Research                                                                                                       Calendar

My primary area of interest include Approximation Algorithms.

For Prospective Phd Students for admssions 2024

Machine learning-assisted congestion control in Computer Networks

Congestion control is a crucial aspect of networking. The main objective of congestion control is to enhance network throughput while preventing congestion, which can result in packet loss, long delays, and other inefficiencies. A congestion control protocol helps a TCP sender determine its sending rate in a TCP/IP network. In this work, we intend to use ML algorithms to determine sender's rate that helps in evaluating various parameters that further help in controlling congestion in the network

Approximation Algorithms

Network design problems occur naturally at many places. For example, a bank may want to install a number of ATMs in a city. Each location in the city is associated with an installation cost. The bank would be interested in identifying the locations to install these ATMs in such a way that the total installation cost plus the cost to service its clients is minimized. This is a typical example of a facility location problem where the ATMs serve as the facilities. Many similar examples can be quoted for the problem such as setting up of fire-brigade stations. In case of the problem of setting up of fire-brigade stations, one may also be interested in connecting these stations so that in case one station does not have a vagon to satisfy a client, the same can be obtained from a nearby station. In such a case, one would be interested in identifying the locations where in addition to the installation cost and service cost, the cost of connecting the fire-brigade stations is also taken into account. This is an example of a \lq connected \rq facility location problem. In another variation of fire-brigade problem, one may specify the maximum number of wagons available at a particular station. This gives rise to what is known as \lq capacitated \rq facility location problem. These problems are known to be NP-hard. The group is working on developing approximation algorithm for variants of facility location problems. In particular, we are looking at capacitated variants of data placement problem, k-median, k-FLP and knapsack median, facility location with penalty/outliers.

For doing Ph.D. with me students with Mathematics background are preferred. Preferably an M.Sc. (Mathematics)/M.Sc.(Operational Research) from University of Delhi.

Research Guidance

Ph.D. Completed


Ph.D. Underprogress

  • Sapna Grover (DoReg: May 5, 2017)
  • Rajni (DoReg: November 11, 2019)
  • Maulein Pathak (DoReg: February 2022) (Jointly with Yogish Sabharwal)
  • Manisha Wadhwa (DoReg: February 2022) (Jointly with Sanjay Madria)

Research Projects (Completed)

  • Principal Investigator, FRP grant, "Efficient and Scalable Parallel Algorithms for Graph Spanners" October 2021 - June 2022.
  • Principal Investigator, University Research Grant for project titled "Approximation Algorithms for capacitated facility location problems" October, 2015 - 2016
  • Principal Investigator, University Research Grant for project titled "Approximation Algorithms for Replica Placement Problem" October, 2014 -2015. completed
  • Principal Investigator, University Research Grant for project titled "Analysis of Gene Expression Data using Bi-Clustering Ensemble Technique" March 2010 - March 2013. Completed.
  • Principal Investigator, University Research Grant for project titled "Analysis of Gene Expression Data" 2007-March 2010, completed.

Master's Projects

2020 - 2021
  • Testing of DUCS OfficeAutomation tool. Hitesh Yadav
2019 - 2020
  • DUCS OfficeAutomation. Ruman Saleem, Swati Gautam and, Tanya Singhal
2018 - 2019
  • Approximation Algorithms for Facility Location Problems with Penalties/Outliers and for ordered $k$-median problem. Abhilasha Gupta and Rajni
  • Approximation Algorithms for Ordered k-median. Rajni
  • Approximation Algorithms for Facility Location with Outliers. Abhilasha Gupta
2016 - 2017
  • Approximation Algorithms for Priority Facility Location Problem. Sachin Kashyap and Moksh Makhija (Jointly with Aditya Pancholi)
2015 - 2016
  • Design and Implementation of Classification Algorithms in OpenMP. Sonika Gupta and Juhi Jain (Jointly with Vipin Kumar)
  • Design and Implementation of Classification Algorithms in Matlab. Aakriti, Darshika and Shikha (Jointly with Vipin Kumar)
  • Design and Implementation of Classification Algorithms in MPI. Saurabh (Jointly with Sandhya Aneja)
2014 - 2015
  • Approximation Algorithms for Replica Placement Problem: Anshul Aggarwal, Sachin Sharma (Jointly with Yogish Sabharwal)

2013 - 2014
  • Approximation Algorithms for Replica Placement Problem : Kanika Gupta, Parul Garg, Ankur Sachdeva, Nirbhay (jointly with Yogish Sabharwal)
  • Developing software for UG Admissions: Manisha, Avni, Tanu, Kirti, Mayank (Jointly with Aditya Pancholi)
  • Developing software for Center Placement Cell: Navdeep and Shailendra (Jointly with Aditya Pancholi)
  • Developing Biclustering Ensemble Tool: Navdeep and Shailendra.

2012 - 2013
  • Approximation Algorithms for Replica Placement Problem: Stuti Chawla (jointly with Yogish Sabharwal)
  • Approximation Algorithms for Interval Scheduling Problem: Mohit Narula (jointly with Sambuddha Roy)

2011 - 2012
  • Approximation Algorithms for Data Placement problems : Sakshi, Archita
  • Approximation algorithms for scheduling problems : Neha Bansal, Vijaya Goel, and Manish Dawar
  • Approximation algorithms for Multi Organisation Scheduling problems : Neha Lawaria and Pankaj Kumar
  • Routing in Delay Tolerant Networks : Garima and Sonia

2010 - 2011
  • Efficient Routing in Delay Tolerant Networks : Divya Gaur and Surbhi Tripathi
  • Ensemble techniques for bi-clustering problem : Deepika Bisht and Soniya Verma
  • Approximation Algorithms for Facility Location Problem : Sapna Grover and Apurv Milind
  • Approximation Algorithms for Data placement problem : Swati Singhal

2009 - 2010
  • Approximation algorithms for clustering problems : Aditya Pancholi
  • Parallel SAT : Ashish Mann and Vashita Arora
  • Multi-core SAT : Amrita Goel and Hunarr Pahwa
  • Routing in ad hoc networks : Megha Gupta and Simmi Singhal

2008 - 2009
  • Subsequence Mining : Neha Jain and Leena Singhal
  • Bi-clustering : Ishan Qureshi, Surbhi Bajaj

2006 - 2007
  • Pattern Discovery Algorithms allowing Wildcard Characters : Ashish Juneja and Savneet Kaur
  • Pattern Discovery Algorithms with Mismatches : Pooja Sinha and Shweta Mehra

2005 - 2006
  • Implementation of Parallel Algorithms for Data Mining (jointly with Dr. Vasudha Bhatanagar) : Ashish Mangla and Ashwani Garg
  • Implementation of Algorithms for identifying modules in Biological Networks.
  • Implementation of Parallel Algorithms for Matrix Computations : Kapil and Tushir Aggarwal
  • Implementation of Parallel Algorithms for Graph Problems.