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Ly DinhView profile

Assistant Professor

• Computational Social Science
• Network Analysis
• Social Simulation Models
• +5 more

Dr. Ly Dinh is an Assistant Professor at the School of Information within the College of Arts and Sciences at the University of South Florida. Her research focuses on computational social science, leveraging network analysis, social simulations, and text mining to study social and organizational systems. She explores network science applications in crisis informatics, emergency response coordination, and interdisciplinary collaboration dynamics. Her work has been recognized with the 2020 Grace Hopper Scholar for Women in Computing and the 2018 Network Science Fellowship. Dr. Dinh holds a Ph.D. and M.A. from the University of Illinois at Urbana-Champaign and a B.A. from the University of Southern California. Her research spans structural balance theory, crisis communication patterns, and computational methods for analyzing social media data during disasters. Recent projects include studying hurricane response networks, disinformation propagation, and the impact of team science on collaboration diversity. Education: Ph.D., University of Illinois at Urbana-Champaign M.A., University of Illinois at Urbana-Champaign B.A., University of Southern California Her publications emphasize methodological advances in network analysis, crisis informatics, and computational linguistics. Recent work highlights the use of ERGM models for peer review networks, taxonomy alignment experiments, and geospatial text mining for disaster zone identification. She has also contributed to understanding public perception disparities through crowdsourced data and the role of structural balance in signed directed networks. Awards: 2020 Grace Hopper Scholar for Women in Computing 2018 Network Science Fellow at Visible Networks Labs Dr. Dinh’s research bridges theory and practice, with applications in emergency response coordination, academic collaboration optimization, and social media-driven crisis management. She is affiliated with interdisciplinary teams advancing computational methods for societal challenges.

• Poverty Studies
• Sociology of Inequality
• Quantitative Methods
• +4 more

Jennifer Laird is an Assistant Professor of Sociology at Lehman College (City University of New York, CUNY). She holds a Ph.D. in Sociology from the University of Washington. Her research focuses on poverty, inequality, and quantitative methods, with recent work examining energy insecurity, public sector employment disparities, and immigration policy impacts on vulnerable populations. She co-authored the 2025 book *Powerless: The People's Struggle With Energy Insecurity in America* (Russell Sage Foundation), advancing analysis of household energy hardships. Her peer-reviewed articles span topics such as job stability for Black public-sector workers, state-level poverty variations, and labor force dynamics among Mexican immigrants. Laird’s work has been featured in outlets like *Health Affairs*, *Socius*, and *Demography*, and she received an Honorable Mention for her 2014 student paper from the ASA Population Section. Laird teaches courses in data visualization and social research methods, emphasizing statistical programming and effective data interpretation. At Lehman College, she instructs Sociology 301 (Methods of Social Research) and Sociology 353 (Visualizing Data). Her teaching background also includes roles as a Teaching Assistant at the University of Washington for courses on stratification and applied statistics. Her research has garnered media coverage in *The New York Times*, *Washington Post*, and *Huffington Post*, highlighting the societal implications of her findings on topics like public sector layoffs during the pandemic and SNAP policy impacts on immigrant children.

David GoldbergView profile

Professor

• Theoretical Cosmology
• Extragalactic Astrophysics
• Gravitational Lensing
• +2 more

David Goldberg is Professor and Head of the Physics Department at Drexel University. His NSF-funded research focuses on theoretical cosmology, gravitational lensing, and large-scale structure. He holds a PhD from Princeton University and authored multiple books including The Standard Model in a Nutshell and A Relatively Painless Guide to Special Relativity . Research highlights: Cosmic structure formation modeling Flexion lensing techniques Cosmological applications of Bayesian statistics Recent publications analyze gravitational lensing signatures, galaxy mass estimations, and cosmic inflation models. He also contributes to cartography science, developing improved flat map projections.

• Spectroscopy
• Atmospheric Chemistry
• Analytical Chemistry
• +3 more

Chris Medcraft is a Postdoctoral Researcher at the Faculty of Science and Engineering, Southern Cross University. He holds a PhD from Monash University. His research focuses on spectroscopic techniques (e.g., microwave, infrared, and rotational spectroscopy) applied to molecular systems, atmospheric chemistry, and astrochemical studies. Key interests include molecular interactions, aerosol dynamics, and chiral analysis. Medcraft collaborates with advanced facilities such as the Environmental Analysis Laboratory and utilizes cutting-edge methods like drone-based measurements for marine aerosol studies. His work spans diverse areas, including the decomposition of fluorinated compounds, hydrogen bonding in complex systems, and structural analysis of organic/inorganic complexes. Notable contributions include studies on the Great Barrier Reef’s atmospheric aerosols and the rotational spectroscopy of astrochemical candidates like vinyloxirane. Medcraft’s research integrates experimental and computational approaches to understand molecular behavior in environmental, atmospheric, and astrochemical contexts. He has contributed to interdisciplinary projects leveraging advanced analytical tools such as broadband rotational spectroscopy and synchrotron-based Fourier transform spectrometry.

Milton HalemView profile

Research Professor

• Climate Science
• Machine Learning
• Quantum Computing
• +5 more

Milton Halem is a Research Professor in the Department of Computer Science and Electrical Engineering at the University of Maryland, Baltimore County (UMBC), affiliated with the College of Engineering and Information Technology. He also holds an Emeritus position as Chief Information Research Scientist at NASA Goddard Space Flight Center's Earth Sciences Directorate. His expertise spans machine learning, quantum computing, atmospheric science, and climate modeling. Halem's research focuses on integrating AI with environmental systems, including wildfire digital twins, planetary boundary layer estimation, and climate forecasting. Key research interests include applying deep learning to wildfire prediction, developing quantum algorithms for optimization, and advancing climate observation systems. His work bridges disciplines like remote sensing, cybersecurity, and geophysical data analytics. Awards include NASA's Exceptional Scientific Achievement Medal (2022), Outstanding Leadership Medal (2018), and the Distinguished Service Medal (1996). Affiliations: UMBC CSEE, NASA Goddard (Emeritus) Notable Projects: Wildfire Digital Twin Initiative, SOAR atmospheric radiances system, AI-enhanced air quality forecasting Publications emphasize machine learning applications in climate science, quantum computing, and environmental monitoring. Collaborations include NASA, NOAA, and academic partners worldwide. Current projects explore AI-driven climate models and real-time wildfire impact assessment systems.

• Atmospheric Physics
• Environmental Science
• Dark Matter Research
• +6 more

Argiriou Athanassios is a Professor of Atmospheric & Environmental Physics at the Department of Physics, University of Patras, Greece. He has served as Head of the Laboratory of Atmospheric Physics (2006–2016), Head of the Applied Physics Section (2010–2011), Deputy Chair of the Physics Department (2011–2013), and Chair of the Physics Department (2013–2015). His academic journey includes a Physics Degree from the University of Patras (1983), a DEA in Physical Energy from the Institut National Polytechnique de Grenoble (1984), and a Doctorat in Physics from Université de Provence (1987). Research interests focus on atmospheric physics, environmental science, dark matter signatures, climate modeling, stable isotope analysis, and energy efficiency. Notable projects include studies on precipitation isotopes in China, quantum computing applications in atmospheric dynamics, and dark matter’s influence on geophysical observations. He has also contributed to energy performance regulations for buildings in Greece and maritime emissions’ impact on air quality in Mediterranean port cities. Recent publications (2020–2025) reveal a strong emphasis on interdisciplinary research, blending geophysics with astrophysics to explore dark universe phenomena and their terrestrial manifestations. His work on Bayesian statistical models for temperature trends and solar energy systems underscores practical applications in climate science and renewable energy. Professional affiliations include membership in the Hellenic Association of Physicists, Hellenic Meteorological Society, and American Geophysical Union. His teaching spans calculus, differential equations, and atmospheric physics courses. Key facilities include the Laboratory of Atmospheric Physics and collaborations with international institutions on climate and energy projects.

Ala'a Al-HabashnaView profile

Adjunct Professor

• Wireless Communication Networks
• 5G/6G Technologies
• Machine Learning Applications
• +5 more

Ala'a Al-Habashna is an Adjunct Professor at Carleton University's School of Computer Science within the Faculty of Engineering and Design. He holds a PhD from Carleton University and focuses on advanced wireless communication systems, 5G/6G networks, machine learning applications, and discrete-event simulation. His research integrates AI-driven solutions for network optimization, spectrum management, and smart urban infrastructure analysis. Education: PhD in Computer Science, Carleton University Research Interests: 5G/6G Networks, Machine Learning for Communications, Cognitive Radio Systems, IoT Integration, and Urban Modeling via Computer Vision Focus on resource allocation algorithms, channel modeling, and AI-driven network architectures Key Article Trends: Recent work emphasizes AI integration in next-gen networks (e.g., RIS-assisted MIMO, GenAI-6G), 5G energy efficiency, and urban analytics using street-view imagery. Earlier contributions include DEVS-based simulation frameworks for fire modeling and D2D video streaming optimization. Collaborations & Funding: Active partnerships include Ericsson Canada for projects like Channel Reconstruction for LTE/NR and Spectrum Sharing with Machine Learning . A funded PhD position is available in Wireless Communication and 5G Networks requiring expertise in Mathematical Optimization. Labs & Teams: Engaged in Ericsson-Carleton Partnership initiatives and leads interdisciplinary teams addressing 6G challenges. Research leverages Carleton's advanced wireless and AI facilities.

Peiyuan PanView profile

Senior Lecturer

• Software System Development
• Web-based Applications
• Artificial Intelligence Technologies
• +3 more

Dr. Peiyuan Pan is a Senior Lecturer in Computer Science at London Metropolitan University, affiliated with the School of Computing and Digital Media. He holds a PhD in Computer-aided Manufacturing Engineering, a postgraduate certificate in Teaching and Learning in Higher Education, and a BSc (Hons) in Computer Science. He specializes in teaching OO programming, web systems development, and e-commerce applications. His research focuses on software system development, AI technologies, embedded systems, e-manufacturing, and supply chain management. Dr. Pan has led several research projects, including a Virtual Surgery system for Java programming education (2010–2011) and an Internet-based supply chain improvement system (1999–2002). He received the Vice Chancellor's Teaching Fellowship Award in 2010–2011. His publications span e-learning methodologies, robotics, and manufacturing automation. He contributes to interdisciplinary work in AI-driven design systems, fuzzy logic protocols, and web-based expert systems. His teaching responsibilities include leading the Computing and Business Information Technology FdSc program. Professional affiliations include the ACM and active participation in international conferences on computing and manufacturing systems.

Liem TranView profile

Professor

• Physical Geography
• Environmental Science
• Spatial Data Science
• +3 more

Liem Tran is a Professor and Associate Dean for Academic Programs in the Department of Geography, Environment, and Earth Sciences at the University of Tennessee. His academic focus lies in Physical Geography and Geographic Information Science and Technology (GIST), with affiliations in the U.S. EPA’s Regional Vulnerability Assessment (ReVA) program. He has been a core team member of ReVA since 2000, contributing integration techniques and distance measures for environmental vulnerability analysis. Dr. Tran holds a Ph.D. from the University of Hawaii. His research emphasizes integrated regional vulnerability assessment, artificial intelligence applications in geographic modeling (e.g., neural networks, fuzzy logic), and GIS-driven spatial analysis. Notable contributions include developing frameworks for ecosystem service evaluation in China and disaster preparedness tools for flooding in rural areas. His work bridges environmental science and public health, addressing topics like healthcare accessibility, sleep disparities, and hospital closure impacts. Teaching focuses include Geography 131 (Natural Environment), GIS, and quantitative methods. He maintains active engagement with environmental justice and health equity through spatial data science. Publications highlight trends in geographic disparities, disaster resilience, and policy-relevant spatial analysis. His interdisciplinary approach combines environmental vulnerability metrics with socioeconomic factors to inform sustainable development strategies.

Mike MooneyView profile

Professor

• Tunneling Engineering
• Underground Construction
• Geotechnical Engineering
• +5 more

Mike Mooney is a Professor of Mechanical Engineering at the Colorado School of Mines, holding the Grewcock Chair in Underground Construction & Tunneling. He directs the Center for Underground and leads the Heavy Construction Studio, focusing on advancing smart, rapid, and cost-effective construction technologies for urban tunneling and challenging ground conditions through instrumentation integration and field experimentation. His educational background includes: PhD in Civil Engineering, Northwestern University, 1996 MS in Civil Engineering, University of California, Irvine, 1993 BS in Civil Engineering, Washington University, St. Louis, 1991 BA in Physics, Hastings College, 1991 Mooney's research integrates instrumentation into tunnel boring machines and horizontal directional drilling systems, studying robotic excavation, soil transformation using polymers/foams, and ground-mechanical interactions via physics models and machine learning. His group conducts extensive field campaigns embedded in real construction projects worldwide, emphasizing data-driven approaches to complex geotechnical challenges. Recent publications (2018-2023) demonstrate expertise in TBM performance optimization, boulder detection systems, soil conditioning, and machine learning applications for ground prediction. Key contributions include real-time vibration monitoring for Venice lagoon restoration, annular pressure management, foam stability analysis, and autonomous tunneling frameworks across major projects in Seattle, Toronto, Los Angeles, and New York City. No scientific awards or fellowships were mentioned in the provided text. Professor Mooney actively advises graduate students as evidenced by extensive student co-authorship in publications. His research is directly applied to international construction projects including the Venice lagoon restoration and urban tunneling initiatives. He is a registered Professional Engineer in Colorado (License #39682) and provides technical consultation for construction projects globally. He directs the Center for Underground at Colorado School of Mines, where his Heavy Construction Studio develops instrumentation systems and conducts field experiments integrated into active construction sites. The lab specializes in real-time monitoring technologies, ground characterization methods, and machine learning applications for tunneling operations.

Saeed BoorboorView profile

Adjunct Assistant Professor

• Visualization
• Computer Graphics
• Medical Imaging
• +4 more

Saeed Boorboor is a Principal Research Scientist at Stony Brook University's Center for Visual Computing and an Adjunct Assistant Professor at the University of Illinois at Chicago. His research focuses on immersive visualization systems, AR/VR/MR, medical imaging, and applied AI. He received his Ph.D. from Stony Brook University under Arie E. Kaufman and a B.S. from LUMS, Pakistan. He will join UIC as an Assistant Professor in Fall 2025. Education: Ph.D. in Computer Science, Stony Brook University (2015–2023) B.S. in Computer Science, LUMS, Pakistan (2010–2014) Research Interests: Dr. Boorboor designs visualization systems for scientific data exploration, leveraging immersive technologies like AR/VR. His work emphasizes human-centered design, applied AI, and novel interaction methods. Recent projects include Explainable XR , Silo , and NeuRegenerate . Publications & Awards: Over 20 peer-reviewed papers, patents in medical imaging, and awards including the IACS Junior Researcher Award and Stony Brook Chair Fellowship. His work has been presented at IEEE VIS, SIGGRAPH, and EuroVis. Teaching: Taught courses like CSE 366 (VR), EMP 532 (Big Data Systems), and mentored 17+ students. Currently recruiting students for his UIC lab.

Joseph Scalia IVView profile

Associate Professor

• Geoenvironmental Engineering
• Geotechnical Engineering
• Contaminant Hydrology
• +4 more

Joseph Scalia IV is an Associate Professor in the Department of Civil and Environmental Engineering at Colorado State University (CSU), affiliated with the College of Engineering. Prior to joining CSU, he worked as a senior associate at Exponent (formerly Failure Analysis Associates) in their Environmental and Earth Sciences Practice. His research focuses on geoenvironmental and geotechnical engineering, particularly the intersection of soil mechanics with contaminant hydrology, environmental engineering, and mine waste management. Scalia holds a B.S. in Civil and Environmental Engineering from Bucknell University and M.S. and Ph.D. in Geological Engineering from the University of Wisconsin-Madison. He leads the Geoenvironmental Lab, which addresses challenges in tailings management, unsaturated terramechanics, and barrier systems for environmental protection. Education: Ph.D., Geological Engineering, University of Wisconsin-Madison (2012) M.S., Geological Engineering, University of Wisconsin-Madison (2009) B.S., Civil and Environmental Engineering, Bucknell University (2007) Research interests include geotechnical stability of tailings storage facilities, hydraulic conductivity of waste materials, and innovative containment systems. His work integrates field experimentation, laboratory testing, and numerical modeling to solve environmental and geotechnical challenges. Recent studies emphasize soil moisture dynamics in mine tailings, chemico-osmotic effects in clay barriers, and polymer-enhanced geosynthetic clay liners for waste containment. Labs/Teams: Geoenvironmental Lab : Focuses on tailings engineering, landfill covers, and geoenvironmental barrier systems. Advancing barrier systems for contaminated sites and optimizing landfill cover designs are central themes. While no specific grants or awards are listed, his extensive publication record reflects sustained contributions to environmental geotechnics and mine waste management.

Ursula EickerView profile

Professor

• Urban energy systems
• 3D City modeling
• Urban data and simulation platform development
• +7 more

Prof. Ursula Eicker is the Canada Excellence Research Chair in Smart, Sustainable and Resilient Cities and Communities at Concordia University , leading cutting-edge research in urban energy systems. Her work integrates 3D city modeling, renewable energy systems, and sustainable transport to develop zero-carbon city strategies. PhD in Solid State Physics (Heriot-Watt University) Habilitation in Renewable Energy Systems (Technische Universitat Berlin) Research Interests focus on urban simulation platforms, district energy networks, and climate-resilient infrastructure. The INSEL4Cities platform enables holistic urban modeling for building demands, transportation, and greenery. Her Residential Densification studies demonstrate 65% energy reduction through retrofits and solar integration. Recent publications explore urban solar shading , transactive energy systems , green infrastructure equity , and decentralized hydrogen production . Awards include the German-African Innovation Incentive and recognition for photovoltaic research in Egypt. Over 50 graduate students in her lab examine zero-carbon pathways. Teaches ENCS 691 on urban energy systems. Secured 10M CAD for the CERC chair and multiple grants. Co-Director of Concordia's Next Generation Cities Institute .

Nathan LenssenView profile

Assistant Professor

• Climate Science
• Statistical Prediction
• Spatial Statistics
• +4 more

Dr. Nathan Lenssen is a Teaching Assistant Professor in the Department of Applied Mathematics and Statistics at the Colorado School of Mines, as well as a Project Scientist at the National Center for Atmospheric Research (NCAR). His academic background includes a B.A. in Mathematics and Physics from Claremont McKenna College, an M.A. in Statistics, and a Ph.D. in Earth and Environmental Sciences from Columbia University. Research Focus: Lenssen's work centers on understanding climate system variability, predictability, and change. He develops statistical and dynamical models to analyze large-scale datasets, addressing challenges like climate model biases and observational uncertainties. Key areas include ENSO predictability, climate impact projections, and spatial statistics for geoscience applications. Teaching & Curriculum: He designs data science and climate science courses emphasizing project-based learning. Current courses include Math 324 (Statistical Modeling), Math 334 (Introduction to Probability), and Math 335 (Introduction to Mathematical Statistics). His pedagogical approach integrates statistical methods with real-world environmental questions. Publications: Lenssen's research spans topics like Arctic warming amplification, ENSO forecast skill, and uncertainty quantification in climate models. His work bridges computational methods, large-scale climate dynamics, and interdisciplinary applications. Professional Engagement: His dual role at Mines and NCAR reflects a commitment to both education and cutting-edge climate science. He collaborates on projects like crop yield forecasting and NASA's GISTEMP uncertainty modeling, highlighting the societal relevance of his work.

• Physical geography
• Environmental geoscience
• Hydrology
• +6 more

Professor Richard Forster from the University of Utah's School of Environment, Society & Sustainability is a leading expert in glaciology and remote sensing with ORCID 0000-0003-3945-5072. His research focuses on firn aquifers, cryospheric processes, and hydrological modeling through advanced geophysical techniques. Current position: Professor (2010-present) Previous academic positions: Assistant Professor (1999-2004), Associate Professor (2004-2010) at University of Utah Research interests include: Firn aquifer dynamics and ice sheet hydrology Remote sensing of snow and glacier systems Climate change impacts on cryospheric regions Hydrological modeling in extreme environments Recent publications demonstrate methodological innovations in SAR coherence analysis, firn aquifer characterization, and snowmelt progression mapping across Greenland, Antarctica, and the Indus Basin. His work integrates field measurements with advanced geophysical techniques. Scientific awards include: College Senior Superior Research Award (2012) Student Choice Teaching Award (2009) Professional activities feature service on NASA's Standing Review Board (2014-2016) and multiple NSF advisory roles (2008-2011). He has secured major grants from NASA and NSF for ice sheet monitoring and snow water equivalent estimation.