Teaching & Speaking
Graduate teaching, engineering instruction, curriculum development, and invited presentations in applied AI.
I teach machine learning as an engineering discipline. I begin with the mathematical foundations, then carry models through data design, evaluation, deployment, and communication with the people who will use them.
CS3 Research Experience for Teachers
Main engineering instructor
For three annual cohorts of the NSF Center for Smart Streetscapes Research Experience for Teachers (RET), I have designed and led the core engineering curriculum for K-12 STEM educators. The four-week program turns current work in computer vision, urban data, and generative AI into material that teachers can adapt for their own classrooms.
ML foundations
Neural networks, backpropagation, training workflows, validation, and model evaluation.
Computer vision
Convolutional networks, object detection and segmentation, tracking, annotation, and dataset design.
Deployable AI
YOLO training, Colab workflows, CVAT, edge deployment, Raspberry Pi systems, and reproducible experiments.
Language systems
Language model foundations, zero-shot methods, generative AI, and responsible classroom use.
Engineering materials
The local curriculum archive preserves the instructor materials used across the 2024 and 2025 cohorts.
Classroom outcomes
Participating teachers developed complete lesson plans that connect AI engineering to science, mathematics, civics, and career education.
2025 lesson plans
2024 lesson plans
Columbia courses
Advanced Deep Learning
An advanced, implementation-centered course on modern visual recognition. Students moved from evaluation and segmentation through two-stage and one-stage detection, tracking, transformers, vision-language models, diffusion, and open-vocabulary systems.
Sequence: Metrics and segmentation · R-CNN, FPN, Mask R-CNN, PointRend · SSD and YOLO · graph embeddings · SORT, ByteTrack, BoT-SORT, DeepSORT · ViT, DETR, Swin, Segmenter · CLIP · diffusion and ControlNet · OWL-ViT · Segment Anything · Generative Agents
Neural Networks & Deep Learning
A theory-to-practice introduction to deep learning, covering mathematical foundations, implementation, model design, and the empirical habits needed to train and evaluate neural networks well.
Sequence: Machine learning review · feedforward networks and backpropagation · optimization · convolutional networks · regularization · recurrent networks · autoencoders · GANs · VAEs · current research directions
I also co-taught Columbia Video Network’s VOAI 0003E: Neural Networks and Deep Learning for the AI Executive Certificate in Fall 2022 and Summer 2023, and previously supported Columbia courses in neural computation and computational neuroscience.
Selected presentations
From Sensors to Systems: Real-Time Traffic Analysis for Faster Decision-Making
Panelist at the CS3 Innovation Summit, discussing how camera, mobility, and model outputs become operational urban intelligence.
Port Authority Tomorrow Summit workshop
I hosted a technical workshop at the Port Authority of New York and New Jersey headquarters as part of its annual Tomorrow Summit, which recognizes technology pilots demonstrated during the previous year.
Evaluating Micromobility & Dangerous Riding Behaviors
Presentation at the Eighth Annual Vision Zero Research on the Road event on using street camera analytics to study micromobility and risky riding behavior.