Jian Kang
· Associate Chair for Research Professor, BiostatisticsUniversity of Michigan · Biostatistics
Active 1988–2024
About
Jian Kang, PhD, MS, is an Associate Chair for Research and a Professor of Biostatistics at the U-M School of Public Health. His primary research interests are in developing statistical methods for large-scale complex biomedical data with applications in precision medicine, imaging, epidemiology, and genetics. His work includes imaging data analysis, Bayesian methods, efficient statistical computation algorithms, ultrahigh-dimensional feature selection, latent source separation methods, graphical models, network inference, composite likelihood approaches, and missing data problems. Dr. Kang has contributed to the development of new statistical learning methods for brain-computer interfaces, scalable Bayesian methods for big imaging data analysis, and statistical ICA methods for multi-dimensional data analysis. His research has led to numerous publications in top journals, and he is actively involved in advancing statistical methodologies for biomedical data analysis.
Research topics
- Computer Science
- Acoustics
- Artificial Intelligence
- Psychology
- Geography
- Engineering
- Sociology
- Architectural engineering
- Environmental science
- Computer vision
- Communication
- Civil engineering
- Statistics
- Medicine
- Speech recognition
Selected publications
Applied Sciences · 2020 · 101 citations
Senior authorCorresponding- Computer Science
- Computer Science
- Acoustics
A protocol for characterizing urban soundscapes for use in the design of Soundscape Indices (SSID) and general urban research as implemented under the European Research Council (ERC)-funded SSID project is described in detail. The protocol consists of two stages: (1) a Recording Stage to collect audio-visual recordings for further analysis and for use in laboratory experiments, and (2) a Questionnaire Stage to collect in situ soundscape assessments via a questionnaire method paired with acoustic data collection. Key adjustments and improvements to previous methodologies for soundscape characterization have been made to enable the collation of data gathered from research groups around the world. The data collected under this protocol will form a large-scale, international soundscape database.
Noise Mapping · 2020 · 154 citations
Senior authorCorresponding- Computer Science
- Geography
- Environmental science
Abstract The implementation of lockdown measures due to the COVID-19 outbreak has resulted in wide-ranging social and environmental implications. Among the environmental impacts is a decrease in urban noise levels which has so far been observed at the city scale via noise mapping efforts conducted through the framework of the Environmental Noise Directive. This study aims to understand how lockdown measures have manifested at a local level to better determine how the person-level experience of the urban soundscape has been affected and how these affects differ across urban space typologies. Taking London as a case study, a series of 30-second binaural recordings were taken at 11 locations representing a cross-section of urban public spaces with varying compositions of sound sources during Spring 2019 (pre-lockdown, N = 620) and Spring 2020 (during-lockdown, N = 481). Five acoustic and psychoacoustic metrics ( LA eq , LA 10 , LA 90 , Loudness, Sharpness) were calculated for each recording and their changes from the pre-lockdown scenario to the lockdown scenario are investigated. Clustering analysis was performed which grouped the locations into 3 types of urban settings based on their acoustic characteristics. An average reduction of 5.4 dB ( LA eq ) was observed, however significant differences in the degree of reduction were found across the locations, ranging from a 10.7 dB to a 1.2 dB reduction. This study confirms the general reduction in noise levels due to the nationally imposed lockdown measures, identifies trends which vary depending on the urban context and discusses the implications for the limits of urban noise reduction.
Building and Environment · 2020 · 153 citations
Senior authorCorresponding- Computer Science
- Artificial Intelligence
- Computer Science
Models of perceived affective quality of soundscapes have been recently included into standards to guide the measurement and improvement of urban soundscapes. Such models have been developed in outdoor contexts and their validity in indoor built environments is unclear. A laboratory listening test was performed in a mock-up living room with a window sight, in order to develop an indoor soundscape model for residential buildings. During the test, 35 participants were asked to rate 20 different scenarios each. Scenarios were defined by combining four indoor sound sources and five urban environments, filtered through a window ajar, on 97 attribute scales. By applying principal component analysis, Comfort, Content, and Familiarity, were extracted as the main perceptual dimensions explaining respectively 58%, 25% and 7% of the total variance. Relationships between the principal component scores, acoustic parameters and indoor and outdoor sound categories were investigated. Comfort, Content, and Familiarity were found to be better predicted respectively by loudness N10, level variability LA10-LA90 and sharpness S. The magnitude of linear-mixed-effect model predictions sensibly improved by accounting for sound categories, thus pointing at the importance of semantic meaning of sounds in indoor soundscape assessment. A measurement system is proposed, based on a 2-D space defined by two orthogonal axes, Comfort and Content, and two additional axes, Engagement and Privacy – Control, rotated 45° on the same plane. The model indicates the perceptual constructs to be measured (e.g. in post-occupancy evaluations), the attribute scales to be employed and actions to improve indoor soundscape quality, thus providing a reference for both research and practice.
Acoustics for Supportive and Healthy Buildings: Emerging Themes on Indoor Soundscape Research
Sustainability · 2020 · 92 citations
- Computer Science
- Architectural engineering
- Sociology
The focus of the building industry and research is shifting from delivering satisfactory spaces to going beyond what is merely acceptable with a wave of new research and practice dedicated to exploring how the built environment can support task performance and enhance people’s health and well-being. The present study addresses the role of acoustics in this paradigm shift. Indoor soundscape research has recently emerged as an approach that brings a perceptual perspective on building and room acoustics in order to shape built environments that “sound good” according to building occupants’ preference and needs. This paper establishes an initial discussion over some of the open questions in this field of research that is still in an embryonic stage. A thematic analysis of structured interviews with a panel of experts offered a range of perspectives on the characterization, management, and design of indoor soundscapes and health-related outcomes. The discussion pointed out the importance of both perceptual and multisensory research and integrated participatory design practices to enable a holistic view regarding the complex building–user interrelations and the design of just cities. Soundscape methodologies tailored to the peculiarities of indoor soundscapes can help to measure and predict the human perceptual response to the acoustic stimuli in context, thus reducing the risk of mismatches between expected and real building experiences. This perceptual perspective is expected to widen the scientific evidence for the negative and positive impacts of the acoustic environment on human health, well-being, and quality of life. This will support prioritizing the role of acoustics in building design and challenge many current design practices that are based on a noise control approach.
Frequent coauthors
- 130 shared
Francesco Aletta
University College London
- 79 shared
Tin Oberman
University College London
- 72 shared
Qi Meng
Ministry of Industry and Information Technology
- 64 shared
Hong Jin
Center for Life Sciences
- 58 shared
Andrew Mitchell
University College London
- 43 shared
Hui Ma
Tianjin University
- 38 shared
Simone Torresin
Eurac Research
- 33 shared
Yue Wu
Shanghai First People's Hospital
Labs
Jian Kang LaboratoryPI
Awards & honors
- Statistics in Biopharmaceutical Research Best Paper Award
- Best Paper in Biometrics by an IBS Member Award
Similar researchers at University of Michigan
- Resume-aware match score
- Save to shortlist
- AI-drafted outreach
See your match with Jian Kang
PhdFit ranks faculty by your research interests, methods, and publications — grounded in their actual work, not templates.
- Free to start
- No credit card
- 30-second signup