Portrait of Vladimir Makarenkov

Vladimir Makarenkov

Affiliate Member
Full Professor, UQAM, Department of Computer Science


Vladimir Makarenkov is a full professor and director of the graduate program in bioinformatics at Université du Québec à Montréal (UQAM). He holds a master's degree in applied mathematics from Lomonosov Moscow State University and a PhD in computer science and mathematics from the École des hautes études en sciences sociales (EHESS) in Paris. Before joining the computer science department at UQAM, he completed a three-year postdoctoral fellowship at the Digital Ecology Lab at Université de Montréal.

He is the author of 80 journal articles and 67 conference papers, and the recipient of the prestigious Simon Régnier Prize and the Chikio Hayashi Prize awarded by the International Society for Mathematical Classification. His research focuses on AI, bioinformatics and data mining. This encompasses the design and development of novel unsupervised and supervised machine learning methods, as well as the use of machine learning techniques, including clustering and deep learning, for the analysis of biological and biomedical data.

Makarenkov’s current research also involves the development of an automated recommendation system based on deep learning to recommend the best clustering algorithm for a given input dataset. Additionally, he is working on creating a generic machine learning model to define the concept of cluster, and on comparing various auto-encoding approaches and clustering algorithms to achieve better clustering results.


A self-attention-based CNN-Bi-LSTM model for accurate state-of-charge estimation of lithium-ion batteries
Zeinab Sherkatghanad
Amin Ghazanfari
Assessing the emergence time of SARS-CoV-2 zoonotic spillover
Stéphane Samson
Étienne Lord
Inertia-Based Indices to Determine the Number of Clusters in K-Means: An Experimental Evaluation
Andrei Rykov
Renato Cordeiro De Amorim
Boris Mirkin
This paper gives an experimentally supported review and comparison of several indices based on the conventional K-means inertia criterion fo… (see more)r determining the number of clusters,
Cache-Efficient Dynamic Programming MDP Solver
Jaël Champagne Gareau
Guillaume Gosset
Éric Beaudry
Inferring multiple consensus trees and supertrees using clustering: a review
Gayane S. Barseghyan
Nadia Tahiri
Low-Rank Representation of Reinforcement Learning Policies
We propose a general framework for policy representation for reinforcement learning tasks. This framework involves finding a low-dimensional… (see more) embedding of the policy on a reproducing kernel Hilbert space (RKHS). The usage of RKHS based methods allows us to derive strong theoretical guarantees on the expected return of the reconstructed policy. Such guarantees are typically lacking in black-box models, but are very desirable in tasks requiring stability and convergence guarantees. We conduct several experiments on classic RL domains. The results confirm that the policies can be robustly represented in a low-dimensional space while the embedded policy incurs almost no decrease in returns.