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Das and Mower Provost Receive NSF CAREER Awards

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Prof. Reetuparna Das

Prof. Emily Mower Provost

Our congratulations go to the CSE faculty who have been selected for NSF CAREER Awards in 2017: Reetuparna Das for her work in repurposing storage structures for use as active computational units and Emily Mower Provost for her use of speech-based mood monitoring.

Reetuparna Das

Assistant Professor Reetuparna Das has been awarded an NSF CAREER grant for her research project, “In-Situ Compute Memories for Accelerating Data Parallel Applications.”

It is predicted that by the year 2020, data production from individuals and corporations is expected to grow to 73.5 zetabytes, a 4.4× increase from the year 2015. This will require a large amount of time and energy in moving data from storage to compute units. Das’ research seeks to design specialized data-centric computing systems that dramatically reduce these overheads.

The central vision of this research is to create in-situ compute memories, which re-purpose the elements used in these storage structures and transform them into active computational units. In-situ compute memories enables computation in-place within each memory array, without transferring the data in or out of it. Such a transformation could unlock massive data-parallel compute capabilities, and reduce energy spent in data movement through various levels of memory hierarchy, thereby directly address the needs of data-centric applications.

More information about the project is available in Prof. Das’ CAREER Award Posting by NSF.

Prof. Das received her PhD in Computer Science and Engineering from Pennsylvania State University, University Park in 2010 and joined the faculty of CSE at the University of Michigan in January 2016. Prior to this she was research scientist at Intel Labs and researcher-in-residence for Center for Future Architectures Research (C-FAR). Her research interests include computer architecture and its interaction with software systems and device/VLSI technologies. Some of her recent projects include energy proportional interconnect architectures, fine-grain heterogeneous core architectures for mobile systems, and low-power scalable interconnects for kilo-core processors. Her thesis research, focused on application-aware on-chip interconnects was recognized by an IEEE Top Picks award. She has received outstanding research and teaching assistantship awards from the Computer Science and Engineering department at Pennsylvania State University. Professor Das has authored over 45 articles in peer reviewed journals and conferences, and filed 5 patents through ARM Inc.

Emily Mower Provost

Prof. Emily Mower Provost has been awarded an NSF CAREER grant for her research project, "Automatic Speech-Based Longitudinal Emotion and Mood Recognition for Mental Health Monitoring and Treatment."

Prof. Mower Provost's research interests are in human-centered speech and video processing, multimodal interfaces design, and speech-based assistive technology. The goals of her research are motivated by the complexities of human emotion expression and perception.

Effective treatment and monitoring for individuals with mental health disorders is an enduring societal challenge. Regular monitoring increases access to preventative treatment, but is often cost prohibitive or infeasible given high demands placed on health care providers. Yet, it is critical for individuals with Bipolar Disorder (BPD), a chronic psychiatric illness characterized by mood transitions between healthy and pathological states. Transitions into pathological states are associated with profound disruptions in personal, social, vocational functioning, and emotion regulation.

Under this grant, Prof. Mower Provost will investigate new approaches in speech-based mood monitoring by taking advantage of the link between speech, emotion, and mood. The approach includes processing data with short-term variation (speech), estimating mid-term variation (emotion), and then using patterns in emotion to recognize long-term variation (mood).

The research investigates methods to estimate mood from longitudinal speech data. Current methods estimate mood from speech acoustics directly. These approaches are not sufficiently accurate for use on speech collected from an individual’s daily life. The proposed work introduces emotion as an intermediary step to simplify the mapping between speech and mood, predicated on the knowledge that emotion dysregulation is a common symptom in bipolar disorder. The proposal advances techniques to improve the robustness and generalizability of emotion recognition algorithms, resulting in low-dimensional secondary features whose variations are due to emotion. These features will be segmented and classified to provide a means to map between speech (a quickly varying signal) and user state (a slowly varying signal), advancing the state-of-the-art. The results provide quantitative insight into the relationship between emotion variation and user state variation, providing new directions and links between the fields of emotion recognition and assistive technology. The focus on modeling emotional data using time series techniques results in breakthroughs in the design of emotion recognition and assistive technology algorithms. The approaches generalize to conditions whose symptoms include atypical emotion, such as post-traumatic stress disorder, anxiety, depression, and stress. 

More information about the project is available in Prof. Mower Provost’s CAREER Award Posting by NSF.

Prof. Emily Mower Provost received her PhD in Electrical Engineering from the University of Southern California (USC), Los Angeles, CA in 2010. She has been awarded a National Science Foundation Graduate Research Fellowship, the Herbert Kunzel Engineering Fellowship from USC, an Intel Research Fellowship, the Achievement Rewards For College Scientists (ARCS) Award, and the UM Oscar Stern Award for Depression Research. She is a member of Tau-Beta-Pi, Eta-Kappa-Nu, and a member of ACM, IEEE, and ISCA. 

About the NSF CAREER Award

The CAREER grant is one of the National Science Foundation's most prestigious awards, conferred for "the early career-development activities of those teacher-scholars who most effectively integrate research and education within the context of the mission of their organization."


Posted: February 8, 2017