Artificial Intelligence (AI): UIC Associate Professor helps MEng students bridge the fundamentals of AI with their engineering knowledge
Learn about Dr. Ian Kash’s computer science background and his online Master of Engineering course.
Artificial Intelligence (AI): UIC Associate Professor Dr. Ian Kash helps MEng students bridge the fundamentals of AI with their engineering knowledge Heading link
Meet Dr. Ian Kash, Associate Professor for the University of Illinois Chicago’s online Master of Engineering with a focus area in AI and Machine Learning program. Earning his Ph.D. in Computer Science from Cornell University, Dr. Kash has worked on a number of topics at the interface between computer science and economics, particularly those connected to AI and machine learning.
Prior to coming to UIC in 2018, Dr. Kash worked at the Microsoft Research Lab in Cambridge, England where he applied AI and economics techniques to a variety of problems, such as challenges that arose with their extensive advertising platform. His current work focuses on economic problems that arise in cloud computing and multi-agent reinforcement learning.
Teaching the CS 411: Artificial Intelligence course for the online MEng program, Dr. Kash uses his expertise to teach Master of Engineering students key AI tools and techniques including search, reinforcement learning, probabilistic models, and game theory. Learn more about Dr. Kash’s background and how he helps MEng students bridge AI fundamentals with their engineering knowledge in the CS 411 course.
Introduction Heading link
Tell us a little bit about yourself.
Dr. Kash started his journey into computer science research as an undergraduate where he worked on serveral projects. Continuing his research in his Ph.D. studies, he immersed himself into the application of both AI and economic methodologies to address issues that arose in distributed systems. This exploration unfolded during the heyday of file-sharing, with a particular focus on peer-to-peer systems. Dr. Kash spent seven years in Microsoft’s research division, where he strategically employed AI and economic techniques to tackle an array of challenges. Towards the end of his time there, he started to conduct research on what’s now called reinforcement learning. This is one of the topics he teaches in his CS 411: Artificial Intelligence course.
Field of AI Heading link
What keeps you excited about the fields of computer science and/or machine learning?
Dr. Kash appreciates how fast-moving the AI field is, and how every day there are new developments. He observes that there is ample space for discussing the success of different AI technologies. In his CS 411 course, Dr. Kash likes to spark discussions linking AI topics and techniques with what’s currently going on in the field of AI. He delves into students’ understanding of various AI technologies and guides them in applying that knowledge to comprehend these new emerging technologies.
CS 411 Course Heading link
What are the valuable skills/knowledge MEng students will earn in your CS 411: Artificial Intelligence course?
The CS 411 course encompasses a broad teaching of AI due to the field’s expansive nature. Dr. Kash introduces students to a number of key AI tools and techniques, plus the fundamentals of how they work. There’s an opportunity for students to then bridge the fundamentals with their engineering knowledge to understand the development of real deployed systems employing these techniques.
CS 411 Real-world Applications Heading link
Are there any real-world applications students can look forward to in your CS 411: Artificial Intelligence course?
In the beginning of the CS 411 course, MEng students will start out searching through the space of possibilities and find a plan to complete a task. Students return to this idea throughout the course in more complex settings, such as a randomness in the world that must be dealt with. Students will develop plans that deal with these situations and learn how other plans will interact with their own plans.
Students will also look at topics that are designed to solve optimization problems such as solutions that satisfy some constraints or optimize some objective. Students will look at a number of techniques, such as constraint satisfaction and mathematical programming. At the end, students will learn a bit of game theory on how to operate in settings where there are multiple decision makers.
Successful Career Tip Heading link
What is the most important thing you’ve learned about having a successful career that you would like to pass on to people considering the AI field?
Dr. Kash acknowledges the evolving state-of-the-art nature of AI and emphasizes the importance of continuously learning and adapting to stay up-to-date with advancements in the field. Throughout his career, Dr. Kash has consistently expanded his toolkit and adjusted his emphasis in response to emerging innovations.
In his CS 411 course, Dr. Kash’s goal is to help students understand their strengths and limitations of different AI techniques, and learn how to apply them to problems they may face during their career. He runs live sessions where the class will work through problems together to understand different algorithms and techniques. He encourages students to continuously incorporate new tools into their skill set and advises on the importance of understanding how newly invented tools complement their existing set of skills.
Exciting Project Heading link
Tell us about the most exciting project you are working on right now using AI tools.
Dr. Kash’s most recent research is looking at applying AI tools to government data to hopefully optimize a process. An example includes food inspections which are crucial to public health. Sanitarians inspect restaurants and make sure the food code is being followed so people don’t develop food born illnesses. One thing he teaches in his course is that AI tools do exactly what you tell them to do, which can also be a challenge. Dr. Kash is currently looking at these rich decision-making settings and trying to figure out how to simplify the decisions that need to be made.