2018 NCWIT Collegiate Award Recipients

NCWIT Collegiate Award Logo

The NCWIT Collegiate Award honors the outstanding computing accomplishments of undergraduate and graduate women. Conferred annually, the award recognizes technical contributions to projects that demonstrate a high level of innovation and potential impact.

The NCWIT Collegiate Award is sponsored by Hewlett Packard Enterprise and Qualcomm.

  • Salam Daher, University of Central Florida, “Physical-Virtual Patient Bed”
    The Physical-Virtual Patient Bed (PVPB), developed in collaboration with experts from medicine, nursing, and computer science, addresses the current difficulty in finding appropriate, responsive patient simulation models for use in training for healthcare providers. The PVPB features a human-shaped, physical, interchangeable shell where multiple projectors render imagery of the patient on the shell, as well as software and hardware systems that allow the "patient" to exhibit dynamic responses to stimuli. (View the project online.)

  • Alankrita Dayal, University of California - Berkeley, “NeuroStroll: A Novel, Rapid, and Accurate Low-Cost Diagnosis Tool for Neurodegenerative Diseases”
    Although neurodegenerative conditions affect the lives of approximately 52 million people worldwide, to date, modern healthcare systems lack a streamlined and effective diagnosis procedure for these infirmities. This project utilizes state-of-the-art hardware and software, including VR and machine learning, to enable healthcare professionals to deliver faster and more conclusive diagnoses. (View the project online.)

  • Kanchana Raja, Columbia University, “Computational Analysis of T-cell Therapy for Pancreatic Cancer, Leukemia, and Multiple Myeloma”
    This project was designed to analyze the effectiveness of SmarT-cells, which are bioengineered cells that can recognize and kill pancreatic cancer cells by delivering intracellular activation signals, causing differential expression of specific genes. By using Java to process massive genetic data files and generate color-coded graphs of biological functions, the researcher was able to prove that this therapy is an effective treatment for pancreatic cancer. (View the project online.)

  • Joanna Rosa Rivero, University of Pittsburgh, “Variable Cross-Sectional Area of Thermoelectric Element Legs for Maximum Performance using Optimization and Thermal-Electric Coupled Methods”
    This project presents a new and unique method for optimizing the efficiency and energy output of thermoelectric generators by varying the cross-sectional area of one semiconductor leg in relation to the other by employing a complex, multi-method mathematical model. (View the project online.)

Honorable mentions include:

  • Katherine Avery, University of Oklahoma, “Automated Bird Roost Detection Using Radar and Artificial Neural Networks”
    This project uses artificial neural networks (ANNs) to identify whether a next generation radar (NEXRAD) image contains visual evidence of a bird roost. The software uses a combination of neural network types, a multilayer perceptron (MLP) and a convolutional neural network (CNN), to analyze the four categories of NEXRAD data that are most relevant in detecting the presence of roost rings. (View the project online.)

  • Tegan Brennan, University of California - Santa Barbara, “Space/Time Analysis for Cybersecurity”
    The Space/Time Analysis for Cybersecurity (STAC) program is an initiative funded by DARPA towards the creation of techniques for detecting software vulnerabilities related to the time or space resource usage behavior of programs. In particular, this project focuses on tools and algorithms for the detection of side-channel vulnerabilities, which use non-functional properties of program execution to obtain information about private data and can potentially leak sensitive information. (View the project online.)

  • Samsara Counts, George Washington University, “The Diverse Cohort Selection Problem”
    To address the problem of how interviewers can most effectively use their available resources in the selection of the strongest possible cohort from a set of applicants, this project models the allocation of interviewing resources and subsequent selection of a cohort as a combinatorial pure exploration problem in the multi-armed bandit (MAB) setting in reinforcement learning. (View the project online.)

  • Bethany Davis, University of Pennsylvania, “Leveraging Computer Vision in Gif Search at GIPHY”
    This project addressed a limitation of the GIPHY searchable gif database by developing and applying optical character recognition algorithms to enable the platform to “read” text within an image. Using Python, PHP, Scala, and Elasticsearch, the researcher coded processes to analyze and re-index eight million gifs, and the resulting improvement to the search engine impacted 200 million daily users. (View the project online.)

  • Catherine Elisa Diaz, University of Colorado - Boulder, “Designing for Depth Perceptions in Augmented Realtiy”
    This study examined the effects of common perceptual and design factors on depth perception within augmented reality. The results of the research showed that the two design factors that have the biggest impact on a user’s depth perception were drop shadow and cast shadow. (View the project online.)

  • Demi Guo, Harvard University, “The Data Calculator: Computing Instead of (only) Inventing Data Access Methods”
    From consumer services to industrial applications to large-scale systems, the platforms we rely on in daily life are increasingly driven by data. As the uses of data proliferate, the efficiency and cost-effectiveness of data access methods are ever more important. This project offers an automated data access design engine framework that can navigate through all possible design spaces, and generate an optimal design based on the use case. (View the project online.)

  • Elizabeth Koning, Calvin College, “Thread Safe Graphics Library”
    The Thread Safe Graphics Library (TSGL) is a multithreaded parallel computing education tool for creating graphical visualizations of the ways different threads may interact with one another when attempting to access the same resources at the same time. The tool includes representations of several common interaction types, and also offers instructors the ability to create new visualizations to illustrate specific situations. (View the project online.)

  • Madolyn MacDonald, University of Delaware, “The Improvement and Quality Evaluation of Reference Genomes to Facilitate the Manufacture of Biotherapeutic Proteins”
    This project addresses the need for a universally accepted, “gold standard” reference genome for Chinese hamster ovary (CHO) cells, which are the preferred platform for the production of biotherapeutics. EvalDNA is a novel computational tool that applies machine-learning methods to combine metrics reflecting the accuracy and completeness of a genome assembly into a single quality score so that labs can confidently choose the best available reference for their work. (View the project online.)

  • Laurel Orr, University of Washington - Seattle, “Probabilistic Database Summarization for Interactive Data Exploration”
    This project has developed an alternative method for querying massive data sets and obtaining approximate values at a speed that allows real-time interaction. The new approach, based on the theory of probabilistic databases and the Maximum Entropy principle, can more accurately predict small populations than traditional approximate query processing (AQP) techniques, and can also process queries more quickly than AQP methods, without increasing the margin of error. (View the project online.)

  • Samantha Runke, Carnegie Mellon University, “NeoCue Trainer”
    In this project, the researcher developed an Android app to effectively train health care providers in the use of the NeoCue system, which is a dynamic guide to providing respiratory care and breathing assistance for newborns. The app familiarizes users with the NeoCue technology and tests them on their proficiency. (View the project online.)

  • Aarti Sathyanarayana, University of Minnesota - Twin Cities, “Computational Sleep Science: A Machine Learning Approach”
    This project addresses limitations in the diagnosis and treatment of sleep disorders and offers a new, revised, data-driven approach to the sleep science process. The project employs a deep-learning approach to leverage the raw data collected on a large scale by consumer wearable devices and offers the possibility of predicting sleep quality based on an individual’s activity levels and other environmental factors. (View the project online.)

  • Prachi Shah, Carnegie Mellon University, “VOXEL - A Tool to Plan Brain Surgeries”
    The VOXEL project addresses limitations in the visualization tools currently in use by neurosurgeons to plan and prepare for brain surgery. By incorporating 3D printing and virtual reality, researchers can create a one to one physical model of an individual patient’s brain, then view brain activity using multiple imaging modalities at the same time. The technology can also be used to model manipulation of the brain in real time. (View the project online.)

  • Katherine Tang, Cornell University, “Euphoria”
    Euphoria is a mental health study focusing on the potential effects of using virtual reality technology for treatment and relaxation. The project seeks to determine how virtual reality may be utilized for mental health treatments by inducing euphoric feelings through various interactions such as touch-induced wings, flight, and exploration of a virtual world. (View the project online.)

  • Courtney Thurston, Embry-Riddle Aeronautical University - Daytona Beach, “Constraint Programming Mapping and Evaluation of Safety-Critical Autonomous Aerial System Flight Pathing”
    The goal of this research is to develop algorithms that decrease the investment of time and money needed to create efficient drone applications, including those used for consumer services, precision agriculture, and search and rescue purposes. This research implements a task-selection decision maker based on constraint-satisfaction programming to cut down on both flight time and risk. (View the project online.)

  • Shelby Ziccardi, Lewis & Clark College, “Individual Calibration of Redirected Walking Thresholds in Virtual Reality”
    Redirected Walking is an important aspect of virtual reality design which plays on the brain’s faculties of perception to allow users to feel that they are exploring a large physical space. This project focuses on creating a modernized method for individually calibrating a user’s perception threshold, beyond which a detectable scaling effect in a VR world may induce sickness. This calibration method will allow for fast, flexible, and accurate adjustments in the VR experience. (View the project online.)