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Faculty + Staff

 Ghassan (Gus) Azar
P 248.204.3659
E gazar@ltu.edu
O MAATHCS S116B

Ghassan (Gus) Azar

Math + Computer Science
Professor, Associate Chair of Math and Computer Science

Office hours by appointment

Azar has been a member of the Mathematics and Computer Science since fall 2000. He has more than 25 years of experience in the computing field. Azar established Computer Software, Inc. in 1992 managing up to 15 consultants. CSI grossed more than $1 million a year providing services for BellSouth, Electronic Data Systems, Ford Motor Company, IBM, Pacific Bell, Southern Bell, Visteon, American Express and an international union.

Azar earned his bachelor's degree from Wayne State University in 1982, his master's degree in 1986, and a doctorate in Computer Science in 1992.. His research interests are Artificial Intelligence, Parallel Processing, Data Warehousing and Data Mining.

He recently earned a Master's of Social Work, specializing in mental health and individual therapy, from the University of Michigan.

Azar’s research interests are:

1) Develop a semi-automated system to help users in classifying mental health illness.

In societies, where there is abundance of qualified and educated classifiers in medical diagnosis, biases lead to inaccurate classification in general and specifically for mental health illnesses [1]. Mentally ill individuals are judged by the way they look and act without deep understanding of their behaviors and symptoms [2]. This phenomenon occurs for two reasons. First, the pressure applied by providers and insurance companies to diagnose individuals within the first 30 minutes of their initial visit to obtain and lock insurance coverage. Hence, insurance coverage may sway a classifier’s decision to secure specific number of treatment sessions for a patient. The second is the lack of knowledge that therapists and/or psychiatrists possess to weed through the many mental health illnesses identified in [3]. Improper diagnosis leads to prescribing and dispensing wrong medications and delivering wrong therapeutic solutions to a client. This improper diagnosis, at worst cases, may lead to irreversible deterioration in the client’s mental health status including hospitalization and/or pre-mature death.

We have identified a specific problem in diagnosing and classifying individuals in the mental health field [3]. In less fortunate countries where there is a shortage of qualified and educated classifiers, the classification problem or the lack thereof, is multiplied several folds. The issue at hand is having unqualified individuals performing diagnosis and classification of mentally ill individuals.

References:

[1] J. Shedler, “The illusion of Mental Health,” American Psychologist, Vol 48 No 11 1117-1131, 1993.

[2] S. Fernando, “Mental Health in a Multi-Ethnic Society,” A Multi-Disciplinary Handbook, Routledge

[3] American Psychiatric Association. [DSM-IV-TR], (2000). Diagnostic and statistical manual of mental disorders (Revised 4th ed.). Washington, DC: Author

2) Brain simulation:

Like the fictional brain in a vat, artificial minds are useless if they are not connected to the real world. Embodied cognitive systems require bodies and worlds where they can interact, explore, and learn. An open framework which connects minds to worlds is therefore of paramount importance. This undertaking involves both building the framework, and building an initial cognitive system which can be instantiated on demand. The Piagetian Autonomous Modeler is a cognitive system architecture which constructs an internal model of its world in the form of a database of neural propositions. The architecture is compatible with Global Workspace Theory and Society of Mind Theory, and is primarily inspired by:


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