Alaa Sheta | Artificial Intelligence | Best Researcher Award

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Alaa Sheta | Artificial Intelligence | Best Researcher Award

Prof Dr Alaa Sheta Southern Connecticut State University, United States

He earned his Ph.D. from the School of Information Technology and Engineering at George Mason University in Fairfax, Virginia, USA, completing his studies from August 1994 to August 1997. His dissertation, titled “Identification and Control of Dynamical Systems Using Genetic Algorithms,” reflects his expertise in artificial intelligence, machine learning, and computational intelligence. During his academic journey, he worked under the co-direction of Prof. Kenneth DeJong and Prof. Ophir Frieder. With a diverse range of research interests, his contributions span across data science, digital image processing, data mining, software engineering, software reliability modeling, software cost estimation, identification and control, autonomous systems, robotics, information systems, computer vision, and computer architecture. His comprehensive knowledge and research in these domains showcase his dedication to advancing technology and understanding complex systems.

Education:

He holds a Ph.D. from the School of Information Technology and Engineering at George Mason University in Fairfax, Virginia, USA, where he conducted groundbreaking research on “Identification and Control of Dynamical Systems Using Genetic Algorithms” from August 1994 to August 1997, under the co-direction of Prof. Kenneth DeJong and Prof. Ophir Frieder. Prior to his doctoral studies, he earned an M.Sc. from the Electronics and Communication Engineering Department at Cairo University, Cairo, Egypt, specializing in “Robust Controller Design of Large Scale Uncertain Dynamical Systems” from August 1989 to August 1994, under the guidance of Prof. Mohamed F. Hassan. His academic journey began with a B.E. from the same department at Cairo University, covering the period from August 1983 to July 1988. His educational background reflects a deep commitment to advancing knowledge in the fields of information technology, engineering, and control systems.

🌐 Profile Links:

Experience:

He currently holds the position of Tenured Professor in the Computer Science Department within the College of Arts and Sciences at Southern Connecticut State University since the fall of 2022. Prior to this, he served as an Associate Professor in the same department from the fall of 2019 to the fall of 2021. His academic journey includes roles as an Assistant Professor and later as a Professional Assistant Professor in the Department of Computing Science at Texas A&M University-Corpus Christi, where he contributed from 2017 to the summer of 2019 and from 2016 to 2017, respectively. Before joining Texas A&M, he served as a Professor and Department Chair in the Computers and Systems Department at the Electronics Research Institute (ERI) under the Ministry of Scientific Research in Cairo, Egypt, from 2015 to 2016. His extensive experience and leadership roles underscore his commitment to advancing computer science education and research.

Honors and Awards:

He is a distinguished professional with a wealth of accomplishments in the field of technology and research. Holding the title of IEEE Senior Member since Fall 2021, he has made significant contributions to the realm of sleep apnea diagnosis, garnering attention for his innovative work. The Southern News featured his pioneering approach in an article titled “Sleep Apnea Diagnosis Simplified” on November 5, 2021, highlighting the impact of his research. His expertise extends to machine learning and AI-based applications for efficient sleep apnea diagnosis, as showcased in a YouTube interview on October 25, 2021, and an insightful session at the Future Technology Conference on November 26, 2019. Notably, he received the Best Paper Award at the 2nd International Conference Europe Middle East & North Africa Information Systems and Technologies (EMENA-ISTL2018) in Fez, Morocco, in October 2018. His accolades also include the Best Citation Prize from Taif University in 2013 and the Best Poster Award at the SGAI International Conference on Artificial Intelligence in Cambridge, UK, in December 2011. With a rich history as a visiting scientist at esteemed institutions such as Tennessee Technological University (TTU) and DeMontfort University, he continues to make groundbreaking contributions to the intersection of technology and healthcare.

Publications:

  1. A mobile robot path planning using genetic algorithm in static environment Cited By : 317, Published By : 2008
  2. Estimation of the COCOMO model parameters using genetic algorithms for NASA software projects Cited By : 205, Published By : 2006
  3. A comparison between regression, artificial neural networks and support vector machines for predicting stock market index Cited By : 180, Published By : 2015
  4. Time-series forecasting using GA-tuned radial basis functions Cited By : 143, Published By : 2001
  5. A novel meta-heuristic search algorithm for solving optimization problems: capuchin search algorithm Cited By : 137, Published By : 2021
  6. Image enhancement using particle swarm optimization Cited By : 107, Published By : 2007
  7. Prediction of software reliability: A comparison between regression and neural network non-parametric models Cited By : 104, Published By : 2001
  8. Business Intelligence and Performance Management – Theory, Systems and Industrial Applications Cited By : 90, Published By : 2013
  9. Development of software effort and schedule estimation models using soft computing techniques Cited By : 81, Published By : 2008
  10. Energy optimization in wireless sensor networks using a hybrid k-means PSO clustering algorithm Cited By : 80, Published By : 2016

 

 

Mohammad Khalooei | Robust Machine learning

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Mohammad Khalooei – Leading Researcher in Robust Machine learning

Congratulations, Mr Mohammad Khalooei, on winning the esteemed Best Researcher Award from ResearchW! Your dedication, innovative research, and scholarly contributions have truly made a significant impact in your field. Your commitment to advancing knowledge and pushing the boundaries of research is commendable. Here’s to your continued success in shaping the future of academia and making invaluable contributions to your field. Well done!

Mr Mohammad Khalooei, a distinguished academic and researcher in the field of renewable energy, Academician/Research Scholar Amirkabir University of Technology, Iran. His academic journey has been marked by a profound dedication to advancing Web Analytics and Metrics, specifically in Big Data Analytics, Block Chain Technologies, Machine Learning into Social Media and Digital Marketing applications.

📚 Education:

He is currently a Ph.D. candidate in Computer Engineering (Artificial Intelligence) at Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran, a position he has held since 2017. Under the supervision of Prof. Mohammad Mehdi Homayounpour and Dr. Maryam Amirmazlaghani, he is actively engaged in advancing his research in the field of Artificial Intelligence. Prior to this, he earned his MSc in Computer Engineering (Artificial Intelligence) from the same institution, graduating in 2017 with an outstanding GPA of 18.79/20. During his master’s program, he worked under the guidance of Prof. Dr. Mohammad Fakhredanesh and Dr. Mohammad Sabokrou, focusing on dominant and rare event detection and localization in video using deep learning methods. His remarkable academic achievements were acknowledged with the first rank among all computer engineering students at the university since 2015.

Before pursuing his master’s degree, he completed his BSc in Computer Engineering (Software Engineering) at Vali-e-Asr University of Rafsanjan, Kerman, Iran, from 2011 to 2015. With a GPA of 17.54/20, he excelled in his academic endeavors. His undergraduate thesis, conducted under the supervision of Dr. Seyyed Mojtaba Sabbagh, Mr. Alireza Mohammadi, and Mis. Fahimeh Dabbaghi, involved replacing the Differential Evolution, Particle Swarm Optimization, and an adaptive Markov Chain Monte Carlo simulation algorithms in the Hydrus modeling project. Notably, he achieved the top position among all computer engineering students at the university from 2011 onwards.

His educational journey began with a Diploma in Mathematics and Physics at Shohada10 High School in Kerman, Iran, where he completed his studies from 1997 to 2011.

🌐 Profile Links:

Awards and Honors:

2020 Full scholarship award at CIFAR Deep Learning and Reinforcement Learning Summer School hosted by CIFAR and Mila, with participation and support from Amii and the Vector Institute.  2020 Invited to attend the Microsoft Research Frontiers in Machine Learning 2020 virtual event. 2020 Reviewer of the International Conference on Learning Representations (ICLR).  2020 Reviewer of the International Conference on Machine Learning (ICML). 2018 Outstanding contribution award in reviewing Elsevier Pattern Recognition Letters Journal, International Association for Pattern Recognition. 2018 Best Hackathon potential Idea award at “Vision is APP to you,” Achieving the best idea of Apple Foundation Program certificate, Italy. 2018 Full scholarship award at Machine and Vision Intelligence school, International Association for Pattern Recognition, Under supervision of Prof. Alfredo Petrosino, Italy. 2017 1st of 12th, Achieving the highest GPA among all Artificial intelligence students at MUT, Tehran, IRAN.

Experience: 

Fall 2019 Teacher Assistant, Speech and audio Signal Processing, Amirkabir University of Technology (Tehran Polytechnic), Under the supervision of Prof. Mohammad Mehdi Homayounpour. Fall 2018 Teacher Assistant, Statistical Machine Learning, Amirkabir University of Technology (Tehran Polytechnic), Under the supervision of Dr. Ahmad Nickabadi.  2019 Lecturer, Robustness of Deep Neural Networks, Sharif University of Technology, Winter Seminar Series (WSS) workshop. Spring 2019 Teacher Assistant, Convex Optimization, Amirkabir University of Technology (Tehran Polytechnic), Under the supervision of Dr. Maryam Amirmazlaghani. 2019 Speaker, Adversarial Machine learning, Amirkabir University of Technology (Tehran Polytechnic), AAIC competition workshop. Fall 2018 Teacher Assistant, Statistical Machine Learning, Amirkabir University of Technology (Tehran Polytechnic), Under the supervision of Dr. Ahmad Nickabadi. 🗣 2018 Speaker, Machine learning in a life of points landscape, Amirkabir University of Technology (Tehran Polytechnic).

Research Experiences:

2017-Now Theoretical and practical research on defending against Adversarial Attack towards Deep Neural Networks, Laboratory of Intelligence and Multimedia Processing, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran. 2017 Theoretical research on Optimization of Generative Adversarial Networks, Laboratory of Intelligence and Multimedia Processing, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran. 2017 Practical research on Self-Attention Generative Adversarial Network, Laboratory of Intelligence and Multimedia Processing, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran. 2017 Practical research on hot topic of Neural network papers (CycleGAN, UNet), Laboratory of Intelligence and Multimedia Processing, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran.

Project Experiences: 🗣

2019 Generating Adversarial Examples for Speech Recognition, Laboratory of Intelligence and Multimedia Processing, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran. 2019 Representational space visualization, Laboratory of Intelligence and Multimedia Processing, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran. 2019 Chief Technology Officer (CTO), 5th Amirkabir Artificial Intelligence competitions, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran. 2019 Artificial Intelligence and the Future of Public Policy of IRAN, National Elites Foundation, Tehran, Iran.

Computer Skills:

  • Languages: Python, Java, C/C++, C#, MATLAB, Linux Shell Script. 🛡
  • Adversarial ML: AdverTorch, CleverHans, Foolbox, IBM-ART. 🤖
  • Deep learning: PyTorch, TensorFlow, Keras. 🌐 Large scale ML: Apache Mahout, Spark MLlib.