Center for AI Business Innovation
Shaping the future of business management, process automation and product development by leveraging the latest developments in artificial intelligence.
The University at Buffalo School of Management’s Center for AI Business Innovation bridges the gap between cutting-edge AI technologies and real-world business challenges, breaking down the barrier between today’s research and tomorrow’s solutions. With an emphasis on training and development, we empower local businesses, communities and students by providing access to AI tools, research and consulting opportunities.
The center stands as a hub for transformative education, innovative thought leadership and meaningful engagement in the Western New York region and beyond. Whether you're looking to embrace AI solutions for your business or expand your understanding of this rapidly evolving field, the Center for AI Business Innovation is your partner in success.
Choose from a suite of services that drive growth and innovation:
Sharpen your skills with tailored programs that introduce businesses and community members to the power of artificial intelligence.
At the Center for AI Business Innovation, education is at the heart of what we do. From foundational learning to advanced applications, our programs ensure you and your organization are prepared to thrive in the AI age:
- AI courses and seminars – Explore the principles of AI and learn how these technologies are reshaping industries
- Executive education programs – Integrate AI into your organization’s strategic vision
- Support for research – Access resources and guidance for AI-driven research projects that advance knowledge and innovation, whether you are a student or a professional
Work with one of our teams of highly skilled students who provide hands-on support to implement AI solutions for your business problems.
What is your challenge? Maybe it’s an inefficient process, a project that is just not getting done, or perhaps it’s something else keeping you up at night? Our student-run consulting teams can help. School of Management students are learning state-of-the-art techniques and are eager to apply those skills to add value to your organization. If you’re interested, complete the project form, and we’ll contact you for details.
Stay ahead of the curve with insights and tools that can transform your business processes.
Whether you need advice, research support, or just some advice and datasets, we’ve got you covered. Contact us and let us know what you need. We will set up some time to understand your needs and explore how we can help.
Partner with leading academics, students and industry experts to tackle the challenges of the AI-driven business landscape, or collaborate with us on a grant opportunity. Contact us and let us know what you need. We will set up some time to explore how we can help.
Learn from your peers through industry events and conferences, focusing on sharing lessons across this rapidly growing field. Check back periodically to learn more about our knowledge-sharing events, including expert speakers, hands-on workshops and upskilling bootcamps.
Our center members are working on cutting-edge AI and machine learning techniques, and strive to apply them to solve real-world business problems. We publish our research in top tier journals and showcase it at premier conferences in both computer science and management science, such as ICML, NeuriPS, KDD, COLT and ISR. We emphasize on the following research areas.
Federated learning (FL), a machine learning framework designed to protect data privacy, has gained significant attention from researchers in recent years. Unlike traditional centralized approaches, FL enables multiple clients to collaboratively train a global model without sharing raw data, thereby preserving privacy and compliance with data governance policies.
Each client independently updates the global model received from the server using its local data and then shares the updated model parameters with the server. The server aggregates these updates to produce a new global model, which is subsequently broadcast to the clients for the next training round. FL has been recognized as an effective method for incentivizing data sharing, which helps address the data availability challenges critical in the era of large language models (LLMs).
By enabling distributed training while respecting privacy, FL fosters collaboration across organizations, institutions and devices, where data cannot be directly pooled due to privacy or regulatory constraints. Despite its promise, FL has several limitations. Its convergence speed and final performance often fall short compared to centralized methods, even when client data is independently and identically distributed (i.i.d.). These challenges are magnified when applying FL to deeper neural networks, which demand more robust optimization techniques.
Another significant issue is data heterogeneity: in many real-world scenarios, client data is non-i.i.d., leading to imbalances that degrade the performance of the global model. Conventional FL methods struggle to handle such variability effectively, resulting in reduced model accuracy and stability. To address these challenges, our center has been actively developing innovative solutions both theoretically and experimentally. We are exploring advanced optimization algorithms to accelerate convergence, strategies to mitigate the impact of non-i.i.d. data, and frameworks tailored for large-scale, deep neural networks in federated settings. These efforts aim to push the boundaries of FL, making it more robust, efficient, and adaptable to diverse real-world applications.
The use of large-language model agents in social simulations has emerged as a trending research topic, driven by advancements in generative AI. Unlike traditional rule-based agent modeling, employing LLMs as agents offers significantly greater flexibility. This approach is also widely regarded as a means of verifying and enhancing LLMs’ ability to perform human-like, deliberate reasoning.
A key question in agent modeling is how accurately these models mirror real-world scenarios. Some studies have demonstrated LLMs' capability to replicate basic human behaviors and reasoning. However, issues such as data contamination and value misalignment may introduce unwanted biases, causing LLMs to become overly familiar with or skewed toward the problems being studied.
These biases can compromise the quality of social simulations, particularly in complex, long-term scenarios where higher-order interactions — such as cooperation, confrontation, deception and persuasion — play a central role. In contrast, our ongoing research emphasizes that in social simulations, agents should operate independently of prior assumptions. Instead, they must focus on contextual factors and actively adapt their actions based on historical interactions, ensuring greater realism and robustness in simulating social dynamics.
Participatory budgeting is a decision-making process in which public resources are allocated based on the preferences of individuals within a community. Traditionally, most studies in this field rely on an additive utility function, where each individual assigns a private utility value to every candidate project, and the total utility of a funded subset is the simple sum of these values.
While this additive model simplifies both analysis and implementation, it often fails to capture the complexities of real-world scenarios. For example, building two playgrounds in the same neighborhood may not yield twice the utility of building a single playground, due to diminishing returns or overlapping benefits. To address these limitations, we broaden the framework of participatory budgeting by introducing general utility functions.