Institute for Mathematical Sciences

Statistical Methods for Developing Personalized Mobile Health Interventions

(4 Feb - 1 Mar 2019)

Visitor List

Jiawei Bai
Johns Hopkins University, USA

Accelerometry data: from micro- to macro-level

Niranjan Bidargaddi
Flinders University, Australia

mHealth systems to advance how we understand and approach mental illness

Bibhas Chakraborty
National University of Singapore, Singapore

Dynamic treatment regimes and "SMART" design

Yi-Hau Chen
Institute of Statistical Science, Academia Sinica, Taiwan

Semiparametric copula-based analysis for treatment effects in the presence of treatment switching

Ngai-Man Cheung
Singapore University of Technology and Design, Singapore

Mobile AI for personalised healthcare

Ying Kuen Cheung
Columbia University in the City of New York, USA

ROADMAP for precision and digital health

Anna Choi
Stanford University, USA and The Chinese University of Hong Kong, Shenzhen, China

Mobile health and personalized recommendation technology

Chuck Chua
BIT, Singapore
Jesse Clifton
North Carolina State University, USA

TBA


Introduction to causal inference

Alex Cook
National University of Singapore, Singapore

Use of RFID to detect proximity of individuals and their location in closed settings for outbreak modelling

Rob Martinus van Dam
National University of Singapore, Singapore
Borame Sue Lee Dickens
National University of Singapore, Singapore

TBA

Naihua Duan
Columbia University in the City of New York, USA

Personalized data science, small data, and N-of-1 trials

Mueller-Riemenschneider Falk
National University of Singapore, Singapore

Personalized lifestyle interventions – opportunities and challenges

Palash Ghosh
Duke-NUS Medical School, Singapore

Dynamic generalized odds-ratio (dGOR): a novel approach to assess dynamic treatment regimes (DTR) with an ordinal outcome

Samiran Ghosh
Wayne State University School of Medicine, USA

Some aspects of SMART design: methodological developments and an application in mHealth intervention

Pei-Yun Sabrina Hsueh
IBM Research in New York, USA

From real-world evidence to person-centered healthcare: applications of computational behavior science and interpretable AI/ML for minimally disruptive medicine

Jonathan Huang
Singapore Institute for Clinical Sciences, Singapore

Introduction to causal inference in complex longitudinal settings

Ta-Cheng Huang
National University of Singapore, Singapore
Joseph Jay Williams
University of Toronto, Canada

Perpetually enhancing technology for human learning and behavior change, through dynamic, personalized, collaborative experimentation

Binyan Jiang
The Hong Kong Polytechnic University, Hong Kong

TBA

Chaeryon Kang
University of Pittsburgh, USA

Hybrid statistical and mechanistic mathematical model guides mobile health intervention for chronic pain

Gi-Soo Kim
Seoul National University, Korea

Contextual multi-armed bandit algorithm for semiparametric reward model

Jane Paik Kim
Stanford University, USA

Statistical inference for online decision-making: in a contextual bandit setting

Santosh Kumar
University of Memphis, USA

Emerging mHealth biomarkers to enable spatio-temporal precision medicine

Tze Leung Lai
Stanford University, USA

Mobile health and personalized recommendation technology


Distinguished Visitor Lecture Series:

Real world data, real world evidence, and decision analytics for precision medicine and health


Distinguished Visitor Lecture Series:

Latent state modeling in mobile health and diagnostic classification: Recent advances in the MCMC approach

Weiwen Leung
University of Toronto, Canada
Jialiang Li
National University of Singapore, Singapore

A model-based multi-threshold method for subgroup identification

Qingling Li
Duke-NUS, Singapore
Brian Y. Lim
National University of Singapore, Singapore

Interpreting step count activity patterns in an incentivized city-wide health intervention

Zoe Zhao-Yan Ming
National University of Singapore, Singapore

App and system for diet and lifestyle tracking for chronic diseases management and research

Andre Matthias Muller
National University of Singapore, Singapore

Cognitive interviewing in e- & mHealth research

Susan A Murphy
Harvard University, USA

Distinguished Visitor Lecture Series:

Stratified micro-randomized trials with applications in mobile health


Distinguished Visitor Lecture Series:

Assessing time-varying causal interactions and treatment effects with applications to mobile health


Public Lecture:

Mobile Health Intervention Optimization

Timothy NeCamp
University of Michigan, USA

Developing mHealth interventions to improve mood, activity, and sleep for medical interns

R. Todd Ogden
Columbia University, USA

Determining optimal treatments based on complex data

Min Qian
Columbia University in the City of New York, USA

Constructing personalized decision algorithm for mHealth applications

Tianchen Qian
Harvard University, USA

Estimating time-varying causal effect moderation in mobile health with binary outcomes

Mahdi Rasouli
National University of Singapore, Singapore
Rui Song
North Carolina State University, USA

Statistical inference for online decision-making: in a contextual bandit setting

Baoluo Sun
National University of Singapore, Singapore

Introduction to causal inference

Chin Gee Jacky Tan
NUS - Institute of Systems Science , Singapore
Mya Thway Tint
National University of Singapore, Singapore
Yuan Wei
Singapore Clinical Research Institute, Singapore
Zhenke Wu
University of Michigan, USA

TBA

Jiali Yao
National University of Singapore, Singapore
Peiling Yap
National Centre for Infectious Diseases, Singapore

A digital community-based syndromic surveillance system for influenza and other acute respiratory infections in Singapore

Yingqi Zhao
Fred Hutchinson Cancer Research Center, USA

Constructing stabilized dynamic treatment regimes for censored data

Qishi Zheng
Singapore Clinical Research Institute, SINGAPORE
Tony Zhong
Icahn School of Medicine at Mount Sinai, USA

A gate-keeping approach to selecting adaptive interventions under general SMART designs