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The Department of Computer Science has a distinguished track record of academic excellence and major achievement in an increasingly vital field that is expanding at a rapid pace. Faculty research is published in the most prominent venues and recognized by significant citations and awards, both national and international. BA, MS, and PhD students are recruited for internships and positions by such industry-leading firms as Motorola Labs, Google, and Microsoft, and are also recruited as PhD students, postdoctoral researchers, and tenure-track professors by some of the best computer science departments in the country.


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  • LAL: linguistically aware learning for scene text recognition 

    Zheng, Yi; Qin, Wenda; Wijaya, Derry; Betke, Margrit (ACM, 2020-10-12)
    Scene text recognition is the task of recognizing character sequences in images of natural scenes. The considerable diversity in the appearance of text in a scene image and potentially highly complex backgrounds make text ...
  • Learning to separate: detecting heavily-occluded objects in urban scenes 

    Yang, Chenhongyi; Ablavsky, Vitaly; Wang, Kaihong; Feng, Qi; Betke, Margrit (Springer, Cham., 2020-12-04)
    While visual object detection with deep learning has received much attention in the past decade, cases when heavy intra-class occlusions occur have not been studied thoroughly. In this work, we propose a Non-Maximum-Suppression ...
  • Deep-learning-driven quantification of interstitial fibrosis in digitized kidney biopsies 

    Zheng, Yi; Cassol, Clarissa A.; Jung, Saemi; Veerapaneni, Divya; Chitalia, Vipul C.; Ren, Kevin Y.M.; Bellur, Shubha S.; Boor, Peter; Barisoni, Laura M.; Waikar, Sushrut S.; Betke, Margrit; Kolachalama, Vijaya B. (2021-08)
    Interstitial fibrosis and tubular atrophy (IFTA) on a renal biopsy are strong indicators of disease chronicity and prognosis. Techniques that are typically used for IFTA grading remain manual, leading to variability among ...
  • Learn to earn: enabling coordination within a ride-hailing fleet 

    Chaudhari, Harshal A.; Byers, John W.; Terzi, Evimaria (2020-12-10)
  • Liquid data networking 

    Byers, John W.; Luby, Michael (ACM, 2020-09-22)
  • A digital fountain retrospective 

    Byers, John W.; Luby, Michael; Mitzenmacher, Michael (Association for Computing Machinery (ACM), 2019-11-08)
    We introduced the concept of a digital fountain as a scalable approach to reliable multicast, realized with fast and practical erasure codes, in a paper published in ACM SIGCOMM '98. This invited editorial, on the occasion ...
  • Bitcoin price prediction using transfer learning on financial micro-blogs 

    Davchev, Jovan; Mishev, Kostadin; Vodenska, Irena; Chitkushev, Ljubomir; Trajanov, Dimitar (2020-12-23)
    We present a methodology for predicting the price of Bitcoin using Twitter data and historical Bitcoin prices. Bitcoin is the largest cryptocurrency that, in terms of market capitalization, represents over 110 billion ...
  • New measures of journal impact based on the number of citations and PageRank 

    Souma, Wataru; Vodenska, Irena; Chitkushev, Lou (Digital Information Research Foundation, 2020-02-25)
    The number of citations has been used for measuring the significance of a paper. Moreover, we have the following question: which paper is the most important if there are some papers with the same number of citations? Some ...
  • Learning to scale multilingual representations for vision-language tasks 

    Burns, Andrea; Kim, Donghyun; Wijaya, Derry; Saenko, Kate; Plummer, Bryan A. (2020)
    Current multilingual vision-language models either require a large number of additional parameters for each supported language, or suffer performance degradation as languages are added. In this paper, we propose a Scalable ...
  • Application of seq2seq models on code correction 

    Huang, Shan; Zhou, Xiao; Chin, Sang (2021)
    We apply various seq2seq models on programming language correction tasks on Juliet Test Suite for C/C++ and Java of Software Assurance Reference Datasets and achieve 75% (for C/C++) and 56% (for Java) repair rates on these ...

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