Seeking advice on graduate school strategy (Computer Science Masters, followed by PhD, from non-traditional / not CS background)

[Matstac]

I am applying for a Masters in Computer Science, specializing in Artificial Intelligence, from a non-traditional background. Afterward, I am targeting a PhD in Computational Neuroscience or interdisciplinary in AI, Neuroscience, and related fields.

Big picture, I want to contribute to mental health at the intersection of Computer Science and Neuroscience (e.g., improved diagnostics methods, computational modeling of mental illnesses, new therapeutic targets).

I am looking for advice on which of the below 2 paths makes the most sense in the long term. Stats at the bottom -- thanks in advance!

1. Post bac before Masters (total of 3 years; 1 year post-bac, 2 years Masters)
   1. Tufts
2. Straight into the Masters (2.5-3 years, assuming a semester or two for pre-reqs)
   1. UPenn (MCIT first), Harvard, NYU, UC Davis, UC Irvine, Boston University, Northeastern

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Stats

• Undergrad from Top 30 school in US, majoring in Psychology and Neuroscience 
  • GPA: 3.04 (high upward trend with 3.65 GPA in latter half)
  • Major/Sciences GPA: 3.67
• 332 GRE (166Q/166V/5.5AW)
• 3 years of management consulting consulting in healthcare/pharma
• 2 years of Psychology research (started in undergrad, continued post-graduation, no publication)

Additional stats, if post bac:

• 1 year of relevant research
• Recommendation letters from professors in target area
• GPA boost, and demonstration of ability to perform in relevant coursework
• Pre-requisites met

In addition to the above benefits from the post bac, making me more competitive for 'better' programs, I think it: allows me to maximize my time during Masters for research (not spent struggling thorough coursework, and to narrow down research interests for the next step).

Any thoughts and advance appreciated. Thanks.

Edited November 10, 2019 by Matstac