جست‌وجوی استاد و پژوهشگر

Muhammad M. Sherifمشاهده پروفایل

استادیار

• Smart Materials
• Structural Systems
• Machine Learning Applications
• +۴ مورد دیگر

Dr. Muhammad M. Sherif is an Assistant Professor in the Department of Civil, Construction, and Environmental Engineering at the University of Alabama at Birmingham (UAB), part of the School of Engineering. He joined UAB in Fall 2019 after completing his Ph.D. at the University of Virginia and M.S. at Carnegie Mellon University, both in structural engineering. His research focuses on smart materials, structural systems, and machine learning applications in civil infrastructure. He is particularly interested in additive manufacturing for construction and the development of innovative materials like engineered cementitious composites. Education: B.S., United Arab Emirates University M.S., Carnegie Mellon University Ph.D., University of Virginia Research Interests: Dr. Sherif’s work spans material characterization, machine learning models for structural analysis, and the integration of advanced materials into infrastructure systems. He explores topics like crack detection using UAVs, superelastic shape memory alloys, and multi-objective optimization in welding processes. His Advanced Materials and Smart Infrastructure Systems (AMSIS) lab emphasizes interdisciplinary approaches to solving civil engineering challenges. Publications: His recent work includes studies on UAV-based pavement crack detection, machine learning for concrete strength prediction, and optimization of tube-to-tubesheet joints. These reflect trends toward AI-driven solutions and sustainable material innovations. Advising: He actively mentors students in multidisciplinary research projects, emphasizing self-motivation and innovation. His lab collaborates on topics like composite materials, structural health monitoring, and infrastructure resilience. Labs/Teams: His AMSIS lab at UAB focuses on advancing smart materials and infrastructure systems through cutting-edge research and collaboration.

• Computational Neuroscience
• Neural Networks
• Machine Learning

Christodoulou Chris is a Professor at the Department of Computer Science, University of Cyprus. He joined in 2005 and holds a Visiting Research Fellowship at Birkbeck College, University of London. His educational background includes a BEng in Electronic Engineering from Queen Mary and Westfield College (1991), a PhD in Neural Networks from King's College London (1997), and a BA in German from Birkbeck College (2008). His research focuses on Computational Neuroscience, Neural Networks, and Machine Learning, with specific interests in neural coding, self-control modeling, computational neuronal modeling, multi-agent reinforcement learning, and practical machine learning applications. Recent publications (2013-2025) demonstrate interdisciplinary approaches combining neuroscience, computer science, and optimization techniques, with emerging emphasis on protein structure prediction and biomedical applications. Scientific awards include: Best Paper Award, ICSR 2015 Best Paper Award, MODELS 2008 Doctoral Symposium He leads the Computational Intelligence and Neuroscience (CIN) research group and has secured funding from European projects (SocioCoast, CYberSafety, TAMIT). He mentors students through Google Summer of Code and collaborates internationally.

Mohamed Boulfizaمشاهده پروفایل

دانشیار

• Bridges
• Cement-based materials
• Computational mechanics
• +۸ مورد دیگر

Mohamed Boulfiza serves as an Associate Professor within the Department of Civil, Geological and Environmental Engineering at the University of Saskatchewan, where his work bridges structural engineering and materials science. His academic credentials include: B.E. from ENTP, Algeria M.A.Sc. from Laval University, Quebec Ph.D. from the University of British Columbia, British Columbia Specializing in infrastructure durability, Dr. Boulfiza's research investigates computational mechanics and constitutive modeling to predict degradation in concrete structures. His expertise spans damage mechanics, corrosion processes in reinforcing steels, and advanced material systems including fiber-reinforced polymers and high-performance concrete. A significant portion of his work focuses on service-life prediction methodologies for bridges and other critical reinforced concrete infrastructure, aiming to enhance structural longevity through innovative modeling of cement-based materials.

Tadanori Kogaمشاهده پروفایل

استادیار

• Green Energy
• Polymer Nanofabrication
• Supercritical Fluids
• +۵ مورد دیگر

Tadanori Koga is an Assistant Professor in the Department of Chemical and Molecular Engineering at Stony Brook University, where he has served since 2006. His research focuses on green energy technologies through polymer nanofabrication, chemical recycling of plastics, and methane hydrate energy resources, utilizing advanced in situ x-ray/neutron scattering techniques at premier facilities like NSLS, APS, and NIST. Education: Ph.D. in Polymer Physics (1998) and M.S./B.S. in Solid State Physics from Kyushu/Kumamoto Universities Honors: Visiting Professor at Kyushu University (2008), Visiting Scientist at Kyoto University (2004) Current projects include: Green nanofabrication using supercritical CO2 Chemical recycling of waste plastics 3D-printing polymer dynamics studies Nanoscale control of methane hydrate formation His work has secured a $1.2M DOE grant for pathogen-preventing nanosurfaces. Recent publications emphasize polymer interface dynamics, self-assembly under stimuli, and scattering-based structural analysis. Collaborative efforts span Oak Ridge Leadership Computing Facility and Brookhaven National Laboratory.

Anthony Comerمشاهده پروفایل

دانشیار

• Composite Materials
• Thermoplastics
• Aerospace Engineering
• +۶ مورد دیگر

Anthony Comer is an Associate Professor at the University of Limerick, holding dual affiliations with the School of Engineering and the Bernal Institute. His research focuses on advanced composite materials, particularly in thermoplastic systems, bio-epoxy composites, and their applications in aerospace, marine, and renewable energy sectors. He leads studies on failure analysis, environmental degradation effects (hygrothermal aging), and structural performance under fatigue and impact loading. Comer's work emphasizes sustainable materials for offshore infrastructure and aircraft maintenance practices. He has authored 43 peer-reviewed articles, with recent contributions analyzing composite repair techniques, cure schedules for sustainable composites, and the mechanical properties of non-crimp fabrics. His research integrates experimental methods (e.g., SEM testing, digital image correlation) with numerical modeling to predict failure modes and optimize material performance. Key collaborations include studies on hydrothermal strength of carbon fiber laminates, automated manufacturing processes, and crowd-structure interaction in grandstands. Despite his extensive publications, no scientific awards are explicitly listed in the provided texts. His advisory role extends to graduate research in composite materials, though specific student names are not documented here. Labs/Teams: Active within the Bernal Institute's advanced materials research group, focusing on composites for energy and transport sectors.

Igor Molybogمشاهده پروفایل

استادیار

• Artificial Intelligence
• Large Language Models
• Multimodal Modeling
• +۵ مورد دیگر

Igor Molybog is an Assistant Professor at the University of Hawai'i at Manoa, holding joint appointments in the Departments of Electrical and Computer Engineering and Information and Computer Sciences. His research focuses on advancing artificial intelligence, particularly through large language models (LLMs), multimodal modeling, and core machine learning optimization. He leads the HawAII research group, exploring applications like LLM alignment, efficient inference systems, and scaling properties of foundation models. Education: Ph.D. in Engineering from UC Berkeley (2022), specializing in optimization algorithms for complex systems. Previously worked at Meta AI on LLaMa model development. Research Interests: Efficient LLM development and evaluation frameworks Multimodal AI integration (video/audio + text) Scalable optimization for large models Computational efficiency in training/ inference Recent Work: Presented REAL alignment method (2024), developed long-context scaling techniques (2023), contributed to Llama 2 chat models (2023). Collaborates with organizations like Epoch AI on scaling challenges. Teaching: Offers courses in AI, machine learning, and optimization across ECE and ICS departments. Labs/Teams: Leads HawAII Initiative fostering AI collaboration at UH Manoa, organizes paper reading seminars, and hosts technical talks with industry experts.

Saskia Kohlhaseمشاهده پروفایل

دانشیار

• Taxation
• International Business Taxation
• ESG Regulation
• +۳ مورد دیگر

Saskia Kohlhase is an Associate Professor at the Department of Accounting, Rotterdam School of Management (RSM), Erasmus University Rotterdam. She is an affiliated member of ERIM (Erasmus Research Institute of Management) since 2016. Her research focuses on taxation, ESG regulation, and corporate social responsibility reporting, particularly examining cross-border tax strategies, loss-firm tax incentives, and transfer pricing mechanisms. She teaches taxation and financial accounting at both undergraduate and master’s levels, coordinating courses like 'Taxation' and 'Financial and Tax Consulting'. Dr. Kohlhase holds a PhD from Vienna University of Economics and Business (2016), where her thesis earned the prestigious Nürnberger Steuergespräche Award. Her work has been published in top journals such as the Journal of Accounting and Economics and Journal of International Business Studies. She actively serves on editorial boards (e.g., Accounting and Business Research) and reviews for leading journals like The Accounting Review. Her research trends emphasize multinational corporate tax strategies, organizational structure impacts of taxation, and ESG regulatory implications. Articles frequently analyze transfer pricing, tax system comparisons, and loss firm behaviors. Beyond academia, she organizes the Dutch Workshop on Corporate Taxation and collaborates with institutions like Duke University. Her advisory work includes mentoring PhD students like Masoud Mohebbi and contributing to policy discussions through RSM Discovery publications. Key awards include the 2016 Nürnberger Steuergespräche Award for her doctoral research. Her collaborative efforts extend to editorial roles and peer-review activities across multiple journals and networks, reinforcing her influence in global accounting and taxation discourse.

Boqing Gongمشاهده پروفایل

استادیار

• Computer Vision
• Machine Learning
• Visual Recognition
• +۱۰ مورد دیگر

Boqing Gong is an Assistant Professor in the Department of Computer Science at Boston University. He concurrently serves as a part-time research scientist at Google. His research focuses on advancing visual recognition, video analysis, and the safety/generalization of AI models through novel algorithms in computer vision and machine learning. He holds editorial roles as an Associate Editor for IEEE TPAMI (since 2024) and TMLR. He has organized major conferences including co-chair roles for WACV 2023, CVPR 2022/2025 tutorials, and ICCV 2025 workshops. Education: Ph.D. Computer Science, University of Southern California (2011-2015) Visiting Graduate Student, University of Texas at Austin (Summer 2012) MPhil Information Engineering, Chinese University of Hong Kong (2008-2010) Bachelor's in Electronic Information Engineering, University of Science and Technology of China (2004-2008) Research Interests: Dr. Gong develops algorithms to understand objects, human activities, and scene relationships. His work emphasizes mathematical structures for effective and efficient solutions with strong analytical guarantees. Key areas include domain adaptation, long-tailed recognition, adversarial robustness, few-shot learning, and generative models. He explores intersections between language, vision, and reinforcement learning. Professional Contributions: Senior/Area Chair for CVPR, ICCV, ECCV, NeurIPS, ICML, ICLR, AISTATS, AAAI Outstanding Reviewer Awards (CVPR 2017/2021)

Edvard P.G. Bruunمشاهده پروفایل

استادیار

• Robotic Automation
• Computational Structural Design
• Material Circularity
• +۳ مورد دیگر

Dr. Edvard P.G. Bruun is an Assistant Professor in the School of Civil and Environmental Engineering at Georgia Tech, specializing in robotic automation for assembling and disassembling large-scale building components. His research develops computational methods for geometrically complex structures requiring robotic fabrication. Education includes: PhD in Civil and Environmental Engineering, Princeton University (2024) MASc in Civil Engineering, University of Toronto (2017) BASc in Civil Engineering, University of Toronto (2015) Research focuses on leveraging cooperative multi-robot systems for construction automation and material circularity. Current projects include scaffold-free robotic assembly techniques, reusable timber structures, and digital workflows for masonry vaulting. Recent publications demonstrate innovations in robotic construction methods and financial risk modeling, with applications spanning sustainable architecture and structural engineering. Scientific awards: Charlotte Elizabeth Procter Fellowship (2023) O.H. Ammann Research Fellowship (2023) Architect Magazine R+D Award (2021) Princeton Catalysis Initiative Award (2020) NSERC Alexander Graham Bell Scholarship (2019) Leads the Bruun Automation Research Lab and supervises graduate students including Chisung Kim. Secured funding from NSF, ASCE, and institutional grants for projects on robotic fabrication and sustainable construction.

Michele Walshمشاهده پروفایل

استاد

• Public Health
• Community-Based Research and Evaluation
• Health Disparities
• +۳ مورد دیگر

Michele Walsh, Ph.D., is a Professor in the Department of Human Development & Family Science at the University of Arizona’s Norton School of Human Ecology. She serves as the Associate Director of Cooperative Extension Family, Consumer and Health Sciences and an Evaluation Specialist. Her work focuses on community-based research and evaluation, particularly addressing health disparities and wellness in rural and marginalized populations across Arizona and the Southwest. Walsh leads the Norton School’s Community Research Evaluation & Development (CRED) Team, which partners with tribal communities, governments, schools, and nonprofits to produce actionable data for program and policy decisions. She is affiliated with the Department of Psychology and the Institute for LGBT Studies. Her research emphasizes social and cultural determinants of health, rural health equity, and applied social measurement. Recent work includes evaluations of the Drug Treatment Alternative to Prison Program, a transgender youth intervention, and the First Things First Regional Needs and Assets Reports. She also explores kindness education in schools and caregiving challenges during the pandemic. Walsh’s publications span public health, education, and sociology, with a focus on translating research into practical solutions. She collaborates with diverse stakeholders to ensure community voices shape policy and practice.

Peter Jansenمشاهده پروفایل

دانشیار

• Natural Language Processing
• Artificial Intelligence
• Cognitive Science
• +۵ مورد دیگر

Peter Jansen is an Associate Professor at the University of Arizona's College of Information Science with a joint appointment at the Allen Institute for Artificial Intelligence (Ai2). He specializes in natural language processing (NLP), cognitive artificial intelligence, and automated scientific discovery. His research focuses on virtual environments for scientific reasoning, such as ScienceWorld and DiscoveryWorld, and methods for explainable AI through projects like the Explanation Bank. Education: PhD in Psychology and Neuroscience from McMaster University, and a Bachelor of Independent Studies from the University of Waterloo. His interdisciplinary background combines NLP, computer science, physics, and electrical engineering. Key projects include open-source hardware initiatives like the science tricorder (featured in over 50 media outlets and exhibited at the German Museum of Technology in Berlin) and TextWorldExpress, a text-game simulator. His work emphasizes grounding science education through sensing and developing AI systems capable of systematic reasoning and explanation generation. Current roles include advising students in automated scientific discovery and contributing to cross-listed courses in Computer Science and Linguistics. He maintains an active presence in academic outreach through teaching, conferences, and publications.

David Ryan Koesمشاهده پروفایل

دانشیار

• Computational Drug Discovery
• Deep Learning
• Discrete Algorithms
• +۳ مورد دیگر

David Ryan Koes is an Associate Professor in the Department of Computational and Systems Biology at the University of Pittsburgh, affiliated with the Joint CMU-Pitt PhD Program in Computational Biology. His research focuses on computational drug discovery, deep learning, and discrete algorithms, aiming to develop novel methods for rapid drug development and open-source software tools. He holds an office at 3064 Biomedical Science Tower 3 and 748 Murdoch Building. Key roles include Associate Director of the CPCB program and leadership in initiatives like CompBio Academy. His software contributions include libmolgrid, gnina, and 3Dmol.js, which advance molecular modeling and visualization. Research interests emphasize AI-driven drug discovery, including molecular docking, pharmacophore modeling, and generative models for molecule design. Recent work involves deep learning for protein structure prediction and pharmacophore elucidation. He has secured NIH grants (e.g., R35GM140753) and collaborations with institutions like CMU and industry partners. Advising over 25 students in computational biology, biotech, and data science programs, Koes bridges academia and industry through projects like Pharmit and the Teach-Discover-Treat initiative. His lab's work spans from foundational ML research to applied drug discovery, with a focus on open science and reproducibility.

• Geotechnical Engineering
• Soil Mechanics
• Earthquake Engineering
• +۳ مورد دیگر

Prof. Alper SEZER is a faculty member at Ege University's Department of Civil Engineering (Faculty of Engineering), specializing in Geotechnics. His research focuses on geotechnical engineering, earthquake effects, soil mechanics, and sustainable development. He has conducted extensive studies on soil liquefaction, seismic site effects, and post-earthquake damage assessments in regions like Antakya, Izmir, and Adıyaman. His work integrates computational methods (e.g., genetic algorithms, artificial neural networks) with experimental geotechnics to improve soil characterization and disaster resilience. He has collaborated on projects analyzing valley effects, ground motion amplification, and the mechanical behavior of soils under cyclic loading. Education: Details not explicitly provided in text, inferred through academic publications and position. Research interests include geotechnical hazard mitigation, soil stabilization techniques (e.g., polymer-modified soils), and the application of fractal analysis to soil properties. His studies address practical challenges such as sulfate resistance in cement-stabilized soils and freeze-thaw resistance of fiber-reinforced materials. He has contributed to microzonation studies and seismic risk assessments in urban areas like Izmir Bay. Publications emphasize field reconnaissance findings from major earthquakes (e.g., 2020 Samos, 2023 Türkiye earthquakes) and laboratory experiments on soil behavior under dynamic loading. Awards and recognitions are not explicitly listed in the text. Grant activities and advising details are not provided, but his extensive publication record suggests sustained research funding. He is affiliated with geotechnical laboratories at Ege University, focusing on experimental testing and computational modeling in geotechnical engineering.

• Performance evaluation of networks
• Wireless networks
• Internet of Things (IoT)
• +۱۲ مورد دیگر

Katia Jaffres-Runser is a full Professor at Institut National Polytechnique de Toulouse (Toulouse INP) , affiliated with the ENSEEIHT engineering school and the IRIT laboratory , where she leads the RMESS team. She holds a PhD from INSA Lyon and completed a prestigious Marie Curie postdoctoral fellowship at Stevens Institute of Technology and INSA Lyon. PhD : Institut des Sciences Appliquées de Lyon (INSA Lyon), 2005 M.Sc. : INSA Lyon, 2002 Diplôme d’Ingénieur en Télécommunications : INSA Lyon, 2002 Her research centers on performance evaluation and optimization of networks , particularly in wireless sensor networks, IoT, embedded systems, and complex networks. She applies advanced techniques like Google Matrix analysis, game theory, and deep reinforcement learning to improve network reliability, synchronization, and efficiency. Her work spans theoretical modeling and practical implementations in avionics, automotive, and industrial systems. Her recent publications highlight a strong trend in time-sensitive networking (TSN) , network synchronization , AI-driven optimization , and complex network analysis using Wikipedia and trade data. She has led major projects like MACACO, GOIA, and the Maison du Quantique Occitanie, focusing on real-time, reliable, and intelligent networking solutions. Scientific Awards: Best Paper Award on Propagation, IEE International Conference on Antennas and Propagation, 2003 STIC Innovation Prize, 2005 Marie Curie Outgoing International Fellowship, 2007–2010 Prime d'Excellence Scientifique (2013–2016) PEDR (2017–2021) RIPEC C3 (2022–2025) She has advised PhD students and contributed to national and international scientific service, including committee memberships in CoNRS , HCERES , and ACM N²Women . She has served on award juries and grant review panels. Her leadership extends to being Vice-President for Education at Toulouse INP (2021–2024) and Team Leader of RMESS at IRIT (since 2024) . She is actively involved in research labs and collaborative teams such as: RMESS Team , IRIT Laboratory – leading research on network modeling and embedded systems MACACO Project – international collaboration on adaptive communication networks GOIA and APPLIGOOGLE Projects – applying Google Matrix to AI and complex networks IRT EDEN – evaluating determinism in embedded networking

• Computational Weld Mechanics
• Finite Element Analysis
• Fatigue and Fracture of Materials
• +۳ مورد دیگر

Professor Zuheir Barsoum is a faculty member at KTH Royal Institute of Technology, serving as Vice Head (Research) in the Department of Engineering Mechanics. His research focuses on computational weld mechanics, fatigue assessment of materials, and structural integrity of welded joints. Key areas include high-frequency mechanical impact (HFMI) treatments for fatigue improvement, finite element analysis, and lightweight metal joining. Funded by VINNOVA, SSAB, Volvo, and others, his work addresses industrial challenges in structural durability. Current PhD students include Martin Edgren (bridge structural health monitoring), Mehdi Ghanadi (fatigue of high-strength steels), Yu Zhu (laser cladding simulations), and Kaushik Iyer (LCC modeling of welded structures). He teaches courses like Advanced Design of Welded Structures (SD2420) and oversees degree projects in Lightweight and Solid Mechanics. Notable achievements include the 2010 Henry Granjon Prize for fatigue design research. His startup Winteria AB commercializes digital quality assurance solutions for welding production, aligning with Industry 4.0 trends. Recent research emphasizes probabilistic fatigue modeling, machine learning for weld geometry analysis, and material defect characterization. Collaborations include Chalmers University and Swerim. His work bridges advanced manufacturing, computational mechanics, and industrial applications to enhance structural reliability and lifecycle cost optimization.