جست‌وجوی استاد و پژوهشگر

Gregor Benzمشاهده پروفایل

پژوهشگر

• Physics Education
• Inquiry-Based Learning
• Big Data Applications in Education
• +۳ مورد دیگر

Dr. Gregor Benz is a postdoctoral Researcher at the Chair of Physics Education , Technical University of Munich (TUM) , focusing on integrating Big Data and Digital Technology into Inquiry-Based Learning and Physics Education . He has held visiting scholar positions at The University of Tennessee and Hofstra University, and previously worked at Karlsruhe University of Education and Sarissa GmbH. Education : PhD in Physics Education (2024) from Karlsruhe University of Education M.Sc. in Electrical Engineering/Physics Teacher Training (2020) from Weingarten University of Education B.Eng. in Electrical Engineering/Physics Teacher Training (2018) from Ravensburg-Weingarten University of Applied Sciences His research explores the role of digital data acquisition systems in enhancing student methodological competencies and argumentation skills , particularly in lab settings. Recent publications analyze the impact of data volume on learning outcomes and strategies for teacher training in digital tools . Key trends in his articles include: Using Big Data to study phenomena like the Stick-Slip Effect in physics Developing TPCK/TPACK frameworks for digital educational technologies Advancing assessment methods for pre-service physics teachers in technological knowledge

• Financial Econometrics
• Statistics
• Empirical Asset Pricing
• +۴ مورد دیگر

Dacheng Xiu is the Joseph Sondheimer Professor of Econometrics and Statistics at the University of Chicago's Booth School of Business, with additional affiliation to the Department of Statistics. He serves as a Research Associate at the National Bureau of Economic Research. His research focuses on developing statistical methodologies and their applications to financial data, with current emphasis on machine learning solutions for big-data problems in empirical asset pricing. His earlier work involved risk measurement and portfolio management with high-frequency data and econometric modeling of derivatives. Xiu's research interests span Financial Econometrics, Statistics, Empirical Asset Pricing, and Quantitative Finance, with increasing integration of Machine Learning and AI techniques. His work demonstrates how statistical methods can extract meaningful economic signals from complex financial datasets, particularly through the application of machine learning to asset pricing problems. His publications reveal a strong trend toward integrating machine learning with traditional financial econometrics, with recent papers focusing on text data analysis, image recognition applications to financial markets, and sophisticated factor modeling approaches. The research shows progression from traditional volatility modeling to cutting-edge applications of deep learning and natural language processing in finance. Fellow of the Society for Financial Econometrics Fellow of the Journal of Econometrics Swiss Finance Institute Outstanding Paper Award AQR Insight Award Dimensional Fund Advisors Prize Bates-White Prize Best Conference Paper Prize at the 44th EFA Poets & Quants' Best 40-under-40 Business School Professors Xiu serves as Co-Editor of Journal of Business & Economic Statistics and Journal of Financial Econometrics, and as Associate Editor for prestigious journals including Journal of Finance, Review of Financial Studies, Journal of the American Statistical Association, Management Science, and Journal of Econometrics. His Risk Lab provides up-to-date daily annualized realized volatilities for individual stocks, ETFs, and future contracts, estimated from high-frequency data, demonstrating practical applications of his methodological research.

• Marine Engineering
• Arctic Technology
• Machine Learning
• +۳ مورد دیگر

Cong Liu is a Postdoctoral Researcher at Aalto University's Department of Energy and Mechanical Engineering, specializing in Marine and Arctic Technology. Active in data-driven maritime research, Liu contributes to winter navigation safety and operational optimization. Affiliation: Aalto University, Department of Energy and Mechanical Engineering Research Focus: Machine learning applications in Arctic shipping, AIS data analysis, icebreaker operations Liu's research bridges artificial intelligence with maritime engineering challenges. Recent work emphasizes predictive models for icebreaker assistance requirements, automated route planning, and maritime traffic safety evaluation using big data analytics. Publications in Engineering Applications of Artificial Intelligence and Cold Regions Science and Technology demonstrate this interdisciplinary approach. Key contributions include developing hybrid deep learning algorithms for ship time headway prediction, creating innovative maritime network mapping techniques, and analyzing crew behavior patterns in harsh ice conditions. These studies leverage AIS data for real-time traffic management and safety enhancement in polar regions. Current projects involve integrating qualitative and quantitative data flows to optimize vessel operations in ice-covered waters. Liu's publications reveal a strong focus on operational analytics, machine learning, and Arctic logistics challenges.

• Signal and Image Processing
• Statistical Machine Learning
• Low-Rank Approximations
• +۱۵ مورد دیگر

Matthieu Puigt is a Professor at Université du Littoral Côte d'Opale, specializing in signal and image processing with a focus on statistical machine learning , low-rank approximations , and sparse component analysis . His research extends to hyperspectral data fusion, unmixing, and restoration, as well as applications in chemistry and computational imaging . He leads the SPECIFI research team. Research Themes : Blind source separation, compressive learning, sensor calibration, and big data analysis Applications : Audio signal processing, environmental monitoring via drones, and urban air quality assessment His recent work includes developing VAE-based hyperspectral image emulators, tensor decomposition methods for multisensor data, and frameworks for butterfly species recognition. He advised Valentin Mullet's 2022 thesis on blockchain traceability systems and actively contributes to IEEE and GRETSI conferences.

Wenbo Heمشاهده پروفایل

استاد

• Machine Learning
• Privacy-Preserving Machine Learning
• Wireless Networks
• +۷ مورد دیگر

Wenbo He is a Professor in the Department of Computing and Software at McMaster University 's Faculty of Engineering. His research bridges Machine Learning , Privacy-Preserving Technologies , and Networked Systems , with a focus on secure federated learning , data anonymization , and wireless network optimization . Contact: hew11@mcmaster.ca Research Interests include: Machine Learning : Robustness under label noise, ensemble models, and 2D/3D classification. Privacy : Differentially private feature operations, encrypted classification, and location privacy. Networking : Software Defined Networking (SDN), wireless ad hoc networks, and crowdsensing. Recent Publications span IEEE Transactions on Mobile Computing , IEEE Infocom , and NeurIPS , with themes in: Security : Data poisoning, web shell obfuscation, and correlation attacks. AI/ML : Noise-robust models, face anonymization, and video action recognition. Systems : RFID optimization, cloud storage, and energy-efficient data centers. Teaching highlights include courses like Real-Time Systems , Computer Networks and Security , and Big Data Systems since 2017.

Hal Hershfieldمشاهده پروفایل

استاد

• Behavioral Decision Making
• Behavioral Economics
• Marketing Psychology
• +۵ مورد دیگر

Hal Hershfield is a Professor of Marketing and Behavioral Decision Making at UCLA Anderson School of Management, where he serves as the Marketing Area Chair. He holds the UCLA Anderson Board of Advisors Term Chair in Management and focuses on how thinking about time transforms emotions and alters judgments and decisions people make. Dr. Hershfield earned his Ph.D. in Psychology from Stanford University in 2009 and completed a post-doc at the Kellogg School of Management at Northwestern University. Prior to joining UCLA, he taught undergraduates at NYU Stern. His educational background includes a B.A. in Psychology from Tufts University (2001). Hershfield's research centers on the psychology of long-term decision making, particularly how time affects people's lives during an era when Americans are living longer but saving less. He is renowned for his discovery that when people confront their 'future selves,' they experience an emotional connection that influences long-term financial and ethical decision making. His work employs diverse methodologies including neuroimaging, eye tracking, archival and big data analyses, and virtual reality to investigate psychological components of saving and retirement planning from psychological, marketing, and managerial perspectives. His publication record demonstrates consistent exploration of future self-continuity, temporal decision making, and the psychological aspects of financial behavior. His research shows how connecting with one's future self can increase savings behavior by approximately 30% and influence various aspects of well-being and decision making across the lifespan. 2021 Fellow, Society for Personality and Social Psychology 2021 Neidorf "Decade" Teaching Award 2018 Citibank Teaching Award 2017 Eric and E Juline Faculty Excellence in Research Award 2017 George J. Robbins Assistant Professor Teaching Award 2017 40 Under 40 Top Business School Professors 2011 Association for Psychological Science Rising Star Award Hershfield actively consults with industry, having worked with Prudential's "Bring Your Challenges" campaign, the Consumer Financial Protection Bureau, Merrill Lynch, and the Principal Financial Group. In teaching, he creates inclusive environments where deep learning occurs through enjoyable, pop-culture infused experiences. His research has been featured in major media outlets including The Wall Street Journal, BBC, The Washington Post, and The New York Times, demonstrating significant real-world impact of his academic work.

Jason Bazilمشاهده پروفایل

استادیار

• Mitochondrial Physiology
• Cardiac Energetics
• Ischemia/Reperfusion Injury
• +۳ مورد دیگر

Jason Bazil is an Assistant Professor in the Department of Physiology at Michigan State University's College of Natural Science, with affiliations to the BioMolecular Science Gateway program. His research laboratory, located in the Biomedical Physical Sciences Building, focuses on cardiac energetics and mitochondrial physiology in ischemic heart disease. Dr. Bazil investigates mitochondrial mechanisms underlying lethal reperfusion injury through an integrative computational-experimental approach. His lab characterizes calcium handling, oxidative stress responses, and mitochondrial volume regulation in cardiac tissue, aiming to identify therapeutic targets for ischemic injury. Additional work develops dynamical modeling frameworks for large-scale biomedical data analysis in pharmacogenomics and systems pharmacology. Recent publications demonstrate a cohesive research trajectory centered on mitochondrial dynamics in cardiac pathophysiology. Key themes include calcium overload-induced permeability transition, adenine nucleotide transporter regulation, polyphosphate metabolism, and ferroptosis mechanisms. The consistent integration of cryo-EM, respirometry, and computational modeling across publications highlights a signature methodological approach for elucidating causal relationships in energy metabolism. No scientific awards are explicitly documented in the provided materials. Dr. Bazil leads the Bazil Lab (bazillab.com) with significant external funding, including an NSF CAREER award as evidenced by 2023 publications. While specific student names aren't listed, his laboratory trains researchers in interdisciplinary cardiac metabolism studies using state-of-the-art instrumentation for mitochondrial analysis. The Bazil Lab operates within MSU's BioMolecular Science Gateway infrastructure, utilizing advanced cryo-electron microscopy, high-resolution respirometry, and computational platforms. Current projects examine mitochondrial ultrastructural remodeling during calcium overload, therapeutic interventions for reperfusion injury, and big data applications in predicting drug responses for cardiac conditions.

• Particle Physics
• High-Energy Physics
• Quantum Chromodynamics (QCD)
• +۴ مورد دیگر

Dr. Jan Kretzschmar is a Senior Lecturer in the Department of Physics at the University of Liverpool, actively contributing to experimental particle physics through the ATLAS and H1 collaborations. His work focuses on precision measurements of W and Z boson production, Higgs boson decays, and searches for beyond-Standard-Model phenomena at the LHC. Academic affiliation: University of Liverpool Key collaborations: ATLAS, H1 Research themes: QCD, proton structure, Higgs phenomenology His research spans precision electroweak measurements, dark matter exploration, and Monte Carlo generator development for detector simulations. He has led analyses of Drell-Yan cross sections, deep inelastic scattering, and Higgs decay modes, contributing to fundamental understanding of particle interactions. Recent publications include studies on jet energy calibration, W boson pair production, and top-quark-associated Higgs signals. He received the Guido Altarelli Award (2016) for his HERA contributions. Professional roles involve convening sessions at EPS-HEP and Moriond conferences, with teaching responsibilities in computational physics and W/Z boson luminosity monitoring.

• Particle Physics
• High Energy Physics
• Experimental Physics
• +۳ مورد دیگر

Dr. Tim Jones is a Research Fellow at the University of Liverpool's Department of Physics, specializing in experimental particle physics through contributions to the ATLAS experiment at the Large Hadron Collider (LHC). His work focuses on detector upgrade projects, jet energy scale calibration, and searches for physics beyond the Standard Model, including supersymmetry and Higgs boson decay anomalies. University of Liverpool, Department of Physics ATLAS Experiment, CERN Collaborations with Rutherford Appleton Laboratory, University of Oxford, and Manchester University His research interests span High Energy Physics , Detector Technology , and Data Analysis , with recent publications analyzing 13 TeV proton–proton collisions . Key subtopics include Jet Substructure , Supersymmetry Searches , and Cloud Computing for Big Data . Current projects involve optimizing detector support structures and securing funding from the Science and Technology Facilities Council (STFC) for particle physics initiatives. Collaborative efforts include institutions like Rutherford Appleton Laboratory, Lawrence Berkeley National Laboratory, and Queen Mary and Westfield College.

Yingyuan Yangمشاهده پروفایل

استادیار

• Cybersecurity
• Machine Learning
• Implicit Authentication
• +۶ مورد دیگر

Yingyuan Yang is an Assistant Professor in the Computer Science Department at the University of Illinois, Springfield within the College of Engineering and Computer Science. His research focuses on the intersection of cybersecurity and machine learning, with specific emphasis on implicit authentication, mobile computing, and malicious application detection. His work spans related disciplines including natural language processing, signal processing, big data analysis, and application security. He actively seeks volunteers for his research projects, involving smartphone usage monitoring over extended periods. Contact details: Email: yyang260@uis.edu Phone: (217) 206-8226 Office: UHB 3010, Springfield, IL

Zhipeng Caiمشاهده پروفایل

استاد

• Machine Learning
• Cybersecurity
• Privacy Preservation
• +۳ مورد دیگر

Dr. Zhipeng Cai is a Professor in the Department of Computer Science at Georgia State University with joint appointments at the Robinson College of Business. As Director of the INSPIRE Center and leader of the Innovative Computing and Networking group, his research spans machine learning, privacy/security, and large-scale distributed systems. Cai is an IEEE Fellow, ACM Distinguished Scientist, and NSF CAREER awardee ranked among the world's top scientists. His research examines fundamental challenges in federated learning systems, privacy-preserving AI, and efficient computing for next-generation networks. Recent publications demonstrate Cai's work in federated open-world learning, hypergraph processing, and wireless-powered IoT networks. His group develops novel methods for distributed data synthesis, adaptive resource management, and physics-inspired communications. Dr. Cai leads significant initiatives in cybersecurity education through Eureka Labs, providing open-access learning materials used globally. His editorial leadership includes roles at IEEE Transactions on Knowledge and Data Engineering, IEEE Transactions on Wireless Communications, and as Editor-in-Chief of High-Confidence Computing. IEEE Fellow (2024) ACM Distinguished Scientist (2025) NSF CAREER Award (2013) World's Top 2% Scientist (Stanford University) The INSPIRE Center conducts federally funded research on adaptive learning systems and secure computing architectures. Current projects focus on privacy-enhancing technologies for large language models, robust optimization under adversarial conditions, and efficient federated learning frameworks.

Yubao Wuمشاهده پروفایل

استادیار

• Big Data Analytics
• Data Mining
• Bioinformatics
• +۳ مورد دیگر

Dr. Yubao (Robert) Wu is an Assistant Professor in the Department of Computer Science at Georgia State University. He holds a Ph.D. from Case Western Reserve University, and B.Eng. and M.Eng. degrees from Dalian University of Technology, China. His research focuses on big data analytics, data mining, and bioinformatics, with emphasis on analyzing large-scale networks in biomedical and social domains. Key areas include graph theory, random walk-based proximity measures, and community detection algorithms. Education: B.Eng. in Electronic and Information Engineering, Dalian University of Technology (2007) M.Eng. in Signal and Information Processing, Dalian University of Technology (2009) Ph.D. in Computer Science, Case Western Reserve University (2016) Dr. Wu’s research emphasizes scalable algorithms for large graphs, including Monte Carlo methods for random-walk-based similarity measures and distributed computing techniques. His work addresses challenges in network analysis such as detecting communities, measuring node similarity, and optimizing search processes in complex networks. He also explores applications in cybercrime analysis (e.g., dark web markets), public health (opioid epidemic prediction via social media), and bioinformatics. His publications span prestigious venues like VLDB, SIGMOD, and ICDE, addressing topics like illicit drug ad detection, dark web certification analysis, and efficient k-vertex connected component discovery. He is based at 1 Park Place, room 638.

Yue Wangمشاهده پروفایل

استاد

• Machine learning
• Pattern recognition
• Signal and image processing
• +۵ مورد دیگر

Yue Wang serves as the Grant A. Dove Professor in the Bradley Department of Electrical and Computer Engineering at Virginia Tech, where he leads pioneering research at the intersection of computational theory and biomedical applications. His work focuses on developing advanced signal processing and machine learning frameworks for genomic analytics, tissue characterization, and medical imaging, driving innovations in precision medicine and diagnostic methodologies. His academic foundation includes: PhD in Electrical Engineering from the University of Maryland (1995) MS in Electrical Engineering from Shanghai Jiaotong University (1987) BS in Electrical Engineering from Shanghai Jiaotong University (1984) Wang's research program centers on pattern recognition, stochastic signal systems, and image processing with deep applications in computational bioinformatics and systems biology. He develops novel algorithms for analyzing complex biological data—from single-cell genomics to radiological images—enabling discoveries in disease mechanisms and therapeutic targeting. His methodologies bridge computational rigor with clinical relevance, particularly in cancer and neurological disorders. Analysis of his recent publications (2022-2025) reveals three dominant trends: 1) Advancement of differential dependency networks for detecting biological network rewiring in disease states; 2) Development of unsupervised deconvolution techniques for cell-type resolution in bulk tissues; and 3) Integration of radiomics with genomics for tumor characterization. These efforts increasingly leverage deep learning and single-cell technologies to address heterogeneity in biological systems. His scientific recognition includes: IEEE Fellow (2016) for contributions to genomic signal analytics and image-based tissue characterization While specific grant details are not provided, Wang's extensive open-source software contributions—including CAM for cell deconvolution, DDN for network analysis, and KDDN for dependency networks—demonstrate sustained research leadership. His mentorship likely involves graduate students in computational biology projects, though formal advisee lists are not documented in available materials. No laboratory or team structure is explicitly described, but his computational focus suggests collaboration with biomedical researchers across Virginia Tech's campus and medical institutions to translate algorithms into clinical tools.

Andrés I. Ávilaمشاهده پروفایل

دانشیار

• Partial Differential Equations
• Numerical methods for PDEs
• High Performance Computing
• +۱ مورد دیگر

Andrés I. Ávila is an Associate Professor in the Department of Mathematical Engineering at the University of La Frontera, Chile. He holds a Ph.D. in Mathematics (1999) and a postdoctoral fellowship (1999) from the University of Missouri. His research focuses on Partial Differential Equations , Numerical Methods for PDEs , High Performance Computing , and Mathematical Modeling . Education Ph.D. in Mathematics (1999), University of Missouri Postdoctoral Fellow (1999–2000), Universidade Estadual de Campinas His work spans nonlinear PDEs (e.g., Schrödinger systems, combustion theory), high-performance computing (Xeon Phi coprocessors, agent-based models), and environmental modeling (floods, wildfires, crop optimization). Articles highlight interdisciplinary applications in physics, biology, and engineering. He has supervised 8 Master’s and 2 Ph.D. students, advised national projects (e.g., Corfo Innovation , Fondef grants), and co-led the National Laboratory for High-Performance Computing (EMC-02) . Collaborations include institutions in Germany, Brazil, and France.

Sanguthevar Rajasekaranمشاهده پروفایل

استاد

• Algorithms
• Big Data Analytics
• AI and Machine Learning
• +۴ مورد دیگر

Sanguthevar Rajasekaran is the Board of Trustees Distinguished Professor and Pratt & Whitney Chair Professor of Computer Science and Engineering at the University of Connecticut. He serves as Director of the School of Computing, leading interdisciplinary research initiatives. His academic roles include overseeing academic programs, research labs, and strategic partnerships in computing and information technology. His research focuses on algorithms, big data analytics, AI/ML, quantum computing, bioinformatics, and high-performance computing. Notable contributions include advancements in record linkage algorithms, IoT time series analysis, quantum algorithms for N-body simulations, and energy-efficient solar PV systems. He has organized major conferences like ICCABS and secured grants such as the NSF Student Travel Grant for computational bio-medical sciences. Rajasekaran has authored over 250 publications across journals and conferences, with recent work emphasizing quantum computing applications, medical data analytics, and cybersecurity in IoT systems. He leads collaborative projects with industry partners and federal agencies, fostering innovation in computational science and engineering.