Research Experience for Undergraduates (REU) Site:
Machine Learning in Natural Language Processing and Computer Vision (NSF IIS-1659788)
Jugal Kalita, Principal Investigator
Jonathan Ventura, Co-Principal Investigator
June 1, 2017 - May 31, 2020 (Estimated)
Amount awarded to date: $379,853.00
The proposal seeks to increase the number of American citizens and permanent resident undergraduates who are attracted to careers in research and advanced studies in Computer Science. Training in theoretical and empirical machine learning will enable participants to contribute to ubiquitous software-based technologies at the highest levels in innovative ways. The proposal will also focus almost exclusively on training future computer scientists from institutions with limited research opportunities, women and under-represented minorities. The research experience will encourage these students to be productive researchers in academic and non-academic environments during their future careers. Computer vision, through applications such as face recognition and autonomous vehicles, has profound implications for society in terms of future of national security and transportation. Natural language processing, through applications such as efficient large volume semantic analysis and summarization of textual documents from disparate sources, also has implications for national security in addition to numerous other possibilities that can be exploited by industry.
The objective of this proposal is to expose bright and motivated undergraduates who want to pursue advanced careers in Computer Science to active research experience early in their careers. This proposal seeks to develop an REU site to broaden the intellectual horizon of participants through exposure to opportunities available in university research. Students will be involved in research projects in machine learning techniques and in emerging applications that exploit machine learning. The applications of interest are in the fields of natural language processing and computer vision. Students will have the opportunity to utilize a combination of theoretical reading, analysis, and research within laboratory environments. The students will be introduced to the topics and helped to obtain in-depth understanding of selected topics through introductory presentations. Then, they will perform hands-on research on novel problems, conduct experiments, and communicate their results through written papers and presentations. The REU students will be involved in research in machine learning and its applications in diverse and emergent areas alongside faculty mentors and graduate students in a university environment. The research will involve undergraduate students in cutting-edge research where they will write software to solve interesting and timely problems and write papers for publication.
For information on how to apply to participate in the program, please visit the project homepage.
Participants for whom I was the primary advisor are marked with a star.
2018 REU Participants
- Mehdi Drissi, Harvey Mudd College
- Chance Hamilton*, Florida Gulf Coast University
- Jacob Krantz, Gonzaga University
- Julian Medina, University of Colorado, Colorado Springs
- Kayleigh Migdol*, Humboldt State University and Carnegie Mellon University
- Tram-Anh Nguyen*, George Mason University
- Kieran Parikh, Middlebury College
- Krishan Rajaratnam, The University of Chicago
- Alisha Sharma*, University of Maryland University College
- Jack St. Clair, Haverford College
- Gia Zhuang, University of Colorado, Colorado Springs
2017 REU Participants
- Diptodip Deb*, Georgia Institute of Technology
- Ryan Griebenow, University of Colorado Colorado Springs
- Sina Masoumzadeh, University of Colorado Colorado Springs
- Adia Meyers*, Clayton State University
- Sridhama Prakhya, BML Munjal University
- Derek Prijatelj, Duquesne University
- Harriet Small*, Brown University
- Adly Templeton, Williams College
- Christopher Towne, New College of Florida
- Kyle Yee*, Swarthmore College
Diptodip Deb and Jonathan Ventura, "An Aggregated Multicolumn Dilated Convolution Network for Perspective-Free Counting", CVPR Workshop on Visual Understanding of Humans in Crowd Scene, Salt Lake City, UT, IEEE, 2018
Derek Prijatelj, Jonathan Ventura, and Jugal Kalita. "Neural Networks for Semantic Textual Similarity," ICON 2017: 14th International Conference on Natural Language Processing, 2017.
Sridhama Prakhya, Vinodini Venkataram and Jugal Kalita. "Open Set Text Classification Using CNNs." ICON 2017: 14th International Conference on Natural Language Processing, 2017.