Fady Alajaji
Scholar

Fady Alajaji

Google Scholar ID: xWJVF30AAAAJ
Queen's University
Information TheoryCommunicationsJoint source-channel codingPolya contagion networksMachine learning
Citations & Impact
All-time
Citations
1,135
 
H-index
16
 
i10-index
27
 
Publications
20
 
Co-authors
20
list available
Contact
No contact links provided.
Resume (English only)
Academic Achievements
  • Research publications cover Shannon coding theorems, capacity, error exponents, feedback, joint source-channel coding, data compression and quantization, error-control codes, communications, channels with memory, digital-analog signaling, network epidemics, contagion processes, Polya urns, opinion dynamics, and information-theoretic methods in machine learning: information bottleneck, data privacy, AI fairness, generative-adversarial networks.
Research Experience
  • Professor in the Department of Mathematics and Statistics at Queen's University. Research focuses on Communications and Information Theory, Applied Mathematics.
Background
  • Primary research interests lie in the area of information theory and communications, specifically coding of signals for transmission over noisy networks. Also interested in Shannon theoretic aspects, effective coding techniques, information bottleneck problems, data privacy, and the use of information-theoretic methods in machine learning, such as generative-adversarial networks and AI fairness. Long-standing interest in probability problems and their applications, including random processes with reinforcement, contagion models, generalized Polya urns, stochastic modeling and analysis of network epidemics, game theory, opinion dynamics, and bounds for the probability of a union of events under partial information.
Miscellany
  • Additional interests include long-term engagement with probability problems and their applications, like reinforced random processes, contagion models, generalized Polya urns, stochastic modeling and analysis of network epidemics, game theory, opinion dynamics, and efficient bounds for the probability of a union of events given partial information.