جستجوی استاد راهنمای دانشگاهی

Elias Aboutaniosمشاهده پروفایل

استاد

• Signal and Image Processing
• Parameter Estimation
• Signal Detection
• +۱۳ مورد دیگر

Professor Elias Aboutanios is a distinguished academic at the University of New South Wales (UNSW), serving as Professor in the School of Electrical Engineering and Telecommunications. With a career spanning over two decades in academia and research, he has established himself as a leading expert in signal processing, radar systems, satellite technology, and NMR spectroscopy. Professor Aboutanios earned his BE in Electrical Engineering from UNSW in 1997 and completed his PhD from UTS in 2002, with research focused on frequency estimation for communications with low earth orbit satellites. Following his doctoral studies, he conducted postdoctoral research at the Institute for Digital Communications at the University of Edinburgh from 2003 to 2007, specializing in space-time adaptive processing for radar target detection. He joined UNSW as a senior lecturer in 2007, was promoted to associate professor in 2019, and achieved the rank of Professor in 2022. His research interests span a broad spectrum of signal processing domains including signal and image processing, parameter estimation, array signal processing, statistical signal processing, positioning and localization, radar and sonar signal processing, NMR signal processing, and space systems. Professor Aboutanios has developed significant expertise in nuclear magnetic resonance spectroscopy, global navigation satellite systems, radar target detection, biologically inspired signal processing, power systems and smart grids, and theoretical signal processing. His work bridges theoretical foundations with practical applications across multiple engineering disciplines. Professor Aboutanios's recent publications demonstrate a strong focus on integrated sensing and communication systems, radar technology, satellite applications, and advanced signal processing techniques. His research shows a clear trajectory toward dual-function radar-communication systems, massive MIMO architectures, CubeSat technology for air traffic monitoring, and innovative approaches to NMR spectroscopy. His work consistently addresses challenging problems in signal parameter estimation, adaptive processing, and system design across multiple application domains. Professor Aboutanios has made significant contributions to engineering education, having developed new courses in electrical engineering design and established the master's program in satellite systems engineering. His educational innovations focus on teaching signal processing through frequent and diverse design experiences, enhancing student learning outcomes in technical subjects. He has led significant space projects including UNSW's involvement in the European QB50 project and the UNSW-EC0 satellite mission, which successfully launched in 2017. As a member of the Space Industry Association of Australia's Legislation Working Group, he has contributed to shaping space policy through multiple submissions to the Australian Government's review of the Space Activities Act.

• Sparse signal processing
• Signal processing for machine learning
• Wireless communications
• +۴ مورد دیگر

Dr. Wei Dai is a Senior Lecturer (Associate Professor) in the Department of Electrical and Electronic Engineering at Imperial College London, part of the Faculty of Engineering. He holds affiliations with the EPSRC Centre for Maths of Precision Healthcare and the Communications and Signal Processing group. His research focuses on sparse signal processing, machine learning applications in signal processing, linear and bilinear inverse problems, wireless communications, and random matrix theory. Notably, he contributed to the first compressive sensing DNA microarray prototype and has a highly cited 2009 paper on compressive sensing reconstruction. Dr. Dai's educational background includes a Ph.D. in Electrical and Computer Engineering from the University of Colorado at Boulder (2007) and postdoctoral research at the University of Illinois at Urbana-Champaign (2007-2010). His work bridges theoretical signal processing with practical applications in sensing, communication systems, and biomedical signal analysis. He leads research initiatives in gridless DOA estimation, robust beamforming, and cortico-muscular coupling analysis using advanced optimization techniques. His research outputs span topics like spectral compressed sensing, Bayesian methods for integrated sensing-communication systems, and dictionary learning for causal discovery. Ongoing work emphasizes low-rank matrix recovery, distributed compressed sensing, and mathematical frameworks for super-resolution localization. Dr. Dai collaborates across disciplines, leveraging signal processing innovations for healthcare technology and next-generation wireless systems.

Lisa Overholtzerمشاهده پروفایل

دانشیار

• Archaeology
• Social Theory
• Material Culture
• +۷ مورد دیگر

Lisa Overholtzer is an Associate Professor and William Dawson Scholar in the Department of Anthropology at McGill University. She directs the Household Archaeology Laboratory, focusing on everyday material practices of ordinary people in Postclassic and Colonial Central Mexico. Her current research centers on the community-engaged household archaeology project at Tepeticpac, Tlaxcala, supported by a SSHRC Insight Grant. She previously led an Indigenous archaeology project at Xaltocan, Mexico. Her education includes a Ph.D. from Northwestern University (2012). Her research integrates social theory with scientific methods, emphasizing household production, social identities (gender, race, class), and ethical archaeology practices. She advocates for inclusive archaeology through collaboration with descendant communities. Key awards include the William Dawson Scholar distinction. She actively mentors graduate students in Mesoamerican archaeology and methodological innovation. Her lab work bridges archaeological theory and science, using Bayesian modeling, geochemical analysis, and ceramic studies to explore materiality and social memory. Her projects emphasize decolonizing methodologies and ethical engagement, particularly in Mexico and Canada. She has pioneered collaborative frameworks like the Field Crew Symposium to democratize archaeological practice.

Xin Liمشاهده پروفایل

استاد

• Mathematical Analysis
• Wireless Communication Systems
• Blockchain Technology
• +۳ مورد دیگر

Professor Xin Li is the Chair of Mathematical Analysis at the School of Mathematics & Statistics, University of Glasgow. His research focuses on interdisciplinary areas at the intersection of mathematical analysis, wireless communication systems, and blockchain technology. He holds a faculty position with expertise in reconfigurable intelligent surfaces (RIS), signal processing, and network optimization. Recent publications highlight his work on RIS-aided information-sensing integrated systems (ISAC), blockchain-based consensus networks in cellular environments, and adaptive beamforming techniques for multipath communication. His research emphasizes practical applications of theoretical mathematical models in telecommunications and distributed systems. Prof. Li's work demonstrates trends in integrating mathematical analysis with emerging technologies like millimeter-wave systems and Byzantine fault tolerance mechanisms. His contributions bridge pure mathematical rigor with real-world communication challenges, addressing both theoretical and applied aspects of modern wireless networks. He currently oversees academic activities within the School of Mathematics & Statistics and maintains an active research program supported by interdisciplinary collaborations. His contact information includes Xin.Li@glasgow.ac.uk and ORCID 0000-0002-2243-3742.

Jill L McNitt-Grayمشاهده پروفایل

استاد

• Biomechanics
• Multijoint Movement
• Motor Control
• +۷ مورد دیگر

Dr. Jill L. McNitt-Gray is a Professor of Biological Sciences and Biomedical Engineering at the University of Southern California (USC), serving as Director of the USC Biomechanics Research Laboratory. She also leads the Integrative and Evolutionary Biology Graduate Program, focusing on interdisciplinary research at the intersection of genomics, evolution, and physiology. Her academic roles include joint appointments in Biomedical Engineering and affiliations with the Center for Robotics and Embedded Systems, Center for Excellence in Genomic Science, and the Department of Veterans Affairs. Education: PhD in Biomechanics (Pennsylvania State University, 1989), MA in Biomechanics (UNC Chapel Hill, 1985), and AB in Mathematics/Statistics (Miami University, 1980). Her research explores mechanisms of multijoint movement control, injury prevention, and human performance optimization across sports, music, and clinical populations. She pioneered biomechanical analysis of wheelchair propulsion mechanics and movement strategies in athletes and musicians. Research Interests: Biomechanics of physically-demanding movements, motor control dynamics, neuromuscular coordination, injury prevention in sports/dance/music, rehabilitation engineering, and comparative biomechanics in mammals. She integrates experimental techniques, computational modeling, and field studies to address these topics. Awards: Recognized as a Fellow of the National Academy of Kinesiology (2023), International Society of Biomechanics (2015), and American Society of Biomechanics (2013). Honored with the Hay Award (2016), Harmon Brown Award (2016), and USC Distinguished Professorship (2016–2019). Her mentorship was acknowledged via the Mellon Award (2010) and leadership of STEM outreach programs. Labs/Teams: Directs USC Biomechanics Research Lab and collaborates with the Sports Science, Engineering, and Technology Academy. Leads the GREU mentorship program and consults for USC Athletics, the Colburn School of Music, and Rancho Los Amigos Rehabilitation Center.

Jesse Wolfsonمشاهده پروفایل

دانشیار

• Geometry
• Topology
• Arithmetic Topology
• +۳ مورد دیگر

Jesse Wolfson is an Associate Professor and Vice-Chair for Inclusive Excellence in the Department of Mathematics at the University of California, Irvine. His research focuses on the interplay of arithmetic, geometry, and topology, with notable contributions to arithmetic topology, resolvent degree, and algebraic geometry. He co-directs the Southern California Geometry and Topology Center (SCGTC), an NSF Research Training Group (RTG) program actively recruiting graduate students in geometry and topology. He holds a PhD and has expertise spanning algebraic structures, homological algebra, and interdisciplinary applications such as fractals in music. His work bridges pure mathematics with educational outreach, including public lectures and collaborations with institutions like St. John’s College, Santa Fe. His email is wolfson@uci.edu , and he maintains an active research blog detailing his projects and collaborations. Wolfson’s research emphasizes foundational questions in mathematics, including Hilbert’s 13th problem and the epistemic role of proofs in AI. His recent talks and articles explore topics like prismatic cohomology, modular functions, and geometric gauge theories, reflecting his commitment to advancing both theoretical and applied mathematical frontiers.

• Mathematical Statistics
• Non-Euclidean Data Analysis
• Algebraic Statistics
• +۳ مورد دیگر

Andrew McCormack is an Assistant Professor in the Department of Mathematical and Statistical Sciences at the University of Alberta. His research focuses on mathematical statistics, with a particular emphasis on non-Euclidean data analysis, algebraic statistics, and information geometry. His research interests include: Non-Euclidean Data Analysis: Developing methodologies for data on manifolds and metric spaces. Algebraic Statistics: Applying nonlinear algebra to understand statistical models, especially graphical models. Tensor Decompositions: Exploring low-dimensional representations of high-dimensional data using matrices and tensors. Information Geometry: Studying statistical models through geometric lenses, incorporating ideas from optimal transport. Statistical Decision Theory: Investigating foundational approaches to evaluating statistical methods from both classical and Bayesian perspectives. Mccormack's recent work spans robust statistical methods, information geometry in covariance modeling, and the application of algebraic techniques to statistical problems. His contributions often bridge theory and computation, addressing challenges in high-dimensional and geometric data analysis. No scientific awards listed. No advisees or grants mentioned. Labs or teams are not specified in the provided information.

Kyle O'Keefeمشاهده پروفایل

استاد

• Global Navigation Satellite Systems (GNSS)
• Indoor Positioning and Navigation
• Ultra-wideband Ranging
• +۶ مورد دیگر

Kyle O'Keefe is a Professor in the Department of Geomatics Engineering at the University of Calgary's Schulich School of Engineering. He holds dual B.Sc. degrees in Geomatics Engineering (University of Calgary, 2000) and Honours Physics (University of British Columbia, 1997), and a Ph.D. in Geomatics Engineering (University of Calgary, 2004). He is a Professional Engineer (P.Eng.) registered with the Association of Professional Engineers and Geoscientists of Alberta since 2005. His research focuses on positioning and navigation technologies, including Global Navigation Satellite Systems (GNSS) advancements Ultra-wideband (UWB) ranging for vehicle/pedestrian navigation Indoor positioning using wireless signals Wearable sensor integration for biomechanics and navigation GNSS spoofing detection and cybersecurity Notable projects include: Development of UWB-augmented GNSS for RTK surveying (2007–present) Wearable sensor systems for rowing/kayaking motion analysis (2017–present) CanX-2 nanosatellite GPS receiver operations (2008) Igliniit project with Inuit hunters for Arctic environmental monitoring (2006–2009) Multi-constellation GNSS evaluation across 20+ years He has received prestigious awards including the Michael Richey Medal (2011) and multiple Best Paper Awards at IPIN and ION conferences. His teaching includes courses like Advanced GNSS Theory and Wireless Location. Active in professional organizations, he co-edits special journal issues and advises industry on emerging navigation technologies.

Quan Zhouمشاهده پروفایل

استادیار

• Markov chain Monte Carlo sampling
• Bayesian methodology
• Variable selection
• +۷ مورد دیگر

Quan Zhou is an Assistant Professor in the Department of Statistics at Texas A&M University, part of the College of Arts & Sciences. His research focuses on developing advanced sampling methods, particularly Markov chain Monte Carlo (MCMC) algorithms, with applications in Bayesian methodology, variable selection, stochastic optimization, and statistical genetics. He holds a BS from Fudan University and a PhD from Baylor College of Medicine, followed by a postdoctoral fellowship at Rice University. He teaches courses such as Mathematical Probability, Multivariate Analysis, and Advanced Stochastic Processes. Notable contributions include work on informed MCMC samplers, Schrödinger bridge theory, and high-dimensional structure learning. He advises PhD students like Hyunwoong Chang (now at UT Dallas) and Guanxun Li (Beijing Normal University). Active in academic service, he served as President of the Southeastern Texas Chapter of the American Statistical Association (SETCASA).

Katherine B. Ensorمشاهده پروفایل

استاد

• Data Science
• Environmental Statistics
• Computational Finance
• +۹ مورد دیگر

Katherine B. Ensor is the Noah G. Harding Professor of Statistics at Rice University's George R. Brown School of Engineering and Computing. She serves as Director of the Center for Computational Finance and Economic Systems (CoFES) and previously led the Kinder Institute Urban Data Platform (UDP). Her expertise spans statistical methods for complex problems in public health, environmental science, and finance. Ensor has a 35-year academic career at Rice, including 14 years as Department Chair. She pioneered wastewater surveillance for pathogens, expanding to monitor 29 communicable illnesses through Houston Wastewater Epidemiology. Education: Ph.D. in Statistics (Texas A&M University), M.S. and B.S.E. in Mathematics (Arkansas State University). Research focuses on high-dimensional time series, spatial processes, Bayesian methods, and computational finance. She leads interdisciplinary initiatives, such as the UDP providing urban data for Greater Houston studies. Research interests include data science applications in public health, environmental analytics, and quantitative finance. Her work bridges academia and community, addressing issues like flood impacts via the Texas Flood Registry and asthma surveillance through collaborations with Houston Health Department. Ensor chairs the National Academies committee on Statistics in Science and Engineering and holds leadership roles in AAAS and ASA. Awards include the ASA Founders Award (2024), AAAS Fellowship (2013), and Texas A&M's Distinguished Former Students honor. She advises on national boards, including NSF's IPAM and ABET's CSAB. Her grants and collaborations drive innovations in statistical methods for societal challenges. Labs/Teams: Co-directs Houston Wastewater Epidemiology and Rice Environmental Statistics & Health (RESH) group with Dr. Loren Hopkins. Active in CoFES, UDP, and the Kinder Institute's urban data initiatives.

Dan S. Wallachمشاهده پروفایل

استاد

• Computer Security
• Cybersecurity
• Electronic Voting Systems
• +۳ مورد دیگر

Dan S. Wallach is a Professor of Computer Science and Electrical and Computer Engineering at Rice University, and a Program Manager at DARPA's Information Innovation Office since June 2023. He holds a PhD (1999) and MA (1995) from Princeton University, and a BS (1993) from UC Berkeley. His research focuses on cybersecurity, electronic voting systems, and mobile security. He directed the NSF-funded ACCURATE Center (2005-2011), led the STAR-Vote project, and advised U.S. election security policies including testifying before state and federal committees. He also served on the Air Force Science Advisory Board (2011-2015), USENIX Board (2011-2013), and IEEE Technical Guidelines Committee (2019-2023). Recent work includes developing ElectionGuard cryptographic tools for verifiable elections and analyzing cyber warfare in Ukraine. His 15+ years of teaching include courses like Introduction to Program Design and Election Systems Technologies. Publications span secure voting protocols, smartphone security, and election auditing. Collaborations include Microsoft and VotingWorks on cryptographic voting systems like ElectionGuard and Arlo-CVR-Encryption.

Jouni Kuhaمشاهده پروفایل

استاد

• Social Statistics
• Latent Variable Modeling
• Survey Methodology
• +۴ مورد دیگر

Jouni Kuha is a Professor of Social Statistics and MSc Social Statistics Programme Director at the London School of Economics and Political Science (LSE), Department of Statistics. His expertise lies in latent variable modeling, survey data analysis, measurement error, and missing data, with applications in social sciences. He collaborates on projects addressing education mobility, public attitudes toward policing, family support dynamics, and global medicine accessibility. He contributed to the UK General Election exit poll analysis and was elected a Fellow of the British Academy in 2021. Education: MSocSc (Statistics) from the University of Helsinki (1992), PhD (Social Statistics) from the University of Southampton (1996). Prior roles include postdoctoral research at Nuffield College, Oxford, and Assistant Professor at Pennsylvania State University. Teaching includes courses on applied regression analysis, multivariate analysis, and statistical modeling. Research interests span latent variable models, categorical data analysis, and methodological advancements in multilevel and latent class analysis. Recent work focuses on digital trace data biases, procedural justice in policing, and intergenerational welfare dynamics. His methodological contributions include R package implementations for multilevel latent class analysis. Scientific awards include Fellowship of the British Academy (2021). Key grants and collaborations involve cross-national studies on citizenship norms, crime victimization, and healthcare policy. He advises on statistical methodologies for social science applications and maintains active involvement in the LSE’s Department of Methodology.

• Statistical Mechanics
• Probability Theory
• Discrete Geometry
• +۳ مورد دیگر

Richard Kenyon is the Erastus L. DeForest Professor of Mathematics at Yale University, serving as Director of Undergraduate Studies. His research focuses on statistical mechanics, probability, and discrete geometry, with notable contributions to dimer models, random tilings, and integrable systems. He has explored topics such as limit shapes, phase transitions, and combinatorial configurations. His work intersects algebraic geometry, combinatorics, and mathematical physics, often involving the analysis of lattice models and their applications. His research includes open problems such as tiling optimization, geometric spanning surfaces, number theory questions, and rigidity of tilings. Kenyon's gallery showcases visualizations of mathematical concepts, including random triangulations, Vinnikov curves, and conformal mappings. His academic contributions span over three decades, with recent articles addressing dimers, webs, and eigenvalue properties. He actively collaborates on projects like the six-vertex model, multiwebs, and renormalizable dynamical systems. Kenyon’s academic service includes directing undergraduate studies at Yale and contributing to initiatives in discrete differential geometry. His work highlights the interplay between pure mathematics and applied probability, with applications in physics and combinatorial optimization.

Sharon L.R. Kardiaمشاهده پروفایل

استاد

• Genetic Epidemiology
• Cardiovascular Disease
• Gene-Environment Interactions
• +۳ مورد دیگر

Sharon L.R. Kardia is the Millicent W. Higgins Collegiate Professor of Epidemiology and Associate Dean for Education at the University of Michigan School of Public Health (SPH). She holds a PhD in Human Genetics (1991) and MA in Statistics (1990), both from the University of Michigan. Her research focuses on genetic epidemiology of chronic diseases, particularly cardiovascular conditions, emphasizing gene-environment and gene-gene interactions. She directs the Public Health Genetics Program and co-directs the Life Sciences and Society Program and the Michigan Center for Genomics and Public Health. Education: PhD in Human Genetics (University of Michigan, 1991); MA in Statistics (University of Michigan, 1990). Postdoctoral training in systems biology at the Department of Microbiology and Immunology. Research Interests: Genetic epidemiology of cardiovascular diseases, chronic disease prevention, genomic/epigenomic analyses in large cohorts, and translational public health genetics. Notable Projects: Co-leads initiatives integrating genomics with public health practice, including the Michigan Center for Genomics and Public Health. Her work spans population studies, analytical method development, and interdisciplinary program leadership. Labs/Teams: Public Health Genetics Program, Life Sciences and Society Program. Teaches courses on AI in public health (PUBHLTH555) and integrative learning experiences for MPH students (PUBHLTH680).

Gaja Jaroszمشاهده پروفایل

استاد

• Phonological Theory
• Computational Linguistics
• Language Acquisition
• +۵ مورد دیگر

Gaja Jarosz is a Professor & Chair of Graduate Admissions in the Department of Linguistics at the University of Massachusetts Amherst, located in the Integrative Learning Center (ILC N410). She holds a PhD in Cognitive Science from Johns Hopkins University and a BA in Mathematics and Social Thought from Washington University in St. Louis. Her research focuses on phonological theory, computational linguistics, and language acquisition, particularly how sound systems and their acquisition can be modeled computationally. She develops formal systems informed by phonological theory and grounded in statistical learning, exploring hidden phonological structure, lexical-grammatical interactions, and learning biases. Since 2015, her work integrates experimental methods with computational modeling. Her publications span computational phonological learning, parametric stress systems, and experimental studies of phonological acquisition. Notable contributions include analyzing Polish phonotactics, stress learning mechanisms, and the role of distributional cues in word segmentation. She co-founded the Society for Computation in Linguistics (SCiL) and organizes conferences like ACL and SCiL workshops. Her research is supported by grants (not explicitly listed) and involves collaborations with scholars like Aleksei Nazarov, Joe Pater, and J. Alex Johnson. She contributes to software tools (e.g., NECPhon) and has taught courses on phonology and computational linguistics.