My research interests lie at the intersection of machine learning and computer vision,
with a focus on developing intelligent systems that are able to
- concisely summarize their beliefs about the world with diverse plausible predictions,
- integrate information and beliefs across different sub-components or `modules' of AI (vision, language, reasoning), and
- and explain why they believe what they believe.
On the machine learning front, I develop tools such as
On the computer vision front, I focus on
- producing diverse outputs from structured and neural models
- structured-output prediction
- large-scale distributed inference and learning in graphical models such as Markov Random Fields
My Google Scholar profile.
- holistic scene understanding
- object/person detection and segmentation
- co-segmentation in multiple images
Honors and Awards
- Google Faculty Research Award, 2015
- Outstanding Reviewer Award,
Conference on Computer Vision and Pattern Recognition (CVPR), 2015
- Outstanding New Assistant Professor award College of Engineering, Virginia Tech, 2015
- National Science Foundation (NSF) CAREER award, 2014
- Army Research Office (ARO) Young Investigator Program (YIP) award, 2014
- Virginia Tech Office of Vice President for Research, Scholar of the Week, 2014
- Virginia Tech Center for Instructional Development and Education Research Teacher of the Week, 2013
- Google Faculty Research Award, 2013
- Carnegie Mellon University College of Engineering Dean’s Fellowship, 2007
My group is supported by the following:
Research Grants and Gifts
- Army Research Lab (ARL) Grant W911NF-15-2-0080, 2015-2018
- Defense Advanced Research Projects Agency (DARPA) Grant HR0011-16-1-0002, 2015-2016
- Google Faculty Research Award, 2015
- Office of Naval Research (ONR) Grant N00014-14-1-0679, 2014-2017
- Army Research Office (ARO) Young Investigator Program (YIP) Award W911NF-14-1-0180, 2014-2017
- National Science Foundation (NSF) CAREER Award IIS-1350553, 2014-2019
- Virginia Tech Institute for Critical Technology and Applied Science (ICTAS)
Junior Faculty Award, 2014-2016
- NSF EAGER Grant IIS-1353694, 2013-2015
Equipment, Educational, Organizational Grants and Gifts
- NRT-DESE: UrbComp: Data Science for Modeling, PI: Naren Ramakrishnan (CS, VT), 2015-2020
- State Council of Higher Education for Virginia (SCHEV) Equipment Trust Grant, 2014
- Oak Ridge Associated University (ORAU) Event Organization Grant
(Co-organizing the 1st Mid Atlantic Computer Vision workshop at Virginia Tech), 2014
- Amazon Web Services (AWS) in Education: Machine Learning Research Grant, 2014, 2015
- Windows Azure for Research Award, 2014
- NVIDIA Academic Hardware Gift, 2014
I lead the VT Machine Learning & Perception Group. Here are the current and past members:
- Harsh Agrawal, MS student
- Latha Pemula, MS student
- Aroma Mahendru, MS student
- Akrit Mohapatra, BS student
- Ashwin Kalyan, Intern (BTech student from NIT Surathkal)
- Abhishek Das, Intern (BTech student from IIT-Roorkee)
- Khushi Gupta, Intern (BTech student from IIT-Gowahati)
- Avi Singh, Intern (BTech student from IIT-Kampur)
MLP Group Alumni (Graduated Students & Former Interns)
- Prakriti Banik, MS student (first job: Bloomberg)
- Neelima Chavali, MS student (first job: Many Trees Inc)
- Clint Solomon, MS student (first job: PhotoKharma)
- Ahmed Osman, Intern (MS student from UManchester; next position: PhD student at VT)
- Faruk Ahmed, Intern (BTech student from IEM; next position: PhD student at UMontreal)
- Senthil Purushwalkam, Intern (BTech student from IIT-G; MS student at RI, CMU)
- Aroma Mahendru, Intern (BTech student from IIT-BHU; next position: MS student at VT)
- Ratnesh Kumar, Intern (PhD student from INRIA; next position: MERL)
- Qi Lou, Intern (MS student from OSU; next position: PhD student at UC Irvine)
- Ankit Laddha, Intern (BTech student from IIT-D; next position: MS student at RI, CMU)
- Adarsh Prasad, Intern (BTech student from IIT-D; next position: PhD student at UT-Austin)
- Harsh Agrawal, Intern (BTech student from DTU; next position: MS student at VT)
- Abhimanyu Dubey, Intern (BTech student from IIT-D)
Former Student Collaborators
On the web
- Towards Transparent VQA Models [slides (pptx)]
- @ CVPR 2016 Workshop on Large Scale Visual Recognition and Retrieval (BigVision), Jul 2015.
- Visual Question Answering [slides (pptx)]
- @ Multimodal Machine Learning Workshop at NIPS, Dec 2015.
- @ Plenary Talk, Western New York Image and Signal Processing Workshop, Dec 2015.
- @ NYU CS Seminar, Sep 2015.
- @ Cornell Tech, Sep 2015.
- @ Data Science Summit & Dato Conference, Jul 2015.
- @ Deep Learning Summit, May 2015.
- Submodular meets Structured: Finding Diverse Subsets in Exponentially-Large Structured Item Sets.
[ slides (pptx) ]
- M-Best and Diverse M-Best MAP Inference in Graphical Models [ slides ]
- CloudCV: Large-Scale Distributed Computer Vision as a Cloud Service [ slides ]
- Should we care about MAP Inference? MAP Inference tools for more than MAP Inference
- Hedging Against Uncertainty via Multiple Diverse Predictions [ slides (pptx) ]
- @ Machine Learning Seminar, University of Toronto, Apr 2015.
- @ VASC Seminar, CMU, Mar 2015.
- @ Indian Institute of Science, Bangalore, Dec 2014.
- @ ONR Workshop on
Structured Learning for Scene Understanding, Oct 2014.
- @ Microsoft Research Cambridge, Sep 2014.
- @ University of Oxford, Sep 2014.
- @ IBM T. J. Watson Research Center, Aug 2014.
- @ Invited Talk at the Workshop on
Graphical models for Scene Understanding at ICCV, Dec 2013.
- @ NICTA/ANU Machine Learning Seminar, Nov 2013.
- @ eBay Research Labs, Aug 2013.
- @ Amazon, Jul 2013.
- @ Recognition Reading Group, University of Washington, Jul 2013.
- @ University of Maryland, Mar 2013.
- @ IST Austria Symposium on Computer Vision and Machine Learning, Oct 2012.
- Structured-Output Models for Computer Vision
- @ Google Research Tech Talk, Apr 2012.
- @ University of Minnesota CSE Colloquium, Apr 2012.
- @ Virginia Tech ECE Colloquium, Apr 2012.
- @ Mitsubishi Electric Research Lab (MERL), Apr 2012.
- @ Adobe Creative Technologies Lab Seattle, Feb 2012.
- @ Microsoft Research Redmond, Feb 2012.
- @ Michigan State University CSE Colloquium, Feb 2012.
- @ Washington University in St. Louis CSE Colloquium, Feb 2012.
- The M-Best Mode Problem [ slides (pptx) ]
- @ Midwest Vision Workshop, UIUC, Sep 2012.
- @ Carnegie Mellon University VASC Seminar, May 2012.
- @ Carnegie Mellon University Select Lab Talk, May 2012.
- @ University of California Berkeley Vision Seminar, Jan 2012.
- Focused Inference and the M-Best Mode Problem [ slides (pptx) ]
- @ University of California Santa Barbara CS/ECE Colloquium, Jan 2012.
- @ University of California San Diego AI Seminar, Jan 2012.
- @ University of California Irvine ICS, Jan 2012.
- Focused Inference with Local Primal-Dual Gaps [ slides (pptx) ]
- On Graph-Structured Discrete Labelling Problems in Computer Vision [ slides (pptx) ]
- @ HP Labs, June 2010.
- @ University of Illinois at Urbana-Champaign, June 2010.
- @ TTI-Chicago Colloquium, May 2010.
- @ Georgia Tech, May 2010.
- @ University of Texas-Autin CS Colloquium, May 2010.
- @ CSAIL, MIT, Apr 2010.
- @ University of Georgia, CS Colloquim, Aprl 2010.
- Beyond Trees: MRF Inference via Outer-Planar Decomposition [ slides (pptx) ]
- @ CMU VASC Seminar, Mar 2010.
- @ TTI-Chicago Colloquium, Dec 2009.
- @ Illinois Vision Meet, Dec 2009.
- @ Microsoft Research Redmond, Dec 2009.