Anish Maheshwar
seattle, wa 47.61° n · 122.33° w
hi! i'm broadly interested in building, understanding, and optimizing intelligent systems.
i'm currently studying applied mathematics and computer science at the university of maryland, college park. my current research interests are reinforcement learning and technical ai safety, though i'm curious about most things ml & math related.
at the moment, i'm interning at amazon web services, building agent infrastructure on the payments team.
previously, i did contract computer vision work with amazon leo, briefly collaborated with the bau lab / ndif, and did computer vision research on parkinson's disease detection.
feel free to contact me about anything at amahesh6@umd.edu!
fun facts
i was born and raised in the greater seattle area, and currently live near dc.
i'm really interested in all kinds of games. i own multiple pokemon monopoly sets, was formerly top ~3000 na on shooter game valorant, and love to play chess.
i'm an effective altruist and rationalist. i got introduced by the non-trivial fellowship in high school (of which i was a finalist, ~200/10k ish), and now help organize umd's ai safety student group and attend various ea / safety workshops.
i love animals; my favorites are the crow and the raccoon. my favorite artists are takato yamamoto and tsutomu nihei.
Blog
random miscellaneous thoughts from me, coming soon :)
Experience
places i've worked and research i've done.
contract computer vision work, incl. segmentation for airport exclusion-zone prediction.
worked on interpretability paper reimplementations using nnsight.
Publications
Academics
awards & coursework i've taken.
apex fund·maryland ai safety·computer science dean's list·presidential scholarship
Coursework* = graduate coursework
cmsc216introduction to computer systems taught by christopher kauffman · fall '25
systems programming in c and assembly — memory and the stack, the unix toolchain, and how programs actually run on the machine.
cmsc250discrete structures taught by fawzi emad · fall '25
propositional & predicate logic, proof techniques and induction, sets and relations, and elementary combinatorics.
cmsc330organization of programming languages taught by clifford bakalian · spring '26
functional, imperative and scripting paradigms across ocaml, ruby and rust; parsing, regular languages, lambda calculus and language semantics.
cmsc351algorithms taught by ting jiang · spring '26
design and analysis of algorithms — asymptotics, recurrences, divide-and-conquer, greedy methods, sorting, and graph algorithms.
textbook: introduction to algorithms — cormen, leiserson, rivest & stein.
cmsc460computational methods taught by ramani duraiswamy · fall '26
numerical computing — interpolation, numerical linear algebra, root-finding, quadrature, and solving differential equations.
*cmsc848pmachine learning theory taught by han shao · fall '26
theoretical foundations of ml — pac learning, vc dimension, generalization bounds, and online learning.
textbook: understanding machine learning — shalev-shwartz & ben-david.
math340multivariable calc, linear algebra & diff eq i (honors) taught by james conway · fall '25
honors sequence, part i: multivariable calculus and linear algebra developed together, with an emphasis on proofs.
math341multivariable calc, linear algebra & diff eq ii (honors) taught by james conway · spring '26
honors sequence, part ii: further linear algebra and ordinary differential equations.
stat410introduction to probability theory taught by sudeshna bhattacharjee · fall '26
probability spaces, random variables and common distributions, expectation, and limit theorems including the clt.