Statement of Objectives | LMU Elite Graduate Program Software Engineering 2025 | Software Engineering Scientist

Statement of Objectives

How can we effectively transfer breakthroughs from scientific research to real-world applications? Given the rapid growth of data and complexity in software systems, how can we enable industries beyond traditional IT domains to harness and interpret this information effectively? Driven by these questions, my current work focuses on improving the usability and accessibility of complex software and neuroscientific data. Specifically, I apply visualization and machine learning methods to simplify intricate datasets, making it easier for users to explore, understand, and manage both software-intensive systems and brain functions. By facilitating clearer interpretation and more informed decision-making, my goal is to support advancements across diverse sectors, from finance and telecommunications to healthcare and cognitive sciences. Motivated by these objectives, I am applying to pursue the Elite Graduate Program in Software Engineering at the University of Augsburg, TUM, and LMU. Finding My Research Interests. With a focus on machine learning and computer vision, my research journey began under Assistant Professor Mohamed Uvaze Ahamed Ayoobkhan at New Uzbekistan University and TechVisionaries Research-Oriented Lab. Image classification results generated by machine learning algorithms often encompass extensive datasets, making it challenging for users to thoroughly interpret and analyze the factors contributing to classification outcomes. To address this complexity, we present an approach that integrates feature extraction summarization techniques into a coherent interface. This method facilitates intuitive visualization and exploration of the data subsets critical to classification accuracy. This work led to the first paper I co-authored, Machine Learning-Based Image Classification with COREL 1K Dataset (ICSDI 2024). My main contributions include investigating a range of Machine Learning (ML) techniques, such as decision trees, k-Nearest Neighbors, and Support Vector Machines (SVM), to determine their respective strengths and limitations while analyzing how feature extraction methods like Local Binary Patterns (LBP) and color histograms influenced classification accuracy. Our work experienced a few submissions. Although I easily felt discouraged at first, I learned to reflect and was encouraged by how much our work had improved after each experiment. I really enjoyed my experience in research more than the industry for the ownership over my work. But, I also had some burning questions regarding my future research interests. Meanwhile I was engaged by the technical aspects of solving real-world problems, I wanted to figure out something that would really excite me - what is the thing that would get me out of bed every morning? And how could I find it? My next project, Developing Advanced AI-Driven Medical Software for Hospitals and Clinics in CA, Uzbekistan, provided clarity on these fundamental questions. Working independently under the supervision of Dr. Mohamed Uvaze Ahamed Ayoobkhan, I aimed to tackle the significant challenge of improving the accuracy of Brain MRI diagnoses while substantially reducing analysis time and making it accessible to underserved areas. Previously, medical decisions related to MRI scans relied heavily on expert doctors, despite the substantial influx of daily patient visits generating vast amounts of data. To address this, I designed and developed CoMedAI, a user-friendly platform that integrates Convolutional Neural Networks (CNN) with Natural Language Processing (NLP) to support radiologists in making swift and accurate diagnoses. Inspired by these findings, I authored another research paper Enhancing Medical Accuracy: A Comparative Study of Fine-Tuned GPT-3.5-Turbo and GPT-4 Models, currently undergoing peer review, demonstrating the superior performance of fine-tuned models. Since its initial deployment for testing, CoMedAI has successfully secured agreements with few hospitals and clinics to utilize observational data for model training. Seeing my academic research translate into tangible, real-world impacts has significantly deepened my enthusiasm for tackling open-ended, impactful problems and motivates my pursuit of advanced formal training through graduate studies. Equal Access in STEM. Another key aspiration during my graduate studies is promoting equal access to technology and education, particularly for people from underrepresented and underserved communities. Throughout my experience conducting tutorials and workshops, I've witnessed firsthand the challenges learners face when encountering advanced concepts like AI, machine learning, and software development methodologies without prior exposure or adequate support. Recognizing that traditional educational resources often leave students confused or discouraged, I developed simplified, practical tutorials that clearly explain complex technological concepts, ensuring accessibility for learners at various skill levels. To further address these disparities, I founded Open Community, a platform dedicated to bringing together diverse individuals to collaboratively learn and innovate through accessible educational resources and supportive mentorship by leading experts. Future Work. My diverse experiences in technology and self-directed learning have converged to shape a clear vision for my future in research. After years of independently teaching myself programming and engaging in various projects, I discovered that my true passion lies at the intersection of software engineering and computational neuroscience. These cumulative experiences solidified my decision to pursue graduate studies in software engineering, where I can apply my technical skills to bridge theoretical models and real neural data, additionally developing comprehensive software systems for better data accessibility and interpretation. I have been inspired by leading researchers whose work exemplifies this integration, such as Prof. Dr. Wolfgang Reif’s pioneering contributions to formal methods, software reliability, and validation techniques. His approach - combining rigorous software specification with practical validation methods - aligns deeply with my goals and demonstrates how software engineering can enhance computational neuroscience. Specifically, I am eager to engage with the Software Engineering research groups affiliated with the Elite Program, whose emphasis on formal methods, distributed systems, and human-computer interaction aligns perfectly with my interests. Through such a program, I aim to refine my skills in model-based software engineering, validation, and data analysis, ultimately developing tools that facilitate understanding of complex software and neural systems. Where I See Myself. Growing up in a low-income family, I learned the values of perseverance and responsibility at a young age. As the oldest child, I always helped care for my siblings and contributed to our household in any way I could. Without access to formal computer science education during high school, I independently explored programming through free online resources, engaging in extensive practice sessions late at night on my mom’s old laptop. While studying full-time, I have also worked part-time as a mentor, developer educator, and freelancer. After completing graduate studies, I aim to pursue a career in academia and research, focusing on developing innovative software solutions and advancing brain-computer interfaces. This will help assist individuals with neurological disorders and facilitate uncovering patterns in neural data, advancing our understanding of cognition across various fields. Joining the Elite Graduate Program in Software Engineering at the University of Augsburg, TUM, and LMU will significantly bring me closer to achieving my goals, including improving equitable access to educational and technological resources for students from marginalized groups.