ladidadida

Type of Student: International male Undergrad Institution: Imperial College London (graduating June 2019) Major: Mathematics GPA: Expected high First Class (probably converts 3.8-4.0) Mathematics Courses Some of my courses were called slightly different things, but roughly in terms of standard US undergrad courses: Calculus, Linear Algebra, Multivariable Calculus, Numerical Analysis, ODEs, Real Analysis, Complex Analysis, (point-set) Topology, Algebra,. At perhaps upper level/early graduate (maybe? this seems to vary a lot looking at different places): Measure Theory, Functional Analysis, (measure based) Probability, Analytic Methods in PDEs, Stochastic Calculus, Algebra, Differential Geometry, Algebraic Topology. Statistics Courses Not really sure when US students normally take most of these: Probability and Statistics, Linear Models, Statistical Theory, Applied Probability, Time Series, Generalised Linear Models, Machine Learning. Additional Info: One (applied) ML software engineering internship at a well known tech company. Will be working there after I graduate. Strong in Python, R, C++, Java. GRE General Test: Q: 169 V: 162 W: 4.5 GRE Subject Mathematics: 910 (97%) Programs Applying: Statistics/Applied math Research Experience: Implemented and studied the efficiency of some ML methods for a problem in astrostatistics over summer. Currently working on senior thesis about brownian motion on Riemannian manifolds (expository work). Letters of Recommendation: One from each of the research experiences, and one from a maths professor who knows me well. Applying to Where: Statistics Stanford Berkeley Harvard Chicago Washington Columbia Yale Applied math UCLA Caltech NYU Princeton Cornell And some places in the UK - haven't quite decided yet. Concerns: 1. My grades weren't great in my first couple of years (just about on the Upper Second Class boundary) but went up significantly last year (to a high First) and should be similar this year. Due to heavier weighting in later years, my final degree classification will be fine, as I am doing well in my upper level/graduate coursework, but my transcript will show weak marks in a lot of the undergrad courses. Are not-fantastic grades on Real Analysis a big deal if I've done a great job on Measure/Functional/PDEs/Stochastics etc? 2. My letter recommenders aren't particularly well known. I know in Mathematics PhD admissions, by far the most important part of an application to strong schools is having excellent recommendations from professors who are known to the departments you're applying to. Is this also the case for Statistics? 3. Is spending time in industry frowned upon? I wanted to delay applications just one year to apply with a stronger averaged grade and having completed my senior thesis. 4. I suppose all concerns really point to this last one in most posts like this - am I being overly ambitious? Thanks!