Research Activities
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Joining my group: I am looking for full time PhD students at IIT Delhi and Research Fellows at Microsoft Research India to work with me on research problems in supervised machine learning, extreme classification, recommender systems and resource constrained machine learning for the Internet of Things. Please e-mail your CV to me directly in addition to formally applying to IIT/Microsoft’s programmes. IIT offers PhD Fellowships in collaboration with Microsoft and many other labs such as Facebook, Google, IBM and TCS. Please look at the CSE and SIT Departments’ web pages for more details and other funding opportunities.
Projects: Unfortunately, I am unable to supervise projects of students outside IIT Delhi. If you are an external student and would like to work with me then the best way would be to join IIT Delhi’s PhD programmes or apply for a Research Fellowship at MSR India.
Internships: If you are a PhD student looking to do an internship with me then please e-mail me directly. I have only one or two internship slots and competition is stiff so please apply early. Please do not apply to me or e-mail me about internships if you are not a PhD student as I will not be able to respond to you.
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- Programme Co-chair: ICVGIP 2014
- Area Chair: IJCAI 2016 – AI on the Web, ICML 2016, ICML 2015, CVPR 2014, ACCV 2014, ICCV 2013, ICVGIP 2012, NIPS 2011, ICVGIP 2010
- Workshop Co-chair: Extreme Classification 2016 (opens in new tab), Extreme Classification 2015 (opens in new tab), RecTech 2015 (opens in new tab), The MSRI Machine Learning Summer School, Extreme Classification 2013 (opens in new tab), WebVision 2012 (opens in new tab), The Mysore Park Computer Vision Workshop (opens in new tab), The MSRI Computer Vision & Graphics Shindig, The Winter School on Machine Learning and Computer Vision
- Keynotes and Selected Invited Talks: DSI@KDD 2016 (opens in new tab), DICTA 2015 (opens in new tab), Big Targets@ECML/PKDD 2015 (opens in new tab), X@ICML 2015 (opens in new tab), Budgeted ML@ICML 2015 (opens in new tab), LSOLDM 2014, ISI Kolkata ML Unit Founder’s Day Lecture 2014, BDA 2013, IIIT Delhi Institute Lecture 2013, ICVGIP 2012, MPMLW 2012
- Committees: ACM India SIGKDD Steering Committee, Shiv Nadar University BDAC Advisory Board, DST-EPSRC 2012 Expert Panel on Applied Mathematics
- Teaching: 2009/CSL864 (opens in new tab) 2013/CSV884 (opens in new tab) 2016/SIV895 (opens in new tab)
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- The Extreme Classification Repository: Multi-label Datasets and Code (opens in new tab)
- PfastreXML: Propensity scored re-ranked FastXML (opens in new tab)
- SLEEC: Sparse Local Embeddings for Extreme multi-label Classification (opens in new tab)
- FastXML: A Fast, Accurate and Stable Tree Classifier for eXtreme Multi-label Learning (opens in new tab)
- M3L: Efficient Max Margin Multi-label Learning (opens in new tab)
- LDKL: Local Deep Kernel Learning for efficient non-linear prediction (opens in new tab)
- SPG-GMKL: Spectral Projected Gradient descent based optimization for Generalized Multiple Kernel Learning (opens in new tab)
- SMO-MKL: SMO based optimization for p-norm regularized Multiple Kernel Learning (opens in new tab)
- GMKL: Generalized Multiple Kernel Learning (opens in new tab)
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- The Extreme Classification Repository: Multi-label Datasets and Code (opens in new tab)
- The Chars74K Dataset: Character Recognition in Natural Images (opens in new tab) and an associated Julia tutorial and Kaggle competition (opens in new tab) (I have no idea how “Google” crept into the dataset name)
- CUReT: The Cropped Columbia-Utrecht Texture Classification Dataset & Associated Filterbanks (opens in new tab)
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