Ildar Akhmetov, PhD

Area of Study / Keywords

CS education software engineering large language models

---

About

I am a Lecturer in the Department of Computing Science at the University of Alberta and a Visiting Scientist at the Khoury College of Computer Sciences at Northeastern University. Before rejoining the department, I taught at Northeastern University in Vancouver.

I serve as Vice President of the Western Canadian Computing Education Society, which organizes the annual WCCCE conference.

---

Research

My research is in computing education and empirical software engineering. On the education side, I study how to teach programming at scale — how large introductory courses and their teaching-assistant teams are organized, and how assessment and curricula should adapt as generative AI changes what beginning programmers need to learn. On the software engineering side, I study how large language models can support development work. Much of my research is done in collaboration with undergraduate students.

CMPUT 191 - Introduction to Data Science

Introduction to data acquisition, basic data manipulation (cleaning, outlier detection), analysis (regression, clustering, classification), basic statistics and machine learning tools, information visualization to communicate information from data. Prerequisite: Math 30-1 or 30-2. This course cannot be taken for credit if credit has been obtained in CMPUT 174, 175, 195, 274, 275, or ENCMP 100.

---

CMPUT 393 - Scalable Data-Intensive Analytics

Introduction to scalable computing paradigms suitable for data-intensive analytics, with a focus on abstractions, algorithms, and infrastructure for scaling data science, machine learning, and data engineering tasks using multiple machines. The concepts will be applied through a substantial, practical project involving collecting, manipulating, and analyzing large datasets, and discussing findings through scientific reports. Prerequisites: CMPUT 200, 201, 204, and 291, and one of CMPUT 191 or 195.

---

INT D 491 - Data Science Capstone

Students will experience the challenges of working in a team to collect, prepare, and analyze real-world data sets in a particular application domain. Students will work with a domain expert to help discover meaningful insights in the data. Students will also apply best practices in teamwork, effective communication, and technical writing. Project experiences will be shared among the teams, to provide an interdisciplinary perspective on the uses of data science in different domains. Prerequisites: one of CMPUT 191 or CMPUT 195, one of CMPUT 200, NS 115, or PHIL 385, and three of CMPUT 267, CMPUT 291, CMPUT 328, CMPUT 361, CMPUT 461, CMPUT 466, CMPUT 467, BIOIN 301, BIOIN 401, BIOL 330, BIOL 331, BIOL 332, BIOL 380, BIOL 430, BIOL 471, IMIN 410, MA SC 475, EAS 221, EAS 351, EAS 364, EAS 405, GEOPH 426, GEOPH 431, GEOPH 438, PHYS 234, PHYS 295, PHYS 420, STAT 441, STAT 471, STAT 479, AREC 313, REN R 201, REN R 426, REN R 480, FIN 440, MARK 312, OM 420, or SEM 330.

---

Browse more courses taught by Ildar Akhmetov

Ludek Kucera, Ildar Akhmetov, Cruz Izu, Olufisayo Omojokun

2025 October; 10.1145/3736251.3754332

---

Ildar Akhmetov, Mirjana Prpa

2025 February; 10.1145/3641555.3705250

---

Logan W. Schmidt, Caitlin J. Kidder, Ildar Akhmetov, Megan Bebis, Alan C. Jamieson, Albert Lionelle, Sarah Maravetz, Sami Rollins, Ethan Selinger

2025 February; 10.1145/3641554.3701928

---

Mohayeminul Islam, Ajay Kumar Jha, Ildar Akhmetov, Sarah Nadi

Proceedings of the ACM on Software Engineering. 2024 July; 10.1145/3643731

---

Ildar Akhmetov, Sadaf Ahmed, Kezziah Ayuno

2024 March; 10.1145/3626252.3630871

---

View additional publications

• 256 Tips for Junior Devs — video series for beginning programmers
• GitHub
• Google Scholar