How much Bitcoin should a moderate-risk household hold alongside a traditional ETF portfolio? This project answers the question end-to-end: data collection, descriptive analysis, mean–variance optimization under multiple risk constraints, and a forward-looking Black–Litterman adjustment. Universe: SPY, VEU, TLT, GLD, and BTC-USD; risk-free rate from the 3-month T-Bill (^IRX); ~2,900 trading days from BTC's 2014 listing to present.

• Computed annualized return, volatility, Sharpe, skew, max drawdown, and the full correlation matrix; estimated CAPM beta of BTC vs SPY over full / pre-2017 / post-2017 sub-samples to show BTC's structural shift from idiosyncratic (β ≈ 0) to risk-asset-like (β ≈ 1).
• Solved five long-only mean–variance scenarios in cvxpy (OSQP / SCS): max-Sharpe with and without BTC, volatility caps at 10% and 15% p.a., a 5% daily CVaR / Expected Shortfall constraint, and max-Sharpe with BTC capped at 10%.
• Implemented He & Litterman (1999) Black–Litterman: derived the equilibrium prior from market-cap weights and blended in three explicit views (BTC outperforms VEU by 15% p.a.; SPY earns 10% p.a.; GLD outperforms TLT by 3% p.a.), then fed posterior expected returns into a max-Sharpe optimizer.
• Including BTC lifted the max-Sharpe portfolio from ~0.97 to ~1.25 (+29%); the unconstrained optimizer wanted ~15% BTC, while capping BTC at 10% cost only ~0.02 in Sharpe.
• Recommendation: allocate 5–10% to BTC, anchored on the “max-Sharpe with BTC ≤ 10%” portfolio and validated by Black–Litterman, with quarterly rebalancing.

Differential expression analyses are susceptible to false positives. permuteDE uses permutation testing to identify which comparisons have a higher number of significant differentially expressed features than would be expected by chance.

• Developed the permuteDE R package, overseeing maintenance, debugging, and feature development.
• Collected and integrated eight large-scale public snRNA-seq datasets from Synapse, performing rigorous data cleaning, metadata curation, and structuring data into Seurat objects and BPCells matrices.
• Applied the permuteDE pipeline across eight datasets to demonstrate its real-world utility.

Note: Currently we are finalizing the package and analyses; permuteDE will be released upon manuscript publication.

Figure Source: Emani et al., Science 2024  ·  Pampari et al., bioRxiv preprint 2025  ·  Ameen et al., Cell 2022

I joined the PsychENCODE project at Prof. Anshul Kundaje's lab. I led this project analyzing scRNA-seq and scATAC-seq multiome data from three brain regions to investigate cell type-specific chromatin accessibility and gene expression patterns underlying schizophrenia and bipolar disorder to better understand their pathogeneses.

• Preprocessed high-dimensional multiome data through quality control, dimensionality reduction, clustering, doublet detection, marker identification, and cell type annotation.
• Performed differential gene expression & peak accessibility analyses (pseudobulk: Wald test; single-cell: Wilcoxon + MAST) across multiple cell types.
• Trained cell type-specific ChromBPNet models to identify causal genomic variants and their downstream effects on chromatin accessibility and gene regulation.

Figure Source: National Cancer Institute 2019  ·  Lowder et al., Cancer Letters 2022  ·  Langfelder & Horvath, BMC Bioinformatics 2008

Black men face higher prostate cancer incidence and mortality than White men. This project leverages LC-MS metabolomics on a cohort of 34 men (17 Black, 17 White) from the IRONMAN Registry to characterize metabolites associated with such disparities. I presented this project as my Master's thesis.

• Applied t-test, PCA, PLS-DA, random forest, logistic & linear regression, chemical similarity enrichment analysis, pathway analysis, and WGCNA to LC-MS metabolomics data, characterizing metabolites associated with Black-White prostate cancer disparities.
• Interpreted complex statistical findings and communicated them through clear scientific writing and effective data visualizations; defended before Stanford Epidemiology faculty and students.

This project visualized the interplay between life expectancy (measured by central death rates) and economic development, healthcare access, energy production, and consumption patterns.

• Developed interactive data visualization dashboards (global heatmaps, cross-country correlation analyses, and time-evolving bubble charts) using JavaScript D3.

Figure Source: Argelaguet et al., 2018  ·  Growing Life

In this project, I practiced NIH-style grant proposal writing by developing a theoretical proposal that integrates single-cell multiomics data to investigate the molecular mechanisms using factor analysis underlying Alzheimer's Disease.

• Drafted the Specific Aims section of a conceptual NIH grant proposal and presented the multi-omics factor analysis plan to ~30 professors and students.

This project evaluated associations between multiple exposures (age [categorical and continuous], sex, race, and smoking) and assessed alcohol as an effect modifier of the smoking–SAH relationship.

• Cleaned and managed case-control datasets in SAS OnDemand: resolved duplicate records, standardized missing values, and created derived variables.
• Conducted descriptive analyses (chi-square tests and t-tests) and applied univariate and multivariable logistic regression to estimate crude and adjusted odds ratios for smoking.
• Performed stratified analysis using Mantel–Haenszel testing to adjust for alcohol use as a confounder.
• Generated summary tables and data visualizations for reporting.

Nominated as Honorable Mention for Best UI for Hack Challenge Spring 2022.

This project developed an iOS app to ease the booking of a study space on campus in one of Cornell's many open libraries and study spaces.

• Programmatically developed a Cornell library study room booking system iOS app by integrating UIKit, AutoLayout, Navigation, UITable & UICollectionView, MVC, Delegation, and Animation.
• Implemented GET all libraries and available rooms, POST new reservation(s), UPDATE reservation history & DELETE reservation(s) that interact with backend API using Alamofire.
• Implemented UI with designers and collaborated with backend teammates for backend requests.