جست‌وجوی استاد و پژوهشگر

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

• Machine Learning
• Control Systems
• Robotics
• +۳ مورد دیگر

Sebastian Trimpe is a Full Professor and Head of the Institute for Data Science in Mechanical Engineering at RWTH Aachen University, concurrently serving as Co-Executive Director of the RWTH Center for Artificial Intelligence since 2023. Previously, he led a Max Planck Research Group at the Max Planck Institute for Intelligent Systems from 2018 to 2022. His educational background includes: Ph.D. in Dynamic Systems and Control from ETH Zurich (2013) Dipl.-Ing. (M.Sc.) in Electrical Engineering from TU Hamburg (2007) MBA in Technology Management from TU Hamburg (2007) B.Sc. in General Engineering from TU Hamburg (2005) Professor Trimpe's research integrates machine learning with control theory to address safety and efficiency challenges in autonomous systems. His work spans theoretical frameworks for robust decision-making under uncertainty and practical implementations in robotics, with particular emphasis on event-triggered control, distributed systems, and data-efficient learning methodologies. Key contributions include novel approaches to safe reinforcement learning and model predictive control with guaranteed stability. Analysis of his recent publications reveals a pronounced focus on bridging machine learning with control engineering, especially in safety-critical robotics applications. Common themes include distribution-aware learning for medical diagnostics, diffusion-based control approximation, and hardware-in-the-loop validation of theoretical frameworks, demonstrating strong alignment between algorithmic innovation and real-world deployment. His scientific achievements have been recognized with prestigious honors: IFAC World Congress Interactive Paper Prize (2011) Klaus Tschira Award for public understanding of science (2014) Best Paper Award at International Conference on Cyber-Physical Systems (2019) Future Prize by Ewald Marquardt Stiftung (2020) As institutional leader, he directs the Institute for Data Science in Mechanical Engineering and co-leads the RWTH AI Center, overseeing strategic research initiatives and industry collaborations. His academic service includes editorial roles for IEEE Control Systems Society conferences and participation in the Cluster of Excellence 'Internet of Production'. The Institute for Data Science in Mechanical Engineering operates as a multidisciplinary hub where fundamental research in learning-based control meets industrial applications. Current projects focus on drone swarm coordination, deformable object manipulation, and medical diagnostics systems, leveraging both simulation environments and physical testbeds like the Mini Wheelbot platform.

• 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.

Kobus Barnardمشاهده پروفایل

استاد

• Computer Vision
• Machine Learning
• Scientific Applications
• +۳ مورد دیگر

Kobus Barnard is a Professor in the Department of Computer Science at the University of Arizona, with his office located in GS 708. His research bridges computer vision, machine learning, and interdisciplinary scientific applications across diverse domains. Education: Ph.D. from Simon Fraser University (1999) His research interests focus on extracting meaningful insights from complex data through computer vision and probabilistic modeling. Key areas include machine learning for environmental monitoring (flood detection, plant disease analysis), social dynamics (interpersonal coordination, emotional coregulation), astronomy (transient classification), and multimodal learning (visual-linguistic integration). His work consistently applies deep learning to real-world problems requiring high-resolution data interpretation. Analysis of his 2022-2025 publications reveals three dominant trends: (1) Environmental applications using satellite imagery for flood mapping and agricultural monitoring, (2) Cognitive modeling of human teams and emotional dynamics through probabilistic frameworks, and (3) Astronomical data analysis leveraging host galaxy properties for transient classification. These threads demonstrate his commitment to solving practical scientific challenges through computational innovation. While scientific awards aren't documented in available sources, his leadership in projects like FloodPlanet and ToMCAT indicates significant contributions to data infrastructure. His advising and grant activities remain unreported in the source material, though his extensive interdisciplinary collaborations suggest substantial mentorship impact. Barnard's work operates at the intersection of multiple scientific communities, evidenced by applications spanning neuroscience, agriculture, astronomy, and social science. His current focus on high-resolution data fusion and multimodal modeling positions him at the forefront of real-world AI deployment.

Seshadri Sravan Kumar V.مشاهده پروفایل

دانشیار

• Power Engineering
• Applied Mathematics
• Time and Frequency Domain Modeling
• +۸ مورد دیگر

Dr. V. Seshadri Sravan Kumar is an Associate Professor in the Department of Electrical Engineering at the Indian Institute of Technology Hyderabad. His research spans Power Engineering and Applied Mathematics, with a focus on Time and Frequency Domain Modeling, Electromagnetic Transients, Phasor and Frequency Estimation, and Machine Learning applications in power systems. Ph.D. and M.Sc.(Engg.) from IISc Bangalore Current research trends include Battery-Ultracapacitor Integration, Electric Vehicle Power Systems, and Non-ideal Component Modeling His group has contributed to 15 recent publications in journals like IEEE Transactions on Vehicular Technology, Renewable Energy (Elsevier), and conferences including IEEE PES General Meeting and PEDES. Key awards include the 2021 IIT Hyderabad Teaching Excellence Award and 2016 POSOCO Power System Award. Students mentored include Ph.D. candidates Sai Vinay Kishore N and alumni such as Anirudh C V S and Naresh Palla. He teaches graduate-level courses like Matrix Theory, Electrical Machine Analysis, and Modeling of Electromagnetic Transients.