Team

Roberto Souza - Associate Professor, University of Calgary

Principal Investigator

Roberto Souza is a dual citizen from Brazil and the United States, working as an Associate Professor at the Electrical and Software Engineering Department at the University of Calgary, Canada, since July 2020. He has a B.Sc. in Electrical Engineering from the Federal University of Pará (2012), an M.Sc. (2014) and PhD (2017), both in Computer Engineering from the University of Campinas (UNICAMP). Before becoming an Assistant Professor, Roberto worked for three years as a postdoctoral scholar in the Radiology Department at the University of Calgary. He has international experience, having worked as an intern at the Grenoble Institute of Technology, France, and the University of Pennsylvania, United States. Dr Souza has extensive expertise in image processing and machine learning. His research is currently focused on innovative strategies for data integration and data mining in imaging applications. Look at his Google Scholar page for a list of relevant publications. 

Mariana Bento - Assistant Professor, University of Calgary

Principal Investigator

Mariana is an Assistant Professor of Biomedical and of Electrical and Software Engineering, a full member of the Hotchkiss Brain Institute, funded by NSERC, who has expertise in medical image processing and machine learning applied to aging and dementia. Mariana has a B.Sc. in Teleinformatics Engineering from the Federal University of Ceará (2011), an M.Sc. (2013) and PhD (2016), both in Computer Engineering from the University of Campinas (UNICAMP). Before the current position, she served as a postdoctoral fellow at the Radiology and Clinical Neurosciences, University of Calgary.  She develops more robust and reliable open tools in neuroimaging and is engaged in open science activities and EDI activities, including conducting workshops. Look at her Google Scholar page for a list of relevant publications. 

Anik Das, MSc - Biomedical Engineering (University of Calgary)

Anik Das is working as a Research Assistant, where his work sits at the intersection of AI and healthcare, focusing on applying machine learning and deep learning to medical imaging, with extensive experience in the pediatric domain. He has expertise in complex data preprocessing pipelines, quality control, data augmentation, domain adaptation, transfer learning, and explainable and trustworthy AI. He also actively contributes to organizing research activities, mentoring peers, and fostering a productive and supportive team environment. Anik Das holds an MSc in Biomedical Engineering from the University of Calgary under Dr. Mariana Bento, along with a BSc and MSc in Computer Science & Engineering.

Pedro Paiva – PDF, Biomedical Engineering (University of Calgary)

Pedro Paiva is a Postdoctoral fellow of AI2Lab and BTLab, working with bias mitigation strategies in medical AI and computer vision on Biometrics. Pedro has a B.Sc. in Computer Science from the Federal University of Alagoas (2016), an M.Sc. (2019), and a Ph.D. (2024), both in Information and Communication Systems from the University of Campinas (UNICAMP). Before becoming a PDF, Pedro was a visiting student at the University of Calgary at BTLab under the supervision of Dr. Marina Gavrilova and fully funded by the Brazilian Ministry of Education. He is interested in Computer Vision, Deep Learning, and AI Fairness. He was a researcher in the Cyber-Physical Division at the Renato Archer IT Center – Brazil. He also has experience as a Computer Vision engineer, working on a wide range of applications for several companies. Pedro was awarded by the Eyes High and Hotchkiss Brain Institute postdoctoral programs. Pedro has published several peer-reviewed papers in major journals and conferences. Look at his Google Scholar page for a list of relevant publications.

Hanna Bugler  – PhD Student, Biomedical Engineering (University of Calgary)

Hanna is a Ph.D. student in Biomedical Engineering under the co-supervision of Dr. Ashley Harris and Dr. Roberto Medeiros de Souza. She completed her BEng in Biomedical and Electrical Engineering at Carleton University. Her research project seeks to analyzes, in real-time, the quality of magnetic resonance spectroscopy data to determine required minimum scan times. Her previous research experience includes developing pre-processing protocol and automating regions of interest selection for second harmonic generation collagen fiber microscopy images.

Farzaneh Dehghani – PhD Student, Biomedical Engineering (University of Calgary)

Farzaneh is a Ph.D. student in Biomedical Engineering at the University of Calgary, working under the supervision of Dr. Mariana Bento and Dr. Sayeh Bayat and funded by the HBI International Graduate Recruitment Scholarship. She received her MSc degree in Biomedical Engineering from the University of Isfahan, where she worked on the Bone Age Assessment of children in an automatic manner. Medical image processing, computer vision, deep/machine learning, and computer-aided diagnosis constitute her areas of interest. The objective of her research is to develop trustworthy machine learning algorithms for Alzheimer’s Disease and healthy aging.

Arshin Soltan  – PhD Student, Biomedical Engineering (University of Calgary)

Arshin received her B.Sc. in Biomedical Engineering from Amirkabir Universit of Technology, where she focused on medical image processing and analyzing brain imaging data in cases related to memory and cognition using AI algorithms. She is interested in working on processing Magnetic Resonance images by employing ML and DL techniques to study various features of the brain and how these techniques perform under specific challenges in image acquisition.

Zeyad Khaled Samir – MSc Student, Biomedical Engineering (University of Calgary)

Zeyad is a master’s student in Biomedical Engineering under the supervision of Dr. Roberto Medeiros de Souza. He completed his BSc in Biomedical and Engineering at Cairo University. Zeyad is interested in computer vision research and its applications in healthcare. He previously worked on problems related to 3D scene understanding and object detection. His current research aims to use deep learning to develop fast brain MR image reconstruction techniques for infants. 

Mansi Singhal  – MSc Student, Biomedical Engineering (University of Calgary)

Mansi Singhal is a graduate student in Biomedical Engineering at the University of Calgary, working under the supervision of Dr. Mariana Bento. She holds a B.Tech degree in Electrical Engineering from Dayalbagh Educational Institute, India (2024). She worked as a research intern in the MITACS Globalink Research Internship at the University of Calgary (2023) and in an international student exchange program at Hong Kong University (2022). She was awarded the Director's Gold Medal for Rank 1 in the Diploma in Electronics Engineering (2021). Her prior research focused on machine learning and image processing models for brain MRI and lung cancer detection. Currently, her research centers on responsible AI and fairness in AI applications for healthcare. Look at her Google Scholar Link for a list of relevant publications.

Brooke Kindleman – MSc Student, Biomedical Engineering (University of Calgary)

Brooke Kindleman is a master's student in Biomedical Engineering at the University of Calgary under the supervision of Dr. Mariana Bento. She completed her B.Sc. in Software Engineering at the University of Calgary. During her undergraduate degree, she focused on computer graphics and modelling and found a passion for image rendering. Her primary interests are applications of AI and computer graphics in medical imaging to improve diagnostic outcomes for patients.

Maya Wadhera - Undergraduate Student (University of Calgary)

Maya is an undergraduate student at the University of Calgary working under the supervision of Dr. Mariana Bento and Arshin Soltan and funded by a Program for Undergraduate Research Experience (PURE) Award. She is interested in learning more about Biomedical Engineering, and her summer project will investigate pediatric MRI datasets, fine-tune the pre-processing pipeline, and identify areas for fair and explainable AI with domain adaptation strategies.