Artificial Intelligence Programs – Pace News

The AI revolution is here, and Pace University is at the forefront. We not only prepare students to thrive in a tech-powered future; we equip them with tools and ethical frameworks to make a positive impact. With our high-tech facilities and innovative faculty research, Pace students are poised to become the next generation of leaders, innovators, and changemakers in an AI-driven workforce.
At Pace, we’re not just embracing the future—we’re helping shape it.
on the Seidenberg School of Computer Science and Information Systems curriculum
incorporate AI at the undergraduate and graduate level
faculty experts teach and research in the field of AI
Every Pace student, regardless of major, can begin their artificial intelligence studies with the curriculum’s foundational course, CIS 101, offering the basics of AI to any student, including those with no prior computer programming or data science knowledge. By instilling a solid understanding of AI from the start, our students can confidently navigate an increasingly AI-powered world.

Pace offers over 11 undergraduate and 17 graduate courses, with plans to expand the number of courses offering AI-infused curriculum. The Seidenberg School of Computer Science and Information Systems is currently planning to launch two new artificial intelligence master’s programs, a MS in AI and a MS in Applied AI. Not only can Pace students learn how to build the technology of tomorrow, but also learn how to apply it in new and innovative ways.
Get coding immediately and apply your new knowledge to portfolio-building projects that will prepare you to enter the professional world with confidence.
Learn how to program, work with big data, and apply sophisticated quantitative techniques (i.e., AI, Machine Learning, Econometrics) to answer questions in economics and business practices.
Gain the skills needed to use analytical programming languages, data science tools, and applications. Learn how to create knowledge from data.
A disgruntled hotel guest triggered a profound realization for Seidenberg grad and serial entrepreneur, Mochii De Castro. She knew she needed change–and that the Seidenberg School’s data science would be the bridge to an incredible journey into technology and artificial intelligence.
Among our faculty are many AI experts and enthusiasts engaged in cutting-edge research and seeking grants in the field. They’re bringing their knowledge into the classroom and collaborating with students and colleagues to explore innovative applications of AI.
A powerhouse in the field of AI, Sharff’s work has earned her numerous grants, and her research spans topics like Senegalese fashion, software engineering, ChatGPT in the classroom, and more.
A philosophy professor and author at Dyson College of Arts and Sciences, Brusseau is leading the conversation about ethical AI, developing workshops and publishing many papers on the topic.
Drawing on her expertise in machine learning, Shan is exploring the intersection of AI and healthcare, focusing on analyzing medical imaging for more accurate diagnoses.
The AI lab—housed in the brand new, cutting-edge building at 15 Beekman in NYC—is the central AI hub for the Pace Community, offering training and opportunities to learn, grow, and collaborate for students, faculty, and staff. In addition, the AI lab extends its training initiatives to local businesses and community members, leveraging Pace’s extensive expertise in AI research and development, which spans over 30 years.
From AI ethics and governance to social media, from pedagogy to healthcare. Our faculty, often in collaboration with students, are at the forefront of AI-related research. Together, they’re asking big questions and exploring innovative solutions, paving the way for a brighter future through the ethical application of AI.
Professor Yegin Genc, PhD, and PhD student Xing Chen’s explore the utilization of AI in clinical psychology, focusing on social media’s influence on mental health. Their findings highlight the underutilization of social media in mental health care, limited collaboration between research communities, and the need for a more human-centric approach.
D. Paul Benjamin, PhD, and PhD student Gunjan Asrani explored the diagnosis of cluttering, a fluency disorder that is often misdiagnosed. Their research utilized machine learning techniques to analyze features of patients’ speech to explore more accurate diagnostic criteria.
Pedro Vasseur, building on past study of data violations, developed a prototype designed to monitor decisions of AI systems based upon ethical frameworks, detecting violations of set ethical rules while working independently of the AI system.

Christelle Scharff, PhD, is fighting bias in AI, one garment at a time. She and a team of students expanded fashion datasets to include African items and refined AI-generated patterns, addressing AI’s tendency to favor a Western viewpoint. She emphasizes exploring diversity bias in AI and expresses optimism as AI discourse increasingly addresses these concerns.
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