I am making an application to study Artificial Neural Networks (ANN) Masters/Ph.D. program in Computer Science at the University of Texas. My core interest behind the application and interest in exploring this program is cementing my career focused on the implementation of artificial intelligence and researching neural networks. With the highly evolving technology, I believe that taking ANN from the University of Texas will be an added advantage to aid in my career development and come up with innovations.

Central passion in ANN developed at one instance when I got a car wreck, which led to a change in careers. Although the car wreck was gruesome, I decided to embrace it as a transition opportunity from a computer operator into a programmer of the same. The transition took a diversion when my medical phases turned to the worse upon a change in treatments. The situation turned out more severe and complication when my doctors and the medical team informed me that it would not be possible to work anymore. At this moment in life, I was not able to read even a paragraph or sum up other routine chores. I was also not able to complete my school assignments as expected and achieve the grades I wanted. However, the hard time I went through and the obstacles I encountered turned out to be motivation roots in my transition phase. I decided to take numerous courses, including psychology, neuroscience, and artificial intelligence. This finally led to the personal grounding of interest on the affiliation thriving between artificial neural networks and the human way of thinking. After several years of struggling with my condition of mental health care, I noticed that artificial brains had the capability of doing much more of what humans could do, which changed the course of thoughts about the human brain. Upon full recovery from my condition, I decided to take full attention in doing research work and noting the implementation aspects associated with ANN, which led to a personal interest development to study the same at higher levels.

I have a job experience that forms some touches and reasons to study ANN from the University of Texas. Much of this experience evolved in the military, where I was able to attain numerous achievements and extensive experiences. Some of these achievements and experiences comprise achieving the top academic score in unmanned aerial vehicles training, emerging as the top 20 percent in Warrior Leadership Course, and gaining an intense experience in the operation of aerial vehicles. I was able to advance my physical fitness for 24 months to arrive at an extended scale through consistent hard work. I was also able to receive an opportunity to rehabilitate other soldiers who were struggling to pass their physical fitness test. These awards and experiences have crucial elements behind my capability in being a leader and team member. My real-world training and extensive experiences with several unmanned vehicles will be essential to me in advancing my ability in the construction of intelligence systems.

After leaving the military, I have exposed myself to other lighter courses due to my condition. I was not able to load or venture into more intense opportunities at it was the situation before the occurrence of the wreck. However, I have been able to learn some basic computer science programs, including Java programming, C++, python, data structures, algorithms and complexity, parallel programming, and matrix math. I have also been able to venture into the field of research by helping one of my professors in the Unity game engine based research project. I have also assisted in the development of mobile virtual reality shooter game with Unity and programmed object-oriented space invaders in Java. Additionally, I have programmed a deep q network for the visdoom setting and enter numerous kaggle competitions using python’s tensorflow/keras library. Further, I have exercised intense python programming, parallel programming, and data cleaning numpy. With these foundations in programming, I have high expectations that I would greatly benefit and learn more from advanced courses in the ANN program from the University of Texas.

The core drive behind my application to the University of Texas is to do more advanced research matching my central interest in ANN. Initially, I was drawn to Dr. Risto Miikkulainen’s research, with the primary objective being to understand the biological thought and development of advanced neuro evolutionally algorithms. This research focuses on studying some crucial research questions that I want to scrutinize more about. This incorporates the evolvement of intelligent agents in fitting their settings, why biological agents have the capability of learning distinct tasks best at distinct periods in their life, and how “backpropagation” might work with neuroevolution. Additionally, I also would like to have a more profound understanding of why distinct neurons enable various tasks in artificial intelligence and why the human brain evolves more complex neurons, unlike other artificial neural networks. Numerous project engagement in the Neural Networks research group forms a cradle of my interest, with the inclusion of A Neuroevolution Approach to General Atari Game playing, using neural networks in the determination of how the word definitions vary across sentences, and the coevolution of competitive and cooperative agent behavior. I am more excited about the development of a technology that shall lead to the modeling of human behavior based on the creation of better settings for people to thrive.

Further exploration of artificial intelligence research at the University of Texas has led to the rooting of more interest in related projects to the same. The Machine Learning Group, under the leadership of Ph.D. Raymond J. Mooney, incorporated various aspects, including using natural language for reward shaping in reinforcement learning. This aspect led to an exposure to the diverse facets applicable to shaping an agent’s behavior. In addition, the Computer Vision Group under the leadership of Ph.D. Kristen Gruman’s focused on Im2Flow: Motion Hallucination from Static Images for Action Recognition that initially used a neural network in the detection of motion and using the same in the prediction of the action. I am confident that exposure to any research experience linked to ANN that I will source from the University of Texas’s artificial intelligence faculty will be a core foundation leading to more rewarding careers after graduation.