I am looking to apply for PhD programs in OR with a focus on Optimization, this would be my first cycle and I am the only one in my family to apply to grad school so any advice/pointers would be greatly appreciated !
Context:
I am an International applicant and will be applying to PhD programs in the US. I have done my bachelor's in Mechanical Engineering from a Tier 3 university in India and my masters in Computational Science from a top university in India. My stats are
Bachelors GPA: 3.44
Masters GPA: 3.77
I have ~ 2 years of research experience in computational electromagnetics, with a paper under review in a decent computational journal (Impact Factor: 4.0).
I have taken the usual mechanical engineering courses(with the usual calculus 1,2,3 + Linear Algebra + Differential Eqns) in undergrad. For my masters I have taken - Probabilty, Numerical methods, Numerical Soln to Differential Equations, Dynamics and Control of Linear Systems and Numerical Linear Algebra. I also audited a course on Finite Element Methods although I don't have it on my transcript.
My program didn't allow me to take more courses as it was a research based masters, which is a big worry for me. As most OR programs require more proof based courses, I have self studied Real Analysis(Abbot), Proof based Linear Algebra(Axler), Combinatorics(Trotter). I'm pretty confident in these topics, but i'm not sure if this would be convincing to the admission commitee as I don't have it on my transcript.
My GRE and TOEFL scores are: 327(164Q, 163V, 4.0AWA) and 113(29R, 29L, 29W, 26S)
I have 2 solid LOR's(One from my research advisor) and one decent.
I'm thinking of applying to: Reach: Georgia Tech, University of Michigan; Target: University of Wisconsin Madison, University of North Carolina, Northwestern University; Safe: University of Minnesota, Ohio State
My biggest concerns are that I have not taken more proof based mathematics classes and my research experience is not in OR. Am I being too ambitious with my Uni's? I'd appreciate any comments !
Question
azurepanda
Hi,
I am looking to apply for PhD programs in OR with a focus on Optimization, this would be my first cycle and I am the only one in my family to apply to grad school so any advice/pointers would be greatly appreciated !
Context:
I am an International applicant and will be applying to PhD programs in the US. I have done my bachelor's in Mechanical Engineering from a Tier 3 university in India and my masters in Computational Science from a top university in India. My stats are
Bachelors GPA: 3.44
Masters GPA: 3.77
I have ~ 2 years of research experience in computational electromagnetics, with a paper under review in a decent computational journal (Impact Factor: 4.0).
I have taken the usual mechanical engineering courses(with the usual calculus 1,2,3 + Linear Algebra + Differential Eqns) in undergrad. For my masters I have taken - Probabilty, Numerical methods, Numerical Soln to Differential Equations, Dynamics and Control of Linear Systems and Numerical Linear Algebra. I also audited a course on Finite Element Methods although I don't have it on my transcript.
My program didn't allow me to take more courses as it was a research based masters, which is a big worry for me. As most OR programs require more proof based courses, I have self studied Real Analysis(Abbot), Proof based Linear Algebra(Axler), Combinatorics(Trotter). I'm pretty confident in these topics, but i'm not sure if this would be convincing to the admission commitee as I don't have it on my transcript.
My GRE and TOEFL scores are: 327(164Q, 163V, 4.0AWA) and 113(29R, 29L, 29W, 26S)
I have 2 solid LOR's(One from my research advisor) and one decent.
I'm thinking of applying to: Reach: Georgia Tech, University of Michigan; Target: University of Wisconsin Madison, University of North Carolina, Northwestern University; Safe: University of Minnesota, Ohio State
My biggest concerns are that I have not taken more proof based mathematics classes and my research experience is not in OR. Am I being too ambitious with my Uni's? I'd appreciate any comments !
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