I like work that holds complexity and clarity at the same time.
I'm a Purdue student double‑majoring in Computer Science and Data Science with a 4.0 GPA. I split my time between founding‑ engineer work at Mutually, product engineering at Flow, and microscopy research in Prabhakar Lab, with teaching and data work grounding everything.
Education
B.S. Computer Science & Data Science
Roles
Founding engineer, product engineer, researcher, and teaching assistant.
Where
Mutually, Flow, Prabhakar Lab, Purdue DataMine + Cisco.
Through-line
Treating code, data, and research as one practice instead of separate lanes.
Purdue as a place to practice systems thinking.
Double‑majoring in Computer Science and Data Science means moving between proofs, systems programming, and statistical thinking, then pulling them back together when I build things outside class.
Purdue University
B.S. Computer Science & Data Science
Systems & core CS
OOP, Computer Architecture, Systems Programming, Algorithms.
Data & information
Information Systems, data pipelines, and statistical tooling.
One thread through startups, research, and teaching.
Instead of a list of disconnected roles, this timeline shows how founding‑engineer work, product engineering, microscopy research, and teaching all reinforce each other.
May 2026 — Aug 2026
Research
Li Group
Summer research on Physics Informed Neural Networks (PINNs), with a focus on optimization where physics constraints meet learned representations.
2025 — Present
Founding Engineer
Mutually
Bloomington, IN
Early engineer on Mutually's first production release, working on cross‑platform UI, Supabase/Postgres models, and recommendation flows that tie event ingestion to a ranked feed.
2025 — Present
Software Engineer
Flow
Remote
Building rider and driver experiences for rideshare, with a focus on onboarding, auth, and analytics that reflect real driving patterns instead of abstract dashboards.
2025 — Present
Undergraduate Research Assistant
Prabhakar Lab · Purdue University
Developing microscopy analysis pipelines where segmentation, tracking, and labeling make image sequences queryable like datasets.
Spring 2025
Teaching Assistant · CS 240
Purdue University
Guided students through systems‑level C (pointers, memory, and debugging) and helped them get comfortable with code that runs close to the metal.
Spring 2024
Data Science Researcher
Cisco through Purdue DataMine
Worked on dashboards and forecasting for Cisco, turning business questions into data pipelines, models, and visual narratives stakeholders could act on.
Jul — Aug 2022
Intern
Leadzen.ai
Mumbai, India · Remote
Interned with the webdev team on front‑end website development, using Elementor and JetEngine to extend functionality and implementing custom JavaScript and CSS to update the pricing section with more engaging visual effects.
Microscopy as a data problem, not just an imaging problem.
In Prabhakar Lab at Purdue, I work on microscopy time‑lapse data where segmenting, tracking, and labeling cells turns raw videos into structured signals you can actually analyze.
I built a microscopy analysis pipeline in Python using tools like Cellpose for segmentation and tracking, then exported labeled datasets for downstream analysis. It sits at the intersection of scientific questions and production‑grade data handling.
Acquire
time-lapse microscopy sequences
Segment + track
using learned models
Label + analyze
turn trajectories into insight
Personal projects that started with real student needs.
StudyBuddy
A full‑stack study planner that treats time as something you design, not just fill. It combines routines, tasks, and focus sessions with simple analytics for how days actually go.
ClassClear
An experiment in syllabus ingestion and structure. It turns unstructured course documents into searchable timelines and requirements so students can see their semester at a glance.
A toolkit that spans systems, data, and product work.
Java, Python, C/C++, SQL (Postgres), JavaScript, HTML/CSS.
React, React Native, Node.js, Flask, JUnit, WordPress.
Git, Docker, Google Cloud Platform, VS Code, PyCharm, IntelliJ.
Pandas, NumPy, Matplotlib, classical ML (XGBoost, Random Forest), and analytics pipelines that connect models back to decisions.
Questions I keep coming back to right now.
Systems & product
- · How early‑stage products can feel coherent even while they move fast.
- · How to design analytics that actually change behavior instead of just reporting it.
Data, research, and signals
- · How to make scientific data pipelines easier to reason about and reuse.
- · Better ways to represent listening history and time‑series behavior so they feel human.
Summer 2026 · Li Group
Research on Physics Informed Neural Networks (PINNs), focused on optimization where physics constraints meet learned representations.