Meredith Calloway

Profile

Computational biologist with 9 years of experience designing and executing large-scale genomic studies in oncology and rare disease. Specializes in single-cell RNA sequencing analysis, machine learning-driven biomarker discovery, and translating bench findings into clinical pipeline candidates. Led research that produced 2 FDA-designated Breakthrough Therapy biomarkers and generated $4.2M in NIH grant funding across three award cycles.

Work Experience

Senior Research ScientistMar 2020 - Present

Verixa Therapeutics, Boston, MA

Designed and executed a 1,200-patient scRNA-seq study that identified a novel tumor microenvironment signature, accelerating a Phase II trial entry by 14 months.
Built an in-house ML pipeline (Python, Seurat, Scanpy) to classify cell subtypes with 94% accuracy, reducing manual annotation time by 60% and enabling same-week turnaround on sequencing batches.
Mentored 4 junior scientists and 2 postdoctoral fellows, with 3 first-author publications submitted to Nature Methods and Cell Genomics during the mentorship period.

Research Scientist IIJun 2017 - Feb 2020

Broad Institute of MIT and Harvard, Cambridge, MA

Developed a CRISPR-based functional genomics screen across 18,000 gene targets in AML cell lines, identifying 37 synthetic lethal pairs that became priority drug discovery targets for two biotech partners.
Authored or co-authored 6 peer-reviewed publications (cumulative impact factor >85) and presented findings at ASHG 2018 and AACR 2019 to audiences exceeding 2,000 researchers.
Secured a $1.6M NIH R01 renewal by designing the experimental framework and writing 80% of the grant narrative, achieving the top 5th percentile score in the study section.

Research Scientist IAug 2015 - May 2017

Genzyme (Sanofi), Framingham, MA

Characterized lysosomal enzyme activity assays for 3 rare disease programs, generating the primary efficacy dataset used in an IND submission approved by FDA on first review.
Optimized a high-throughput cell viability platform that increased assay throughput from 96-well to 384-well format, cutting reagent cost by 38% and doubling weekly sample capacity.
Collaborated with biostatistics to apply mixed-effects models to longitudinal patient data, reducing variability in primary endpoint measurements by 22% across a 6-month natural history study.

Education

Ph.D. in Computational Biology2010 - 2015

MIT, Cambridge, MA

Dissertation: 'Integrative Multi-Omics Approaches to Identifying Oncogenic Driver Networks in Pediatric Glioma'; NSF Graduate Research Fellowship recipient

B.S. in Biochemistry, Minor in Statistics2006 - 2010

University of Michigan, Ann Arbor, MI

Summa Cum Laude; Undergraduate Thesis Award, Department of Chemistry

Skills

Single-cell RNA sequencing (scRNA-seq) · CRISPR functional genomics · Python (Scanpy, Seurat, scikit-learn) · R (DESeq2, edgeR, ggplot2) · NIH grant writing (R01, R21) · Biomarker discovery & validation · Flow cytometry & FACS · Regulatory documentation (IND, BLA support) · Statistical modeling (mixed-effects, survival analysis)

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