PSL 2025 Cognitive Science, Statement of Objectives

Statement of Objectives

How can we propagate breakthroughs in the scientific community to the real world? With the explosion of data in neuroscience, how can we help fields outside of computational neuroscience and cognitive science make sense of this information? Inspired by these questions, my current research focuses on improving how people interact with brain data. Specifically, I apply visualization and machine learning methods to reduce the complexity of neuroscientific data, making it easier for users to explore and understand brain functions. By helping users better interpret this data, I aim to support informed decision-making in various fields, from education to healthcare. It is with these broad goals in mind that I am applying to pursue a Master’s Degree in Cognitive Science at ENS-PSL & EHESS.

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-driven learning have converged to shape a clear vision for my future in research. After years of teaching myself programming, building projects, I have discovered that my true passion lies at the intersection of neuroscience and computation. These cumulative experiences solidified my decision to pursue graduate studies in computational neuroscience, where I can apply my technical skills to understanding the brain’s complex networks. I have been inspired by leading researchers whose work exemplifies the marriage of computation and neuroscience. Dr. Étienne Koechlin’s pioneering investigations into higher-order cognitive function are a prime example. His research mapping how the frontal lobe governs complex decision-making and reasoning – and how this understanding guides both fundamental neuroscience and applied domains like artificial intelligence – resonates deeply with my own goals. It demonstrates how computational modeling and experimental data can be combined to illuminate the neural basis of cognition. This alignment between Dr. Koechlin’s work and my interests reassures me that I am on the right path, and it motivates me to contribute new insights to the field. Specifically, I am eager to join the Network Dynamics and Computations Lab because its research focus aligns perfectly with what I hope to pursue. The lab’s mission to understand how thousands of neurons work together to produce behavior – by combining mathematical modeling of cortical networks with analysis of neural activity – fits my desire to bridge theoretical models with real neural data. In such a setting, I aim to refine my skills in computational modeling and data analysis, and ultimately develop tools that help explain how our brains compute the algorithms of mind.

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. With no formal computer science education available 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 graduate studies, I aim to pursue a career in academia, so that I can develop the research and tools to address these challenges and more. Furthering my education at ENS-PSL & EHESS would bring me one step closer to my goal of advancing brain–computer interfaces to assist people with neurological disorders and uncover patterns in neural data that advance our understanding of cognition in a wide range of fields and improving equal access to educational resources for students like me in marginalized groups.