The research case for a PhD in artificial intelligence: directions I want to work on, and the infrastructure, papers, and computational studies behind them. Each entry states its type and how AI was used.
I am applying to PhD programs in artificial intelligence. The work on this site is the case for it: research infrastructure at 12.2M rows, working papers, open machine-learning tools benchmarked against published baselines, and production AI systems that keep a human in the loop. Quantum computing and biophysics show up here as application areas where the machine-learning question is sharp; the doctoral work I want sits in AI itself.
Models that make a scientific measurement cheaper or a hypothesis testable: protein stability prediction, Markov state models of molecular dynamics, ADMET property prediction, and quality control for cryo-EM and electrophysiology. Benchmarked against published baselines, CPU-only, open source.
Retrieval and generation grounded in primary sources: hybrid BM25 and dense retrieval over full-text papers, answers that carry their citations, and a 12.2M-row provenance-tracked graph of the research-funding economy underneath.
How to measure the output of AI-assisted research: verification levels, disclosure of the human and AI division of work, agent orchestration with review gates, and what degrades once a person stops checking.
Neural decoders for quantum error correction inside a real-time budget, learned search over quantum-optimization circuits, and how to partition a workload across CPU, GPU, and QPU.
These are active study questions rather than completed results. Each one links to code, a benchmark, or a working paper where one exists.
I use AI as a research and engineering instrument. I define the questions, scope, and evaluation criteria; orchestrate the workflows; review intermediate and final outputs; and make the final technical and editorial decisions. Each artifact states its verification level and the division of human and AI work.