جستجوی استاد راهنمای دانشگاهی

Konstantinos Papamichaelمشاهده پروفایل

استاد

• Lighting Technology
• Daylighting Systems
• Energy Efficiency
• +۳ مورد دیگر

Konstantinos Papamichael is Professor of Design at the University of California, Davis, and Co-Director of the California Lighting Technology Center (CLTC). At CLTC, he leads initiatives to advance energy-efficient lighting and daylighting technologies through research partnerships with utilities, manufacturers, government agencies, and design professionals. His research focuses on lighting technology innovation with emphasis on adaptive systems, daylight harvesting, spectral optimization, and human-centered design. Recent projects address dynamic lighting controls, neural network modeling for daylight prediction, and application-specific spectral power distributions. Analysis of his recent publications reveals strong emphasis on energy-efficient lighting solutions, human factors in lighting design, and technological innovation in daylight harvesting. His work consistently bridges theoretical research with practical applications in sustainable building design. Papamichael directs the CLTC facility at 633 Pena Drive in Davis, where he oversees technology demonstrations and educational outreach programs that accelerate adoption of advanced lighting solutions across residential, commercial, and institutional sectors.

• Lighting Technology
• Energy Efficiency
• Human-Centric Lighting
• +۲ مورد دیگر

Michael Siminovitch is Professor of Design and Rosenfeld Chair in Energy Efficiency at the University of California, Davis, where he directs the California Lighting Technology Center (CLTC). His research advances energy-efficient lighting, human-centric design, and sustainable technologies. Recent grants include projects on discrete color spectra’s impact on stress and alertness (with Toyota Boshoku America and Seoul Semiconductor) and full-spectrum LEDs for health applications. Research interests span lighting technology, energy conservation, color science, and industrial innovation. The CLTC bridges academic research and industry implementation. Articles focus on energy-efficient lighting systems, color fidelity in clinical settings, and policy initiatives. Trends include human-centric lighting design, sustainable technology deployment, and interdisciplinary collaborations with healthcare and manufacturing sectors. No awards or students are listed in the source material. Grants include $200,000 for discrete color research (2023) and $100,000 for full-spectrum LED studies. The CLTC collaborates globally with industry partners like Seoul Semiconductor.

Jelena Kovačevićمشاهده پروفایل

استاد

• Biomedical Imaging
• Signal Processing
• Wavelets and Frames
• +۵ مورد دیگر

Jelena Kovačević is Dean Emeritus of the NYU Tandon School of Engineering and the Paulette Goddard Professor of Electrical and Computer Engineering at NYU. She holds a Dipl. Electrical Engineering from the University of Belgrade (1986), and MS/PhD from Columbia University (1988/1991). Her career includes roles at Bell Labs, co-founding xWaveforms, and leadership at Carnegie Mellon University. Her research focuses on biomedical imaging, wavelets, and signal processing. She has pioneered frameworks for graph signal processing and semi-supervised learning, with impactful applications in healthcare and engineering. As Dean, she drove initiatives to double women’s representation in engineering, established interdisciplinary research areas, and secured $59M in annual research expenditures. Awards include IEEE’s EMBS Career Achievement Award and fellowships from IEEE and EUSIPCO. She has authored influential books like Wavelets and Subband Coding and Foundations of Signal Processing , and served as Editor-in-Chief of IEEE Trans. on Image Processing. Her leadership extended to forming the Northeast Regional Deans (NeRDs) council and advancing diversity initiatives like Tandon’s Office of Inclusive Excellence. Education: Columbia University (PhD 1991), University of Belgrade (1986) Key Roles: Dean of NYU Tandon (2018-2024), Carnegie Mellon Department Head (2003-2018) Research: Biomedical imaging, graph signal processing, multiresolution techniques Her awards reflect her technical contributions and leadership, including the Belgrade October Prize, E.I. Jury Award, and recognition for foundational papers in wavelet theory. She has been a plenary speaker at major conferences and a key figure in signal processing and biomedical imaging communities.

Aidan Gilchristمشاهده پروفایل

استادیار

• Biomaterials Engineering
• Stem Cell Biology
• Tissue Engineering
• +۷ مورد دیگر

Aidan Gilchrist is an Assistant Professor of Biomedical Engineering at the University of California, Davis. His research focuses on developing biomaterials and in vitro models to address aging-related health challenges, particularly in regenerative medicine and disease progression modeling. The Gilchrist Lab integrates stem cell engineering, materials design, and systems biology to investigate cellular aging mechanisms and translate findings into clinical applications. Dr. Gilchrist holds a Ph.D. in Materials Science and Engineering from the University of Illinois Urbana-Champaign and a B.S. in Chemical Engineering from the University of Massachusetts Amherst. His work bridges engineering and biology through projects such as hydrogel design for organoid models, mechanotransduction studies in cancer, and metabolic analysis of stem cell niches. Key research areas include: Biomaterials engineering for 3D cellular microenvironments Stem cell aging and rejuvenation strategies Organoid models of liver disease and cancer Dynamic hydrogel systems for controlled drug delivery Recent work highlights stiffness-mediated chemoresistance in pancreatic cancer organoids and the role of extracellular matrix mechanics in disease progression. His lab also develops injectable hydrogels and microgel systems for therapeutic applications, emphasizing biocompatibility and tunable material properties. No scientific awards or grants are explicitly listed in the provided materials, though his prolific publication record reflects active research engagement. The Gilchrist Lab collaborates on translational projects targeting human tissues and patient-derived models for personalized medicine applications.

Yiannis Sakellaridisمشاهده پروفایل

استاد

• Automorphic forms
• Number theory
• Representation theory
• +۳ مورد دیگر

Yiannis Sakellaridis is a Professor of Mathematics at Johns Hopkins University (JHU), affiliated with the Krieger School of Arts & Sciences. Previously, he held positions at Rutgers University–Newark (2014–2019) and the National Technical University of Athens (2015–2017). He earned his PhD in Mathematics from Stanford University (2006) under Daniel Bump, with a focus on unramified representations of p-adic groups on spherical varieties. Education: PhD in Mathematics, Stanford University (2001–2006) Diploma in Electrical & Computer Engineering, National Technical University of Athens (1996–2001) Research Interests: Automorphic forms, number theory, representation theory, with emphasis on spherical varieties, relative trace formulas, and L-functions. His work bridges geometric and analytic aspects of representation theory, including functoriality, harmonic analysis on spherical varieties, and applications of trace formulas. Grants & Awards: Fellow of the American Mathematical Society (2023) NSF grants totaling over $650,000 (2011–2024) Simons Fellow in Mathematics (2021–2022) Professor of the Year Award, JHU Mathematics (2020) Academic Service: Editor, Tunisian Journal of Mathematics (2022–present) Co-organizer of conferences including the 2022 International Congress of Mathematicians section on Lie Theory Extensive editorial and organizing roles in workshops on the Langlands program and trace formulas Teaching & Mentorship: Current advisees: Rui Chen, Xin Zou (JHU) Prior students include Junqi Wang (Rutgers Newark, 2018) and Ioan Filip (Columbia, 2016) Teaching spans advanced graduate courses (e.g., Number Theory, Representation Theory) and undergraduate calculus/linear algebra Labs/Teams: Active in collaborative networks including the Automorphic Project , and co-leads initiatives such as the JHUnior Number Theory Days (2024/25).

Mark Behrensمشاهده پروفایل

استاد

• Algebraic Topology
• Homotopy Theory
• Chromatic Homotopy
• +۳ مورد دیگر

Mark Behrens is the John and Margaret McAndrews Professor of Mathematics at the University of Notre Dame. His research focuses on computational algebraic topology, particularly homotopy groups of spheres, chromatic homotopy theory, and applications to motivic homotopy. Education includes a PhD from University of Chicago (2003) and BS degrees in Mathematics/Physics from University of Alabama. Research develops connections between tensor-triangular geometry, topological automorphic forms, and Goodwillie calculus. Current projects investigate equivariant analogs of Mahowald's theorem and derived functors in unstable homotopy. Publications provide spectral algebra models for v-periodic phenomena, resolutions of telescope conjectures, and computational frameworks for tmf-based spectral sequences. Recent work establishes Lie algebra models for unstable homotopy and equivariant periodicity phenomena. Awards include AMS Fellowship (2022), NSF CAREER grant (2011), and Sloan Fellowship (2007). Supervises 21 PhD students and postdocs investigating equivariant homotopy, factorization homology, and operad theory. Current grants support research on equivariant and motivic deformations of stable homotopy theory.

Soham Janaمشاهده پروفایل

استادیار

• High-dimensional statistics
• Robust estimation
• Statistical Machine Learning
• +۵ مورد دیگر

Soham Jana is an Assistant Professor in the Department of Applied and Computational Mathematics and Statistics (ACMS) at the University of Notre Dame, part of the College of Science. He holds a Ph.D. in Statistics and Data Science from Yale University (2022), an M.Stat. and B.Stat. from the Indian Statistical Institute (ISI), Kolkata. His research focuses on high-dimensional statistics, robust estimation, statistical machine learning, causal inference, and empirical Bayes methods. Previously, he was a Post-doctoral Research Associate at Princeton University's Operations Research and Financial Engineering department. Education: Ph.D., Yale University, 2022 (Thesis: Learning non-parametric and high-dimensional distributions via information-theoretic methods) M.Stat. (Hons.), Indian Statistical Institute, 2017 (Dissertation: Characterization of single-integral non-kernel divergences) B.Stat. (Hons.), Indian Statistical Institute, 2015 Research interests include theoretical and methodological aspects of high-dimensional statistics, robust estimation, neural networks, causal inference, and empirical Bayes. He has received awards such as the INSPIRE Scholarship and the Kaneb Center Course Design Academy Award (2024–2025, $5000). Grants and professional contributions include organizing sessions at conferences like JSM and CFE-CMStatistics. His work spans collaborations with institutions like Princeton, Yale, and the First Republic Bank Research Program. He teaches courses such as Introduction to Probability and Modern Machine Learning Techniques at Notre Dame.

• Partial Differential Equations
• Inverse Problems
• Control Theory
• +۲ مورد دیگر

Matthias Eller is a Professor in the Department of Mathematics and Statistics at Georgetown University. His research focuses on Partial Differential Equations, Inverse Problems, and Control Theory. Publications span spectral optimization for Maxwell systems, wave propagation in viscoelastic media, thermoacoustic tomography, and controllability problems. Research emphasizes rigorous analysis of boundary conditions, stability properties, and solution techniques for complex physical models. Education: Ph.D., Wichita State University

John F Mustardمشاهده پروفایل

استاد

• Planetary Geoscience
• Environmental Science
• Earth Sciences
• +۱۲ مورد دیگر

John F. Mustard is a Professor of Earth, Environmental, and Planetary Sciences at Brown University, with a secondary appointment in Environment and Society. His research focuses on planetary surfaces and environmental processes, particularly Mars exploration. He is a co-director of the CIFAR Earth 4D program and has led major NASA missions, including Deputy PI of the Mars Reconnaissance Orbiter's CRISM instrument and chair of the Mars 2020 Science Definition Team. Mustard's work bridges planetary science and environmental studies, addressing topics like Martian mineralogy, climate history, and subsurface habitability. Affiliations: Earth, Environmental and Planetary Sciences Institute; Brown Institute for Environment and Society. Research Themes: Planetary geology, environmental processes, remote sensing, and Mars/Solar System evolution. Research Interests: Mustard investigates Earth and planetary systems using advanced satellite technologies. Key areas include Martian surface evolution, aqueous alteration processes, and subsurface habitability. His work integrates field studies (e.g., Antarctic analogs) with orbital data analysis, contributing to missions like the Mars Sample Return Program. Key Contributions: Mustard has identified clay-rich regions on Mars, linked to early habitable environments, and advanced techniques for spectral analysis of planetary surfaces. He also explores terrestrial applications, such as crop monitoring and land-use change in the Amazon. Grants and Funding: Active grants include NASA projects on Martian phyllosilicates, lunar mineralogy, and interdisciplinary studies on societal-environmental interactions. Labs/Teams: Mustard Lab Group focuses on remote sensing and planetary surface processes. Collaborations span NASA instruments (CRISM), international space agencies, and interdisciplinary programs like CIFAR’s Earth 4D initiative.

Stephen Roessnerمشاهده پروفایل

استادیار

• Psychoacoustics of Distributed Mode Loudspeakers
• Audio Archival Preservation
• Microphone Theory
• +۳ مورد دیگر

Stephen Roessner is an Assistant Professor of Electrical and Computer Engineering at the Hajim School of Engineering & Applied Sciences, University of Rochester. He holds a PhD from the same institution (2023). His research focuses on audio engineering and acoustics, emphasizing psychoacoustics of distributed mode loudspeakers, audio preservation, microphone theory, and spectral analysis. He has conducted extensive studies on listener preferences for flat-panel loudspeakers and temporal patterns in music. His work integrates signal processing, room acoustics, and human-computer interaction. Education: PhD in Electrical and Computer Engineering, University of Rochester (2023) Key research areas include audio latency masking, real-time signal processing frameworks, and ensemble performance timing analysis. His articles frequently explore loudspeaker design, reverberation effects, and audio preservation strategies. Notably, he has investigated trends in US Billboard music tempo and structural acoustics of glass loudspeakers. His research has been published across multiple venues, including the Proceedings of Meetings on Acoustics and specialized journals in audio engineering. While no specific awards are listed, his contributions reflect sustained innovation in acoustic technologies and music informatics.

Simon Denmanمشاهده پروفایل

دانشیار

• Computer Vision
• Machine Learning
• Deep Learning
• +۵ مورد دیگر

Dr. Simon Denman is an Associate Professor in the School of Electrical Engineering and Robotics at Queensland University of Technology (QUT). He co-leads the Applied Data Science research programme at the QUT Centre for Data Science. His research focuses on computer vision, machine learning, and signal processing, with applications in security, healthcare, and sports analytics. Education: PhD, Bachelor of Information Technology, and Bachelor of Engineering (Electronics) from Queensland University of Technology. Research Expertise: Dr. Denman's work spans action recognition, trajectory prediction, biometrics, and medical signal processing. His lab develops solutions for video surveillance, sports analytics, and healthcare diagnostics using deep learning architectures like neural memory networks and 3D perspective networks. Publication Trends: His recent articles (2023-2025) demonstrate a focus on radar signal processing, cross-domain adaptation, and geometric deep learning. Key applications include renewable energy assessment, emotion recognition, and real-time pose estimation in complex environments. Grants: DP200101942: Unlocking Mass Mobile Video Analytics with Advanced Neural Memory Networks (2021-2025)

Richi Nayakمشاهده پروفایل

استاد

• Text Mining
• Data Mining
• Machine Learning
• +۲ مورد دیگر

Professor Richi Nayak is an internationally recognized expert in data mining, text mining and web intelligence at Queensland University of Technology's School of Computer Science. As the Applied Data Science Program Leader of the Centre for Data Science, she develops novel methods for text classification, clustering, and information extraction using deep learning, matrix factorization, and ranking-centered approaches. Her research spans three main streams: Text Mining for data organization and understanding, applications of data mining in solving real-world problems across domains including education, healthcare and transportation, and algorithms for automation and personalization. She has successfully commercialized technologies including marketing strategy automation and bias detection systems deployed by Fortune 500 companies. Professor Nayak's recent publications focus on practical applications of machine learning including traffic crash analysis, multimodal learning, biomass modeling, and offensive text detection. Her work consistently addresses real-world problems through innovative machine learning approaches.

Thirunavukarasu Balasubramaniamمشاهده پروفایل

پژوهشگر ارشد

• Tensor Factorization
• Machine Learning
• Data Mining
• +۲ مورد دیگر

Thiru Balasubramaniam is a Research Fellow in Computer Science at QUT specializing in tensor/matrix factorization for data science applications. With a PhD from QUT, his work applies computational methods to IoT, social media analysis, and agricultural informatics, with recent focus on climate-pasture yield relationships and misinformation detection. Developed efficient NMF/CMTF algorithms for high-dimensional data. Current projects analyze COVID-19 misinformation dynamics, EV usage patterns, and pasture monitoring using remote sensing. Recipient of QUT-CDS research funding. Teaches data mining, analytics, and web computing courses across undergraduate and postgraduate programs.

• Group Theory
• Finite Groups
• Permutation Groups
• +۳ مورد دیگر

Dr. Luca Sabatini is a Research Fellow at the University of Warwick. His work focuses on advanced topics in group theory, including finite groups, permutation groups, nilpotent subgroups, and abelian sections. He explores structural properties of groups through probabilistic methods and their applications to graph theory and expander constructions. His research bridges abstract algebra with combinatorial and probabilistic techniques. Key research interests include Sylow subgroup interactions, stabilizer subgroup analysis, quasirandom group properties, and the growth of abelian sections within finite transitive groups. His work often involves studying subgroup structures, element orders, and maximal subgroup behaviors in both theoretical and computational contexts. His publications from 2021–2025 highlight a focus on group diameter, random Schreier graphs, and extremal subgroup constructions. These studies contribute to understanding the probabilistic and structural limits of finite groups. No awards or grants are explicitly mentioned in the provided data. Labs/Teams: No specific lab or collaborative team affiliations are noted in the text.

Erin Kado-Fongمشاهده پروفایل

پژوهشگر ارشد

• Astrophysics
• Galaxy Formation and Evolution
• Dwarf Galaxies
• +۳ مورد دیگر

Erin Kado-Fong is a YCAA Postdoctoral Fellow at Yale University's Department of Astrophysical Sciences. Her research focuses on observational studies of galaxy structure and formation, particularly the role of environment and star formation in low-mass dwarf galaxies. She utilizes wide-field surveys like SAGA and Merian to analyze large samples of galaxies beyond the Local Volume. Education: PhD in Astrophysical Sciences from Princeton University (2022, advised by Meg Urry); B.S. in Astrophysics from Tufts University (2017). Awards include the Porter Ogden Jacobus Fellowship (Princeton) and the 2021 Ohio State CCAPP Prize. Grew up in Davis, California. Research interests include: Tracing star formation histories in dwarf galaxies Environmental effects on galaxy evolution Structural properties of low-mass galaxies Development of observational survey techniques Her recent work examines tidal interactions between dwarf galaxies and their impact on star formation, as well as mass-size relations in Milky Way analog satellite systems. She collaborates on projects like the Subaru Prime Focus Spectrograph (PFS) and contributed to the HFF-DeepSpace photometric catalogs. Awards and recognition include specialized fellowships and prizes recognizing her contributions to understanding galaxy evolution mechanisms.