Faculty

David Crandall
David Crandall received the Ph.D. in Computer Science from Cornell University in 2008 and the M.S. and B.S. degrees in Computer Science and Engineering from the Pennsylvania State University, University Park, in 2001. He worked as a Postdoctoral Associate at Cornell from 2008-2010, and as a Research Scientist at Eastman Kodak Company from 2001-2003.

Dr. Crandall’s main research interest is computer vision, the area of computer science that tries to design algorithms that can “see”. He is particularly interested in visual object recognition and scene understanding. He is also interested in other problems that involve analyzing and modeling large amounts of uncertain data, like mining data from the web and from online social networking sites.

David’s website: www.cs.indiana.edu/~djcran/

Postdocs

Sven Bambach
Sven received a B.Eng. degree with a focus in media and imaging technology from Cologne University of Applied Sciences in Germany (2010). He came to Indiana in 2011 and received his M.S. in computer science in 2013 and PhD in both Computer Science and Cognitive Science 2016. His main research interest is in understanding egocentric video, from both technical and human perspectives.

Sven’s websites: homes.soic.indiana.edu/sbambach/www.svenbambach.com

Current Students

PhD Students

Chenyou Fan
Chenyou received his M.S. in Computer Science in 2014 from Indiana University and his B.S. in Computer Science from Nanjing University, China. He is currently pursuing his PhD in Computer Science with a Statistics minor and his primary research interest is image-sentence generation with deep learning methods.

Chenyou’s website: homes.soic.indiana.edu/fan6/


Jangwon Lee
Jangwon is a PhD student in robotics track of Informatics at the School of Informatics and Computing at Indiana University. He was a software engineer at Samsung Electronics before coming to Indiana University. He received his B.S. in Information and Communication Engineering from Sungkyunkwan University (SKKU) in 2006 and his M.S in Electrical and Computer Engineering from SKKU in 2008. His broad research interests are Human-Robot Interaction and computer vision for robotics.

Jangwon’s website: homes.soic.indiana.edu/leejang/


Jingya Wang
Jingya received her B.E. from Nankai University in 2009 and her M.S. in Computer Science at IU in 2011. Now she is pursuing her Ph.D in Computer Science program. Her research interest is computer vision and social media data mining. More specifically her passion is to find insights in images with object recognition and scene classification techniques.

Jingya’s website: mypage.iu.edu/~wang203/


Mingze Xu
Mingze received his M.S. in Computer Science from Indiana University (2014) and his B.E. in Software Engineering from Jilin University (2012) in China. He is currently pursuing his PhD in Computer Science with a Statistics minor. His research is primarily in the area of Computer Vision and Machine Learning. In particular, he focuses on developing better models and techniques that provide a more compelling sense in 3D reconstruction from multiple images.

Mingze’s website: homes.soic.indiana.edu/mx6/


Zehua Zhang
Zehua received his B.E. in Automation from Xi’an Jiaotong University in 2015 and came to IU to pursue his Ph.D. in Computer Science in 2016. As an undergraduate, he mainly focused on object tracking and applying machine learning and computer vision techniques to improving web security. Now he is more interested in object recognition and generative adversial networks.​

Master’s Students

Benjamin Cutilli
Ben graduated from Haverford College with a Bachelor of Science degree in computer science. He is a master’s student at Indiana University Bloomington focusing on computer vision and dabbling in natural language processing. He worked for NVIDIA as a Solutions Architect intern in the summer of 2017, focusing on style transfer using deep learning. In his free time he runs, skis, and follows his unrealistic dream of becoming good at Counter-Strike: Global Offensive.


Ramya Rao
Ramya is pursuing M.S. in Computer Science in Indiana University. She was a software engineer at Accenture Services Private limited before coming to Indiana University. She received her B.E. in Electronics and Communications Engineering from Visveswaraya Technological University in 2012.

Ramya’s LinkedIn profile: https://goo.gl/Z8Uc7c

Alumni

PhD Students

Kun Duan
Kun’s work has studied Conditional Random Field Models for Structured Visual Object Recognition, presenting novel CRF-based solutions for three computer vision applications: human pose recognition, large-scale multimodal image clustering, and local attribute discovery for object recognition. He received his PhD in August 2014 and worked as a Computer Scientist at GE before joining SnapChat in 2016.

Kun’s website: www.cs.indiana.edu/~kduan/


Jeffrey Johnson
Jeff Johnson’s research is in planning and decision making in multi-agent robotic systems. His thesis developed a general framework to efficiently compute navigation solutions in partially observable systems. He has since worked to integrate the framework tightly with computer vision to enable robust, camera-based navigation. He received his PhD in September 2017 and since 2012 has worked as a robotics and software engineer at major automotive and Silicon Valley companies.

Jeff’s website: http://jeffreykanejohnson.com/


Mohammed Korayem
Mohammed’s PhD research studied large scale textual and visual mining in social media under the supervision of Prof. David Crandall. His other research interests include Machine Learning, Computer Vision, Text Mining, Web Mining, and Soft computing. He received his PhD in June 2015 and is now a Data Scientist at CareerBuilder, Inc.

Citations of his research as complied by Google scholar: http://scholar.google.com/citations?hl=en&user=6NukOWsAAAAJ
Mohammed’s website: www.mohammedkorayem.com/


Stefan Lee
Stefan received his B.S. in Computer Science from the University of West Florida in 2011, his M.S. in Computer Science in 2013 from Indiana University, and his Ph.D. from IU in 2016. His thesis research was primarily focused on uncertainty in large-scale weakly labeled vision problems and inference in probabilistic graphical models. He is now a Postdoc at Virginia Tech, working on theory and applications of deep learning in computer vision.

Stefan’s website: homes.soic.indiana.edu/steflee/


Haipeng Zhang
Haipeng’s work has studied how to use online and mobile data to estimate properties of and find connections in the physical world, demonstrated with applications to predicting consumer behavior, ecological events, mobile user properties, and concept relationships. He received his PhD in August 2014 and is now working as a Research Scientist at IBM Research China.

Haipeng’s website: www.cs.indiana.edu/~zhanhaip/

Master’s Students

Devendra Dhami
Devendra received his M.S. degree in Computer Science from Indiana University in 2015. His Master’s thesis work was on ‘Morphological classification of galaxies into spirals and non-spirals’ using computer vision and machine learning techniques under the guidance of Prof. David Crandall. He is now in the PhD program at Indiana University.

Devendra’s website: homes.soic.indiana.edu/ddhami/
Devendra’s Master’s thesis: homes.soic.indiana.edu/ddhami/Master%20Thesis.pdf


Sumit Gupta
Sumit received his M.S. in Computer Science from Indiana University Bloomington in 2016. His master’s thesis work involved application of deep learning techniques in infrared spectrum images, fine-grained recognition and egocentric scene classification.

Sumit’s LinkedIn profile: www.linkedin.com/in/isumitg


Harsh Seth
Harsh received the M.S. degree in Computer Science from Indiana University in 2015. His Master’s thesis work (under the guidance of Prof. David Crandall) involved developing automated answering apps for the visually impaired, using Google Glass. He is now at Sears.


Manu Singh
Manu Singh received his Masters degree in Computer Science from the School of Informatics and Computing in 2016. His master’s thesis work involved investigating tag selection and propagation methods for large scale social image classification. His research interests include computer vision, machine learning and data mining. He now works as a Cognitive Engineer at IBM Watson.

Manu’s LinkedIn profile: www.linkedin.com/in/imanusingh
Manu’s GitHub profile: www.github.com/msingh23


Tiangang Song
Tiangang Song received his Masters degree in Computer Science from the School of Informatics and Computing in 2015, and
received his B.E. from Wuhan University in 2013. His Master’s work investigated semi-automatic recognition using Human-in-the-loop techniques. His research interests include computer vision and machine learning. He now works at ESRI.


Tingyi Wanyan
Tingyi is currently a PhD student in the Intelligent Systems Engineering department at IU. His current research direction is Artificial Intelligence, Computer Vision and Neural Imaging. He received his M.S degree at Indiana University in 2016, majoring in Computer Science and working in the Computer Vision Lab.

Undergraduate Students

  • Dennis Chen is a junior in Electrical and Computer Engineering at Olin College. He was an undergraduate intern at IU during Summer 2014. His research interests include deep learning and applying computer vision to image classification problems. – LinkedIn, GitHub

  • Demetris Coleman studied Artificial Neural Networks at IU during the summer of 2015. He is an undergraduate student in the 5th year of his 3/2 engineering program, and plans to graduate in the Fall 2016 with degrees from Auburn University and Berea College in Electrical Engineering and Applied Math and Sciences. – LinkedIn
  • Russell Conard, CEO and Founder of Ornicept, is a passionate entrepreneur and computer scientist. He left the lab to pursue the commercialization of his computer vision research created under Professor Crandall’s mentorship. This research continues today under a Department of Energy grant. At Ornicept, he leads the development of SPECTEO, an enterprise software package designed to improve the collection, processing, and accuracy of data and fieldwork for engineers, scientists, and researchers. – LinkedIn
  • Tayla Frizell (REU from Mississippi Valley State University)
    Handwriting recognition and scoring to study child development, Summer 2015.
  • Gustavo Goncalves (REU from Dillard University)
    Object recognition and tracking for quadcopters, Summer 2014.
  • Paul Grubbs
    Accelerating computer vision algorithms with GPUs, 2011
  • Alan Lu is a sophomore majoring in Electrical and Computer Engineering at the University of Illinois at Urbana-Champaign (UIUC). He was a student in the summer of 2017 and worked on the project, Examining Computer Vision Techniques for Providing Useful Information to People with Visual Impairments. On the UIUC campus, Alan spends most of his free time involved in other research topics or in engineering groups. – LinkedIn
  • Ethan Petersen is a Computer Science and Mathematics double major at Rose-Hulman Institute of Technology, graduating in 2018. He worked with Dr. David Crandall to use deep learning to study attention in driving. The research focused on teaching a convolutional net to extract the most relevant regions in high-resolution video data, Summer 2017 – Website
  • Tyler Rarick is a student at Rose-Hulman Institute of Technology, studying Computer Engineering and Computer Science, 2018. Personally interested in computer vision, artificial intelligence, deep learning, neural networks, and embedded systems – LinkedIn
  • Alex Seewald (REU from Earlham College)
    Deep learning for scene classification, Summer 2014.
  • Joshua Sherfield (REU from Norfolk State University)
    Studying color distributions on Flickr, Summer 2013.
  • Dylan Vener is a Computer Science and Mathematics double major at Rose-Hulman Institute of Technology, graduating 2019. He worked on evaluating viability and reliability of various computer vision techniques for application towards helping visually impaired people, Summer 2017.
  • David Zhang
    Optical music recognition, Summer – Fall 2013.