
- 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 and incomplete data), optimization (robust optimization and convex optimization for large data), data analysis, network analysis, bio-informatics (micro-array data analysis and molecular network analysis) .
Summary:
I am currently working on completing incomplete graph or network using machine 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.
January -April 2020
CS5512: Machine Learning (S5 Research Scholar , Elective)
CS3145: Introduction to Database Systems Lab (S6, Core, Laboratory)August -December, 2019
CS4801: Principles of Machine Learning (S5, Elective)
August -December, 2018
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:
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:
Soumya Suvra Ghosal, 2nd year, NIT Durgapur
Organizing:
CODS-COMAD 2019
Program committee member:
NIPS 2019, IJCAI, 2019, ICML 2019, NIPS 2018, IJCAI 2018, DSAA 2018, KDD 2017
Reviewer:
IEEE PAMI, Bioinformatics, PlosOne
Participation:
Represent IITPKD in WOMEN SCIENTIST & ENTREPRENEUR’S CONCLAVE in Indian International Science Festival 2017
'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