In an ongoing effort to create lasting pipelines of diverse scientific talent and differentiated research, Amazon today announced a collaboration with Howard University, a historically black college or university (HBCU) located in Washington, DC. Founded in 1867, Howard is a research university that includes 14 schools and colleges.
As part of the collaboration, which will be housed in the College of Engineering and Architecture, Amazon will fund faculty research projects with an initial focus on machine learning and natural language processing.
“Through their research, funded in part by Amazon, our faculty and students are working to advance the fields of artificial intelligence, machine learning, natural language processing, e-commerce and robotics,” said John MM Anderson, dean of the College of Engineering and Architecture at Howard. “Importantly, the underrepresented minority students educated at Howard in these fields will become future innovators who create technology-driven companies and diversify the engineering and computer science workforce. Amazon’s continued support of our faculty and students has advanced our research and education activities and resulted in an exchange of mutually beneficial technological expertise.”
As part of these awards, faculty are assigned Amazon Research Associates who will maintain regular contact with the awardees over the course of their projects. Amazon Research Liaisons are technical subject matter experts who keep abreast of project progress and act as a bridge to Amazon’s scientific community.
“Serving as the Amazon Research Liaison for this important partnership has been rewarding, both personally and professionally,” said Omid Sadjadi, a senior applied scientist at Amazon Transcribe. “We believe that the faculty and students working on these projects will have a meaningful impact.”
Last October, Amazon officials visited Howard’s campus to help build on the growing research partnership between Amazon and Howard faculty. The visit included an information session covering Amazon Science internships for graduate students at Howard, including a panel discussion with Amazon scientists. In the evening, Amazon hosted a community engagement dinner for College of Engineering and Architecture faculty, research liaisons and Amazon researchers.
“As soon as the opportunity arose to engage with Howard on an effort that my team could be a part of, I was all in,” said Maureen Stewart Nordberg, Principal Product Manager, Amazon Benchmarking. “Having worked with Howard alumni and with some of the fellows in the program, I’m excited to see the engagement and see what further effort and innovation is to come.”
The collaboration builds on an existing relationship between Amazon and Howard, including an ongoing collaboration between Howard and Amazon Studios to “diversify the entertainment industry by creating a pipeline for black and other underrepresented students” and a 10-week summer internship program sponsored by Amazon Web Services (AWS) that enabled Howard University undergraduate students to pursue both a applied cloud internship course with AWS.
“We are confident that the scientific works produced by this important research collaboration will advance technological innovation in strategic domains shared by Howard and Amazon,” said Harry Keeling, associate professor of computer science. “Furthermore, we believe this collaboration will attract additional funding as well as accelerate our graduates’ preparation for careers within Amazon.”
The following is information about the funded research projects and the faculty’s recipients:
“Detection and prediction of adverse drug effects with deep learning”
Saurav Keshari Aryal, Lecturer, Electrical Engineering and Computer Science, and Legand L. Burge III, Professor of Computer Science and Executive Director of the Howard West Initiative
“Clinical notes contain rich information about patients’ health status, clinical outcomes, and safety issues. The flexibility of free-text narratives allows physicians to record complex details outside of standardized data fields. Often, clinical notes record ADE occurrences and the physician’s assessment of the causes of adverse drug events (ADEs).
“The rich contextual information in clinical notes holds great promise for improving ADE surveillance. We propose a deep learning-based approach to unlock the value of the unstructured information in clinical notes. The proposed method integrates traditional natural language processing techniques and deep sequential learning to extract ADE events (occurrence, event, non-clinical actions, and clinical levels) implicit patient factors for ADE events at the individual patient level.”
“Development of a Hybrid Recommender System by Modeling Online Shopping Demand at the Household Level”
Md. Sami Hasnine, Assistant Professor of Civil and Environmental Engineering
“The study aims to develop a series of advanced econometric models to estimate household online shopping demand. Then a recommendation system will be developed, which will be enhanced by using machine learning models so that consumers do not need to search for each item before ordering. Instead, a consumer can just go through the suggested shopping list and select the necessary items. Each household typically has repeated daily, weekly orders online or demand. recurring, seasonal, one-time purchases etc. The output of this study is to generate a set of daily, weekly and monthly shopping lists for consumers to review and order the audited items. This will greatly save consumers from browsing time while buying the same products. In addition, the machine learning model will learn individuals’ behavior over time and increase the accuracy of the recommendation system.”