Prof. Sunghoon Ivan Lee, Director of the Advanced Human-Health Analytics (AHHA) Lab at UMass Amherst, is accepting a PhD student (with full funding) for Fall 2022. Please read the project description and necessary skillsets below. If interested, please reach out to Prof. Lee at firstname.lastname@example.org along with your CV. You will also need to apply to the Ph.D. program in the College of Information and Computer Sciences (Deadline: 12/15). Applicants are encouraged to specify that they are interested in working with Prof. Lee in their SOP. Please also visit the lab webpage: http://www.ahhalab.org/
Explicitly focusing on individuals undergoing long-term rehabilitation – such as stroke survivors and TBI survivors – we are interested in how personal health information could help patients better engage in at-home rehabilitation and self-management of their conditions outside the clinic. Various health-related personal information has been studied, but current solutions primarily focus on tracking patients’ history of compliance behavior and training performance. We are currently investigating how machine learning-generated personal information with inherent uncertainties could help patients establish stronger engagement to long-term rehabilitation and positively affect their compliance behavior. I am specifically looking for students who are passionate about at least one or preferably combination of the followings: 1) designing machine learning algorithms to extract clinically relevant information from data obtained from patients’ home settings via wearable and mobile devices, 2) investigating real-world needs of the stakeholders (i.e., patients and clinicians), and 3) design a technological solution to provide the information back to the stakeholders (e.g., mobile app to visualize personal data).
Related published papers from this project include the following:
- Remote Assessment of Cognitive Impairment Level based on Serious Mobile Game Performance
Hee-Tae Jung, Hyunsuk Lee, Kwangwook Kim, Byeongil Kim, Sungji Park, Taekyeong Rye, Yangsoo Kim, and Sunghoon Ivan Lee, IEEE Journal of Biomedical and Health Informatics (IEEE JBHI), 2019, Cover Article of the Issue [PDF]
- Towards the Design of a Ring Sensor-based mHealth System to Achieve Optimal Motor Function in Stroke Survivors
Yoojung Kim, Hee-Tae Jung, Joonwoo Park, Yangsoo Kim, Nathan Ramasarma, Paolo Bonato, Eun Kyoung Choe*, Sunghoon Ivan Lee*, ACM Interactive, Mobile, Wearable and Ubiquitous Technologies (ACM IMWUT), 2019, [PDF]
- Rehabilitation Games in Real-World Clinical Settings: Practices, Challenges, and Opportunities
Hee-Tae Jung, Taiwoo Park, Narges Mahyar, Sungji Park, Taekyeong Rye, Yangsoo Kim, Sunghoon Ivan Lee, ACM Transactions on Computer-Human Interaction (ACM ToCHI), 2020, [PDF].
Please let me know if you would like to know more about the most recent papers related to this topic (under review), then I can share them. Also, this line of work is related to recently funded grants below and these articles (article 1, article 2, article 3, article 4).
- National Institutes of Health. NIBIB R01. Achieving Optimal Motor Function in Stroke Survivors via a Human-Centered Approach to Design an mHealth Platform, Lee, S.I. (Principal Investigator, UMass Amherst), Bonato, P. (Site PI, Harvard Medical School), Choe, E.K. (Site PI, UMD), Ramasarma, N. (Industry Collaborator, FormSense).
- National Institutes of Health. Patient-Centered Serious Games for Remote Cognitive Training in Older Adults with Mild Cognitive Impairment, Lee S.I. (Principal Investigator, UMass Amherst), Daneault, J.-F. (multi-PI, Rutgers University)
Knowledge, Skills, and Abilities
The Ph.D. students will closely work with Prof. Lee and other team members in a weekly basis. They are expected to have excellent oral and written communication, creativity for algorithm and/or qualitative anlaysis, and teamwork spirit. I prefer candidates with at least one of the following skills:
- Strong understanding and preferably prior experience in human-centered design approaches, such as semi-structured interviews, participatory design with clinicians and patients, and in-lab and deployment studies.
- Strong understanding of the fundamentals of machine learning and its implications to domain specific data. Having experience in analyzing wearable time-series data to construct regression or classification models is a plus.
Note that we do not require applicants to have domain-specific knowledge in rehabilitation medicine. I welcome students with diverse backgrounds who have strong interests in the subject matter & enthusiasm to advance healthcare using technologies.
Information about Principal Investigator
Sunghoon “Ivan” Lee is an Assistant Professor of Computer Science at UMass Amherst. He received his PhD in Computer Science, MS in Computer Science, and MS in Electrical Engineering, all from the University of California Los Angeles in 2010, 2013, and 2014, respectively. From 2014 to 2016, he was a post-doctoral fellow in the Department of Physical Medicine & Rehabilitation at Harvard Medical School.
Ivan is a recipient of the NSF CRII Award and NIH Trailblazer Award. He is currently serving as Editor for the Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) and Associate Editors for the ACM Transactions on Computing for Healthcare (HEALTH) and IEEE Open Journal of Engineering in Medicine and Biology (OJEMB). He is a Senior Member of the IEEE and an elected Member of the Technical Committee on Wearable Biomedical Sensors and System of the IEEE Engineering in Medicine and Biology Society (EMBS). Ivan has served as a technical program committee member for several flagship conferences in the area of wearable computing and health informatics. He is also a recipient of the 2021 Lilly Fellowship for Teaching Excellence and 2021 ADVANCE Faculty Fellowship for Equity at UMass Amherst.