// about

So, why artificial intelligence?

As far as we know, the brain may be the only thing in the universe capable of studying itself. Humans are curious creatures, and it's in our nature to question everything, even our own capacity to question. So maybe it's not surprising that intelligence, once you start looking for it, seems to show up everywhere. I mean, we live in a world where trees warn each other of danger through underground networks, bees communicate through dance, ants build sprawling colonies, and elephants mourn over the bones of their dead. Intelligence is remarkably ubiquitous, and AI has become the most compelling framework we have for studying it. Rather than simply observing intelligent behavior, it enables us to build systems that reproduce it.

I blame my neuroscience background, but it's this perspective I hold that draws me from studying intelligence to trying to build it. But the deeper I've gotten into AI, the more I've realized that every breakthrough seems to expose another gap in our understanding. We have language models that outperform the average human on many benchmarks, yet they still struggle with arithmetic or spelling. The same models can produce remarkably introspective writing about consciousness yet our deepest intuitions insist that they're not conscious at all. And perhaps most strikingly, they consume enormous amounts of energy to answer questions that the human brain solves on just about twenty watts.

Building the future of intelligence on this planet means sitting with these exciting contradictions. What do we mean by understanding? By reasoning? By learning? By sentience? Building increasingly capable systems has, somewhat paradoxically, become one of the fastest ways to discover how incomplete our theories of intelligence really are. it convinces me that the next leap forward won' come from simply scaling today's ideas, but from developing a deeper understanding of intelligence itself. I want to help build AI that earns its place by solving real problems and extending human capabilities in meaningful ways, not by treating massive datasets and compute budgets as an indefinite cure-all. It's as humbling as it is exciting, and I don't think I'll run out of things to be curious about anytime soon.

With that motivation out of the way, below is a descending timeline of the milestones that best illustrate my journey so far.

// timeline
MAY 2026 — PRESENT
AI Engineer Intern @ Cisco Systems
This summer, I'm working with the Silicon One team to build an AI-powered validation suite that will be used to accelerate the development of next-generation ASICs. I'll also be building out an multi-agent RCA system for effective customer-facing error triage in the next few weeks.
JAN 2026 — PRESENT
Teaching Assistant @ Carnegie Mellon University
Who would've thunk I'd be doing this again! This time working with Prof. Matt Gormley to write homeworks and exams and leading recitation sections. It's been a great excuse to spend even more time in a subject I'd probably be studying anyway.
SEP 2025 — PRESENT
M.S. AI & Innovation @ Carnegie Mellon University
Of course I loved academia so much I went back for round two! Between coursework, TAing, a startup on the side, and more recently some research endeavors, I've basically been tackling AI from all angles. I've been leaning into applied AI systems and RL, which seems to be an exciting combination to approach all sorts of challenges. Turns out saying "yes" to everything propels me to do more things and meet more cool people at a scale I could never imagine before.
JUN 2024 — JUL 2025
Machine Learning Engineer @ Ericsson
I started as summer intern and got to develop the first AI-powered anomaly detection system for some of our biggest customers, easily the largest-scale ML system I'd shipped at this point. In addition to working around with multiple teams in my org, I also got to be on the other side, leading company-wide GenAI workshops and mentoring four interns through their own projects.
FEB 2024 — DEC 2024
Machine Learning Intern @ San Diego Supercomputer Center
My time here was largely spent publishing research tutorials to help researchers get their ML workloads running on some serious HPC systems, plus building out prompt-engineering guides for many of the frontier LLM chat models as part of the CIML project. I also put together single- and multi-GPU resource-management walkthroughs for PyTorch Lightning, and TensorFlow, learning a lot about GPU management and orchestration along the way!
SEP 2023 — DEC 2024
Instructional Assistant @ UC San Diego
Acting more purposefully on the passion for teaching I had since high school, these three quarters scratched an itch. Aside from the usual discussion leading and grading obligations, I was given the chance to prepare and deliver four lectures introducing machine learning and ethics. Huge shoutout to Prof. Shannon Ellis for entrusting the class with me that week and helping me realize this absolutely surreal moment!
JUN 2023 — AUG 2023
Platform Engineering Intern @ Nokia
My first taste of the corporate world, and I got to touch pretty much the whole software stack. I spent most of my time on router topology configuration tooling, cleaning up config management, error tracking, and packaging it all into a saner UX for our testing engineers.
SEP 2021 — DEC 2024
B.S. Cognitive Science & Machine Learning @ UC San Diego
These were definitely a fun-filled four years with tons of new experiences. I was deeply involved with Triton Gaming and the Data Science Student Society, encouraging me to put myself out there more and eventually make some of my best friends. Flexible schedules also enabled my mix of cognitive science, machine learning, and data science coursework which greatly contributed to me settling into a career in AI.