Asif Salekin
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Mail code: 9709Campus: Tempe
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Asif directs the Laboratory for Ubiquitous and Intelligent Sensing (UIS Lab) at the Ira A. Fulton Schools of Engineering, serving as an assistant professor at the School of Biological and Health Systems Engineering (SBHSE) and as a graduate faculty member in Computer Science, advising PhD students in the Computer Science program.
Asif’s research sits at the intersection of Human-Centered Computing, Machine Learning, Cyber-Physical Systems, and Usable Sensing Security and Privacy within the realm of Ubiquitous Computing, where a core focus is to integrate human-centered computing and sensing solutions to advance Smart and Mobile Health. His works have been featured in top-tier computer science venues, including IMWUT/Ubicomp, DAC, AAAI Applied Intelligence, EWSN, INTERSPEECH, etc., and prestigious journals, such as Nature Molecular Psychiatry, 2023, and PNAS 2022. Notably, one of his papers on Preclinical-stage Alzheimer’s Disease Detection received the prestigious ‘IAAI Deployed Application Award’ in 2021. In 2016, his paper, titled AsthmaGuide, was nominated for the Best Paper award at the Wireless Health 2016. He received the Graduate Student Award for Outstanding Research from the UVA CS Department in 2018.
To date, his research has been funded by two NSF grants and three NIH grants. Currently, he is serving as an Associate Editor (AE) for the journal ‘The Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT)’ and for the conference ‘UbiComp,’ which is the leading publication platform for ubiquitous computing research. He is also an Associate Editor (AE) for ‘ACM Transactions on Computing for Healthcare.’
PhD in the Department of Computer Science
University of Virginia
Advisor: Professor John A. Stankovic
Graduation: 2019
Asif's research takes a multi-disciplinary approach to developing novel and practical human-centric computing and sensing solutions. His research interests go beyond the conventional learning or sensing approaches and address the research challenges, such as:
- Investigating the natural distribution shifts within real human-centric applications, understanding their impact, and developing solutions to attain robustness against these shifts.
- Addressing issues of Fairness and bias mitigation in human-centric computing, especially in scenarios where the factors subject to inequitable treatment remain unidentified.
- Multimodal-integration and Co-teaching-based ubiquitous and human-centric computing solutions.
- Interpretability of ML inference, particularly in the context of healthcare applications.
- Scalable and compressed ubiquitous computing solutions addressing the challenges of resource-constraint edge computing platforms.
- Trustworthiness and reliability in automated human-centric sensing, computing, and actuation/intervention.
- Addressing the privacy and security challenges associated with human-centric computing, sensing, and Internet-of-things (IoT) applications.
A core focus of his research program is to integrate computing solutions to advance health assessment, identify latent markers, automate health monitoring, and facilitate automated interventions. His recent and ongoing projects include real-world health applications, such as:
- Passive assessment of mental health (e.g., depression, social anxiety disorder, PTSD, etc.) and affective states.
- Automated assessment of opioid craving in opioid-use-disorder (OUD) individuals and providing reliable, trustworthy, and just-in-time interventions to mitigate craving.
- Understanding mental, speech, and sensorimotor factors of children-stuttering and providing automated interventions.
- Automated assessment of socio-behavioral well-being of dairy cattle and providing adaptive just-in-time interventions to the farmers.
- Automated monitoring of asthma and dementia patients.
- Predicting COVID-19 mortality, risk factors, and its effect on mental health.
- Identify, visualize, and interpret health-related (for Autism, Alzheimer's, and PTSD) atypical brain-activity patterns from EEG, MRI (fMRI), and fNIRS signals, etc.
- Identifying markers of chronic kidney disease (CKD).
Check Asif's group's publication on his website: https://asalekin.github.io/#publication
Current Ph.D. Students:
- Yi Xiao (ASU CS PhD)
- Harshit Sharma (ASU CS PhD)
- Shaily Roy (ASU CS PhD)
- Sawinder Kaur (SU CS PhD)
Graduated Ph.D. Students:
- Dr. Jingyu Xin
- Brian Philip Testa (Pending under revision)