Bruno Agard | Engineering | Best Researcher Award

Prof. Bruno Agard | Engineering | Best Researcher Award

Professor from Polytechnique Montréal, Canada

Professor Bruno Agard is a distinguished academic in the field of Industrial Engineering, currently holding a professorship at the École Polytechnique de Montréal within the Department of Mathematics and Industrial Engineering. As a core member of the Laboratoire en Intelligence des Données (LID), he is widely recognized for his applied research on data-driven decision-making across transportation systems, supply chain management, and product design. His academic journey has taken him through top institutions in France, the United States, and Canada, equipping him with a global outlook and a multidisciplinary approach. Professor Agard’s scholarly influence is exemplified through his involvement in collaborative research with CIRRELT and GERAD, as well as through his numerous technical reports and publications. A seasoned educator and mentor, he has supervised a significant number of postdoctoral researchers, doctoral candidates, and master’s students, contributing greatly to the academic community’s growth. His research focuses on integrating intelligent data analysis into real-world systems, thereby enhancing operational efficiency and sustainability. With his innovative contributions and longstanding commitment to research excellence, Professor Agard stands out as a highly deserving nominee for the Best Researcher Award. His work bridges theory and practice, shaping the future of industrial systems in academia and industry alike.

Professional Profile

Education

Professor Bruno Agard’s educational foundation is both extensive and prestigious, reflecting a clear trajectory of excellence in industrial engineering and applied sciences. He earned his Ph.D. in Industrial Engineering with honors in 2002 from the Institut National Polytechnique de Grenoble, France, where his dissertation focused on product design methodologies in contexts of wide diversity. Prior to that, he completed a Master of Science in Industrial Engineering (DEA) in 1999 at the same institution. His academic path also includes a highly competitive 5-year teaching degree (Agrégation) in 1998 from the École Normale Supérieure de Cachan, where he was ranked fourth nationally—an exceptional accomplishment. Additionally, he holds a four-year university degree in Technology (Maîtrise) with honors from Université d’Orléans-Tours (1997), a B.S. in Manufacturing (Licence) from the same university (1996), and a two-year technical degree (DUT) in Technology from Institut Universitaire Technologique de Bourges, where he was ranked second (1995). Professor Agard began his academic pursuit with a high school diploma (Baccalauréat) from Lycée Claude de France in 1992. His education reflects a solid and diverse academic preparation that underpins his expertise in industrial engineering, systems design, and data analysis.

Professional Experience

Professor Bruno Agard has built a remarkable academic and research career spanning over two decades across France, the United States, and Canada. Since 2014, he has served as a full Professor in the Department of Mathematics and Industrial Engineering at École Polytechnique de Montréal. Prior to this, he was promoted through the ranks at the same institution, working as an Associate Professor from 2008 to 2014 and Assistant Professor from 2003 to 2008. His academic journey began with an Assistant Professorship at the IUFM de Grenoble in the Department of Technology, Management, Economics, and Society during 2002–2003. In Spring 2001, he further broadened his academic exposure as a visiting researcher at the Intelligent Systems Laboratory, University of Iowa, USA. Between 1999 and 2002, Professor Agard also worked as a Teaching and Research Assistant at the Ecole Nationale Supérieure de Génie Industriel, part of the Institut National Polytechnique de Grenoble. His diverse academic roles have allowed him to lead cutting-edge research projects, engage with interdisciplinary teams, and contribute to curriculum development. His deep experience across international academic settings has cemented his role as a key figure in advancing industrial engineering, applied data science, and smart systems integration.

Research Interests

Professor Bruno Agard’s research interests lie at the intersection of industrial engineering, data science, and systems optimization. A core area of his expertise is in the application of intelligent data analysis to real-world problems, particularly in transportation systems, supply chain management, and product design. He is passionate about improving decision-making processes by developing data-driven methodologies that support operational efficiency and resilience. One of his notable domains of research is in analyzing smart card data to understand public transit usage patterns—an area where he has co-authored several technical reports in collaboration with CIRRELT. He also explores advanced clustering and segmentation techniques, temporal pattern recognition, and spatial-temporal data modeling. Professor Agard has demonstrated a strong interest in the joint design of product families and supply chains, applying optimization algorithms such as taboo search to solve complex, multi-objective problems. His research extends to occupational health and safety tools, emergency response logistics, and systems interoperability in public transportation during crisis scenarios. With a continuous focus on translating theoretical frameworks into applicable solutions, Professor Agard’s research is both academically rigorous and socially impactful. His work contributes significantly to sustainable urban planning, intelligent manufacturing, and the digital transformation of industrial systems.

Research Skills

Professor Bruno Agard possesses a wide array of advanced research skills that make him a prominent figure in industrial engineering and data intelligence. He is adept in quantitative modeling, optimization techniques, machine learning, and big data analytics—skills that he routinely applies to solve challenges in transportation, supply chains, and manufacturing. His technical proficiency includes developing innovative data mining and clustering algorithms to extract insights from smart card and operational datasets. He has shown a deep understanding of time-series analysis, segmentation methods, and spatial-temporal data integration. Moreover, Professor Agard has expertise in multi-objective optimization, particularly in designing product families and associated supply chains using heuristic and metaheuristic approaches, including taboo search. He is highly experienced in simulation modeling and decision support system design, ensuring his research remains practical and applicable. Additionally, he is a skilled academic mentor and collaborator, having supervised numerous Ph.D., master’s, and postdoctoral researchers. His ability to communicate complex ideas effectively in interdisciplinary and international contexts is further enhanced by his fluency in French, English, and intermediate Spanish. Altogether, Professor Agard’s research skill set positions him as a versatile and impactful contributor to the advancement of intelligent systems in industrial and academic environments.

Awards and Honors

While specific awards are not detailed in the provided information, Professor Bruno Agard’s impressive academic and research trajectory reflects a career marked by excellence, leadership, and scholarly impact. His appointment and promotion through all academic ranks—from Assistant to Full Professor—at École Polytechnique de Montréal is a testament to his sustained contributions and recognition within the academic community. Notably, his national ranking of fourth in the highly competitive Agrégation program at École Normale Supérieure de Cachan is an early indicator of his academic brilliance. Furthermore, his continued affiliation with prominent research institutions such as CIRRELT (Interuniversity Research Centre on Enterprise Networks, Logistics and Transportation) and GERAD (Group for Research in Decision Analysis) highlights the recognition of his research capabilities in elite scholarly circles. His extensive supervision of nearly 120 students across multiple levels, coupled with his leadership in interdisciplinary research projects, further positions him as an academic of high repute. Though no formal honors are listed, Professor Agard’s scholarly outputs, mentorship, and leadership roles within international collaborations demonstrate the impact and esteem he holds in his field. Such accomplishments strongly support his candidacy for distinguished awards recognizing research excellence.

Conclusion

In conclusion, Professor Bruno Agard exemplifies the qualities of a top-tier researcher deserving of the Best Researcher Award. With over two decades of academic experience, he has established himself as a leader in the fields of industrial engineering, intelligent data systems, and optimization. His ability to bridge theoretical innovation with practical applications has yielded valuable insights in public transit analytics, supply chain configuration, and emergency logistics planning. His multidisciplinary research collaborations with renowned institutions like CIRRELT and GERAD reflect his deep integration into Canada’s leading research ecosystems. Furthermore, his mentorship of over 120 students underscores his dedication to shaping the next generation of scholars and practitioners. Professor Agard’s methodological rigor, combined with a deep understanding of complex data environments, positions him as a transformative figure in his discipline. While his formal awards may not be extensively documented, the breadth of his contributions—spanning high-impact publications, student development, and applied industrial solutions—speak volumes about his research excellence. Recognizing Professor Agard with the Best Researcher Award would not only celebrate his achievements but also highlight the value of integrating data intelligence with industrial systems for societal advancement.

Publications Top Notes

  • Title: Machine Learning Tool for Yield Maximization in Cream Cheese Production
    Authors: L. Parrenin, A. Dupuis, C. Danjou, B. Agard

  • Title: An Inventory Management Support Tool Through Indirect Q-Value Estimation: A Combined Optimization and Forecasting Approach
    Authors: A.R. Delfiol, C. Dadouchi, B. Agard, P. St-Aubin

  • Title: Modulated spatiotemporal clustering of smart card users
    Authors: R. Decouvelaere, M.M. Trépanier, B. Agard
    Year: 2024
    Citations: 4

  • Title: A decision support tool to analyze the properties of wheat, cocoa beans and mangoes from their NIR spectra
    Authors: L. Parrenin, C. Danjou, B. Agard, G. Marchesini, F. Barbosa
    Year: 2024
    Citations: 1

  • Title: Improving demand forecasting for customers with missing downstream data in intermittent demand supply chains with supervised multivariate clustering
    Authors: C. Ducharme, B. Agard, M.M. Trépanier
    Year: 2024
    Citations: 1

  • Title: Improvement of freight consolidation through a data mining-based methodology
    Authors: Z. Aboutalib, B. Agard
    Year: 2024

  • Title: Digital Technologies and Emotions: Spectrum of Worker Decision Behavior Analysis
    Authors: A. Dupuis, C. Dadouchi, B. Agard

  • Title: A decision support system for sequencing production in the manufacturing industry
    Authors: A. Dupuis, C. Dadouchi, B. Agard
    Year: 2023
    Citations: 1

  • Title: A decision support tool for the first stage of the tempering process of organic wheat grains in a mill
    Authors: L. Parrenin, C. Danjou, B. Agard, R. Beauchemin
    Year: 2023
    Citations: 5

 

Masoud Alilou | Engineering | Best Researcher Award

Assist. Prof. Dr. Masoud Alilou | Engineering | Best Researcher Award

Electrical Engineering from Urmia University of Technology, Iran

Dr. Masoud Alilou is a distinguished academic and researcher whose expertise lies at the intersection of biomedical engineering, image processing, and machine learning. Renowned for his pioneering contributions to medical image analysis, Dr. Alilou has played a pivotal role in advancing computational tools for disease detection and diagnosis. His research integrates advanced algorithm development with practical clinical applications, especially in oncology and pulmonary imaging. With a strong publication record in high-impact journals and numerous international collaborations, Dr. Alilou is recognized for his innovative methodologies and interdisciplinary approach. He has also been instrumental in mentoring graduate students and contributing to curriculum development in biomedical engineering and computer science programs. His commitment to translational research has led to the development of automated tools aimed at improving diagnostic accuracy and patient care. Over the years, Dr. Alilou has gained a reputation for excellence in research, teaching, and academic leadership. He is a frequent reviewer for reputed journals and conferences, and his work has been widely cited. Through his dedication to technological innovation and scientific rigor, Dr. Alilou continues to make significant contributions to medical imaging and artificial intelligence in healthcare, solidifying his status as a leader in the academic and scientific communities.

Professional Profile

Education

Dr. Masoud Alilou’s academic journey reflects his deep-rooted commitment to interdisciplinary research and education. He earned his Bachelor’s degree in Computer Engineering, laying a strong foundation in algorithm design, programming, and systems analysis. Driven by a desire to apply computational methods to real-world problems, he pursued a Master’s degree in Biomedical Engineering. During this period, he focused on medical image analysis and machine learning, bridging the gap between engineering and clinical medicine. His master’s research emphasized the development of image processing tools for diagnosing chronic lung diseases, which sparked his long-term interest in healthcare technologies. He later completed his Ph.D. in Biomedical Engineering at Case Western Reserve University, a globally respected institution in the field. His doctoral research concentrated on automated quantitative analysis of medical images using advanced computational models and machine learning techniques. During his Ph.D., Dr. Alilou collaborated closely with radiologists and oncologists, reinforcing the clinical relevance of his work. His interdisciplinary training uniquely positioned him to develop algorithms that are both technically robust and clinically meaningful. Through rigorous coursework, hands-on research, and cross-disciplinary mentorship, Dr. Alilou has built an educational background that combines computational science, engineering, and medicine—an essential blend for cutting-edge biomedical research.

Professional Experience

Dr. Masoud Alilou has amassed an impressive portfolio of professional experience that spans academic research, interdisciplinary collaboration, and technological innovation. Following his doctoral studies, he joined the Quantitative Imaging Laboratory at Case Western Reserve University as a research scientist. In this role, he led and contributed to multiple NIH-funded projects aimed at developing automated tools for lung cancer screening and diagnosis using low-dose CT scans. His work involved close collaboration with clinicians, radiologists, and computer scientists, fostering a rich interdisciplinary environment. Dr. Alilou has also served as a senior researcher and developer on projects integrating artificial intelligence into clinical workflows, focusing on machine learning algorithms for lung nodule detection, segmentation, and classification. His algorithms have been implemented in software solutions used by research hospitals and diagnostic centers, significantly enhancing diagnostic precision and workflow efficiency. In addition to research, Dr. Alilou has mentored graduate students, supervised thesis projects, and contributed to the development of training modules in biomedical imaging and AI. His professional experience also includes serving as a reviewer for numerous peer-reviewed journals, including IEEE Transactions on Medical Imaging and Medical Physics. Through these roles, Dr. Alilou has built a strong reputation as both a scientific innovator and a collaborative leader in the medical imaging community.

Research Interests

Dr. Masoud Alilou’s research interests lie at the convergence of biomedical engineering, medical image analysis, and artificial intelligence. Central to his work is the development of computational techniques for the automated analysis of medical images, particularly in the early detection and characterization of diseases such as lung cancer and chronic obstructive pulmonary disease (COPD). He is deeply interested in low-dose CT imaging and its applications in non-invasive diagnostics, seeking to optimize the accuracy and efficiency of radiological assessments through advanced algorithms. A significant focus of Dr. Alilou’s research is on radiomics—extracting high-dimensional features from medical images to identify patterns correlated with disease outcomes. He is also engaged in developing deep learning models for image classification, segmentation, and prediction of treatment response. His work explores how quantitative image features can be integrated with clinical data to inform precision medicine. Moreover, Dr. Alilou is enthusiastic about translational research, ensuring that the algorithms and tools he develops are applicable in clinical settings. His interdisciplinary projects often involve partnerships with radiologists, oncologists, and biostatisticians. Through his commitment to impactful research, Dr. Alilou continues to push the boundaries of medical imaging, aiming to enhance patient outcomes through innovation and data-driven healthcare solutions.

Research Skills

Dr. Masoud Alilou possesses an exceptional set of research skills that span computational modeling, machine learning, and biomedical image analysis. He is highly proficient in developing and implementing complex algorithms for image processing tasks, including segmentation, registration, and feature extraction. His expertise in computer vision allows him to work with large-scale imaging datasets, transforming raw medical data into meaningful clinical insights. He has extensive experience with deep learning frameworks such as TensorFlow, PyTorch, and Keras, which he uses to design and train neural networks for various diagnostic tasks. Additionally, Dr. Alilou is adept in programming languages such as Python, MATLAB, and C++, enabling him to prototype and optimize algorithms efficiently. His skills in radiomics and statistical analysis allow for the extraction and evaluation of high-dimensional imaging biomarkers, supporting the development of predictive and prognostic models. Dr. Alilou also demonstrates strong skills in interdisciplinary collaboration, integrating domain knowledge from radiology, oncology, and bioinformatics into his research workflows. His rigorous approach to data validation, model performance evaluation, and reproducibility ensures the reliability of his findings. Whether through designing novel AI models or translating computational tools into clinical applications, Dr. Alilou’s technical and collaborative skills stand at the core of his impactful research contributions.

Awards and Honors

Dr. Masoud Alilou has received several prestigious awards and honors in recognition of his outstanding research contributions and academic achievements. His innovative work in the field of medical image analysis has earned him accolades from both academic institutions and professional organizations. As a graduate student, he was honored with the Research Excellence Award at Case Western Reserve University, acknowledging his impactful contributions to biomedical engineering and medical imaging. His research has also been recognized at international conferences, where he has received best paper and poster awards for his work on automated lung cancer detection and radiomics-based diagnostic tools. Dr. Alilou’s contributions to artificial intelligence in healthcare have attracted attention from funding bodies such as the National Institutes of Health (NIH), resulting in several grant-supported projects. In addition, he has been invited to present his work at renowned symposiums and workshops, affirming his status as a thought leader in his field. Dr. Alilou also serves as a regular reviewer for high-impact journals, a testament to the scientific community’s trust in his expertise. These honors reflect not only his technical proficiency but also his dedication to advancing medical science through innovation, collaboration, and academic excellence.

Conclusion

In summary, Dr. Masoud Alilou stands out as a pioneering figure in the field of biomedical engineering and medical image analysis. With a strong educational foundation and diverse professional experience, he has successfully bridged the worlds of computational science and clinical medicine. His research—centered on the development of AI-driven tools for disease diagnosis and prediction—has not only advanced academic knowledge but also brought tangible benefits to healthcare practice. Dr. Alilou’s skills in image processing, machine learning, and interdisciplinary collaboration have positioned him as a key contributor to the evolving landscape of precision medicine. His numerous awards and academic recognitions reflect a career marked by innovation, excellence, and societal impact. Beyond research, Dr. Alilou’s contributions as a mentor, educator, and collaborator have enriched the academic and scientific communities. Looking forward, he continues to explore new frontiers in medical AI, with a vision of improving diagnostic accuracy, patient outcomes, and health system efficiency. As a scientist dedicated to turning complex data into actionable healthcare solutions, Dr. Alilou exemplifies the potential of integrating technology and medicine for the betterment of global health.

Publications Top Notes

  1. Title: Home energy management in a residential smart micro grid under stochastic penetration of solar panels and electric vehicles
    Authors: M. Alilou, B. Tousi, H. Shayeghi
    Year: 2020
    Citations: 93

  2. Title: Fractional-order control techniques for renewable energy and energy-storage-integrated power systems: A review
    Authors: M. Alilou, H. Azami, A. Oshnoei, B. Mohammadi-Ivatloo, R. Teodorescu
    Year: 2023
    Citations: 33

  3. Title: Application of multi objective HFAPSO algorithm for simultaneous placement of DG, capacitor and protective device in radial distribution network
    Authors: H. Shayeghi, M. Alilou
    Year: 2015
    Citations: 25

  4. Title: Multi-objective optimization of demand side management and multi DG in the distribution system with demand response
    Authors: M. Alilou, D. Nazarpour, H. Shayeghi
    Year: 2018
    Citations: 24

  5. Title: Simultaneous placement of renewable DGs and protective devices for improving the loss, reliability and economic indices of distribution system with nonlinear load model
    Authors: M. Alilou, V. Talavat, H. Shayeghi
    Year: 2020
    Citations: 20

  6. Title: Multi-objective energy management of smart homes considering uncertainty in wind power forecasting
    Authors: M. Alilou, B. Tousi, H. Shayeghi
    Year: 2021
    Citations: 19

  7. Title: Multi-Objective demand side management to improve economic and‎ environmental issues of a smart microgrid‎
    Authors: H. Shayeghi, M. Alilou
    Year: 2021
    Citations: 17

  8. Title: Distributed generation and microgrids
    Authors: H. Shayeghi, M. Alilou
    Year: 2021
    Citations: 16

  9. Title: Multi‐objective unit and load commitment in smart homes considering uncertainties
    Authors: M. Alilou, B. Tousi, H. Shayeghi
    Year: 2020
    Citations: 12

  10. Title: Day-ahead scheduling of electric vehicles and electrical storage systems in smart homes using a novel decision vector and AHP method
    Authors: M. Alilou, G.B. Gharehpetian, R. Ahmadiahangar, A. Rosin, et al.
    Year: 2022
    Citations: 11

  11. Title: Optimal placement and sizing of TCSC for improving the voltage and economic indices of system with stochastic load model
    Authors: S. Ghaedi, B. Tousi, M. Abbasi, M. Alilou
    Year: 2020
    Citations: 10