Education
Teaching Experience
Instructor of Record
Developed complete syllabi, lecture notes, discussion pages, and assignments to facilitate courses ranging from 15 to 140 students. Presented lectures, from asynchronous online to fully in-person, on linear algebra, probability, statistics, data analysis, and differential equations.
In this two-week pre-college program, students work in groups to construct and present a data analysis project which collects, organizes, cleanses, and visualizes a dataset of their choosing. Topics include exploratory data analysis, data visualization, probability distributions, data scraping and cleansing, the basics of hypothesis testing, and regression modeling.
An introduction to the basic concepts of linear algebra, matrix theory, and differential equations that are used widely in modern engineering and science.
Course Development
Contributed to the design and development of new mathematics and engineering courses. Authored original lecture material, guided Excel practice problems, engaging problem sets, and instructional resources tailored to enhance pedagogical clarity and student engagement.
Collaborated with the Director of Online Programs to develop a comprehensive College Algebra course aimed at preparing incoming students for success in higher-level mathematics. Designed instructional content to reinforce key algebraic concepts through accessible and engaging materials.
Developed and launched a summer course for high school students introducing the fundamentals of data analysis, probability, and statistics. Encouraged students to master affective presentation skills and collaborative work. Produced a full suite of materials, including lecture videos, online quizzes, interactive assignments, and guided Excel tutorials, delivered to over 50 students annually.
Teaching Assistant
Supported instruction in twenty undergraduate and graduate-level courses through grading, writing lecture notes, designing assignments, leading weekly discussion sections, and providing academic support to students. Collaborated with faculty to reinforce core course concepts and foster a strong learning environment.
Direct Reading Program Mentees and Presentations
Paired with up to three undergraduate students, mentoring a self-contained course on gradient descent, its convergence theory, and its guarantees. Wrote a series of lecture notes and exercises for students who were, in addition to weekly hour one-on-one sessions, expected to prepare a presentation to conclude the semester.
Service
Performed review of the structure and design of the graduate program in the applied math and statistics department. Participated in discussion about course structure, qualifying exams, and various requirements for incoming and current Ph.D. students.
Participated in a group of faculty, post-doctorates, graduate students, and undergraduate student workers to research, develop, and implement AI into pedagogy. With the goal to enhance student learning, I beta-tested various course-specific AI tools, provided feedback, and integrated into various Johns Hopkins courses' LMS. Further work included providing feedback on textbook manuscripts about the use, benefits and fallback, of AI for students, instructors, and researchers.
Research Interests
Conducting theoretical research on algorithm design and analysis to unify the regimes between smooth and nonsmooth convex problem classes (e.g. function exhibiting Hölder smoothness or uniform convexity). Prior work focused on heterogeneously smooth and convex compositions, calculus results expanding and characterizing dual notations between Hölder smoothness and uniform convexity, interpolation theory for inexactly smooth convex functions, performance estimation over respective problem classes, and universal algorithm design. Future work entails characterizing the class of minimax optimal methods for convex Lipschitz minimization.
Experimentation and implementation of various AI derived course assistants. Herein, we discuss, analyze, and test different methods for embedding the rising LLM tools into the education system. By supplying agents with structured prompts, focused on providing students with motivation and step-by-step guidance instead of direct answers, we aim to improve comprehension and intellectual capability.
Preparing to investigate spectral properties of the Discrete Fourier Transform and its connections to signal representation and harmonic analysis. Further investigation will include advancing understanding of the Fractional Fourier Transform, smoothly interpolating between the signal and frequency domains.