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Feb
4
Tue
Maia Jacobs, Harvard University – “One Size Doesn’t Fit Anyone: Tailoring Digital Tools for Personal Health Journeys”
Feb 4 @ 10:45 am – 11:45 am

Location

Hackerman B-17

Abstract

Personal technologies for everyday health management have the potential to transform healthcare by empowering individuals to engage in their own care, scaffolding access to critical information, and supporting patient-centered decision-making. Currently, many personal health tools often focus only on a single task or isolated event. However, chronic illnesses are characterized by information needs and challenges that shift over time; thus, these illnesses are better defined as a dynamic trajectory than a series of singular events.

In this talk, I discuss my work designing and implementing novel computing systems that: 1) support chronic illness trajectories and 2) reduce patients’ barriers to accessing information necessary for effective personal health management. I create technologies that have the flexibility and robustness to conform to individuals’ evolving health situations. By connecting individuals with personalized and actionable feedback, my approach can lead to long-term engagement with health tools. This is evidenced by participants’ motivations for using these systems as well as longitudinal usage patterns. Using results from longitudinal field deployments, I demonstrate the ability for personalized and adaptive health tools to facilitate patients’ proactive health management and engagement in their care. I also discuss opportunities for future work: looking at personalization as a strategy for addressing health disparities, designing for illness trajectories in which uncertainty is paramount, and integrating machine learning models into clinical workflows.

Bio

Dr. Maia Jacobs is a postdoctoral fellow at Harvard University’s Center for Research on Computation and Society. Jacobs’ research focuses on the development and assessment of novel approaches for health information tools to support chronic disease management. She completed her PhD in Human Centered Computing at Georgia Institute of Technology with the thesis, “Personalized Mobile Tools to Support the Cancer Trajectory��.

Jacobs’ research was recognized in the 2016 report to the President of the United States from the President’s Cancer Panel, which focuses on improving cancer-related outcomes. Her research has been funded by the National Science Foundation, the National Cancer Institute and the Harvard Data Science Institute. Jacobs was awarded the iSchools Doctoral Dissertation Award and the Georgia Institute of Technology College of Computing Dissertation Award. Jacobs was also recognized as a Foley Scholar, the highest award given by the GVU center to PhD candidates at Georgia Tech. Prior to joining Georgia Tech, Maia received a B.S. degree in Industrial and Systems Engineering from the University of Wisconsin-Madison and worked as a User Experience Specialist for Accenture Consulting.

Host

Chien-Ming Huang

Feb
11
Tue
Qian Yang, Carnegie Mellon University – “TBD”
Feb 11 @ 10:45 am – 11:45 am

Location

Hackerman B-17

Host

Chien-Ming Huang

Feb
13
Thu
Hui Guan, North Carolina State University – “TBD”
Feb 13 @ 10:45 am – 11:45 am

Location

Hackerman B-17

Host

Yair Amir

Feb
18
Tue
Emma Pierson – “TBD”
Feb 18 @ 10:45 am – 11:45 am

Location

Hackerman B-17

Host

Ben Langmead

Feb
25
Tue
Maria De Arteaga, Carnegie Mellon University – “Machine Learning in High-Stakes Settings: Risks and Opportunities”
Feb 25 @ 10:45 am – 11:45 am

Location

Hackerman B-17

Abstract

Machine learning (ML) is increasingly being used to support decision-making in critical settings, where predictions have potentially grave implications over human lives. Examples include healthcare, hiring, child welfare, and the criminal justice system. In this talk, I will characterize how societal biases encoded in data may be reproduced and amplified by ML models, and I will present an algorithm to mitigate biases without assuming access to protected attributes. Moreover, even if data does not encode discriminatory biases, limitations of the observed outcomes still hinder the effective application of standard ML methods to improve decision-making. I will discuss some of these challenges, such as the selective labels problem and omitted payoff bias, and I will propose methodology to estimate and leverage human consistency to improve algorithmic decision making.

Bio

Maria De-Arteaga is a joint PhD candidate in Machine Learning and Public Policy at Carnegie Mellon University’s Machine Learning Department and Heinz College. She holds a M.Sc. in Machine Learning from Carnegie Mellon University (2017) and a B.Sc. in Mathematics from Universidad Nacional de Colombia (2013). She was an intern at Microsoft Research, Redmond, in 2017 and at Microsoft Research, New England, in 2018. Prior to graduate school, she worked as a data science researcher and as an investigative journalist. Her work has been awarded the Best Thematic Paper Award at NAACL’19, the Innovation Award on Data Science at Data for Policy’16, and has been featured by UN Women and Global Pulse in their report Gender Equality and Big Data: Making Gender Data Visible. She is a co-founder of the NeurIPS Machine Learning for the Developing World (ML4D) Workshop, and a recipient of a 2018 Microsoft Research Dissertation Grant.

Host

Ilya Shpitser

Mar
3
Tue
Yang Zhang – “TBD”
Mar 3 @ 10:45 am – 11:45 am

Location

Hackerman B-17

Host

Chien-Ming Huang

Mar
5
Thu
CS Seminar – “TBD”
Mar 5 @ 10:45 am – 11:45 am

Location

Hackerman B-17

Host

Chien-Ming Huang

Mar
10
Tue
Rui Zhang – “TBD”
Mar 10 @ 10:45 am – 11:45 am

Location

Hackerman B-17

Host

Xin Jin

Mar
12
Thu
Ravi Karkar – “TBD”
Mar 12 @ 10:45 am – 11:45 am

Location

Hackerman B-17

Host

Chien-Ming Huang

Mar
24
Tue
Noah Stephens-Davidowitz – “TBD”
Mar 24 @ 10:45 am – 11:45 am

Location

Hackerman B-17

Host

Abhishek Jain

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