Describe what your organization does, and how it relates to data science.
Data science is a core component of industrial and systems engineering. Industrial and systems engineers work to design and improve processes and systems in every field imaginable. To do so, they must access data, analyze and interpret data, and make decisions based on that data.
Why is sponsoring the Research Bazaar important to you?
The relatively recent explosion of access to data is thrilling, but also daunting. It’s important for us to be both a leader and a supporter in this important field.
How has the integration of data analytics or artificial intelligence influenced the product or service offerings of your organization?
In fall of 2023, our department introduced a “Certificate in Engineering Data Analytics”. This certificate is available to undergraduates from any engineering major, and will help the College of Engineering meet the explosive demand for engineers with data science skills.
Can you share a specific instance where your organization has effectively translated data into actionable insights or outcomes?
One (of many) ways in which we engage with the community is through government entities. Much of our research applies data analysis to challenges in the public sector, which requires partnerships with local and sometimes national organizations. For example, last year one of our research teams partnered with the City of Madison Police Department to analyze the effectiveness of their response to the opioid epidemic.
Are there any notable collaborations or partnerships your organization has formed to advance data science research or applications?
As experts in this evolving field, UW-ISyE Professors Yontan Mintz and Laura Albert were featured guests in this summer’s UW-Madison Division of Information Technology (DoIT) webinar series exploring the topic of AI in an academic environment.
How does your organization approach ethical considerations and responsible use of data in your data science endeavors?
Dr. Mintz’ research focuses on the application of machine learning and automated decision making to human-sensitive contexts. He is specifically attracted to the issue of ethics and privacy implications in the rise of AI. His conclusions take into account the need to address things like the historical context of data, where data is collected, and the inherent bias that is unavoidable with the human beings behind the models.