- Ph.D. in Computer Science, University of Virginia
- Master of Computer Science, University of Virginia
- B.S. in Computer Science and Engineering, Bangladesh University of Engineering and Technology
Ubiquitous and Intelligent Sensing (UIS Lab)
Areas of Expertise:
- Pervasive and Ubiquitous Computing
- Machine Learning
- Internet of Things (IoT)
- Human Centric Computing and Sensing
- Wireless, Connected, and Mobile Health.
I am directing the Laboratory for Ubiquitous and Intelligent Sensing (UIS Lab) at Syracuse University. My research takes a multi-disciplinary approach to develop novel and practical human event sensing technologies that capture observable low-level physical signals from human bodies and surrounding environments and employ new machine learning, signal processing, and natural language processing techniques to rectify the existing sensing technologies. My research exquisition goes beyond the conventional learning or sensing approaches and addresses the research challenges, such as the uncertainties in physical world sensing, interpretability of ML inference, human factors such as the user-context and mobility, limitation of current technologies (i.e., IoT, CPS), and resource constraints of the sensing data and computation platform. A core focus of my research program is to integrate passive sensing and interpretable AI to advance human health assessment, identify latent markers, and automate health monitoring and interventions. Major ongoing funded research projects are (1) NSF SCH (Medium): Psychophysiological Sensing to Enhance Mindfulness-Based Interventions for Self-Regulation of Opioid Cravings, (2) NIH R021 and NIH R-01: Understanding speech, speech-motor-control, and emotional process in early childhood stuttering, (3) NSF CPS (Small): Developing a Socio-Psychological CPS for the Health and Wellness of Dairy Cows.
Honors and Awards:
- IAAI Deployed Application Award, The Thirty-Third Annual Conference on Innovative Applications of Artificial Intelligence (IAAI-21)
- Graduate Student Award for Outstanding Research, Department of Computer Science, UVA, 2018
- Nominated for the best paper award (AsthmaGuide), Wireless health 2016
- Harshit Sharma, Yi Xiao, Victoria Tumanova, Asif Salekin, “Psychophysiological Arousal in Young Children Who Stutter: An Interpretable AI Approach”, Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT), 2022. (and Ubicomp 2022)
- Jingyu Xin, Vir V. Phoha, Asif Salekin, “Combating False Data Injection Attacks on Human-Centric Sensing Applications”, Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT), 2022. (and Ubicomp 2022)
- Cramer et al., “Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States”, The Proceedings of the National Academy of Sciences USA (PNAS), 2022.
- Fatih Altay, Guillermo Ramón Sánchez, Yanli James, Stephen V. Faraone, Senem Velipasalar, Asif Salekin. Preclinical Stage Alzheimer’s Disease Detection Using Magnetic Resonance Image Scans, The Thirty-Third Annual Conference on Innovative Applications of Artificial Intelligence (IAAI-21).
- Tianjia He, Lin Zhang, Fanxin Kong, and Asif Salekin. Exploring Inherent Sensor Redundancy for Automotive Anomaly Detection, The 57th Design Automation Conference (DAC), 2020.
- Salekin, Jeremy W. Eberle, Jeffrey J. Glenn, Bethany A. Teachman, and John A. Stankovic. 2018. A Weakly Supervised Learning Framework for Detecting Social Anxiety and Depression, ACM Interactive, Mobile, Wearable, and Ubiquitous Technologies (IMWUT), Vol. 2, No. 2, Article 81 (June 2018), 26 pages. (and Ubicomp 2018)
- Salekin, Z. Chen, M. Ahmed, J. Lach, D. Metz, K. de la Haye, B. Bell, and J. Stankovic, Distance Emotion Recognition, ACM Interactive, Mobile, Wearable, and Ubiquitous Technologies (IMWUT), Vol. 1, Issue 3, Sept. 2017, 96:1-96:24 (Ubicomp 2017)