I am a PhD student at the School of Information at The University of Texas at Austin, advised by Dr. Danna Gurari. My research interests are primarily focused on computer vision and its applications in the fields of biomedical sciences and assistive technologies. I am also interested in systems that enable collaborations between humans and machines by leveraging their individual strengths. Prior to joining UT, I worked as a Research Associate at Qatar Computing Research Institute with Prof. Michele Ceccarelli on analyzing multidimensional medical datasets to address problems in cancer research. I received my masters degree in Computer Science from University of California, Irvine, and worked with Dr. Charless Fowlkes. I completed my bachelors degree in Computer Science from Carnegie Mellon University, where I was advised by Dr. Brett Browning and Dr. Bernardine Dias.

Research Interests:


Medical Image Analysis

Human-Machine Collaboration

Machine Learning


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University of Texas, Austin

PhD in Information Science, 2018 -
Focus: Computer Vision
Advisor: Dr. Danna Gurari

University of California, Irvine

MS in Computer Science, 2014
Focus: Computer Vision
Full sponsorship by QRLP

Carnegie Mellon University

BS in Computer Science, 2011
Minor in Mathematics
University and College Honors

Work Experience


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CrowdMOT: Crowdsourcing Strategies for Tracking Multiple Objects in Videos

Anjum, S., Lin, C., & Gurari, D. (2020). CrowdMOT: Crowdsourcing Strategies for Tracking Multiple Objects in Videos. To Appear, In Proceedings of the ACM on Human Computer Interaction (PACM HCI), 2020

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CTMC: Cell Tracking with Mitosis Detection Dataset Challenge

Anjum, S., & Gurari, D. (2020). CTMC: Cell Tracking with Mitosis Detection Dataset Challenge. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops (pp. 982-983).

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Dataset bias: A case study for visual question answering.

Das, A., Anjum, S., & Gurari, D. (2019). Dataset bias: A case study for visual question answering. Proceedings of the Association for Information Science and Technology, 56(1), 58-67.

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Identification of genetic determinants of breast cancer immune phenotypes by integrative genome-scale analysis.

Hendrickx, W., Simeone, I., Anjum, S., Mokrab, Y., Bertucci, F., Finetti, P., Curigliano, G., Seliger, B., Cerulo, L., Tomei, S., Delogu, L., Tomei, S., Delogu, L., Maccalli, C., Wang, E., Miller, L., Marincola, F., Ceccarelli, M., & Bedognetti, D. (2017). Oncoimmunology, 6(2), p.e1253654.

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Molecular profiling reveals biologically discrete subsets and pathways of progression in diffuse glioma.

Ceccarelli, M., Barthel, F. P., Malta, T. M., Sabedot, T. S., Salama, S. R., Murray, B. A., Anjum, S., ..., & Robertson, A. (2016). Cell, 164(3), 550-563.

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VEGAWES: variational segmentation on whole exome sequencing for copy number detection.

Anjum, S., Morganella, S., D’Angelo, F., Iavarone, A., & Ceccarelli, M. (2015). BMC bioinformatics, 16(1), 315.

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RAFNI: Robust Analysis of Functional NeuroImages with Non–normal α-Stable Error.

Bensmail, H., Anjum, S., Bouhali, O., & El Anbari, M. (2012, November). In International Conference on Neural Information Processing (pp. 624-631). Springer, Berlin, Heidelberg.

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MAQSA: a system for social analytics on news.

Amer-Yahia, S., Anjum, S., Ghenai, A., Siddique, A., Abbar, S., Madden, S., Marcus. A., & El-Haddad, M. (2012, May). In Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data (pp. 653-656). ACM.

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Place Recognition for Indoor Blind Recognition.

Senior Honors Thesis. Undergraduate, 2011. Carnegie Mellon University. Advisors: Dr. Brett Browning and Dr. Bernardine Dias

Awards and Honors

Collaborators: Anubrata Das, Dr. Danna Gurari
Scholarly paper concerning issues of diversity and inclusion in the field of information science.

The Thanaa Award is given to employees of Qatar Foundation for Education, Science and Community Development and its centers to reward and recognize praiseworthy performance, professional excellence and outstanding contribution to Qatar Foundation.

An academic scholarship, awarded based on academic performance, leadership, and impact on the community of women in tech. The award is a 7,000 Euros scholarship for outstanding female undergraduate and graduate students completing their degrees in computer science and related fields.

Awarded annually to seniors who have demonstrated a balance of good grades and participation in extracurricular activities, while providing exemplary service to the university, the student body and the community.

Awarded annually to seniors at Carnegie Mellon University in recognition of their outstanding leadership qualities.

Awarded annually to undergraduates at Carnegie Mellon University (Qatar Campus) in recognition of their outstanding scholastic achievements and contributions to the well-being of all people.