- M.S., Business Analytics | University of California San Diego (June 2025)
- B.S., Engineering | Vellore Institute of Technology (June 2021)
Senior Data Analyst @ The Smart Cube (Jan 2023 - June 2024 )
- Built investment planner to simulate media budget changes and forecast sales impact, improving marketing ROI by 3.5%
- Created ROI and brand dashboards used by directors and VPs, saving 20+ hours/month in manual reporting
- Integrated data from 9+ teams to provide real-time visibility into brand health and vendor compliance
Data Analyst @ The Smart Cube (_August 2021 - December 2022)
- Cleaned and combined sales data from different regional sources using SQL in Google Big Query, improving report accuracy by reducing monthly data discrepancies from ~25 to fewer than 5
- Built a P&L Tableau dashboard with advanced calculations (e.g., revenue projections, variance metrics) for the CFO, replacing manual reports and saving 12 hours/month
To determine whether sending incentive-based email reminders increases attendance at student-led events.
| Component | Description |
|---|---|
| Sample | 48 pre-registered students for internal events at UCSD Rady School |
| Randomization | Done at the individual level using a Python script |
| Treatment Group | Received a reminder email 24–48 hours before the event |
| Control Group | Received no reminder |
| Tracking | Attendance was measured using event sign-in records |
| Incentives Highlighted | Free food 🍔, networking 🤝, insider insights 💡 |
📩 Sample Reminder Email: “Just a reminder: Don’t miss tonight’s event! ✅ Free food, ✅ Meet UCSD’s supply chain leader, ✅ Insider insights — it’s all happening at 5:15 PM in Room 3N128!”
• T-statistic: -2.8284 • P-value: 0.0076 (statistically significant) • Interpretation: Attendance significantly increased with reminders t-Test
• Effect Size: 0.82 (large) • Interpretation: Reminders had a strong real-world impact
• Result: Observed vs. expected attendance showed significant differences • Interpretation: Reminder group had a clearly higher turnout Chi-Square Test
| Metric | Control Group | Treatment Group | Impact |
|---|---|---|---|
| Attendance Rate | 58.3% | 91.6% | +33.3% |
| No-Show Rate | 41.6% | 8.3% | −33.3% |
• Simple behavioral nudges can drive measurable behavioral change • Personalization + timely delivery (24–48 hours before) enhances effectiveness • Highlighting tangible incentives (e.g., free food) improves engagement
Developed objective strategy for discovering optimal EEG bands based on signal power spectra using Python. This data-driven approach led to better characterization of the underlying power spectrum by identifying bands that outperformed the more commonly used band boundaries by a factor of two. The proposed method provides a fully automated and flexible approach to capturing key signal components and possibly discovering new indices of brain activity.
Used Matlab to train over 100 machine learning models which estimated particulate matter concentrations based on a suite of over 300 biometric variables. We found biometric variables can be used to accurately estimate particulate matter concentrations at ultra-fine spatial scales with high fidelity (r2 = 0.91) and that smaller particles are better estimated than larger ones. Inferring environmental conditions solely from biometric measurements allows us to disentangle key interactions between the environment and the body.
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Causality: The new science of an old question - GSP Seminar, Fall 2021
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Guest Lecture: Dimensionality Reduction - Big Data and Machine Learning for Scientific Discovery (PHYS 5336), Spring 2021
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Guest Lecture: Fourier and Wavelet Transforms - Scientific Computing (PHYS 5315), Fall 2020
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A Brief Introduction to Optimization - GSP Seminar, Fall 2019
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Weeks of Welcome Poster Competition - UTD, Fall 2019
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A Brief Introduction to Networks - GSP Seminar, Spring 2019