About Me Education Activities Teaching/Courses Talks/writeups Miscellaneous CV

Nilava Metya

নীলাভ মেট্যা

"All convex optimization problems are easy" is a myth.
"All non-convex optimization problems are hard" is a myth.


Rutgers - the State University of New Jersey

About me

Nilava I am a second year PhD student in Mathematics at Rutgers University - New Brunswick. My advisors are Arunesh Sinha and Paul Feehan. Email me at nilava.metya@rutgers.edu if you have any question (about anything: life in general, the math community at Rutgers, ...)

Something I want to clarify for readers of this webpage: This is my professional webpage written from my personal point of view and I have different optinions on learning vs research. For me, learning includes research; I refer to learning from a book/paper/others as second-hand learning and learning from my own insights/research/results as first-hand learning. So if you see the word 'learn', it does include research sometimes. I have a strong urge to do second-hand learning from experts only, because of my strong belief that second-hand learning, by definition, cannot be done third-hand.

My learning interests lie in applied areas of Distributionally Robust Optimization, Polynomial Optimization, Applied Algebraic Geometry and theoretical Machine Learning.
I love solving problems (and mathematical puzzles), especially those similar to olympiad style and competitive coding problems.

Here is a CV.

News:



Goals to reach within 2023:

Goals to reach within 2024:

Goals to reach within 2025:

Goals to reach within 2027:

Education

Doctor of Philosophy Sep 2022 - (expected) 2027 Rutgers - the State University of New Jersey
Mathematics
Master of Science Sep 2022 - May 2024 CGPA: 4.0 / 4.0
 
Bachelor of Science (Hons) Aug 2019 - May 2022 Chennai Mathematical Institute
Mathematics and Computer Science
CGPA: 9.72 / 10
Position: 3rd (out of 57)
 
Indian School Certificate Exam 2019 Don Bosco School, Liluah
Science stream
Percentage: 97.25%
Position: 2nd (in a batch of ~180), 1st (in Science batch of ~55)
 
Indian Certificate of Secondary Education 2017 Don Bosco School, Liluah
Percentage: 96.6%
Position: 1st (out of ~180)
 

Activities

(Aug 2024) Princeton Machine Learning Theory Summer School at Princeton University.

(July 2024) Equilibrium Summer School at Rutgers University - New Brunswick.

(May 2024) DIMACS Workshop on Efficient Algorithms for High Dimensional Metrics: New Tools at DIMACS (Rutgers, New Brunswick).

(Dec 2023) Bayesian Statistics and Statistical Learning: New Directions in Algebraic Statistics at IMSI, Chicago.

(Oct 2023) Algebraic Statistics for Ecological and Biological Systems at IMSI, Chicago.

(Oct 2023) Apprenticeship Week at IMSI, Chicago.

(Sep 2023) Algebraic Statistics and Applications at IMSI, Chicago.

(Aug 2023) Permutations and Causal Inference at IMSI, Chicgo.

(Jun 2023) Algebraic Methods in Biochemical Reaction Networks at MPI, Leipzig (organized by MPI Leipzig and SLMath).

(May 2023) Attending as the head counsellor at PROMYS India.

(Jan-Apr 2023) Organizing ANGeLS - the Algebra and Geometry Learning Seminar for graduate students. Please drop by at HILL 525 at 9AM every Wednesday to enjoy bagels over some wonderful talks on Quiver Representations.

Coursework at Rutgers

Teaching at Rutgers

  1. Representation Theory
  2. Algebraic Geometry II
  3. Homological Algebra
  1. Measure Theoretic Probability
  2. Lie Algebras
  3. Theory of Schemes  at Princeton
  1. Topological Data Analysis
  2. Data Mining
  3. Topics in Algebraic Geometry  at Princeton
  1. Learning Theory
  2. Convex optimization  at Princeton
  1. Advanced Algorithm Design  at Princeton
  1. Topoplogy grader
  2. Theory of Numbers grader
  1. Analysis II grader
  2. Topics in Applied Algebra grader
  1. Calculus 2 workshop leader
  2. Linear Algebra and Applications grader
  1. Calculus 2 workshop leader
  2. Algebra 2 grader
  1. Mathematical theory of statistics grader

Coursework at CMI

Teaching Assistantship at CMI

  1. English
  2. Classical Mechanics I
  3. Functional Programming in Haskell
  4. Algebra I
  5. Analysis I
  1. Advanced Programming in Python
  2. Discrete Mathematics
  3. Probability Theory
  4. Algebra II
  5. Analysis II
  6. Quantum Computation and Quantum Information     just attended the corurse
  1. Theory of Computation
  2. Design and Analysis of Algorithms
  3. Calculus
  4. Algebra III
  5. Analysis III
  1. Classical Mechanics II
  2. Formal Security Analysis
  1. Representation theory of Algebras and Quivers
  2. Introduction to Manifolds
  3. Graduate Analysis I
  4. Algebraic Number Theory
  5. German
  1. Linear Algebra and its Applications
  2. Graduate Topology 2
  1. Functional Programming in Haskell
  2. Algebra I
  1. Functional Programming in Haskell
  2. Algebra I
  1. Algebra 2
 
 

Talks/Write-ups

  1. Inference on growth process of a network at Rutgers for the course on Data Mining
    Reference Paper.
    Slides. Report. Video.

  2. Principal Components along Quiver representations at Rutgers for the course on Topological Data Analysis
    Reference Paper: Principal Components along Quiver Representations.
    Slides.

  3. Representations as sections of line bundles at Princeton for the course on Algebraic Geometry
    Notes.

  4. Complexity of Optimization at Rutgers Math department Pizza Seminar
    Slides.

  5. Quvier representations - geometry and invariants at Rutgers Algebra aNd GEometry Learning Seminar
    References: a geometric view.
    Notes.

  6. Quvier representations at Rutgers Graduate Algebra and Representation Theory Seminar
    References: Quiver representations via reflection functors, a geometric view, beyond Dynkin quivers.
    Notes.

  7. Burnside's paqb theorem at Rutgers Graduate Number Theory Seminar
    Reference: Abstract Algebra by Dummit and Foote.

  8. Kneser Graph Coloring at Rutgers Graduate Combinatorics Seminar
    Reference: A new short proof of Kneser's conjecture.

  9. Very Basic Lie Theory at Rutgers Geometry and Topology Seminar
    Reference: Very Basic Lie Theory by Howe.

  10. Well-definedness of the Brauer group at Rutgers Algebra aNd GEometry Learning Seminar
    Reference: Associative Algebras by Pierce.

  11. Fiedler Vector Methodat CMI for the course on Matrix Computations
    These are the slides, written report, and video based of a group project on the Fiedler vector method - an approximate way to find a balanced graph cuts.
    Slides. Report. Video.

  12. Cantor Set
    Here are the notes for a talk on Cantor set I gave in a tutorial in Graduate Analysis I course.
    Notes.

  13. Markov Chain Monte Carlo
    This is the presentation based on an internship with Prof R V Ramamoorthi.
    Presentation.

  14. Quantum Computing
    These are the write-ups for a series of four talks I gave at a PROMYS counsellor seminar on Quantum Computing.
    Talk 1. Talk 2. Talk 3. Talk 4.

  15. Lie Algebras and their Representations
    These are the slides and the writeup for a series of talks I gave at a PROMYS counsellor seminar on Representation theory of Lie algebras.
    Writeup. Talk 1. Talk 2. Talk 3.

  16. Introduction to Hyperbolic Geometry
    These are the write-ups for a series of four talks I gave at a PROMYS counsellor seminar on the calculus on the upper half plane in Hyperbolic geometry.
    Writeup.

  17. Computer Project in grade 12
    These are the project writeups for my project for grade 12 in high-school. One is a compilation of codes we did throughout the year, another is a larger project to imitate a retail shop and implement an inventory of items.
    Compilation. Inventory Code.

Miscellaneous

  1. FAQ on applying abroad after bachelors/masters

  2. Access your CMI account remotely
    This is an article about accessing your CMI account sitting at your home. This allows you to do a few things like opening CMI local links and creating your own homepage - and any other task for which you want local access to a physical computer at CMI.

  3. Distributing grade details in the online semester
    This is an article for graders to share information with their students, keeping all the information available to them. I wrote this article in hope that graders learn and use new techniques, in order to adapt to the online semester. Technology should not be interfere with one's right to information. Hope that this article gives some momentum.

  4. Distributing grade details in the online semester from your university mail (secure)
    This article does the same job as the previous article. The only difference is that the email id, from which mails are sent, is the user's university account. This makes use of the mail command in Unix. This much more secure, because one will be using the institute's local machine to send all mails.

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