sahely

Research interest:

Machine Learning (learning from noisy, incomplete data and multi-view data), optimization (robust optimization and convex optimization for large data), Time series data (anomaly detection), bio-informatics (micro-array data analysis and molecular network analysis).

Summary:

I am currently working on detecting and predicting extreme events in time series data using machine learning models and deep learning models and on handling missing data for kernel method when data has multiple sources. Previously, I have worked on robust optimization to learn form noisy data. My recent work proposes a new pre-processing method to correct noisy labels via mutual consistency check using a Parzen window classifier. It has also used the Spannogram framework to solve the problem with approximation guarantees. During my graduation I have been working on building robust classifiers to deal with interval-valued uncertainty in data as well as kernel matrices. I have also dealt with scalability and robustness of these classifiers. A novel distribution free large deviation inequality has been proposed which handles uncertainty. A mirror descent algorithm (MDA) like procedures have been applied to make learning of proposed robust formulations scalable. In addition to working on the mathematical aspects, I focused on applying new formulations on various real life scenarios.

Awards:

                 Indo-Finish Mobility grand 2019-2020 from DST and Academy of Finland (June-July 2019).

                 IMPECS postdoc fellowship, 2012-2014.

                 IBM Ph.D. Fellowship Award 2010 - 2011.

                 Best Runner Up paper award in PAKDD, 2009.

Research Grant:

1.  Core Research Grand (CRG) from SERB, DST of around INR 3.5m on Project titled "Robust Multi-view Learning for Extreme Events Detection and Prediction in Time Series Data “. 2021-2024.
2. Automatic Pathogen Detection in plants (as Co-investigator ) a project in TIH of IITPalakkad