Asim Smailagic
Research Professor, Electrical and Computer Engineering
Courtesy Appointment, Computer Science Department
Asim Smailagic is a research professor in the Department of Electrical and Computer Engineering. His research is in the area of computer systems, and he is the director of the Laboratory for Interactive Computer Systems and Wearable Computers.
Education
1984 Ph.D., Computer Science, University of Sarajevo
1976 MS, Computer Science, University of Sarajevo
1973 BS, Electrical Engineering, University of Sarajevo
Affiliations
Research Interests
- AI and ML for security
- Applications of security and privacy
- biometrics
- computer engineering
- context-aware computing
- cyber physical systems security and privacy
- data security and privacy
- embedded systems security
- emerging applications security
- hardware security
- Internet of Things (IoT)
- intrusion and anomaly detection and prevention
- IoT security and privacy
- manufacturing security
- ML and AI
- mobile and app security and privacy
- network security
- privacy
- privacy engineering
- security of AI and ML
- software security
- systems security
- usable privacy and security
- virtual coaches
- web security
Media mentions
ECN Magazine
Noh and Smailagic improve medical image recognition AI
CEE’s Hae Young Noh and the Engineering Research Accelerator’s Asim Smailagic have developed an active learning technique to significantly reduce the amount of data and training images required to train image recognition systems for medical diagnostics.
MedAL: Accurate and Robust Deep Active Learning for Medical Image Analysis
Smailagic and Noh win Best Paper Award at IEEE Conference on Machine Learning and Applications
ECE's Asim Smailagic and CEE's Hae Young Noh won the Best Paper Award at the IEEE Conference on Machine Learning and Applications for their work on MedAL, an active learning approach that overcomes the limitations of traditional artificially intelligent medical imaging technologies.
CMU Engineering
Recognizing disease using less data
Artificially intelligent medical imaging technology is a promising tool to help doctors diagnose disease. CMU researchers have found a way to use less data to train these tools.