جست‌وجوی استاد و پژوهشگر

Thomas G. Costantinoمشاهده پروفایل

استاد

• Emergency Medicine Ultrasound
• Diagnostic Imaging
• Point-of-Care Ultrasound (POCUS)
• +۳ مورد دیگر

Thomas G. Costantino, MD, is a Professor in the Department of Emergency Medicine at Lewis Katz School of Medicine at Temple University. He specializes in Emergency Medicine Ultrasound, with a focus on diagnostic imaging, vascular access, and musculoskeletal ultrasound. Dr. Costantino has held academic positions at multiple institutions, including Drexel University and Medical College of Pennsylvania, and has received numerous accolades for teaching excellence and research contributions. Education: MD from University of Michigan School of Medicine Training: Fellowship and Residency in Emergency Medicine at Medical College of Pennsylvania His research involves advancing point-of-care ultrasound (POCUS) techniques in emergency diagnostics, particularly for aortic dissections, pleural effusions, and acute visual complaints. His publications highlight the efficacy of ultrasound-guided procedures over traditional methods. Scientific Awards & Memberships National Alzheimer’s Federation Research Award University of Michigan Alumni Merit Scholarship Excellence in Teaching Awards at Temple, Drexel, and MCP/Hahnemann Medical Schools Fellow of American Academy of Emergency Medicine and American College of Emergency Physicians Dr. Costantino actively contributes to emergency medicine education, including associate editor roles in medical textbooks and ultrasound training programs. His work emphasizes improving diagnostic accuracy and patient outcomes through innovative imaging techniques.

• Statistical signal processing
• Robust statistical methods
• Array processing
• +۶ مورد دیگر

Esa Ollila serves as Associate Professor in the Department of Signal Processing and Acoustics at Aalto University, Finland, and holds an adjunct professorship in Statistics at the University of Oulu. His academic appointments include Academy of Finland Research Fellow (2010-2015) and prior senior research/lecturing roles at both institutions. His educational background features: M.Sc. in Mathematics, University of Oulu (1998) Ph.D. in Statistics (with honors), University of Jyväskylä (2002) D.Sc.(Tech) in Signal Processing (with honors), Aalto University (2010) Professor Ollila's research centers on statistical signal processing and robust statistical methodologies , with significant contributions to array processing, high-dimensional data analysis, and covariance matrix estimation. His work bridges theoretical statistics with practical applications in radar systems, wireless communications, and big data analytics, emphasizing robustness against outliers and computational efficiency in modern data-intensive environments. Current focus areas include compressed sensing, sparse approximation, and blind source separation techniques. Analysis of his 15 most recent publications (2024-2025) reveals three dominant trends: (1) robust covariance learning for massive random access systems, (2) integrated sensing and communications (ISAC) for 6G networks using advanced beamforming, and (3) geometric approaches to elliptical distributions in statistical inference. His work increasingly incorporates deep learning (GANs, graph neural networks) while maintaining strong foundations in classical signal processing theory. Key recognitions include: Academy of Finland Postdoctoral Fellowship (2004-2007) Academy of Finland Research Fellowship (2010-2015) His research has been supported through prestigious Academy of Finland grants totaling over a decade of continuous funding. Professor Ollila currently leads an active research group at Aalto University, supervising doctoral candidates and collaborating internationally with institutions including Princeton University (where he served as Visiting Post-doctoral Research Associate during 2010-2011). He maintains strong ties with the University of Oulu through his adjunct professorship and has contributed to EURASIP's Special Area Team on Theoretical and Methodological Trends in Signal Processing. The Esa Ollila Research Group focuses on cutting-edge challenges in statistical signal processing, with current projects spanning robust DOA estimation under non-Gaussian noise, covariance matrix learning for massive MIMO systems, and machine learning-enhanced radar-communication integration. The group actively develops open-source tools like the fitHeavyTail R package for heavy-tailed distribution modeling and maintains collaborations with industry partners in wireless communications.

Kwantae Kimمشاهده پروفایل

استادیار

• Power-Efficient Analog/Mixed-Signal Integrated Circuits
• Biomedical Sensor Systems
• Neuromorphic Sensor ICs
• +۳ مورد دیگر

Kwantae Kim is an Assistant Professor at the Department of Electronics and Nanoengineering within Aalto University's School of Electrical Engineering . He leads the Tiny Systems and Circuits (TSirc) Group , focusing on power-efficient analog/mixed-signal ICs for biomedical and neuromorphic sensor systems. IEEE Senior Member (2025) Collaborates with institutions across Europe, Asia, and America Specializes in ultra-low-power AI-embedded IoT platforms His research emphasizes Tiny, Sensory, Intelligent, and Wireless IoT systems through: Development of energy-efficient IC architectures Democratizing access to advanced chip design Hardware-software co-design for edge computing Recent publications highlight innovations in: Spoken-language-understanding SoCs Temporal-sparsity-aware keyword spotting Open-source silicon frameworks Awards include: 2025 IEEE Senior Member 2023 Best Poster Award (AICAS) 2019 Samsung HumanTech Silver Award Research partnerships span: Prof. Tobi Delbruck (UZH/ETH Zurich) Prof. Hoi-Jun Yoo (KAIST) Prof. Shih-Chii Liu (UZH) Prof. Sohmyung Ha (NYU Abu Dhabi)

William A. Goddard, IIIمشاهده پروفایل

استاد

• Quantum Chemistry
• Molecular Dynamics
• Statistical Physics
• +۷ مورد دیگر

William A. Goddard, III is the Charles and Mary Ferkel Professor of Chemistry, Materials Science, and Applied Physics at the California Institute of Technology. With a career spanning over five decades, he has held positions from Noyes Research Fellow (1964–66) to his current professorship since 2001. His educational background includes a B.S. from UCLA (1960) and a Ph.D. from Caltech (1965). Quantum chemistry and first-principles simulations Multiscale modeling (QM→MD→mesoscale) Catalysis and protein structure prediction Nanotechnology and bionanotechnology Energy storage (batteries, supercapacitors) Recent publications emphasize applications in metal-organic frameworks , electrocatalysis , and space manufacturing , reflecting his interdisciplinary approach. His work on G-protein coupled receptors and Li-S batteries demonstrates methodological innovation through quantum mechanics and machine learning . Horizon Prize , Royal Society of Chemistry Over 1548 total publications (1967–2022) As Director of Caltech's Material and Process Simulation Center , he leads development of software like ReaxFF for reactive dynamics. He teaches Ch 120 ab (Nature of the Chemical Bond) and Ch 121 ab (Atomic-Level Simulations), emphasizing hands-on computational applications for experimentalists and theorists.

Christos Gatzidisمشاهده پروفایل

استاد

• Computer Graphics
• Virtual Reality
• Serious Games
• +۵ مورد دیگر

Christos Gatzidis serves as Executive Dean of Bournemouth University's Faculty of Science and Technology since October 2023, overseeing six departments including Computing and Informatics, Creative Technology, and Psychology. Previously Deputy Dean (2021-2023) and Head of Creative Technology Department (2016-2021), he was appointed Professor in Creative Technology in November 2019. The Faculty leads five REF Units of Assessment and secures funding from AHRC, NIHR, and Innovate UK for research spanning digital healthcare, gaming technologies, and cultural heritage applications. His educational qualifications include: PhD in Information Science, City University London (2010) PG Cert in Research Degree Supervision, Bournemouth University (2010) MA in Computer Animation, Teesside University (2003) BSc (Hons) in Computer Studies (Visualisation), University of Derby (2002) Professor Gatzidis specializes in computer graphics with research spanning virtual reality, serious games, and digital healthcare applications. His work bridges technical innovation and practical implementation, particularly in mindfulness prototypes for healthcare and stroke rehabilitation systems. Current projects focus on the multidisciplinary application of gaming technologies in medical contexts, emphasizing user experience and therapeutic outcomes through collaborations with industry partners. His publication record (2014-2025) demonstrates evolving expertise from foundational computer graphics research in terrain generation and deformation to applied work in healthcare, cultural heritage, and music education. Key trends include virtual reality for therapeutic mindfulness, usability studies in mobile gaming, and digital cultural presentation techniques, reflecting a trajectory toward socially impactful technological solutions with strong industry translation. No scientific awards are documented in the provided information. He has supervised PhD students and secured competitive research funding, including two Innovate UK Knowledge Transfer Partnerships. His principal investigator role in a virtual reality mindfulness prototype project exemplifies his approach to translating academic research into practical industry solutions, with current focus on expanding knowledge exchange activities to support the University's civic engagement mission. As Executive Dean, he leads faculty-wide research strategy across six departments, fostering interdisciplinary collaborations particularly in digital healthcare and cultural heritage. His personal research integrates computer graphics expertise with clinical applications through partnerships with healthcare providers and technology companies, driving innovation in therapeutic VR systems and educational gaming platforms.

Jennifer Dyمشاهده پروفایل

استاد

• Machine Learning
• Computer Vision
• Explainable AI
• +۲ مورد دیگر

Jennifer Dy is a Distinguished Professor at Northeastern University with joint appointments in Electrical and Computer Engineering and Khoury College of Computer Sciences. As Director of AI Faculty at the Institute for Experiential AI, she leads research in machine learning, computer vision, and explainable AI. Her work spans biomedical applications (COPD phenotyping, neuroimaging) and fundamental algorithms (active learning, continual learning). She holds a PhD from Purdue University and is an AAAI Fellow. Research Focus: Dy develops methodologies for robust and interpretable machine learning, including techniques for model stability in continual learning, dependency-aware active learning, and axiomatic explanation frameworks. Her applied research advances diagnostic tools using Raman spectroscopy, CT imaging, and multi-omics biomarker discovery. Awards: Recognized with the NSF CAREER Award, Faculty Research Team Award, and AAAI Fellowship for contributions to unsupervised learning and medical AI. Publication Trends: Recent articles demonstrate strong cross-disciplinary integration, combining theoretical advances in explainability/robustness with applications in healthcare, wireless systems, and particle physics. Methodological themes include optimal transport theory, probabilistic modeling, and transformer architectures.

Guohong Tianمشاهده پروفایل

مدرس ارشد

• Automotive Engineering
• Internal Combustion Engines
• Renewable Energy
• +۷ مورد دیگر

Dr. Guohong Tian is a Senior Lecturer in Automotive Engineering at the University of Surrey's School of Mechanical Engineering Sciences. He holds roles including Departmental representative for the Faculty International Relations Committee and coordinator for Headstart and Summer School programs. His research focuses on advanced engine technologies, alternative fuels, and thermal management systems. Prior to Surrey, he was at Newcastle University (2010–2015) and Birmingham University as a Research Fellow (2008–2010). Education: PhD in Mechanical Engineering (Birmingham University). Collaborations: Jaguar Land Rover, Cummins, BP, Shell, JCB, and Avid. Research areas include: Internal combustion engines: novel designs (free piston, scroll engines), waste heat recovery via ORC, and desalination integration. Battery thermal management using capillary-driven cooling and heat pipes. Soot oxidation mechanisms and catalytic diesel particulate filters. Key projects: KTP project with William Medcalf Limited improving vintage engine performance. ADVICE-ADvancing thermal energy management in hybrid vehicles. Publications emphasize combustion diagnostics, emission reduction, and scroll expander optimization. He supervises students on pyrolysis oils and scroll expander development. His lab includes a state-of-the-art engine test bench with FTIR emission analysis and high-speed imaging systems.

Sarah L. Kellerمشاهده پروفایل

استاد

• Biophysics
• Physical Chemistry
• Chemical Biology
• +۳ مورد دیگر

Sarah L. Keller is a Professor of Chemistry at the University of Washington, holding the Duane and Barbara LaViolette Endowed Chair. She earned her Ph.D. in Physics from Princeton University and completed postdoctoral research at UC Santa Barbara and Stanford University. Her research focuses on self-assembling soft matter systems, particularly lipid membrane biophysics and prebiotic chemistry. She investigates how lipid mixtures form complex phases and their biological implications. Education: Ph.D. in Physics, Princeton University (1995); postdoctoral training at UC Santa Barbara (Presidential Fellow) and Stanford University (NIH NRSA Fellow). Research Interests: Keller's lab explores phase behavior in lipid membranes, prebiotic vesicle formation, and membrane-driven cellular processes. They employ interdisciplinary approaches combining physics, chemistry, and bioengineering to study systems like yeast vacuoles and protocell models. Awards: Biophysical Society Avanti Award, Cottrell Scholar, NSF CAREER Award, Fellow of the American Physical Society, and recognition for mentoring and teaching excellence. Her students frequently win NSF Graduate Fellowships and international awards. Advising & Grants: Keller has mentored over 50 students/postdocs, many of whom hold academic or industry roles. Her lab emphasizes interdisciplinary collaboration, with funding from NSF, NIH, and private foundations. The Keller Lab also supports career development resources for early-career researchers. Labs/Teams: The lab is part of the UW Chemistry Department and collaborates with the UW Biophysics program. Research is conducted in the Bagley Hall laboratory, with access to advanced microscopy and simulation tools.

Philipp Neumannمشاهده پروفایل

استاد

• High-Performance Computing
• Scientific Computing
• Parallel and Distributed Systems
• +۴ مورد دیگر

Philipp Neumann is a Professor and Chair of High-Performance Computing at Helmut Schmidt University since 2019. Previously, he held roles including Senior Researcher at the German Climate Computing Center (2016–2019), Postdoc at the University of Hamburg (2017–2019), and completed his Habilitation in Scientific Computing at TU Munich (2019). He transitioned to DESY/Universität Hamburg in May 2024. Education : PhD (Dr. rer. nat.) in Scientific Computing at TU Munich (2008–2013) Habilitation in Scientific Computing at TU Munich (2019) Studies in Technomathematics at Friedrich-Alexander University Erlangen-Nuremberg (2003–2008) Research Interests focus on high-performance computing, parallel and distributed systems, computational science, and applications in climate modeling. His work bridges computational methods with medical challenges, including surgical simulation training, wound healing mechanisms, and gastrointestinal pathology. Professional Activities include leadership roles in major conferences such as steering committee member for ISPDC (since 2021) and program committee roles in IPDPS, ICCS, and IEEE Cluster. He also managed the DFG priority program SPPEXA (2013–2016). Labs/Teams include the Chair for High-Performance Computing at Helmut Schmidt University and collaborations with the German Climate Computing Center. His current work at DESY/Universität Hamburg likely expands these computational efforts into interdisciplinary research.

Dan McCammonمشاهده پروفایل

استاد

• X-ray Astronomy
• Astrophysics
• Diffuse X-ray Background
• +۵ مورد دیگر

Dan McCammon is a Professor in the Department of Physics at the University of Wisconsin-Madison, affiliated with the College of Letters & Science. His research focuses on X-ray astronomy, including studies of the diffuse X-ray background, interstellar and intergalactic media, and the development of advanced X-ray instrumentation. He is a key contributor to the XRISM (X-ray Imaging and Spectroscopy Mission) satellite, leading efforts in high-resolution X-ray spectroscopy and mission operations. McCammon's work emphasizes understanding cosmic plasma dynamics, galaxy cluster physics, and supernova remnant evolution through cutting-edge observational techniques and detector technology. His research interests span multiple subfields, including the thermodynamic properties of galactic clusters, charge-exchange processes in astrophysical plasmas, and the design of cryogenic microcalorimeters for space-based observatories. He has pioneered advancements in transition-edge sensors (TES) and superconducting detectors, enhancing the precision of X-ray spectral measurements. McCammon has contributed to numerous sounding rocket missions, such as Micro-X, and has been instrumental in the development of the Line Emission Mapper (LEM) probe concept, aimed at mapping the soft X-ray sky with unprecedented resolution. His work on the Hitomi (ASTRO-H) satellite demonstrated breakthroughs in resolving the thermal and dynamic properties of cosmic plasmas, such as the Perseus galaxy cluster and the Crab Nebula. His publications highlight a focus on high-resolution X-ray spectroscopy of cosmic sources, including galaxy clusters, active galactic nuclei, and supernova remnants. He has explored topics like non-thermal pressure contributions in cluster cores, ionized plasma diagnostics, and the role of charge-exchange emissions in interpreting diffuse X-ray backgrounds. McCammon's instrumentation innovations have enabled breakthroughs in measuring spectral features with sub-eV resolution, advancing our understanding of astrophysical processes. Despite the absence of explicitly listed awards or grants in the provided text, his leadership in major space missions and pioneering detector technologies underscores his contributions to the field. His research team collaborates on international projects, such as XRISM and LEM, reflecting a commitment to advancing observational astrophysics through interdisciplinary collaboration.

Ronald G. Larsonمشاهده پروفایل

استاد

• Complex Fluids
• Fluid Mechanics
• Polymer Physics
• +۶ مورد دیگر

Ronald G. Larson serves as the George Granger Brown Professor of Chemical Engineering and A. H. White Distinguished University Professor at the University of Michigan's College of Engineering, with additional appointments in Mechanical Engineering and Macromolecular Science & Engineering. His research leadership spans multiple departments within the Chemical Engineering Division, where he directs the Larson Lab focused on fundamental and applied soft matter physics. His research program investigates complex fluids through computational and theoretical frameworks, emphasizing polymer physics, rheology, and molecular simulations. Key thrusts include polymer melt processing, biomembrane dynamics, colloidal systems, and polyelectrolyte coacervation. The group employs advanced techniques like Brownian dynamics, coarse-grained modeling, and multiscale simulation to address challenges ranging from industrial polymer processing to biomedical applications. Recent publications (2023-2025) reveal strong momentum in rheological modeling of complex fluids, with particular emphasis on self-healing materials, wax deposition in pipelines, and crystallization mechanisms. The work bridges fundamental molecular insights with industrial applications, demonstrating consistent high-impact output across polymer science, soft matter physics, and chemical engineering domains. The Larson Lab operates as a collaborative hub within the Chemical Engineering Department, leveraging computational resources to advance understanding of fluid mechanics and material properties. Current projects integrate machine learning with traditional modeling approaches, reflecting the group's commitment to methodological innovation while maintaining strong connections to experimental validation and real-world engineering problems.

Joyoung Leeمشاهده پروفایل

دانشیار

• Connected Vehicle (CV) Systems
• Traffic Management
• Autonomous Vehicle Control
• +۵ مورد دیگر

Dr. Joyoung Lee is an Associate Professor in the Department of Civil and Environmental Engineering at New Jersey Institute of Technology (NJIT). He previously served as Laboratory Manager at the Federal Highway Administration's Saxton Transportation Operations Laboratory. His research focuses on Connected Vehicle (CV) systems, including applications in traffic management, signal control optimization, and autonomous vehicle infrastructure integration. Dr. Lee holds a Ph.D. (2010) and M.S. (2007) in Transportation Engineering from the University of Virginia, and a B.S. (2000) in Transportation Engineering from Hanyang University. His work emphasizes CV-based solutions for real-time traffic systems, cooperative vehicle-infrastructure systems (CVIS), and autonomous vehicle integration. Notable achievements include the 2019 IEEE CAVS Best Paper Award and multiple best paper recognitions from PTV User Group Meetings. His research also addresses traffic safety through innovations like the Virtual Guide Dog system for visually impaired pedestrians and advanced traffic monitoring frameworks using LiDAR and computer vision. Education: Ph.D., Transportation Engineering, University of Virginia (2010) M.S., Transportation Engineering, University of Virginia (2007) B.S., Transportation Engineering, Hanyang University (2000) Dr. Lee's research interests span smart city infrastructure, edge computing for traffic systems, and sustainable transportation solutions. He has pioneered algorithms for cooperative intersection management, automated platooning systems, and federated learning-based traffic optimization. His work bridges theoretical models with real-world implementation through partnerships with FHWA and industry stakeholders. Key contributions include development of the Cumulative Travel-Time Responsive (CTR) traffic signal control system, smart arrival notification systems for paratransit services, and advanced microsimulation calibration techniques. His lab focuses on translating CV data into actionable strategies for safer, more efficient transportation networks. Awards: IEEE CAVS Best Paper Award (2019) ASCE Grand Challenge Innovation Contest Honorable Mention (2017) PTV VISSIM Best Paper Awards (2012, 2008) Excellence in Research Award (University of Virginia, 2011) Ongoing projects include semi-decentralized graph neural networks for traffic forecasting and low-cost LiDAR-based traffic monitoring systems. His work addresses critical challenges in autonomous vehicle integration, incident management, and infrastructure resilience through interdisciplinary collaborations.

• Graph Representation Learning
• Graph Neural Networks
• Deep Generative Models
• +۵ مورد دیگر

Jian Tang is an Assistant Professor at HEC Montréal and a core member of the Montreal Institute for Learning Algorithms (MILA). His research focuses on graph representation learning, generative models, and their applications in drug discovery and material science. Prior to this, he was a postdoctoral researcher at the University of Michigan and Carnegie Mellon University, and a researcher at Microsoft Research Asia (2014-2016). He has received several prestigious recognitions, including the Canada CIFAR Artificial Intelligence Chairs (CCAI Chair) and best paper nominations at WWW’16. His work on LINE (WWW’15) was recognized as the most cited paper in its year. Tang’s research spans theoretical foundations and practical systems, such as GraphVite for scalable graph embedding and TorchDrug for drug discovery. Key research interests include geometric deep learning for molecular structures, generative models for protein design, and neural-symbolic reasoning for knowledge graphs. He actively collaborates with leading biological labs and leverages industry partnerships for GPU resources. Recent publications emphasize molecular property prediction, 3D conformation generation, and algorithmic reasoning frameworks. He has secured grants from IBM/MILA, Amazon, and the National Research Council Canada, supporting projects like molecular pretraining and geometric representation learning. Tang teaches courses on graph representation learning and deep learning, and mentors a vibrant team of PhD and master’s students. His lab has developed impactful software tools like LINE, PTE, and LargeVis, widely used in the research community.

• Heterogeneous Catalysis
• Catalyst Synthesis
• Lignin Depolymerization
• +۷ مورد دیگر

An Verberckmoes is an Associate Professor at Ghent University in the Faculty of Engineering and Architecture, specifically within the Department of Materials, Textiles and Chemical Engineering. She is affiliated with multiple research units including the Biomolecules Center for Sustainable Chemistry, ChemTech Materials, and the Industrial Catalysis and Adsorption Technology group. Her research expertise centers on heterogeneous catalysis with a strong focus on sustainable chemical processes. Dr. Verberckmoes specializes in catalyst synthesis, particularly zeolite-based catalysts for bio-alcohol conversion and lignin valorization. Her work bridges fundamental catalyst design with practical applications in biomass conversion, aiming to develop more efficient and environmentally friendly processes for producing renewable chemicals and materials. Analysis of her recent publications (2024-2025) reveals a dominant research trajectory focused on lignin depolymerization technologies, with particular emphasis on catalytic approaches using noble and non-noble metals. She has made significant contributions to understanding reaction mechanisms in zeolite catalysis, especially for dehydration reactions of bio-alcohols to valuable chemicals like butadiene. Her work often combines experimental approaches with kinetic modeling to optimize both catalyst performance and process conditions. Dr. Verberckmoes collaborates extensively within Ghent University and with external partners on projects related to sustainable chemistry and biomass conversion. Her research group appears to focus on developing integrated approaches that combine catalyst design, process engineering, and advanced analytical techniques to advance lignin valorization and sustainable chemical production.

Carolina Osorioمشاهده پروفایل

استاد

• Transportation Science
• Operations Research
• Artificial Intelligence
• +۴ مورد دیگر

Carolina Osorio is a Professor at HEC Montréal, holding the Scale AI Research Chair in Artificial Intelligence for Urban Mobility and Logistics. She is affiliated with the Department of Decision Sciences and is a member of the Group for Research in Decision Analysis (GERAD) and the Interuniversity Research Centre on Enterprise Networks, Logistics and Transportation (CIRRELT). Her research focuses on transportation optimization, urban mobility, and data-driven simulation-based methods. She has been recognized among the world’s most influential researchers in 2023 and 2024. Education: Ph.D. in Mathematics, École Polytechnique Fédérale de Lausanne (EPFL) M.Sc. in Statistics, University College London (UCL) Bachelor’s in Engineering, École nationale supérieure d'informatique et de mathématiques appliquées de Grenoble (ENSIMAG) Research Interests: Her work emphasizes scalable transportation modeling, simulation-based optimization, and AI applications for urban logistics. She develops methods for large-scale network analysis, traffic demand estimation, and sustainable urban mobility solutions. Key areas include traffic signal optimization, car-sharing service design, and high-dimensional stochastic systems. Publications: Recent articles highlight advancements in scalable traffic demand estimation, Bayesian optimization for transportation systems, and simulation-based toll optimization. Her work addresses challenges in global highway networks, urban congestion dynamics, and multi-city calibration. Awards: Scale AI Research Chair (Artificial Intelligence for Urban Mobility and Logistics) Recognition as a world-leading researcher in transportation science Advising & Grants: Osorio collaborates on projects funded by Scale AI and leads research initiatives through GERAD and CIRRELT. Her supervision activities include teaching courses such as Decision Analysis and Sample Efficient Optimization at HEC Montréal. Labs & Teams: She contributes to interdisciplinary teams at GERAD and CIRRELT, focusing on integrating advanced analytics into urban transportation systems.