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Yassin A. HassanView profile

Professor

• Computational Thermal Hydraulics
• Experimental Thermal Hydraulics
• Reactor Safety
• +12 more

Dr. Yassin A. Hassan is a Professor at the College of Engineering , Texas A&M University , with joint appointments in Nuclear Engineering and Mechanical Engineering . He holds the L.F. Peterson '36 Chair II , is a University Distinguished Professor , and directs the Center for Advanced Small Modular and Microreactors (CASMR) . Ph.D., Nuclear Engineering, University of Illinois – 1980 M.S., Nuclear Engineering, University of Illinois – 1975 B.S., Engineering, University of Alexandria in Egypt – 1968 His research interests include: Computational & Experimental Thermal Hydraulics Reactor Safety Fluid Mechanics Two-Phase Flow Turbulence & Laser Velocimetry Imaging Techniques His recent publications focus on: Thermal hydraulics of heat pipes and microreactors AI integration in nuclear thermal-fluid systems Flow regime transitions in wire-wrapped fuel assemblies CFD validation for pebble bed and molten salt reactors Uncertainty quantification in reactor simulations Flow visualization techniques under elevated pressures Scientific awards include: American Nuclear Society Seaborg Medal (2008) James N. Landis Medal (ASME, 2017) Akiyama Medal (ICONE 24, 2016) Arthur Holly Compton Award (ANS, 2003) Texas A&M TEES Research Impact Award (2018-2019) Honorary professor, Bangor University, UK Dr. Hassan leads the Thermal-Hydraulics Research Laboratory and has pioneered advancements in reactor safety, digital twin technologies, and AI-driven thermal-fluid simulations.

Sahani PathirajaView profile

Lecturer

• Data Science
• Bayesian Inference
• Stochastic Analysis
• +7 more

Dr. Sahani Pathiraja is a Lecturer (tenure track assistant professor) at UNSW Sydney , specializing in Data Science . Her research bridges mathematical and statistical foundations with practical applications in environmental and biomedical sciences. Research Focus : Sequential Bayesian inference, Monte Carlo methods, stochastic analysis of non-linear filtering, uncertainty quantification, and real-time parameter estimation. Current Projects : Co-investigator in the ARC Industrial Transformation Training Centre: Data Analytics for Resources and Environment (DARE) and the Next Generation Graduate Program (NGGP) in Sports Data Science and AI . Research Supervision : Dr. Pathiraja supervises PhD students in areas including: Bayesian inference Stochastic differential equations Data assimilation Non-linear filtering Scientific Collaborations : Her work intersects with environmental science, biomedical applications, and machine learning. Projects include stochastic hydrology, SDEs, and operator learning for environmental systems. Contact Information : Email: s.pathiraja@unsw.edu.au Phone: +61 2 8065 0836 Office: Room 2070, Level 2, The Red Centre, UNSW Sydney

Owais KhanView profile

Assistant Professor

• Cardiovascular Biomechanics
• Computational Fluid Dynamics
• Medical Imaging
• +3 more

Owais Khan serves as an Assistant Professor in the Department of Biomedical Engineering at Toronto Metropolitan University, where he leads research in cardiovascular biomechanics to improve heart disease diagnosis and treatment through engineering-driven approaches combining computational simulations, medical imaging, and biomechanics. His research program focuses on three interconnected pillars: developing physics-based computational models for blood flow simulation in patient-specific anatomies; advancing medical imaging techniques like dynamic CT myocardial perfusion and vessel wall MRI for quantitative physiological assessment; and conducting fundamental biomechanics studies to optimize prosthetic valve designs. This work directly addresses critical clinical challenges including heart surgery complications, aneurysm rupture prediction, and vein graft failure in coronary bypass patients. Khan's publication record demonstrates consistent innovation in cardiovascular computational modeling, with recent work emphasizing personalized medicine through physics-informed neural networks, multi-fidelity uncertainty quantification, and integration of CT perfusion imaging for coronary hemodynamics. His research bridges engineering principles with clinical cardiology to enable virtual treatment planning and risk stratification without additional patient risk. His scientific contributions have been recognized with prestigious awards including the American Heart Association Postdoctoral Fellowship, NSERC Postdoctoral Fellowship, Baxter Young Investigator Award, and MITACS Globalink Research Award. As director of the Cardiovascular Imaging and Modeling Biomechanics Lab (CIMBL), Khan maintains active collaborations with clinicians and radiologists at major hospitals, facilitating direct translation of engineering solutions to clinical cardiovascular medicine through a multi-disciplinary approach focused on personalized treatment strategies.

Eric BarthView profile

Professor

• Dynamic systems and control
• Mechatronic systems
• Fluid power systems
• +7 more

Eric Barth is Professor of Mechanical Engineering and Professor of Neurological Surgery at Vanderbilt University's School of Engineering. He serves as Director of the C* Control laboratory (also known as the Laboratory for the Design and Control of Energetic Systems) and is affiliated with the Vanderbilt Institute for Surgery and Engineering (VISE), an interdisciplinary entity bringing engineers and physicians together to impact healthcare. His educational background includes: Ph.D. in Mechanical Engineering from Georgia Institute of Technology M.S. in Mechanical Engineering from Georgia Institute of Technology B.S. in Engineering Physics from University of California - Berkeley Professor Barth's research focuses on dynamic systems and control with applications spanning multiple domains. His primary interests include the design, modeling and control of mechatronic and fluid power systems, free-piston internal combustion and free-piston Stirling engines, energy storage and harvesting systems, and MRI compatible pneumatic robots for medical applications. His work applies a system dynamics and control perspective to problems involving the control and transduction of energy, encompassing multi-physics modeling, control methodologies formulation, and model-based design. His recent publications reveal a strong trajectory connecting mechanical engineering principles with medical applications, particularly in neurosurgery. The research spans energy systems (especially Stirling engines and novel energy storage approaches) and advanced medical robotics for MRI-guided interventions. This dual focus demonstrates his ability to bridge theoretical control systems with practical applications in both energy and healthcare domains. Professor Barth actively advises several doctoral students including David Comber, Joshua J Cummins, Alexander V. Pedchenko, and E. Bryn Pitt. His research is supported by significant funding, notably from the Center for Compact and Efficient Fluid Power, an NSF Engineering Research Center. The C* Control laboratory he directs occupies approximately 1000 square feet and contains specialized equipment including an 8-camera high-bandwidth optical tracking system, mechanical breadboard tables, pneumatic equipment with high-bandwidth servo-valves, specialized pressure sensors, a thermographic camera, high-speed video equipment, 3D printers, and a 2D laser cutter. Computational facilities include a network of approximately 20 machines running MATLAB/Simulink and SolidWorks, with access to additional CNC machining resources through the School of Engineering and the University.

Steven ShechterView profile

Professor

• Stochastic Optimization
• Simulation
• Medical Decision Making
• +3 more

Steven Shechter is a Professor of Business Administration and holds the WJ VanDusen Chair in the Operations and Logistics Division at the University of British Columbia's Sauder School of Business. He holds a BS in Mathematics from Loyola University Chicago, an MS in Operations Research from Georgia Tech, and a PhD in Industrial Engineering from the University of Pittsburgh. His research focuses on stochastic optimization, simulation methodologies, and healthcare operations, with particular emphasis on medical decision-making and healthcare system efficiency. His academic contributions include groundbreaking work in multi-objective optimization (e.g., electoral apportionment models), patient monitoring systems, and surgical capacity allocation. He teaches advanced decision modeling courses to MBA and MBAN students, including Simulation Modeling and Optimal Decision Making modules. Key research trends in his work include applying stochastic processes to healthcare challenges (e.g., alarm fatigue management in patient monitoring systems), optimizing surgical workflows, and developing adaptive treatment protocols using Bayesian methods. His recent studies address pressing issues like kidney transplantation decision models and pediatrician scheduling in healthcare facilities. While no formal awards are listed, his extensive publication record reflects sustained impact in operations research and healthcare analytics. His work often bridges theoretical models with practical healthcare applications, emphasizing system efficiency and patient-centered outcomes.

• Transportation Engineering
• Asphalt Materials
• Viscoelasticity
• +5 more

Ramez M. Hajj is an Assistant Professor in the Department of Civil and Environmental Engineering at the University of Illinois at Urbana-Champaign (UIUC). He holds affiliations with the Grainger College of Engineering and has served in multiple academic and professional roles, including editorial board memberships and leadership in organizations like the Transportation Research Board. His research focuses on asphalt materials and flexible pavements, spanning molecular-level investigations to large-scale infrastructure applications, with particular emphasis on viscoelasticity, composites, and machine learning. Education: Bachelor of Science in Civil Engineering with a minor in Engineering Science and Mechanics, Virginia Tech (2014) Master of Science in Civil Engineering, University of Texas at Austin (2016) Doctor of Philosophy in Civil Engineering, University of Texas at Austin (2019) Research Interests: Asphalt binder rheology and chemistry Computational modeling of infrastructure materials Pavement design, maintenance, and recycling Application of AI and machine learning in materials engineering Self-healing asphalt technologies Sustainable infrastructure solutions Publications: His work spans over 50 peer-reviewed articles, emphasizing innovations in asphalt material science and infrastructure resilience. Recent research highlights include AI-driven predictive models for asphalt properties and novel methods for evaluating pavement performance using ultrasonic techniques. Awards and Honors: Outstanding Reviewer awards from leading journals (2021–2022) Teaching excellence recognitions from the Center for Innovation in Teaching and Learning Illinois-Indiana Sea Grant Faculty Fellowship Grants and Funding: Research is supported by agencies such as IDOT, USDA, MnDOT, and industry partners. Projects include developing self-healing asphalt capsules and optimizing pavement design algorithms. Labs and Teams: Leads research initiatives in advanced material characterization and AI-driven infrastructure solutions within UIUC’s Civil and Environmental Engineering department.

David JensenView profile

Professor

• Machine Learning
• Causal Modeling
• Artificial Intelligence
• +3 more

David Jensen is a Professor in the College of Information and Computer Sciences (CICS) at the University of Massachusetts Amherst. He directs the Knowledge Discovery Laboratory and the Computational Social Science Institute. His research focuses on machine learning, causal modeling, and analyzing large social, technological, and computational systems. Jensen's work is supported by organizations like the National Science Foundation and DARPA. Education: DSc in Engineering and Policy, Washington University in St. Louis (1992) MS in Engineering and Policy, Washington University in St. Louis (1988) BS in Mechanical Engineering, University of Nebraska (1986) Research Interests: Causal inference in relational and dynamic systems Machine learning applications in security and privacy Computational social science Large-scale network analysis Achievements: Recipient of teaching awards from UMass College of Natural Sciences (2011) and CICS (2022) 2017 IEEE INFOCOM Test of Time Paper Award Leadership roles in conferences and journals, including action editor for the Journal of Machine Learning Research Labs and Affiliations: Founder of the Knowledge Discovery Laboratory (2000) Director of the Computational Social Science Institute (2018-2022) Member of the Computing Community Consortium (CCC) Council

Hao ZhangView profile

Associate Professor

• Robotics
• Artificial Intelligence
• Computer Vision
• +6 more

Hao Zhang is an Associate Professor in the Department of Computer Science at the Manning College of Information and Computer Sciences (CICS), University of Massachusetts Amherst. He directs the Human-Centered Robotics Laboratory (HCRLab), focusing on lifelong collaborative autonomy, robot adaptation, and human-robot teaming. His research integrates robotics, AI, and machine learning to develop algorithms for real-world applications like manufacturing, autonomous driving, and environmental monitoring. He holds an NSF CAREER Award and DARPA Young Faculty Award, among other recognitions. Dr. Zhang earned a PhD from the University of Tennessee, Knoxville (2014) and an MS from the Chinese Academy of Sciences (2009). His work addresses challenges in unstructured environments through innovations like self-reflective terrain adaptation and graph-based perception systems. He actively promotes equity in robotics through his PROGRESS outreach program. His research sponsors include NSF, DARPA, and industry partners such as Toyota. Publications span conferences like RSS, ICRA, and IROS, with best paper awards. He serves on editorial and program committees for top-tier journals/conferences including RA-L, NeurIPS, and AAAI.

David CastañónView profile

Professor

• Stochastic Control
• Estimation Theory
• Game Theory
• +6 more

David Castañón is a Professor of Electrical and Computer Engineering (ECE) and Systems Engineering (SE) at Boston University. He holds a PhD from MIT (1976) and has held leadership roles including Department Chair of BU ECE (2010-2014) and President of the IEEE Control Systems Society (2008). His research focuses on stochastic control, optimization, game theory, and distributed computing, with applications in sensor management, inverse problems, and autonomous systems. Education: PhD, Massachusetts Institute of Technology (1976). Key affiliations include the Center for Information and Systems Engineering, the Rafik B. Hariri Institute for Computing, and the ALERT Department of Homeland Security Center of Excellence. He teaches courses such as EC702 Recursive Estimation and EC719 Statistical Learning Theory. Research interests span stochastic control, estimation theory, optimization algorithms, and multi-agent systems. Notable contributions include work on sensor management, cooperative operations, and inverse problem solutions for medical and security imaging. His work often integrates theoretical frameworks with practical applications in autonomous systems and distributed computing. Scientific achievements include IEEE Fellow status (2006), CSS Distinguished Member Award, and leadership roles in major conferences like the IEEE Conference on Decision and Control (2007 as General Chair). He has also served on the Air Force Advisory Board and the IEEE Society Review Committee. Grants and lab affiliations include the NSF Engineering Research Center for Subsurface Sensing (2001-2013) and the SENTRY DHS Center of Excellence (2021-present). His interdisciplinary collaborations bridge robotics, medical imaging, and security systems.

Raymond H. CuijpersView profile

Associate Professor

• Cognitive Robotics
• Human-Robot Interaction
• Socially Assistive Robotics
• +5 more

Raymond H. Cuijpers is an Associate Professor at Eindhoven University of Technology in the Human Technology Interaction group. His research focuses on Cognitive Robotics , Human-Robot Interaction , and Artificial Intelligence for cognitive agents, with applications in healthcare robotics and aging population support. PhD in Physics of Man from Utrecht University (2000) Postdoctoral research at Erasmus MC Rotterdam and Radboud University Nijmegen Key research areas include: Developing socially intelligent robots with proper social cue interpretation Hybrid AI approaches for real-world complexity handling Visual-haptic perception integration in human motor control Service robots for COPD patient assistance (KSERA project) Rescue robotics and tele-operation applications Recent research output (2025) includes studies on: Personalization in human-robot communication Optimal lighting for elderly visual perception Human-robot bonding mechanisms Interactive sensorized platforms for homecare (GUARDIAN) Audiovisual temporal integration in virtual environments He coordinates large-scale European projects like GUARDIAN and previously KSERA, contributes to sustainable development goals through healthcare robotics, and serves on editorial boards of leading journals including International Journal of Social Robotics . His work spans both technical robotics development and human-centric interaction studies.

• Biomedical Engineering
• Neuroscience
• Neural Engineering
• +6 more

Hayriye Cagnan is a Senior Lecturer (Associate Professor) in the Department of Bioengineering at Imperial College London’s Faculty of Engineering. She specializes in neural engineering and movement disorders, focusing on deep brain stimulation (DBS) and tremor pathophysiology. Her research integrates computational modeling, signal processing, and clinical neuroscience to develop adaptive neurotherapies. Cagnan holds a Ph.D. in Neuroscience from the University of Amsterdam and Philips Research, and has held postdoctoral positions at the University of Oxford and University College London. Education: B.Sc. in Electrical and Electronics Engineering (Cornell University, 2000–2004) – Fulbright Scholar M.Sc. in Engineering and Physical Science in Medicine (Imperial College London, 2004–2005) – Chevening Scholar Ph.D. in Neuroscience (University of Amsterdam/Philips Research, 2010) Research Interests: Her work addresses neural mechanisms underlying Parkinson’s disease, essential tremor, and other movement disorders. Key areas include: Development of adaptive DBS systems for tremor management Neural circuit dynamics and oscillations in basal ganglia networks Non-invasive neurostimulation techniques (e.g., TMS, tRNS) Machine learning for optimizing neurotherapeutic interventions Publications: Recent work emphasizes closed-loop systems, phase-specific stimulation, and translational neuroscience. Her studies explore how DBS modulates movement speed, reward processing, and neural oscillatory patterns in Parkinsonian patients. Awards: MRC Career Development Award (2018) MRC Skills Development Fellowship (2015) British Chevening Scholarship (2004) Lab & Collaboration: Leads the Neuroengineering and Dynamic Systems Lab at Imperial, collaborating with clinicians and engineers to advance neuromodulation therapies. Active in initiatives like NEUROMOD+ for next-generation neurotherapies.

• Optimal Control
• Numerical Analysis
• Finite Element Methods
• +2 more

Jakob Wagner is a Research Fellow at the Technical University of Munich (TUM), affiliated with the School of Computation, Information and Technology and the Department of Mathematics. He works within the Chair of Optimal Control under Prof. Boris Vexler and serves as an Invited Lecturer at Kutaisi International University in Georgia since 2022. His educational background includes a Master's degree in Mathematics from TUM (2019-2020, grade 1.0) and a Bachelor's degree in Mathematics from TUM (2014-2018, grade 1.2). Wagner's research focuses on Optimal Control of fluid dynamics equations, particularly the Navier-Stokes and Stokes systems. He specializes in Finite Element Methods and rigorous Error Estimates for discretizations of partial differential equations. His work addresses time-dependent problems, state constraints, and boundary control mechanisms in computational fluid dynamics, with significant contributions to stream-function formulations and pressure boundary conditions. Analysis of his publication record reveals a concentrated research trajectory in numerical analysis of incompressible flow control. His work consistently advances theoretical foundations for finite element discretizations, emphasizing fully discrete error analysis and pointwise constraints across Stokes and Navier-Stokes frameworks. Key innovations include novel approaches to blood flow modeling and ocean current reconstruction through coupled ODE systems. Wagner actively mentors students, supervising Master's theses by Alexandro Jedaidi (2025, ocean current reconstruction), Chenhong Lin (2025, Stokes equations), and Hendrik Bruhse (2023, parabolic problems), plus Noah An der Lan's Bachelor thesis (2025, Bayesian experiment design). His teaching portfolio spans Analysis, Optimization, and Numerical Methods courses at TUM and Kutaisi International University. He operates within TUM's Chair of Optimal Control research ecosystem, contributing to international collaborations through conference presentations at GAMM Annual Meetings and specialized symposia, while maintaining active involvement in computational mathematics projects including robotic swarm applications for radio imaging.

Michael ChoiView profile

Assistant Professor

• Markov Chains
• Stochastic Algorithms
• Statistical Physics
• +6 more

Michael Choi is an Assistant Professor in the Department of Statistics and Data Science at the National University of Singapore (NUS), affiliated with the Institute of Operations Research and Analytics (IORA). Previously, he held a joint appointment with Yale-NUS College (2022–2025) and worked at the School of Data Science (SDS) at the Chinese University of Hong Kong, Shenzhen. He earned his PhD from Cornell University’s School of Operations Research and Information Engineering (ORIE), advised by Prof. Pierre Patie, and holds an undergraduate degree in Actuarial Science from the University of Hong Kong. His research focuses on Markov chains and processes, stochastic algorithms (e.g., MCMC, simulated annealing, Langevin dynamics), and their applications in statistical physics, optimization, Bayesian statistics, information theory, game theory, and theoretical computer science. He explores interdisciplinary connections with quantum computation, control theory, and computational chemistry. Choi actively contributes to the academic community, serving as an Associate Editor of Statistics and Computing since August 2023. He has presented at numerous international conferences and workshops, including the IMS APRM 2026 (Hong Kong), INFORMS International Meeting 2025 (Singapore), and BayesComp 2025 (Singapore). His work bridges theoretical foundations with practical computational methods in stochastic systems.

• Biostatistics
• Public Health
• Epidemiology
• +9 more

Dr. Shuvo Bakar is a Senior Lecturer in the Sydney School of Public Health at the University of Sydney, within the Faculty of Medicine and Health. He holds a PhD in Statistics from the University of Southampton, UK, and has prior experience as an Assistant Professor at Yale University, Lecturer at the Australian National University, and Scientist at Data61 (CSIRO). His research focuses on statistical methods applied to public health challenges, including Bayesian hierarchical modeling, machine learning, spatio-temporal analysis, and their applications in epidemiology, clinical trials, and environmental health. Dr. Bakar's research interests span statistical methodologies such as Bayesian adaptive designs, small area estimation, and spatial risk modeling, alongside applications in child health, infectious diseases, and extreme weather impacts on health. He is an active member of academic communities, including the Royal Statistical Society (RSS Fellow), Statistical Society of Australia, and the Australian Trials Methodology Research Network. His work also involves collaborations on grants totaling millions in funding, addressing topics like climate change impacts on health inequity and cardiovascular disease prevention in remote regions. Education: PhD in Statistics (University of Southampton, UK) Key Research Themes: Obesity, Diabetes, Cardiovascular Disease; Reproductive, Maternal & Child Health Grants/Projects: Includes NHMRC-funded trials on respiratory infections in First Nations children and MRFF grants for cardiovascular risk reduction in regional Australia. Dr. Bakar's contributions extend to editorial roles for Nature Scientific Reports and Discover Public Health , and his research has been published in journals like PloS One , Climatic Change , and Journal of the Royal Statistical Society .

Luis NovoaView profile

Associate Professor

• Business Analysis
• Management Science
• Supply Chain Management
• +3 more

Luis Novoa is an Associate Professor in the Department of Computer Information Systems and Business Analytics at James Madison University’s College of Business, where he also contributes to the MBA program. His academic journey includes a Ph.D. in Decision Sciences from The George Washington University (2016), an M.S. and B.S. in Industrial Engineering from Universidad de los Andes (2007 and 2005, respectively). He has held roles such as Assistant Professor at JMU since 2017, Visiting Assistant Professor at The George Washington University (2016–2017), and Instructor at Universidad de los Andes (2007–2011). His research focuses on business analysis, management science, and applied decision analysis under uncertainty, particularly in supply chain management, logistics, education, and healthcare. Notable work includes optimizing automated package sorting systems, developing educational tools like STRATA for operations research assessment, and Bayesian methods for academic motivation analysis in business analytics courses. Recent articles highlight contributions to bike-sharing system decision-making, supply chain curriculum alignment with industry practices, and stochastic optimization in energy systems. His awards include JMU College of Business Distinguished Teacher (2021–2022) and recognition for summer research excellence (2011). Dr. Novoa’s teaching and research emphasize practical applications of analytics, bridging academic theory with real-world challenges in business and public sectors. He has authored or co-authored over 15 peer-reviewed publications spanning operations research, education, and industry case studies.