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FALL 2025 Senior Project Descriptions

Professor: Zhigang Zhu

BEAT+: Adding Branding and Entrepreneurship for Real-World Applications Using Emerging Technologies: AI, AR, AT, and Apps

Undergraduate Seniors in CS and CpE will work in teams to design emerging technical solutions in Artificial Intelligence (AI), Augmented and Virtual Reality (AR/VR), Assistive Technology (AT), data analytics and app development skills to solve real-world application problems for social good and national priorities. Teams will learn basic branding principles (how to design user interfaces and communicate with end users and community partners about their technical solutions) and basics of entrepreneurship (customer discovery, business models, etc). Teams will be encouraged to participate in academic research projects, the CCNY Zahn Innovation competition and NYSID’s CREATE competition. We will invite collaborators from both academia and industry as the BEAT+ mentors and evaluators. Students are expected to learn new software/hardware tools and skills by doing their projects.

Professor: Huy Vo

This course provides students with technical preparations for defining projects and crafting a strategy to identify user requirements, possibilities for new designs and engineering solutions to support their project objectives. Topics in data science will be recommended; however, students are welcome to propose their own projects. Students are encouraged to strengthen their skills in full-stack, web and/or mobile development throughout the course. At the end of Senior Project I, students are expected to form teams, complete a project proposal, and a mock-up/prototype of the product with a clear timeline of deliverables. A prototype must be completed by the end of Senior Project II.

Professor: Jianting Zhang

GPU-Accelerated Data Science

With fast increasing availability of Graphics Processing Units (GPUs) in data centers and personal computing systems, there are significant interests to adopt the massively data parallel computing power on GPUs for Data Science, including data management, data analytics, machine learning and AI applications.

In the two-semester project-oriented capstone courses, the first semester (Capstone I) will introduce the basics of GPU hardware, the leading data parallel programming model, i.e., Nvidia CUDA, its integration with Python and various applications in Data Science. Small project templates will be provided to students to gain hand-on experiences in GPU programming using C++ and Python Interfaces. Students are expected to develop a group-based project proposal that can focus either on computing efficiency or domain-specific applications. The proposal will be implemented in the second semester (Capstone II).

Students are expected to have reasonable skills in C++ programming (from CSc 212) and basic knowledge of Python-based Data Science software (from CSc 113 or self-learning); but more importantly, enthusiasm in parallel computing and its realizable efficiency in practical applications.

Instructor: Thomas Sessa

This section of Senior Project I, led by an industry executive with deep expertise in healthcare data, offers students the unique opportunity to explore real-world challenges and collaborate with key stakeholders, including sponsors, internship providers, and industry leaders in the healthcare sector. The course will equip students with the technical skills necessary to define complex problems, develop strategic solutions, assess available and needed datasets, and create innovative engineering designs to address project goals. Through case studies and hands-on projects, the course will place a strong emphasis on solving healthcare-related challenges—ranging from epidemiology and public health to patient access, equity, and population health.

By the end of Senior Project I, students will work in teams to craft a comprehensive project proposal that includes a detailed timeline and clear deliverables, setting the stage for successful project execution.

While the majority of suggested projects will focus on healthcare challenges (especially those impacting NYC), students are encouraged to propose their own project ideas. To ensure that each proposal aligns with the course’s technological objectives, all projects must be thoroughly discussed and approved by the instructor before moving forward.