Living docs
Plans
Trajectory layer from the Academics vault — Plan A (US research path), Plan B (international contingency), applicant profile, and skill development toward computational neuroscience.
- Plan A: strengthen the CS / psych profile at Purdue, REUs, applications aimed at the Fall 2027 computational neuroscience cycle
- Plan B: international contingency (Mexico / Europe) — transcripts, networking, fellowship scouting
- Skill track: Python scientific stack, networks from scratch, Git, viz, MATLAB for neuro labs
What “plans” means here
Not a mood board — the structured roadmaps in the Academics vault. They answer: what am I building toward, on what timeline, and what is the backup if the primary path stalls.
The north star is applied abstraction propelled by scientific curiosity — not rote software production for its own sake. Plans turn that sentence into dated quests. Polaris is where those quests get done.
Plan A — US research path
Primary strategy: strengthen the applicant profile while continuing Computer Science and Psychology at Purdue — computational neuroscience research, REU applications, faculty networking, GRE when needed, and a focused Statement of Purpose and letters for computational neuroscience / related PhD programs.
Program scouting includes strong computational and systems neuroscience environments (e.g. UW, MIT BCS, Stanford, UCLA, Michigan, Chicago, CMU, plus UK programs such as Oxford, UCL, Imperial). Lists are living; fit scores and professor notes get filled as research deepens.
Plan B — international contingency
Parallel admin and networking so academic goals can continue in Mexico or Europe if needed: apostilled transcripts, passport validity, researcher outreach, and fellowship landscapes (e.g. CONACYT, La Caixa) on a longer clock.
Public site stays high-level; operational checklists stay in the vault where they belong.
Skills & profile inventory
Skill development board targets data-science Python, a neural net from scratch, solid Git hygiene, visualization projects, and MATLAB literacy for lab environments — alongside NumPy/SciPy, pandas, classical ML, and PyTorch as the long stack.
Profile quests: research experience (poster or paper path), three strong letters, SoP that connects life story to research goals, polished CV, and this site as the inspectable public surface.