Bzdok Lab

Danilo Bzdok’s research team is focused on data-guided analysis techniques for large datasets from a systems neuroscience perspective. We believe that a strong interdisciplinary approach, with an equal footing in research object and research method, is a prerequisite for forward progress in quantitative neuroscience and personalized medicine. We collaborate with institutions across the globe, identifying pressing questions in medical imaging and health, reframing them as machine learning problems, and translating new insight into biomedicine.

Bzdok Lab
Danilo Bzdok

Danilo Bzdok

Principal Investigator

McGill University

Mila Institute

Danilo Bzdok is a medical doctor and computer scientist with a dual background in systems neuroscience and machine learning algorithms. After medical training at RWTH Aachen University (Germany), Université de Lausanne (Switzerland), and Harvard Medical School (USA), he completed one Ph.D. in brain-imaging neuroscience (Research Center Juelich, Germany, 2012) and one Ph.D. in computer science in machine learning statistics at INRIA Saclay and Neurospin (France, 2016). Danilo currently serves as Associate Professor at McGill’s Faculty of Medicine and as Canada CIFAR AI Chair at Mila - Quebec Artificial Intelligence Institute, Montreal, Canada, including cross-appointments at the McConnell Brain Imaging Center, Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute, and the School of Computer Science at McGill University. In his free time, he enjoys French and Italian culture, language and languages, playing chess or Go, and consuming excessive amounts of music.


Meet The Team

Jakub Kopal

Jakub Kopal

Postdoctoral Fellow

McGill University

University of Montreal

Jakub Kopal completed his Ph.D. in neuroscience under joint supervision from the University of Chemistry and Technology in Prague and Université Toulouse III – Paul Sabatier in 2021. Currently, he is working as a postdoctoral fellow at the intersection of neuroscience and genetics in the group of Danilo Bzdok at McGill University and Sébastien Jacquemont at CHU Saint-Justine. His research focuses on the influence of genetic mutations on brain structure and function. Otherwise, he enjoys data visualization, pickles and random facts.


Andrea Luppi

Andrea Luppi

Postdoctoral Fellow

McGill University

Andrea obtained undergraduate and master’s degrees in philosophy, psychology, and neuroscience at Oxford before completing his PhD in neuroscience at Cambridge and the Alan Turing Institute (UK). His doctoral work combined multimodal, multi-species neuroimaging to investigate the neuroscience of altered states of consciousness (coma, anaesthesia, psychedelics) through the lens of complex systems and information dynamics. He is now conducting postdoctoral work with Dr. Bratislav Misic (MNI) and Dr. Danilo Bzdok (MILA), at the interface of AI and neuroscience. Combining whole-brain computational modelling and neuromorphic computing, he investigates how the network architecture and neuromodulatory landscape of the brain jointly shape information processing and cognition. A native of Italy, Andrea enjoys hiking in the mountains, cooking, indoor skydiving, and reading and writing fiction.


Badr Hammou

Badr Hammou

Postdoctoral Fellow

McGill University

Badr Ait Hammou is a postdoctoral researcher at the intersection of Artificial intelligence (AI) and neuroscience. His current research focuses on machine learning, data-driven analysis techniques for large-scale biomedical datasets. Before joining the lab, he worked as a postdoctoral researcher at the University of Montreal, where he conducted research on deep learning techniques to solve a variety of medical computer vision problems applied to Ophthalmology, including video action recognition, image generation, and image classification. He completed a Ph.D. in Computer Science at Mohammed V University in Rabat. His doctoral work lies in the area of AI for Big Data analytics. It covers a set of intelligent systems based on distributed machine learning, distributed deep learning techniques, and natural language processing (NLP) to solve several challenges related to real-world problems in the context of big data, such as personalized recommendations, group recommendations, and real-time social media analysis.


Gregory Bell

Gregory Bell

Research Assistant

McGill University

Greg completed his bachelor’s degree in Physics, summa cum laude, at Temple University. He then did a M.Sc. at Mcgill in Physics. After working and gaining interest in data science and engineering, Greg joined the Bzdok Lab and currently works with LLM’s and medical data.


Liam Hodgson

Liam Hodgson

Doctoral Student

McGill University

Prior to joining the Bzdok Lab at McGill, Liam completed his bachelor’s degree in Engineering Physics at UBC and subsequently worked on the R&D team at an additive manufacturing startup. As a doctoral student in the Computer Science department, he is investigating the function and disfunction of the human brain through the lens of single-cell omics, as well as developing computational methods to better analyze and interpret this high-dimensional biological data.


Karin Saltoun

Karin Saltoun

Doctoral Student

McGill University

Karin Saltoun is a PhD student in Neuroscience at McGill University. She studied biophysics at York University (Toronto) where she developed a love for using math and physics to understand biological problems on a more fundamental level. During her undergraduate career, first through work in an experimental lab focusing on precision measurements in atomic physics, before moving to a computational neuroscience lab developing algorithms for distinguishing sleep states from brain behaviour. She is currently working in Dr. Bzdok’s lab to understand brain asymmetry at a population level using a variety of machine learning techniques. When she’s not glued to her computer, she enjoys baking desserts or reading fiction.


Jack Stanley

Jack Stanley

Doctoral Student

McGill University

Jack is a PhD student in the Quantitative Life Sciences program at McGill. Prior to joining the lab, Jack completed an Honours BSc in Statistics and Biochemistry at the University of Toronto, where he was a Schulich Leader and National Scholar. His current research is focused on untangling the complexity of human disease and biology using increasingly powerful deep learning approaches. An avid distance runner, Jack is also captain of McGill’s varsity cross country team, and a member of the varsity track team.


Anwesha Bhattacharya

Anwesha Bhattacharya

Doctoral Student

McGill University

Anwesha is starting her PhD in the Biological and Biomedical Engineering department at McGill university. After completing her Masters in Aersopace Engineering, she went on to apply her engineering skills as a Quantitative Researcher at JP Morgan Chase, Mumbai. She developed her interest in machine learning techniques while working on her Masters thesis for object detection and 6D pose estimation. She also did a summer internship at CERN where she worked on High Granularity Calorimeter data and applied machine learning techniques to classify fundamental particles. She now hopes to use ML techniques to further the domain of neuroscience and better understand the brain. In her leisure time, Anwesha loves reading fantasy, hiking, and yoga. She also enjoys dancing and is trained in Bharatnatyam.


Le Zhou

Le Zhou

Doctoral Student

McGill University

Le is pursuing his PhD in the Integrated Program of Neuroscience at McGill University. He finished his bachelor’s degree in computer science and master’s degree in psychology at the University of Electronic Science and Technology of China. He is interested in the relationship between human behavior and the brain and is currently studying the effect of chronotype (circadian rhythms) on the brain pattern by applying machine learning methods on population datasets. In his free time, Zhou reads fiction, watches movies, and plays games.


Chloé Savignac

Chloé Savignac

Doctoral Student

McGill University

Chloé Savignac is a PhD student in the Integrated Program in Neuroscience at McGill University, interested in the applications of machine learning to dementia research. She joined the lab in January 2021 after graduating with First Class Honours from a B.A. & Sc. in Cognitive Science at McGill University. Her PhD work combines Big Data analytics tools with state-of-the-art machine learning techniques to derive factors of Alzheimer’s disease (AD) susceptibility in population datasets of up to half a million participants. Her master’s thesis leveraged the power of structural brain scans from ~40,000 participants of the UK Biobank imaging cohort to find population signatures of familial AD risk as a function of APOE haplotypes. She has since shifted focus to single-cell genomics, aiming to identify patterns of transcriptomic markers uniquely linked to AD disease in male and female patients. Outside the lab, Chloé enjoys learning new languages and exploring cuisines from around the world.


Kimia Shafighi

Kimia Shafighi

Master’s Student

McGill University

Kimia is starting her Master’s in the Integrated Program of Neuroscience at McGill University after receiving her bachelor’s in biomedical engineering. She dedicated her undergraduate studies to create the McGill Biodesign team and worked on transformational applications in biotechnology. Now, she is using machine learning techniques to investigate a variety of neuroscience questions at the population scales and discover statistical patterns in large datasets.


Karam Ghanem

Karam Ghanem

Master’s Student

McGill University

Karam Ghanem is a Master’s student in Biomedical Engineering at McGill University. He finished his Bachelor’s degree in Engineering Physics at McMaster University where he immersed himself in the application of physics in a number of engineering and scientific fields. Karam worked as a Firmware Engineer on the R&D team at a telecommunications company where he developed his engineering skillset. He is currently investigating Diffusion Models in a theoretical context.


Justin Marotta

Justin Marotta

Master’s Student

McGill University

Justin is a student in the Biological & Biomedical Engineering Master’s program at McGill. Prior to joining the lab, he completed his undergraduate degree in Electrical Engineering at Queen’s University and worked as a sales engineer for Cisco Systems focused on cybersecurity solutions. Currently, he is developing a data-driven machine learning approach to perform a phenome-wide investigation of population-level drivers behind adolescent brain and cognitive development. Factors of equity, diversity, and inclusion are a focal point of this investigation. In his free time he enjoys music, reading, and getting outside to ski and hike.


Zilong Wang

Zilong Wang

Master’s Student

McGill University

Zilong is starting his MSc in Neuroscience at McGill University, after graduating from Honors Cognitive Science also at McGill. He is interested in understanding human higher cognitive functions by applying various machine learning methods on large population data. Currently he is investigating the relationship between different cerebellum subregions and cortical areas to learn more about the novel functions recently discovered in cerebellum. In his free time, Zilong enjoys meeting people and welcomes any outdoor activities.


Nicole Osayande

Nicole Osayande

Master’s Student

McGill University

Nicole is a Master’s student in the Biological & Biomedical Engineering program, and she was selected for the inaugural cohort of McCall MacBain Scholars at McGill. She graduated from Queen’s University in spring of 2021 with a bachelor’s degree in Computer Science, specializing in Biomedical Computing. Prior to joining the lab, Nicole worked at IBM as a software developer for the Watson Orchestrate AI team, where she gained hands-on experience with machine learning algorithms and automation tools. Nicole has a strong connection to EDI initiatives as she founded the Queen’s Student Diversity Project at her alma mater and collaborated with the undergraduate admissions and recruitments office to encourage students of diverse backgrounds to pursue their post-secondary studies at Queen’s. She now hopes to bridge her interest in machine learning with her passion for EDI initiatives to introduce the Neuroscience community to diversity-aware population modeling of large-scale datasets via Bayesian hierarchical regression. In her free time, she likes dancing, boxing, content creation, and watching Korean dramas!


Shambhavi Aggarwal

Shambhavi Aggarwal

Master’s Student

McGill University

Shambhavi Aggarwal is a student in the Biological & Biomedical Engineering Master’s program at McGill University. She obtained her undergraduate degree in Information Technology, during which she cultivated her interest in Machine Learning and Computer Vision. Her passion led her to pursue research internships in Computer Vision and Quantum Machine Learning at IISc Bangalore and Purdue University, respectively. After graduation, she applied her skills by working as a Computer Vision Engineer at Claritas HealthTech, where she used deep learning techniques to analyze medical data. Her ambition is to expand her knowledge of machine learning algorithms and explore how they can be applied to medical data. During her free time, she relishes activities such as music, singing, and cooking, while also engaging in interesting conversations with people.


Former Lab Members


Avatar

Chris Zajner

Research Assistant 2020-2021

Pursuing medical studies at Western University.

Avatar

Nadejda Zaharieva

Lab Manager & RA 2021

Research at École de technologie supérieure.

Avatar

Nahiyan Malik

Master's Student 2019-2021

Meta Platforms, Inc.

Avatar

Julius Kernbach

Doctoral student 2017-2019

Neurosurgeon at RWTH Aachen Medical School

Avatar

Anthony Ma

Bachelor Student 2018-2022

Pursuing masters at Harvard University

Avatar

Hannah Kiesow

Doctoral student 2018-2022

Senior Psychologist at Zortify

Avatar

Hasnain Mamdani

Master's Student 2019-2021

Data Scientist at Hamilton Health Sciences

Avatar

Enning Yang

Master's Student 2021-2023

Chief Technology Officer of Eureka AI startup

Avatar

Devin Kreuzer

Research Assistant 2020-2022

ML Engineer at Pintrest

Publications

A complete list of our publications can be found on ResearchGate.

Selected Publications

Bzdok D, Altman N, Krzywinski M. Statistics versus machine learning. Nature Methods, 2018, 15:233-234.
Bzdok D, Ioannidis JPA. Exploration, inference and prediction in neuroscience and biomedicine. Trends in Neurosciences, Cell Press, 42:251-262, 2019.
Bzdok D, Nichols TE, Smith SM. Towards Algorithmic Analytics for Large-scale Datasets. Nature Machine Intelligence, 1:296-306, 2019.
Bzdok D, Engemann D, Thirion B. Inference and prediction diverge in biomedicine. Patterns, Cell Press, 2020.
Hartwigsen G, Bengio Y, Bzdok D. How does hemispheric specialization contribute to human-defining cognition? Neuron, Cell Press, 2021.
Smallwood J, Bernhardt B, Leech R, Bzdok D, Jefferies E, Margulies D. The role the default mode network in cognition: a topographic perspective. Nature Reviews Neuroscience, 2021.
Kernbach J, Yeo BTT, Smallwood J, Margulies D, Thiebaut de Schotten M, Walter H, Sabuncu M, Holmes A, Gramfort A, Varoquaux G, Thirion B, Bzdok D. Subspecialization within Default Mode Nodes Characterized in 10,000 UK Biobank Participants. Proceedings of the National Academy of Sciences of the USA, 115:12295-12300, 2018.
Kiesow H, Dunbar RIM, Kable JW, Kalenscher T, Vogeley K, Schilbach L, Marquand AF, Wiecki TV, Bzdok D. 10,000 Social Brains: Sex Differentiation in Human Brain Anatomy. Science Advances, AAAS journal, 6:aaz1170, 2020.
Schulz MA, Yeo BTT, Vogelstein JT, Mourao-Miranada J, Kather JN, Kording K, Richards B, Bzdok D. Different scaling of linear models and deep learning in UKBiobank brain images versus machine-learning datasets. Nature Communications, 2020.
Spreng RN, Dimas E, Mwilambwe-Tshilobo L, Dagher A, Koellinger P, Nave G, Ong A, Kernbach JM, Wiecki TV, Ge T, Li Y, Holmes A, Yeo BTT, Dunbar RIM, Bzdok D. The Default Network of the Human Brain Is Associated With Perceived Social Isolation. Nature Communications, 2020.
Bzdok D, Michael Eickenberg, Gaël Varoquaux, Bertrand Thirion. Hierarchical Region-Network Sparsity for High-Dimensional Inference in Brain Imaging. Information Processing in Medical Imaging (IPMI), 2017, pp. 323-335.
Bzdok D, Eickenberg M, Grisel O, Thirion B, Varoquaux G. Semi-supervised Factored Logistic Regression for High-Dimensional Neuroimaging Data. Advances in Neural Information Processing Systems (NeurIPS), 2015.

Teaching

Related teaching on machine learning/data science:

Course outline from 2020 for BMDE 520
Course outline from 2021 for BMDE 520/COMP 598
Course outline for 2022 for BMDE 520/COMP 598

Contact

Dr. Danilo Bzdok is currently looking for post-docs, PhD students, and software engineers to build the labs at McGill University and Mila Artificial Intelligence Institute in Montreal, Canada. Drop him an if you are interested.

  • Department of Biomedical Engineering, Faculty of Medicine, McGill University, 3775 Rue University, Montréal, QC H3A 2B4
  • 09:00 to 17:00 Monday to Friday
  • Publications

McGill University Mila Institute