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: US path — foundation at PFW, research experience, REUs, applications aimed at Fall 2027 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 under phd-prep / phd-roadmap and $ACADEMICS - META. They answer: what am I building toward, on what timeline, and what is the backup if the primary path stalls.
The north star in the vault meta home is applied abstraction propelled by scientific curiosity — not rote software production for its own sake. Plans turn that sentence into dated quests and profile work.
Plan A — US research path
Primary strategy: strengthen the applicant profile while finishing dual training in Computer Science and Psychology at Purdue Fort Wayne — lab or independent research, REU applications, networking with faculty, GRE when needed, then a focused Statement of Purpose and letters for computational neuroscience / related PhD programs.
Program scouting in the vault 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/TensorFlow 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.