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SysML4Health: Scalable Systems for ML-driven Analytics in Healthcare
Alexey Tumanov · Jimeng Sun · Tushar Krishna · Vivek Sarkar · Dawn Song

Fri Apr 09 07:45 AM -- 04:00 PM (PDT) @
Event URL: https://sysml4health.github.io/ »

"This workshop focuses on the challenges involved in building integrated scalable distributed systems for the healthcare analytics domains. Healthcare analytics offers a unique opportunity to explore scalable system design since there has been a tectonic shift in the ability of medical institutions to capture and store unprecedented amount of structured and unstructured medical data, including the new ability to stream unstructured medical data in real time. This shift has already contributed to an ecosystem of Machine Learning (ML) models being trained for a variety of clinical tasks. However, new approaches are required to build systems that can develop and deploy ML models based on distributed healthcare data that must necessarily be accessed with privacy-preserving constraints.

The goal of this workshop is to attract leading researchers to share and discuss their latest results involving approaches to building scalable platforms for privacy-aware collaborative learning and inference that can be applicable to the domain of healthcare analytics. The scope of the workshop includes (but is not limited to) the following challenges:
* Scalable and distributed learning
* Continuous federated learning with privacy constraints
* Enforcing soft real-time constraints for streaming data analytics
* Specialized heterogeneous hardware for learning and inference
* Scalable runtime and resource allocation systems
* Productive systems for developing scalable data analytics applications"

Author Information

Alexey Tumanov (Georgia Tech)
Jimeng Sun (UIUC)
Tushar Krishna (Georgia Institute of Technology)
Vivek Sarkar (Georgia Tech)
Dawn Song (UC Berkeley)

Dawn Song is a Professor in the Department of Electrical Engineering and Computer Science at UC Berkeley. Her research interest lies in AI and deep learning, security and privacy. She is the recipient of various awards including the MacArthur Fellowship, the Guggenheim Fellowship, the NSF CAREER Award, the Alfred P. Sloan Research Fellowship, the MIT Technology Review TR-35 Award, ACM SIGSAC Outstanding Innovation Award, and Test-of-Time Awards and Best Paper Awards from top conferences in Computer Security and Deep Learning. She is an ACM Fellow and an IEEE Fellow. She is ranked the most cited scholar in computer security (AMiner Award). She obtained her Ph.D. degree from UC Berkeley. She is also a serial entrepreneur. She is the Founder of Oasis Labs and has been named on the Female Founder 100 List by Inc. and Wired25 List of Innovators.

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