MIE’s Professor Samin Aref teaches courses that build the skills needed to tackle challenges in an increasingly AI-driven world
With the rise of low-cost embedded sensors and internet-connected devices, these days engineers have more data to work with than ever before. But how can we turn this data into insights that improve efficiency or enhance performance? That’s where Professor Samin Aref of the Department of Mechanical & Industrial Engineering (MIE) comes in.
Aref teaches courses in the Master of Engineering (MEng) program’s Data Analytics and Machine Learning (DAML) emphasis.
Open to MEng students from multiple programs, the DAML emphasis builds the skills that future engineers will need to understand data, predict future outcomes and determine optimal actions.
“My courses cover descriptive, predictive and prescriptive analytics,” says Aref.
“Another way to put it is to say that I teach students about what happened, what will likely happen and what should be done from a data perspective.”
Aref says students who study all three forms of analytics are better prepared to tackle the kinds of complex problems they will face in their future careers, many of which require a combination of analytics approaches.
“Let’s say the owner of a restaurant chain wants to see where they should open a single new location, and they give you the data to figure it out. You could use descriptive and predictive analytics to come up with the answer,” says Aref.
“However, if they want to open multiple new locations, descriptive and predictive analytics alone won’t give you the answer. You’d have to use prescriptive analytics to find the best places for new locations.”
By building skills across all three areas of analytics, MEng graduates learn to recognize different types of data-driven problems and apply the appropriate tools to solve them.
This breadth of training helps them stand out in the competitive AI and data analytics job market.
Prescriptive analytics is particularly relevant to today’s professional landscape since currently, it remains a weak spot for large language models.
Aref points to the 2025 International Math Olympiad where fine-tuned large language models from Google DeepMind and OpenAI solved five out of six given problems but failed the one that required the type of mathematical reasoning involved in prescriptive analytics.
He says it’s an example of the type of problem that MEng students learn to mathematically formulate and solve in MIE1653H: Integer Programming Applications.
“In the DAML MEng emphasis, we’re training our students to be the most effective and competent engineers for jobs involving AI and analytics.”
Right now, Aref is working with Zihe Zhou (MIE MEng student), to develop a new machine learning algorithm for the task of network clustering.
Used in applications ranging from market research to drug discovery, network clustering identifies groups within a network based solely on patterns of connection.
“Part of the challenge is that the computer only knows the connection patterns, but nothing else, says Aref.
“It’s like having a bunch of customers and only knowing how they know each other, not their demographics nor spending habits; so the computer has to figure out how to cluster the customers given the limited information.”
Zhou’s work involves identifying weaknesses in current clustering algorithms and designing a new scalable algorithm with fewer flaws, improving the accuracy or reliability of network clustering.
Aref’s own path to the University of Toronto has spanned four countries and four continents.
“I got my MSc in Industrial Engineering in Iran,” says Aref.
“Then I completed a PhD in Computer Science in New Zealand. After that, I worked for three years as a research scientist in Germany before coming to Canada.”
Having lived around the world, Aref appreciates the diversity of the MEng program, both in terms of their personal backgrounds and their chosen areas of expertise.
“It is an absolute privilege for me to teach the MEng students,” says Aref.
“Our MIE MEng students not only come from different corners of the world, but from a wide range of undergrad programs.”
“Despite their variety in undergraduate training, nearly half of MIE MEng students pursue the DAML emphasis. I absolutely love seeing the students’ enthusiasm for learning and helping them progress towards their goals.”
Outside of his work, Aref is taking advantage of all Toronto has to offer, including the unexpected. For example, he is now a devotee of a sport known as underwater hockey.
“I play with a U of T club called Hart House Underwater Club,” says Aref.
“I went to one of their events just for fun, and now I play every Saturday. Underwater hockey is as strange as you would imagine it to be.”
“I love learning about different cultures and Toronto offers unmatched opportunities for that every day.”