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جست‌وجوی استاد و پژوهشگر

استاد راهنما، استاد دانشگاه یا پژوهشگر مناسب برای مسیر پژوهشی‌تان را پیدا کنید و با او ارتباط بگیرید.

نمایش ۱-۱۵ از ۴۳۳ استاد راهنما

اطلاعات این پروفایل‌ها از صفحات عمومی دانشگاه‌ها جمع‌آوری شده و ممکن است به‌روز نباشد.

Junfu Zhangمشاهده پروفایل

استاد

Clark UniversityUnited States

Junfu Zhang is a Professor of Economics and Department Chair at Clark University. His research focuses on applied microeconomics, particularly urban and regional economics, with an emphasis on urbanization in China, entrepreneurship, regional development, and racial housing segregation. He holds a B.A. from Renmin University of China (1993), and M.A. and Ph.D. from Johns Hopkins University (1999, 2001). Prior to joining Clark in 2006, he was a research fellow at the Public Policy Institute of California and a Brookings Institution fellow (2000–2001). He has been an IZA Research Fellow since 2007 and served as President of the Chinese Economists Society (2019–2020). Education: B.A., International Economics, Renmin University of China, 1993 M.A., Economics, Clark University, 1997 M.A., Economics, Johns Hopkins University, 1999 Ph.D., Economics, Johns Hopkins University, 2001 Research Interests: Dr. Zhang's research explores urbanization dynamics in China, including land use policies, housing tenure systems, and economic segregation. His work also addresses entrepreneurship, high-tech industrial clusters, and the economic impacts of urbanization. Recent studies include analyses of land regulation, Super Bowl economic effects, and gender competition in labor markets. Recent Research Trends: His articles reflect a focus on China's socio-economic challenges, such as urban land policies, housing wealth effects, and regional development disparities. Collaborative projects with institutions like IZA and CES highlight interdisciplinary engagement. Awards and Affiliations: IZA Research Fellow (2007–present) Former President, Chinese Economists Society (2019–2020) Advising and Grants: No specific grant or student advising details are listed, though his research often involves collaborative teams. His work is supported by institutional affiliations and academic networks. Labs and Teams: No dedicated labs are mentioned, but his research engages interdisciplinary teams focusing on urban economics and regional policy.

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Gert Cauwenberghsمشاهده پروفایل

استاد

University of California, San DiegoUnited States

Gert Cauwenberghs is a Professor of Bioengineering at the University of California San Diego (UCSD), affiliated with the Jacobs School of Engineering. He co-directs the Institute for Neural Computation and holds a visiting professorship at MIT. His research focuses on neuromorphic engineering, energy-efficient neural interfaces, and wearable biosensors. Key contributions include silicon-based adaptive neural circuits, implantable neural recording systems, and in-ear biosensing devices. Education: M.Eng. in Applied Physics (University of Brussels, 1988), M.S. and Ph.D. in Electrical Engineering (Caltech, 1989–1994). Prior roles include Professorships at Johns Hopkins University and Visiting Professor at MIT. Research Interests: Biomedical integrated circuits, neuromorphic computing, brain-machine interfaces, and energy-efficient neural systems. His work bridges neuroengineering and clinical applications, emphasizing adaptive intelligence and low-power designs. Recent Work: Development of femtojoule-efficient neural chips, high-density neural interfaces, and closed-loop wearable systems. Projects include neurobench benchmarking frameworks and RRAM-based neuromorphic hardware. Awards: NSF Career Award (1997), ONR Young Investigator (1999), PECASE (2000), IEEE Distinguished Lecturer (2003–2004). Grants & Labs: Active in NIH and DoD-funded projects, co-directs the UCSD Institute for Neural Computation. Collaborates with industry on neural interface technologies. Labs/Teams: Cauwenberghs Lab at UCSD focuses on integrated neuroengineering systems, including neural recording systems and neuromorphic computing architectures.

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Farhad Rachidi-Haeriمشاهده پروفایل

استاد

Swiss Federal Institute of Technology in LausanneSwitzerland

Farhad Rachidi-Haeri is a Titular Professor and Head of the Electromagnetic Compatibility (EMC) Group at EPFL. His expertise spans EMC research, lightning electromagnetics, time reversal techniques, and fault location in power systems. He has led the EMC Group since the 1980s, with funding from the Swiss National Science Foundation, European Union, and private sector collaborations. His work involves international partnerships with institutions like the University of Toronto and KTH. Education: PhD in Electrical Engineering from EPFL (1991), M.S. from EPFL (1986). Roles: President of Swiss National Committee of URSI (2012–2020), Editor-in-Chief of IEEE Transactions on EMC (2013–2015), and member of the Academy of Sciences of Bologna Institute (2019). Research Focus: Lightning interaction with infrastructure, electromagnetic field modeling, time reversal applications for fault detection, and high-frequency transient analysis. His work bridges theoretical physics and engineering, addressing challenges in power systems, lightning protection, and aerospace. Awards: IEEE EMC Technical Achievement Award (2005), Berger Award (2016), and Distinguished Honorary Professor at Tsinghua University (2024). Over 400 peer-reviewed papers and 500 conference contributions reflect his prolific research output. Labs/Teams: Leads the EMC Laboratory at EPFL, focusing on experimental and numerical studies of electromagnetic phenomena. Collaborates with global networks on projects like Laser Lightning Control and structural lightning protection for wind turbines.

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Elisa Donatiمشاهده پروفایل

پژوهشگر

University of ZurichSwitzerland

Dr. Elisa Donati is a researcher and independent group leader at the Institute of Neuroinformatics , affiliated with both the University of Zurich and the Swiss Federal Institute of Technology Zurich . Her work bridges neuromorphic engineering, biomedical signal processing, and wearable healthcare technologies, with a focus on creating brain-inspired systems for neuroprosthetics and rehabilitation. Affiliation: Institute of Neuroinformatics, University of Zurich & ETH Zurich Email: elisa@ini.uzh.ch Elisa’s research emphasizes developing neuromorphic signal processing strategies for wearable and embedded systems, enabling real-time closed-loop interactions with the nervous system. She specializes in translating neuroscience insights into energy-efficient technologies for digital health applications, including neuroprosthetics and personalized biomedical devices using neuromorphic hardware. Her recent publications (2024–2025) highlight advancements in gesture recognition via EMG and event-based systems, neuromorphic heart rate monitoring , and spiking neural network architectures . These works span biomedical signal processing, low-power computing, and adaptive algorithms, reflecting her commitment to robust, real-time, and brain-inspired solutions for healthcare. Elisa’s contributions to neuromorphic computing are evident in her exploration of heterogeneous population encoding , event-driven processing , and ultra-low-power microcontrollers . Her projects often integrate wearable systems with neuroscience, aiming to improve prosthetic control and rehabilitation technologies .

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Michael Klasenمشاهده پروفایل

استاد

Heidelberg UniversityGermany

Prof. Dr. Michael Klasen is a leading theoretical physicist at the Institute of Theoretical Physics at the University of Münster, where he heads his eponymous research group. His work bridges nuclear and particle physics, with significant contributions to quantum chromodynamics and physics beyond the Standard Model. His research focuses on Particle Physics , Quantum Chromodynamics , and Physics beyond the Standard Model , with particular emphasis on understanding the quark-gluon structure of atomic nuclei and dark matter phenomena. His innovative approach connects microscopic quark-gluon dynamics with nuclear binding phenomena, creating a crucial bridge between nuclear and particle physics. Prof. Klasen's recent work analyzing nucleon binding at the quark-gluon level was recognized as a "Breakthrough of the Year 2024" by Physics World. His research group's publication in Physical Review Letters demonstrated how quarks and gluons behave differently in nucleon pairs than in free nucleons, fundamentally advancing our understanding of nuclear binding. Breakthrough of the Year 2024 from Physics World Leadership of Research Training Group 2149 "Strong and weak interactions - from hadrons to dark matter" Supervision of award-winning doctoral research including the Infineon Dissertation Prize 2025 Prof. Klasen has successfully mentored numerous PhD students, with 20 of his group's graduates continuing their academic careers at prestigious institutions including CERN and Stanford University. His research has been supported by major funding bodies including the German Research Foundation (DFG), the Helmholtz Alliance for Astroparticle Physics, and BMBF collaborative research programs. The Klasen working group maintains active collaborations with international research networks including CTEQ, DM@NLO, and RESUMMINO.

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Damir Filipovicمشاهده پروفایل

استاد

Swiss Federal Institute of Technology in LausanneSwitzerland

Academic Profile: Damir Filipovic is a Full Professor and the Swissquote Chair in Quantitative Finance at the College of Management of Technology (CDM) of École Polytechnique Fédérale de Lausanne (EPFL), Switzerland. He previously held academic positions at the University of Vienna, University of Munich, and Princeton University, and served as Head of the Vienna Institute of Finance. Research Focus: Quantitative finance, risk management, stochastic processes, term structure modeling, volatility risk, and machine learning applications in financial markets. Industry Collaboration: Co-developed the Swiss Solvency Test for insurance capital requirements while consulting for the Swiss Federal Office of Private Insurance. Publications: Contributed extensively to journals like Journal of Financial Economics, Mathematical Finance, and Annals of Applied Probability, with a textbook on Term-Structure Models. Academic Service: Editorial board member of multiple journals and organizer of advanced workshops on systemic risk and financial technology. Recent Research: His work emphasizes machine learning for portfolio risk management, kernel-based yield curve estimation, and robust stochastic modeling. Keynote speaker at international conferences on finance and insurance mathematics, with over 15 recent publications in 2023-2025 addressing high-dimensional financial problems, neural control systems, and causal inference in market data. Education: Ph.D. in Mathematics from ETH Zurich (2000). Graduate of ETH Zurich and University of Vienna. Teaching & Mentorship: Supervises current and former EPFL Ph.D. students in quantitative finance, including Nicolas Camenzind, Joshua Hayes, Andrea Ruglioni, and ten others. Former students like Damien Ackerer and Lotfi Boudabsa now lead research in risk management. Labs & Programs: Directs EPFL's Finance and Technology Programme, leads the Computational Finance Group (CSF) at EPFL, and contributes to Swiss Finance Institute initiatives. Scientific Leadership: Served on EPFL Committee of Academic Evaluation and Doctoral Program Finance committee.

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Sally Gibsonمشاهده پروفایل

پژوهشگر

University of CambridgeUnited Kingdom

Sally Gibson is a researcher at the Department of Earth Sciences, University of Cambridge, specializing in mantle geodynamics and volatile cycling processes. Her work integrates field observations, geochemical analysis, and numerical modeling to investigate how deep Earth processes influence surface environments over 3.5 billion years of planetary evolution. Research focuses on volatile cycling (CO₂, H₂O, F, Cl, S) in mantle systems Key projects include mantle plume-ridge interactions with collaborators in the US and Ecuador Operates a LA-ICP-MS laboratory for high-resolution geochemical analyses Supervises PhD students in petrology, geochemistry, and numerical modeling Her research addresses fundamental questions about Earth's habitability through studies of mantle-derived volatiles critical for climate regulation and energy transition metal deposits. Fieldwork in remote regions like Antarctica, Lesotho, and the Galápagos Islands provides empirical data for her interdisciplinary approach. Recent publications highlight her expertise in mantle xenolith analysis, plume dynamics, and volatile quantification in large igneous provinces. Her group's work combines 3He/4He isotopic analysis with seismic tomography to constrain lithospheric evolution and mineral deposit formation. Students under her supervision develop expertise in petrology and geochemical modeling while engaging with environmental and societal impacts of geological research. She actively promotes scientific outreach and community engagement, fostering connections between academia and broader society.

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Themistoklis Sapsisمشاهده پروفایل

استاد

Massachusetts Institute of TechnologyUnited States

Themistoklis Sapsis is a Professor in the Department of Mechanical Engineering at the Massachusetts Institute of Technology (MIT), where he also holds an affiliation with the MIT Institute for Data, Systems, and Society. He earned his Ph.D. in Mechanical Engineering from MIT in 2011 and previously served as an Assistant Research Scientist at NYU’s Courant Institute of Mathematical Sciences. His research focuses on developing analytical, computational, and data-driven methods to predict and quantify extreme events in high-dimensional nonlinear systems, such as turbulent fluid flows and mechanical systems. Key areas include probabilistic modeling of climate extremes, machine learning for climate simulation corrections, and uncertainty quantification in complex dynamical systems. Recent work emphasizes applications in ocean engineering (e.g., vortex-induced vibrations, wave energy systems) and environmental science (e.g., spatially resolved climate extremes, bias correction in Earth system models). His methodologies combine stochastic emulators, Bayesian experimental design, and neural networks to address challenges in data sparsity and model fidelity. Notable contributions include frameworks for correcting coarse-scale climate simulations using machine learning, real-time ocean temperature reconstruction from satellite data, and data-driven modeling of hydrodynamic interactions in marine risers. His research bridges theoretical developments with practical applications in energy systems, structural monitoring, and autonomous systems. Prof. Sapsis collaborates with interdisciplinary teams and has contributed to initiatives such as FIRSTLING-DIGIMAR (a marine riser digital twin) and multi-fidelity frameworks for autonomous seakeeping. His work is supported by grants focused on advancing machine learning in scientific modeling and extreme event prediction.

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Boris Murmannمشاهده پروفایل

استاد

University of TrierGermany

Boris Murmann is Professor at Stanford University, specializing in integrated circuit design, mixed-signal computing, and energy-efficient AI hardware. His research advances neural interface technologies, analog design automation, and tinyML systems. Recent work develops ultra-low-power neural recording ICs for brain-computer interfaces, RRAM-based memory systems, and open-source semiconductor design frameworks. Publications demonstrate innovations in compressive sensing for neural data, hardware-algorithm co-design, and reinforcement learning for analog circuit synthesis. Significant contributions include Medusa (TinyML processor), EMBER (RRAM macro), and methodologies for coarsely-quantized computer vision and analog design automation.

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Akihiko Nishimuraمشاهده پروفایل

استادیار

Johns Hopkins UniversityUnited States

Akihiko Nishimura is an Assistant Professor in the Department of Biostatistics at the Johns Hopkins Bloomberg School of Public Health. He holds a PhD from Duke University (2017) and MS and BS degrees from Stanford University (2011 and 2010). His research focuses on Bayesian methods, statistical computing, and public health data science, with applications in precision medicine and observational health data analytics. PhD, Duke University, 2017 MS, Stanford University, 2011 BS, Stanford University, 2010 Nishimura's research centers on developing advanced statistical and computational methodologies for real-world health data. His work emphasizes Bayesian inference, large-scale computing, and software development for reproducible research. He is particularly interested in using observational health data to improve clinical decision-making and advance precision medicine. He co-leads the Bayesian Learning and Spatio-Temporal modeling group (BLAST Group) and the inHealth/OHDSI Lab , collaborating with clinicians and data scientists across institutions. His recent publications reflect a strong trend in methodological innovation in Monte Carlo methods (e.g., Hamiltonian and Zigzag samplers), scalable Bayesian inference, and applications in pharmacovigilance, diabetes management, and infectious disease modeling. The articles span disciplines including biostatistics, computational statistics, public health, and bioinformatics, demonstrating a consistent focus on high-impact, computationally intensive problems in health data science. Nishimura actively contributes to the scientific community through methodological development and open science. He develops statistical software and shares teaching materials on GitHub, emphasizing reproducibility and performant computing. His involvement in the OHDSI community enables large-scale, multi-institutional studies that would not be feasible with single-source data. His work has been recognized through publications in top-tier journals such as the Journal of the American Statistical Association , Biometrika , and JAMA Ophthalmology , and has been picked up by numerous news outlets and social media platforms, indicating broad scientific and public impact. Nishimura teaches courses on performant statistical computing and advanced Monte Carlo methods, training the next generation of data scientists in efficient algorithm and software design. He mentors students and collaborators in statistical methodology and software development, fostering a culture of rigorous, reproducible, and impactful research.

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Sean O'Rourkeمشاهده پروفایل

دانشیار

University of ColoradoUnited States

Sean O'Rourke is an Associate Professor in the Department of Mathematics at the University of Colorado Boulder. His research focuses on probability, random matrix theory, and random polynomials. He has organized multiple workshops and minisymposia, including events at the Canadian Discrete and Algorithmic Mathematics Conference (CanaDAM) and ICERM, demonstrating his active role in academic community engagement. His research spans spectral properties of random matrices (e.g., singular values, elliptic matrices, Laplacian matrices), probabilistic behavior of polynomial roots under operations like differentiation and summation, and applications of free probability theory. Recent work includes universal behavior in eigenvalue gaps, non-Hermitian matrix controllability, and asymptotic refinements of classical theorems for random polynomials. Publications highlight collaborations with researchers such as Andrew Campbell, Kyle Luh, David Renfrew, and Van Vu. His work appears in leading journals like Annals of Probability , Electronic Journal of Probability , and Transactions of the American Mathematical Society , covering topics from Gaussian fluctuations to low-rank perturbations and noncommutative harmonic analysis.

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John Martinمشاهده پروفایل

دانشیار

University of Illinois at SpringfieldUnited States

John Martin is an Associate Professor at the University of Illinois Springfield, affiliated with the School of Integrated Sciences, Sustainability, and Public Health. Since 2006, he has taught introductory physics for science majors and astronomy courses while directing the Henry R. Barber Research Observatory and hosting public UIS Star Parties. His research focuses on stellar astrophysics, particularly the life cycles of massive stars and phenomena like Eta Carinae. Stellar Astrophysics Massive Star Evolution Luminous Blue Variables (LBVs) Supernova Impostors Be Stars and Disks Photometry and Spectroscopy His publications, including over 100 peer-reviewed works, emphasize Eta Carinae's 5.5-year spectroscopic events, LBVs in M31/M33, Be star dynamics, and supernova impostors like SN 2009ip. Awards include the 2011 University of Illinois University Scholar title, JJ Nassau Fellowship (1997), and Dean's List recognitions. He earned his PhD in Astronomy from Case Western Reserve University (2003) and BS in Astro-Physics from the University of Virginia (1995). Scientific awards include 2011 University of Illinois University Scholar 1997 JJ Nassau Graduate Research Fellowship 1995 Mastin Graduate Fellowship 1995 Dean's List, University of Virginia 1994 Dean's List, University of Virginia

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Galatia Cleanthousمشاهده پروفایل

مدرس

Maynooth UniversityIreland

Dr. Galatia Cleanthous is a Lecturer in the Department of Mathematics and Statistics at Maynooth University, Ireland, affiliated with the Faculty of Science & Engineering and the Hamilton Institute. She joined Maynooth in 2020 after postdoctoral positions at Trinity College Dublin, Newcastle University, and University of Cyprus, and holds a PhD in Pure Mathematics from Aristotle University of Thessaloniki (2014). Education PhD in Mathematics, Aristotle University of Thessaloniki, Greece (2014) MSc in Mathematics, Aristotle University of Thessaloniki, Greece Diploma in Mathematics, Aristotle University of Thessaloniki, Greece Research Interests Her research bridges pure and applied mathematics, focusing on Mathematical Analysis , Probability , and Statistics . Specifically, she explores Geometric Analysis , Geometric Function Theory , and Harmonic Analysis on manifolds and metric spaces. In statistics, she works on Nonparametric , Spatial , and Environmental Statistics , developing adaptive estimation techniques and studying Gaussian random fields on spheres and other domains. Publication Trends From 2025 back to 2013, her work has consistently appeared in top journals such as Annals of Statistics , Bernoulli , Journal of Nonparametric Statistics , and Transactions of the American Mathematical Society . A clear trend emerges: early publications concentrate on pure analytic topics like Fourier multipliers and function spaces, while recent outputs integrate these theoretical tools into modern nonparametric statistics, density estimation on manifolds, and stochastic modeling of environmental and seismological data. Scientific Awards Master’s degree ranked first with grade 9.8/10, Aristotle University of Thessaloniki (2011) Diploma ranked first among ~200 students, grade 9.7/10, Aristotle University of Thessaloniki (2009) Undergraduate merit awards for three consecutive academic years (2005-2008), State Scholarship Foundation of Greece National first place in Cypriot high-school mathematics entrance exams (2005), Ministry of Education, Cyprus Advising & Outreach Dr. Cleanthous has supervised BSc and MSc students, including Ultán Doherty (BSc, 1st Class Honors, 2021) and Anush Harish (MSc, 2022). She serves as Chair of the Department PR Committee, Member of the University STEM Promotions Committee, and Member of the departmental Equality, Diversity & Inclusion committee. Beyond campus, she trains young mathematicians at the North Kildare Maths Problem Solving Club and organizes public engagement events for Science Week. Labs & Teams She is associated with the Hamilton Institute at Maynooth University, a multidisciplinary research institute fostering collaboration between mathematics, computer science, and engineering.

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Jack Puleoمشاهده پروفایل

استاد

University of DelawareUnited States

Jack Puleo is a Professor and Chair in the Department of Civil and Environmental Engineering at the University of Delaware (UD), and a core faculty member of the Center for Applied Coastal Research (CACR). He holds a Ph.D. from the University of Florida, a Master’s from Oregon State University, and a Bachelor’s from Humboldt State University. His research focuses on coastal hydrodynamics, sediment transport, and nature-based solutions for coastal resilience. He has served as Associate Chair and Director of CACR, and was a Fulbright Scholar and Visiting Professor at Plymouth University (2011-2012). Research Interests: Small-scale hydrodynamic processes and sediment transport in coastal zones Remote sensing and sensor networks for coastal monitoring Nature-based solutions for coastal protection Munitions mobility in nearshore environments Climate change impacts on coastal flooding Awards and Honors: NSF CAREER Award (2007) ASCE Teaching Awards University of Delaware Teaching Awards (twice) Chi Epsilon Advising Award ASBPA Robert G. Dean Award German DAAD Scholarship Labs and Collaborations: Core member of the Center for Applied Coastal Research (CACR), collaborating on projects such as UXO mobility studies, coastal flooding modeling, and military infrastructure resilience. Active in interdisciplinary work with the Naval Research Laboratory and joint bases like Langley-Eustis.

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Laura Blechaمشاهده پروفایل

دانشیار

University of FloridaUnited States

Laura Blecha is an Associate Professor in the Physics Department at the University of Florida, specializing in astrophysics. Her research focuses on supermassive black hole (SMBH) and galaxy evolution through numerical simulations and observational collaborations. PhD from Harvard University (2012) Full Member of NANOGrav pulsar timing collaboration Associate Member of the LISA Consortium Her work spans three primary areas: SMBH Formation & Evolution : Origins of SMBHs, galaxy merger-driven growth, and intermediate-mass black hole demographics AGN Fueling & Feedback : Hydrodynamic simulations of AGN activation mechanisms and observational bias in AGN detection Binary SMBH Dynamics : Gravitational wave recoil effects, three-body interactions, and pulsar timing array detection strategies Recent publications (2025) focus on dual AGN detection with Keck AO, JWST studies of primordial galaxies, and NANOGrav gravitational wave background analysis. Her group develops sub-grid models for SMBH dynamics in cosmological simulations and investigates signatures of black hole mergers in galaxy clusters. Laura's research combines computational methods (Illustris, BRAHMA simulations) with observational validation through: JWST NIRSpec spectroscopy Pulsar Timing Array analysis Multiwavelength imaging campaigns

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