Iowa City, IA 52246

Email: sarah.aboutalib@gmail.com

Phone: (412) 945-0772

Website: http://www.sarahaboutalib.com

Website Design: portfolio

I have experience and interest in Artificial Intelligence, Human-Computer Interaction, Machine Learning, Deep Learning and Cognitive Science research problems.

• Ph.D. in Computer Science - Carnegie Mellon University (May 2011)
• B.S. in Cognitive Science (Specialization in Computation) w/ Honors - Overall GPA: 3.7 Minor in Computer Science and Engineering University of California, San Diego (June 2005)

Languages: Python, Java, C/C++, Matlab, Visual Basic, LaTeX, HTML, PHP, XML, SQL, CSS, CSH, JavaScript and Bash Script, easily learns new languages. Content Management Systems: Drupal, Wordpress. Deep Learning Platforms: Caffe. Applications: Photoshop, Flash, Access. Other languages and applications also, feel free to contact with additional questions. Some Arabic Comprehension.

• Postdoctoral Scholar, Univ of Pittsburgh, Biomedical Informatics (October 2016 - December 2018)
  Research in applying machine learning and computer vision techniques to radiological image data to improve breast cancer diagnosis, recall rates, and performance of radiologists.
• User Experience Consulting, Projects: Architectural Design Company - Orchard Lane; Pediatric Dentistry Group - University of Florida Student Dentistry; Non-Profit Organization - Alfaoz; Physical Therapy Office - Pure Medical Services. (December 2014 - December 2015)
  Interview users and improve experience and navigation flow. Create modern designs that are intuitive to update.
• User Experience Consulting, University of Pittsburgh, Global Studies Department, CERIS (May 2012 - April 2013)
  Manager: Elaine Linn
  Interview users and create a content management system (Drupal) to improve experience of updating and interaction.
• Database Consulting, Pure Medical Services (May 2011 - June 2013)
  Manager: Armen Badalyan
  Design and implemented a Database system, including front and backend for a medical office.
• Research Assistant, Carnegie Mellon University (September 2005 - Present)
  Advisor: Manuela Veloso
  Investigating an approach to integrating multiple cues of diverse types (visual, activity, speech, etc.) for more robust object recognition using video data and probabilistic relational learning techniques.
• Internship, Lockheed Martin (June 2009 - August 2009, participating in discussions until present)
  Manager: Mark Gersh
  Supervisor: Andy Zimdars, Dave Tyler Two projects: (1) Developed vision and assessed feasibility of visual navigation, feature recognition and obstacle avoidance with the robots' limited on-board processors for DARPA LANdroids project (2) Image registration and its Cramer-Rao lower bound for spectral images. Please contact me for more details.
• Teaching assistant, Carnegie Mellon University (January 2009 - May 2009)
  Assisting in Computer Vision course with Prof. Tai Sing Lee explaining basic principles and techniques to students, preparing programming assignments, including running sessions and giving one lecture.
• Teaching assistant, Carnegie Mellon University (January 2007 - May 2007)
  Assisting in Cognitive Robotics, a course with David Touretzky teaching robot programming with high level primitives for perception and action, using cognitive science concepts such as visual routines, dual coding theory, and affordances with Tekkotsu software framework and the Sony Aibo robot dog.
• Honors Project & Independent Research, UC San Diego (September 2004 - June 2005)
  Developed a computer model of the use of saccades in developing a positioninvariant representation in object recognition. Also, assisted in computer vision project researching object recognition using shared-features.
• Other Courses and Studies (September 2001 - Present)
  Familiar with techniques in AI and cognitive science subjects such as Ethnography, Computer User Design, Genetic Programming, Path Planning.

RSNA Trainee Research Prize (November 2017)

Women @ IT fellowship (September 2005 - June 2006)

Cognitive Science Honors (June 2005)

Gates Millennium Scholarship (June 2001-2005)

• Sarah S. Aboutalib, Aly A. Mohamed, Shandong Wu, et al. Deep Learning to Distinguish Recalled but Benign Mammography Images in Breast Cancer Screening. Clinical Cancer Research, 2018. http://clincancerres.aacrjournals.org/content/early/2018/09/26/1078-0432.CCR-18-1115
• Aboutalib, S., Mohamed, A., Wu, Shandong, et al. Do pre-trained deep learning models improve computer-aided classification of digital mammograms? International Society of Optics and Photonics (SPIE): Medical Imaging, 2018.
• Aboutalib, S., Mohamed A., Zhang L., Wu S. Deep Learning for Reducing Breast Cancer Recall Rate in Screening Mammogram. NLM Training Conference, 2018
• Aboutalib, S., Mohamed, A., Wu, Shandong, et al. Automatic identification of nuanced imaging features in recalled but biopsy benign mammogram images. Radiology Society of North America (RSNA), 2017.
• Aboutalib, S. and Veloso, M. Multiple-Cue Object Recognition on Outside Datasets. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2010.
• Aboutalib, S. and Veloso, M. Cue-based equivalence classes and incremental discrimination for object recognition. IROS, 2009.
• Aboutalib, S. and Veloso, M. Simulation and Weights of Multiple Cues for Robust Object Recognition. IROS, 2007.
• Aboutalib, S. and Veloso, M. Towards Using Multiple Cues for Robust Object Recognition. In Proceedings of AAMAS'07, the Sixth International Joint Conference on Autonomous Agents and Multi-Agent Systems, Honolulu, HI. May 2007.
• Murphy-Chutorian, E., Aboutalib, S., Triesch, J. Analysis of a Biologically- Inspired System for Real-time Object Recognition Cognitive Science Online, 3.2, pp. 1-14. 2005. http://cogsci-online.ucsd.edu/3/3-3.pdf

* Thesis proposal document also available, upon request.

• Aboutalib S, Mohamed A., Zhang L., Wu S. Deep Learning for Reducing Breast Cancer Recall Rate in Screening Mammogram. NLM Training Conference. Abstract Oral Presentation: Focus Session (June 2018)
• Aboutalib S, Mohamed A., Zhang L., Wu S. Multi-View Deep Learning for Reducing False Recalls in Screening Mammography. AMIA Annual Meeting, Accepted for Oral Presentation (November 2018)
• Aboutalib S, Mohamed A., Wu S. Do pre-trained deep learning models improve computer-aided classification of digital mammograms? SPIE Medical Imaging Conference Proc. (27 February 2018); Oral Presentation
• Aboutalib S. Applying Deep Learning to Improve Interpretation of Digital Mammograms towards Reducing False Recall Rates. Open Mic Session at NLM Training Conference, (June 5-6 2017), San Diego, CA.
• Aboutalib S, Wu S. Automatic identification of nuanced imaging features in recalled but biopsy benign mammogram images. Radiological Society of North America (RSNA) Conference, (November 29-December 1 2017), Abstract Oral Presentation.