جستجوی استاد راهنمای دانشگاهی

David E. Bernal Neiraمشاهده پروفایل

استاد مدعو

• Optimization in Chemical Engineering
• Quantum Computing Algorithms
• Process Systems Engineering
• +۲ مورد دیگر

David E. Bernal Neira is an Adjunct Professor at Carnegie Mellon University's Tepper School of Business, focusing on Operations Management and Quantum Computing. His research integrates optimization, quantum algorithms, and chemical engineering applications. Education: PhD in Chemical Engineering, Carnegie Mellon University (2021) BS in Physics, Universidad de los Andes (2018) MS in Chemical Engineering, Universidad de los Andes (2016) BS in Chemical Engineering (Cum Laude), Universidad de los Andes (2014) Research Interests: He specializes in Process Intensification, Quantum Algorithm Design, Nonlinear Optimization, and Machine Learning applications in chemical systems. His work emphasizes scalable solutions for industrial challenges, from refinery planning to quantum hardware efficiency. Publication Trends: Recent articles (2020-2023) reveal a strong focus on quantum computing advancements (35%), convex MINLP methodologies (40%), and chemical process optimization (25%). Key themes include algorithm scalability, hybrid modeling, and cross-disciplinary quantum applications. Awards: Best Talk Award, Quantum Computing Workshop (AIChE/DTU, 2022) AIChE CAST Directors’ Student Presentation Finalist (2020) Mark Dennis Karl Teaching Award (CMU, 2019) Cum Laude in Chemical Engineering (UniAndes, 2014) Advising and Labs: While specific student engagements are undisclosed, his research involves collaborations across computational chemistry and quantum information groups. No dedicated lab is mentioned, but affiliations center on CMU's optimization and quantum initiatives.

Mohammad Kohandelمشاهده پروفایل

استاد

• Cancer Biology
• Drug Delivery Systems
• Cancer Stem Cells
• +۵ مورد دیگر

Dr. Mohammad Kohandel is a Professor in the Department of Applied Mathematics at the University of Waterloo, cross-appointed in the School of Computer Science and Chemical Engineering. He holds a PhD from the Institute for Advanced Studies in Basic Sciences (Iran). His research focuses on applying mathematical, computational, and machine learning techniques to biomedical challenges, including cancer treatment optimization, drug delivery systems, and quantum sensor technology. He is affiliated with the Centre for Mathematical Medicine at the Fields Institute, Waterloo Institute for Nanotechnology, and the Waterloo Data & Artificial Intelligence Institute. His work spans mathematical oncology, scientific machine learning, and computational modeling of cancer stem cells and tumor microenvironments. Recent publications highlight advancements in deep learning for tumor classification, physics-informed neural networks, and synergistic therapies targeting tumor heterogeneity. Dr. Kohandel leads the Kohandel Lab, which actively seeks graduate students and researchers to advance interdisciplinary projects in computational biomedicine.

• Monte Carlo radiation transport simulation
• Medical physics
• Radiation protection
• +۴ مورد دیگر

Professor Susanna Guatelli is a leading academic in radiation physics and medical applications at the University of Wollongong (UOW), affiliated with the School of Physics within the Faculty of Engineering and Information Sciences. She is currently the Interim Head of the School of Physics and Theme Leader for 'Monte Carlo simulations' in the Centre for Medical Radiation Physics at UOW. Her research focuses on Monte Carlo simulation techniques for radiation protection, medical physics, and space exploration, with significant contributions to Geant4 software development and benchmarking. Guatelli obtained her Master's and PhD in Physics from the University of Genova (Italy) and INFN Genova, with early career work at CERN on radiation effects in space missions. She has held key roles in international collaborations, including the Geant4 International Collaboration Steering Board and leadership of medical physics benchmarking groups. Her research interests span radiation protection for astronauts, medical radiation therapy optimization, and advanced imaging techniques. Notable recognitions include the Australian Institute of Physics' Women in Physics Award (2021) and inclusion in the UOW Women of Impact (2016). She actively participates in public outreach to inspire youth in physics and serves as an Associate Editor for journals like Physica Medica . Guatelli has secured over 30 grants since 2012, focusing on medical radiation research, space radiation monitoring, and machine learning in therapy planning. Her current projects include DoseGAN for microbeam radiation therapy and Space RAdiation Monitoring System (SRAMS) for lunar missions.

Benjamin Learمشاهده پروفایل

استاد

• Nanoparticle Chemistry
• Electron Transfer
• Photothermal Chemistry
• +۳ مورد دیگر

Benjamin J. Lear is a Professor of Chemistry at Pennsylvania State University and a faculty member of the Institute for Energy and the Environment (IEE). His research focuses on understanding how ligands on nanoparticles influence their electronic properties and leveraging molecular-scale photothermal effects to drive chemical transformations. Key areas of study include electron transfer mechanisms, photothermal curing of polymers, and surface chemistry controls on nanoparticle behavior. His work bridges theoretical and experimental chemistry, employing advanced computational methods like ab initio calculations to identify nuclear coordinates for electron transfer. Recent contributions include studies on gold nanoparticle electronic properties, carbon black photothermal applications, and AI-enhanced data visualization in chemical education. Notable achievements include a $4.5M grant for 3D-printed ceramics research (2024) and a Fulbright Fellowship in Finland (2020). His lab explores applications in materials science, catalysis, and sustainable polymer processing, with over 70 peer-reviewed publications since 2000.

Madhusanka Liyanageمشاهده پروفایل

دانشیار

• Network Security
• 5G
• 6G
• +۴ مورد دیگر

Madhusanka Liyanage is an Associate Professor/Ad Astra Fellow and Director of the Network Softwarization and Security Labs (NETSLAB) at University College Dublin's School of Computer Science. He holds adjunct and honorary professorships at the University of Oulu and universities in Sri Lanka. His research focuses on network security, 5G/6G technologies, blockchain, and AI-driven systems. He has authored/co-authored over 250 publications, secured €10M+ in research funding, and led major projects like the EU Horizon Europe initiatives. Notable awards include the 2020 IEEE ComSoc Outstanding Young Researcher Award and recognition as a World's Top 2% Scientist (2020–2024). Education: B.Sc. (First Class Honours) from University of Moratuwa (2009), M.Eng. (Asian Institute of Technology, 2011), M.Sc. (University of Nice Sophia Antipolis, 2011), D.Sc. (University of Oulu, 2016) Research Interests: 5G/6G, Blockchain, AI/ML, Federated Learning, IoT, Network Slicing His work spans security orchestration, quantum-resistant cryptography, and smart grid innovations. He has advised 24+ students, secured grants from EU, IRC, and industry partners, and contributed to standards via ENISA consultancy. Current projects include CONFIDENTIAL6G and ROBUST-6G, addressing privacy and security in next-gen networks.

• Precision Manufacturing
• Micro/Nano Manufacturing
• Optical Freeform Manufacturing
• +۴ مورد دیگر

Professor Fengzhou Fang is a Full Professor of Precision Manufacturing at the School of Mechanical and Materials Engineering, University College Dublin. He holds dual appointments as a Full Professor at Tianjin University (China) and leads the UCD Centre of Micro/Nano Manufacturing Technology (MNMT-Dublin). His academic roles include Editor-in-Chief of Nanomanufacturing and Metrology and Founding President of the International Academy of Engineering and Technology (AET). Education: BSc (Mechanical Engineering) from Heilongjiang University, MEng and PhD (Manufacturing Engineering) from Harbin Institute of Technology (China). Research focuses on precision manufacturing, micro/nano technologies, optical freeform systems, and bio-medical implants. Key innovations include defect-free nanoimprinting molds and atomic-scale machining processes. His 300+ publications and 60 patents span optical manufacturing, surface finishing, and material characterization. Awards: Fellowships from ISNM, CIRP, AET, and SME; Royal Irish Academy membership; multiple paper awards including the SME Albert M. Sargent Award. Current grants include an IRC-funded project on AI-driven bioimplant manufacturing. Labs/Teams: Leads the MNMT-Dublin center advancing freeform optics, bio-medical devices, and atomic-scale fabrication techniques.

Joshua Schrierمشاهده پروفایل

استاد

• Physical Chemistry
• Computational Chemistry
• Machine Learning for Chemical Experiments
• +۳ مورد دیگر

Joshua Schrier is the Kim B. and Stephen E. Bepler Chair and Professor of Chemistry at Fordham University. His research focuses on Physical Chemistry, Computational Chemistry, and Machine Learning for chemical experiments. He leads a lab in JMH 620 and maintains an office in JMH 616. His work emphasizes autonomous experimentation, AI-driven materials discovery, and open-source laboratory automation. Schrier’s research bridges theoretical and experimental approaches, leveraging machine learning to accelerate discovery in areas like perovskite nanocrystals, rare-earth separation, and self-assembling vesicles. His research interests include developing self-driving labs to reduce experimental bias, optimizing synthesis workflows with active learning, and enhancing reproducibility through data science. Key projects include the 'Dark Reactions' initiative, which uses failed experiments to train predictive models, and the 'Frugal Twin' concept for low-cost automation. Schrier collaborates on the Materials Acceleration Platform (MAP) for energy materials discovery. Publications highlight themes like machine learning for crystal polymorph prediction, AI-guided synthesis protocols, and automated pH estimation. While no scientific awards are listed, his contributions to lab automation and data-driven methodologies have advanced experimental chemistry practices. Advising and grants are not detailed here, but his lab engages in collaborative, interdisciplinary research with undergraduate and graduate students. His lab’s infrastructure includes custom-built robots like Sidekick (a 3D-printed liquid handler) and software frameworks like ESCALATE for experiment management. Current work explores AI’s role in hypothesis generation, sustainable materials development, and democratizing access to advanced experimental tools.

Howard Liمشاهده پروفایل

استاد

• Robotics
• Artificial Intelligence
• Control Systems
• +۳ مورد دیگر

Howard Li is a Professor in the Department of Electrical and Computer Engineering at the University of New Brunswick's Faculty of Engineering. His research focuses on robotics, artificial intelligence, unmanned vehicles, control systems, and quantum computing. Dr. Li completed his Ph.D. at the University of Waterloo and has conducted research internationally at institutions including EPFL (Switzerland) and University of Pavia (Italy). He has industry experience developing training systems for fighter aircraft and control systems for autonomous vehicles. His research interests span robotics, autonomous systems, quantum computing, and AI applications in mechatronics. Recent publications demonstrate focus on LiDAR simulation, deep learning architectures, satellite imagery analysis, and geospatial algorithms. Dr. Li has secured over $1,000,000 in research grants from agencies including NSERC, CFI, and DRDC. He chairs the IEEE Autonomous Robotics Group and contributed to the first IEEE robotics standard. Harrison McCain Foundation Young Scholars Award NSERC Postgraduate Scholarship IEEE Senior Member recognition He established the COBRA research group and open-source Quad Rotor Simulator. All his former postdocs/PhDs secured positions at prestigious institutions including MIT and NASA.

Tianyu Zhuمشاهده پروفایل

استادیار

• Polymer Synthesis
• Ceramic Engineering
• Coating Technology
• +۷ مورد دیگر

Tianyu Zhu is an Assistant Professor in the Department of Materials Science and Engineering at Clemson University. He holds a Ph.D. in Chemistry from the University of South Carolina (2019) and a B.S. from Nanjing University (2015). His research focuses on synthesizing and processing soft materials and ceramics for energy storage applications, particularly in battery electrodes, polymer-ceramic composites, and sustainable manufacturing. Prior to Clemson, he worked at Quantumscape Inc. (2022-2023) and the Lawrence Berkeley National Laboratory (2020-2022). His work addresses challenges in energy frontiers, including battery recyclability, electrode stability, and green manufacturing. He has published over 50 peer-reviewed articles and holds 4 patents. Education: Ph.D., Chemistry, University of South Carolina, 2019 B.S., Nanjing University, 2015 Research Interests: Development of bio-based and recyclable polymers for sustainable materials Advanced manufacturing of battery components (electrodes, binders, electrolytes) Design of ceramic and composite materials for high-performance energy storage Optimization of lithium-ion and solid-state battery chemistries His recent publications highlight innovations in conductive polymer coatings, fluorine-free binders, and bio-inspired materials. His lab emphasizes green chemistry principles and cross-disciplinary approaches to energy storage challenges. No awards or grants are explicitly listed in the provided text.

Su Yanمشاهده پروفایل

دانشیار

• computational electromagnetics
• multiphysics modeling
• high-performance computing
• +۳ مورد دیگر

Su Yan is an Associate Professor and Director of Graduate Studies in Howard University's Department of Electrical Engineering and Computer Science. He directs the IBM-HBCU Quantum Center and leads research in computational electromagnetics, nonlinear multiphysics modeling, and machine learning-enhanced simulations. His group develops extreme-scale algorithms for electromagnetic systems with applications in RF devices, quantum technologies, and computational imaging. Current projects involve AI-accelerated metasurface design, microwave-assisted hydrogen generation, and randomized multiscale solvers. Honors include DOE/NSF Early Career Awards, ACES Early Career Award, and IEEE prize papers. He currently advises 9 PhD students and has graduated multiple doctoral candidates in computational electromagnetics. Research Grants: DOE Early Career Award for randomized multiscale algorithms NSF CAREER for neural network-enhanced RF device simulation NSF grants for AI-enhanced metasurfaces and hydrogen catalyst design Ansys collaboration on non-conforming solvers

• Quantum Computing
• Machine Learning
• Optimization
• +۳ مورد دیگر

Maryam Zomorodi Moghaddam is an Associate Professor in the Department of Computer Science at Cracow University of Technology's Faculty of Computer Science and Telecommunications. Her research focuses on quantum computing, machine learning, and optimization techniques, with applications in distributed systems and medical informatics. Research interests include: Quantum circuit optimization and teleportation Distributed quantum systems architecture Machine learning applications in healthcare Evolutionary computation and genetic algorithms Natural language processing using quantum techniques Recent publications demonstrate strong focus on quantum computing optimizations and AI medical applications, with quantum circuit efficiency and healthcare AI emerging as dominant themes. Notable contributions include distributed quantum gate protocols and medical classification systems using neuroevolution.

Stella Quinonesمشاهده پروفایل

دانشیار

• Engineering Education
• Biomedical Sensors
• Nanoscale Manufacturing
• +۴ مورد دیگر

Dr. Stella Quinones is an Associate Professor and Department Chair in the Department of Metallurgical, Materials and Biomedical Engineering (MMBME) at the University of Texas at El Paso (UTEP). With over 20 years of academic experience, her work bridges engineering education and materials science. Her research focuses on engineering education strategies to increase diversity in STEM, particularly for women and first-year students. She also develops innovative sensors for biomedical applications, including non-invasive glucose monitoring, and investigates materials for aerospace and energy technologies like CdTe thin films and high strain-rate materials. Her educational initiatives include hands-on coursework in sensor design and quantum mechanics tailored for electrical engineers, which have improved student outcomes. Dr. Quinones emphasizes active learning through technology integration and mentorship programs to support academic success. Her research interests span multiple disciplines, including biomedical sensors, nanoscale manufacturing, and materials characterization. She has contributed to advancements in 3D manufacturing and the analysis of materials under extreme conditions such as hypervelocity impacts. Her pedagogical innovations include a first-year hands-on sensor design course that increased passing rates in subsequent engineering courses. Dr. Quinones also leads efforts in curriculum development and collaborative educational programs to enhance STEM retention. Dr. Quinones’ articles highlight her work in materials science, sensor technology, and educational methodologies. Her recent studies explore corrosion behavior of high-entropy alloys, machine learning in glucose sensing, and modular motion control systems for printed electronics. She has published extensively on semiconductor materials, quantum device physics, and the microstructural analysis of metals exposed to space environments. In advising and grants, Dr. Quinones focuses on mentorship programs for at-risk students and interventions to improve academic and career trajectories. She has been instrumental in securing funding for educational initiatives and materials research through partnerships with organizations like ProQuest/Pivot. Her commitment to fostering an inclusive academic environment is central to her work at UTEP.

Peimeng Yinمشاهده پروفایل

استادیار

• Computational Mathematics
• Applied Mathematics
• Numerical Analysis
• +۷ مورد دیگر

Peimeng Yin is an Assistant Professor in the Department of Mathematical Sciences at the University of Texas at El Paso (UTEP). His research focuses on computational mathematics and applied mathematics, emphasizing numerical analysis, partial differential equations, scientific computing, and data science. He designs and implements numerical algorithms for PDEs with applications in physics, astrophysics, engineering, biology, energy, and oncology. Key interests include discontinuous Galerkin methods, structure-preserving numerical techniques, dynamical low-rank approximation, and PDE-based data-driven modeling. His work bridges rigorous mathematical theory with practical computation, addressing challenges like high derivatives, singularities, and multiscale phenomena. Recent research involves adaptive finite element methods, positivity-preserving schemes, and machine learning-enhanced algorithms for parabolic equations. Yin's studies often emphasize energy stability and error estimation in gradient flow systems, with applications ranging from fluid dynamics to biomedical decision support systems. Notable contributions include advancements in biharmonic problem solvers, neutrino kinetic equations, and surgical outcome prediction models. He maintains an active publication record in top-tier computational mathematics journals, with a focus on developing robust numerical frameworks for complex physical systems.

Lamia Goualمشاهده پروفایل

استاد

• Petroleum Engineering
• Environmental Engineering
• Nanomaterials
• +۶ مورد دیگر

Dr. Lamia Goual is a Professor of Petroleum Engineering at the University of Wyoming, holding the A.J. Castagne Professorship in the College of Engineering and Applied Science. She serves as the Graduate Program Coordinator and leads the Goual Research Group, focusing on interfacial science and thermodynamics in porous media with applications to oil recovery, carbon sequestration, and environmental remediation. Her work emphasizes multi-scale analysis linking microscale fluid-rock interactions to macroscale flow behavior in unconventional energy resources. Dr. Goual holds a B.S. in Chemical Engineering from Ecole Nationale Polytechnique (Algeria), and M.S./Ph.D. in Petroleum Engineering from Imperial College London. Before joining UW in 2007, she conducted postdoctoral research at the University of Alberta and held roles at the Enhanced Oil Recovery Institute. Her research spans four core areas: Aquifer Remediation (NSF CAREER-funded) Hydrocarbon Recovery (multi-scale experiments and molecular dynamics) Fouling Mitigation (asphaltene deposition mechanisms) Carbon Engineering (graphene-based materials from coal/petroleum) Recent publications highlight nanomaterial applications in oil recovery, surfactant-enhanced displacement, and asphaltene behavior in porous media. Notable awards include the NSF CAREER Award (2014–2020) and A.J. Castagne Professorship. She advises multiple graduate students and collaborates with industry partners like Halliburton. Dr. Goual’s lab facilities include advanced imaging (SEM, AFM) and high-pressure flow experiments. She has supervised over 20 students and actively engages in outreach programs for K-12 students, including Wyoming Summer High School Institute demonstrations.

Chang Liuمشاهده پروفایل

استاد

• Software Engineering
• Mobile Health
• 3D Immersive Learning Applications
• +۳ مورد دیگر

Chang Liu is a Professor in the Department of Electrical Engineering and Computer Science at Ohio University's Russ College of Engineering and Technology. He is affiliated with the Center for Scientific Computing and Immersive Technologies. His research focuses on applying software engineering big data to developer knowledge and integrating software principles into healthcare and educational technologies. He earned his Ph.D. in Information and Computer Science from the University of California, Irvine in 2002. Research interests include software engineering, mobile health applications, and 3D immersive learning tools. Notable projects include the VIADS visual analytic tool for clinical hypothesis generation and a VR app for Mal de Debarquement syndrome treatment. His work spans wearable health devices, quantum algorithm verification, and pediatric risk stratification using deep learning. Recent articles emphasize clinical research tools, healthcare data analytics, and immersive educational technologies. Over 60+ conference presentations and 28+ journal articles highlight contributions to medical informatics, software engineering, and edutainment systems. His patents cover authentication methods, gesture-based CAPTCHA, and health-related VR applications. Chang has led interdisciplinary STEM education initiatives, including summer camps and virtual learning environments. Collaborative projects involve medical researchers, educators, and industry partners. Current work explores neuromorphic AI agents and scalable software development methodologies.