. Candidate
Brent Lab, Washington University in St. Louis
09.2019 - Current
- Led the transition of the research lab's focus from regulatory network inference to human genetics, while mentoring 4 Ph.D. students to help them develop expertise in computational genomics.
- Generated and implemented three novel research ideas, one published and two currently being prepared as manuscripts, while leading and managing a team of 13 scientists as co-authors, ensuring successful collaboration and project completion.
- Presented research findings at three conferences, with an upcoming platform presentation at the International Genetic Epidemiology Society (IGES), where the work has been nominated as a finalist for the Best Oral Presentation by a Ph.D. candidate.
Graduate Research Assistant
Long Life Family Study (LLFS) Consortium
01.2020 - Current
- Collaborated with prominent leaders in statistical genetics, applying complex statistical models to advance their research, which resulted in 3 publications in Aging Cell, JACC: Basic to Translational Science, and The Journals of Gerontology.
- Presented novel computational methods at 3 LLFS Consortium bi-annual Meetings to foster collaboration opportunities and educate geneticists on leveraging large-scale genomic data analysis techniques for identifying biomarkers of healthy aging.
- Initiated weekly literature review and analysis planning meetings at Brent Lab for Ph.D. students involved in the LLFS consortium, fostering collaboration, innovation, and teamwork. This effort has resulted in 4 novel computational method development projects currently led by fellow Ph.D. students.
Graduate Teaching Assistant
Algorithms in Computational Biology (CS 587)
08.2022 - 12.2023
- Delivered 6 lectures on two Transcription Factor Motif Discovery methods and Hidden Markov Models to around 100 masters and Ph.D. students across two fall semesters.
- Conducted two hour-long office hours weekly to mentor students one-on-one with their assignments.
Software Development Intern
CUNA Mutual Group
06.2017 - 08.2018
- Managed 2 developers, worked closely with 6 business owners, and architected the design of the
time tracking system in use by more than 1000 employees to help CUNA Finance assign a budget to CUNA IT. - Spearheaded the development process of the application, eliminating the need for a manually typed spreadsheet and saving the company more than 7000 hours per year (C Sharp, .NET MVC, MS SQL,JavaScript).
- Created an automated process for builds/releases for 5 internal applications in the development, demo, and production environments, saving the company approximately 1000 hours annually.
Education
Ph.D. - Data Science
Washington University in St. Louis
St Louis, MO11.2024
Bachelor of Science -
Beloit College
Beloit, WI05.2019
Skills
- Programming Languages: Proficient in Python, R, Java, and C Sharp; Experienced in Linux HPC, bash scripting, Docker, Singularity, and GIT version control; Familiar with MATLAB and Mathematica; Applied Nextflow
- Machine Learning and Statistics: Experienced in deploying ML models including linear and logistic regression, Generalized linear mixed-models, k-means clustering, random forest, and XG Boost; Familiar with neural network, deep learning, and SVM
- Bioinformatics: Expert in GWAS, TWAS, rare-variant association tests, multi-omics integration, meta-analysis, mendelian randomization analysis, biological network propagation, RNA-Seq QC, and TF-Gene regulatory network inference
- Graduate Coursework: Advanced Machine Learning, Advanced Algorithms, Computational Statistical Genetics, Algorithms for Computational Biology, Introduction to Data Wrangling, Introduction to Artificial Intelligence, Data Mining
Publications
Sandeep Acharya, et. al. “A Methodology for Gene Level Omics-Was Integration Identifies Genes Influencing Traits Associated with Cardiovascular Risks: The Long Life Family Study.” Human Genetics, Sept 14, 2024. https://doi.org/10.1007/s00439-024-02701-1
Adam J Santanasto, Sandeep Acharya, et. al., Whole Genome Linkage and Association Analyses Identify DLG Associated Protein-1 as a Novel Positional and Biological Candidate Gene for Muscle Strength: The Long Life Family Study,The Journals of Gerontology: Series A, 2024, glae144, https://doi.org/10.1093/gerona/glae144
Mary F Feitosa, Shiow J Lin, Sandeep Acharya, et. al., Discovery of genomic and transcriptomic pleiotropy between kidney function and soluble receptor for advanced glycation end-products using correlated meta-analyses: The Long Life Family Study (LLFS), Aging Cell, 00, e14261. https://doi.org/10.1111/acel.14261
Tomohiro Hayashi, Sajal K. Tiwary,Kory J. Lavine, Sandeep Acharya, et. al., The Programmed Death-1 Signaling Axis Modulates Inflammation and LV Structure/Function in a Stress-Induced Cardiomyopathy Model, Journal of the American College of Cardiology: Basic Trans Science. 2022 Nov, 7 (11) 1120–1139.https://doi.org/10.1016/j.jacbts.2022.05.006
Deidra A Ressler, Ryan K Cvejkus, Emma Barinas-Mitchell, Mary Feitosa, Joanne M. Murabito, Sandeep Acharya, et. al., Epidemiology and Genetic Determination of Measures of Peripheral Vascular Health in the Long Life Family Study: Manuscript under review.
Sandeep Acharya, et. al., FISHNET: FInding Significant Hits in biological NETworks : Manuscript in preparation.
Scientific Presentations
Acharya, S., et.al., (2024, November). FISHNET: FInding Significant Hits in biological NETworks. Platform presentation.
Annual Conference of the International Genetic Epidemiology Society (IGES), Denver, Colorado. \textbf{(Accepted for a platform presentation and selected as a Williams Award finalist.)
Acharya, S., et.al., (2023, November). Multi-omics Integration to Identify Genes Affecting Cardiovascular Diseases Related Traits. Poster presented at the Annual Conference of the American Society of Human Genetics (ASHG), Washington DC.
Acharya, S., et.al., (2023, November). Multi-omics Integration to Identify Genes Affecting Cardiovascular Diseases Related Traits. Poster presented at the Annual Conference of the International Genetic Epidemiology Society (IGES), Nashville, TN.
Acharya, S., et. al., (2022, July). Global TF Network Inference in Humans. Poster presented at the Annual Conference of the International Society for Computational Biology (ISCB), Madison, Wisconsin.
Timeline
Graduate Teaching Assistant
Algorithms in Computational Biology (CS 587)
08.2022 - 12.2023
Graduate Research Assistant
Long Life Family Study (LLFS) Consortium
01.2020 - Current
Ph.D. Candidate
Brent Lab, Washington University in St. Louis
09.2019 - Current
Software Development Intern
CUNA Mutual Group
06.2017 - 08.2018
Ph.D. - Data Science
Washington University in St. Louis
Bachelor of Science -
Beloit College
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