Eklavya: Research Projects

1. Fair Division of Indivisible Items

   (August 2021 - present) Topics: cooperative game theory. Supervisor: Prof. Jugal Garg, ISE, UIUC.

2. Algorithms for packing problems

   (Jan 2020 - April 2021) Topics: approximation algorithms, bin packing, online algorithms. Supervisor: Prof. Arindam Khan, CSA, IISc Bangalore.
   • Designed approximation algorithms for rectangle bin packing when items are skewed, i.e., each item either has small width or small height.
   • Designed approximation algorithms for a variant of bin packing that generalizes geometric bin packing and vector bin packing.
   • Designed an approximation algorithm for d-dimensional geometric bin packing when items are allowed to be rotated. This algorithm gives the best-known approximation factor for d ≥ 3.
   • Worked on the online knapsack problem in the random-order model. Obtained hardness results and improved algorithms for some special cases (profit=size and profit=1).

3. Mitigating DNS-related DoS attacks using SDN

   (Sept 2017 - Dec 2017) Topics: computer networks, network security, SDN. Supervisor: Prof. Vishal Gupta, BITS Pilani.
   • Studied DNS-related DoS attacks and Software-Defined Networking (SDN).
   • Devised a new mechanism for mitigating DNS amplification attacks, which uses a set of geographically-distributed SDN routers.
   • I presented a paper on it at ICACCI in September 2018.

4. Internship at American Express, Gurgaon

   (Jan 2018 - June 2018) Topics: neural networks, machine learning, big data.
   • Trained a neural network from almost-raw data to estimate the probability of a credit-card applicant defaulting.
   • The data was in a unique format, so a custom neural network architecture was devised.
   • The neural network's performance was at par with the model then in production, which was tuned over many years and utilized several complex hand-engineered features.

5. CT-means clustering algorithm

   (Nov 2017 - Jan 2018) Topics: machine learning, algorithms, math. Supervisor: Prof. Surekha Bhanot, BITS Pilani.
   • Invented a clustering algorithm, which I named CT-means. It is an approximation to C-means fuzzy clustering. It uses KD-trees to reduce running time.
   • Mathematically proved its convergence and approximation guarantees.
   • Implemented the algorithm and benchmarked its performance on different datasets. It was not significantly faster in practice and its applicability was limited.