sahely

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Dr. Sahely Bhadra
Asst. Professor
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Biosketch
  • 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)
    • Helsinki Institute of Information Technology, Aalto University, Finland (Oct,2014 - Dec,2016)
    • Max-Planck-Institute for Informatik and Saarland University, Germany (Sep, 2012 - Aug, 2014)
  • Research Internship
  • PhD in Computer Science (Aug, 2006 - Sep, 2012)
  • 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

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:

 

Teaching

August -December, 2019

CS4801: Principles of Machine Learning (S5, Elective)

January - April, 2019

    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)

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)

Research Group

PhD Students:

   Ms Abilasha (Joining August,2018)

   Ms Aparna (Joining August,2018)

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

Research Area
Machine Learning
Optmization
Bioinformatics
Additional Information
Title
Other Professional Positions
Description

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

Title
Invited Talk
Description

'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

Title
Tutorial
Description

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

Recent Publications

Viivi Uurtio , Sahely Bhadra and Juho Rousu
Proceedings of the 36th International Conference on Machine Learning(ICML) 6383--6391 (2019)
Anish Mathew, Deepak P and Sahely Bhadra
ICML 2019 Time Series Workshop (2019)
Sahely Bhadra
P D., Jurek-Loughrey A. (eds) Linking and Mining Heterogeneous and Multi-view Data. Unsupervised and Semi-Supervised Learning. Springer, Cham 1-25 (2019)
Viivi Uurtio, Sahely Bhadra, Juho Rusu
IEEE International Conference on Data Mining (ICDM'18) (2018)
Timothy Larock, Timothy Sakhrov, Sahely Bhadra and Tina Eliassi - Rad.
International School and Conference of Network Science (NetSci) (2018)