Prof. Demetrios Kazakos
Texas Southern University, USA

Title: Artificial Intelligence for Bioinformatics and Housing Development.

Abstract: Artificial Intelligence (AI) can be applied in both bioinformatics and housing development, though the approaches and specific applications vary across these fields.

AI in Bioinformatics
In bioinformatics, AI, particularly machine learning (ML) and deep learning, plays a transformative role in analyzing complex biological data. Here are some core applications: Genomic Analysis: AI can analyze vast genomic datasets to identify genetic markers for diseases, understand mutations, and predict disease susceptibility. Deep learning models, for example, are applied in identifying gene expressions that correlate with specific diseases. Protein Structure Prediction: Tools like AlphaFold by DeepMind predict protein structures, a key component in drug discovery and understanding cellular processes. Accurate protein folding predictions have far-reaching implications for medical research and treatment development. Drug Discovery and Development: AI models analyze biological data and predict how new drugs interact with human targets, improving lead compound identification, reducing the time to market, and optimizing the drug development process. Personalized Medicine: AI can help in developing tailored treatments by analyzing an individual’s genetic information, lifestyle, and medical history, predicting the most effective treatments for each person. Imaging and Diagnostics: Machine learning algorithms are used to analyze medical images (e.g., MRIs, CT scans) to diagnose conditions such as cancer or neurodegenerative diseases early on.

AI in Housing Development
AI in housing development focuses on improving the efficiency of construction processes, managing housing markets, and creating smart living spaces. Key applications include: Predictive Analytics for Real Estate: AI models analyze real estate trends, economic indicators, and buyer demographics to forecast housing prices and market demand. This helps developers and investors make better decisions on where and when to build. Automated Design and Planning: Generative design algorithms can optimize floor plans, energy usage, and material costs by simulating thousands of designs and selecting the best options based on developer and client requirements. Smart Cities and Sustainable Building: AI can optimize energy use, manage waste, and improve water usage in smart buildings and communities. Sensors and IoT (Internet of Things) devices collect data, while AI models adjust resource allocation dynamically. Construction Robotics and Automation: AI-powered robots can handle repetitive or hazardous tasks on construction sites, such as bricklaying, painting, and welding, improving efficiency and reducing human risk. Risk Management and Quality Control: AI can assess risks in construction projects, predicting issues that may arise due to weather, supply chain disruptions, or labor shortages, ensuring timely and quality project delivery.

BIO: Prof. Demetrios Kazakos received his Diploma in Electrical and Mechanical Engineering from the National Polytechnic University of Greece. He then started graduate his graduate studies in the United States. He received a Master of Arts degree in Electrical Engineering from Princeton University and a Doctor of Philosophy degree from the University of Southern California, specializing in Statistical Communication Theory. In 1980, he joined the Electrical Engineering Department of the University of Virginia,where he stayed until 1993.
In 1992, he was elevated to the grade of Fellow of IEEE, for his research in two areas: Enhanced Algorithms for Multiuser Multiaccess Networks and Statistical Pattern Recognition. In 2009, he was elevated to the grade of IEEE Life Fellow.
In 1993 he accepted the position of Head of the Electrical and Computer Engineering of the University of Southwestern Louisiana. At the same time he has always been a very active participant in IEEE conference organizing and editorial activities. He was Editor of the IEEE Transactions on Communications for 5 years, Technical Program Chair for two major IEEE Conferences, and member of the Technical Program Committee for several IEEE and other conferences.
In 1983 he started a new company named HITEC, INC, which undertook several Research and Development projects in Information Technology, funded by the U.S. Department of Defense and the European Community.
In 2001, he undertook the position of Professor and Chair of the Electrical Engineering and Computer Science Department at the University of Toledo. In 2004, he moved to the University of Idaho, as Professor and Chair of the Electrical and Computer Engineering Department.
From 2006 to 2008, he was Dean of the College of Science and Technology at Texas Southern University. From September 2009 to September 2011, he was at the National Science Foundation in the position of
Program Director responsible for the Program: “Centers of Research Excellence in Science and Technology”.
Overall, he has published about 165 refereed journal papers, book chapters and conference proceeding papers, as well as two books.