جست‌وجوی استاد و پژوهشگر

Dave Bennettمشاهده پروفایل

استاد

• Geographic Information Science (GIScience)
• Environmental Decision-Making
• Human-Environment Interactions
• +۱۲ مورد دیگر

Dave Bennett is a Professor in the Department of Geography at the University of Iowa, within the College of Liberal Arts and Sciences. His work bridges geographic information science, environmental policy, and complex systems theory, with a strong emphasis on interdisciplinary research. He is actively involved in teaching and mentoring graduate students, offering courses in GIS, field methods, and environmental applications. Research Interests: His research lies at the intersection of technology, policy, and science, focusing on geographic information science (GIScience) and environmental decision-making. He is particularly interested in human-environment interactions and how complex, nonlinear responses emerge from system interactions. Much of his work is framed within complexity theory and employs agent-based modeling, evolutionary algorithms, and cyberinfrastructure to study land use change, watershed management, and disaster response. Publication Trends: His most recent publications (2010–2016) demonstrate a consistent focus on agent-based modeling, parallel computing, land use dynamics, and socio-environmental systems. These works often integrate high-performance computing and service-oriented architectures to simulate complex spatial processes. Key themes include resilience, opinion diffusion, mobility, and provenance in geosimulation. Scientific Awards: No specific awards or fellowships are mentioned in the provided text. Advising and Grants: Dave Bennett has advised numerous PhD and Master’s students, including current advisees Patrick Bitterman, Haoyi Xiong, Shuang Xu, and James Madden. He has served as principal investigator or senior personnel on multiple major grants from the NSF, USDA, NIH, and NGA, totaling millions of dollars. Key projects include 'People, Water, and Climate,' 'Geoinformatics for Environmental and Energy Modeling and Prediction (GEEMaP),' and 'Understanding Water-Human Dynamics with Intelligent Digital Watersheds.' Labs and Teams: While no specific lab name is mentioned, his research is conducted within multidisciplinary teams and leverages cyberinfrastructure platforms at the University of Iowa. His work often involves collaboration with engineers, ecologists, and social scientists, particularly through NSF-funded interdisciplinary programs.

• Earthquake Engineering
• Structural Dynamics
• Steel Structures
• +۱ مورد دیگر

Dr. David Roke is an Associate Professor in the Department of Civil Engineering at the University of Akron's College of Engineering and Polymer Science. A member of the American Society of Civil Engineers (ASCE) and Chi Epsilon, Dr. Roke was honored with the 2019 Excellence in Teaching Award from The National Society of Leadership and Success (Sigma Alpha Pi). His research program focuses on earthquake engineering and structural resilience, with particular emphasis on developing advanced structural systems for seismic resistance. Key research areas include self-centering structural systems, buckling-restrained elements, soil-structure interaction effects, and life-cycle cost analysis of resilient infrastructure. His investigations employ experimental testing combined with computational methods including evolutionary algorithms, sensitivity analysis, and machine learning techniques. Dr. Roke's recent publications demonstrate significant contributions to economical seismic-resistant design, with innovative work on self-centering concentrically braced frames and rocking core systems. His research integrates structural performance assessment with economic feasibility studies to advance practical applications in earthquake engineering. His educational background includes a Ph.D. in Structural Engineering from Lehigh University, an M.S. in Civil Engineering from University of Pittsburgh, and a B.S. in Civil Engineering from University of Pittsburgh.

Steven O. Kimbroughمشاهده پروفایل

استاد

• Artificial Intelligence
• Computational Rationality
• Decision Support Systems
• +۶ مورد دیگر

Steven O. Kimbrough is a Professor of Operations, Information and Decisions at the Wharton School, University of Pennsylvania. His research spans artificial intelligence, computational rationality, and strategic optimization with applications to political science, economics, and service innovation. He teaches courses like Agents, Games, and Evolution and Thinking With Models , focusing on experimental approaches to bounded rationality and uncertainty in decision-making. Primary Email: kimbrough@wharton.upenn.edu Office: 3730 Walnut Street, 565 Jon M. Huntsman Hall, Philadelphia, PA 19104 His research interests include: Artificial intelligence and metaheuristics for constrained optimization Evolutionary computation in electoral redistricting Agent-based modeling of market dynamics Logic modeling for normative reasoning Text mining applications in event analysis Publications demonstrate expertise in computational economics, political modeling, and service analytics. Recent work focuses on: Empirical validation of electoral compactness Strategic learning in oligopolies Multi-objective matching algorithms Feasible-infeasible solution spaces Service network optimization Teaching emphasizes: Game-theoretic approaches to strategic behavior Modeling life-cycle for energy sustainability Computational experiments in social science

Kiril Kuzminمشاهده پروفایل

مدرس

• Bioinformatics
• Machine Learning
• Discrete Optimization
• +۳ مورد دیگر

Dr. Kiril Kuzmin is a Lecturer in the Department of Computer Science at Georgia State University, where he teaches Data Structures, Algorithms, Data Science, and Machine Learning. He holds a Ph.D. in Computer Science (2024) with a concentration in Bioinformatics from Georgia State University and a Ph.D. in Mathematics (2009) from the National Academy of Sciences of Belarus. His academic journey includes roles as Assistant and Associate Professor at Belarusian State University and a postdoctoral fellowship at the University of Turku, Finland. Dr. Kuzmin’s research focuses on Bioinformatics, Machine Learning, Discrete Optimization, and Graph Theory. He has published over 50 papers, with notable contributions in stability analysis of combinatorial optimization problems and applications of machine learning in genomics. His work includes predicting host specificity of coronaviruses and developing algorithms for heterogeneous genomic population analysis. Dr. Kuzmin has received the Scopus Award in Mathematics (2013) and served as PI/co-PI on three international projects. His teaching spans Java programming, Data Structures, and advanced mathematical courses like Calculus and Algebra. He is affiliated with Georgia State’s bioinformatics research group and has actively contributed to academic and political advocacy in Belarus.

Daniel Tauritzمشاهده پروفایل

استاد

• Artificial Intelligence for National Security
• Cyber Security
• Critical Infrastructure Protection
• +۵ مورد دیگر

Daniel Tauritz is a Professor in the Department of Computer Science and Software Engineering at Auburn University and serves as Director for National Laboratory Relationships. He holds a Ph.D. and M.S. in Computer Science from Leiden University, along with propaedeutic studies in Computer Science and Mathematics. His research focuses on AI-driven cybersecurity, automated algorithm design using hyper-heuristics, computational game theory, and evolutionary computation. Tauritz leads initiatives such as the Auburn Cyber Research Center and collaborates with institutions like Los Alamos National Laboratory on critical infrastructure protection and satellite network security. His work bridges academia and national security through projects like the Cyber Fire Puzzles competition and the Satellite Tycoon economic simulation game. His educational background includes advanced studies at Leiden University, with a strong foundation in computer science and mathematics. Research contributions span evolutionary algorithms for molecular evolution, coevolutionary defense strategies, and generative hyper-heuristics. Tauritz has secured grants including an NSF award for AI-cybersecurity education and contributed to Auburn’s partnerships with national laboratories. He is actively involved in fostering student engagement through ethical hacking clubs and experiential learning programs. Notable achievements include moderating panels on AI in cybersecurity and AI workforce development, as well as developing frameworks like Galaxy for network emulation and DCAFE for automated cyber experiments. His publications emphasize applying evolutionary computation to real-world challenges, including satellite constellation economics and adversarial network defense strategies.

• Ecohydrology
• Water Quality Management
• Environmental Impact Assessment
• +۶ مورد دیگر

Dr. Pouyan Nejadhashemi is an MSU Research Foundation Professor in the Departments of Plant, Soil and Microbial Sciences and Biosystems & Agricultural Engineering at Michigan State University. He serves as Director of the MSU Institute of Water Research and the Center for Intelligent Water Resources Engineering. His expertise spans ecohydrology, climate change adaptation, environmental impact assessment, and decision support systems for ecosystem sustainability. He holds a Ph.D. in Biological Resources Engineering from the University of Maryland. Dr. Nejadhashemi's research focuses on water quality modeling, non-point source pollution prevention, and integrating machine learning with hydrological systems. Key areas include irrigation optimization, PFAS contamination monitoring, and watershed management. He teaches courses on water resources systems analysis and ecohydrology. Awards: MSU Research Foundation Professor Title Grants: FAA grant for PFAS remediation, grants for developing agricultural innovations in Senegal and Bangladesh Labs/Teams: MSU Institute of Water Research, Center for Intelligent Water Resources Engineering His work bridges computational methods with environmental challenges, addressing global water security, food systems, and sustainable resource management. Recent projects include modeling water quality in the Chesapeake Bay and developing AI tools for agricultural extension platforms.

Marta Zanioloمشاهده پروفایل

استادیار

• Water Resources Planning and Management
• Drought Modeling
• Climate Change Adaptation
• +۵ مورد دیگر

Marta Zaniolo serves as an Assistant Professor in Civil and Environmental Engineering at Duke University since January 2024, addressing critical water sustainability challenges through integrated hydrology, machine learning, and systems modeling approaches. Her work focuses on ensuring equitable water access amid climate change, scarcity, and competing demands across diverse spatial scales. Her academic foundation includes: B.S. in Environmental Engineering from Politecnico di Milano (2014) M.S. in Environmental Engineering from Politecnico di Milano (2017) Ph.D. in Information Technology (Environmental Intelligence Lab) from Politecnico di Milano (2020) Postdoctoral Research at Stanford University (2020-2023) Dr. Zaniolo's research integrates water resources planning , drought modeling , and climate adaptation with cutting-edge machine learning techniques including reinforcement learning and feature extraction. The ZEDD Lab develops computational models that synthesize climate, hydrological, and socio-economic data to enhance decision-making for water security. Her work emphasizes real-world applicability through stakeholder engagement and participatory design, particularly in vulnerable regions like the Omo-Turkana basin where mismanaged infrastructure operations can trigger socio-ecological conflicts. Analysis of her 2021-2025 publications reveals three dominant research trajectories: (1) development of synthetic weather generators (e.g., FIND model) for precise drought characterization, (2) neuro-evolutionary algorithms for multi-objective reservoir control under climate uncertainty, and (3) quantification of flexibility value in water infrastructure planning. These studies consistently bridge technical innovation with equity considerations, particularly in transboundary water systems. She leads the Zaniolo Lab for Environmental Data and Decisions (ZEDD Lab), which operates at the intersection of hydrology, systems modeling, and machine learning. The lab's framework combines computational water system dynamics with stakeholder-inclusive approaches to develop decision support tools for extreme event preparedness. Current projects focus on urban drought resilience, dam reoperation strategies for climate extremes, and heat-substance use risk intersections in rural communities, all grounded in case studies spanning California, Ethiopia, and the American South.

Amlan Chatterjeeمشاهده پروفایل

دانشیار

• High-Performance Computing
• Big Data
• Graph Compression
• +۵ مورد دیگر

Dr. Amlan Chatterjee is an Associate Professor in the Department of Computer Science at California State University, Dominguez Hills. His work focuses on high-performance computing, big data analytics, and GPU-based graph compression techniques. He has held academic roles since 2015, including Assistant Professor (2015-2021) and currently serves as Associate Professor. His research explores efficient computation on large datasets using multi-core architectures and GPUs, alongside cloud computing optimization and IoT applications in aviation and health monitoring. Education: Ph.D., Computer Science, University of Oklahoma, 2014 M.S., Computer Science, State University of New York, 2009 B.Tech., Computer Science & Engineering, West Bengal University of Technology, 2007 Research Interests: Dr. Chatterjee's research spans graph compression, social network analysis, cloud resource optimization, and IoT-driven solutions for aviation safety and health monitoring. He has mentored students in projects like GPU-based big data processing and cloud computing efficiency. Awards: Graduate Computer Science Scholarship (University of Oklahoma, 2012-13) Computer Science Advisory Board Scholarship (2012) Phillips Petroleum Scholarship (2010-11) Top Undergraduate Rank (1st/68 students) Academic Contributions: He has served on numerous committees, including the Research Chair for Untenured Faculty (2016-17), IEEE conference session chairs, and accreditation boards. His teaching includes courses on data structures, operating systems, and introductory computer science.

Kalyanmoy Debمشاهده پروفایل

استاد

• Multi-Objective Optimization
• Evolutionary Algorithms
• Genetic Algorithms
• +۵ مورد دیگر

Kalyanmoy Deb is a Professor at the Department of Electrical and Computer Engineering, Michigan State University. Previously affiliated with Indian Institute of Technology Kanpur and Indian Institute of Technology Madras, he is a leading researcher in evolutionary algorithms and multi-objective optimization. Research Focus : Multi-Objective Optimization, Genetic Algorithms, Constraint Handling, Machine Learning-assisted Optimization Key Contributions : Development of NSGA (Non-dominated Sorting Genetic Algorithm) series, hybridization of machine learning with evolutionary algorithms, fidelity evaluation techniques Scientific Accomplishments : With over 347 publications and 19,618 citations, his work on NSGA algorithms has become foundational in evolutionary computation. His recent research explores mixed-fidelity evaluations, innovized knowledge extraction, and neural architecture search.

Jianjun Huمشاهده پروفایل

استاد

• Machine Learning
• Deep Learning
• Data Mining
• +۷ مورد دیگر

Jianjun Hu is a Professor in the Department of Computer Science and Engineering at the Molinaroli College of Engineering and Computing , University of South Carolina. His research focuses on machine learning, deep learning, data mining, and evolutionary computation applied to bioinformatics, material informatics, and health informatics . Current projects include developing deep learning algorithms for intelligent audio processing , data-driven materials discovery , and medical image analysis . Education: Ph.D. in Computer Science (2004), Michigan State University Postdoc at University of Southern California (2005-2007) and Purdue University (2004-2005) Research Empowerment: Secured funding from NSF, NIH, Nvidia , and the South Carolina Department of Transportation Developed materials informatics platforms like Materialsatlas.org Created algorithms for protein-peptide binding prediction and crystal structure discovery Scientific Contributions: Editorial roles at PLOS ONE and Journal of Health & Medical Informatics Key conference reviewer for GECCO and BIBM Academic Mentorship: PhD advisors: Ananda Mondal, Jhih-Rong Lin, Emrah Atigan, Swain Mrutyunjaya MS thesis advisors: Guoyu Lu, Emrah Atilgan, Kevin Chien, Mythri Sunkara Laboratory: Machine Learning and Evolution Group (MLEG) at University of South Carolina Focus on deep learning for materials discovery , healthcare analytics , and evolutionary algorithms

Philip K. McKinleyمشاهده پروفایل

استاد

• Evolutionary Robotics
• Autonomous Systems
• Cyber-Physical Systems
• +۳ مورد دیگر

Philip K. McKinley is a Professor in the Department of Computer Science and Engineering at Michigan State University's College of Engineering. He received his Ph.D. from the University of Illinois at Urbana-Champaign. His research focuses on enhancing the robustness and security of autonomous vehicles through evolutionary robotics and adaptive software systems. His EvoRally platform enables experimentation with scaled autonomous vehicles capable of high-speed operation in challenging environments. McKinley investigates evolutionary computation approaches to develop resilient systems capable of handling uncertain conditions. His work spans cyber-physical systems, digital twins, and learning-enabled components within the Robot Operating System (ROS) framework. He teaches courses in computer networks and operating systems, with publications exploring evolutionary robotics, adaptive middleware, and biologically-inspired system design.

Joseph Kasprzykمشاهده پروفایل

دانشیار

• Water Resources Planning
• Decision Making Under Uncertainty
• Multiobjective Evolutionary Algorithms
• +۳ مورد دیگر

Joseph Kasprzyk is an Associate Professor and Associate Chair for Graduate Education in the Department of Civil, Environmental and Architectural Engineering at the University of Colorado. He specializes in hydrology, water resources, and environmental fluid mechanics. His research focuses on decision making under uncertainty, multiobjective optimization, and scientific visualization in water systems planning. He holds a PhD (2013), MS (2009), and BS (2007) in Civil Engineering from Penn State University. Education: PhD in Civil Engineering, Penn State University, 2013 MS in Civil Engineering, Penn State University, 2009 BS in Civil Engineering with Honors, Penn State University, 2007 Research Interests: Optimization of water resources systems Climate change adaptation strategies Participatory decision support frameworks Multiobjective evolutionary algorithms Model diagnostics and visualization Key Contributions: Developed the Many Objective Robust Decision Making (MORDM) framework Pioneered participatory tools like ParFAIT for decision support Advances in linking water-energy systems through optimization Scientific Awards and Recognition: EPA STAR Graduate Fellowship (2010-2013) Outstanding Reviewer for ASCE Journal of Water Resources Planning and Management Invited presenter at SIAM Computational Science conferences Professional Engagement: Member of ASCE Environmental Water Resources Institute Reviewer for major journals including Environmental Modelling and Software Active in curriculum development for water systems analysis Labs and Teams: Involved in interdisciplinary water resources research groups, including collaborations with organizations like NOAA and the NSF-funded projects on robust decision making in infrastructure systems.

Jacob Schrumمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Evolutionary Computation
• Neural Networks
• +۴ مورد دیگر

Dr. Jacob Schrum is an Associate Professor of Mathematics and Computer Science at Southwestern University. He specializes in Evolutionary Computation applied to video games and AI, focusing on neuroevolution, procedural content generation, and multiobjective optimization. His work includes winning the BotPrize 2012 competition for human-like game bots and advancing AI in games like Ms. Pac-Man and Super Mario Bros. Education: PhD in Computer Science (UT Austin, 2014), MS (UT Austin, 2009), BS (Southwestern U, 2006) Research: AI behavior evolution, generative adversarial networks (GANs), and interactive evolution of artistic content Notable achievements include first-place in the 2018 Ms. Pac-Man vs. Ghosts competition and multiple best-paper awards at GECCO and CEC conferences. He mentors undergraduate research through SCOPE, publishing with students on topics like Tetris AI and Mario level generation. Scientific Awards: BotPrize 2012 Grand Prize 2018 Ms. Pac-Man Competition First Place UTCS Teaching Excellence Award (2012) His work spans game AI, artistic GANs, and educational outreach. Current projects involve evolving Minecraft structures and text-to-level diffusion models. He actively contributes to ACM SIGEVO/SIGCSE and IEEE Computational Intelligence Society.

Prabhat Hajelaمشاهده پروفایل

استاد

• Aerospace Engineering
• Structural Optimization
• Multidisciplinary Design
• +۷ مورد دیگر

Prabhat Hajela is the Edward P. Hamilton Professor of Aerospace Engineering at Rensselaer Polytechnic Institute (RPI). He has held leadership roles including Provost and Vice Provost, overseeing academic strategy and undergraduate programs. His academic journey includes degrees from the Indian Institute of Technology Kanpur (B.Tech, 1977), Iowa State University (MS, 1979), and Stanford University (MS and PhD, 1981–1982). He joined RPI in 1990 and became a full professor in 1992. His research focuses on structural and multidisciplinary optimization, with applications in aerospace systems, composite materials, and uncertainty modeling. Notable contributions include over 300 publications and four co-authored books. Hajela’s administrative leadership includes expanding undergraduate research programs, international collaborations, and the CLASS initiative. He is a Fellow of AIAA, ASME, and AeSI, and received the AIAA Biennial Multidisciplinary Design Optimization Award in 2004. Education: B.Tech (Aeronautical Engineering), Indian Institute of Technology, Kanpur (1977) MS (Aerospace Engineering), Iowa State University (1979) MS (Mechanical Engineering), Stanford University (1981) PhD (Aeronautics and Astronautics), Stanford University (1982) Research Interests: Hajela’s work spans structural optimization, multidisciplinary design, computational mechanics, and aerospace systems. He pioneered methods for reliability-based design, uncertainty quantification, and evolutionary algorithms. His research addresses challenges in composite materials, aeroelasticity, and the integration of artificial intelligence in engineering design. Recent projects include virtual reality in STEM education and adaptive systems for Mars missions. Key Contributions: Developed EVOLVE, a genetic search-based optimization code Advanced methodologies for multiscale composite analysis and failure prediction Contributed to CAN-SPAM legislation as a Congressional Fellow (2003) Chaired ASME’s Aerospace Division and served on National Academies panels Awards and Recognition: AIAA Biennial Multidisciplinary Design Optimization Award (2004) Fellowships: AIAA, ASME, and AeSI Recipient of the Boeing-A.D. Welliver Fellowship (1995) Administrative Impact: Hajela led RPI’s academic innovation, including undergraduate research expansion and the implementation of living-learning communities. His focus on global education and interdisciplinary collaboration has shaped RPI’s strategic vision. Current roles include chairing the NRC Technical Assessment Board for the Army Research Laboratory.

• Natural Language Processing
• Machine Learning
• Biomedical Informatics
• +۸ مورد دیگر

Feifan Liu is an Associate Professor at UMass Chan Medical School with appointments in Population and Quantitative Health Sciences, Radiology, and Health Informatics and Implementation Science. Holding a PhD in Computer Science from the Chinese Academy of Sciences and postdoctoral training in Machine Learning at the University of Texas at Dallas, he founded the AI for Health (iAI4Health) Lab , focusing on AI-driven healthcare analytics. Education: BS & MS in Electrical Engineering - Shandong University PhD in Computer Science - Chinese Academy of Sciences Postdoc in Machine Learning - University of Texas at Dallas His research bridges Natural Language Processing with Biomedical Informatics , concentrating on suicide risk prediction, HIV prevention, cancer informatics, and cardiometabolic disease management using Deep Learning and Interpretable AI . He develops computational frameworks for adverse drug reaction detection, radiology report analysis, and health equity assessment. Recent work focuses on Health Equity Algorithms and Multi-site Validation through the OHDSI network. His 15 most recent publications (2019-2025) span topics like Medical Visual QA Systems , Gene Mutation Extraction , Citation Network Analysis , and Pharmacovigilance , with keywords indicating cross-disciplinary work in AI, Data Science, and Clinical Research. Scientific Awards: NIH AIM-AHEAD Leadership Fellow (2022) International Challenge Winner (Medical QA 2018, Gene Relation Extraction 2019) As PI/MPI on multiple NIH grants (R01, R21, R34), he leads projects on suicide risk algorithms, PrEP care prediction, and diagnostic uncertainty measurement. The iAI4Health Lab under his direction explores AI applications for clinical decision support and population health analytics.