Eklavya: Research Projects
Algorithms for packing problems
- 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).
- 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.
- 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.
- 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.
the algorithm and benchmarked its performance on different datasets.
It was not significantly faster in practice and its applicability was limited.