جست‌وجوی استاد و پژوهشگر

Juan-Pablo Correa-Baenaمشاهده پروفایل

دانشیار

• Materials Science
• Photovoltaics
• Semiconductor Technology
• +۳ مورد دیگر

Juan-Pablo Correa-Baena is an Associate Professor at the Georgia Institute of Technology , holding the Goizueta Early Career Faculty Chair in the School of Materials Science and Engineering. He leads the Materials for Solar Energy Harvesting and Conversion research initiative at the Institute for Materials (IMat) and Strategic Energy Institute, aiming to consolidate Georgia Tech's expertise in photovoltaics and interdisciplinary energy research. Education: PhD in Environmental Engineering, University of Connecticut (2014) MS in Environmental Engineering, University of Connecticut (2011) BS in Management and Engineering for Manufacturing, University of Connecticut (2008) His research focuses on the chemistry-structure-property relationships of low-cost semiconductors for optoelectronic applications. Key areas include halide perovskites , nanoscale control , and advanced deposition/characterization techniques . He develops atomic layer deposition and synchrotron-based imaging to address metastable material behavior. Recent publications highlight innovations in dimensional control , machine learning for thermal stability , and flexible photovoltaic devices . His work integrates materials synthesis , quantum phenomena , and industrial scalability . Scientific recognition: Highly Cited Researcher (Web of Science, 2019–2021) Nature Index Leading Early Career Researcher in Materials Science (2019) NSF, DoE, and industry-funded projects Students and team: He advises 14 graduate students and postdocs, including Sanggyun Kim, Diana LaFollette, and Leonardo Josué Lugo Salas, fostering interdisciplinary collaboration through workshops and symposia.

Darren Robinsonمشاهده پروفایل

استاد

• Urban Energy Systems
• Building Physics
• Multi-agent Stochastic Simulation
• +۳ مورد دیگر

Professor Darren Robinson holds the Chair in Architectural and Urban Sciences at the University of Sheffield 's School of Architecture and Landscape, where he serves as Director of Research. His work bridges social, building, and urban physics through multiscale modeling approaches. RCUK Innovation Fellowship (2018-2021, £268k) Leverhulme Research Programme Grant (2015-2020, £3.4M) EPSRC grant for Model-Predictive Control in buildings (2016-2019, £541k) His research focuses on statistical modeling of human behavior in buildings, urban energy simulation , and integrated assessment modeling for climate policy. Key contributions include stochastic occupant behavior models and urban metabolism frameworks. Notable awards include the Sustainability Science Best Paper Award (2020) , CIBSE Napier-Shaw Medal (2007), and Fellowships from FIBPSA and the Research Council of Norway. He leads the People, Environments and Performance and Multiscale Simulation research groups.

• Geotechnical Engineering
• Disaster Response and Mitigation
• Digital Data Collection Systems
• +۴ مورد دیگر

Dr. J. David Frost is the Elizabeth and Bill Higginbotham Professor of Civil Engineering at Georgia Institute of Technology and a Regents' Entrepreneur. He has held academic positions at Purdue University and Georgia Tech, with a focus on geotechnical engineering and disaster response. As founding director of Georgia Tech's Savannah campus and head of the Geosystems Engineering Group, Frost has shaped academic programs and research initiatives. Education: B.A.I and B.A. in Civil Engineering and Mathematics, Trinity College, Dublin (1980) M.S. and Ph.D. in Civil Engineering, Purdue University (1986, 1989) Research Interests span geotechnical engineering, bio-inspired design, and disaster resilience. His work emphasizes digital data collection systems for subsurface hazard assessment, soil-polymeric material interactions, and geotechnical responses to earthquakes, hurricanes, and anthropogenic disasters. Recent projects integrate ant nest geometry, plant root mechanics, and geosynthetic innovations into infrastructure solutions. Scientific Contributions include two U.S. patents for subsurface data systems, leadership in NSF-funded post-disaster reconnaissance missions (e.g., 9/11, Türkiye earthquakes), and co-founding the Geotechnical Extreme Events Reconnaissance (GEER) Association. His articles reflect expertise in bio-inspired geotechnics, machine learning for disaster modeling, and advanced computational simulations. Awards & Recognition: ASCE Huber Civil Engineering Research Prize NSF National Young Investigator Award Georgia Society of Professional Engineers Engineer of the Year in Education Coastal Business & Education Technology Alliance Leadership Innovation Award Professional Engagement includes chairing the Savannah Area GIS board, advising on ASCE Geo-Legislative Committees, and founding a software company serving 350+ global clients. His work bridges academia, policy, and industry innovation.

Johan Meyersمشاهده پروفایل

استاد

• Wind Energy
• Turbulent Flow Simulation
• Flow Control & Optimization
• +۵ مورد دیگر

Johan Meyers is a full Professor at KU Leuven's Faculty of Engineering Science, Department of Mechanical Engineering, where he heads the Applied Mechanics and Energy conversion (TME) research unit. He serves as a contact person for TME and is an active member of the KIES – KU Leuven Institute for Energy and Society. His administrative roles include membership on the Council of the Faculty of Engineering Science, the Mechanical Engineering Department Council and Board, and chairing the HPC Steering Committee. Professor Meyers' research focuses on turbulent flow simulation and optimization, with particular emphasis on wind energy applications, atmospheric pollutant dispersion, and computational methods. His work spans Direct Numerical Simulation (DNS), Large-Eddy Simulation (LES), and model reduction techniques for applications in energy engineering. Current research categories include flow control & optimization, wind farm engineering, and atmospheric pollutant dispersion modeling, with specific applications in radioactive release scenarios and wind turbine system optimization. His recent publications demonstrate a strong trend toward wind energy applications, particularly in optimizing wind farm layouts and operations through advanced computational methods. The research shows significant emphasis on Large-Eddy Simulation techniques to study atmospheric boundary layer interactions with wind farms, with growing interest in hybrid wind-solar energy systems and the effects of surface temperature heterogeneity on flow patterns. His work increasingly integrates machine learning approaches to enhance computational efficiency in wind farm modeling. Professor Meyers actively supervises numerous PhD students including Bon, T., Janssens, N., Jamaer, S., and ALREWENY, A., among others. His research is supported by multiple ongoing projects through 2028, including 'Wind-farm co-design in the North-Sea basin given climate and market uncertainty' and 'Reconstruction of turbulence from partial observations,' primarily funded by research councils and industry partnerships. He leads the Turbulent Flow Simulation and Optimization (TFSO) research group, which develops efficient supercomputing simulation tools for turbulent flow applications in energy engineering. The group specializes in wind farm optimization, atmospheric pollutant dispersion modeling, and airborne wind energy systems, with a particular focus on LES studies of wind farm interactions with the atmospheric boundary layer.

J. Ilja Siepmannمشاهده پروفایل

استاد

• Chemical Theory and Computation
• Chemical Physics
• Environmental and Green Chemistry
• +۴ مورد دیگر

J. Ilja Siepmann is a Distinguished McKnight University Professor and Distinguished University Teaching Professor at the University of Minnesota's Department of Chemistry, with affiliations spanning Chemical Engineering, Materials Science, and Data Science. His research integrates molecular simulations, force field development, and machine learning to study adsorption phenomena, phase equilibria, polymer chemistry, and nanoporous materials. Education: Undergraduate: University of Freiburg, Germany (1983-1987) Graduate: University of Cambridge, UK (PhD, 1988-1991) Post-doctoral: IBM Zurich Research Lab, Koninklijke/Shell Lab, and University of Pennsylvania (1991-1994) Research interests focus on chemical theory, materials genomics, and environmental chemistry, with emphasis on energy-efficient separations, nanostructured materials, and sustainable chemical processes. Computational methods like Monte Carlo algorithms and machine learning underpin his investigations into fluid interfaces, nucleation, and catalytic systems. Recent publications emphasize adsorption thermodynamics, molecular simulations of complex fluids, data-driven materials discovery, and polymer self-assembly. Trends include integration of machine learning with molecular modeling, nanoporous materials for clean energy, and phase behavior of refrigerants. Awards: Distinguished McKnight University Professor Distinguished University Teaching Professor Advises graduate and undergraduate researchers in computational chemistry projects. Leads the Siepmann Group at Kolthoff Hall, part of the Chemical Theory Center and Nanoporous Materials Genome Center. Research funded through MURI and industry partnerships.

Georgios Sermpinisمشاهده پروفایل

استاد

• Machine Learning
• Financial Trading
• Forecasting
• +۳ مورد دیگر

Professor Georgios Sermpinis is a faculty member in Accounting & Finance at the Adam Smith Business School, University of Glasgow. He joined the institution in 2011, bringing expertise in machine learning, financial trading, forecasting, econometrics, and financial risk management. Degrees from National Kapodistrian University of Athens and Liverpool John Moores University Former roles at University of Bedfordshire and Liverpool John Moores University Consultancy for major banks including Goldman Sachs and BNP Paribas Research Interests: Focus on machine learning applications in finance, including portfolio optimization, cryptocurrency markets, and risk modeling. His work integrates AI with traditional financial theories, emphasizing sustainability and behavioral aspects. Scientific Awards: Acanto Research Grant (2017) Santander & University of Oviedo Grant (2017) Mobility Grant, Universidad de La Laguna (2017) University of Oviedo Grant (2014) Early Career Research Grant, University of Bedfordshire (2010) Students: Supervised PhD candidates include Feng Xin and Sun Longguang, focusing on ESG investment and financial markets. Editorial Roles: Senior Editor of Decision Support Systems, Associate Editor of Information Systems and Operational Research, and Guest Editor for journals like Quantitative Finance.

April Weiمشاهده پروفایل

استادیار

• Computational Genetics
• Population Genetics
• Statistical Genetics
• +۱۰ مورد دیگر

April Wei is an Assistant Professor in the Department of Computational Biology at Cornell University, where she leads the Wei Lab in Computational Genetics. Her research focuses on developing scalable computational methods to analyze massive genomic datasets, addressing fundamental questions in evolutionary processes and human genetics. B.Sc. in Biology, Fudan University (2013) Ph.D. in Computational Biology, University of Michigan (2018) Postdoctoral Research at UC Berkeley and UCLA Joined Cornell in January 2022 The Wei Lab explores diverse areas such as population genetics , computational genetics , and natural selection , with applications in human evolution , gene conversion , and complex traits . Their work leverages graph-based data structures and deep learning to improve genomic analysis scalability. April's research trajectory, as reflected in her publications, emphasizes genomic data compression , selective sweep detection , and ancestral recombination graph (ARG) analysis . Her lab's outputs span algorithm development for biobank-scale data, evolutionary modeling of overlapping genes, and statistical frameworks for gene conversion landscapes. April mentors a team of Ph.D. students and postdoctoral researchers, including current advisees Ziqing Pan, Meera Chotai, Jinmin Li, Aditya Girish, and Lin Yuan, with notable alumni such as Siddharth Avadhanam (2023). The lab is supported by NIH and NSF grants. Lab culture prioritizes collaboration , inclusivity , and scientific curiosity , with social activities like lab barbecues, game nights, and group hikes. April is also a passionate fan of Star Wars and water sports.

Mark Burrisمشاهده پروفایل

استاد

• Travel behavior economics
• Value pricing for highways
• Price elasticity of travel demand
• +۷ مورد دیگر

Mark Burris is the Herbert D. Kelleher Professor in the Department of Civil & Environmental Engineering at Texas A&M University's College of Engineering, where he also serves as Division Head of Transportation & Materials Engineering. He is additionally a Research Engineer with the Texas A&M Transportation Institute, demonstrating his dual commitment to academic research and practical transportation solutions. With a career spanning over two decades since joining Texas A&M in 2001, Burris has established himself as a leading expert in transportation economics and traveler behavior. Burris's research focuses on the intersection of transportation economics, behavioral psychology, and infrastructure management. His work primarily investigates traveler responses to pricing mechanisms, particularly value pricing and high-occupancy toll (HOT) lanes. He has pioneered research combining traditional transportation engineering with behavioral economics to understand seemingly irrational traveler choices, such as paying to use express lanes that are sometimes slower than toll-free alternatives. His research has significantly advanced the understanding of travel time value, reliability valuation, and how psychological factors influence transportation decisions. Analysis of Burris's recent publications reveals a strong trend toward integrating behavioral economics with transportation engineering, with increasing attention to equity considerations in road pricing, the impacts of emerging technologies like autonomous and connected vehicles, and innovative methods for measuring traveler responses. His work consistently addresses practical transportation challenges while advancing theoretical understanding of travel behavior. Burris has served in prominent leadership roles, including a six-year term as chair of TRB's transportation economics committee. He has advised numerous federal agencies, serving on NCHRP panels and participating in FHWA expert forums on road pricing. His expertise is widely recognized in both academic and professional transportation circles. As an educator, Burris has advised over 60 graduate students and numerous undergraduates, teaching core courses including CVEN 307 (Introduction to Transportation Engineering), CVEN 454 (Urban Planning for Engineers), and CVEN 632 (Transportation Engineering: Economics). His research portfolio includes substantial funding from FHWA, NCHRP, and various state transportation agencies, with recent projects focusing on behavioral economics applications to managed lanes, vehicle miles traveled fee equity, and the impact of emerging mobility technologies.

Roop Aparajita Subhra Purushottamمشاهده پروفایل

دانشیار

• Machine Learning
• Extreme Classification
• Optimization Techniques
• +۵ مورد دیگر

Roop Aparajita Subhra Purushottam is an Associate Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology Kanpur. His research focuses on machine learning foundations and applications, particularly in extreme classification, optimization techniques, robust learning, and educational technology. He has developed scalable algorithms for web-scale applications and innovative teaching tools for programming education. His research interests span: Design and analysis of machine learning algorithms Statistical learning theory and online optimization Non-convex optimization for large-scale problems Robust learning against adversarial corruptions Applications in information retrieval, education, and environmental monitoring Recent publications demonstrate a strong focus on extreme classification techniques, efficient deep learning architectures, and educational technologies. His work consistently appears in top-tier conferences including KDD, ICML, NeurIPS, and CVPR, with innovations in scaling machine learning systems to handle millions of labels and users. Significant Awards: Gopal Das Bhandari Distinguished Teacher Award (2024) PK Kelkar Faculty Fellowship (2024-2027) Microsoft Bing Ads Greatness Award (2021) Computer Society of India Faculty Award (2018) Multiple best paper awards and nominations at major conferences He leads several research grants and consults for industry partners including Microsoft Research and Tower Research. His team develops open-source tools like Prutor for programming education and DEFRAG for efficient feature agglomeration in extreme classification. He has advised numerous PhD and Master's students who have received prestigious awards for their research contributions.

• Artificial Intelligence
• Semantic Web
• Ontology Engineering
• +۲ مورد دیگر

Asunción Gómez Pérez is a Spanish computer scientist and Full Professor at the Technical University of Madrid (UPM) . She currently serves as Vice-Rector for Research, Innovation and Doctoral Studies at UPM and holds a seat at the Real Academia Española . She has authored over 300 publications and accumulated 20,000 citations. Education : PhD in Computer Science (UPM, 1993), MBA (Comillas Pontifical University) Leadership Roles : Director of the Department of Artificial Intelligence (2008–2016), Academic Director of AI Master’s/PhD programs (2009–2016), Executive Director of UPM’s Artificial Intelligence Lab (1995–1998) Her research focuses on Semantic Web and Ontology Engineering , with applications in knowledge representation, machine-machine communication, and multilingual data integration. She pioneered methods for ontology validation, metadata licensing, and AI-driven social inclusion. Key publication trends include: Ontology evaluation frameworks (e.g., OOPS!) Linked Data quality models and validation tools Multilingual and cross-lingual AI applications Interoperability solutions for smart cities and healthcare Machine Learning for social exclusion prediction Ontology-driven library and lexicography systems Scientific Awards Fellow of the European Academy of Sciences Ada Byron Prize She has led projects like the NeOn Methodology for ontology development and contributed to the European framework for linked data rights (LD Terms). Her work bridges theoretical research with practical implementations in AI and Semantic Technologies.

Thomas Koopمشاهده پروفایل

استاد

• Atmospheric Chemistry
• Physical Chemistry
• Ice Nucleation
• +۴ مورد دیگر

Prof. Dr. Thomas Koop is a Professor of Physical Chemistry at Bielefeld University, where he leads the Atmospheric and Physical Chemistry research group within the Faculty of Chemistry. He has served as Dean of the Faculty of Chemistry from 2022-2024 and currently serves as Vice Dean (2024-2025). His research focuses on phase transition phenomena, particularly ice nucleation and growth, supercooled liquids, and the formation of amorphous glassy materials. His work has significant implications for understanding atmospheric aerosols, cloud formation mechanisms, and cryobiological processes. The group employs experimental techniques such as differential scanning calorimetry and optical cryo-microscopy, developing specialized equipment for studying phase transitions at micro and nanoscales. Prof. Koop's publication record shows a consistent focus on atmospheric chemistry with increasing exploration of biological ice nucleators, planetary atmospheres (including Venus), and the physical properties of atmospheric aerosols. His most cited work includes 'Water activity as the determinant for homogeneous ice nucleation in aqueous solutions' (Nature, 2000), which established fundamental principles in the field. 2024-2025: Vice Dean of Faculty of Chemistry 2022-2024: Dean of Faculty of Chemistry 2001-2022: Co-founder and Executive Editor of Atmospheric Chemistry and Physics Since 2004: Coordinator of Graduate School of Chemistry and Biochemistry Prof. Koop has mentored numerous students and postdoctoral researchers, contributing significantly to the development of the next generation of atmospheric scientists. His research has been supported by various funding agencies and has led to collaborations with institutions worldwide, from MIT and UC Berkeley to research centers in Switzerland and Israel.

Ankit Kariryaaمشاهده پروفایل

استادیار

• Machine Learning
• Remote Sensing
• Geospatial Analysis
• +۴ مورد دیگر

Ankit Kariryaa is a Tenure Track Assistant Professor at the Department of Computer Science and Department of Geosciences and Natural Resource Management , University of Copenhagen. His work bridges Machine Learning and Environmental Informatics , focusing on remote sensing, geospatial analysis, and ecological modeling. University of Copenhagen, Denmark Machine Learning Section, Department of Computer Science Geography, Land, Environment and Society, Department of Geosciences Kariryaa specializes in applying deep learning and computer vision to environmental challenges. His research includes: Automated tree detection and biomass estimation via satellite imagery Multi-modal geospatial representation learning Monitoring farmland tree decline and carbon sequestration potential Agroforestry system mapping using AI Developing AI tools for climate policy and sustainability Recent work trends show a focus on quantum-inspired machine learning , environmental monitoring , and cross-cultural AI applications . His 15 most recent publications span topics in remote sensing , ecological modeling , and AI ethics , with methods ranging from neural networks to tensor-based learning. He collaborates across disciplines, notably with researchers in ecology , climate science , and quantum computing . His outreach includes seminars on AI in agroforestry and ecosystem management , while his team contributes to global tree resource databases like TreeSense.

Marzieh Aminiمشاهده پروفایل

دانشیار

• Computer Vision
• Sensor Fusion
• Autonomous Vehicles
• +۷ مورد دیگر

Dr. Marzieh Amini is an Associate Professor at Carleton University, cross-appointed to the School of Information Technology and Department of Systems and Computer Engineering . She coordinates the Optical Systems and Sensors undergraduate program and leads research in computer vision, sensor fusion, and biomedical signal processing . PhD in Electrical and Computer Engineering (2016), Concordia University Postdoctoral Fellow (2020), McGill University Research Interests focus on autonomous vehicle perception systems integrating machine learning and statistical modeling . Her work addresses multi-sensor integration for reliable operation in diverse environments, including biomedical applications and critical infrastructure monitoring . Recent publications emphasize wildfire management , LiDAR-based infrastructure monitoring , and adverse weather adaptation in autonomous systems . She has received grants from NSERC, NRC, and FRQNT . Honors & Awards include: Volunteer Recognition Awards (IEEE Montreal, 2022 & 2019) FRQNT Postdoctoral Fellowship (2018) IEEE ISCAS Travel Support (2016) Professional Service includes leadership roles in IEEE committees and conference organization.

• Construction Robotics
• Human-Robot Collaboration
• Wearable Sensors
• +۴ مورد دیگر

Houtan Jebelli is an Assistant Professor in Civil and Environmental Engineering at the University of Illinois. His research focuses on construction robotics, human-robot collaboration, and wearable sensing technologies for worker health and safety monitoring. He directs research on exoskeleton applications, fall risk detection, and AI-enabled monitoring systems for construction environments. Research interests include: Human-robot collaboration in construction sites Physiological monitoring using wearable sensors Exoskeleton technology and ergonomic assessment AI-enabled safety management systems Robotic inspection and defect detection Jebelli's recent work demonstrates strong interest in bridging robotics with occupational health, particularly studying cognitive and physiological impacts of wearable robotics. His publications frequently address real-time monitoring systems and human factors in construction technology adoption.

• Developmental Psychology
• Child Mental Health
• Anxiety Disorders
• +۵ مورد دیگر

Dr. Kathryn Lester is an Associate Professor in Developmental Psychology at the University of Sussex's School of Psychology. She leads internationally recognized research on childhood anxiety, focusing on intergenerational transmission, cognitive biases, and school mental health interventions. Her work includes developing evidence-based programs for emotionally-based school avoidance and evaluating whole-school approaches to mental health. She holds leadership roles in Sussex’s senior management team, including Subject Group Lead for Developmental Psychology and Deputy Director for Postgraduate Research. She co-leads the Sussex Foundation Partnership Trust School Mental Health Research Team Clinic and has secured funding from the National Institute for Health Research, ESRC, and The National Lottery Community Fund. Her academic journey includes a D.Phil. in Psychiatry from the University of Oxford (2008) and postdoctoral research at the University of Sussex and King’s College London. Key research interests include anxiety prevention, school-based interventions, and the impact of parenting behaviors on child mental health. She has collaborated with organizations like the Sussex Wildlife Trust and provided consultancy for educational content development, such as children’s book series on emotions and ITV’s ‘Planet Child’ series. Her research spans mixed-methods approaches, including participatory methods with children and caregivers. Notable projects include NIHR-funded studies on digital mental health toolkits and Kavli Trust-funded interventions to reduce anxiety transmission from parents to children. She actively engages in knowledge exchange and mentoring early-career researchers.