جستجوی استاد راهنمای دانشگاهی

• Food process engineering
• Food physics
• Alternative proteins
• +۷ مورد دیگر

Michael Beyrer is a Professor at the University of Applied Sciences Western Switzerland Valais-Wallis, School of Engineering, leading the Sustainable Food Systems research group. His work focuses on non-thermal food preservation technologies, plant-based protein engineering, and food extrusion systems. Research areas: Food process engineering, alternative proteins, non-thermal processing, and equipment design Affiliation: Department of Life Sciences Engineering, School of Chemistry and Life Sciences His research investigates non-thermal preservation methods like pulsed electric field (PEF) and cold plasma treatments for protein-rich food matrices. Key studies include: Microbial stability enhancement in plant protein extracts Sublethal bacterial damage mechanisms Texture optimization in meat analogues through pectin-protein interactions Flavor retention models for high-protein foods Sustainable microalgae integration in plant-based foods Recent publications analyze anisotropic structure formation in plant-based meat alternatives via high-moisture extrusion, biofilm mitigation using cold plasma, and functional property preservation during multiple extrusion cycles. His team develops innovative extrusion nozzles (patent WO2022018084A1) to enable shear-controlled fibrous structures.

Cong Shenمشاهده پروفایل

دانشیار

• Machine Learning
• Wireless Communications
• Federated Learning
• +۷ مورد دیگر

Cong Shen is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Virginia, where he leads a research group focused on machine learning, wireless communications, and networking. He is affiliated with the UVA Link Lab and serves as Deputy Director of Collaboration at SpectrumX, an NSF Spectrum Innovation Center. He has previously held faculty positions at the University of Science and Technology of China (USTC) and maintains strong industry ties with companies such as Qualcomm, SpiderCloud Wireless, Silvus Technologies, and Xsense.ai. Education: B.E. and M.E., Electronic Engineering, Tsinghua University, China Ph.D., Electrical Engineering, University of California, Los Angeles (UCLA), USA His research lies at the intersection of machine learning and communication systems, with a focus on in-context learning, transformers, federated learning, reinforcement learning, distributed optimization, multi-armed bandits, and AI for wireless . His work aims to bridge theoretical foundations with engineering applications in next-generation wireless networks and intelligent systems. His recent publications (2023–2025) reveal a strong trend toward integrating foundational models with communication constraints, particularly in federated and decentralized settings. Key themes include in-context learning with provable guarantees, efficient prompt optimization using bandit methods, privacy-preserving federated learning, and reinforcement learning for wireless resource management. His work frequently appears in top-tier venues such as NeurIPS, ICML, ICLR, AISTATS, and IEEE ICC. Scientific Awards: NSF CAREER Award (2022) Best Paper Award, IEEE ICC (2021) Excellent Paper Award, ICUFN (2017) Best Paper of 2024, Science Robotics Finalist for Best Student Paper Award, Asilomar (2024) Dr. Shen advises a dynamic group of graduate and undergraduate students, including PhD candidates Chengshuai Shi, Zhoubin Kou, Di Wu, and others. He leads multiple NSF-funded projects, including initiatives under the SWIFT, ECCS Core, MLWiNS, and CAREER programs, focusing on spectrum access, resource rationing in wireless FL, and domain-knowledge-enriched RL for network optimization. His lab actively contributes to open science through GitHub repositories and code releases. He also serves as an associate or editor for several IEEE Transactions journals and participates in program committees of major AI and communications conferences.

• Turbulence and combustion modeling
• Fluid mechanics
• Multi-phase flows
• +۲ مورد دیگر

Haifeng Wang is an Associate Professor at Purdue University's School of Aeronautics and Astronautics, specializing in turbulence and combustion modeling. His work focuses on fluid mechanics, multi-phase flows, propulsion systems, and machine learning applications in combustion. He leads the Combustion Energy and Propulsion Lab and has contributed to over 50 peer-reviewed articles since 2012. Key achievements include: Development of advanced flamelet models for turbulent combustion simulation Pioneering studies on differential molecular diffusion effects in turbulent flames Investigations into combustion instabilities in rocket and turbine combustors His research integrates experimental validation with high-fidelity numerical methods like LES/PDF and Eulerian Monte Carlo fields. Awards include the 2014 American Chemical Society Doctoral New Investigator Award and 2012 Bernard Lewis Fellowship. He holds a PhD in Mechanical Engineering from Cornell University (2011).

Zhaoran Wangمشاهده پروفایل

دانشیار

• Machine Learning
• Optimization
• Reinforcement Learning
• +۳ مورد دیگر

Zhaoran Wang is an Associate Professor at Northwestern University's McCormick School of Engineering, holding a joint appointment in Industrial Engineering and Management Sciences and Computer Science. His research focuses on developing data-driven decision-making methods that integrate machine learning, optimization, and game theory to address societal challenges. Key areas include autonomous agents, multi-agent systems, and provably efficient reinforcement learning algorithms. His work emphasizes computational and statistical efficiency, particularly in scenarios with partial observability or confounded data. Notable contributions include frameworks for mitigating overoptimization in reinforcement learning and scalable solutions for partially observed systems. He has published widely in top venues like ICML, NeurIPS, and ICLR. Research interests span machine learning theory, reinforcement learning, optimization, and their applications in control systems and multi-agent coordination. Recent publications emphasize theoretical guarantees for algorithms and practical scalability in real-world domains like autonomous driving and economic systems.

Irem Sepilمشاهده پروفایل

پژوهشگر ارشد

• Evolutionary Biology
• Reproductive Ageing
• Parental Effects
• +۳ مورد دیگر

Dr. Irem Sepil is a Royal Society Dorothy Hodgkin Fellow and Associate Research Fellow at St Hilda's College, University of Oxford. She holds a dual affiliation with the Department of Biology at two Oxford locations: Mansfield Road (OX1 3SZ) and South Parks Road (OX1 3RB). Her primary research focuses on evolutionary biology, particularly reproductive ageing, non-genetic parental effects, sexual selection, and life-history theory. Using the fruit fly Drosophila melanogaster as a model, she investigates how paternal age and diet influence offspring fitness, exploring mechanisms like ejaculate-mediated transgenerational effects and interventions to delay reproductive senescence. Her work integrates experimental, theoretical, and genomic approaches to address unresolved questions in paternal effects, gametic ageing, and life-history trade-offs. Notably, she has contributed to studies on seminal fluid proteomics, sperm competition dynamics, and demographic buffering theory. Dr. Sepil is also actively involved in academic service, serving as Graduate Student Mentor Representative in her department and participating in the Biology Research Staff Network. Her scientific achievements include the prestigious Royal Society Dorothy Hodgkin Fellowship, recognizing her innovative contributions to evolutionary and reproductive biology. She leads the Fly Lab, where she mentors researchers and explores cutting-edge questions at the intersection of ageing, parental effects, and evolutionary mechanisms.

César A. Uribeمشاهده پروفایل

استادیار

• Distributed Optimization
• Decentralized Control
• Algorithm Analysis
• +۵ مورد دیگر

César A. Uribe is the Louis Owen Assistant Professor in the Department of Electrical and Computer Engineering at Rice University, part of the George R. Brown School of Engineering. He also serves as a Visiting Professor at the Moscow Institute of Physics and Technology (MIPT). His research focuses on distributed optimization, decentralized control, algorithm analysis, and computational optimal transport, with an emphasis on fundamental limits of distributed optimization and scalable algorithms for networked systems. Uribe holds a BSc in Electronic Engineering from Universidad de Antioquia (2010), MSc in Systems and Control from Delft University of Technology (2013), an MSc in Applied Mathematics from the University of Illinois at Urbana-Champaign (2016), and a PhD in Electrical and Computer Engineering from UIUC (2018). He was a Postdoctoral Associate at MIT’s Laboratory for Information and Decision Systems (LIDS) before joining Rice in 2021. His research explores distributed learning algorithms, non-asymptotic analysis of social learning, and optimal transport theory. Key themes include geometric convergence rates in distributed inference, resilient optimization under adversarial conditions, and applications in networked systems such as epidemics and control systems. Uribe has received numerous awards, including the 2020 INFORMS DEI Ambassadors Program Award and the 2019 Yahoo! FREP Award. His work spans over 50 peer-reviewed publications and includes collaborations on distributed algorithms for machine learning, signal processing, and control theory. He actively mentors students in PhD and postdoctoral programs, emphasizing diversity and inclusion in STEM. Current research initiatives include optimal transport methods for network regression, competitive virus spread models over hypergraphs, and PID-based neural network architectures for adaptive control.

• Network Optimization
• Quantum Communication
• Machine Learning
• +۶ مورد دیگر

Dr. Avishek Nag is an Assistant Professor in the School of Computer Science at University College Dublin, Ireland. His work focuses on network optimization, quantum communication, machine learning, and 6G technologies. He holds a PhD from the University of California, Davis, and has over 100 publications in journals, conferences, and book chapters. Education: B.E. (Honours) in Computer Science from Jadavpur University (2005) M.Tech. from Indian Institute of Technology Kharagpur (2007) Ph.D. in Computer Science from University of California, Davis (2012) Research Interests: Quantum Networks & Key Distribution (QKD) 6G Networks & Explainable AI IoT Security & Edge Computing Optical Network Optimization Machine Learning for Biometrics & Healthcare Recent Publications: Recent work includes hybrid noise modeling for quantum satellite communication, XAI-driven network slicing for 6G, and ECG-based biometric systems. His research bridges theoretical advancements with practical implementations in telecommunications and cybersecurity. Awards: 2008 IEEE Advanced Networks Best Paper Award Grants & Leadership: Outreach Lead for IEEE UK & Ireland Blockchain Group Coordinated Cross-Atlantic EU-US testbed experiments Teaching: Teaches Distributed Systems, Wireless Communications, and Software Engineering at UCD.

Christine Aikensمشاهده پروفایل

استاد

• Nanomaterials
• Chemical Catalysis
• Energy Materials
• +۳ مورد دیگر

Christine Aikens is a University Distinguished Professor of Chemistry at Kansas State University (KSU), where she leads the Aikens Research Laboratory. Her work focuses on theoretical and computational studies of nanomaterials, particularly noble metal nanoparticles and their applications in catalysis, energy, and biorenewables. She holds a B.S. from the University of Oklahoma (2000) and a Ph.D. in Physical Chemistry from Iowa State University (2005), followed by postdoctoral research at Northwestern University (2005–2007). Her research emphasizes understanding structure-property relationships in nanomaterials, including optical properties, electron dynamics, and catalytic reactivity. Major areas include plasmon-enhanced photocatalysis, bioinspired water splitting catalysts, and ligand effects on nanoparticle stability. She collaborates with experimental groups globally, including the Di Sun and Ackerson groups for nanocluster characterization and synthesis. Dr. Aikens has received numerous awards, including the NSF CAREER (2010), Sloan Fellowship (2011), Camille Dreyfus Teacher-Scholar (2011), and 2020 ACS Rising Star Award. Her lab has pioneered computational methods like TDDFT+TB for nanoscale systems and contributed to software development for teaching nanoscience (NUITNS program). Grants: DOE, NSF, Air Force Office of Scientific Research Students: Advised 15+ graduate students and postdocs, including current researchers Sulalith Samarasinghe and Gayathri Habarakadage Active in professional service, she co-organized the 2024 International Symposium on Monolayer-Protected Clusters and frequently presents at Gordon Research Conferences. Her lab's work bridges theory and experiment, advancing sustainable materials and energy solutions.

• Space Physics
• Magnetospheric Dynamics
• Plasma Turbulence
• +۱ مورد دیگر

Dr. Matthew Argall is a Research Professor in Physics & Astronomy at the University of New Hampshire's College of Engineering and Physical Sciences. His research focuses on space plasma physics, magnetospheric dynamics, and space weather phenomena using NASA mission data. Research explores magnetic reconnection processes, plasma turbulence, and energy conversion mechanisms in space environments. Recent work develops machine learning tools for space weather prediction and VR systems for science education. Publications extensively utilize data from MMS, Wind, and Ulysses missions. Educational Contributions: Teaches graduate-level Optics (PHYS 708) Supervises senior design projects (PHYS 797) Mentors doctoral researchers (PHYS 999)

Dipayan Dattaمشاهده پروفایل

استادیار

• Quantum Chemistry
• Computational Chemistry
• Excited State Dynamics
• +۲ مورد دیگر

Dipayan Datta is Assistant Professor of Chemistry and Biochemistry at Ohio University, leading the Quantum Molecular Theory Group. His research develops quantum-mechanics methods to simulate molecular electronic structure and chemical processes. Current projects include excited state dynamics simulations, catalysis in solution environments, and non-covalent interaction quantification. The group creates high-performance computing algorithms for CPU-GPU platforms enabling large-scale quantum chemistry simulations. Datta's methodological work includes coupled-cluster implementations for heterogeneous architectures and spin-adapted open-shell theories. Applications span materials science, biochemistry, and energy research.

Chris Barrettمشاهده پروفایل

استاد

• Materials Chemistry
• Sustainable Materials
• Biomaterials
• +۴ مورد دیگر

Chris Barrett is a Professor of Chemistry at McGill University, affiliated with the Faculty of Science. He holds adjunct roles in Physics and Astronomy and serves as Co-Director of the McGill Institute for Advanced Materials. His research focuses on interdisciplinary biomaterials science, emphasizing sustainable materials derived from natural polymers like silk and cellulose, as well as light-responsive molecular systems. He has supervised over 30 graduate students and 20 postdoctoral researchers, contributing to 150+ publications. Education: B.Sc. (Queen’s University, 1992), M.Sc. (Queen’s, 1994), Ph.D. (Queen’s, 1997). Postdoctoral work at MIT (1998–1999) and Harvard-MIT (1997–1998). Awards include the Leo Yaffe Teaching Award and Fulbright Visiting Research Chair. Research interests span materials chemistry, sustainable engineering, and bio-inspired design. His lab develops eco-friendly polymers, optical devices, and bio-interface materials. Recent projects include photo-degradable materials and light-harvesting systems modeled after natural structures. Lab: Barrett Lab, Otto Maass Building (OM430), McGill University Key Collaborations: Institute for Sustainable Engineering, School of Environment, Montreal Neurological Institute Grants: NSERC, FRQNT, and international visiting professorships Awards and recognition include over 150 invited lectures and 50 public outreach talks on sustainability and materials science. His work bridges chemistry, physics, and engineering to address global environmental challenges.

Pietro Mililloمشاهده پروفایل

استادیار

• Remote Sensing
• Earth Science
• Cryosphere Monitoring
• +۵ مورد دیگر

Pietro Milillo is an Assistant Professor in the Department of Civil and Environmental Engineering at the University of Houston, with additional appointments as a Guest Scientist at the German Aerospace Center (DLR) and former roles at NASA JPL, UC Irvine, and Caltech. He holds a PhD in Environmental Engineering from the University of Basilicata and MS/BS degrees in Physics from the University of Bari. Research Interests: Focus on synergistic use of remote sensing data and computational analysis for Earth and Cryosphere Science, natural/anthropogenic hazard response, and near real-time disaster monitoring. Key areas include SAR constellations, InSAR techniques, machine learning for geophysics, and infrastructure health assessment. Publication Trends: Recent work emphasizes AI-driven SAR analysis (2025), Antarctic ice monitoring (2025), infrastructure deformation mapping (2025), and integration of altimetry with SAR for environmental applications (2024). Scientific Recognition: 2021: Italian Full Professor Habilitation in Earth Science Physics 2020: Remote Sensing Young Investigator Award 2019: ISSNAF Anna Maria Molteni Award 2016: NASA Postdoctoral Program Fellowship Advising: Mentors PhD students at the University of Houston (2023-2024) and international institutions like the University of Bath (UK) and Politecnico di Torino (Italy). Institutional Leadership: Coordinated €2MM/year international satellite campaigns for Greenland/Antarctica (2017-2021), contributed to NASA's Surface Deformation observing system design (2019-2020), and chaired IGARSS sessions on remote sensing applications.

Juntao Huangمشاهده پروفایل

استادیار

• Computational Mathematics
• Applied Mathematics
• Numerical Analysis
• +۶ مورد دیگر

Juntao Huang is an Assistant Professor at the University of Delaware , specializing in computational mathematics and applied mathematical modeling. His research bridges numerical analysis, machine learning, and physical systems, focusing on hyperbolic partial differential equations, kinetic theory, and high-order numerical methods. Dr. Huang's work emphasizes machine learning-based moment closures for equations in fluid dynamics, radiative transfer, and semiconductor physics. He develops implicit-explicit (IMEX) schemes for stiff hyperbolic systems and investigates bound-preserving discontinuous Galerkin methods in reactive flows. His publications highlight adaptive sparse grid algorithms and stability-preserving time integrators . His recent article trends include machine learning integration into kinetic models (BGK equations, Boltzmann transport), multi-scale hyperbolic systems , and thermodynamically stable PDE formulations . This aligns with his interest in non-equilibrium flow modeling and numerical methods for Vlasov-Maxwell equations in plasma physics. Dr. Huang's advising and grant information isn't present in the provided texts, but his technical contributions span from lattice Boltzmann methods for convection-diffusion equations to curved interface treatments in kinetic simulations. His work also explores information theory through generalized Kullback-Leibler divergences in Tsallis statistics.

Gowtham Mohanمشاهده پروفایل

استادیار

• Solar Energy Systems
• Thermal Engineering
• Energy Storage
• +۴ مورد دیگر

Gowtham Mohan is an Assistant Professor in the Department of Engineering Technology at the University of Houston's Cullen College of Engineering. His research focuses on advancing renewable energy systems, thermal engineering, and sustainable technologies. He leads projects in solar thermal systems, particle flow dynamics, and energy storage materials. His work integrates experimental and computational methods to optimize energy efficiency and system performance. Key research areas include solar agrivoltaic systems, high-temperature particle receivers, and PVT (Photovoltaic Thermal) systems. He has conducted extensive studies on thermal energy storage materials, corrosion-resistant alloys, and non-intrusive measurement techniques for particle plumes. His contributions span interdisciplinary applications such as sustainable desalination, combined cycle power plant optimization, and environmental integration of energy systems. Dr. Mohan's publications emphasize novel methodologies for particle velocity estimation, thermal imaging analysis, and system feasibility studies. His work bridges theoretical innovation with practical applications in energy sustainability and thermal engineering. He collaborates closely with industry and academic partners to translate research into real-world solutions.

Dennis L. Parkerمشاهده پروفایل

استاد

• Medical Imaging
• Magnetic Resonance Imaging (MRI)
• Magnetic Resonance Angiography (MRA)
• +۴ مورد دیگر

Dennis L. Parker is a Professor in the Departments of Radiology & Imaging Sciences and Biomedical Informatics at the University of Utah. He serves as the Mark H. Huntsman Endowed Professor and founded the Utah Center for Advanced Imaging Research (UCAR), directing it from 2003–2016. His work focuses on the mathematics and physics of medical imaging , particularly MRI and MR-guided thermal therapies . Education: PhD in Medical Biophysics and Computing (University of Utah), MS in Physics (Brigham Young University), BS in Physics (Brigham Young University) Dr. Parker has pioneered MRI thermometry for thermal therapy guidance, MR angiography techniques (MOTSA), and carotid plaque analysis using diffusion-weighted MRI. His research integrates acoustic radiation force , shear wave elastography , and focused ultrasound for non-invasive interventions. Recent publications emphasize dynamic T1/T2* mapping , skull microstructure modeling , and AI-driven tissue property estimation . His work has been funded by NIH R01 grants and VA Merit Awards . Scientific Recognition: Distinguished Research Award, University of Utah (2000) Fellow, American Institute for Medical and Biomedical Engineering (2008) Fellow, International Society for Magnetic Resonance in Medicine (2015) He has mentored over 80 trainees , many of whom now hold academic positions. His Neurovascular Imaging Group develops non-invasive MRI methods to replace risky X-ray angiography, advancing global standards in vessel wall imaging and atherosclerosis detection .