Mukta's CV

GitHub: Mukta Palshikar
Publications: Google Scholar, PubMed

Summary

• I am a PhD-level computational biologist, specializing in the end-to-end analysis and integration of bulk/single-cell/spatial transcriptomics and proteomics data, from QC and initial processing up to the biological interpretation.
• My postdoctoral work in Ilya Korsunsky’s lab at Brigham and Women’s Hospital focused on building multi-modal spatial atlases in the contexts of colorectal cancer and interstitial lung disease. I gained extensive experience in spatial statistics, and analyzing spatial transcriptomic data from Xenium, Visium, and MERFISH, and histopathological images.
• My graduate work in Juilee Thakar’s lab at the University of Rochester focused on building tools to learn and simulate immune signaling network models using multiple layers of omics data - bulk and single-cell RNA-seq, proteomics, and phosphoproteomics - in the contexts of HIV and atherosclerosis. I gained extensive experience in pathway and network analysis.
• I am proficient in Python and R, with exposure to machine learning for omics data.

I am a U.S permanent resident (I have a ‘green card’) and don’t need visa sponsorship.

Research Experience

• Research Scientist (Jan 2026 - present)
  • Digital Twin Innovation Hub @ University of Nebraska-Lincoln (working remotely from Boston, MA).
  • Advisor: Tomas Helikar, PhD
  • Transitioned academic research methodologies into scalable analytical workflows for a newly formed spin-off venture.
  • Designed and implemented computational pipelines for the logical modeling of immune networks to identify therapeutic targets in autoimmune kidney disease
• Post-doctoral research fellow (Feb 2023 - Dec 2025)
  • Harvard Medical School; Brigham and Women’s Hospital, Boston, MA
  • Advisor: Ilya Korsunsky, PhD
  • Led cross-functional spatial analysis of the tumor microenvironment in human colorectal cancer with the Broad Institute and MGH, characterizing anti-tumor immunity hubs and T-cell exhaustion.
  • Developed a comprehensive spatial atlas of interstitial lung disease, integrating multi-modal data to map cellular and molecular architecture.
  • Developed and optimized computational pipelines to translate tissue morphology and cellular phenotypes from clinical pathologists into quantitative features for analysis.
  • Analyzed and integrated data from multiple spatial transcriptomics platforms (Visium, MERFISH, Xenium) and single-cell RNA-sequencing (scRNA-seq).
• Doctoral Researcher (Aug 2017 - Jan 2023)
  • University of Rochester, Rochester NY
  • Advisor: Juilee Thakar, PhD
  • Developed a novel computational framework to construct executable models of cell signaling pathways directly from multi-omics data.
  • Integrated phosphoproteomic, transcriptomic, and proteomic datasets to build context-specific models that predict signaling dynamics and pathway activity.
  • Applied these models to investigate dysregulated immune signaling in human diseases, including HIV-associated atherosclerosis (cardiometabolic disease) and B-cell malignancies.
  • Authored and maintained open-source Python packages to make modeling workflows accessible to the broader research community.

Education

Publications & Presentations

* indicates co-first authorship

Preprints & In Preparation

1. Chen JH*, Chen H*, Palshikar MG*, Spurrell M, Demaray J, …, Hacohen N, Korsunsky I, Pelka K. Spatial analysis of human colorectal cancer identifies anti-tumor immunity hubs at epithelial/stromal interface as site enriched for effector and exhausted T cells. Under review.
2. Tran M, Gao C, Palshikar MG, …, Kim E, Wei K, Korsunsky I. Spatial Reconstruction of Interstitial Lung Disease. In preparation.
3. Reshef Y, Sood L, …, Palshikar MG, … Korsunsky I, Raychaudhuri S. Powerful and accurate case-control analysis of spatial molecular data with deep learning-defined tissue microniches. bioRxiv. 2024; doi:10.1101/2024.02.07.537149. [Preprint]

Published

1. Millard N, Chen JH, Palshikar MG, …, He J, Wei K, Korsunsky I. Batch correcting single-cell spatial transcriptomics count data with Crescendo improves visualization and detection of spatial gene patterns. Genome Biol. 2025 Feb 25;26(1):36. [PMID:40001084]
2. Bolton C, Mahony CB, …Palshikar MG, Garnett J, Raychaudhuri S, Frank MM. Synovial tissue atlas in juvenile idiopathic arthritis reveals pathogenic niches associated with disease severity. Sci Transl Med. 2025 Jul 2;17(805):eadt6050. [PMID:40601776]
3. Kassis G, Palshikar MG*, Hilchey SP, Zand MS, Thakar J. Discrete-state models identify pathway specific B cell states across diseases and infections at single-cell resolution. *J Theor Biol. 2024 Apr 21:583:111769. [PMID:38423206]
4. Palshikar MG, Min X, Crystal A, Thakar J. Executable Network Models of Integrated Multiomics Data. J Proteome Res. 2023 May 5;22(5):1546-1556. [PMID:37000949]
5. Palshikar MG, Palli R, …, Zand MS, Thakar J. Executable models of immune signaling pathways in HIV-associated atherosclerosis. npj Syst Biol Appl. 2022 Sep 21;8(1):35. [PMID:36131068]
6. Hilchey SP, Palshikar MG*, …, Zand MS, Thakar J. Cyclosporine A Modulates LSP1 Protein Levels in Human B Cells to Attenuate B Cell Migration at Low O2 Levels. *Life (Basel). 2022 Aug 22;12(8):1284. [PMID:36013463]
7. Palshikar MG, Hilchey SP, Zand MS, Thakar J. WikiNetworks: translating manually created biological pathways for topological analysis. Bioinformatics. 2022 Jan 12;38(3):869-871. [PMID:34636843]
8. Hilchey SP, Palshikar MG, …, Elliott MR, Zand MS, Thakar J. Cyclosporine A Directly Affects Human and Mouse B cell Migration in vitro by Disrupting a HIF1α-Dependent, O2 Sensing, Molecular Switch. BMC Immunol. 2020 Mar 18;21(1):13. [PMID:32183695]
9. Palli R, Palshikar MG, Thakar J. Executable pathway analysis using ensemble discrete-state modeling for large-scale data. PLoS Comput Biol. 2019 Sep 3;15(9):e1007317. [PMID:31479446]

Conference Presentations

1. Palshikar MG, Spurrell M, Demaray J, …, Hacohen N, Chen JH, Korsunsky I. Abstract 5492: Spatial organization and cellular composition of immunity hubs in human colorectal cancer. Cancer Res. 2024; 84 (6 Supplement):5492. American Association for Cancer Research (AACR) Annual Meeting, 2024.
2. Palshikar MG. WikiNetworks: translating manually created biological pathways for topological analysis. Computational Modeling in Biology Network (COMBINE), 2021.
3. Palshikar MG. Executable models of pathways built using single-cell RNAseq data reveal immune signaling dysregulations in people living with HIV and atherosclerosis. American Society for Microbiology (ASM) Eastern New York Fall Symposium, 2021.
4. Palshikar MG. Executable models of pathways built using single-cell RNAseq data reveal immune cell heterogeneity in people living with HIV and atherosclerosis. Conference on Intelligent Systems for Molecular Biology (ISMB), 2020.

Technical Instruction & Mentorship

Graduate Student Mentor | 2019 – 2023 University of Rochester | Rochester, NY

• Mentored five undergraduate students and one visiting scientist in computational research projects involving network-based analysis of omics data.
• Provided training in algorithm design, data analysis in Python and R, and scientific presentation skills.

Instructor & Teaching Assistant | 2019 – 2022 University of Rochester | Rochester, NY

• Instructed a graduate-level module on unsupervised machine learning for the Quantitative Biology course, including workshops in R.
• Led weekly workshops and graded assignments for the core graduate course, Foundations in Modern Biology.

Honors and Awards

• Spring 2023: George V. Metzger Award, University of Rochester School of Medicine and Dentistry. Awarded to a Biophysics PhD student based on the quality of the dissertation and the supporting research. Please read more about George Metzger’s life here.
• Fall 2017: Graduate Alumni Fellowship Award, University of Rochester School of Medicine and Dentistry. Awarded to an incoming graduate student in the School of Medicine and Dentistry with promise for exceptional accomplishment in graduate study