/home/srujan

last login: ——— on tty1

srujan@profile:~$

srujan@profile:~$cat experience.txt

01/

Renaissance Computing Insitute

— Software Engineer Intern

University of North Carolina · Chapel Hill, NC · Jun 2026 – Aug 2026

Built and shipped a desktop tool (Python, PySide6, Watchdog) that watches local directories and automatically ingests new or modified files into iRODS, released to a user base of universities, national laboratories, government agencies, and companies managing petabytes of data worldwide. Wrote the project's test suite from scratch and presented the design and release plan to RENCI leadership and iRODS Consortium members.

data_infrastructure
research_tooling
open_source
systems

02/

AIMS Lab

— Undergraduate Researcher

NC State University (AIMS Laboratory) · Raleigh, NC · Jan 2026 – Present

Developing physics-informed graph neural networks in Python to predict the environmental half-life and persistence of PFAS compounds, backed by an RDKit-based feature engineering pipeline covering 14,000 chemical structures. Implementing active learning and uncertainty quantification so predictions carry confidence bounds for an environmental decision-support platform.

AI_for_science
PFAS
RDKit
molecular_descriptors
uncertainty

srujan@profile:~$cat questions.txt

[01] what are you studying?
CS, Econ, and Math. Call it either interdisciplinary ambition or a controlled intellectual spiral.
[02] what do you want to build?
Infrastructure, AI, governance, research, finance. In other words, systems that make other systems less stupid.
[03] are you more of a cs person or an econ person?
Computer science gives me the tools to build; economics reminds me that those tools eventually have to meet people, incentives, and reality.
[04] what kind of work are you looking for?
Technical work with institutional consequences: AI, data infrastructure, research tooling, finance, civic technology, or systems that help serious organizations operate better.

srujan@profile:~$cat interests.md

01.
AI as infrastructure
How machine learning systems become part of ordinary institutional life.
02.
Computational social science
Using technical tools to understand economics, governance, behavior, and inequality.
03.
AI for scientific discovery
Especially where models help researchers reason about complex systems.
04.
Civic and nonprofit technology
Tools that help organizations with limited resources operate like they have real infrastructure.
05.
Institutional integrity
How systems fail, how people trust them, and how to design against decay.

srujan@profile:~$./contact.sh

email:: sponnap2 [at] ncsu [dot] edu
github:: github.com/srujanponnapalli
linkedin:: linkedin.com/in/srujanponnapalli

/home/srujan — Srujan Ponnapalli