جست‌وجوی استاد و پژوهشگر

Mohammad Dehghaniمشاهده پروفایل

استاد آموزشی

• Reinforcement Learning
• Simulation Optimization
• Healthcare Operations
• +۵ مورد دیگر

Mohammad Dehghani is an Associate Teaching Professor in the Department of Mechanical and Industrial Engineering at Northeastern University, where he also serves as Program Director of the Galante Engineering Business Program. He holds a Ph.D. in Engineering Management from Western New England University (2016), an M.S. in Industrial Engineering from Tarbiat Modares University (2011), and a B.S. in Industrial Engineering from Yazd University (2008). His research focuses on Reinforcement Learning (RL), Simulation Optimization, and Healthcare Operations, with applications in manufacturing, digital twin systems, and UAV routing. He has developed multiple courses in Industrial Engineering and Data Analytics, receiving the 2020 Fostering Engineering Innovation in Education Award and the 2025 DAIS Data Analytics Teaching Award. Education: Ph.D. in Engineering Management, Western New England University, 2016 M.S. in Industrial Engineering, Tarbiat Modares University, 2011 B.S. in Industrial Engineering, Yazd University, 2008 Dehghani’s research bridges AI and operations research, emphasizing practical applications. His work includes developing RL frameworks for manufacturing scheduling and UAV routing, as well as simulation-optimization models for healthcare and pandemic preparedness. He has collaborated on projects addressing supply chain resilience during the COVID-19 pandemic and multi-objective supplier selection processes. His publications span journals like Simulation and conferences such as Winter Simulation Conference (WSC). His honors include the 2015 Best Ph.D. Paper Award at WSC and recognition from the Institute of Industrial and Systems Engineers (IISE). He actively contributes to professional societies, including the American Society of Engineering Management and Institute of Industrial Engineers. His teaching focuses on integrating data analytics and simulation tools into engineering curricula, with courses emphasizing Python integration, simheuristics, and digital twin technology. Dehghani leads initiatives in the Galante Program to enhance engineering-business synergies, preparing students for industry roles through interdisciplinary training. His work emphasizes practical problem-solving, with grants supporting projects in healthcare logistics and sustainable construction in cold climates.

Xiaoqiang Wangمشاهده پروفایل

استاد

• Numerical Analysis
• Applied Partial Differential Equations
• Mathematical Biology
• +۶ مورد دیگر

Xiaoqiang Wang is a Professor in the Department of Scientific Computing at Florida State University (FSU). His research focuses on numerical analysis, applied partial differential equations, mathematical biology, image processing, and scientific computing. He holds a Ph.D. from Pennsylvania State University (2005). His work emphasizes phase-field modeling for elastic bending energy, biological microstructures, and computational methods for complex systems. Notable contributions include advancements in centroidal Voronoi tessellation algorithms for image segmentation and high-performance computing techniques for scientific visualization. Recent publications highlight innovations in topology-preserving phase-field models, neural network-based energy minimization, and stochastic resource competition models. His research bridges theoretical mathematics with practical applications in biophysics, materials science, and biomedical engineering. Wang collaborates actively with interdisciplinary teams, contributing to FSU's computational science initiatives. His lab focuses on developing novel numerical methods and simulations for biological and physical systems, reflecting a commitment to both foundational and applied research.

• Higher Education Didactics
• Mathematics Education
• Digital Learning Tools
• +۳ مورد دیگر

Eva Glasmachers is a Lecturer and Managing Director of the Faculty of Mathematics at Ruhr University Bochum. She holds a PhD in Differential Geometry (2010) and has been actively involved in academic leadership since 2008. Her roles include student advising, curriculum development, and managing faculty operations. Her research focuses on university-level mathematics education, emphasizing interactive teaching methods, digital learning tools (e.g., STACK), and student motivation strategies. She leads projects like VORsprung for digital study preparation and trains tutorial group instructors. She is a founding member of HDM@RUB (Center for Higher Mathematics Education) and serves on the Ruhr University Senate and the Excellence Network for Teaching. Notable publications include works on gamification in education, adaptive learning systems, and dropout prevention strategies. She collaborates extensively with educational technology centers and contributes to national didactics conferences.

Fiona M. Scott Mortonمشاهده پروفایل

استاد

• Industrial Organization
• Antitrust Policy
• Competition Policy
• +۵ مورد دیگر

Fiona M. Scott Morton is the Theodore Nierenberg Professor of Economics at the Yale University School of Management, where she has been a faculty member since 1999. She also holds appointments as Professor in the Health Policy & Management department and as an Adjunct Professor at Yale Law School. Her primary academic home is the School of Management, where she serves as a full Professor of Economics. Professor Scott Morton earned her BA in Economics from Yale University in 1989 and her PhD in Economics from the Massachusetts Institute of Technology in 1994. Her educational background has provided a strong foundation for her work at the intersection of economics, law, and public policy. Her research focuses on industrial organization, with particular expertise in competition policy and antitrust enforcement. Professor Scott Morton has become a leading voice on competition issues in digital markets and healthcare, with her work frequently cited by policymakers and practitioners. She has pioneered research on the economic implications of market concentration, platform regulation, and healthcare pricing dynamics. Her work bridges theoretical economic analysis with practical policy applications, making significant contributions to both academic discourse and real-world competition policy. A review of her recent publications reveals a strong focus on digital market regulation, particularly the implementation and enforcement of the European Union's Digital Markets Act. She has also continued her groundbreaking work on healthcare markets, examining issues like hospital acquisitions of physician practices and surprise medical billing. Her research demonstrates a consistent application of industrial organization theory to contemporary policy challenges across multiple sectors. Professor Scott Morton has received numerous accolades for her work, including: Yale School of Management Faculty Teaching Excellence Award (2020) Yale SOM Alumni Association Teaching Award (2015-16, 2006-07) Health Care Research Award for "The Effect of Medicare Part D on Pharmaceutical Prices and Utilization" (2011) National Science Foundation Research Grants (multiple awards) As Associate Dean from 2007-10 and through her founding of the Thurman Arnold Project at Yale, Professor Scott Morton has played a significant role in shaping competition policy education and discourse. Her government service as Deputy Assistant Attorney General for Economic Analysis at the U.S. Department of Justice (2011-12) provided practical experience that informs her academic work. She has secured substantial research funding, including grants from the Washington Center for Equitable Growth and multiple National Science Foundation awards. Professor Scott Morton directs the Thurman Arnold Project at Yale, which serves as a hub for competition policy research, education, and engagement. Through this initiative, she connects academic research with practical policy challenges, fostering dialogue between scholars, practitioners, and policymakers in the antitrust community.

Zhang Yi-Chengمشاهده پروفایل

استاد

• Econophysics
• Information economics
• Statistical physics
• +۲ مورد دیگر

Zhang Yi-Cheng is a Full Professor of Theoretical Physics at the University of Fribourg, Switzerland, since 1992. His academic career includes visiting professorships at Nordita (Denmark) and INFN (Italy), and postdoctoral research at Brookhaven National Lab (USA). He specializes in interdisciplinary fields such as Econophysics , Statistical physics , and Complex network sciences , focusing on applications in financial markets, social systems, and global trade networks. His research explores topics like market dynamics, network structures, and algorithmic ranking systems. Notable awards include the 2011 Honorary Director of the Complexity Sciences Research Center and recognition as a 2011 Chinese '1000 Talents' awardee . His work bridges physics-based methodologies with socio-economic systems, addressing challenges in information-driven economies and networked societies. Zhang has contributed to influential studies on ranking algorithms, percolation theory in networks, and the interplay between economic complexity and trade. His interdisciplinary approach has led to advancements in understanding systemic risks, market inefficiencies, and the role of information in shaping global economic interactions.

Sara Ahmadianمشاهده پروفایل

پژوهشگر

• Optimization Algorithms
• Machine Learning
• Big Data Analysis
• +۵ مورد دیگر

Dr. Sara Ahmadian is a Researcher at the University of Waterloo's Department of Combinatorics and Optimization. She completed her Ph.D. in 2017 under the supervision of Prof. Chaitanya Swamy, earning the 2017 University of Waterloo Outstanding Achievement in Graduate Studies award. Her research focuses on designing efficient algorithms for optimization problems in machine learning and big data analysis, particularly in facility location and clustering. She has held visiting research positions at the University of Alberta, Hausdorff Research Institute for Mathematics, and École polytechnique fédérale de Lausanne. Education: Ph.D. in Combinatorics and Optimization, University of Waterloo (2017) Master's in Combinatorics and Optimization, University of Waterloo (2010) Bachelor's in Computer Engineering, Sharif University of Technology (2008) Research interests include approximation algorithms, online algorithms, and algorithmic game theory applied to clustering and facility location problems. Her work has led to advancements in k-means and k-median problems, with a notable improvement in the fundamental k-means algorithm. Scientific Awards: 2017 University of Waterloo Outstanding Achievement in Graduate Studies (Ph.D.) designation Advising and Grants: No specific advising or grant information is provided in the text. Labs/Teams: No specific lab or team affiliations mentioned.

• Human-Robot Interaction
• Social Robotics
• Group Dynamics
• +۳ مورد دیگر

Sarah Gillet is a Postdoctoral Researcher at the Division of Robotics, Perception, and Learning at KTH Royal Institute of Technology, where she focuses on developing social robot behaviors to foster collaboration and inclusion in human groups. Her research addresses challenges like in-group favoritism through computational approaches to shape group interactions. She holds a Doctoral Thesis (2024) titled Computational Approaches to Interaction-Shaping Robotics . Her work emphasizes group dynamics , robot-mediated inclusion , and pedagogical robotics , particularly in children and adolescent populations. Key areas include gaze behavior analysis, equitable participation promotion, and social robot roles such as mediators in educational settings. Dr. Gillet teaches the Social Robotics (DD2413) course and supervises master theses. Her recent publications explore robot gaze behaviors for participation balance, socially appropriate listening, and influence prediction models like RoSI. She actively participates in conferences like ACM/IEEE HRI and IEEE RO-MAN. Her research integrates computational methods with social science insights to design robots that actively improve human group interactions, with applications in education, collaboration, and bias mitigation.

• Quality Management
• Organizational Culture
• Agility
• +۶ مورد دیگر

André M. Carvalho is an Assistant Professor of Quality Engineering and Management at the NOVA School of Science and Technology, NOVA University Lisbon, Portugal. He holds a PhD (2020) in Engineering Design and Advanced Manufacturing from the University of Minho/MIT Portugal Program. His career includes postdoctoral research at the Technical University of Denmark (2020) and visiting roles at MIT (2018–2020) and Northeastern University (2019). His research focuses on sociotechnical systems, quality management, organizational culture, agility, and sustainability. He explores how organizations adapt to global challenges through methodologies like Lean Six Sigma and Industry 4.0 frameworks. Education: PhD in Engineering Design and Advanced Manufacturing (2020) – University of Minho/MIT Portugal Program Research Interests: Carvalho’s work bridges quality management with organizational agility, emphasizing digital transformation, Industry 4.0/5.0, and sustainable practices. He investigates frameworks for operational excellence, such as the Quality 4.0 Roadmap and the QOE-SME model for small/medium enterprises. His studies address challenges like supply chain quality, data-driven decision-making, and organizational culture’s role in innovation. Article Trends: His recent publications (2022–2025) highlight themes like Lean Six Sigma applications in healthcare, AI-driven quality forecasting, and Industry 4.0 integration. He also explores ESG factors in performance optimization and the evolving profile of quality leaders in the digital era. Awards and Recognition: Acknowledged by the Industrial Engineering and Operations Management (IEOM) Society, International Academy for Quality (IAQ), and American Society for Quality (ASQ) for research contributions. Grants and Advising: While specific grants are not detailed, his postdoctoral and visiting roles suggest institutional support. Advising focuses on doctoral/master’s students (not listed explicitly in the text).

Qihe Tangمشاهده پروفایل

استاد

• Insurance
• Finance
• Risk Management
• +۴ مورد دیگر

Professor Qihe Tang is a SHARP Professor at UNSW Business School's School of Risk and Actuarial Studies since 2017, where he also serves as Co-Director of the Innovations in Risk, Insurance and Superannuation (IRIS) Knowledge Hub. Previously, he was a Full Professor and held the F. Wendell Miller Endowed Chair at the University of Iowa (2006-2019), following academic appointments at the University of Hong Kong, University of Amsterdam, and Concordia University. Dr. Tang earned his PhD in Statistics from the University of Science and Technology of China in 2001. His research expertise centers on extreme value theory for insurance, finance, and quantitative risk management, with current focus areas including catastrophe risk modeling, systemic risk in financial networks, decision making under uncertainty, pricing in incomplete markets, and climate change impacts on insurance. His recent scholarly output demonstrates significant contributions to tail risk analysis, risk measurement methodologies, and insurance-linked securities pricing. The articles reveal a strong emphasis on extreme events, systemic risk propagation, and innovative approaches to quantifying and managing catastrophic risks in increasingly complex financial and environmental contexts. Elected Member of the International Statistical Institute (ISI) SHARP Professor at UNSW Sydney F. Wendell Miller Endowed Chair at University of Iowa Professor Tang has supervised over a dozen PhD students who now hold positions in academia and industry worldwide. He serves as Editor of Insurance: Mathematics and Economics and has been Principal Investigator for multiple major research grants including three ARC Discovery Projects, NSF funding, and several Society of Actuaries research grants. His research has achieved an H-index of 43 according to Google Scholar.

Dong Gongمشاهده پروفایل

مدرس ارشد

• Computer Vision
• Machine Learning
• Deep Learning
• +۳ مورد دیگر

Dr. Dong Gong is a Senior Lecturer and ARC DECRA Fellow (2023-2026) at the School of Computer Science and Engineering (CSE), UNSW. He holds an adjunct position at the Australian Institute for Machine Learning (AIML), University of Adelaide. His research focuses on machine learning challenges in dynamic environments, including continual learning, foundation models, generative models, and applications in interdisciplinary areas like mining and agriculture. Research interests include learning with non-ideal supervision, foundation model adaptation, generative models, and interdisciplinary problems combining CV/ML with domain-specific applications. His work often addresses real-world scenarios such as mineral exploration and soil trait analysis using CV/ML technologies. Outstanding Reviewer: NeurIPS 2018 Outstanding Area Chair: ACM MM 2024 ARC DECRA Fellowship (2023-2026) Advising and grants: Actively supervises PhD/MPhil students in computer vision and ML. Collaborates with industry and government on research projects. Utilizes advanced infrastructure like UNSW's Katana supercomputing cluster and Gadi (NCI). Labs/Teams: Involved in interdisciplinary research groups at UNSW CSE and AIML, focusing on dynamic learning paradigms and real-world applications of AI.

Jianbing (Jason) Liمشاهده پروفایل

استاد

• Environmental pollution control
• Petroleum waste management
• Soil and groundwater remediation
• +۳ مورد دیگر

Dr. Jianbing Li is a Professor and Professional Engineer (P.Eng.) in the Environmental Engineering Program at the University of Northern British Columbia (UNBC), holding prestigious fellowships from CSCE, CSSE, EIC, and Engineers Canada. His research program addresses critical environmental challenges with significant real-world impact, particularly in northern and remote communities of British Columbia. Education: PhD in Environmental Systems Engineering, University of Regina Research Focus: Dr. Li's work centers on environmental pollution control , petroleum waste management , soil and groundwater remediation , environmental modeling , risk assessment , and oil spill response . His innovative approaches integrate machine learning, advanced materials, and sustainable engineering principles to develop practical solutions for complex environmental problems, with particular emphasis on resource recovery from waste streams. Publication Trends: Analysis of his 15 most recent publications (2023-2025) reveals a strategic focus on oil spill response technologies, wastewater treatment innovations, and waste valorization. Key advancements include nano/micro bubble flotation systems, chitosan-based adsorbents, and machine learning models for pyrolysis optimization, demonstrating his leadership in translating laboratory research to field applications. Scientific Recognition: 2024 Fellow of Engineers Canada and Engineering Institute of Canada 2023 CSCE Dr. Albert E. Berry Medal (Canada's top environmental engineering award) Multiple UNBC Research Excellence Awards (2010, 2014, 2019, 2023) 2013 Northern BC Business and Technology Award with Husky Energy Best paper awards from International Academy of Science and Environmental Geotechnology Society Research Leadership: Dr. Li has secured over $800,000 in 2023 and $1.9 million in 2020 for oil spill response research through NSERC, DFO, and NRCan. His current portfolio includes groundwater protection for Indigenous communities, next-generation decanting technologies, and water security for remote regions. He actively mentors PhD, MSc, and MASc students while serving on NSERC evaluation committees and co-directing the UNBC/UBC environmental engineering program (2013-2017). Collaborative Networks: Dr. Li leads multi-institutional partnerships with UBC, government agencies, industry (including Husky Energy), and Indigenous communities like Lheidli T'enneh First Nation. His work through the Multi-Partner Research Initiative addresses practical challenges in rural British Columbia while advancing fundamental knowledge in environmental systems engineering.

• 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.

James M Martin-Haydenمشاهده پروفایل

دانشیار

• Hydrogeology
• Groundwater Modeling
• Environmental Geology
• +۳ مورد دیگر

James M Martin-Hayden serves as an Associate Professor in the Department of Earth, Ecological and Environmental Sciences within the College of Natural Sciences and Mathematics at the University of Toledo. With over 25 years of service at the institution, he has established himself as a prominent hydrogeologist specializing in groundwater systems and environmental geology. Education: B.A. from University of Maine, M.S. and Ph.D. from University of Connecticut (1994) Current Position: Associate Professor of Geology Department: Earth, Ecological and Environmental Sciences University: University of Toledo Dr. Martin-Hayden's research focuses on hydrogeology, with particular emphasis on groundwater-surface water interactions in wetland ecosystems, numerical groundwater modeling, and hydrogeologic field methods. His work primarily investigates the Oak Openings Region of northwest Ohio, examining how drainage modifications have altered natural groundwater flow regimes that support wet prairies. He has made significant contributions to understanding wellbore flow dynamics, groundwater sampling methodologies, and the impacts of evapotranspiration on groundwater recharge. His research integrates field observations, numerical modeling, and geophysical techniques to address complex hydrogeological problems. Analysis of Dr. Martin-Hayden's publication record reveals a consistent research trajectory focused on hydrogeological systems, particularly in the Great Lakes region. His work spans from fundamental investigations of wellbore flow dynamics to applied studies of regional aquifer systems and wetland hydrology. Recent publications demonstrate increasing integration of geophysical methods with traditional hydrogeological approaches, reflecting an evolution toward more comprehensive characterization of complex subsurface systems. His research shows particular strength in addressing the hydrological impacts of human modifications to natural systems, especially in wetland ecosystems. Dr. Martin-Hayden has mentored several students, including Lucas Groat (thesis on Hydrogeology of Wet Prairies) and Pryanka More (undergraduate thesis on Influences of ET on Groundwater Recharge). His collaborative work shows strong connections with researchers including Timothy G. Fisher, Kennedy Okioghene Doro, and Richard H. Becker, indicating active participation in research networks focused on Great Lakes geology and hydrogeology.

Amal Zouaqمشاهده پروفایل

استاد

• Artificial Intelligence
• Natural Language Processing
• Semantic Web
• +۵ مورد دیگر

Dr. Amal Zouaq is a Full Professor in the Department of Computer Engineering and Software Engineering at Polytechnique Montréal. She holds the FRQS (Dual) Chair in AI and Digital Health, serves as Director of the LAMA-WeST research laboratory, and is an Associate Member of MILA. Her work bridges artificial intelligence with applications in digital health, cultural heritage, and educational technologies, positioning her at the forefront of interdisciplinary AI research in Canada. Her research focuses on Artificial Intelligence , particularly Natural Language Processing and the Semantic Web . Specific interests include knowledge representation, ontology learning, SPARQL query generation, bias mitigation in language models, and clinical text processing. Her work spans multiple domains including healthcare, cultural heritage, and educational technology, with emphasis on developing practical AI solutions that address real-world challenges in knowledge management and information extraction. Analysis of her recent publications reveals a strong trajectory in advancing NLP techniques for knowledge-intensive applications. Her work increasingly focuses on domain-specific applications in healthcare and cultural heritage, with growing emphasis on ethical AI considerations like bias mitigation. The research demonstrates progression from foundational semantic web technologies toward more sophisticated neural approaches while maintaining strong theoretical grounding in knowledge representation. Scientific Recognition: Holder of the FRQS (Dual) Chair in AI and Digital Health Dr. Zouaq has supervised 23 graduate students to completion, including 1 PhD and 22 Master's theses, with research spanning ontology learning, knowledge representation, and NLP applications. Her supervision record demonstrates consistent mentorship in cutting-edge AI research with practical applications across multiple domains. She actively serves on program committees for major conferences in knowledge engineering, data mining, and semantic web technologies. She directs the LAMA-WeST (Web, Semantics and Text) laboratory , which specializes in natural language processing and artificial intelligence research. The lab focuses on knowledge representation, semantic technologies, and their applications in healthcare, cultural heritage, and educational contexts. As a member of IVADO and MILA, she collaborates with leading AI researchers across Montreal's vibrant AI ecosystem.

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.