Me

Hi! This is Archie (Anubhavnidhi Abhashkumar). I am a grad student pursuing PhD in Computer Sciences at University of Wisconsin - Madison. I work with Professor Aditya Akella. My areas of interest include Network Policy Management in SDN (Software Defined Network) and Big Data Systems. I'm part of the Wisconsin Internet Systems Research (WISR) Lab and Wisconsin Institute on Software-defined Datacenters in Madison (WISDoM).

Publications

Supporting Diverse Dynamic Intent-based Policies using Janus [pdf]
Anubhavnidhi Abhashkumar, Joon-Myung Kang, Sujata Banerjee, Aditya Akella, Ying Zhang and Wenfei Wu.
CoNEXT 2017, Seoul/Incheon, South Korea.

P5: Policy-driven optimization of P4 pipeline [pdf], [pptx]
Anubhavnidhi Abhashkumar, Jeongkeun Lee, Jean Tourrilhes, Sujata Banerjee, Wenfei Wu, Joon-Myung Kang and Aditya Akella.
SOSR 2017, Santa Clara, CA.

Paving the Way for NFV: Simplifying Middlebox Modifications using StateAlyzr [pdf], [pptx]
Junaid Khalid, Aaron Gember-Jacobson, Roney Michael, Anubhavnidhi Abhashkumar and Aditya Akella.
NSDI 2016, Santa Clara, CA.

Experiences

Research Assistant

2015 - Present

I am working with Professor Aditya Akella on topics related to SDN and Big Data Systems.

Software Engineering Intern

2017

I worked with Hyojeong Kim on creating a high sampling tailer for tagging TCP and HTTP data.

Research Associate

2016

I worked with Joon-Myung Kang and Sujata Banerjee on representing and configuring diverse dynamic intent-based policies.

Research Associate

2015

I worked with Jeongkeun "JK" Lee and Sujata Banerjee on Programming the switch data-path from high-level policies.

Teaching Assistant

2014 - 2015
  • Introduction to Programming Language (in Java), Fall 2014, Spring 2015
  • Introduction to Computer Networks, Fall 2014

Course Projects

Kubernetes: A Profile and Evaluation Study - A deep dive into Google's open source container orchestrator Kubernetes. The study profiles the policies and behaviour of Kubernetes in scheduling, admission control, crash scenarios and auto-scaling.
Large Scale Empirical Study of Linear Classifiers for Multi Class Problems - Experiment to compare the performance of Logistic Regression, Linear SVM, Naïve Bayes, ECOC (Error Correcting Output Code using Naive Bayes) and Weighted Majority over large datasets. Performance was measured using Cohen’s Kappa and G-mean.

Hobbies

I follow Pro-Wrestling. Some companies that I follow are WWE, NJPW, PWG and RoH
I like playing video games. I currently own a PS4 and Nintendo 3DS.
I like reading books, comics and mangas. Favorite genres are popular science, myth-fiction, fantasy and scifi.
I am also learning to play all 3 types of guitars: acoustic, electric and bass