Dr. Amin Keramati is an Assistant Professor of Supply Chain Management at Widener University’s School of Business Administration. He holds a PhD in Transportation & Logistics from North Dakota State University and previously served as a graduate research assistant at the Upper Great Plains Transportation Institute. His work includes federally funded projects like the Mountain-Plains Consortium’s MPC-550 project, focusing on highway-rail grade crossing safety. Dr. Keramati teaches courses in enterprise resource planning, decision analytics, database management, transportation/logistics, project management, and data analytics. Education: PhD in Transportation & Logistics, North Dakota State University Graduate Research Assistant at Upper Great Plains Transportation Institute Research Focus: Dr. Keramati develops mathematical, statistical, and machine learning approaches to solve complex problems in transportation, supply chain, and logistics. Key areas include data mining, big data decision support, transportation network analytics, project scheduling, smart manufacturing, and accident analysis. His interdisciplinary work bridges supply chain optimization with emerging technologies like blockchain for healthcare and clinical trials. Awards & Recognition: Student Paper Award, American Association of State Highway and Transportation Officials (2017) Grants & Projects: Lead researcher on the Mountain-Plains Consortium’s MPC-550 project, which expanded his dissertation work on safety systems for highway-rail crossings. Collaborates on federally funded transportation safety initiatives and supply chain optimization for bioethanol and pharmaceutical industries. Labs/Teams: Active in Widener’s School of Business research groups focusing on supply chain innovation and interdisciplinary projects combining logistics with emerging technologies.
Ann B. Lee is a Professor and Co-Director of the PhD Program in Statistics at Carnegie Mellon University , with a joint appointment in the Department of Statistics & Data Science and the Machine Learning Department. Prior to joining CMU, she held positions as a J.W. Gibbs Assistant Professor at Yale University and a visiting research associate at Brown University. PhD in Physics, Brown University MSc/BSc in Engineering Physics, Chalmers University of Technology, Sweden Her research focuses on statistical methodology for complex data in the physical sciences , emphasizing trustworthy inference, uncertainty quantification, and integration of classical statistics with machine learning. Recent work includes likelihood-free inference, calibrated forecasting, and diagnostics for generative models. The STAMPS research group , which she co-founded in 2018, hosts weekly meetings and public webinars. In Fall 2024, STAMPS will transition into a CMU Research Center. Recent publications span likelihood-free inference , climate modeling , and astronomy . Notable collaborations include applications to hurricane intensity guidance , galaxy redshift estimation , and cosmological parameter biases . She mentors PhD students and has advised multiple award-winning researchers, including ASA Best Student Paper Award winners. Her teaching includes advanced courses on probability, regression, and AI for climate sciences.
Leif Eriksson is a Professor at Chalmers University of Technology , specializing in Radar Remote Sensing within the Department of Space, Earth and Environment . His career at Chalmers began in 2004, and he was promoted to Professor in 2022 after serving as Group Leader (2012–2017) and Head of Faculty Assembly (2017–2020). His research focuses on developing advanced methods for environmental monitoring using radar data, particularly synthetic aperture radar (SAR) from satellites and aircraft. Leadership Roles: Group Leader (Radar Remote Sensing), Faculty Assembly Head Key Collaborations: Rymdstyrelsen, EU Horizon, VINNOVA, European Space Agency Research Interests : Dr. Eriksson’s work spans forest biomass estimation , sea ice dynamics , and ocean surface current/wind retrieval . He integrates SAR data with in situ observations and climate models to study: Forest degradation (clear cuts, storm damage) via multi-temporal SAR Sea ice concentration, drift patterns, and thickness in Arctic regions Wind vectors and surface currents using interferometric SAR techniques Applications for maritime navigation safety and polar shipping optimization Article Trends : His recent publications emphasize SAR’s role in transport infrastructure monitoring (e.g., Iron Ore Line degradation), pan-Arctic landfast ice stability , and multi-frequency SAR fusion for enhanced sea ice observations. Collaborative work with teams across Europe and the U.S. highlights interdisciplinary approaches to climate and marine research. Projects & Grants : Dr. Eriksson leads or contributes to projects such as: CAISA (2022–2024): Air-ice-sea data assimilation EONav (2016–2019): Copernicus data for maritime navigation SEDNA (2017–2020): Safe Arctic shipping Forest Biomass Monitoring (2017–2018): Spaceborne SAR applications His work is supported by Rymdstyrelsen, EU Horizon, and industry partners like Trafikverket. Labs & Teams : He is central to the Radar Remote Sensing Group at Chalmers, collaborating with institutions like Lund University and international bodies such as ESA. His research often involves satellite campaigns (e.g., TanDEM-X, Sentinel) and field studies in polar regions.
Dr. Kevin Kochersberger is an Associate Professor in the Department of Mechanical Engineering at Virginia Tech , with a career spanning academic research, technical innovation, and educational leadership. His work focuses on autonomous aerial systems , robotic control , and applied aerodynamics , particularly through the Uncrewed Systems Laboratory . Kochersberger's research has pioneered UAV-based radiation detection , 3D terrain mapping , and low-resource drone applications , including establishing the African Drone and Data Academy in Malawi . Education: Ph.D., Mechanical Engineering, Virginia Tech (1994) M.S., Mechanical Engineering, Virginia Tech (1984) B.S., Mechanical Engineering, Virginia Tech (1983) A.S., Engineering Science, Jamestown Community College (1981) Kochersberger's publications demonstrate expertise in UAV path planning , smart material actuation , and radiation source localization , with over $9M in research funding. His scientific awards include AIAA Associate Fellow (2009) and Aviation Week Aerospace Laureate (2003). Notable projects involve helicopter-deployable robotic systems and urban canyon navigation without GPS. Recent articles highlight BVLOS drone simulators , 2.5D terrain mapping , and autonomous negative obstacle traversal , reflecting his focus on real-time adaptive control and heterogeneous robotic systems . He teaches Drone Technology and Flight Operations and Advanced Design Projects , emphasizing student-driven innovation and industry collaboration .
Gregory Paradis is an Assistant Professor in the Department of Forest Resources Management at the University of British Columbia (UBC) Faculty of Forestry. His research focuses on sustainable forest management, integrating operations research, mathematical optimization, and systems modeling to address complex interactions between ecosystems, industries, and society. He works with the FRESH Lab and collaborates with the Integrated Remote Sensing Studio, emphasizing ecological and economic integration in forest planning. Sustainable Forest Management Operations Research Forest Economics Data Science Risk Assessment GIS-based Methods His research spans forest inventory optimization, climate change adaptation strategies, wildfire risk modeling, and decision support systems for invasive species. He develops computational frameworks to enhance wood supply planning, carbon management, and ecological resilience. Recent work includes machine learning applications for fire safety in timber structures and automated road planning tools for wildlife conservation. Paradis’s publications highlight trends in applying optimization methods to sustainable forestry, with a focus on biodiversity, climate adaptation, and value chain innovation. He advocates for interdisciplinary approaches that bridge silviculture, industrial engineering, and data science to tackle emerging challenges in forest ecosystems. As an educator, he seeks motivated students with quantitative and creative problem-solving skills. His lab collaborates on remote sensing integration, risk assessment models, and policy-relevant forest management strategies, ensuring plans account for uncertainties like insect infestations or windthrow events.
Saurabh Amin is a Professor in the Department of Civil and Environmental Engineering at the Massachusetts Institute of Technology (MIT), where he also serves as the Edmund K. Turner Professor and Undergraduate Officer. He is a Principal Investigator at the Laboratory of Information and Decision Systems and holds affiliations with the Operations Research Center and the Center for Computational Science and Engineering. His educational background includes: B.Tech. 2002, Indian Institute of Technology (IIT) Roorkee M.S. 2004, University of Texas (UT) Austin Ph.D. 2011, University of California (UC) Berkeley Saurabh Amin's research focuses on the design and control of infrastructure systems using game theory and optimization in networks. His work spans three main areas: resilient network control, information systems and incentive design, and optimal resource allocation in large-scale infrastructure systems. By concentrating on critical infrastructure domains including highway transportation, electric power distribution, and urban water networks, his research develops innovative theory and tools to enhance system performance against both stochastic and adversarial disruptions. His approach involves modeling cyber-physical interactions in infrastructures to assess vulnerabilities, developing detection and response tools for failures at various scales, and designing economic incentive schemes that improve aggregate public good while accounting for dependencies and private information among strategic entities. Amin's work bridges mathematical systems theory with practical civil engineering applications, creating a rigorous theoretical foundation for infrastructure resilience that addresses diverse failure mechanisms from natural disasters to deliberate malicious actions. His recent publications demonstrate a strong focus on decarbonization of energy systems, resilient infrastructure planning under climate uncertainty, optimization methods for complex networked systems, and game-theoretic approaches to sustainable infrastructure management. His work increasingly integrates artificial intelligence and machine learning techniques with traditional control theory to address contemporary challenges in infrastructure resilience and sustainability. The research shows a clear trajectory toward addressing climate change impacts on infrastructure systems while maintaining economic efficiency and operational reliability. Professor Amin has received numerous prestigious awards and honors: Common Ground Excellence in Teaching Award, 2025 HSCC Test-of-Time Award, 2024 MIT CEE, Distinguished Service and Leadership Award, 2023 Samuel M. Seegal Prize (SoE) – inspiring students in pursuing and achieving excellence, 2022 Earll M. Murman for Excellence in Undergraduate Advising, 2022 C3.ai Digital Transformation Institute Research Award, 2020 MIT, Ole Madsen Mentoring Award, 2020 MIT, Energy Initiative Research Award, 2020 National Academy of Engineering, China-America Frontiers of Engineering Symposium speaker, 2019 MIT, Robert N. Noyce Career Development Professor, 2015-2018 Google Faculty Research Award, 2015 National Science Foundation CAREER Award, 2015 Siebel Energy Institute Research Award, 2015 MIT, Solomon Buchsbaum AT&T Research Fund Award, 2012 Professor Amin has been actively involved in significant research projects including the C3.ai DTI project on Causal Reasoning for Real-Time Attack Identification in Cyber-Physical Systems and another on Learning in Routing Games for Sustainable Electromobility. He serves as the chief scientist on multi-institutional NSF grants, including the $9 million Foundations of Resilient Cyber-Physical Systems (CPS) project. His teaching portfolio includes courses such as 1.008 Engineering for a Sustainable World, 1.104 Sensing and Intelligent Systems, 1.020 Engineering Sustainability: Analysis and Design, and 1.208 Resilient Networks. As Undergraduate Officer, he plays a key role in shaping the educational experience for civil and environmental engineering students at MIT. Professor Amin leads the Resilient Infrastructure Networks Lab at MIT, where his team develops theoretical foundations and practical tools for infrastructure resilience. The lab focuses on the intersection of control theory, game theory, and optimization applied to cyber-physical infrastructure systems. Current research directions include pandemic-resilient urban mobility and hurricane-resilient smart grid operations, reflecting the lab's commitment to addressing pressing societal challenges through rigorous systems engineering approaches.
Professor Vasyl Stepanovych Kravtsiv is a distinguished academic and researcher at the Institute of Regional Studies named after M. I. Dolishnyi, part of the National Academy of Sciences of Ukraine. He serves as both the Director of the Institute (from 2014 to July 2025) and Head of the Department of Regional Environmental Policy and Nature Management. With a career spanning over four decades in regional studies, environmental policy, and economic development, Professor Kravtsiv has established himself as a leading expert in regional economics and environmental management in Ukraine. Professor Kravtsiv's educational background includes a degree in Geography from Ivan Franko Lviv State University (1980). His academic achievements include becoming a Candidate of Economic Sciences (1987), Senior Researcher (1994), Doctor of Economic Sciences (2007), and Professor (2010). He has also been honored as a 'Honored Worker of Science and Technology of Ukraine' for his significant contributions to the field. His scholarly work examines the theoretical and practical aspects of regional environmental policy formation, particularly during Ukraine's transition to a market economy. Professor Kravtsiv's research primarily focuses on regional economics, environmental policy, spatial development, and cross-border cooperation. His work examines the intersection of economic development and environmental sustainability, with particular emphasis on the Carpathian region of Ukraine. He pioneered economic methods of environmental quality management in Ukraine, which now operate as pollution fees. Professor Kravtsiv has developed numerous regional environmental safety programs approved by Western Ukrainian authorities and contributed significantly to Ukraine's State Regional Policy Concept. His recent publications address contemporary challenges including the impact of the war and pandemic on tourism, spatial economic development amid European integration, and sustainable growth in decentralization contexts. His scholarly contributions include over 100 scientific works, with 18 monographs exploring regional development, environmental policy, and cross-border cooperation. Professor Kravtsiv's work demonstrates consistent evolution from theoretical foundations to practical policy implementation, with recent publications showing adaptation to Ukraine's wartime conditions and European integration requirements. Honored Worker of Science and Technology of Ukraine Editor-in-chief of the journal 'Regional Economy' Editor-in-chief of the collection 'Socio-economic problems of the modern period of Ukraine' Author of over 100 scientific works including 18 monographs Key contributor to Ukraine's environmental policy framework As Director of the Institute of Regional Studies, Professor Kravtsiv oversees extensive research activities, coordinates international collaborations through Erasmus+ and Visegrad Fund programs, and guides scholarly publication efforts. His leadership has positioned the Institute as a key contributor to Ukraine's regional development policies and environmental management strategies. He actively participates in developing Ukraine's adaptation to European standards and contributes to reconstruction planning amid wartime conditions, demonstrating both academic rigor and practical policy relevance.
Dr. Yanchao Liu is an Associate Professor at Wayne State University's College of Engineering, Department of Industrial and Systems Engineering. He has received research funding from the National Science Foundation and the State of Michigan, including the NSF Career Award. His academic career spans prior industry roles as a Data Scientist and Manager of Advanced Analytics at Sears Holdings Corporation (2016-2017) and Director of Brand Marketing Analytics at Catalina Marketing Corporation (2017). He teaches courses in data science, IoT, and stochastic processes. B.S. Industrial Engineering, Huazhong University of Science and Technology (2006) M.S. Industrial Engineering, University of Arkansas (2008) Ph.D. Industrial and Systems Engineering, University of Wisconsin-Madison (2014) Dr. Liu's research focuses on mathematical modeling for transportation systems, industrial AI, and data analytics. His work addresses drone traffic management, battery-constrained delivery routing, and optimization algorithms for urban mobility. He has developed novel methods for UAV safety diagnostics, random forest implementations, and fairness-aware path planning in urban air mobility. His publications span journals like Journal of Guidance, Control and Dynamics , Transportation Research Part C , and IEEE Transactions on Intelligent Transportation Systems , with conference contributions at IISE and FAIM. His research combines theoretical advancements with practical applications in smart cities and logistics. NSF Career Award (2020) Faculty Research Excellence Award (2021) IEEE PES Best Conference Paper (2015) IEEE Transactions on Smart Grid Best Reviewer (2015) Hubei Province Distinguished Bachelor’s Thesis Award (2006) Dr. Liu advises PhD students like Zhenyu Zhou and J. Chen. He has contributed to energy market modeling (with M.C. Ferris) and published extensively on drone operations, machine learning algorithms, and stochastic processes. His work includes U.S. patent pending applications for UAV safety systems.
Noah Molotch is a Professor of Geography at the University of Colorado Boulder, where he serves as Director of the Mountain Hydrology Group and is a Fellow of INSTAAR (Institute of Arctic and Alpine Research). His research and teaching focus on hydrologic processes in mountainous regions with expertise in snow hydrology, remote sensing, and ecohydrology. Molotch earned his Ph.D. from The University of Arizona in 2004. His research utilizes ground-based observations, remote sensing, and computational modeling to understand hydrological processes, particularly snow distribution and water-carbon-nitrogen fluxes in mountain ecosystems. His work has significant implications for sustainable resource management and environmental policy. His recent publications show a strong focus on snow water equivalent estimation, snowmelt timing impacts on forest productivity, and the development of advanced remote sensing techniques for monitoring mountain hydrology. His research increasingly addresses climate change impacts on water resources, with particular attention to the Western United States. Fellow of INSTAAR Art+Science fellowship in partnership with artist Hannah Taylor Molotch advises numerous graduate students and leads significant research projects including those funded by NASA and NSF. His Mountain Hydrology Group operates field sites across the Western U.S., including Storm Peak Laboratory and Niwot Ridge Long Term Ecological Research Program. The group produces near-real-time snow water equivalent estimates and contributes to the Snow Today website at the National Snow and Ice Data Center.
Dr. Heesung Woo is an Assistant Professor of Advanced Forestry at the College of Forestry, Oregon State University , specializing in robotics, sensor integration, and precision forestry. His work focuses on autonomous forestry machinery, AI-driven forest management, and sustainable practices. He advises two graduate students and collaborates internationally through research projects. Research Interests: Autonomous Forest Machinery Development Sensor Integration & ICT Solutions Precision Forestry via Remote Sensing/LiDAR/GIS Machine Learning for Forest Inventory Advanced Forestry Practices for Sustainability Publications emphasize innovative applications of technology in forestry, including LIDAR integration, harvester data analytics, and carbon offset project modeling. His work bridges engineering, environmental science, and policy. Dr. Woo leads the Advanced Forestry Lab at Oregon State, focusing on real-world deployment of cutting-edge technologies to address challenges in forest operations, sustainability, and resource optimization.
Leila Character is an Assistant Professor at Texas A&M University, with expertise in machine learning and geospatial analysis. Her research bridges disciplines like archaeology, environmental science, and geospatial intelligence, often involving fieldwork and computational modeling. She has a multidisciplinary background as a professional geologist, environmental scientist, and AI researcher. Ph.D. and M.A. in Geography and the Environment from University of Texas at Austin B.S. in Geology with a minor in Anthropology/Archaeology from Sewanee: The University of the South Her active projects focus on underwater aircraft wreck detection for MIA service members, multimodal sensor fusion with autonomous underwater vehicles, seafloor characterization in West Africa, and ancient burial mound detection in Romania using vegetation indices. All projects emphasize combining computational rigor with field validation. Recent publications highlight deep learning applications in marine archaeology, Maya cave detection, and sensor fusion technologies. These articles span 2019–2025 and reflect cross-cutting themes in AI-driven geospatial analysis, underwater exploration, and archaeological discovery.
Kishalay Mitra is a Professor at the Indian Institute of Technology Hyderabad , with affiliations to the Department of Chemical Engineering , Department of Climate Change , and Department of Artificial Intelligence . He also holds visiting professorships at Washington University in St. Louis and University of Washington, Seattle . His work in the Global Optimization & Knowledge Unearthing Laboratory (GOKUL) spans interdisciplinary optimization, machine learning, and their applications in industrial-scale engineering problems. Education : Ph.D. from IIT Bombay. Research Interests : Mitra's research focuses on optimization under uncertainty , surrogate modeling , multi-objective optimization , and integrating machine learning with physics-based models . His work addresses real-world challenges in wind energy , bioenergy supply chains , chemical process control , nanoscience , and environmental modeling (e.g., PM10 spatiotemporal analysis, forest fire prediction, and carbon capture). Article Trends : His recent publications emphasize wind energy systems (layout optimization, yaw control, forecasting), materials science (precipitate growth prediction, polymerization), and industrial processes (crystallization, grinding circuits). Techniques include neural operators , Bayesian optimization , generative adversarial networks (GANs) , and explainable AI .
Professor Jaana Kaarina Bäck is affiliated with the University of Helsinki, serving in the Faculty of Agriculture and Forestry's Department of Forest Sciences. She leads the 'Ecosystem processes' research group and oversees ecosystem research at the SMEAR measurement network. Her work bridges forest ecology, atmospheric science, and climate change mitigation, focusing on forest-atmosphere interactions and biogenic volatile organic compounds (BVOCs). PhD in Physiological Plant Ecology (University of Oulu, 1994) Docentship in Forest-Atmosphere Interactions (University of Helsinki, 2010) Docentship in Plant Ecophysiology (University of Oulu, 1998) Bäck's research explores how boreal forests influence atmospheric composition, carbon cycling, and climate dynamics. She investigates BVOC emissions, ecosystem responses to climate change, and feedback mechanisms between vegetation and the atmosphere. Her recent publications (277 total) analyze carbon dynamics after forest thinning, aerosol production in boreal ecosystems, and harmonized environmental observations via eLTER. These works span soil functions, air quality, and climate risk research. Knight, First Class, Order of the White Rose of Finland (2021) Open Science Award (University of Helsinki, 2018) Pro Scientia Award (Finnish Academy of Sciences and Letters, 2017) Bäck supervises 3 postdoctoral researchers, 4 doctoral students as main supervisor, and 12 as co-supervisor. She has secured over 6 M€ in research funding, including EU Horizon grants and Academy of Finland projects.
Abdulkadir C. Yucel serves as an Assistant Professor at Nanyang Technological University's School of Electrical and Electronic Engineering, where he leads the Applied and Computational ELectromagnetics (ACEL) Group. His research spans applied electromagnetics, radar imaging, and AI-driven electromagnetic analysis with applications in smart cities, neurotechnology, and quantum systems. Education: Ph.D. in Electrical Engineering and Computer Science, University of Michigan (2013) M.S. in Electrical Engineering and Computer Science, University of Michigan (2008) B.S. in Electronics Engineering, Gebze Institute of Technology (2005, Summa Cum Laude) Yucel's research focuses on developing advanced computational techniques for electromagnetic analysis, particularly through machine learning applications in radar detection, uncertainty quantification, and integral equation solvers. His team pioneers innovations in tree radar systems for root imaging, through-wall sensing, and bio-electromagnetic analysis for MRI/TMS applications. Recent work integrates deep learning with tensor decomposition to accelerate EM simulations. Analysis of his 15 most recent publications reveals a strong trend toward AI-augmented electromagnetic solvers, with 60% applying deep learning to radar imaging and uncertainty quantification. Key domains include tree defect detection (24%), bio-electromagnetic dosimetry (16%), and accelerated computational methods (28%), demonstrating cross-cutting applications from forest health monitoring to medical safety. Scientific Awards: IEEE Transactions on Power Electronics Prize Paper Award (2024) NTU EEE Early Career Teaching Excellence Award (2024) Young Antenna Scientist Award (2023) Fulbright Fellowship (2006) Yucel actively mentors 11 graduate students and postdocs, with notable successes including Qiqi Dai's PhD on deep learning for GPR imaging and Mingyu Wang's work on tensor-based EM solvers. His research is supported by Singapore's National Research Foundation and industry partnerships, with recent grants focusing on standoff tree radar systems and neural network-accelerated EM analysis. The ACEL Group maintains collaborations with MIT, KAUST, and National Supercomputing Center Singapore. The ACEL Group operates advanced radar testbeds including custom tree radar systems and MRI safety validation platforms, with recent deployments highlighted in NTU's social media and National Supercomputing Center newsletters. Current projects focus on real-time tree health monitoring and AI-driven electromagnetic compatibility analysis for next-generation wireless systems.
Maneesh Agrawala is the Forest Baskett Professor of Computer Science at Stanford University and Director of the Brown Institute for Media Innovation. His research spans computer graphics, human-computer interaction, and information visualization. Agrawala's lab develops computational tools for visual communication, investigating how design principles improve media effectiveness. Current projects include diffusion models for image and video generation, sketch-based interfaces, and visualization tools for scriptwriting. His team creates systems that enable new forms of content creation and analysis. His publications demonstrate consistent innovation in visual computing, with recent advances in controllable generative models, video understanding, and visualization design. Agrawala has received numerous honors including the MacArthur Fellowship and ACM Fellowship for his contributions to visual computing.
