جستجوی استاد راهنمای دانشگاهی

Xiaofeng Shaoمشاهده پروفایل

استاد

• Econometrics
• Time Series Analysis
• Change-Point Analysis
• +۳ مورد دیگر

Xiaofeng Shao is a Professor of Statistics & Data Science at Washington University in St. Louis, with a joint appointment in the Department of Economics. He holds a PhD from the University of Chicago and previously served at the University of Illinois at Urbana-Champaign for 18 years. He is a Fellow of the Institute of Mathematical Statistics and the American Statistical Association. His research focuses on econometrics, time series analysis, change-point detection, high-dimensional statistics, nonparametric methods, and functional data analysis. Recent work emphasizes object-valued time series modeling and machine learning applications in high-dimensional and imaging data. Notable contributions include the dependent wild bootstrap method and self-normalization techniques for time series inference. Key awards include Fellowships from leading statistical societies. His publications span over 20 years, addressing topics like change-point detection in climate projections, statistical methods for COVID-19 infection trends, and high-dimensional dependence testing.

• Optics in Medicine
• Cancer Imaging
• Photodynamic Therapy
• +۵ مورد دیگر

Brian W. Pogue, Ph.D., is the Robert A. Pritzker Chair in Biomedical Engineering at Dartmouth College's Thayer School of Engineering, with a joint appointment as an Honorary Fellow in Medical Physics at the University of Wisconsin-Madison. His academic background includes a Ph.D. in Medical/Nuclear Physics from McMaster University and a Research Fellowship at Harvard Medical School's Wellman Center for Photomedicine. He has led significant administrative roles, including Dean of Graduate Studies at Dartmouth (2008–2012) and Chair of Medical Physics at Wisconsin (2022–2025). Research Focus : Dr. Pogue pioneers Optics in Medicine , specializing in cancer imaging, photodynamic therapy, and surgical guidance. His work integrates fluorescence imaging, radiation therapy monitoring, and molecular diagnostics to improve cancer treatment precision. Key innovations include Cherenkov imaging for radiotherapy dosimetry and hypoxia-sensitive probes for tumor resection. Publication Trends : Recent articles (2023–2025) emphasize real-time surgical guidance, hypoxia quantification, and multimodal imaging systems. Dominant themes include fluorescence tomography, radiation dosimetry, and low-cost diagnostic devices, reflecting a translational focus from preclinical validation to clinical applications. Awards & Honors : Fellow, Optica (formerly OSA) Fellow, American Institute for Medical and Biological Engineering (AIMBE) Fellow, American Association of Physicists in Medicine (AAPM) Fellow, SPIE (International Society for Optics and Photonics) Funding & Innovation : Continuously funded by the NIH since 2001 ($52M+ total), Dr. Pogue founded three startups: DoseOptics LLC (radiotherapy dose imaging) and Hypoxia Surgical LLC (tissue hypoxia cameras), bridging academic research to clinical tools.

Hsueh-Chia Changمشاهده پروفایل

استاد

• Microfluidics
• Nanofluidics
• Electrokinetics
• +۵ مورد دیگر

Hsueh-Chia Chang is the Bayer Professor of Chemical Engineering in the Department of Chemical and Biomolecular Engineering at the University of Notre Dame's College of Engineering. He also holds a concurrent faculty position in the Department of Aerospace and Mechanical Engineering. As Principal Investigator of the Chang Lab, he leads cutting-edge research in microfluidic and nanofluidic technologies for biomedical applications. Prof. Chang received his B.S. in Chemical Engineering from Caltech in 1976, followed by M.S. and Ph.D. degrees in Chemical Engineering from Princeton University in 1977 and 1980, respectively. His academic journey has established him as a leader in the field of microfluidics and biosensing technologies. Chang's research focuses on developing low-cost liquid biopsy nanotechnologies for cancer screening and therapy management. His lab specializes in microfluidic and nanofluidic research coupled with electrokinetics, optics, plasmonics and acoustics for biosensing applications. Key research areas include Electrokinetics & Biosensing, Optics & Plasmonics, Nanoelectrokinetics, and Droplet Microfluidics. The Chang Lab has developed innovative platforms that can isolate and sort tumor cells, exosomes, microvesicles, lipoproteins, and stress granules from blood samples, then lyse these structures to release RNA/protein biomarkers for detection and quantification. His recent publications reveal a strong focus on extracellular vesicle diagnostics, cancer biomarker detection, and point-of-care diagnostic technologies. The research spans multiple disciplines including oncology, cardiology, and neurology, with applications for pancreatic, liver, breast, ovarian, and lung cancers as well as myocardial infarction diagnosis. Fellow of American Institute of Medical and Biological Engineering (2025) Provost Research Achievement Award, Notre Dame (2024) Fellow of the National Academy of Inventors (2020) Lifetime Achievement Award, American Electrophoresis Society (2019) Fellow of the American Physical Society (1997) Presidential Young Investigator Award, NSF (1985) Prof. Chang has advised over 50 PhD students and postdocs who have gone on to prominent positions in academia and industry. His lab has secured significant funding and has commercialized several technologies through startups like Aopia Biosciences, which launched NanoEx at ISEV 2024. The Chang Lab maintains active collaborations with medical researchers for validating their diagnostic platforms against various cancers and cardiac conditions.

• Computer Vision
• Machine Learning
• Robotics
• +۶ مورد دیگر

Dr. Feras Dayoub is a Senior Lecturer at the School of Computer and Mathematical Sciences (Faculty of Sciences, Engineering and Technology) at the University of Adelaide , specializing in Embodied AI and Robotic Vision within the Australian Institute for Machine Learning (AIML) . He co-directs the CROSSING French-Australian laboratory for human-autonomous agent teaming and holds an Adjunct position at the Queensland University of Technology (QUT) , serving as an Associate Investigator at its Centre for Robotics . Previously, he was a Chief Investigator at the ARC Centre of Excellence for Robotic Vision . His research focuses on advancing reliable deployment of computer vision and machine learning on mobile robots in real-world environments. Applied projects include agricultural automation , environmental conservation , and autonomous infrastructure monitoring . He has published extensively on topics like object detection , domain adaptation , 3D representation learning , and vision-language navigation , with a particular emphasis on robustness in dynamic and partially observed environments. Dr. Dayoub is also an educator specializing in programming , computer vision , and robotic perception . He contributes to open-source robotics research through tools like AARK (Autonomous Racing Toolkit) and has led teams developing solutions for precision agriculture (e.g., Deepfruits fruit detection system) and environmental monitoring (e.g., Crown-Of-Thorns starfish detection ). Key Collaborations : CROSSING Lab, QUT Centre for Robotics Research Themes : Embodied AI, Robust Perception, Domain Adaptation

Jan Dirk Wegnerمشاهده پروفایل

دانشیار

• Machine Learning
• Computer Vision
• Remote Sensing
• +۷ مورد دیگر

Jan Dirk Wegner is an Associate Professor at the University of Zurich, holding the chair in 'Data Science for Sciences' and leading the EcoVision Lab. He previously served as a Postdoc (2012–2016) and senior scientist (2017–2020) at ETH Zurich's Photogrammetry and Remote Sensing Group, following his PhD (with distinction) from Leibniz University Hannover (2011). His research bridges machine learning, computer vision, and remote sensing to address environmental and geoscience challenges, focusing on large-scale environmental data analysis, vegetation monitoring, and climate change mitigation. Education: PhD (with distinction) in Geodesy, Leibniz University Hannover (2011) Postdoc, ETH Zurich (2012–2016) Senior Scientist, ETH Zurich (2017–2020) Research Interests: Machine Learning, Computer Vision, Remote Sensing, Environmental Science, Climate Science, Geosciences, Explainable AI, Uncertainty Quantification, and Applications in Sustainability. The EcoVision Lab develops data-driven methods for global-scale environmental monitoring, including vegetation parameter mapping, flood prediction, forest degradation detection, and AI-driven ecological modeling. Awards: ETH Postdoctoral Fellowship (2012–2016) Science Prize of the German Geodetic Commission WEF Young Scientist Class 2020 (Top 25 globally under 40) Advising & Leadership: Director of the University of Zurich's Doctoral School in Data Science, leading the EcoVision Lab, and coordinating the CVPR EarthVision Workshops. His roles include Vice President of ISPRS Technical Commission II, member of the ETH AI Center, ELLIS, and UN-ETH Partnership. Labs/Teams: EcoVision Lab focuses on interdisciplinary AI applications for environmental challenges, collaborating with NGOs, governments, and industry to translate research into societal impact.

• Dental Informatics
• Teledentistry
• Oral Health Policy
• +۴ مورد دیگر

Professor Heiko Spallek is Head of School and Dean at the University of Sydney’s Sydney Dental School, leading the school’s integration with the Faculty of Medicine and Health. He also serves as Academic Lead for Digital Health and Health Service Informatics at the faculty level. His research focuses on advancing dental informatics, teledentistry, and evidence-based practice, while advocating for improved oral health policy and public health initiatives. He holds roles including director at Community Connections Australia and membership in the Charles Perkins Centre. Education: DMD, Dr. med. dent., MSBA(CIS), FACD, FAIDH Leadership: Oversees Sydney Dental School’s academic and clinical programs, emphasizing interprofessional education and digital health innovation. Research interests include leveraging big data and machine learning in dentistry, improving access to oral healthcare through teledentistry, and addressing systemic issues in dental education and policy. He has led projects on laser dentistry applications, dental caries prevention, and healthcare workforce regulation. Notable contributions include the OpenWide conference series, the BigMouth dental data repository, and advocacy for equitable dental funding in Australia. Grants include initiatives on oral hygiene in aged care and analysis of healthcare advertising perceptions. Media engagements highlight his role in public discourse on dental access and policy, including commentary on Australia’s dental crisis and aged care reforms. He actively promotes interdisciplinary collaboration through platforms like the Dental Informatics Online Community. Labs/Teams: Directs the Sydney Dental School’s research programs in informatics and public health, collaborating with national and international networks such as the National Dental PBRN.

Simon Makمشاهده پروفایل

استادیار

• Statistical Modeling
• Bayesian Statistics
• Gaussian Process
• +۵ مورد دیگر

Simon Mak is an Assistant Professor of Statistical Science at Duke University and a Faculty Network Member of the Duke Institute for Brain Sciences. His educational background includes: Ph.D. in Statistics, Georgia Institute of Technology (2018) M.S. in Statistics, Georgia Institute of Technology (2018) B.S. in Statistics, Simon Fraser University (2013) Dr. Mak's research focuses on advanced statistical methodologies for complex scientific problems. His expertise spans statistical modeling , Bayesian inference , Gaussian process emulation , and uncertainty quantification . He applies these methods to nuclear physics (heavy-ion collisions), engineering (engine control systems), and music information retrieval, emphasizing scalability and interpretability in scientific computing. Analysis of his 2023-2025 publications reveals dominant trends in scalable Gaussian process methods for massive datasets and multi-fidelity simulations, particularly applied to high-energy physics and engineering systems. He has pioneered innovations in Bayesian optimization for expensive simulators and developed novel frameworks for online change-point detection in streaming data, demonstrating exceptional cross-disciplinary impact. Dr. Mak leads multiple significant research initiatives: Collaborative Research: Cost-Efficient and Confident Sampling for Modern Scientific Discovery (2023-2026) Science-Integrated Predictive modeLing (SCINPL) for scalable scientific computing (2022-2025) The X-SCAPE collaboration for statistically advanced nuclear collision modeling (2020-2025) These projects fund his development of statistical frameworks for scientific discovery in complex systems. He actively contributes to the JETSCAPE collaboration, developing multi-stage frameworks for studying jet quenching in heavy-ion collisions, and applies statistical methods through the Duke Institute for Brain Sciences to advance neuroscience research.

Daniel Varonمشاهده پروفایل

استادیار

• Atmospheric Science
• Environmental Science
• Satellite Remote Sensing
• +۳ مورد دیگر

Daniel Varon is the Boeing Assistant Professor in Aeronautics and Astronautics at MIT, joining in July 2025. He is also affiliated with the MIT Institute for Data, Systems, and Society (IDSS). His research focuses on atmospheric composition, satellite remote sensing of greenhouse gases, and air pollution. Varon holds a PhD in Atmospheric Chemistry from Harvard University (2020), an MSc in Applied Mathematics, and dual undergraduate degrees in English Literature and Physics from McGill University. He has held postdoctoral roles at Harvard and Princeton University. His work uses satellite data to quantify methane and nitrogen oxide emissions, with applications in climate policy and environmental monitoring. Notable contributions include developing methods for detecting methane super-emitters via hyperspectral satellites and quantifying emissions from oil/gas fields. Varon has received over 3,000 citations and an h-index of 24 as of 2025, with extensive media coverage for his Nord Stream pipeline leak analysis. Varon has secured grants totaling $785K, including NOAA funding for geostationary satellite methane monitoring. He mentors postdocs and graduate students in satellite data analysis and machine learning applications. His teaching includes Harvard’s Atmospheric Chemistry course, where he received the Harvard Certificate of Distinction in Teaching. Varon serves as an Associate Editor for Atmospheric Measurement Techniques and contributes to initiatives like the Methane Emissions Detection Using Satellites Assessment (MEDUSA) Advisory Board. His lab focuses on integrating machine learning with satellite data to advance climate science.

• Environmental Engineering
• Virology
• Water Treatment
• +۳ مورد دیگر

Helen Nguyen is a Professor in the Department of Civil and Environmental Engineering at the University of Illinois at Urbana-Champaign , where she has held positions since 2006. She also serves as an Affiliate at the Carle Illinois College of Medicine and the Institute for Genomic Biology . Her academic roles include chairing the Environmental Engineering and Science Program (2017-2019) and prior positions as Associate and Assistant Professor at UIUC. Ph.D. (2005), M.S. (2004) in Environmental Engineering from Johns Hopkins University M.S. (2000) in Earth and Environmental Science from University of Illinois at Chicago B.S. (1995) in Geology from Ivan Franko National University of L'viv Nguyen's research focuses on pathogens and biofilms in drinking water distribution systems , environmental surveillance of pathogens , and water and food safety . Her work spans microbial inactivation mechanisms, biofilm dynamics, and innovative water treatment strategies. Her recent 15 articles (2023-2025) emphasize coronavirus stability , legionella contamination , rotavirus on produce , biofilm mechanics , and climate-pathogen interactions . Trends include environmental virology, microbial risk assessment, and disinfection science. Scientific awards include the NSF CAREER award , Fulbright Specialist , AEESP/CH2M Hill Outstanding Dissertation Award , and University of Illinois College of Engineering Research Excellence Awards at both Assistant and Associate Professor levels. Nguyen advises 10 undergraduate students on pathogen removal and has mentored award-winning graduate students like Ruiqing Lu (2015) and Chamteut Oh (2024). She leads Engineers Without Borders teams and collaborates on USDA-funded food safety workshops .

Carey E. Priebeمشاهده پروفایل

استاد

• computational statistics
• statistical pattern recognition
• statistical image analysis
• +۵ مورد دیگر

Carey E. Priebe is a Professor in the Department of Applied Mathematics and Statistics at the Whiting School of Engineering, Johns Hopkins University. He maintains strong affiliations with multiple research centers including the Johns Hopkins University Center for Imaging Science, the Mathematical Institute for Data Science, and the Human Language Technology Center of Excellence. His academic career spans several decades with significant contributions to statistical methodology and theory. Dr. Priebe's research focuses on computational statistics, statistical pattern recognition, and statistical inference for high-dimensional and graph data. His work bridges theoretical statistics with practical applications in areas such as brain connectome mapping, network analysis, and image processing. He has made significant contributions to spectral graph theory, graph matching, and vertex nomination, with applications ranging from neuroscience to national security. His publication record demonstrates consistent contributions to statistical methodology, with a notable emphasis on graph-based statistical methods. His research trajectory shows increasing focus on network data analysis, particularly in the last decade, with applications to brain mapping and connectome analysis as evidenced by his NSF BRAIN Initiative grant and Nature publication. 2013 Erskine Fellow (University of Canterbury) 2011 McDonald Award for Excellence in Mentoring and Advising 2010 ASA SDNS Distinguished Achievement Award 2009 Erskine Fellow (University of Canterbury) 2008 National Security Science and Engineering Faculty Fellow 2008 Pond Award for Excellence in Teaching NSF BRAIN EAGER grant recipient (2014) Professor Priebe has supervised an extensive number of doctoral students whose work spans statistical methodology, network analysis, and machine learning. His students have secured positions at prestigious institutions including academia (University of Wisconsin, Boston University), government research labs, and major technology companies (Microsoft, Facebook, Amazon). His research has been supported by significant grants from NSF, DARPA, and other agencies focused on national security applications and fundamental statistical methodology development. He maintains active collaborations across multiple disciplines and institutions, as evidenced by his numerous conference presentations and visiting appointments including at The Alan Turing Institute and The Isaac Newton Institute. His work bridges theoretical statistics with practical applications in neuroscience, security, and data science.

• Quality and Reliability Engineering
• Design of Experiments
• Time Series Analysis
• +۶ مورد دیگر

Rong Pan is a Professor at the School of Computing and Augmented Intelligence, Arizona State University (ASU). He holds a Ph.D. in Industrial Engineering from Pennsylvania State University (2002), an M.S. from Florida A&M University (1999), and a B.S. in Materials Science from Shanghai Jiao Tong University (1995). His research focuses on quality and reliability engineering, design of experiments, time series analysis, and statistical learning theory. Key projects involve NSF-funded research on reliability prediction, accelerated life testing, and degradation modeling. He serves as an Associate Editor for the Journal of Quality Technology and has authored over 80 publications. Courses taught include Reliability Engineering, Design of Experiments, and Statistics for Data Analysts. His academic service includes roles as a referee for IEEE Transactions and IIE journals. Research interests emphasize statistical methods for reliability improvement, with recent work on Bayesian inference models, optimal experimental design, and machine learning applications in industrial systems. Grants include collaborations with the NSF, Arizona Department of Transportation, and Science Foundation Arizona. His work bridges theoretical advancements and practical applications in manufacturing, energy systems, and semiconductor reliability. Education: Ph.D. (2002), M.S. (1999), B.S. (1995) Key Research Areas: Reliability Engineering, Bayesian Methods, Time Series, DOE Active Grants: NSF CMMI, SUNY IT Visiting Scholar Program Teaching: IEE 573 Reliability Engineering, DSE 501 Statistics Service: Journal of Quality Technology (Associate Editor), IEEE Transactions (Referee)

• Climate Change Attribution
• Extreme Weather Events
• Agricultural Impacts of Climate Change
• +۳ مورد دیگر

Mariam Zachariah serves as a Research Fellow at the Centre for Environmental Policy within the Faculty of Natural Sciences at Imperial College London. She is a core contributor to World Weather Attribution (WWA), an international scientific collaboration conducting rapid climate change attribution analyses for extreme weather events globally. Her work bridges climate science, vulnerability assessment, and policy-relevant research. Her educational foundation includes a PhD from the Indian Institute of Technology Bombay (IITB), where she investigated climate impacts on Indian agriculture. This research focused on drought and extreme temperature effects on crop yields in major agrarian regions, recognizing agriculture's critical role in India's climate-vulnerable economy. Zachariah's research centers on near-real-time attribution of extreme events to quantify human-induced climate change influences. Her expertise spans climate modeling, statistical analysis of extreme weather, and integrating vulnerability frameworks to assess compound impacts on communities. She examines how climate change interacts with socioeconomic factors to exacerbate disasters, particularly in agricultural systems and flood-prone regions worldwide. Analysis of her recent publications reveals a dominant focus on rapid attribution of droughts, floods, and heatwaves across diverse global contexts - from the Horn of Africa to Central Europe and South America. These studies consistently demonstrate climate change as a significant amplifier of event severity, while emphasizing how pre-existing vulnerabilities determine actual impacts. Her work increasingly addresses compound hazards and the intersection of climate change with infrastructure failures and land management. As a key member of the World Weather Attribution initiative, Zachariah collaborates with climate scientists, social scientists, and vulnerability experts in a unique operational framework that delivers scientific assessments within days of extreme events. This work directly informs policymakers, media, and affected communities about climate change's role in contemporary disasters.

Andrea Tapiaمشاهده پروفایل

دانشیار

• Information Systems
• Disaster Management
• Social Media Analysis
• +۳ مورد دیگر

Andrea Tapia is an Associate Professor of Information Sciences and Technology at Pennsylvania State University. She holds a Ph.D. in Sociology from the University of New Mexico (2000). Her research focuses on the intersection of social theory, ICT, and crisis response, with a particular emphasis on leveraging social media for disaster resilience and humanitarian action. She has secured over $3.7 million in external funding, supervised 16 graduate committees (including 10 PhDs and 6 master’s theses), and authored over 40 journal articles, 60 conference papers, and 12 book chapters. Tapia is an elected leader in the American Sociological Association and the International Association for Information Systems for Crisis Response and Management. Her work directly influences UN policy, international relief organizations, and U.S. governmental initiatives. She has pioneered platforms like Aurorasaurus and contributed to frameworks for social media integration in emergency dispatch systems. Her research spans crisis informatics, crowdsourced early warning systems, and inter-organizational collaboration in humanitarian contexts. Notable achievements include developing methodologies for trust detection in social media data and analyzing disaster response coordination networks. Tapia has presented at 73 conferences, including 31 invited talks, and her scholarship emphasizes actionable insights for policymakers and practitioners. Tapia teaches 12 courses across undergraduate, Honors, and graduate levels, reflecting her commitment to education. Her current focus includes refining frameworks for social media data adoption in public safety answering points and advancing resilience analytics for cyber-physical-social systems. She leads interdisciplinary teams addressing challenges in disaster response, digital volunteering, and technology-mediated collaboration.

Abdallah Chehadeمشاهده پروفایل

دانشیار

• Deep Learning
• Prognostics
• Reliability Analysis
• +۸ مورد دیگر

Dr. Abdallah Chehade is an Associate Professor in the Department of Industrial and Manufacturing Systems Engineering at the University of Michigan-Dearborn , where he leads the Informatics, Reliability, and Data Analytics (IRDA) lab . He holds a Ph.D. in Industrial Engineering from the University of Wisconsin-Madison (2017), with minors in Computer Sciences and Statistics, alongside an M.S. in Mechanical Engineering and a B.E. in Mechanical Engineering from the American University of Beirut. Research Interests span safe and robust deep learning solutions , explainable AI , data fusion for degradation modeling , and Bayesian statistical modeling . His work integrates AI/ML with prognostics and Internet of Things (IoT) to address challenges in reliability analytics and industrial data science . Publications highlight advancements in deep autoencoders , LSTM networks , and hybrid models for warranty forecasting , with applications in battery cells , sheet metal stamping , and rail transportation . His grants from Ford, Honda, and the U.S. Army focus on smart manufacturing , AI for sensor modeling , and digital twins . Lab Members include Ph.D. students working on topics like physics-based AI , computer vision , and deep learning for prognosis . He serves on the INFORMS Quality, Statistics, and Reliability (QSR) Council and maintains affiliations with IEEE , INFORMS , and IISE .

• Quantum Nanophotonics
• Plasmonics
• Spintronics
• +۷ مورد دیگر

Maiken H. Mikkelsen is the James N. and Elizabeth H. Barton Associate Professor in the Department of Electrical and Computer Engineering at Duke University, with a joint appointment in the Department of Physics . Her research focuses on quantum nanophotonics , plasmonics , and light-matter interactions in nanoscale materials, aiming to advance optoelectronics, quantum science, and biomedical diagnostics. Education B.S. in Physics, University of Copenhagen (2004) Ph.D. in Physics, University of California, Santa Barbara (2009) Postdoctoral Fellowship, University of California, Berkeley Her work explores nanophotonic engineering for quantum optics , spintronics , and ultrafast optoelectronics , with recent studies on nonlinear metasurfaces and plasmonic enhancement of immunoassays for point-of-care diagnostics. Publications highlight 2D semiconductor emission control , ultrafast single-photon sources , and metasurface-based photodetectors . Scientific Awards Maria Goeppert Mayer Award (2017) NSF CAREER Award (2015) Moore Inventor Fellow (2021) ONR/Air Force/Army Young Investigator Awards (2015-2017) Cottrell Scholar (2016) Stansell Family Distinguished Research Award (2021) She advises graduate students in Duke’s Electrical & Computer Engineering and Physics programs and leads the Mikkelsen Lab , which emphasizes ultrafast spectroscopy and quantum material development . The lab has graduated PhD students like Eunso Shin and Hengming Li (2025).