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LAB DIRECTOR

Dr. Safwan Wshah

University of Vermont
Computer Science Department
417 Innovation
safwan.wshah at uvm.edu

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About Us

At the Vermont Artificial Intelligence Laboratory (VaiL) we work at the intersection of machine learning theory and application. Our mission is to improve the adaptability and generalization of machine learning methods, in order to allow higher-quality applications to broader classes of real-life problems. Our core area is object understanding and geo-localization from the ground and satellite images. We also collaborate with groups from other fields. 

News

Join Us

Applications are invited for THREE doctoral research positions in Spring/Fall 2023 in the Computer Science or Complex Systems and Data Science Programs at the computer Science Department of the University of Vermont. Successful candidates will work under the supervision of Prof. Safwan Wshah at the University of Vermont on projects related to 1) Generative models such as Adversarial Networks and 2) Computer Vision applications related to Satellite images. The candidates should have a strong machine learning and software engineering background and preferably have publications in the one of the machine learning fields and experience in open source toolkit such as tensorflow or pyTorch. 


Interested candidates should send a CV with a publication list, a short description of research interests, a course transcript, and names of three references from previous research positions to safwan.wshah@uvm.edu. Candidates will also need to apply and be admitted to the University of Vermont Computer Science graduate program.
 
Review of applications will begin immediately and continue until a suitable candidate is identified. More information about Prof. Wshah’s group and UVM can be found at www.wshahaigroup.com

Latest Publications

Objects detection, geo-localization and enhancement in ground and satellite level images

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The concept of geo-localization refers to the process of determining where on earth some ‘entity’ is located, typically using GPS coordinates. The entity of interest may be an image, sequence of images, a video, satellite image, or even objects visible within the image.  As massive datasets of GPS tagged media have rapidly become available due to smartphones and the internet, and deep learning has risen to enhance the performance capabilities of machine learning models, the fields of visual and object geo-localization have emerged due to its significant impact on a wide range of applications.

For the full list of publications, please refer to  Dr. Wshah

Google Scholar

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