I’m a San José State student working across data, systems, and real-world questions. I build projects to understand what the numbers are actually saying, then figure out what to do with them.
Two projects at different stages. Both are less about presenting a perfect answer and more about making the reasoning visible.
I wanted to see whether football transfer values could be estimated with data I could actually explain. The study uses performance, appearance, transfer, and player data across seven leagues.
A growing decision-intelligence project combining relational data, SQL analysis, Python, Tableau, and future classification work.
One-on-one academic coaching, study planning, goal-setting, and support for student workshops.
Guest operations, reservations, payments, StarRez, Excel, and maintaining accurate records across conference groups.
Start with the question.
Before choosing a model or a chart, define what decision the work is supposed to support.
Make the data earn its place.
Clean, join, filter, and inspect the inputs before treating them as trustworthy.
Leave room for the caveat.
A useful result includes what worked, what did not, and what the analysis still cannot tell us.
The work is technical, but the reason I care about building it is personal.
Coming to the United States as an international student has made me pay attention to how people learn, communicate, and find their footing in unfamiliar systems.
That perspective shows up in the way I work: I ask questions before reaching for answers, explain decisions clearly, and keep improving the parts that are not obvious at first.
Some interests stay separate. Some end up influencing the work.
I love football, so building something around it felt pretty natural. I’m a strong Barça and Messi supporter, and that interest eventually led me to look at what actually drives a player’s market value.
Probability, incomplete information, and decisions that make sense even when the outcome goes the other way. Useful habits outside the card table too.
For long work sessions, study breaks, and the background of ordinary days. A fairly consistent rotation of pop, alternative, and Hindi music.