CS Degree Curriculum Design
As Head of the Software Engineering track at HIT, I developed a new CS curriculum proposal centered on Innovation-First Learning principles.
Curriculum Development
I do my best to align CS curricula with the real world, based on three decades of hiring, mentoring, and leading graduates in industry.
Curriculum Development
B.Sc. Concentration Programs
Six third-year specialization tracks built on a shared CS foundation at HIT, each turning broad CS knowledge into job-ready expertise with a portfolio.
CS Graduate Readiness
I established and led an industry advisory board to define practical readiness profiles for graduate roles, including AI engineer and full-stack developer tracks.
Innovation-First Learning
Innovation-First Learning bridges traditional CS education and current AI practice through deep theory, modern tools, and project work.
CS Elective Design Guidelines
A five-criteria framework for evaluating advanced computer science electives by depth, industry relevance, system building, communication, and consolidation.
Talks and Presentations
Would I Hire My Own Graduates?
A talk on Innovation-First Learning for job-ready CS graduates in the AI era. Drawing on three decades spent on parallel academia and industry tracks, it traces the growing disconnect between the two, how the software engineering role is shifting, and what over 100 student projects show about the response.
CS Elective Courses Refresh
A talk on redesigning the CS elective track. It diagnoses where the current electives fall short, no clear prerequisite sequence, overlapping content that earns credit twice, and a narrow focus that leaves role gaps, then argues for electives that give students the complete, end-to-end toolkit industry expects.
Course Development
Generative AI: From Variational Autoencoders to World Models
A 13-week graduate course on generative modeling, spanning variational autoencoders, diffusion models, and world models.
Large Language Models and Agentic AI
A 13-week graduate course on large language models and agentic AI, covering modern LLM architectures, alignment, tool use, and multi-agent systems.
Deep Generative Models for Audio-Visual Data
A 13-week graduate course on deep generative models for audio-visual data, covering VAEs, GANs, diffusion and the Stable Diffusion family, and synthetic data generation.
Large Language Model for Natural Language Processing
A 13-week undergraduate course applying large language models to NLP tasks with a code-first approach, spanning foundation models, fine-tuning, retrieval-augmented generation, and agentic AI.
Deep Generative Models for Visual Data
A 13-week undergraduate course on deep generative models for visual data, covering VAEs, GANs, diffusion and the Stable Diffusion family, and synthetic data generation.
Images and Vision: From Pixels to Deep Learning
A 13-week undergraduate course tracing computer vision from image formation and classical image processing through classical vision and 3D reconstruction to deep learning for detection and segmentation, taught with a code-first approach.