Andrew T. Campbell | |
|---|---|
| Born | 15 May 1959 |
| Nationality | British |
| Citizenship | UK, USA |
| Alma mater | Aston University, England City University, London, England Lancaster University, England |
| Occupation(s) | Computer scientist, academic and researcher |
| Known for | Mobile sensing, mental health, mobile computing, applied machine learning |
| Scientific career | |
| Fields | Ubiquitous computing, applied machine learning, HCI |
| Institutions | Dartmouth College Columbia University |
| Doctoral advisor | David Hutchison |
| Website | https://www.cs.dartmouth.edu/~campbell/ |
Andrew Thomas Campbell (born 15 May 1959) is a computer scientist who works in the field of ubiquitous computing. He is best known for his research on mobile sensing, applied machine learning, mental health and human behavioral modeling. [1] [2]
Campbell is the Albert Bradley 1915 Third Century professor in computer science at Dartmouth College. He joined Dartmouth Computer Science in 2005 after spending 10 years as a professor of Electrical Engineering at Columbia University. Prior to being on the faculty at Columbia, Campbell spent 10 years in the software industry working on the research and development of wireless networks and operating systems. He has worked on digital health as a visiting research scientist in the Android group at Google and at Verily Life Sciences. [3]
Campbell has received a number of awards including the ACM SIGMOBILE Test of Time Paper Award for pioneering sensing and machine learning on smartphones and the ACM Ubicomp 10-year Impact Award for paving the way for numerous efforts in the area of stress detection from sensory data and also for pioneering the integration of mental health research with mobile data mining. [4] [5] [6]
Campbell was born in Coventry, England, on 15 May 1959. He received a BSc in Mechanical Engineering from Aston University (1981) and then an MSc. in Computer Science from City, University of London. He worked in the software industry for a decade in England, the Netherlands and the USA before returning to university. He received his PhD in Computer Science from Lancaster University (1996). [3] [7]
He started as an assistant professor of Electrical Engineering at Columbia University in 1996 and was promoted to an associated professor with tenure in 2003. After a sabbatical year as a visiting professor at Cambridge University, he joined Dartmouth College in 2005. In 2018, he was named the Albert Bradley 1915 Third Century professor at Dartmouth College. [3]
Campbell is best known for his work in ubiquitous computing, where he and his students first implemented sensing and machine learning algorithms on the iPhone when it was released in 2007. [8] [4] He is also known for the development of the StudentLife app (2014). [9]
At Dartmouth, he led the StudentLife Study [10] tracking over 200 undergraduate students across their 4 years of college using smartphone sensing and brain imaging [11] to better understand the dynamics of mental health of students across their college years. [12] [13] [14] [15] This study was also the first to use mobile sensing to capture the impact of COVID-19 on student behavior and mental health outcomes during the pandemic. [16] He has also studied mental health and performance in the workplace using mobile sensing. [17]
Campbell has received an NSF CAREER Award, IBM Faculty Award, AT&T Faculty Award, Google Faculty Award and EPSRC Fellow Award. His group has received multiple 10 year impact awards, including the ACM SenSys Test of Time Award (2018) [18] and the ACM SIGMOBILE Test of Time Award (2019) for their work on the CenceMe app (2008), [8] the ACM Ubicomp 10 Year Impact Award (2022) for their work on the StressSense app. [6] More recently, his group also received the ACM Ubicomp 10-Year Impact Award (2024) [5] for their work on the StudentLife Study. [9] The citation for this 10-Year Impact Award said, "This groundbreaking paper has had an exceptional influence and significant impact on the field. It pioneered the integration of mental health research with mobile data mining, addressing real-world challenges in innovative ways. The availability of the dataset to the community also exemplifies exemplary research practices."