Biography
I am a Postdoctoral Fellow at The University of Texas in Austin. I recently graduated with a PhD in the Computer Science from University of Colorado Boulder, where I was 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. I transferred from The University of Texas in Austin after completing three years in the doctoral program. Previously, I worked as a Research Associate at Qatar Computing Research Institute with Prof. Michele Ceccarelli, Dr. Sihem Amer-Yahia, and Dr. Halima Bensmail. 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, and was advised by Dr. Brett Browning and Dr. Bernardine Dias.
Research Interests:
Computer
Vision
Medical Image Analysis
Human-Machine Collaboration
Deep Learning
Education
University of Colorado, Boulder
PhD in Computer Science, 2023
Focus: Computer Vision
Advisor: Dr. Danna Gurari
The University of Texas, Austin
PhD in Information Science, 2018-2021 (transferred)
Focus: Computer Vision
Advisor: Dr. Danna Gurari
The University of California, Irvine
MS in Computer Science, 2014
Focus: Computer Vision
Advisor: Dr. Charless Fowlkes
Full sponsorship by QRLP
Carnegie Mellon University
BS in Computer Science, 2011
Minor in Mathematics
University and College Honors
Advisors: Dr. Brett Browning and Dr. Bernardine Dias
Work Experience
Postdoctoral Fellow 2023-present
Graduate Research Assistant 2021-2023
Graduate Research Assistant 2018-2021
Research Associate 2014-2016
Research Assistant 2011-2012
Student Research Assistant 2010-2011
Teaching Assistant 2008-2011
Publications
Grounding Answers for Visual Questions Asked by Visually Impaired People
Chen, C., Anjum, S., & Gurari, D. (2022). Grounding Answers for Visual Questions Asked by Visually Impaired People. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June, 2022. Oral Presentation
Exploring the Use of Deep Learning with Crowdsourcing to Annotate Images
Anjum, S., Verma, A., Dang, B., & Gurari, D. (2021). Exploring the Use of Deep Learning with Crowdsourcing to Annotate Images. Human Computation 8 (2), 76-106.
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
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).
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.
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.
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.
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.
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.
Awards and Honors
Computer Science Departmental Student Award
Course: Neural Nets and Deep Learning
ACM CHI Conference on Human Factors in Computing Systems 2021
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.