Mission

The Human AI Technology Lab seeks to understand human health and behavior through machine learning and computational models of mobile device data streams. We strive to use mobile technology and intelligent computing to make health and social change in the community. We want to optimize opportunities for positive interactions between community members.

By harnessing the capabilities and data of everyday mobile technology, the Human AI Technology Lab aims to intelligently explore the patterns and behavior inherent to unique individuals. From this knowledge, we can make predictions about potential health outcomes and suggest individualized improvements to promote a healthier, happier community. 

Announcements

The Human AI Technology Lab seeks to understand human health and behavior through machine learning and computational models of mobile device data streams. We strive to use mobile technology and intelligent computing to make health and social change in the community. We want to optimize opportunities for positive interactions between community members.

By harnessing the capabilities and data of everyday mobile technology, the Human AI Technology Lab aims to intelligently explore the patterns and behavior inherent to unique individuals. From this knowledge, we can make predictions about potential health outcomes and suggest individualized improvements to promote a healthier, happier community. 

Research Projects

NSF-IIS-1816687: Computational Modeling of Human Rhythms to Improve Health and Quality of Life

Recent research indicates balance is vital to health and well-being, however humans experience stress and imbalances through lack of attention to mental and physical states when meeting work and life demands. This research proposes to improve health and wellbeing in individuals through awareness of their personal rhythms, which are repeated cycles of internal and external events including biological, mental, social, and environmental read more...

MoodRing: Mobile Application Detecting Changes in Adolescent Depression

Adolescent depression and suicide are increasingly prominent health issues that require thorough diagnosis, assessment and treatment initiatives by pediatric health care. Minimal reassessment and follow up care of adolescents’ depression can lead to worsening symptoms, increased costs to the child’s health, and increased healthcare utilization read more...

Predicting the Probability of Readmission After Cancer Surgery with LSTM

Hospital readmissions cost the US healthcare system billions of dollars annually, are associated with high mortality rates and are a source of stress and suffering for patients and family members. Traditional approaches to readmission risk stratification rely on static administrative and medical record data and generally classify all surgical oncology patients at high-risk. However, different factors related to daily behavior and activities may contribute to or signal increased or decreased risk of readmission. Our research read more...

Connected Steps

Physical inactivity and social isolation are growing epidemics linked to increased morbidity and mortality, particularly among aging Americans. This research aims to address both problems by encouraging co-productive physical activities, specifically walking together. We aim to build an intelligent technological system read more...

People

Afsaneh Doryab

Principal Investigator

For more information, please visit my FACULTY PAGE


Email: ad4ks@virginia.edu

Runze Yan

PhD Student

Runze is working towards developing a computational framework for automated discovery and modeling of human rhythms. This project aims to understand the complex principles behind the physical and psychological abnormalities of human bodies, to plan life schedules, and avoid persisting fatigue and mood and sleep alterations due to the desynchronization of those rhythms.

Personal site: https://runz96.github.io

Email: ry4jr@virginia.edu

Sid Shenoy

PhD Student 

My research is focused on Human-Robot-Interaction using emotion recognition and adaptation in humanoid robots. I am particularly interested in using these adaptive humanoid robots for applications such as pain management in children, mental health wellbeing, and fighting loneliness. I use reinforcement learning methods along with facial expression recognition and voice quality analysis for emotion recognition. With the help of these adaptation methods, I hope to explore the capabilities of these robots for detecting unique individual patterns and behaviors for improvement in health outcomes and positive interactions with members of our community. In my free time, I am interested in filmmaking and participating in debates.

Email: sks6bu@virginia.edu

Tahsin Mullick

PhD Student 

My research interest lies in the confluence of artificial intelligence and human mental health analysis and prediction. My current research looks into the role of passive mobile and fitness sensors in developing the MoodRing application for detecting and predicting changes in adolescent depression. The aim of the project is to enable patients, caregivers, and parents with early intervention alerts to prevent the worsening of symptoms. This project is a reflection of the lab's goals to promote health and well being through intelligent learning of patterns and trends.

Personal site: https://tahsinmullick.github.io/

Email: tum7q@virginia.edu

Matt Landers

PhD Student 

I am interested in modeling for complex, real-world systems. My research focuses on machine learning techniques for times series anomaly detection and risk prediction. I am especially interested in machine learning methods that can account for domain-based constraints or other types of domain knowledge; for example, in the identification and treatment of Parkinson's Disease, hospital readmission prediction, and in other health care applications. I also research techniques for reducing bias in models deployed in consequential real-world settings.

Personal site: https://mattlanders.net

Email: qwp4pk@virginia.edu

Matt Clark

PhD Student 

My research focuses on developing technology dedicated to supporting people's well-being, focusing on mental health and social wellness. To accomplish this, I hope to develop and utilize technology capable of detecting human emotion through sensors, machine learning, and human computer interaction. I plan to use these technologies to help people better understand their mental health and to communicate with others.

Email: wdd6ts@virginia.edu

Staff

Anna Barrick, Research Coordinator

Alex Zummo, Research Programmer

Yusheng Jiang, Research Programmer

Undergraduate Students

Alex Schmid Tyler Lynch Lauren Manuel Nicole Casco

Alumni

Kiara Gan Yillin Huang Rachel Lew Jessie Li Youran Wu Jaijai Liang