جستجوی استاد راهنمای دانشگاهی

Adam Feinberg is a Professor in the Departments of Biomedical Engineering and Materials Science and Engineering at Carnegie Mellon University (CMU). He leads the Regenerative Biomaterials & Therapeutics Group, focusing on cell-material interactions, 3D bioprinting, and bioengineered tissues. His work integrates nanofabrication, molecular biology, and 3D imaging to address challenges in muscle repair, corneal regeneration, and cancer. Key innovations include the FRESH bioprinting platform, enabling soft ECM gel-based constructs, and ECM shrink-wrapping techniques for cell encapsulation. Feinberg holds a Ph.D. and MS in Biomedical Engineering from the University of Florida (2004, 2002) and a BS in Materials Science and Engineering from Cornell University (1999). He has secured major grants, including ARPA-H funding for diabetes treatments and Canada’s New Frontiers Fund for heart disease therapies. His research has led to over 45 peer-reviewed articles and 20 patents. His scientific awards include the NIH Director’s New Innovator Award and NSF CAREER Award. Media highlights include breakthroughs in vascularized tissue models and biodegradable actuators. Feinberg collaborates widely, advancing clinical translation of bioprinted tissues and sustainable bio-bots.

• Biomedical Engineering
• Tissue Engineering
• 3D Bioprinting
• +۶ مورد دیگر

Jason Szafron is an Assistant Professor in the Department of Biomedical Engineering at Carnegie Mellon University’s College of Engineering. He leads a research group focused on developing computational tools to understand and treat cardiovascular diseases, particularly congenital heart defects, pulmonary vascular diseases, and fetal growth restriction. His work integrates biomechanical modeling with experimental data to improve treatment planning and medical device design. Education: B.S. in Biomedical Engineering, Texas A&M University (2015) M.S. and Ph.D. in Biomedical Engineering, Yale University (2018, 2020) Postdoctoral Research Fellow at Stanford University’s Department of Pediatrics (2023) Research Interests: Simulation tools for cardiopulmonary disease progression Biomechanical modeling of vascular adaptation Computational frameworks for organ-scale growth remodeling Optimization of tissue-engineered vascular grafts His lab combines experimental and computational approaches to study disease mechanisms, with a focus on mechanobiological and immunological factors driving vascular pathology. Publications highlight advancements in pulmonary hypertension modeling, vascular graft design, and open-source diagnostic tools like HemoLens. Recent work emphasizes personalized treatment planning via neural network-based digital twins. Awards: Parker B. Francis Fellow (2022–2025) for pulmonary disease research Labs/Teams: Director of the Szafron Lab at CMU, focused on translational cardiovascular engineering. Collaborates with Stanford University and Mayo Clinic on clinical applications.

• Biomedical Engineering
• Cardiovascular Disease
• Pulmonary Vascular Disease
• +۶ مورد دیگر

Carmel Majidi is a Professor in the Department of Mechanical Engineering at Carnegie Mellon University's College of Engineering. He leads the Soft Machines Lab, where his research focuses on soft robotics, wearable computing, and advanced materials that mimic biological systems. His work enables robots and machines to safely interact with humans through soft, deformable technologies. Education: Ph.D. in EECS, University of California, Berkeley (2007) B.S. in Civil and Environmental Engineering, Cornell University (2001) His research interests span soft robotics, stretchable electronics, liquid metal materials, and bio-inspired engineering. He develops soft electromechanical materials for applications in haptics, medical devices, and human-machine interfaces. His group pioneers fabrication methods for soft composites and microfluidic systems that function as artificial skin, nervous tissue, and muscle. The recent trend in his publications highlights a strong focus on soft actuators, self-healing materials, liquid metal composites, and haptic interfaces for virtual and augmented reality. His work integrates materials science, robotics, and advanced manufacturing to enable scalable, robust, and practical soft systems. Scientific Awards: 2023 Inno Fire Award for Trailblazing Innovators, Pittsburgh Business Times Majidi leads a major research thrust in a new NSF Engineering Research Center (ERC) dedicated to improving robot dexterity, funded up to $52 million. He has been instrumental in developing soft robotic systems inspired by prehistoric organisms, such as the pleurocystitid, and in creating healthcare devices powered by body heat using liquid metal technologies. His research has been featured in Popular Science , Communications of the ACM , and Ars Technica . He is actively involved in advancing scalable manufacturing of soft electronics and enabling robust integration between soft materials and conventional microelectronics. His lab collaborates across disciplines to push the boundaries of physical AI and wearable robotics.

• Soft Robotics
• Wearable Robotics
• Soft Electronics
• +۱۲ مورد دیگر

James Bain is a Professor in the Electrical and Computer Engineering (ECE) Department at Carnegie Mellon University, with a courtesy appointment in the Department of Materials Science and Engineering. He serves as Associate Director of the Data Storage Systems Center (DSSC) within the College of Engineering. B.S. in Materials Science and Engineering from the University of Pennsylvania (1988) M.S. (1991) and Ph.D. (1993) in Materials Science and Engineering from Stanford University His research spans magnetic, optical, electrical, thermal, and mechanical devices for information storage. Current programs focus on heat-assisted magnetic recording and resistive switches for memory and reconfigurable electronics, with interdisciplinary applications in energy security, industrial decarbonization, and nanofabrication. His work intersects materials science , electrical engineering , and nanotechnology . Scott Institute Seed Grant (2018) for energy research Member of Materials Research Society and IEEE Magnetics, Electron Devices, and Photonics Societies Bain has co-authored over 225 publications and leads research at the interface of data storage and energy systems . He actively contributes to multiphysics modeling, nanoscale thermal transport, and phase-change materials. His lab is affiliated with Carnegie Mellon’s Data Storage Systems Center, advancing technologies for grid-interactive and high-performance buildings.

• Magnetic recording
• Information storage technology
• Resistive memory devices
• +۵ مورد دیگر

James A. Bain is a Professor in the Electrical and Computer Engineering (ECE) Department at Carnegie Mellon University’s College of Engineering. He also holds a courtesy appointment in the Department of Materials Science and Engineering and serves as Associate Director of the Data Storage Systems Center (DSSC). Bain’s research focuses on magnetic, optical, and resistive switching devices for information storage, including heat-assisted magnetic recording (HAMR) and phase change materials. Education : B.S. (1988) in Materials Science and Engineering from the University of Pennsylvania; M.S. (1991) and Ph.D. (1993) in Materials Science and Engineering from Stanford University. His work spans nanotechnology, energy-efficient computing, RF metrology, and multiphysics modeling. Bain’s research has received recognition through the Scott Institute seed grant for energy research. He has co-authored over 225 papers and holds a leadership role as ECE’s Associate Department Head for Academic Affairs since 2018. His laboratory investigates thermal transport, device reliability, and emerging memory architectures. Recent publications highlight advancements in HAMR thermal management, resistive switching dynamics, and phase-change RF switches. Bain’s expertise intersects with the IEEE Magnetics, Electron Devices, and Photonics Societies, as well as the Materials Research Society.

• Magnetic Recording
• Resistive Switching
• Phase Change Materials
• +۳ مورد دیگر

Zachary Manchester is an Assistant Professor in the Robotics Institute at Carnegie Mellon University, with a courtesy appointment in Electrical and Computer Engineering. His research focuses on enabling robotic systems to achieve human/animal-like agility and robustness through control theory, optimization, and physics-based algorithms. Key areas include legged robots, drone swarms, and space systems. He leads the Robotic Exploration Lab, which develops both hardware and software for advanced robotic applications. Current PhD students include Arun Bishop, Swaminathan Gurumurthy, and Jeong Hun Lee, among others. His work spans theoretical foundations and practical implementations, with applications in space exploration, terrestrial robotics, and deployable structures. Education: Not explicitly detailed in text. Advising: Supervises multiple PhD and master’s students, with past advisees now at institutions like SpaceX and NASA. Grants/Labs: Leads the Robotic Exploration Lab, collaborating on projects like PyCubed (open-source cubesat framework) and deployable space structures.

• Robotics
• Control Systems
• Optimization
• +۳ مورد دیگر

Noelia Grande Gutiérrez is an Assistant Professor in Mechanical Engineering and Biomedical Engineering at Carnegie Mellon University's College of Engineering. She holds a courtesy appointment in Biomedical Engineering. Her research integrates computational engineering with cardiovascular medicine, developing multiphysics models to support clinical decision-making for cardiovascular diseases. Key areas include bioengineering, biomechanics, fluid dynamics, and medical device technology. Her computational frameworks simulate complex physiological processes from platelet deposition to arterial occlusion, enabling patient-specific thrombosis investigations in coronary artery disease. Recent work focuses on multiscale modeling approaches bridging molecular interactions with systemic hemodynamics. Grande Gutiérrez received the American Heart Association Fellowship (2016-2018) for her innovative cardiovascular research.

• bioengineering
• computer-aided design (CAD)
• biomechanics
• +۲ مورد دیگر

Jason Howell is a Teaching Professor and Associate Department Head in the Department of Mathematical Sciences at Carnegie Mellon University. He holds a Ph.D. in Mathematical Sciences from Clemson University. His research focuses on numerical analysis and computational mathematics, particularly finite element methods for fluid and structural dynamics, fast linear solvers, and applications in natural and social sciences. He actively mentors undergraduate research projects in fluid-structure interaction, numerical linear algebra, and differential equations. His work spans theoretical foundations of finite element methods, including compatibility conditions and mixed formulations, as well as applied projects with chemists and physicists on topics like solubility parameters and nanostructures. Current research activities include computational fluid dynamics, iterative solvers for large systems, and mathematical modeling in biomedical and engineering contexts. Notable contributions include studies on cantilever dynamics, sparse matrix preconditioning, and viscoelastic flow modeling. He collaborates across disciplines, integrating numerical methods with real-world applications in materials science and engineering systems. His teaching and research emphasize bridging mathematical theory with practical scientific challenges.

• Numerical Analysis
• Computational Mathematics
• Finite Element Methods
• +۵ مورد دیگر

Paul A. Salvador is a Professor in the Department of Materials Science and Engineering at Carnegie Mellon University's College of Engineering. He also serves as Director of the Energy Science, Technology & Policy (EST&P) Program. Ph.D. in Materials Science and Engineering from Northwestern University (1997) B.S.E. in Materials Science and Engineering from University of Pennsylvania (1992) His research focuses on advanced materials processing for energy conversion and storage, particularly: Photocatalysts for solar fuel production Electrocatalysts in solid oxide fuel cell electrodes Thin film epitaxy for metastable materials synthesis Recent publications (2021-2025) address: Quantifying microstructural evolution in SOFC anodes Combinatorial substrate epitaxy for polytypic oxide growth Polymerization-induced microstructural heterogeneity in ceramic materials Domain-specific reactivity in ferroelectric photocatalysts Computational modeling of electrochemical degradation High-throughput screening of doped photocatalysts Scientific recognition includes: Elevated to Fellow of The American Ceramic Society (ACerS) in 2021 He leads collaborative research with: National Energy Technology Laboratory Laboratoire CRISMAT (France) Multiple research groups at Carnegie Mellon University

• Materials Science and Engineering
• Solid Oxide Fuel Cells
• Photocatalysis
• +۵ مورد دیگر

Francis Ogoke is an incoming Assistant Professor in the Department of Mechanical Engineering at Carnegie Mellon University, set to begin in Fall 2025. He is currently a postdoctoral associate at the Massachusetts Institute of Technology. His academic journey includes a Ph.D. in Mechanical Engineering from Carnegie Mellon University (2024) and a B.S.E. in Chemical and Biological Engineering from Princeton University (2019). His research lies at the intersection of artificial intelligence and engineering systems, with a focus on developing foundational AI methods for complex engineering problems. Key areas include: Physics-informed deep learning Uncertainty quantification and probabilistic modeling Representation learning for generalization Applications in additive manufacturing, digital twins, and cyber-physical systems The recent articles reflect a strong trend in leveraging deep learning—especially vision transformers, generative models, and reinforcement learning—for accelerating simulations, enhancing in-situ monitoring, and improving control in additive manufacturing. His work consistently bridges AI innovation with real-world engineering challenges, particularly in metal 3D printing and multiphysics modeling. Notable scientific awards include: Presidential Fellowship in the College of Engineering, Carnegie Mellon University G.E.M. Fellowship Francis Ogoke advises emerging researchers and is expected to lead a research group focused on AI-driven engineering systems. His lab will likely focus on developing intelligent frameworks for digital twins and autonomous manufacturing. He has not yet advised any students as per current records. He is actively involved in pioneering research that integrates AI into core engineering workflows, supported by advanced computational and experimental infrastructure. He is affiliated with the College of Engineering at Carnegie Mellon University and conducts research relevant to advanced manufacturing, sensing technologies, and intelligent systems.

• Artificial Intelligence
• Deep Learning
• Physics-Informed Machine Learning
• +۷ مورد دیگر

Liwei Wang is an Assistant Professor in the Department of Mechanical Engineering at Carnegie Mellon University's College of Engineering, where he leads the Computational and Physical Intelligence Laboratory (CPhI Lab). His research integrates computational modeling, machine learning, and mechanics to design advanced materials and smart systems. Education: Ph.D., Mechanical Engineering, Shanghai Jiao Tong University (2022, Honors) B.S., Mechanical Engineering, Shanghai Jiao Tong University (2017, Honors) Liwei Wang's research interests lie at the intersection of machine learning, computational engineering, and advanced materials . He develops data-driven and physics-informed frameworks for topology optimization, metamaterials, 3D/4D printing, soft robotics, and programmable materials systems . His work emphasizes physical intelligence —designing materials that can sense, adapt, and respond to their environments. Applications span mechanical protective cloaks, flexible electronics, minimally invasive surgery, and mechanical computing . His recent publications reveal a strong trend toward multi-scale, data-efficient design of metamaterials using advanced machine learning techniques such as Gaussian processes, latent variable models, and neural networks. He focuses on scalable, differentiable, and task-aware optimization methods that enable rapid discovery and deployment of functional material systems. Scientific Awards: ASME Design Automation Dissertation Award ASME Design Automation Conference Best Paper Award Institution-level Outstanding Ph.D. Dissertation Award Dr. Wang advises a growing research group at CMU and leads the CPhI Lab, where his team develops next-generation computational tools for materials innovation. While specific grants are not listed, his research is likely supported by federal and private funding given the scope and impact of his work. His lab emphasizes interdisciplinary collaboration and translation of computational designs into physical prototypes via additive manufacturing. Laboratory & Team: The Computational and Physical Intelligence Laboratory (CPhI Lab) focuses on co-designing materials and structures with embedded intelligence, combining simulation, data science, and experimental validation to push the boundaries of what engineered materials can achieve.

• Machine Learning
• Computational Engineering
• Data-Driven Design
• +۱۹ مورد دیگر