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Dr. Sahely Bhadra
Asst. Professor [Research interest Machine Learning and Robust optimization]
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  • 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 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) .


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.




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: Principal of Machine Learning (S5, Elective)

CS2110 Computer Programming Lab (S3, Core, Laboratory)

Research Group

PhD Students:

Viivi Uurtio (co-supervising with Prof Juho Rousu of Aalto University, Finland)


Soumya Suvra Ghosal, 2nd year, NIT Durgapur

Research Area
Machine Learning
Additional Information
Other Professional Positions



Program committee member:

NIPS 2018, IJCAI 2018, DSAA 2018, KDD 2017




Represent IITPKD in WOMEN SCIENTIST & ENTREPRENEUR’S CONCLAVE in Indian International Science Festival 2017

Invited Talk

'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

Submitted Manuscript

Viivi Uurtio, Sahely Bhadra, Juho Rousu. Sparse non-linear CCA through HSIC.

Sahely Bhadra. Book Chapter : Multi view data completion


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

Timothy Larock, Timothy Sakhrov, Sahely Bhadra and Tina Eliassi - Rad.
International School and Conference of Network Science (NetSci) (2018)
Timothy LaRock, Timothy Sakharov, Sahely Bhadra and Tina Eliassi-Rad
Accepted for 14th International Workshop on Mining and Learning with Graphs (MLG) (2018)
Sahely Bhadra and Juho Rousu
Springer Book : Data Mining for Systems Biology (2018)
Sahely Bhadra, Peter Blomberg, Sandra Castillo, and Juho Rousu.
Bioinformatics (2018)
Sahely Bhadra, Samuel Kaski, Juho Rousu
Machine Learning 106 (5) 713-739 (2017)