
- Assistant Professor (Jun, 2017 -Till date)
- Computer Science and Engineering, IIT Palakkad, India
- Postdoctoral researcher
- Network Science Institute, Northeastern University, USA (Jan, 2017 - May 2017)
- Adviser: Tina Eliassi-Rad
- Helsinki Institute of Information Technology, Aalto University, Finland (Oct,2014 - Dec,2016)
- Adviser: Samuel Kaski, Juho Rousu
- Max-Planck-Institute for Informatik and Saarland University, Germany (Sep, 2012 - Aug, 2014)
- Adviser : Matthias Hein
- Network Science Institute, Northeastern University, USA (Jan, 2017 - May 2017)
- Research Internship
- NetApp, Bangalore (Jun, 2010 - May, 2011)
- Adviser: Siddhartha Nandi
- Yahoo! Labs, Bangalore (May, 2009 - Jul, 2009)
- Adviser: Rajeev Rastogi, Sundararajan Sellamanickam
- NetApp, Bangalore (Jun, 2010 - May, 2011)
- PhD in Computer Science (Aug, 2006 - Sep, 2012)
- Indian Institute of Science (IISc), Bangalore
- Adviser: Chiranjib Bhattacharyya
- Thesis: Learning Robust Support Vector Machine Classifiers with Uncertain Observations
- Software Engineer (Jul, 2004 - Jul, 2006)
- Wipro Technology, Bangalore
- B.Tech in Information Technology (Apr, 2000-May, 2004)
- RCC Institute of Information Technology, Kalyani University, Kolkata
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:
- 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.
- Automatic Pathogen Detection in plants (as Co-investigator ) a project in TIH of IITPalakkad
February - May, 2021
CS3700: Introduction to Database Systems (S6, Core, Theory)
December -February 2020
CS5512: Machine Learning (MTech Data Science S1 , Research Scholar, Elective)
CS5101: Machine Learning Lab(MTech Data Science S1, Research Scholar, Elective)
January -April 2020
CS5512: Machine Learning (S5 Research Scholar, Elective)
CS3145: Introduction to Database Systems Lab (S6, Core, Laboratory)
August -December 2019
CS2300: Data Structure and Algorithm (S3, Core, Theory)
CS2310: Data Structure and Algorithm Lab (S3, Core, Laboratory)
January - April 2019
CS3700: Introduction to Database Systems (S6, Core, Theory)
CS3145: Introduction to Database Systems Lab (S6, Core, Laboratory)
August -December 2018
CS4801: Principles of Machine Learning (S5, Elective)
CS5001: Topics in Machine Learning(S7, Research, Elective)
January - April, 2018
CS4804: Convex Optimization (S6, elective)
CS3700: Introduction to Database Systems (S6, Core, Theory)
CS3145: Introduction to Database Systems Lab (S6, Core, Laboratory)
August -December, 2017
CS4801: Principles of Machine Learning (S5, Elective)
CS2110 Computer Programming Lab (S3, Core, Laboratory)
PhD Students:
Ms Abilasha (since August,2018 in IITPKD)
Ms Jishy (since August 2019, External registration in IITPKD)
MSc Students:
Shikha (since August 2019 in IITPKD)
Graduated PhD Students:
Viivi Uurtio (co-supervising with Prof Juho Rousu of Aalto University, Finland)
BTech projects students:
Ahmed Zaheer Dadarkar (2017-2021): Extreme Value prediction in Time series data
Vipin Kumar Seth (2017-2021): Rainfall prediction in a small region around Palakkad
Ipsita Singh (2016-2020): Learning Graph Embedding for Graph Classification
Adrian McDonald Tariang (2015-2019): Automated Newspaper Typesetting based on News Values and Prior Editorial Style
Akshat Choube(2015-2019): Study of Multiview Face Synthesis Using Generative Adversarial Networks (GANs)
Anish MM (2015-2019): Unsupervised Lower Dimensional Representation Learning for Time Series Data
Prabal Vashisht (2015-2019): Multi-view data interpretation and completion using Deep Networks
Intern:
Kaveri Mukundan, BTech.
Soumya Suvra Ghosal, 2nd year, NIT Durgapur
Organizing:
- Workshop on Smart and Precise Agriculture in PAKDD 2021.
-
One-day Symposium on Data Science in IIT Palakkad 2020.
-
Young Researchers’ Symposium Chairs in CODS COMAD 2019
Program committee member:
NIPS (2018,2019,2020), IJCAI (2018, 2019,2020), ICML (2019,2020,2021) DSAA 2018, KDD 2017
Reviewer:
IEEE PAMI, Bioinformatics, PlosOne
Participation:
Represent IITPKD in WOMEN SCIENTIST & ENTREPRENEUR’S CONCLAVE in Indian International Science Festival 2017
'Principle Flux mode analysis', Indo-German Frontiers of Engineering Conference (INDOGFOE 2021) Symposium, by IIT KGP, 24 February - 26 February 2021
'Linear Models for Classification', The ACM India summer school on ML and Data Science, by IITM, 4th June - 13th June 2018.
'Machine Learning in Bioinformatics and Health care', ICMR workshop " Harnessing the power of oncology data analytics for early diagnosis and treatment" 15th February - 16th February 2018
Problems with Partially Observed (Incomplete) Networks: Biases, Skewed Results, and Solutions (with Tina Eliassi-Rad and Sucheta Soundarajan ) [SIAM SDM 2018] Abstract, Slides, and Resources