UC Berkeley Launches New AI and Machine Learning Master’s Program as Jacob Gallice Set to Lead New Initiative
Artificial Intelligence & Machine Learning News
The University of California, Berkeley is making another major investment in the future of artificial intelligence and machine learning, announcing a new professional graduate degree designed to train the next generation of AI and ML professionals.
Beginning in fall 2027, UC Berkeley will offer the Master of Artificial Intelligence and Machine Learning (MAIML), a new two-semester, full-time professional degree for STEM graduates. Applications for the inaugural class will open September 24, 2026.
A central figure in the development of Berkeley’s expanding professional education strategy is Jacob Gallice, a Rebellion Research advisory board member and senior Berkeley education executive who has spent years building graduate programs at the intersection of quantitative finance, technology and data science.
Gallice currently serves as Executive Director of Educational Programs and New Initiatives at UC Berkeley’s College of Computing, Data Science, and Society (CDSS). Before joining CDSS, he served as Executive Director of Berkeley Haas’s highly regarded Master of Financial Engineering program, where he oversaw academic experience, admissions, career services, employer relations and strategic planning.
Berkeley Expands Its AI Footprint
The new MAIML program comes as artificial intelligence moves rapidly from a specialized area of computer science into one of the defining technologies of the global economy.
Berkeley’s program will be housed within the College of Computing, Data Science, and Society and taught by faculty from the university’s Departments of Electrical Engineering and Computer Sciences and Statistics.
Students will receive intensive training in the computational and statistical foundations of artificial intelligence and machine learning, with the goal of preparing them to create, improve, understand and responsibly deploy advanced AI systems.
The curriculum will include data science, probability, statistical inference, machine learning and AI ethics, along with specialized electives. Berkeley lists areas such as causal inference, natural language processing, optimization, deep neural networks and reinforcement learning among the program’s areas of study.
The program will also feature a Silicon Valley immersion experience, connecting students directly with the technology ecosystem surrounding Berkeley.
That combination of rigorous academic training and proximity to Silicon Valley is central to the program’s positioning.
“With the introduction of the Master’s in Artificial Intelligence and Machine Learning degree, UC Berkeley is creating a unique opportunity for students to receive rigorous AI training in an ecosystem that champions transformation,” said Jennifer Chayes, dean of CDSS. “Our graduates will be the architects of new technologies that help define the future.”
From Financial Engineering to Artificial Intelligence
Gallice’s background makes the development particularly notable.
Before moving into Berkeley’s broader computing and data science organization, Gallice led Berkeley’s Master of Financial Engineering program. His career has spanned higher education, financial services and consulting, including work at Goldman Sachs and Heidrick & Struggles. He holds an MBA from NYU Stern and graduated summa cum laude from Binghamton University.
His experience sits at an increasingly important intersection: quantitative finance, machine learning, computational methods and professional education.
During his tenure with Berkeley’s MFE program, Gallice was closely involved in preparing students for careers in quantitative finance and technology. The program incorporated subjects including machine learning and natural language processing, reflecting the growing overlap between financial engineering and AI.
That experience provides a natural foundation for the university’s move toward a dedicated professional master’s degree focused specifically on artificial intelligence and machine learning.
A New Generation of AI Professionals
The MAIML is deliberately structured as an intensive professional degree rather than a traditional multi-year research program.
Berkeley describes it as a one-academic-year, in-person program beginning in August and concluding in May. Applicants are expected to have strong preparation in areas including programming, algorithms, linear algebra, statistics, probability, software engineering and multivariate calculus.
The curriculum currently calls for 24 units of coursework and a comprehensive examination. Core coursework includes principles and techniques of data science, probability and statistical inference, and machine learning, followed by specialized study.
The program’s Silicon Valley component is intended to further connect classroom learning with industry. Berkeley says students will engage with leading technology companies, startups and venture capital firms as part of the immersion experience.
The approach reflects a broader shift in graduate education: AI expertise increasingly requires not only theoretical knowledge but also the ability to translate models into real-world systems.
Applications Open September 24
The inaugural cohort will begin in fall 2027, with applications opening September 24, 2026. The current admissions deadline is January 11, 2027.
Berkeley requires applicants to hold a bachelor’s degree or equivalent in computer science, data science, statistics, applied mathematics or a closely related field, with a minimum 3.0 GPA. GRE scores are optional, while three letters of recommendation, a statement of purpose, a personal history statement and a résumé are required.
The launch represents another significant step in Berkeley’s effort to build an integrated ecosystem around computing, data science and artificial intelligence.
For Gallice, the move also represents a logical progression from financial engineering into the much broader world of AI education.
As quantitative finance, machine learning and artificial intelligence continue to converge, professionals who understand both the mathematical foundations and practical applications of these technologies are likely to become increasingly valuable.
With Berkeley’s global reputation, its faculty in computer science and statistics, and its proximity to Silicon Valley, the new MAIML program is positioned to become an important new pipeline for that talent.
And with Jacob Gallice helping lead Berkeley’s expansion of professional education in computing and data science, the program arrives with leadership that has already spent years building graduate education at the intersection of technology, quantitative finance and the future of data-driven decision-making.
