A Multidisciplinary Journey in AI

My journey started with a deep interest in robots and how they move. This curiosity led me to study Electrical Engineering for my first Master’s degree, focusing on control systems – the complex parts that make robots work.

But, I found myself drawn to the world of mathematics. After spending a year learning about fascinating topics like real analysis and optimization, I fell in love with the beauty and strength of math. This love inspired me to start a Ph.D. in Applied Mathematics at UMBC, guided by Professor Jinglai Shen.

During my Ph.D., I was drawn to the exciting area of machine learning. I gained extensive knowledge in computer vision while working under the guidance of Professor Hamed Pirsiavash, who advised me for my second master’s degree. Together, we developed the ASNI algorithm, a novel method for compressing deep neural networks. This project ultimately formed the basis of my second Master’s degree.

The concept of enhancing deep neural networks’ efficiency with fewer parameters, known as sparse optimization, greatly intrigued me. However, understanding it from a mathematical perspective was quite challenging. To tackle this, I began exploring it from different angles, particularly through mathematics and statistics. The development of algorithms for training neural networks often involves using the stochastic gradient descent algorithm. Eager to comprehend its mechanics, I embarked on my third Master’s degree. This pursuit was to deepen my understanding of data handling and the learning process.

I consider myself fortunate to have had the opportunity to teach Basic Statistics at Towson University. This experience allowed me to reinforce my knowledge in areas such as point estimation, sample distribution, bootstrapping, confidence intervals, and hypothesis testing. In my view, teaching helps organize your thoughts and provides various perspectives to your audience, leading to a deeper grasp of the subject. This is why I regard myself as extremely lucky to have also taught Linear Algebra at UMBC. Teaching this subject granted me invaluable insight into Linear Algebra, the essential language of machine learning.

My journey through different subjects wasn’t just by chance. I believe that to really get AI and make it better, you need to see the big picture and know how different subjects connect with each other.

I’m now at a place where electrical engineering, mathematics, and statistics merge. My educational journey includes three Master’s degrees: one in Electrical Engineering where I focused on control systems, a second in Electrical Engineering aimed at making deep neural networks more efficient, and a third in Statistics. Currently, I’m in the final stages of my Ph.D. in Applied Mathematics, focusing on sparse optimization and algorithm development. This wide-ranging educational path reflects my deep interest in fully understanding the areas important for artificial intelligence. I believe that creating great algorithms requires an understanding of how different fields connect. Armed with a strong foundation in mathematics, electrical engineering, and statistics, I am ready to contribute significantly to the future of AI.

All my studies have given me a unique set of skills that help me face the challenges and opportunities in the field of deep learning.

With my varied background and a strong belief in working together, I see myself doing well in a place that values teamwork across different fields. I’m excited to join others in pushing the limits of what we can do in this fascinating area.

My story is about never stopping learning and exploring different subjects. I hope it inspires others to see how connecting different areas of knowledge can lead to new discoveries and innovations.