Short Bio

I am a PhD student at Harvard University working with Professor Hanspeter Pfister, closely collaborating with the department of Brain and Cognitive Sciences at MIT. My research involves explaining the performance of deep neural networks through the lens of Statistics and Visualization.

Before this, I was research assistant at MIT working with Fredo Durand on understanding the generalization behaviour of neural networks across variations in viewpoints, light source distributions, object poses and other scene atttributes.

This summer I will be joining in Adobe Research for an internship with Connelly Barnes and Eli Schectman.

In the past, I have been an intern at Microsoft Research Redmond, a master's student at Harvard University, and an undergraduate at IIT Delhi back home in India.

Apart from research, I am extremely passionate about teaching, and spend significant time writing tutorials/giving lectures in and around Boston. Click on the "Talks" or "Tutorials" section above for those. Actively seeking collaborators for side projects. This includes tutorials, and other tools I am interested in building. If interested, please drop me a mail.

Research Goal

Broadly, I am interested in exploring how the decision making process of deep learning models differs from human visual cognition. I believe that building the right tools to dissect and run controlled experiments on neural networks is an important part of this process, and that is what my current research focusses on.

Awards and Scholarships

  • Harvard SEAS Departmental PhD Scholarship, 2019-20
  • Snapchat Research Scholarship, 2018
  • UIST conference honorable mention paper award, 2017
  • Harvard SEAS Fellow, 2016-2018
  • Government of India MHRD scholarship, 2014-2015
  • One of thirteen students in the Viterbi India Program, 2012


Computer Vision

  • ZoomMap: Using Zoom to Capture User Areas of Interest on Images
    Bylinskii, Z., Newman, A.P., Tancik, M., Madan, S., Durand, F., Oliva, A. 2019.
  • Effects of title wording on memory of trends in line graphs[PAPER]
    Newman, A., Bylinskii, Z., Haroz, S., Madan, S., Durand, F., Oliva, A., 2018.
  • Synthetically trained icon proposals for parsing and summarizing infographics. [PAPER]
    Madan, S.*, Bylinskii, Z.*, Tancik,M.*, Zhong, K., Recasens, A.,Alsheikh, S., Pfister, H., Durand, F., 2018.
  • Understanding Infographics through Textual and Visual Tag Prediction. [PAPER]
    Madan, S.*, Bylinskii, Z.*, Alsheikh, S.*, Recasens, A.*, Zhong, K., Pfister, H., Durand, F. and Oliva, A., 2017.
  • Learning Visual Importance for Graphic Designs and Data Visualizations (Honorable Mention Award). [PAPER][CODE][WEB]
    Zoya Bylinskii, Nam Wook Kim, Peter O'Donovan, Sami Alsheikh,Madan, S., Hanspeter Pfister, Fredo Durand, Bryan Russell, Aaron Hertzmann.

Machine Learning in Biology

  • An ensemble micro neural network approach for elucidating interactions between zinc finger proteins and their target DNA. BMC Genomics, 17(13), 97 (2016). [PAPER]
    Shayoni Dutta , Spandan Madan, Harsh Parikh, Durai Sundar.
  • Exploiting the recognition code for elucidating the mechanism of zinc finger protein-DNA interactions. BMC Genomics, 17(13), 109. (2016). [PAPER]
    Shayoni Dutta , Spandan Madan, Durai Sundar.


  • Understanding Infographics through Textual and Visual Tag Prediction. NECV'17.
    Madan, S.*, Bylinskii, Z.*, Alsheikh, S.*, Recasens, A.*, Zhong, K., Pfister, H., Durand, F. and Oliva, A., 2017.

    Invited Talks

    • U.C. Berkeley vision seminar.

    • MIT graphics seminar.

    • Harvard Business School - machine learning for managers[LINK]
    • HackMIT - AI v/s Deep Learning v/s Machine Learning.
    • MIT Blueprints - an introduction to computer vision.[LINK]

    Reviewing Experience

    • One paper for TPAMI
    • Two papers for CVPR'18