My junior year led me to reconsider my academic interests and adopt a more interdisciplinary academic plan. I am still studying computer science and statistics, and I have greatly enjoyed the contrast between these two fields and their approaches to problem solving. Following my sophomore year, I had the privilege to participate in the Undergraduate Program in Neural Computation at the Center for the Neural Basis of Cognition, a joint cognitive science institute between Carnegie Mellon University and the University of Pittsburgh. I worked with another student under Professor Robert Kass, a joint professor of statistics and machine learning. During this work, I analyzed neural data recorded from mouse brains using dense arrays of probes through the visual cortex and related areas of the thalamus. I discovered some interesting dynamics exhibited by these populations of neurons in response to various types of visual stimuli, and I learned quite a bit about statistical properties of machine learning techniques in the process. Before the summer of 2021, I intended to pursue a PhD in some deep learning subfield with an emphasis on creating trustworthy deep neural networks. After a closer experience with neuroscience, I have the same general interest, but I would like to approach the problem from a different angle: instead of developing analytical and machine learning techniques to characterize the vulnerability of deep neural networks to malicious inputs, I am now motivated to seek inspiration for new neural network architectures by studying biological brains and understanding their dynamics better. Other students in this summer program informed me of faculty who do exactly this, and I currently intend to pursue a PhD under the supervision of such a person.
Sophomore Year in Review
Last summer, I was able to participate in an NSF REU program at California State Polytechnic University, Pomona, where I first encountered machine learning. Under the guidance of two wonderful professors, I began work on a project in adversarial machine learning, generating
printable patches to fool image classification networks. This experience provided me with many new skills, from analyzing cutting-edge research papers and understanding deep learning architectures well enough to exploit their weaknesses to working in Linux and executing code on
an HPC. My supervisors and I hope to publish a full paper on this work before the end of the summer of 2021. I will also work with a student in this summer’s REU cohort to further develop the project and start a new one.
Beyond extracurricular research, I also began research under Professor Raghu Machiraju this year. The research is part of a project funded by the National Science Foundation called Autonomous Computing and Memory Materials. The lab incorporates students and faculty from multiple universities to investigate the potential for neuromorphic computers, which are synthetic computers characterized by key properties from biological brains that allow for high-power computation with incredible energy efficiency. In the lab, I have worked to implement a machine learning model to analyze neural data from rats.
In addition to research, I am deepening my knowledge of the intersection of neuroscience and computing through coursework this semester. In the spring of 2021 I was enrolled in Quantitative Neuroscience, a course covering the mathematical foundations of neuroscience from differential
equation-based models like the Hodgkin-Huxley model to the use of dynamical systems methods to interpret brain data. In addition, I took a small graduate seminar in decision science taught by Professor Roger Ratcliff, the creator of the Drift Diffusion Model of decision making. Later in the semester, that course was taken over by the neurosurgeon Professor Ammar Shaikhouni who detailed new data collection methods and discussed how they are used for diagnosis and research. These courses have already expanded my understanding of the brain, and I am excited to apply this knowledge in future courses and research.
G.O.A.L.S.
Global Awareness
Many different media exist through which students may cultivate global awareness and understanding of cultures which are very fundamentally differ from their own; academic courses, participation in extracurricular activities, and immersive experiences in other countries can all contribute to one’s sense of worldliness and ability to assimilate knowledge of customs previously unknown into one’s perception of the world. Having experienced other cultures in all of these ways, I intend to most actively engage with further immersion. I was given the opportunity to study abroad in Spain during the summer after my junior year of high school, and my understanding of the world and the role of the United States within it dramatically changed during my visit despite its brevity.
Original Inquiry
At this point in my life, I have never been involved in any original creative inquiry. This is one of my deepest regrets and is one of my primary sources of excitement regarding being in a college environment. I plan to pursue creative inquiry on multiple fronts; whether most of that inquiry occurs in the form of creative projects, assisting members of other disciplines with their research, or conducting research within the field of computer science is yet to be determined. However, regardless of the relative sizes of research and side projects in extending my participation in the field of computer science outside of the classroom, I hope to participate in some research regarding animation and the creation of accurate simulations in addition to working on some film projects or video game.
Academic Enrichment
Currently, I am taking courses that afford me a high level of latitude within my major. I am currently not entirely certain of my major path; I am exploring the implications of majoring in Computer and Information Science as well as Data Analytics, and while both fields are appealing for different reasons, I have found some clarity so far through speaking with older students and attending career-related events. I have chosen to continue my math education in the case of a choice to work more extensively with 3D systems or data visualization, and I am taking a further economics class in the case of choosing to use computer science or data analysis techniques in the context of conducting market research or aiding companies through consulting. Finally, I am exploring ancient Greece through literature to provide contrast to my other courses and because I am deeply interested in the formation of the hero cycle and other staples of modern literature within pages written by the ancient Greeks.
Leadership Development
I have not had many opportunities to pursue leadership roles at this point. However, I plan to pursue leadership roles in the future so that I may facilitate collaboration between groups in order to align their visions. I lead by sharing my ideas and by encouraging others to share and pursue theirs, and by understanding the space in which student organizations operate, I hope to make effective and lasting contributions.
Service Engagement
I have joined an organization which joins technology and activism to the end of combating the opioid epidemic within central Ohio, and I hope that this organization will expose me to the nature of student organizations on campus in the coming months so that I may more effectively organize around my ideas in the future.
Career
Summer 2020: NSF REU Site for Big Data Security and Privacy at Cal Poly Pomona
Following my first year at OSU, I had the privilege of participating in a National Science Foundation Research Experiences for Undergraduates program at Cal Poly Pomona. With seven other undergraduate students from New Jersey to California, I participated in a short course regarding principles of responsible information handling, machine learning, and modern encryption schemes before embarking on an eight-week research project with the objective of applying adversarial machine learning to computer vision. I worked with Drs. Hao Ji and Tingting Chen to create a pipeline for generating small printable patches that can fool deep neural network-based object classifiers, a process that involves generating a 3D digital representation of the target object, perturbing color data on an area of the object that the classifier perceives as highly characteristic of that object, and realizing these perturbed regions with a color printer to fool classifiers with real images of physical objects. This was my first experience with machine learning and computer vision techniques and with technical research, and following the experience, I am motivated to continue to pursue research. Drs. Ji and Chen and I continue to develop this project, and we hope to publish a full paper detailing our results in the spring of 2021.
Fall 2020: IDEAS Lab
At the outset of my second year at OSU, I became involved with a multi-institution collaborative project with the goal of creating a new paradigm for computing inspired by biological brains. I am overseen at OSU, the unifying data analysis arm of the project, by Dr. Raghu Machiraju, and we work to synthesize experimental results from a phononics lab at UC Boulder, a calcium brain imaging lab at UCSD, a photonics lab at MIT, and a nanoparticle simulation lab at Johns Hopkins University. I work with Dr. Rigoberto Hernandez as well as a collection of graduate and undergraduate students to interpret the results of simulations involving gold nanoparticles wrapped with polymer linkers that are subjected to electric fields. We hope to develop a model for interpreting the system’s states to store and process information.
About Me
Clay Washington is a second-year Eminence Fellow at the Ohio State University. He attended a suburban high school in Canton, Ohio, and he is enamored by the near-endless opportunities presented by Ohio State’s vibrant urban campus. He is a double major in Statistics and Computer and Information Science and is interested in computer vision, neuromorphic computing, and the application of materials science to data storage.