2017 NCWIT Summit – Collegiate Award

LUCY SANDERS: The first segment, the Collegiate Award, the NCWIT Aspirations in Computing Collegiate Award. I have two people who will come on stage and help us with that, Janice Zdankus and Vicki Mealer-Burke. Janice is from HPE, HP Enterprise, and she’s a vice president of quality in the Customer Experience group, and Vicki is the newly-appointed chief diversity officer from Qualcomm, a lot to like about Janice and Vicki. First of all, they sponsor this award. Yay! That’s awesome. Thank you. They care very deeply about working inside their organizations, and also outside their organizations to help build the STEM and computing pipeline. And they work with various organizations like NACNE and others in the community to really work on our issue as a community. And so with that, I’d like to welcome Janice and Vicki to the stage.

JANICE ZDANKUS: Thanks, Lucy. Vicki and I are so excited to present this year’s NCWIT Collegiate Award recipients. And as NCWIT board members, we’re also impressed and inspired by the many accomplishments of the NCWIT community members that are represented here today.

VICKI MEALER-BURKE: The NCWIT Collegiate Award honors outstanding technical accomplishments of college women in any year of study. Conferred annually, the award recognizes technical projects that demonstrate a high level of creativity and potential social impact.

JANICE ZDANKUS: HPE is proud to sponsor this award along with Qualcomm. And this year, together, we are awarding six recipients and 12 honorable mentions. Many of them are here today.

VICKI MEALER-BURKE: So now the good part. Each Collegiate Award winner receives a $10,000 cash award and an engraved trophy. I know, right? And each Collegiate Award honorable mention receives $2,500 cash prize and a covered certificate.

JANICE ZDANKUS: So let’s begin by acknowledging these wonderful technologists with the NCWIT 2017 Collegiate Award honorable mentions. Freshman, sophomore, or two-year degree program winners are: Sharon Chen from Columbia University, Rae Lasko from Carnegie Mellon University, Vinitha, uh-oh. Sorry, Vinitha. So, Vinitha Ranganeni from Carnegie Mellon University, and also Maya Varma from Stanford University who could not be with us today.

VICKI MEALER-BURKE: From the junior and senior level are: Alankrita Dayal, University of California, Berkeley, Kelsey D’Souz, Columbia University, Emily Greene, Dartmouth College, and Yamini Nambia, Georgia Tech.

JANICE ZDANKUS: And at the graduate level, we have: Danielle Bragg, University of Washington, Seattle, Asmaa Elbadrawy from University of Minnesota, Twin Cities, Rachel Harsley, University of Illinois, Chicago, Stacey Truex, Georgia Tech.

VICKI MEALER-BURKE: All right. Let’s have another round of applause for all 18 of these outstanding technologists. Congratulations, and we look forward to hearing of your continued success and achievements. Thank you.

JANICE ZDANKUS: Oh, okay. Part of the application process for the Collegiate Award is to submit a video summary of your project. All of these videos, along with bios and photos, can be found on the Summit app. We’ve compiled a montage of our 2017 winning submissions, so let’s watch that now.

[Video] POOJA CHANDRASHEKAR: My project is called Towards the Rapid Diagnosis of Mild Traumatic Brain Injury in a Clinical Setting. Basically, what that means, is that I worked in developing a new way to diagnose concussions. And there are approximately 1.6 to 3.8 million sports-related concussions each year. We developed a rapid, accurate, and inexpensive screening test for NTBI, combining a dynamic motor tracking test and machine learning techniques.

[Video] DIVYA MAHAJAN: As we see around us these days, a lot of data is being generated. So we have all this data. We would like to make some sense out of it. That’s where machine learning algorithms come in. To tackle all this, specifically for a range of machine learning algorithms, we create Tabla, so it works top-down. It looks at the algorithmic properties of machine learning algorithms, and it creates a template which is common across a range of these machine learning algorithms. The Tabla framework makes it easy for software developers to generate hardware which is

[Video] BETHY DIAKABANA: Malaria is a life-threatening parasitic disease caused by protozoan parasites of the genus Plasmodium and is transmitted through the bite of a female Anopheles mosquito. The objective of this project is to develop a fully-automated, lightweight computeration system to detect malaria parasites present in blood smears at any stage. This algorithm and application will be helpful in areas where experts in microscopic analysis are not available.

[Video] MANISHA MOHAN: Care is a smart diaper for infants and toddlers that automatically detects unexpected disrobing and activates a signal that alerts the parents, caregiver, or the police station about the situation. CAPS proposes two solutions, active and passive. When forceful removal of clothing is detected by the sensor, a message is sent to the wearer’s phone, asking if the act was done with consent. The passive system consists of olfactory capsules which are activated by the user when they feel threatened. The capsules are worn as discreet accessories like jewelry, so are highly portable and easily accessible in emergencies. I hope that the proposed solution is going to help people in terms of safety and security, and reduce the number of cases of sexual abuse in the future.

[Video] ANVITA GUPTA: Precision medicine refers to creating individualized medicine based on a person’s genes. Thus, patients might not have to take drugs for the rest of their lives to treat a disease. In this project, I’ve harnessed the power of deep neural networks to create algorithms that generate new DNA sequences. These sequences can be spliced into a person’s genome to treat diseases such as cancer. MotifGAN is a novel approach that uses artificial intelligence to generate new DNA that binds correctly to proteins. Thus, deep neural nets allow us to treat genetic diseases in a personalized way.

[Video] VALERIE CHEN: Embedded software is everywhere. We can find them in cars, appliances, medical technology, and more. However, finding critical errors is a complex problem. Thus, my objective, was develop a software-testing method that generates a minimal and effective test suite for any embedded software system. I developed a data mining algorithm to choose states within the system from which to begin testing. My algorithm systematically extracts information from the user-entered system requirements based on rule-based classification, to choose a minimal and effective number of states. I tailor to the system’s structure using a variety of rules, including current system modes, endpoints and breakpoints of energy ranges, event tables, and operator functions. In the end, I programmed a prototype implementation of what I envision this tool to look like in the future that would aim maybe researchers in developing better software.

VICKI MEALER-BURKE: And now, I’d like to call the 2017 winners to the stage, beginning with freshmen and sophomores. Please come up to the stage when I call your name. Valerie Chen from Yale University, for her project A Novel Combinatorial Method with Data Mining to Detect Critical Errors in Embedded Software Systems. Come on up. Anvita Gupta from Stanford University, for her project entitled Deep MotifGAN for Personalized Medicine.

JANICE ZDANKUS: And now for our junior and seniors. Pooja Chandrashekar from Harvard University for her project entitled Towards the Rapid Diagnosis of Mild Traumatic Brain Injury in a Clinical Setting. Bethy Diakabana from Wentworth Institute of Technology for Accessible Malaria Identification.

VICKI MEALER-BURKE: And now our last two winners from the graduate level. Divya Mahajan from Georgia Tech for her project entitled Tabla: A Unified Template-based Framework for Accelerating Statistical Machine Learning. And Manisha Mohan from Massachusetts Institute of Technology for Wearable Technology to Detect and Deter Sexual Abuse.

JANICE ZDANKUS: So let’s hear another round of applause for all 18 of these outstanding technologists. Ladies, you represent the future of the computing discipline, and speaking on behalf of HPE and Qualcomm, we couldn’t be more proud of your accomplishments. And to all the academics in the audience, please remember that this wonderful opportunity is available to all of your female students pursuing a degree in computing and participating in the Aspirations in Computing community. So we hope to see more of them in the coming years to come. Thank you.

LUCY SANDERS: Thank you, Janice and Vicki, and congratulations again to all the winners.

Scroll to Top