My background spans upstream process development, synthetic biology, and metabolic engineering, coupled with graduate training in statistics (MS) and bioinformatics. I have research experience across biopharma (Bristol Myers Squibb, Takeda), academic (UT Austin, MIT), and startup (BioReNuva) environments. I have worked across bacterial, yeast, and mammalian cell culture.
At BioReNuva I developed the company’s first bioreactor fermentation process and led its transfer to manufacturing. My doctoral work in the Alper lab centered on the oleaginous yeast Yarrowia lipolytica: I developed Golden Gate based genome editing and promoter library screening, performed adaptive laboratory evolution to raise tolerance to industrial waste streams, and integrated CFD modeling of high-oil-density fermentations.
Bioprocess
- Developed BioReNuva's first bioreactor fermentation process, from initial design through to routine production runs. Interned in upstream process development at Bristol Myers Squibb, running metabolite-controlled fed-batch experiments for monoclonal antibody production in CHO cell lines, and at Takeda, optimizing Vero cell culture conditions for a next-generation vaccine manufacturing system and running tangential flow filtration (TFF).
- Contrasted simulated three-phase bioreactors at both 3 L bench and 4,000 L pilot scale using CFD (Ansys CFX, multiphase MUSIG), measuring the interfacial tension and viscosity needed to parameterize the models and tracking how oxygen transfer, gas holdup, and Reynolds number shift with oil loading. Validated the bench scale experimentally, running Y. lipolytica fermentations at up to 50% (v/v) waste cooking oil, among the highest substrate loadings in the published literature.
- Led technology transfer at BioReNuva, handing the process from development over to the manufacturing team.
- CHO cell culture for biologics at Bristol Myers Squibb and Vero cell culture for vaccine production at Takeda, both in regulated (cGLP) process development environments.
- Developed and validated a plate-reader lipase-activity assay tracking 4-nitrophenol release, and chemical oxygen demand quantification for wastewater bioremediation.
- HPLC with refractive-index and UV detection, used to quantify triacetic acid lactone alongside the acetic, butyric, and propionic acids in wastewater fermentations; GC-MS for fatty acid (FAME derivatization) and fatty alcohol profiling; headspace GC; LC-MS/MS for targeted metabolomics.
Synthetic biology & strain engineering
- Trained in the Alper lab at UT Austin engineering Y. lipolytica to turn waste streams into higher-value products such as polyketides, long-chain fatty alcohols, and lipids. Co-author on two papers in Metabolic Engineering, covering pathway engineering in Y. lipolytica and multi-biosensor-guided production in E. coli.
- Genome editing (Golden Gate assembly, CRISPR). Built a Type IIS Golden Gate system for Y. lipolytica, integrated cassettes by split-marker homologous recombination into selectable loci, and quantified promoter strength by spectral flow cytometry against RNA-seq transcript abundance. Co-first author on a linear mixed-effects framework for evaluating synthetic gene circuits in ACS Synthetic Biology, validated against synthetic data, published data, and our own two- and three-input logic gates, with sample-size guidelines for experimentalists and a web app for other researchers.
- Ran adaptive laboratory evolution campaigns of 144 and 244 generations on industrial waste streams, raising Y. lipolytica tolerance to hydrothermal liquefaction wastewater from 10% to 30% (v/v). One evolved isolate reached a nearly 3-fold increase in triacetic acid lactone titer while reducing the chemical oxygen demand of the waste stream, and whole-genome variant analysis traced candidate causal mutations.
- Bacterial and yeast work across Yarrowia, Saccharomyces cerevisiae, E. coli, Pseudomonas, and Moorella thermoacetica, covering strain engineering, electroporation, aseptic technique, anaerobic assay development, and growth quantification by optical density, dry cell weight, and automated cell counting.
Data & statistics
- Built RNA-seq and Tag-seq pipelines end to end using cutadapt, STAR, HTSeq, and DESeq2, plus genomic variant calling with bowtie2 and samtools, genome annotation with AUGUSTUS, orthology by BLAST, functional enrichment through DAVID, and protein structure prediction with AlphaFold2. Run on TACC's high-performance computing systems.
- MS in Statistics concurrent with the PhD, followed by two years of postdoctoral work in environmental biostatistics at UT Austin with Dr. Roger Peng, building epidemiological and statistical models of large environmental-health datasets and advising medical faculty on statistical methodology and experimental design.
- Factorial screens crossing nitrogen source, nitrogen concentration, and substrate loading while optimizing media for a yeast alkane assimilation panel, analyzed by three-way ANOVA with post-hoc correction; mixed-effects and growth-curve models; PCA over fermentation and omics data.
- Python (pandas, NumPy, scikit-learn) and R (tidyverse) for statistical modeling and reproducible analysis, with published code accompanying peer-reviewed work. Taught both to a 100-student data science course as instructor of record. Won first place in the professional division of the JSM Data Challenge Expo, an exploratory analysis competition.
Leadership
- Mentored 4 undergraduate researchers and 2 junior PhD students during the PhD, and presented results to project sponsors and collaborators. As instructor of record for a 100-student data science course, managed 3 teaching assistants.
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