Research Areas

My research interests are driven by a passion for pushing the boundaries of artificial intelligence and optimization techniques. At the forefront of my academic pursuits are Machine Learning and Deep Learning, where I continuously strive to develop cutting-edge algorithms that can learn from data and make intelligent decisions. These techniques have transformative potential across various domains, from computer vision and image processing to robotics and generative AI. My research delves into creating models that can efficiently process vast amounts of data and uncover complex patterns to drive innovation and solve real-world problems.

The fascinating realm of Generative AI captivates my curiosity. Here, I explore the development of advanced generative models that can create synthetic data resembling the characteristics of real-world datasets. This has significant implications in various fields, such as generating realistic images and videos, data augmentation, and even creating artistic works. By pushing the boundaries of Generative AI, I aim to unlock the potential of these models for creative and practical applications, leading to novel solutions and insights.

In the domain of Computer Vision, I am dedicated to enhancing the interpretation and understanding of visual data through machine learning techniques. My research revolves around developing state-of-the-art algorithms for object recognition, image segmentation, and unpaired image translation. These advancements in Computer Vision have wide-ranging applications, from medical imaging and surveillance to augmented reality. By empowering computers to perceive and interpret visual information with human-like proficiency, we can revolutionize industries and improve the quality of life.

Beyond machine learning and computer vision, my interests extend into Evolutionary Multiobjective Optimization. I focus on designing algorithms that can optimize multiple, often conflicting, objectives simultaneously. This approach yields a set of diverse and optimal solutions, known as the Pareto front, and has applications in engineering, finance, and resource management. I aim to harness Evolutionary Multiobjective Optimization to tackle complex, multi-dimensional problems and find robust and efficient solutions in real-world scenarios.

Lastly, my fascination with Quantum-Inspired Optimization stems from the potential of quantum mechanics principles to revolutionize optimization algorithms. I explore how quantum-inspired techniques can surpass classical approaches in solving computationally challenging problems. By harnessing quantum-inspired optimization, I believe we can unlock novel solutions in various critical fields, including Machine Learning Model Training, feature selection, drug discovery, Supply Chain Management, and Financial Portfolio Optimization.

In pursuit of these research interests, I am committed to fostering a collaborative and innovative environment. I aspire to mentor students and collaborate with peers to propel the frontiers of artificial intelligence, optimization, and computer vision. Through interdisciplinary research and creative problem-solving, I aim to create a lasting impact on society by advancing technology and addressing global challenges. As an academician and researcher, I look forward to contributing to the ever-evolving landscape of technology and inspiring the next generation of innovators.