I teach machine learning and reinforcement learning at Georgia Tech with a student-first, evidence-based approach— reproducibility, diagnostics, transparent rubrics, and timely feedback at scale.
1400+ students/term. Directed 30+ TAs, revamped structure with student-centered pedagogy and TA professional development, and launched a public blog for transparency and methods.
Course pageLed multi-section operations while continuing curriculum improvements and student support. Prior TA work emphasized analysis-first approaches to policy/value methods.
Course pageLed instructional teams across both courses, continuing curriculum improvements and high-touch student support.
Designed and delivered GTA pedagogy and classroom practice foundations.
Supported instruction, assessment, and student mentoring across multiple semesters.
Facilitated hands-on learning and international cohort coordination.
Mentored incoming freshmen on innovation projects and problem-solving. Contacts: Dr. Wes Wynens, Dr. Jeff Davis (wes.wynens@gatech.edu, jeff.davis@ece.gatech.edu).
Design in service of diverse learners with transparent structures, multiple modalities, and inclusive norms.
Align outcomes, activities, and assessments (Wiggins & McTighe, 2005) and ground methods in how learning works (Ambrose et al., 2010).
High expectations paired with scaffolding, timely feedback, opportunities for recovery, and metacognitive reflection.
As IoR for CS7641 and CS7642 in OMSCS, I’ve supported thousands of graduate students. I lead large TA teams, run data-informed course ops, and maintain predictable rhythms (weekly updates, checkpoints, dependable office hours).
I teach because I believe learning is transformative. Whether in person or online, higher education is a unique space where students expand their ways of thinking, develop critical skills, and redefine their sense of what is possible. As an instructor, my core philosophy is that teaching must be designed in service of the student. This means recognizing that learners come from diverse backgrounds with varied levels of preparation, confidence, and motivation, and intentionally building pathways that support each student’s success. Effective instruction begins not with content delivery, but with a commitment to students’ growth and intellectual development.
My teaching philosophy is grounded in student-centered design and equity-minded pedagogy. I draw on the framework of backward design as articulated by Wiggins and McTighe (2005), which emphasizes identifying clear learning outcomes and then aligning instruction and assessment strategies with those goals. This model ensures that every component of a course—lectures, assignments, feedback—supports the specific skills and knowledge students are expected to develop. It also reinforces the principle that teaching should be evaluated by what students genuinely learn, not simply by what is “covered.”
Equally important is acknowledging that students do not all start in the same place. As Ambrose et al. (2010) emphasize in How Learning Works, students bring a wide range of prior knowledge, beliefs, identities, and experiences that shape their learning. My responsibility is to recognize these differences and create inclusive environments in which all students have the opportunity to thrive. This includes maintaining clear and transparent course structures, offering flexible modes of engagement, and combining foundational support with meaningful academic challenge.
My instructional approach emphasizes active learning, frequent feedback, and metacognitive reflection. I design courses around a flipped classroom model, where students engage with core materials—such as readings, video lectures, or guided walkthroughs—before synchronous sessions like class meetings or office hours. This design ensures that our live interactions focus on application, clarification, and collaborative problem-solving. In both in-person and online formats, I incorporate short in-class quizzes, breakout discussions, and structured peer activities to reinforce core concepts and promote engaged learning.
Assignments are scaffolded to support deep learning and sustained intellectual growth. Early-stage exercises provide accessible entry points into complex material, meeting students at various levels of experience. As the course progresses, students encounter layered challenges that involve synthesis, abstraction, and independent exploration. Major assignments serve as low-stakes practicums, encouraging students to apply course content to industry-relevant problems. These projects are intentionally sequenced: students move from learning how to read and critique research papers, to articulating hypotheses, designing experiments, and developing nuanced discussions grounded in theory. Graduates frequently report that this structured, open-ended project model builds both confidence and competence in their professional applications of machine learning.
Formative assessment is central to my teaching practice. I integrate frequent, low-stakes opportunities such as concept checks, self-assessments, and auto-graded quizzes to provide continuous feedback. These tools help students reflect on their progress and adjust their learning strategies. While summative assessments are more comprehensive, I design them with openness and flexibility in mind. Assignments include clearly defined goals and grading rubrics, but also leave space for creative exploration. This encourages students to go beyond minimum requirements, collaborate with peers, and engage with the teaching team—resulting in deeper, more authentic learning.
In my role as Instructor of Record for CS7641: Machine Learning and CS7642: Reinforcement Learning in Georgia Tech’s OMSCS program, I have taught and supported thousands of graduate students. These courses are integral to the Machine Learning specialization and are consistently among the largest and most active offerings at Georgia Tech, with a high volume of students progressing through them each semester. Leading these courses has deepened my understanding of how to deliver rigorous, high-quality instruction at scale while maintaining personal connection and responsiveness.
My responsibilities extend beyond teaching content. I lead large instructional teams, oversee ongoing curriculum development, and mentor new teaching assistants as they enter the program. I work closely with TAs to ensure consistency in grading and communication, and I routinely analyze student performance data to identify common challenges and adapt our support strategies. This feedback-driven process allows me to refine both course materials and the overall learning experience across diverse delivery formats and learner backgrounds.
Teaching at scale has also reinforced the importance of accessibility and inclusion. In asynchronous, online environments, students may feel disconnected from their peers and instructors. To address this, I establish clear and predictable rhythms: weekly updates, structured checkpoints, and dependable office hour availability. I also create space for student voice through discussion prompts, mid-course surveys, and reflective check-ins at the end of each module. These channels provide insight into students’ needs while reinforcing a sense of belonging and support, even in large, distributed classrooms.
At the core of my teaching philosophy is the belief that intellectual rigor and compassionate support must coexist. I design courses that are challenging but not exclusionary. Students are asked to engage with open-ended, real-world problems that require sustained effort, and I pair these expectations with timely feedback, structured scaffolding, and opportunities for recovery and reflection.
Equity and inclusion are essential to this process. I regularly review my materials and assessments to ensure clarity and fairness, and I actively seek student feedback to evaluate the effectiveness of my teaching. I apply universal design principles, providing content and assessments in multiple modalities. I strive to make expectations transparent and to demystify success by sharing models of high-quality work. I also acknowledge the challenges students may face and actively normalize help-seeking, iterative learning, and resilience.
Ultimately, I want students to leave my courses with more than technical knowledge. I hope they develop curiosity, confidence, and a capacity to solve hard problems thoughtfully. Whether they become researchers, engineers, or educators, I want them to view themselves as capable learners and agile thinkers, prepared to contribute meaningfully in their fields.
Looking ahead, I am excited to contribute this teaching philosophy to institutions that prioritize equity, innovation, and academic excellence. I seek environments where pedagogy is treated as a scholarly, collaborative process—where instructors are encouraged to reflect, adapt, and contribute to the evolving goals of the academic community. I am eager to collaborate with fellow educators, help shape inclusive curricula, and support students in developing not only mastery of content but confidence and purpose in their intellectual work.
Whether designing new courses, mentoring instructional teams, or supporting cross-departmental teaching initiatives, I am prepared to contribute meaningfully to a culture of engaged, student-centered learning. I look forward to joining a community committed to impactful teaching, continuous improvement, and transformative educational experiences.