Pei-yun S. (Sabrina) Hsueh  Pei-yun S. (Sabrina) Hsueh photo       

contact information

Behavioral Analytics, Cognitive Learning and Adaptation, Multimdoal and Text analytics, Human Computer Interaction
Thomas J. Watson Research Center, Yorktown Heights, NY USA



My current research focuses is on innovative approaches of computing personalization and incorporating personalization analytics into service design. In the healthcare domain, I am working on active characterization of personal wellness status and active recommendation that are driven by outcome prediction. The importance of personalization research and system design arises from the need of serving the long tail of user need. While many multi-year structured programs have verified the effectiveness of individualized intervention on preventive care and chronic disease management (Helmrich et al, 1991; Bailey, 2001; Finland National Type II Diabetes Prevention Programme, 2007; CDC Diabetes Prevention Program, 2008), the task of offering personalized services dynamically in users’ context has posed grand challenges to existing service providers. On the one hand, integrated care models have shown promises in satisfying the long tail of demand. On the other hand, the success of an integrated model in clinical trials is not enough to secure a disruption in the service market. In fact, its reliance on the constant updates of user wellness status and tailoring of intervention accordingly requires solid system support from both the vendors and system operators. Some key competencies to be provided include: (1) inferring risks from multiple heterogeneous sources (and whenever necessary, going back to the user and care team to solicit for more information); (2) handling multi-faceted risk stratification; and (3) “on the fly” assessment and recommendation with respect to trends shown in the incoming data stream. During the process of data analytics, I am also quite interested in using social signals to improve compliance feedback strategy.

I am involved in the development of an evidence-based wellness management platform in a cloud computing environment. The platform provides an API for healthcare applications to (i) integrate information from heterogeneous data source (Sense), (ii) draw predictions by applying or extending models in a repository (Predict), and (iii) trigger proper responses (Respond). The development side of goal is to enable any independent software vendor (ISV) to use the API and the Sense-Predict-Respond framework to implement their services and exchange information with 3rd party applications.

My roles in IBM Watson Research Center include: 

- Global GTO Healthcare topic industries Co-Lead 2014

- Mobile-First Far Reaching Research Tech Lead 2014

- Wellness analytics Lead 2013-present

Computational Behavior and Decision Science Group

Healthcare Informatics Research, Industry & Solutions Research Department


My current projects include: 

 - Personalized system of Insights

  Design hypothesis-driven exogenous data analytics framework for enterprise data curation, consumption and cross-layer clinical/consumer insight generation

  Execution of adherence behavior adherence modeling and the design of prospective

study in the context of PHM for self-ensured employers

 Liaison with Mobility Competency Center on iOS wellness app development and

develop healthcare use cases with wearables and biosensors.

 Ecosystem building and Client/partner relationship management

 Technology consultation for outcome-based business models with partners/clients

 Identify strategies to increase ecosystem value through technology initiatives, assess

technical feasibility and strategic options enabled by new technologies

 IP portfolio/Thought leadership (liaison to Science & Technology department)

 Personalized healthcare platform and mobile applications

 Wearable/IOT/bio-sensor application in healthcare/wellness

 “Precision medicine at Nano-scale”


My past projects include: 

- Analytics Lead of In-market Experiment, Taiwan Collaboratory

 Design personalization analytics on Wellness Cloud

 Enable personalized services with clinical insight generation, sampling,

context-aware recommendation, adherence monitoring and adaptation.

 Develop AaaS (Analytics-as-a-Service) to deploy insights to 3rdparty SP

 Lead the development of health literacy tool/app (dynamic accretion of patient

engagement instruments with collaborative crowdsourcing)


Social media analytics (Trend detection from crowd-sourcing data)

Predictive Modeling Group, Business Analytics and Math Science Department

 Social Media Analytics for marketing intelligence

 Blog Analysis of Network Topology and Evolving Responses (BANTER)

 Mining crowd wisdom from unstructured data sources (w. Amazon Mechanical Turk)

 Patent Quality Index for legal communication

 Statistical analysis of quality-indicative features in patents applications/transactions

 Big-data analytics (sampling for natural language processing)


Overarching Theme & Previous Work


The overarching them of my research interest ties closely to the marriage of artificial intelligence and human computer interaction, with a focus on integrating machine learning and empirical analysis approaches for natural language understanding. My previous research concerns the development of spoken language understanding applications in spontaneous speech, using a variety of approaches ranging from statistical analysis, empirical study to machine learning. This is no secret that people speak differently under different circumstances. Some of the differences are systematic and can be attributed to deeper differences, such as the intention of speaker.

My contribution to this problem is to develop a learning framework that can be used to identify multimodal features (and patterns) that are characteristics of the systematic differences in human conversations and to build automatic detection mechanisms that are robust to spontaneous speech effects. Current projects include automatic topic segmentation and labeling and automatic decision detection. The overarching goal is to provide visual aids at the right level of details for the users to find information from the often-lengthy archives of conversation recordings.


Recognition & Awards

2015 Technology Leadership Event: speaker (Disruptive technology) & Manager

Choice Award

2014 IBM Manager Choice Award

2013 IBM Invention Achievement Award

2011 IBM Invention Achievement Award

2009 IBM Invention Achievement Award

2007 GOOGLE European Anita Borg Scholar

2005 – 2008 EU FP6 Project: AMIDA (Augmented Multi-party Interaction with Distant

Access)+AMI (Augmented Multi-party Interaction) (FP6-506811)

2004 Winner of Taiwan Merit Scholarship (National Science Council)

2003 Top Scholar Award, University of Washington


Community Services

  • Co-chair, IBM Healthcare informatics Professional Interest Group
  • Organizer, IBM Precision Medicine and Wellness Day
  • Board of Director & Treasurer: Chinese Institute of Engineers Greater New York Chapter (CIE-GNYC) 2013-2016

  • Managing committee, Emerging Information and Technology Association

  • Invited book chapter: Health Information Management textbook (Springer)

  • Invited speaker: US-Taiwan Biotech Business Form

  • Invited speaker: IBM Technology Leadership Event (TLE) -- disruptive technologies

  • Invited Session Chairs: Applied Human Factors and Ergonomics Conference (AHFE), Industrial and Systems Engineering Research Conference (ISERC), IEEE International Conference of Service Operations, Logistics and Informatics (IEEE SOLI), IEEE CollaborateCom Healthcare, CIE-GNYC conference (Healthcare session)

  • Workshop Organization: MEDINFO (largest medical informatics conference) 2015, MIE (largest European medical informatics conference) 2015: Effective adherence management with exogenous data analytics

  • Panel Organization MEDINFO 2013: Personalized healthcare and management: potentials and challenges

  • Workshop Organization MIE 2014: gaps analysis of patient-controlled devices

  • Organizing Committee for the Standardization Work Group on Data for Science and Technology: Chronic Disease Management and Independent Living for the Aged (2011- present)
  • IBM Health Care and Life Science (HCLS) Webinar Series Organizer
  • IBM Academy of Technology Committee
  • IBM HCI PIC Research Coordinator (2010-present)
  • IBM Westchester Toastmaster Club, President(2011-present), Secretary (2010), Sergeant-at-arm (2009)
  • UC Berkeley Alumni Society
  • PC: Annual Conference of Human Language Technology (HLT), European Association of Computational Linguistics (EACL), North American Association of Computational Linguistics (NAACL)
  • Journal review: IEEE Intelligent Systems Transactions on Knowledge and Data Engineering Statistical Analysis and Data Mining IEEE Journal of Selected Topics in Signal Processing Journal of Natural Language Engineering
  • Conference paper review: NAACL, EACL, HLT, ICML, ICIS, ACL, CHI
  • Statistical Natural Language Processing Reading Group
  • Women in Machine Learning Workshop (WiML)
  • EUROMASTERS Summer School in Speech Technology
  • IGK Summer School in Computational Linguistics and Psycholinguistics, Univ. of Saarland
  • European Summer School in Logic, Language, and Information (ESSLII)