Mike Terekhov
MS CS @ University of Southern California
Recent college graduate from Boston University with a B.S. in mechanical engineering with a concentration in machine learning and currently pursuing a M.S. in computer science at the University of Southern California
Current Research Projects
HIV Research AI Regimen Project
I am developing an AI-powered clinical decision tool using Retrieval-Augmented Generation (RAG) to optimize Antiretroviral Therapy recommendations for HIV patients globally. My project features a round-table style AI framework where multiple AI agents simulate perspectives of virologists, clinicians, and pharmacists to collaboratively refine treatments. I am implementing agentic workflows with Agents, Planning, and Reasoning Chains to analyze drug-mutation interactions and generate personalized treatment recommendations.
Vision Transformer Research
I am exploring methods to detect voids in Composite Oriented Strand Boards using machine learning, aiming for up to 95% accuracy. I formulated and implemented a Vision Transformer architecture with an encoder and 8 attention heads to explore advanced image representation techniques. My approach includes a preprocessing pipeline that segments 128×128 images into 16×16 patches for the Vision Transformer, enabling efficient processing of micro-CT scan data for void detection.
Previous Research Projects
AI vs Human Text Classifier
In this project, my team and I set out to explore whether machine learning models could effectively distinguish between human-written and AI-generated high school essays. We gathered a diverse dataset that included original human essays, AI-generated essays using models like GPT-4, GPT-2, Mistral, and Gemma, as well as both AI- and human-paraphrased texts. We applied classical text analysis methods such as Bag of Words, TF-IDF, and SVMs, alongside a neural network using GloVe embeddings. While traditional methods performed well on standard AI text, we found that they struggled with identifying AI-paraphrased versions of human essays. However, our GloVe-based model showed strong performance even in these more nuanced cases. Through this work, we demonstrated both the potential and the challenges in detecting AI-generated content, particularly as language models become more adept at mimicking human writing.
View PaperCourses Taken
Computer Science
Natural Language Processing
Reinforcement Learning
Software Engineering
Machine Learning
Advanced Algorithms
Advanced Computer Vision
Computer Security Systems
Networks
Mechanical Engineering
Mechanics of Materials
Manufacturing
Instrumentation
Statics
Mechanics
Heat Transfer
Thermodynamics
Fluid Mechanics