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SPRING 2026 Senior Project Descriptions

Professor: George Wolberg

In this project-based course, students are grouped into teams of up to three students per group to work on building apps for iOS, Android, web, and Windows/Mac platforms. The capstone course will last two semesters. In the first semester, we will study how to build, test, and deploy mobile, web, and desktop apps from a single codebase. After fundamental principles are introduced, each team chooses one topic of interest from the fields of image processing, computer vision, computer graphics, and gaming. Groups will perform research and development to specify deliverables, milestones, and implementation considerations. Each group must research similar apps in their chosen topic and resolve how their own app will distinguish itself from the available offerings, present these findings to the class, demonstrate a deep understanding of the principles and algorithms that the app will implement, and outline a working plan to implement the software. 

In the second semester, each team will focus on implementation and presentation of ongoing results with detailed design, implementation, integration, testing, and experiment evaluation. Projects will be selected based on the interests of the students and professor. The goals of the course are to gain deeper insights into the workings of real-world software engineering, to receive valuable hands-on experience in basic research, and to better understand how to build, test, and deploy mobile, web, and desktop apps from a single codebase. 

Professor: Saptarashmi Bandyopadhyay 

AI Agents for Decision Making in the Real World: Artificial Intelligence (AI) Agents are special autonomous models that can take reasonable actions in the real world. The class objective is to design and implement Agentic Machine Learning Algorithms that allow AI to observe their environment, take an action that impacts the environment, and find qualitative and quantitative approaches (including receiving environmental feedback) to evaluate the goodness of the AI's decision making. Students will be training and building Multimodal AI Agents (e.g. Large Language Models or LLM Agents, Audio-Vision-Language Models or AVLM Agents, Robotic Agents, and other Multimodal AI Agents) in the two-semester project-oriented courses.

The first semester (Spring 2026) will introduce the basics of training, finetuning and inferencing of AI Agents. Students will be specializing in AI Agent algorithms, and will have the ability to determine when to follow a Multi-Agent AI solution vs a Single-Agent AI solution. Students will also develop the skills to understand when single-processing is more efficient vs when multi-processing and distributed processing is more effective for deploying such AI Agents to learn in real-time, while balancing system parameters like latency, energy utilization, network bandwidth utilization, reliability and effective job utilization with queuing among other computing parameters. Students will be taught AI Agent Algorithms from Deep Reinforcement Learning (RL), Multi-Agent RL, Imitation Learning, Self-Supervised Learning, Computational Game Theory, Model Predictive Control, Continual Learning and Evolutionary Learning. From these algorithms, students can specialize and create their own algorithms in a group-based project proposal that can focus on computing efficiency and domain-specific applications. 

Students are expected to develop an open-source code base, preliminary prototype Demo at the end of CSc 59866 and write a research report on their findings. This proposal prototype and Demo will be expanded further in CSc 59867 in Agentic Application Areas in the Real World. Students are expected to develop an open-source code base, prototype Demo at Technology Readiness Level (TRL) 2, 3 or 4, at the end of CSc 59867 and write an Advanced research report on their findings.

Students are expected to have reasonable skills in Python programming (either from coursework or self-taught); but more importantly, show openness in picking up new AI skills. As a part of the Demo, students may learn new programming languages like JAX, or Kotlin, based on their relevant projects. Students can email the Professor at  sbandyopadhyay@ccny.cuny.edu  to ask any questions.

Instructor: Yunhua Zhao

In this sequence, students will work toward completing a fully functional project related to deep learning. During the first semester (59866), the focus will be on learning to build a strong foundation in deep neural network (DNN) and their practical applications in language, image, code and audio processing. Students will be divided into groups, where they will select a project topic, conduct background research, and study current trends in their chosen area. Each group will then prepare and submit a detailed project proposal outlining objectives, methodology, and expected outcomes at the end of the semester. This stage ensures that students understand both the technical and contextual aspects of their project before implementation. 

In the second semester (59867), students will transition from planning to execution, developing their project entirely from scratch. This phase emphasizes hands-on design, implementation, testing, and refinement of their deep learning solution. Teams will apply the concepts and techniques learned in the first semester to build a functional system that addresses their proposed problem. By the end of the course, students are expected to deliver not only a working project but also thorough documentation and a final presentation that demonstrates both technical proficiency and critical thinking about real-world applications of deep learning.