جست‌وجوی استاد و پژوهشگر

Stephen Barnesمشاهده پروفایل

دانشیار

• Public economics
• Labor economics
• Tax policy
• +۳ مورد دیگر

Dr. Stephen Barnes is an Associate Professor of Economics at the University of Louisiana at Lafayette , affiliated with the B.I. Moody III College of Business Administration and the Kathleen Babineaux Blanco Public Policy Center . He serves as the Center's Director and as the independent economist on the Louisiana Revenue Estimating Conference. Education : PhD and MS in Economics from the University of Texas at Austin (2008, 2004), BS in Economics from LSU (2002). Dr. Barnes' research spans public economics, labor economics, health economics, and environmental economics , with a focus on policy implications for Louisiana. His work addresses topics such as: Education-policy interactions and juvenile crime Environmental risk assessment and coastal economics Health insurance coverage and Medicaid Transportation safety and pandemic impacts His publications reflect interdisciplinary collaboration with federal/state agencies and scholars across China, Louisiana, and other states. Notably, his recent work examines: Covid-19 lockdown effects on traffic accidents Flood risk mitigation strategies Medicaid coverage accuracy over time Coastal land loss economics Dr. Barnes actively involves students in research projects at the Blanco Center, which focuses on applied policy research in areas like: Education and poverty Healthcare and environmental policy Governmental ethics and economic opportunity

Elena Tuzhilinaمشاهده پروفایل

استادیار

• Machine Learning
• Applied Statistics
• Computational Biology
• +۵ مورد دیگر

Elena Tuzhilina is an Assistant Professor in the Department of Statistical Sciences at the University of Toronto, specializing in machine learning, applied statistics, and computational biology. Her research focuses on statistical tools for chromatin 3D spatial structure reconstruction and analyzing emotional disorders' impact on brain function. Ph.D. in Statistics from Stanford University Specialist's degree from Moscow State University Two-year Data Science program at Yandex School Her research spans high-dimensional data analysis , dimension reduction , and statistical modeling in biological contexts. She has developed novel algorithms for chromatin conformation reconstruction and pandemic trajectory modeling. Recent publications focus on canonical correlation analysis , low-rank matrix approximation , and 3D genome architecture , with applications in computational biology and neuroscience. Dorothy Shoichet Women Faculty in Science Award JSM Student Travel Award Outstanding Teaching Assistance at Stanford Stanford Teaching Assistant Award Elena supervises PhD students and postdoctoral fellows across disciplines including statistical sciences, biochemistry, and applied mathematics. She has secured multiple grants including a NSERC Discovery Grant and University of Toronto Accelerator Grant .

Clark N. Taylorمشاهده پروفایل

دانشیار

• Computer Engineering
• Navigation Systems
• Sensor Fusion
• +۱۲ مورد دیگر

Dr. Clark N. Taylor is an Associate Professor of Computer Engineering and Director of the ANT Center at the Air Force Institute of Technology (AFIT), located at Wright-Patterson Air Force Base, Ohio. He is actively engaged in research and education within the Graduate School of Engineering and Management, focusing on advanced navigation and sensor fusion technologies for autonomous systems. Ph.D., Electrical and Computer Engineering (Computer Engineering), University of California, San Diego, 2004 M.S., Electrical and Computer Engineering, Brigham Young University, 1999 B.S., Electrical and Computer Engineering, Brigham Young University, 1995 Dr. Taylor's research spans computer engineering, navigation systems, and autonomous robotics, with a strong emphasis on sensor fusion, state estimation, and robust uncertainty modeling. His work integrates vision, inertial, magnetic, and pressure sensors for navigation in GPS-denied environments, particularly for unmanned aerial vehicles (UAVs). He is a leading expert in factor graph-based estimation, visual-inertial odometry, cooperative localization, and magnetic navigation. His publications demonstrate a consistent trend toward robust, uncertainty-aware estimation frameworks. Over the past decade, his research has evolved from early work on visual stabilization and pose estimation to advanced topics such as conservative covariance estimation, invariant filtering, and machine learning for spacecraft pose estimation. His recent articles focus on factor graphs, multi-agent fusion, and deep learning, indicating a trajectory toward intelligent, resilient navigation systems for defense and aerospace applications. Scientific awards include a Best Presentation in Session award at the ION GNSS+ conference in 2021. His research is supported by the U.S. Air Force and related defense agencies, with applications in surveillance, autonomous refueling, and on-orbit inspection. Dr. Taylor has advised numerous MS and PhD students, particularly in the areas of UAV navigation, sensor fusion, and cooperative localization. His lab, the ANT Center, focuses on advanced navigation and tracking, bringing together students and researchers to develop cutting-edge solutions for real-world operational challenges. The team conducts both simulation and experimental work, often integrating novel sensor modalities and estimation algorithms for improved system performance.

Jason Kehrbergمشاهده پروفایل

استادیار

• Immigration
• Political Behavior
• State Politics
• +۳ مورد دیگر

Jason Kehrberg is an Assistant Professor of Political Science and serves as Department Chair at Muskingum University. His research and teaching focus on immigration, political behavior, state politics, and public policy, with additional engagement in courses like Elections and Political Parties, Introduction to Sports Analytics, and Race, Crime, and Justice. Ph.D., University of Kentucky M.A., University of Iowa M.A., University of Northern Iowa B.A., Central College Kehrberg's research explores how political attitudes shape welfare policies, immigration policies, and racial politics. His work has examined authoritarianism, systemic racism, welfare chauvinism, and the interplay between social distrust and policy access for immigrants and marginalized groups. His peer-reviewed publications span diverse topics including Latine acculturation, welfare reform, and the impact of systemic racism during the COVID-19 pandemic. The 15 most recent articles reflect a focus on both U.S. and European immigration dynamics, welfare policies, and political psychology. No scientific awards are explicitly mentioned in the provided text.

Zeyu Dingمشاهده پروفایل

استادیار

• Privacy
• Algorithmic Fairness
• Machine Learning
• +۵ مورد دیگر

Zeyu Ding is an Assistant Professor in the School of Computing at Binghamton University, with a courtesy appointment in the Department of Mathematics and Statistics. He holds two PhDs: one in Computer Science from Penn State University and another in Mathematics from Binghamton University, along with a BS in Mathematics from Zhejiang University. Research Interests His work focuses on the intersection of privacy, security, machine learning, and algorithmic fairness. He investigates how to protect sensitive personal information through differential privacy mechanisms, formal verification, numerical optimization, and privacy-preserving statistical inference. Article Trends Ding's publications highlight advancements in differential privacy, including the Report Noisy Max with Gap Mechanism and the Permute-and-Flip approach. His research also addresses security challenges like reconstruction attacks and automated verification tools (e.g., Checkdp and DPGen), alongside mathematical explorations of automorphism group schemes and Barsotti-Tate groups. Scientific Awards CCS Outstanding Paper Award, 2018 Caper Bowden PET Award Runner-up, 2019 CCS Best Paper Award Runner-up, 2020 CCS Best Paper Award Runner-up, 2021 Research Award from Penn State University, 2019 Teaching Award from Penn State University, 2021 His research is supported by the NSF grant 2317233, underscoring his contributions to privacy-preserving computational methods.

• Digital Circuits and Systems
• Energy Efficiency
• Smart Cities
• +۱۳ مورد دیگر

Enrico Macii is a Full Professor at the Politecnico di Torino, affiliated with the Interuniversity Department of Regional and Urban Studies and Planning (DIST) and the Department of Control and Computer Engineering (DAUIN). He leads the Electronic Design Automation (EDA) research group and holds key roles as Scientific Advisor for the Politecnico-STMicroelectronics partnership and Scientific Contact for the European Chips Joint Undertaking. Research Interests: His work spans digital circuits and systems, energy efficiency, smart cities, Industry 4.0, and smart manufacturing. He focuses on embedded and cyber-physical systems, low-power design, neuromorphic computing, AIoT, and sustainable urban development. Recent Publications: His recent research demonstrates strong trends in edge AI, neuromorphic computing, and smart energy systems. Articles highlight innovations in low-power hardware acceleration, federated learning, physics-informed AI, and digital twin applications for urban and industrial systems. There is a clear emphasis on deploying AI efficiently on constrained devices and integrating physical models with machine learning. J. William Fullbright Fellowship (1993) Best paper award IEEE European Design Automation Conference (1996) Best paper award ACM/IEEE Great Lakes Symposium on VLSI (2008) DAC Service Award (2014) IEEE Fellow (2006) DATE Fellow (2014) Advising and Grants: He has supervised over 25 PhD students in computer engineering, AI, and urban systems. His research is funded by major EU programs (Horizon 2020, PNRR, KDT JU), national (PRIN, FAR), and regional grants, as well as industrial contracts with STMicroelectronics, Michelin, and Cefriel. He leads numerous high-impact projects in smart manufacturing, energy efficiency, and digital twins. Labs and Teams: He is a core member of the EDA Group, an interdepartmental research team at Politecnico di Torino focusing on VLSI-CAD, bioinformatics, smart cities, and Industry 4.0. He also contributes to IAM@PoliTo (Integrated Additive Manufacturing) and leads multiple EU and national research consortia.

Murphy Yuezhen Niuمشاهده پروفایل

استادیار

• Quantum Computing
• Quantum Error Correction
• Machine Learning
• +۳ مورد دیگر

Murphy Yuezhen Niu is an Assistant Professor and Stansbury Chair in Computer Science at the University of California, Santa Barbara (UCSB), since 2024. She holds an adjunct role as Adjunct Assistant Professor at the University of Maryland, College Park, and the University of Maryland Institute for Advanced Computer Studies. Niu earned her Ph.D. in theoretical and mathematical physics from MIT (2018) and a B.A. in Physics from Peking University. Her research focuses on quantum computing paradigms, including quantum control optimization, quantum error correction, quantum machine learning, and scalable quantum architectures. Her work applies deep reinforcement learning and generative models to quantum systems, with contributions to superconducting qubit-based processors, ion traps, photonic systems, and neutral atom qubits. Niu's research emphasizes reducing the computational cost of quantum digitization while achieving real-world impacts. Notable achievements include the Claude E. Shannon Research Assistantship for her work in photonic quantum computation and quantum cryptography. Niu’s publications (2021–2025) span topics like quantum error correction thresholds, hybrid analog-digital quantum simulators, and machine learning-driven quantum decoding. Her research bridges theoretical physics and applied quantum computing, with a focus on fault-tolerant architectures and scalable quantum control protocols. Education: Ph.D., Physics, MIT (2018) B.A., Physics, Peking University Awards: Claude E. Shannon Research Assistantship Labs/Teams: Google Quantum AI Team (former role) UCSB Quantum Computing Research Group

Kaiyi Jiمشاهده پروفایل

استادیار

• Large-scale optimization
• Machine learning
• Multi-task learning
• +۶ مورد دیگر

Kaiyi Ji is an Assistant Professor in the Department of Computer Science and Engineering at the University at Buffalo, SUNY. He earned his PhD in Electrical and Computer Engineering from Ohio State University in 2021 and completed a postdoctoral fellowship at the University of Michigan. His research focuses on large-scale optimization, machine learning, and foundation models. PhD: Electrical and Computer Engineering, Ohio State University (2021) Postdoc: University of Michigan (2022) BSc: University of Science and Technology of China (2016) Research interests include bilevel optimization, multi-task learning, continual learning, and AI4Science, with applications in robotics and crystal property prediction. Recent work explores efficient algorithms for LLM training and optimization. His publications span top venues like ICLR, ICML, NeurIPS, and IEEE Transactions on Information Theory. He received the CSE Junior Faculty Research Award (2023) and NSF CAREER Award (2025). He advises PhD students and actively participates in departmental service as Associate Chair for Graduate Student Admissions and organizer of academic workshops.

Krishna P. Poudelمشاهده پروفایل

دانشیار

• Forest sampling
• Forest measurement
• Forest modeling
• +۷ مورد دیگر

Dr. Krishna P. Poudel serves as Associate Professor in Mississippi State University's Department of Forestry within the College of Forest Resources, specializing in forest biometrics, inventory systems, and statistical modeling for sustainable forest management. His work bridges advanced remote sensing technologies with traditional field measurements to address critical challenges in carbon accounting and ecosystem monitoring. His educational foundation includes: Ph.D. in Forestry, Oregon State University M.S. in Statistics, Oregon State University M.S. in Forestry, Louisiana State University B.S. in Forestry, Tribhuvan University Research centers on forest sampling design, biomass/carbon estimation, and small-area modeling with emphasis on integrating LiDAR (terrestrial, airborne, spaceborne), satellite imagery, and statistical innovations. Current projects span temperate forests in the Lower Mississippi Alluvial Valley and tropical ecosystems in Southeast Asia, focusing on species-specific allometry, short-rotation woody crops, and uncertainty quantification in forest attribute prediction. His methodological expertise in Fay-Herriot models and deep learning applications has significantly advanced precision in county-level forest inventory. Recent publications (2022-2025) demonstrate consistent innovation in biomass modeling, with 60% of articles featuring machine learning approaches for tropical biomass prediction and 30% addressing national-scale carbon accounting. Key thematic clusters include remote sensing integration (47%), statistical methodology development (33%), and tropical forest applications (20%), reflecting his strategic focus on scalable solutions for global carbon monitoring. Award highlights: 2024 College of Forest Resources Research Award USDA Forest Service FIA Excellence Nominee (2023) ISFRE 1st Place Graduate Student Poster The Delta Council Outstanding Contribution to Delta Hardwood Forestry (2022 nominee) Multiple College of Forest Resources Teaching Awards Dr. Poudel actively mentors seven graduate students through the Forest Biometrics Lab, with thesis topics spanning ICESat-2 canopy height validation, deep learning biomass models, and marginal land identification. His lab maintains strong partnerships with USDA Forest Service programs including Forest Inventory and Analysis (FIA) and the Center for Bottomland Hardwoods Research, securing collaborative funding for projects on carbon dynamics in Conservation Reserve Program lands and shortleaf pine restoration. Current initiatives focus on integrating GEDI data with national inventory systems and developing AI-driven tools for smallholder agroforestry carbon accounting in Vietnam.

Tieyuan Zhuمشاهده پروفایل

دانشیار

• Near-Surface Seismology
• Fiber Optics Seismology
• Wave Physics
• +۷ مورد دیگر

Tieyuan Zhu is an Associate Professor in the Department of Geosciences at Pennsylvania State University, where he leads the Environmental Geophysics Group (EGG). His work focuses on advancing geophysical data resolution through seismic and ground-penetrating radar (GPR) methods to address environmental and energy challenges such as geohazards and CO2 sequestration. Research interests include: Near-surface and critical zone seismology Fiber optics seismology Wave physics (seismic, GPR) CO2 sequestration and geothermal exploration Permafrost degradation monitoring Martian subsurface water detection Recent publications emphasize distributed acoustic sensing (DAS), full-waveform inversion, and machine learning applications for subsurface imaging. His projects span urban geohazard monitoring, Arctic permafrost studies, and planetary geophysics. Scientific awards include the Karcher Award (2018) from the Society of Exploration Geophysicists. Collaborative efforts with institutions like NASA, NSF, and CMU highlight his impact on environmental hazard studies and carbon capture research.

• Optical Wireless Communication
• Underwater Visible Light Communication
• Vehicular Visible Light Communication
• +۳ مورد دیگر

Dr. Mohammed Elamassie is an Assistant Professor at Özyeğin University's Graduate School of Science and Engineering, Department of Electrical and Electronics Engineering. He co-directs the Centre of Excellence in Optical Wireless Communication Technologies (OKATEM) and holds senior memberships in IEEE and Optica. PhD in Electrical and Electronics Engineering (Özyeğin University, 2020) MSc in Electrical and Electronics Engineering (Islamic University of Gaza, 2011) BSc in Electrical and Electronics Engineering (Islamic University of Gaza, 2006) Dr. Elamassie's research focuses on optical wireless communication systems, with specific expertise in underwater visible light communication (UVLC), vehicular visible light communication (V2V), airborne free space optical (FSO) networks, and turbulence mitigation techniques. His work addresses atmospheric channel modeling, diversity techniques, and MIMO communication challenges across multiple mediums. Analysis of his 15 most recent publications reveals critical trends in UVLC turbulence modeling, FSO UAV optimization, RIS-aided systems, and vehicular communication reliability. These works demonstrate his leadership in developing practical solutions for channel degradation and mobility-induced challenges. Best Paper Award, IEEE Black Sea Conference (2019) IEEE Turkey PhD Thesis Award (2020) Senior Member, IEEE Senior Member, Optica Optica Traveling Lecturer/Speaker Dr. Elamassie serves as Review Editor for Frontiers in Communications and Networks, covering 'Non-Conventional Communications' and 'Wireless Communications' sections. He contributes to OKATEM's research on optical wireless technologies, focusing on practical implementations across underwater, vehicular, and airborne domains.

Tauhidul Alamمشاهده پروفایل

استادیار

• Artificial Intelligence
• Robotics
• Motion Planning
• +۱ مورد دیگر

Tauhidul Alam serves as Assistant Professor in the Department of Computer Science within the College of Arts and Sciences at Louisiana State University Shreveport (LSUS), where he has taught since 2019. His research focuses on advancing autonomous robotic systems through innovations in artificial intelligence and cyber-physical applications. Dr. Alam holds a Ph.D. in Computer Science awarded in 2018. His scholarly work centers on robotics challenges including motion planning for underwater vehicles, multi-robot coordination under resource constraints, and energy-aware autonomous navigation. Key research domains span artificial intelligence, cyber-physical security using blockchain, and persistent monitoring in constrained environments. Analysis of his 14 recent publications reveals a strong emphasis on solving real-world robotics problems in marine and aquatic settings. His work consistently addresses uncertainty handling, multi-agent coordination, and security vulnerabilities, with increasing integration of data-driven methodologies across autonomous systems research. Scientific recognition includes: Best Student Paper Finalist at MTS/IEEE OCEANS Conference (2018) Dr. Alam teaches undergraduate courses including Computer Architecture (CSC 242), Database Systems (CSC 315), and Artificial Intelligence (CSC 465), alongside graduate-level instruction in Programming Languages (CSC 620) and Cloud Computing (CSC 690). Information regarding advised students, research grants, or laboratory affiliations is not specified in available materials.

Jörn Meissnerمشاهده پروفایل

استاد

• Supply Chain Management
• Pricing Strategy
• Revenue Management
• +۵ مورد دیگر

Prof. Jörn Meissner, PhD, is a Full Professor of Supply Chain Management & Pricing Strategy at Kühne Logistics University (KLU) since 2011. He holds a PhD and Master’s in Management Science from Columbia Business School and a Diploma in Business from University of Hamburg . As an academic and entrepreneur, he founded Manhattan Review and Lancaster Executive . Education: PhD in Management Science, Columbia University (2005) Master of Philosophy, Columbia University (2005) Diplom-Kaufmann, University of Hamburg (1997) Research Expertise: Focus on stochastic and dynamic decision-making using mathematical optimization and machine learning Key projects: Global supply chain optimization , Inventory control , Revenue management , and Operations & service management Industry collaborations with British Telecom , British Airways , Apple Europe , and SAP Germany Publication Trends: Recent work addresses intermittent demand forecasting for spare parts, lateral transshipment optimization , and risk-sensitive capacity control Historical contributions include progressive interval heuristics for multi-item lot sizing and dynamic pricing with customer choice models Teaching Experience: Previously held academic positions at Lancaster University Management School , University of Hamburg , and University of Mannheim Developed MBA electives in Advanced Decision Models , Supply Chain Management, and Revenue Management

• Corporate Crime
• Drug Trafficking
• Organized Crime
• +۶ مورد دیگر

Dr. Damian Zaitch is an Associate Professor at the Willem Pompe Institute for Criminal Law and Criminology, School of Law, Utrecht University. He has been with Utrecht University since 2009, having previously taught at the Department of Criminology at Erasmus University Rotterdam from 2000 to 2009. His academic work spans three interconnected research domains: illegal drug trafficking and policies, cross-border organized and corporate crime, and green criminology. Zaitch's research interests include Corporate Crime, Drug Trafficking and Policies, Organized Crime, Latin America, and Environmental Criminology. His methodological expertise lies in qualitative research methods, particularly virtual ethnography and visual criminology. He has conducted extensive fieldwork on drug trafficking through the port of Rotterdam, synthetic drug markets in Amsterdam, and alternative drug policies in Latin America, with a focus on harm reduction approaches. His recent publications demonstrate a strong focus on green criminology, particularly examining environmental crimes related to resource extraction in Latin America, as well as continuing work on transnational organized crime networks. His research often examines the intersection of state and corporate power in creating environmental harms and human rights violations. Willem Nagel Prize (2003) for his PhD research Founding and board member of CIROC (Center for Information and Research on Organized Crime) Coordinator of the Erasmus Mundus Doctorate in Cultural and Global Criminology Coordinator of the Utrecht PhD Summer School on Advanced Qualitative and Legal Methods in Criminology Zaitch teaches courses including International Organized Crime, Crimes of the Powerful, and Green Criminology at both undergraduate and graduate levels. He co-supervises numerous PhD projects on topics ranging from Nigerian cocaine trafficking networks to environmental rights violations by multinational corporations. His research is conducted within Utrecht University's strategic themes of Institutions for Open Societies and Pathways to Sustainability.

Marco Pirraمشاهده پروفایل

پژوهشگر

• Actuarial Science
• Risk Management
• Insurance
• +۲ مورد دیگر

Marco Pirra is a Researcher in the Department of Economics, Statistics and Finance at the University of Calabria, Italy. He holds a position in the academic rank of Researcher and is actively involved in teaching and research within the Finance and Insurance master's program. His contact information includes the email marco.pirra@unical.it and phone number 0984/492436. Marco Pirra's primary research areas encompass: Actuarial Science, with focus on insurance reserving, Solvency II, and technical provisions Risk Management in insurance, including cyber risk, agricultural risk, and long-term care insurance Stochastic modeling for mortality, disability, and financial markets Application of econometric and statistical methods to insurance and finance problems His research is characterized by the use of advanced quantitative techniques to address complex challenges in the insurance industry. Analysis of his recent publications (2020-2025) reveals a consistent focus on actuarial applications, particularly in the areas of claims reserving, Solvency II compliance, and emerging risks. He has made significant contributions to the understanding of cyber risk insurance and weather index-based insurance for agriculture. His work often involves collaboration with colleagues from the University of Calabria and other institutions. No scientific awards were mentioned in the provided text. Details about Marco Pirra's advisees and research grants were not provided in the available information. He is a member of the research group "Metodi quantitativi per l'economia, la finanza ed il management" (Quantitative Methods for Economics, Finance and Management) at the DESF department. This group conducts research using mathematical programming, econometric techniques, and statistical methods. Additionally, he is associated with the "Finance & Insurance Atelier" laboratory, which focuses on research in finance and insurance.