2017 NCWIT Collegiate Award Recipients

​We are pleased to announce the recipients of the 2017 NCWIT Collegiate Award, sponsored by Hewlett Packard Enterprise and Qualcomm, who were honored at the 2017 NCWIT Summit on May 23, 2017.

  • Pooja Chandrashekar, Harvard University, “Towards the Rapid Diagnosis of Mild Traumatic Brain Injury in a Clinical Setting”
    This project focuses on more accurate, rapid, and inexpensive diagnostic testing for a mild traumatic brain injury (mTBI), also known as a concussion, which is prevalent among athletes and military personnel. Clinical trials are in progress at the National Rehabilitation Hospital. (View the project online.)

  • Valerie Chen, Yale University, “A Novel Combinatorial Method with Data Mining to Detect Critical Errors in Embedded Software Systems”
    This project detects critical errors in embedded software, which can be found in everyday technology used for vehicles and more. The Combined Covering Array Testing (CCAT) method maximizes the amount of system behavior covered and the number of errors found, while minimizing the test size. (View the project online.)

  • Bethy Diakabana, Wentworth Institute of Technology, “Accessible Malaria Identification (AMI)”
    This project uses a fully automated lightweight computer vision system to detect malaria parasites present in blood smears at any stage. If this algorithm is incorporated in routine tests, the presence of malarial parasite can be detected without the risk of human error or the expenses of robust medical equipment. (View the project online.)

  • Anvita Gupta, Stanford University, “Deep MotifGAN for Personalized Medicine”
    MotifGAN uses artificial intelligence (AI) to generate new DNA that binds correctly to proteins. Sequences generated from MotifGAN can be used for personalized treatments for diseases like colorectal cancer. (View the project online.)

  • Divya Mahajan, Georgia Institute of Technology, “TABLA: A Unified Template-based Framework for Accelerating Statistical Machine Learning”
    TABLA is a framework that generates accelerators for a class of machine learning algorithms, offering an energy efficient solution for analyzing the vast amounts of data generated by a wide range of commercial and enterprise applications, such as social networking and financial analysis. (View the project online.)

  • Manisha Mohan, Massachusetts Institute of Technology (MIT), “Wearable Technologies to Detect and Deter Sexual Abuse”
    This project investigates loopholes in technological solutions for preventing sexual abuse and proposes solutions for ignored populations and scenarios. For example, many current systems expect the victim to press the panic button, which assumes the victim is conscious or able-bodied. (View the project online.)

Honorable mentions include:

  • Danielle Bragg, University of Washington - Seattle, “Smartfonts: Improving Legibility through Letterform Redesign”

  • Sharon Chen, Columbia University, “Wordcradle”

  • Kelsey D'Souza, Columbia University, “Ebohub: Infectious Disease Surveillance and Containment”

  • Alankrita Dayal, University of California - Berkeley, “Robodycare, Mind-Controlled Massager: An Integrated Biofeedback System”

  • Asmaa Elbadrawy, University of Minnesota - Twin Cities, “Domain-aware Grade Prediction and Top-n Course Recommendation”

  • Emily Greene, Dartmouth College, “Secure Sharing of Wearable Healthcare Data”

  • Rachel Harsley, University of Illinois - Chicago, “Empowering People to Control Their Digital Trail”

  • Rae Lasko, Carnegie Mellon University, “The Implications of Temporal Association Rules in the Design of Intelligent Tutoring Systems”

  • Yamini Nambiar, Georgia Institute of Technology, “Spatio-temporal Spectral Variability in Cassiopeia A”

  • Vinitha Ranganeni, Carnegie Mellon University, “User Interface for Collecting Workspace Trajectories”

  • Stacey Truex, Georgia Institute of Technology, “Fast, Privacy Preserving Linear Regression Over Distributed Datasets, Based on Pre-Distributed Data”

  • Maya Varma, Stanford University, “Autonomous Smartphone-Controlled Robotic Wheelchair with Bluetooth Beacon-Assisted Navigation and RGB-D Vision”