Spring 2025. I’m starting a PhD in Computer Science at SIUE.
Four months post-stage-4 diagnosis. Fourteen months after defending my math master’s. Uncertain time horizons, clear research priorities.
Why Now?
Stage 4 cancer with a 2 to 5 year median survival. Why start a 4 to 6 year program?
Because the research matters regardless of completion.
A PhD gives me time for deep research, access to computational resources, legitimate space to work on hard problems, collaboration, and freedom from commercial constraints. Whether I finish or not, the work will exist. Papers, code, ideas, tools.
That’s the goal.
Why Spring 2025 (Not Fall 2024)
I actually tried to start in Fall 2024, right after the stage 4 diagnosis.
September 2024: stage 4 confirmed, started chemo. Immediately tried to get PhD admission for the fall semester. Problem: I needed GRE scores. Had an appointment scheduled. While driving to the test center, they canceled on me. Rescheduled. By the time I got scores back and submitted paperwork, the deadline had passed.
At the time, it felt like a frustrating setback.
In retrospect, it was perfect timing. The chemo regimen they put me on in fall 2024 was way too intense for academic work. I was barely functional. No way I could have handled coursework.
Over the winter, treatment stabilized. Energy returned. Brain fog cleared enough to think. By spring 2025, I’m actually ready.
Sometimes administrative delays save you from yourself.
The Research Direction
I’m focusing on a few areas.
Machine learning foundations: understanding how these systems actually work, not just using them.
AI safety and alignment: making sure we build systems that minimize harm.
Complex networks: analyzing AI conversations and reasoning as navigable graphs.
Statistical computing: methods that bridge theory and large-scale data.
The connecting theme is building and analyzing intelligent systems responsibly.
Constraints
Let me be explicit.
Medical: chemo cycles, scans every 3 months, unpredictable energy, surgeries as needed.
Time: unknown but likely finite. Optimize for meaningful work now.
Motivation: cancer clarifies what matters. No time for bullshit.
Productivity: expect 50 to 70 percent of healthy baseline. Plan accordingly.
This isn’t tragic. It’s just parameters for the optimization problem.
What I’m Not Doing
I’m not collecting credentials. Not pursuing safe, incremental research. Not following traditional PhD timelines. Not caring about academic politics. Not pretending I have unlimited time.
I’m working on problems that matter, publishing results as I get them, building tools others can continue, documenting reasoning thoroughly, and optimizing for research quality over completion.
First Semester
Spring 2025 courses:
AI: classical search (A*, DFS, BFS), MDPs, reinforcement learning, Bayesian networks, all unified under utility maximization.
Research methods: setting up the dissertation track.
The AI course is perfect timing. I need to understand how intelligent systems optimize, what utility functions actually mean, how learning and planning relate, and where alignment problems emerge.
The Bigger Picture
Two master’s degrees gave me foundations.
CS (2015): how to build systems. Encrypted search, cryptography, distributed systems.
Math/Stats (2023): how to reason rigorously. Proofs, statistical inference, probability theory.
PhD (2025 onward): combining both for AI research.
This progression wasn’t planned. But it makes sense in retrospect.
Research Questions I Care About
Can we analyze AI reasoning as complex networks? Treating conversations and knowledge graphs as navigable structures.
How do we ensure AI systems minimize suffering? Not just performance, actual ethical properties.
What does alignment mean formally? Beyond buzzwords to mathematical specifications.
Can we make LLMs more interpretable? Understanding what’s happening, not just what’s output.
These aren’t all solvable in a PhD. But they’re worth working on.
The Time Calculation
Optimistic (5 years): complete PhD, publish multiple papers, build lasting tools.
Realistic (3 years): significant research contributions, useful software, incomplete degree.
Pessimistic (1 year): one good paper, open source tools, documented ideas.
All of those are worth doing. The PhD structure supports the work. The degree itself is secondary.
What Success Looks Like
Not “got the PhD.” Instead: published research that advances the field, built tools that others use, left clear documentation for continuation, contributed to AI safety understanding, made my reasoning legible to future researchers.
If I do those things, the PhD was worth it whether I finish or not.
The Cancer Paradox
Weirdly, cancer makes me more productive, not less.
Not because I’m racing against time (though I am). Because it removes everything that doesn’t matter. No more imposter syndrome (who cares?). No more perfectionism (done beats perfect). No more fear of failure (worse things have happened). No more academic posturing (no time).
Just: do meaningful work while you can.
What’s Different This Time
I did my CS master’s as a younger, healthier person. It was about learning and credentials.
I did my math master’s while managing cancer. It was about foundations and rigor.
PhD with stage 4: it’s about making the work count.
Every project gets filtered through “is this worth my remaining time?” Most things fail that filter. A few pass. Those few get everything I have.
First Steps
This semester: AI course to understand utility maximization frameworks. Start complex networks research. Set up infrastructure for the dissertation. Begin building analysis tools. Document everything.
The work begins now.
PhD Year 1, Spring 2025. Uncertain timeline. Clear priorities.
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