In August 2021 CAIDA was successfully awarded the Faculty of Science Hiring Cluster Grant, resulting in the formation of a new interdisciplinary hiring cluster in the Faculty of Science, named "Artificial Intelligence Methods for Scientific Impact” (AIM-SI, pronounced “aim sci”). Composed of AI methods researchers who have an interest in achieving scientific impact via interdisciplinary collaborations, AIM-SI aspires to help fuel artificial intelligence innovation and research excellence. The cluster currently consists of 26 existing UBC faculty members spanning Computer Science, Statistics, Math, Electrical & Computer Engineering, and Earth, Ocean and Atmospheric Sciences--including UBC’s existing 14 Canada CIFAR AI chairs.
Over the past several years, the university has faced unprecedented demand for AI teaching and AI research collaborations across all scientific disciplines. Better meeting this demand will lead to more scientific breakthroughs across the university and produce a much larger pool of highly-qualified graduates for Vancouver’s red-hot tech sector. The Faculty of Science hiring cluster grant makes filling this demand possible: UBC has hired 6 new assistant professors in total: 2 in Statistics (Geoff Pleiss started in 2023; Saifuddin Syed to start in 2025), 1 in Mathematics (Ahmet Alacaoglu started in 2024), and 3 in Computer Science (Peter West started in 2025; Peter Yichen Chen (funded entirely by CS) started in 2025; Serena Wang to start in 2026). The new hires for the cluster will be funded as part of the President’s Academic Excellence Initiative (PAEI), which is driving the largest recruitment of faculty in UBC history.
AIM-SI strives to increase UBC's strength in AI methods beyond the "steady state" we’ve been maintaining, thereby meeting the immediate demand for teaching, supervision, and collaboration in AI at UBC, and making us more comparable to other top institutions both in Canada and worldwide. By building a strong interdisciplinary center on the base of AI methods we hope to fuel artificial intelligence innovation and research excellence in Vancouver’s AI community. These hires are a great first step in actualizing this vision.
Meet our new hire: Ahmet Alacaoglu, Assistant Professor, Mathematics
Dr. Ahmet Alacaoglu received his Ph.D in Computer and Communication Sciences from École Polytechnique Fédérale de Lausanne (EPFL) in August 2021. In 2021 he began his work as a Postdoctoral Research Associate for the Wisconsin Institute for Discovery at the University of Wisconsin–Madison, where he remained until July 2024 when he started at UBC as an Assistant Professor for the Mathematics Department, and a member of both CAIDA and AIM-SI.
Dr. Alacaoglu’s research interests consist of optimization, machine learning, and reinforcement learning, with a focus on continuous optimization. In particular, his work is concerned with designing fast algorithms and providing mathematical guarantees on these algorithms’ behaviour, often in the context of machine learning and AI.
“Optimization problems arise in many different contexts that are of interest to different disciplines.”
Dr. Alacaoglu’s research focuses on continuous optimization and is particularly concerned with designing fast algorithms and providing mathematical guarantees on these algorithms’ behaviour, often in the context of machine learning and AI. Fitting to AIM-SI’s core value of interdisciplinarity, the area of optimization sits at the intersection of several disciplines including mathematics, computer science, electrical engineering, operations research, and statistics.
“There are many mysteries involving the behaviour of optimization algorithms for training or fine-tuning neural networks.”
Perhaps unsurprisingly, optimization also has a central role in AI. Optimization already plays a key role in neural networks, but as new research findings empirically demonstrate the benefit of different, and often new, architectures in different applications, new questions appear both about developing fast optimization algorithms in these contexts or analyzing these algorithms theoretically.
“AI methods are being used in areas ranging from healthcare to engineering and scientific discovery.”
For Dr. Alacaoglu, AI methods for scientific impact goes beyond the methodological role and instead refers to the role of AI as a unifying theme for solving problems in different scientific disciplines. AI methods are being used in almost every area of science and beyond, as such researchers across almost every discipline are learning and using AI methods and terminology. This allows a stronger interaction between research communities, resulting in the enrichment of both theory and applications at the same time.
“CAIDA’s effort to bring departments together is essential to foster this atmosphere.”
Such interactions are encouraged by CAIDA and AIM-SI, and Dr. Alacaoglu has been an active participant in such activity. He notes the impact that this highly interdisciplinary and intellectually stimulating environment has had – both within CAIDA/AIM-SI and at UBC as a whole. He appreciates the celebration of the impact of AI in new contexts and the encouragement for researchers to pursue opportunities for new and cross-disciplinary collaborations. We are overjoyed having Dr. Alacaoglu as a member of CAIDA and AIM-SI, and we look forward to seeing what he does next. Welcome to UBC Dr. Ahmet Alacaoglu!