جست‌وجوی استاد و پژوهشگر

• Catalysis engineering
• Electrochemical energy conversion
• Photochemical process
• +۸ مورد دیگر

Yijiao Jiang is a Professor at Macquarie University's School of Engineering and Co-Director of the Transforming Energy Markets Research Centre. She specializes in sustainable technologies for converting greenhouse gases, plastics, and biowaste into fuels and chemicals. Her work is supported by grants from ARC Discovery Projects, Industry Transformation Hubs, and international organizations like the European Commission and CSIRO. She leads a research group of 2 postdocs, 5 PhD candidates, and 5 undergraduate students. Education: PhD in Chemical Technology (University of Stuttgart, Germany) BEng/MEng in Environmental Engineering (Kunming University of Science and Technology) Postdoctoral research at ETH Zurich and UNSW Australia Research Interests: Her research focuses on catalysis engineering, electrochemical energy conversion, and photochemical processes. Key areas include CO2 reduction, waste plastics upcycling, and hydrogen production. She explores novel catalyst designs and in situ characterization techniques to enhance reaction efficiency and sustainability. Publications: Her articles highlight advancements in CO2 electroreduction mechanisms, waste valorization via transesterification, and novel catalyst materials. Recent work emphasizes improving electrolyte stability and catalyst durability in energy conversion systems. Awards & Roles: Fellow of the Royal Society of Chemistry (FRSC) ARC College of Experts Member Recipient of the Monash Engineering Women's Leadership Award (2017) Associate Editor for ACS Energy & Fuels and Carbon Capture Science & Technology Grants & Supervision: Secured over 40 grants totaling millions in funding. Supervised 21 HDR students and 57 thesis students, with notable completions like Wenwen Zhang (VC Commendation) and Alena Kochubei (Executive Dean’s Commendation). Labs & Networks: Leads research on energy markets and participates in the NSW Decarbonisation Innovation Hub. Collaborates globally on projects like Powerfuels and Hydrogen Networks.

Anuradha Agarwalمشاهده پروفایل

پژوهشگر

• Integrated Photonics
• Microphotonics
• Electronic Materials
• +۳ مورد دیگر

Dr. Anuradha Agarwal is a Principal Research Scientist at the Massachusetts Institute of Technology , affiliated with the Microphotonics Center and Materials Research Laboratory . She serves as Director of the Electronic-Photonics Packaging Materials Research Laboratory and leads the Lab for Education and Application Prototypes (LEAP) . Her work bridges fundamental materials science with scalable photonic device applications. Ph.D., Electrical Engineering , Boston University Dr. Agarwal’s research focuses on mid-infrared photonic sensors for chem-bio sensing and hyperspectral imaging, utilizing CMOS-compatible silicon fabrication to enable large-scale manufacturing. She pioneers sustainable microchip production through the FUTUR-IC initiative and develops education programs for integrated photonics workforce training. Her recent publications highlight advancements in photonic couplers , metasurfaces , and self-healing materials , reflecting a strong emphasis on resource efficiency and environmental resilience in photonic technology. She has authored over 250 peer-reviewed papers and holds 17 awarded patents. Optica Fellow (2022) As director of Electronic-Photonic Packaging (EPP) , she explores innovative testing and packaging solutions. Her LEAP lab focuses on K-Gray workforce development, including virtual/augmented reality training tools to address skill gaps in the integrated photonics industry.

Luis Felipe Pereiraمشاهده پروفایل

استاد

• Mathematical Modeling
• Numerical Simulation of Multiphase Flows
• Uncertainty Quantification
• +۵ مورد دیگر

Luis Felipe Pereira is a Professor in the Department of Mathematical Sciences at the School of Natural Sciences and Mathematics, University of Texas at Dallas. His research focuses on mathematical modeling and numerical simulation of multiphase flows in porous media, uncertainty quantification, CO2 storage, oil recovery, contaminant transport, and high-performance scientific computing. He specializes in developing advanced multiscale methods and parallel algorithms to address complex subsurface flow problems. His work combines computational mathematics with applications in energy and environmental systems, including reservoir engineering and geomechanics. Key contributions include the development of multiscale mixed methods, Bayesian frameworks for subsurface characterization, and efficient parallel implementations for billion-cell reservoir simulations. Recent research trends emphasize improving algorithm scalability, integrating machine learning techniques, and addressing challenges in heterogeneous media. His computational tools enable predictive modeling of subsurface processes under uncertainty, with applications in CO2 sequestration, groundwater contamination, and enhanced oil recovery. No scientific awards or grants are explicitly listed in the provided texts. Advising information is not documented here, though his research group likely engages in graduate student mentorship in applied mathematics and computational science. He collaborates on interdisciplinary projects involving porous media flow across multiple scales.

Pallavi Dhagatمشاهده پروفایل

استاد

• Magnetic Materials
• Biomedical Imaging and Sensing
• Data Storage Technology
• +۵ مورد دیگر

Pallavi Dhagat is a Professor and Dean's Professor in the College of Engineering at Oregon State University, specializing in Electrical Engineering and Computer Science. She leads the Applied Magnetics Lab and holds a D.Sc. in Electrical Engineering from Washington University, St. Louis (1999) and an MBA from Oregon State University (2010). Her research focuses on advanced magnetic materials, biomedical applications of magnetic nanoparticles, and magneto-acoustic signal processing technologies. She previously worked at Seagate Technology, contributing to perpendicular recording technology development. Education Background: D.Sc., Electrical Engineering, Washington University, St. Louis, 1999 M.S., Electrical Engineering, Washington University, St. Louis, 1996 B.E. (Honors), Electrical Engineering, Birla Institute of Tech. and Science, Pilani, India, 1993 MBA, Oregon State University, 2010 Research Interests: Dr. Dhagat pioneers 3D printed magnetic materials, biomedical imaging/sensing with magnetic nanoparticles, and novel data storage devices leveraging spin-wave and acoustic-wave interactions. Her work includes developing advanced magnetic measurement techniques and applying magnetic hyperthermia for cancer treatment. Recent research explores magnetoelastic hybrid devices and graphene-based spintronics. Publications Overview: Her articles span magneto-acoustic device design (2023–2025), nanoparticle synthesis for hyperthermia (2020–2022), and foundational studies in spin-wave dynamics (2015–2024). Key themes include material characterization, device integration, and biomedical applications. Awards: Recipient of the NSF CAREER Award (2007) for innovative research in surface acoustic wave technologies. Advising & Labs: As faculty advisor for the Applied Magnetics Lab, she mentors students in cutting-edge projects. No specific grants listed in the provided text, but her work aligns with NSF-funded initiatives.

Wojciech Dmowskiمشاهده پروفایل

عضو هیئت علمی

• Atomic structure and dynamics in liquids and glasses
• Nanoscale structures and local atomic ordering in materials
• Advanced structural characterization techniques
• +۴ مورد دیگر

Wojciech Dmowski is an Associate Research Professor in the Department of Materials Science and Engineering at the University of Tennessee, Knoxville, affiliated with the Tickle College of Engineering. His research focuses on atomic-level structural characterization of liquids, glasses, and metallic materials. He is associated with the Shull Wollan Center (UT-ORNL) and has held positions such as Visiting Professor at Tohoku University (2013). His work integrates advanced techniques like neutron scattering and X-ray spectroscopy to study dynamics in metallic glasses, high-entropy alloys, and aqueous systems. Education: PhD from Warsaw University of Technology. Research interests emphasize real-space atomic dynamics, medium-range structural ordering, and deformation mechanisms in amorphous materials. Key areas include stress-induced structural anisotropy in glasses, correlated motions in liquids, and the interplay between structure and mechanical properties. His studies on metallic glasses address ductility, plasticity, and rejuvenation effects under mechanical loading. Publications span over two decades, with recent focus on high-entropy alloys, proton dynamics in water, and nanoscale structural heterogeneity. Awards include research grants from Poland's Ministry of Education and a visiting professorship in Japan. Professional service includes roles as Secretary of the SIG Amorphous Materials (ACA, 2000-2003) and membership in TMS, MRS, and other societies. His work bridges fundamental materials science with applications in energy storage and structural engineering.

Jack Marquezمشاهده پروفایل

عضو هیئت علمی

• High Performance Computing (HPC)
• Cloud Computing
• Heterogeneous Storage Systems
• +۳ مورد دیگر

Jack Marquez is a Research Assistant Professor at the University of Tennessee, Knoxville, affiliated with the College of Engineering and the Department of Electrical Engineering and Computer Science. He is a member of the Global Computing Laboratory directed by Michela Taufer. PhD in Software Engineering (2022), Universidad Autónoma de Occidente, Colombia BS in Software Engineering (2013), Universidad Autónoma de Occidente, Colombia His research focuses on High Performance Computing (HPC) and Cloud Computing, with a particular emphasis on heterogeneous storage systems, scientific workflows, and performance optimization techniques. He has developed scalable algorithms for motif counting, dynamic simulation management, and data fabric integration, bridging computational efficiency with data-intensive science. His recent publications highlight expertise in: Optimizing HPC performance in cloud environments In-situ analysis for molecular dynamics simulations Machine learning-driven resource allocation Checkpointing and fault tolerance mechanisms Community-driven scientific data infrastructure Previously, he served as a researcher and adjunct professor at Universidad Autónoma de Occidente (2013–2016), contributing to software engineering pedagogy and industry collaboration as a software developer. He is based in the Min H. Kao Building, Room 351, and can be reached at jmarque4@utk.edu .

Andrew D. Parsekianمشاهده پروفایل

دانشیار

• Environmental Geophysics
• Hydrology
• Cryosphere Science
• +۷ مورد دیگر

Andrew D. Parsekian is an Associate Professor in the Department of Geology & Geophysics at the University of Wyoming, part of the College of Engineering and Physical Sciences. He serves as Director of the Hydrologic Science Program and is actively engaged in research and graduate education in environmental geophysics. Ph.D., Rutgers-Newark, 2011 B.S., Dickinson College, 2005 His research focuses on environmental geophysics, particularly the application of non-invasive geophysical methods to study groundwater and cryospheric systems. His group specializes in ground-penetrating radar, electrical resistivity tomography, seismic refraction, and nuclear magnetic resonance. These tools are used to investigate permafrost dynamics, active layer processes, snow distribution, and hydrogeophysical inversion in complex terrains. His recent publications (2024–2025) span high-impact journals such as Geophysics , Journal of Geophysical Research , and The Cryosphere . The work demonstrates a strong trend toward integrating geophysical data with hydrological models, incorporating uncertainty quantification, and applying machine learning to hydrofacies classification. Many of his papers feature student lead-authors, highlighting his active mentorship in graduate research. Scientific contributions include leadership in the ABoVE airborne radar surveys and the Permafrost Dynamics Observatory. His work bridges geophysics, hydrology, and climate science, with growing emphasis on remote sensing and big data analytics. Machine learning-based hydrofacies classification Uncertainty quantification in geophysical inversion Permafrost and active layer monitoring Snow distribution and non-Gaussian statistics Hydrogeophysical data fusion Remote sensing of boreal and Arctic systems Dr. Parsekian teaches Fundamentals of Research for incoming graduate students and Engineering and Environmental Geophysics every odd-year spring. He advises several graduate students, many of whom have led recent publications. His research is supported by active collaborations and data-sharing initiatives, though specific grants are not listed in the text. He leads a dynamic research group focused on advancing near-surface geophysical techniques for environmental applications. He is affiliated with the Hydrologic Science Program and conducts fieldwork in diverse environments, from Arctic permafrost regions to tropical peatlands. His team combines field data collection with advanced data processing and modeling, contributing to both theoretical and applied geophysics.

Liu Chuanlaiمشاهده پروفایل

پژوهشگر

• Computational Materials Science
• Energy Storage Materials
• Microstructure Physics
• +۵ مورد دیگر

Liu Chuanlai is a computational materials scientist with dual affiliations at the Max Planck Institute for Sustainable Materials (formerly Max Planck Institute for Iron Research) in Germany and the National Engineering Research Center of Light Alloy Net Forming at Shanghai Jiao Tong University in China. His research focuses on the development and application of advanced computational methods to understand and design energy storage materials and structural alloys. His primary research interests include computational materials science, microstructure physics, alloy design, phase-field modeling, crystal plasticity, and solid-state battery materials. Liu employs multi-scale modeling approaches combining phase-field methods, crystal plasticity, and CALPHAD-informed thermodynamics to investigate phenomena such as spinodal decomposition, deformation twinning, grain boundary precipitation, and chemo-mechanical degradation in various alloy systems including aluminum, magnesium, and battery electrode materials. Analysis of his recent publications reveals a strong emphasis on multi-physics modeling of microstructural evolution in metallic systems, particularly focusing on the interplay between chemo-mechanical effects and microstructural features. His work often combines advanced computational techniques with experimental validation through collaborations with leading materials research groups worldwide. Liu has established productive research collaborations with prominent scientists including Dierk Raabe, Franz Roters, and other members of the Max Planck research community. His publications appear in top-tier materials science journals such as Nature Communications, Acta Materialia, and Advanced Science, demonstrating the high impact of his research in the computational materials science community.

Shengrong Buمشاهده پروفایل

دانشیار

• Smart Grids
• Wireless Network Security
• Deep Reinforcement Learning
• +۳ مورد دیگر

Shengrong Bu is an Associate Professor at the Department of Engineering, Brock University, Canada. She holds a Ph.D. in Electrical and Computer Engineering from Carleton University and has held academic positions at the University of Glasgow and industrial roles at Huawei Technologies. Her research bridges smart grid communications, wireless network security, and deep reinforcement learning applications. Ph.D., Electrical & Computer Engineering, Carleton University MEng by Research, Electrical Engineering, University of Wollongong BEng, Mechanical Engineering & Automation, Huazhong University Research focuses on: Multi-vector energy microgrids and smart grid communications Deep reinforcement learning for network optimization Game theory applications in energy systems Big data analytics for grid resilience Articles show expertise in P2P energy trading, fog computing resource allocation, and wireless security in smart grid environments, with funding from EPSRC and NSERC. Awards include multiple IEEE best papers and prestigious research fellowships. She supervises Ph.D. and MSc students in energy systems research.

• Heterogeneous Catalysis
• Alternative Energy
• Environmental Catalysis
• +۵ مورد دیگر

Dr. Mihaela Diana Lazar is a Principal Researcher (CS I) at the National Institute for Research and Development in Isotopic and Molecular Technologies (INCDTIM) , Romania, affiliated with the Department of Materials, Energy, and Advanced Technologies . Her research focuses on heterogeneous catalysis for environmental and energy applications, particularly involving graphene-metal nanocomposites , metal-organic frameworks (MOFs) , and carbon nanostructures . PhD in Chemistry (2010) - Babeș-Bolyai University, Cluj-Napoca Her expertise spans catalyst preparation techniques (impregnation, sol-gel), CO2 methanation , hydrogen storage , and environmental pollutant removal . She leads projects like EFILUX (electrolytic copper recovery) and GRAFTID (graphene-TiO2 for pollutant degradation), and contributes to CO2-HyBrid (hybrid CO2 capture) and ME-MOS (MOF-based energy storage). Recent publications highlight her work on graphene-metal hybrid systems for pharmaceutical detection, MIL-101-supported catalysts for nitrobenzene hydrogenation, and zinc oxide-graphene nanocomposites for solar supercapacitors. Her technical skills include BET surface analysis , catalyst characterization , and green synthesis methods .

• White adipocyte biology
• Lipid metabolism
• Carbohydrate metabolism
• +۳ مورد دیگر

Mikael Rydén is a Professor and Senior Physician specializing in adipose tissue research at the Department of Medicine, Huddinge, Karolinska Institutet. He co-leads the Mikael Rydén & Niklas Mejhert lab, focusing on white adipocyte biology, lipid metabolism, and immunometabolism. His research spans 25 years and includes studies on energy storage, obesity-related inflammation, and metabolic pathways. He has contributed to groundbreaking work using single-cell and spatial transcriptomics to map adipose tissue heterogeneity. Key research areas include the role of epigenetic changes in obesity, lipid droplet biology, and the interplay between metabolism and inflammation. Recent articles highlight findings on ER stress in adipocytes and adipose tissue memory post-weight loss. The lab collaborates widely, receiving grants from the Swedish Research Council and others. Mikael advises a team including postdocs and PhD students, overseeing clinical studies and lab management. His lab is part of the Center for Reproduction, Metabolism and Molecular Medicine (CeRM), emphasizing translational research to combat metabolic diseases. Notable achievements include developing the Lipid Droplet Knowledge Portal and advancing understanding of adipocyte subpopulations and insulin resistance.

Carmine D'Agostinoمشاهده پروفایل

مدرس ارشد

• Catalysis
• Porous Materials
• Nuclear Magnetic Resonance (NMR) Spectroscopy
• +۵ مورد دیگر

Dr Carmine D'Agostino is a Senior Lecturer in Chemical Engineering at the University of Manchester, specializing in analytical methods like NMR spectroscopy to study catalytic systems and porous materials. His research focuses on improving industrial processes through advanced characterization techniques, with applications in sustainable chemistry and renewable energy. Education: BEng (2004) and MEng (2006) from Università di Napoli Federico II, Italy; PhD (2012) in Chemical Engineering from the University of Cambridge under Prof. Lynn Gladden. Previously a research scientist at the University of Cambridge's Department of Chemical Engineering and Biotechnology. Research interests include catalysis, porous materials, ionic liquids, and multi-component diffusion. He has received awards such as the Young Scientist Award at the International Congress on Catalysis and a Wolfson College Junior Research Fellowship. His work contributes to UN Sustainable Development Goals related to clean energy and sustainable production. Active in projects like SPACING (sustainable acrylic acid production) and low-field NMR studies in rock pore structures. Supervises PhD students and collaborates internationally, contributing to journals like Advanced Materials and Physical Chemistry Chemical Physics .

• Machine Learning
• Wireless Communications
• Federated Learning
• +۴ مورد دیگر

Mohammad Mohammadi Amiri is an Assistant Professor in the Computer Science Department at Rensselaer Polytechnic Institute (RPI), joining in Fall 2023. Previously, he held postdoctoral positions at MIT Media Lab (2022–present) and Princeton University (2019–2021), working with Prof. Vincent Poor and Prof. Sanjeev Kulkarni. He earned his Ph.D. in Electrical and Electronic Engineering from Imperial College London (2019), with prior degrees from Iran University of Science and Technology (B.Sc., 2011) and the University of Tehran (M.Sc., 2014). His research focuses on decentralized intelligence, federated learning, and wireless edge computing. He explores enabling distributed machine learning while preserving privacy, particularly in IoT and smart systems. Key areas include federated learning over wireless channels, data valuation, and optimization of edge computing resources. His articles address topics like federated learning convergence, device selection in wireless systems, and decentralized data valuation. Recent work includes papers accepted at AAAI 2023, CISS 2023, and IEEE Transactions on Wireless Communications. Awards : IEEE Communications Society Young Author Best Paper Award (2022) Best PhD Thesis Award (2019), IEEE Information Theory Chapter Eryl Cadwallader Davies Prize (2019), Imperial College London Advising & Grants : Mentoring Ph.D. students in machine learning and distributed systems Recipient of multiple scholarships and awards during his academic career Labs/Teams : Camera Culture Group (MIT Media Lab) Princeton University's Electrical Engineering Department

• Coding Theory
• Distributed Storage Systems
• Matroid Theory
• +۳ مورد دیگر

Thomas Westerbäck is a Senior Lecturer at Mälardalen University, affiliated with the School of Education, Culture and Communication and the Division of Mathematics and Physics. His research focuses on coding theory, distributed storage systems, matroid theory, combinatorics, information theory, and algebraic coding. He has contributed to advancements in locally repairable codes, matroid structures in storage systems, and applications of combinatorial methods in coding. His publications highlight theoretical bounds, code constructions, and practical implementations in storage efficiency and error resilience. Key research themes include matroid minors in coding, alphabet-dependent code bounds, and polymatroidal frameworks for heterogeneous storage systems. His work bridges abstract mathematical concepts (e.g., Möbius inversion in lattices) with applied coding challenges in distributed environments. Publications span topics from binary matroid flats to exact regenerating codes, demonstrating a blend of theoretical depth and practical storage solutions. His research emphasizes resilience against data erasures and optimizing storage efficiency through combinatorial and algebraic innovations.

• Cloud Computing
• Distributed Systems
• High-Performance Computing
• +۵ مورد دیگر

Raja Appuswamy is an Assistant Professor in the Department of Data Science at EURECOM, focusing on data management on modern hardware and molecular information storage. His research integrates cutting-edge storage technologies with bioinformatics, particularly in DNA-based data storage. He teaches courses in cloud computing, distributed systems, and databases. His work spans Optimizing storage hierarchies with DNA archival systems Hardware-conscious algorithms for GPUs and heterogeneous architectures Error correction in molecular storage Long-term database preservation strategies Key research interests include Cross-architecture data joins (e.g., OneJoin, XJoin) Cold storage innovation with CMOSS and OligoArchive Integration of bio-inspired storage with traditional computing and explores interdisciplinary challenges at the intersection of computer science and molecular biology. Publications emphasize scalable solutions for DNA storage density, error tolerance, and archival systems. Awards highlight contributions to DNA-based image encoding, storage reliability, and page cache checksums. Current projects focus on enzymatic DNA ligations for storage density improvement, motif-based error correction, and hardware acceleration for knowledge graphs. His work targets both theoretical advancements and practical implementations in storage systems.