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Santosh S. VempalaView profile

Professor

• Algorithms
• Convex Geometry
• High-Dimensional Sampling
• +4 more

Santosh S. Vempala is the Frederick P. Storey II Chair and Professor of Computer Science at Georgia Institute of Technology's College of Computing with joint appointments in the H. Milton Stewart School of Industrial and Systems Engineering (ISyE) and the School of Mathematics. He teaches courses including CS6150: Computing for Good (C4G) and CS6550/CS8803DAA: Continuous Algorithms: Optimization and Sampling. His research spans multiple interconnected domains: Algorithmic convex geometry and high-dimensional sampling Continuous optimization methods Computational models of brain function Randomized algorithms with applications to machine learning Vempala's recent publications reveal a strong focus on developing efficient algorithms for high-dimensional problems, particularly logconcave sampling and convex body integration. His work bridges theoretical computer science with practical applications in optimization and neuroscience, with increasing attention to the intersection of theoretical frameworks and brain computation models through his collaboration with Christos Papadimitriou. He leads the Computing for Good (C4G) initiative which applies computational approaches to social challenges, including projects like Safe and Easy Passwords!, LifeNet, C4G BLIS, and Shelter-to-Home that address problems in resource-constrained settings. Vempala currently advises PhD students Xinyuan Cao, Mirabel Reid, Max Dabagia, and Yunbum Kook, and has authored influential books including 'Spectral Algorithms' and 'The Random Projection Method' that have shaped research in algorithmic convex geometry. His tutorials at major conferences, including STOC 2015 on 'Sampling and Volume Computation in High Dimension' and FOCS 2020 on 'Computation in the Brain,' demonstrate his leadership in connecting theoretical computer science with broader scientific challenges.

Thomas GoebelView profile

Assistant Professor

• Induced Seismicity
• Fault Structure
• Earthquake Source Processes
• +3 more

Dr. Thomas Goebel is an Assistant Professor at the Center for Earthquake Research and Information (CERI), University of Memphis. He holds a PhD from the University of Southern California (2013). His research focuses on induced seismicity, fault structure, and earthquake source processes, integrating rock mechanics, seismology, and hydrogeology. Key projects include studies on fault roughness effects, aftershock clustering, and induced seismicity mitigation. He leads the Earthquake Physics Group (EPG), comprising 1 PostDoc and 5 graduate students, and collaborates internationally on volcano monitoring and geothermal energy projects. Dr. Goebel has received the 2023 Tigers Ascending to Excellence Award. Education: PhD in Earth Sciences, University of Southern California, 2013. Research interests emphasize interdisciplinary approaches to understanding stress storage/release in the crust, earthquake size prediction, and fault responses to fluid perturbations. His work bridges laboratory experiments, numerical modeling, and statistical analyses to address fundamental seismological questions. Recent publications highlight contributions to induced seismicity spatial footprints, laboratory-based aftershock dynamics, and volcano-seismic network development. He actively mentors students, with recent accolades including NSF internships and travel awards. Labs/Teams: Earthquake Physics Group (EPG) at CERI, collaborating with institutions in France, El Salvador, and the U.S. on projects like volcanic seismic networks and fault hydrology studies.

• Structural Engineering
• Structural Health Monitoring
• Civil Engineering
• +3 more

Dr. Andy Nguyen is a Senior Lecturer in the School of Engineering at the University of Southern Queensland. He holds a PhD from Queensland University of Technology (QUT), an MEng from the National University of Civil Engineering (NUCE), and a BEng from NUCE. His research focuses on structural health monitoring, integrating machine learning and deep learning techniques to assess infrastructure integrity. Key areas include damage detection in bridges, pavements, and buildings, as well as sustainable construction materials like bamboo. Nguyen leads projects such as the 'Next Generation Living Laboratory for Engineering Education and Engagement,' emphasizing real-world applications of technology in civil infrastructure. His work spans crack detection algorithms, finite element model updating, and vibration-based structural analysis. He collaborates on AI-driven solutions for autonomous vehicle object detection and smart maintenance planning. Nguyen’s contributions include over 50 peer-reviewed publications and active supervision of postgraduate research in composite materials and transport infrastructure. His research outputs highlight advancements in computational mechanics, sensor technologies, and data-driven methods for infrastructure resilience. Nguyen’s expertise bridges civil engineering challenges with cutting-edge machine learning, advancing both theoretical and applied solutions for sustainable and safe structures.

Bob KoppView profile

Professor

• Climate Change
• Sea Level Rise
• Paleoclimate
• +3 more

Bob Kopp is a Professor at Rutgers University's Department of Earth & Planetary Sciences and Co-Director of the University Office of Climate Action. He leads the NSF-funded Megalopolitan Coastal Transformation Hub (MACH), focusing on climate risk management in the Northeast U.S. and advancing understanding of coastal climate interactions. He co-directs the Climate Impact Lab, a multidisciplinary collaboration assessing climate economic risks. His research spans climate uncertainty, sea-level dynamics, and climate policy, with leadership roles in IPCC assessments and U.S. National Climate Assessments. Education: Ph.D. in Geobiology from Caltech (2000s) and B.S. in Geophysical Sciences from the University of Chicago. Prior to Rutgers, he served as AAAS Science & Technology Policy Fellow at the U.S. Department of Energy and postdoctoral researcher at Princeton University. Affiliations include Rutgers Climate Institute, Energy Institute, and graduate programs in Atmospheric Sciences, Geological Sciences, Oceanography, Statistics, and Planning & Public Policy. Research interests emphasize climate change impacts, sea-level projections, and policy integration. Key contributions include frameworks for probabilistic sea-level assessments (e.g., PaleoSTeHM) and adaptive strategies for coastal resilience. His work bridges scientific analysis with actionable policy, emphasizing stakeholder engagement in megaregions like the New York–Philadelphia urban corridor. Grants include NSF-funded projects totaling millions, supporting interdisciplinary climate solutions. Awards: Not explicitly listed, but recognized for leadership in IPCC and U.S. climate reports. Grants: Includes NSF support for MACH and Climate Impact Lab. Labs/Teams: Directs MACH, Climate Impact Lab, and collaborates with Rutgers' EOAS and Climate Institute.

Long NguyenView profile

Professor

• Nonparametric Bayesian Statistics
• Optimal Transport
• Machine Learning
• +4 more

Long Nguyen is a Professor of Statistics at the University of Michigan, Ann Arbor, with a courtesy appointment in Electrical Engineering and Computer Science. He is affiliated with the Michigan Institute for Data Science (MIDAS) and the Vietnam Institute for Advanced Study in Mathematics (VIASM). His research focuses on Bayesian nonparametrics, optimal transport, machine learning, and spatiotemporal data analysis. Nguyen holds a PhD in Computer Science from UC Berkeley and has held postdoctoral positions at Duke University and the Statistical and Applied Mathematical Institute. Education: B.Sc. in Computer Science from Pohang University of Science and Technology; M.Sc. in Mathematics from Arizona State University; Ph.D. in Computer Science from UC Berkeley (2007). Research Interests: Bayesian nonparametric methods, optimal transport theory, statistical inference for complex models, and applications in spatiotemporal data, functional data analysis, and hierarchical modeling. He emphasizes developing scalable algorithms and geometric approaches for statistical learning. Editorial Roles : Annals of Statistics Journal of Machine Learning Research SIAM Journal on Mathematics of Data Science Bayesian Analysis Awards : IMS Fellow, ASA Fellow NSF CAREER Award IEEE Signal Processing Young Author Award L. J. Savage Dissertation Award (via student Aritra Guha) Advising & Collaborations : Guided over 20 PhD students and postdocs, many now in academia and industry. Collaborates on projects in AI ethics, music theory, and environmental data science. Active in organizing summer schools in Vietnam on Bayesian statistics and machine learning. Labs & Teams : Co-leads the Statistical Machine Learning reading group at U-M and collaborates with the VIASM on advanced mathematical research in Hanoi.

• Decision Intelligence
• Strategic Management
• International Management
• +5 more

Dr. Roger Moser is a Senior Lecturer at Macquarie University's Department of Management Innovation, Strategy and Entrepreneurship Research Centre. He also holds adjunct roles at the Indian Institute of Management Udaipur (since 2012) and the University of St. Gallen (since 2012). His research focuses on Decision Intelligence, Strategic Management, and International Management, particularly exploring how executives leverage data (small/big) to enhance decision-making frameworks. He has over 99 publications since 2005, with notable works in journals like Journal of Service Research and Journal of Business Research . Dr. Moser holds a Dr. rer. pol. from EBS European Business School (2006) and a lic. oec. HSG (MSc) from the University of St. Gallen (2003). His research interests include Decision Model Innovation, Social Capital in emerging markets, and strategic alignment in supply chains. He has conducted impactful studies on access-based services in India, supplier integration in China's automotive sector, and humanitarian logistics using satellite data. His recent work emphasizes technology integration in B2B value creation, decision frameworks for uncertainty reduction (e.g., in agriculture and disaster management), and AI-driven expert systems. He has presented at global forums on topics like digital transformation and decision intelligence applications, contributing to both academic and practitioner discourse. External Roles: Adjunct Professor of Business Policy, Indian Institute of Management Udaipur Titularprofessor / Permanent Lecturer, University of St. Gallen Notable impacts include a project providing clean drinking water in India through access-based solutions, recognized for quality-of-life and societal contributions. His research has been cited over 1,666 times with an h-index of 21.

Michael BodeView profile

Professor

• Applied Mathematics
• Ecological Applications
• Conservation Biology
• +6 more

Michael Bode is a Professor in the School of Mathematical Sciences at Queensland University of Technology (QUT). His research focuses on applying mathematical and computational methods to ecological and conservation challenges, particularly in marine ecosystems and coral reef management. He holds a PhD in Applied Mathematics from the University of Queensland. Research Interests: Professor Bode specializes in larval dispersal modeling, conservation prioritization, and the application of ecosystem models to real-world management scenarios. His work integrates disciplines like applied mathematics, statistics, and ecology to address issues such as coral reef resilience, invasive species control, and climate change impacts. Grants & Projects: Securing Antarctica's Environmental Future (2020) Conserving Coral Reef Fish and Sustaining Fisheries in the Anthropocene (2019) Tackling Pests Using Game Theory (2019) New Methods for Conserving Dispersing Species on Coral Reefs (2017) Awards: 2020 Fenner Medal (Australian Academy of Science) Eureka Prize finalist (2020) Australian Research Council Future Fellowship (2017) Labs & Teams: He leads the QUT Applied Mathematical Ecology Group (QUTAMEG), which develops quantitative tools for ecological and conservation problems. His work is published in journals like Conservation Biology , Nature Sustainability , and PLoS Biology .

Dan SheldonView profile

Professor

• Computational Biology
• Bioinformatics
• Machine Learning
• +5 more

Dan Sheldon is a Professor in the Department of Computer Science at the University of Massachusetts Amherst, holding a Five College joint faculty position with Mount Holyoke College. His research focuses on developing algorithms to address environmental challenges using large datasets, emphasizing computational sustainability. Key areas include spatial optimization for endangered species conservation, continent-scale bird migration modeling, and interpreting weather radar data for ecological insights. Methodologically, his work leverages probabilistic inference, network modeling, and machine learning. Sheldon earned a PhD in Computer Science from Cornell University and an AB in Mathematics from Dartmouth College. His postdoctoral training at Oregon State University was supported by an NSF Bioinformatics Fellowship. He co-leads the BirdCast project, an NSF-funded initiative applying novel machine learning to avian migration studies. His research affiliations include the Center for Data Science and the Computational Social Science Institute. Research interests span computational biology, machine learning, and data privacy. Notable contributions include algorithms for ecological decision-making, differentially private synthetic data techniques, and Gaussian process applications in environmental forecasting. Awards include an NSF Fellowship in Bioinformatics. Current projects integrate radar data analysis, biodiversity tracking, and privacy-preserving statistical methods. Grants include the BirdCast NSF grant and collaborations in computational sustainability. His work bridges theoretical computer science with applied ecological challenges, emphasizing interdisciplinary approaches to global-scale environmental problems.

Eralp DemirView profile

Researcher

• Materials Science
• Mechanical Engineering
• Finite Element Analysis
• +3 more

Eralp Demir is a Post-Doctoral Researcher at the Department of Engineering Science, University of Oxford. His research focuses on materials mechanics, crystal plasticity, and finite element methods. He holds a PhD from RWTH Aachen University and has conducted research at institutions including Carnegie Mellon University, Max Planck Institute, and Cornell University. His current work involves developing the OXFORD-UMAT framework for fusion energy materials in collaboration with UKAEA. He specializes in in-house finite element code development and commercial software integration (e.g., Abaqus, MSC Marc). His expertise spans computational materials modeling, microstructural analysis, and experimental validation using techniques like 3D XRD. Education: PhD in Engineering Science, RWTH Aachen University Advanced Studies at Carnegie Mellon University (Mechanical Engineering), Cornell University (MAE), and others Research Interests: Crystal plasticity modeling, fusion energy materials, finite element method development, microstructural mechanics, and additive manufacturing. His work bridges computational simulations with experimental techniques to understand material behavior under extreme conditions. Labs/Teams: Collaborates with the Tarleton Research Group at Oxford and UKAEA on fusion energy projects. Active in developing open-source tools for material modeling.

• Computer Architecture
• Hardware Acceleration
• Approximate Computing
• +3 more

Akash Kumar is a Professor at Ruhr University Bochum, Germany, with affiliations to multiple institutions including TU Dresden and National University of Singapore. His research focuses on computer architecture, hardware acceleration, and approximate computing, with a strong emphasis on FPGA-based systems and neural network optimization. He has contributed extensively to embedded systems, mixed-criticality systems, and security in emerging technologies like reconfigurable nanodevices. His work spans cross-layer approximation methodologies, graph neural networks, and energy-efficient processing for edge AI. Collaborations with industry and academia highlight his leadership in VLSI design and secure hardware implementations. Notable contributions include frameworks for bounding time in mixed-criticality systems and resilient logic locking techniques. Research interests include hardware-software co-design, real-time systems, and efficient neural network architectures. His publications in top-tier conferences (DAC, DATE, FCCM) and journals (IEEE Trans. CAD, ACM TECS) underscore his impact in the field.

Rani MoranView profile

Lecturer

• Decision Making
• Reinforcement Learning
• Cognitive Maps
• +5 more

Dr. Rani Moran is a Lecturer in Psychology at Queen Mary University of London's School of Biological and Behavioural Sciences. She is affiliated with the Centre for Brain and Behaviour. Her research focuses on decision-making, memory, and learning mechanisms, employing methods like computational modeling, neuroimaging, and pharmacological interventions. Key interests include reinforcement learning, cognitive maps, and exploration-exploitation dilemmas. Her work integrates experimental designs with advanced statistical analysis to understand flexible behavioral control. Recent studies explore model-based vs. model-free learning, credit assignment mechanisms, and disinformation's impact on learning biases. She has published in top journals such as Psychological Review and Nature Communications . Dr. Moran collaborates on projects involving neurocomputational models of confidence, meta-cognition, and social learning. Her research aims to develop interventions for optimizing cognitive processes, with applications in mental health and decision-making contexts.

Stefan WrobelView profile

Professor

• Machine Learning
• Data Mining
• Artificial Intelligence
• +3 more

Prof. Stefan Wrobel is a Professor of Computer Science at the University of Bonn and Director of the Fraunhofer Institute for Intelligent Analysis and Information Systems (IAIS). He holds leadership roles, including Co-Director of the Lamarr Institute for Machine Learning and Artificial Intelligence and Managing Director of the Bonn-Aachen International Center for Information Technology (b-it). His research focuses on AI, machine learning, and big data applications in industry and society. He earned his PhD from the University of Dortmund and has held academic positions at Magdeburg University and Berlin Technical University. Active in national/international AI initiatives, he chairs the Fraunhofer Strategic Research Field on Artificial Intelligence and co-leads the Machine Learning Rhine-Ruhr (ML2R) Competence Center. Education: Master's (Georgia Tech), PhD (University of Dortmund). Research emphasizes intelligent algorithms, data analysis, and AI ethics. Awarded GI-Fellow (2022) and honored by the German Computer Science Society for contributions to AI history. Key roles include Editorial Board member of Machine Learning journals and advisory roles in AI ethics and certification. Scientific contributions span over 100 publications in machine learning, data mining, and visual analytics. Advised numerous PhD students on topics like graph mining and trustworthy AI. Leadership in institutions like Fraunhofer Technology Hub for Machine Learning and the German Computer Science Society's Special Interest Group on Knowledge Discovery.

Katja FennelView profile

Professor

• Chemical Oceanography
• Biological Oceanography
• Physical Oceanography
• +3 more

Katja Fennel is a Professor in the Department of Oceanography at Dalhousie University, Faculty of Science. Her research focuses on coupled physical-biogeochemical modeling to understand marine ecosystems, carbon and nutrient cycling, and climate change impacts. She specializes in coastal and open ocean dynamics, with expertise in hypoxia, ocean deoxygenation, and carbon sequestration. Fennel holds significant academic roles including Killam Professorship (2019-2024) and former Co-editor-in-Chief of Biogeosciences (2013-2021). She has led projects on ocean prediction, marine carbon dioxide removal (CDR), and coastal acidification. Education: PhD in Marine Biology and Diploma in Numerical Mathematics from University Rostock, Germany Affiliations: Adjunct roles at University of Maryland Center for Environmental Science, University of Maine, and Rutgers University Research Interests: Development of high-resolution coupled models to study biogeochemical processes in shelf and open ocean systems. Key areas include the impact of climate change on marine ecosystems, hypoxia formation mechanisms, and the role of ocean alkalinity enhancement in carbon removal. Fennel's work integrates observational data with advanced modeling to address ecological and climate challenges. Publications highlight contributions to understanding coastal-ocean carbon dynamics, biogeochemical forecasting, and Arctic carbon uptake. Recent focus includes expanding Argo float networks and evaluating marine CDR strategies like ocean alkalinity enhancement (OAE). Awards: AGU Fellow (2024), CMOS President's Prize (2023)

Bing YanView profile

Assistant Professor

• Power System Optimization
• Grid Integration of Renewables
• Microgrid Operation
• +3 more

Bing Yan is an Assistant Professor in the Department of Electrical and Microelectronic Engineering at Rochester Institute of Technology (RIT), affiliated with the Kate Gleason College of Engineering. She holds a B.S. in Information Management from Renmin University of China (2010), and M.S. and Ph.D. degrees in Electrical Engineering and Statistics from the University of Connecticut (2012–2017). Prior to RIT, she was an Assistant Research Professor at the University of Connecticut. Dr. Yan’s research focuses on power system optimization , including grid integration of renewables (wind/solar), microgrid operations, distributed energy systems, and manufacturing scheduling. She has published over 30 peer-reviewed articles and secured grants from the National Science Foundation (including a CAREER Award), Department of Energy, and industry partners like Brookhaven National Laboratory and ABB. Her work emphasizes mixed-integer linear programming and machine learning applications in energy systems. Notable contributions include stochastic unit commitment models for wind farms, voltage control via deep reinforcement learning, and multi-layer weather models for PV prediction. She advises on projects involving grid resilience, smart manufacturing, and data-driven optimization. Awards: National Science Foundation Faculty Early Career Development (CAREER) Award Multiple NSF grants, DOE grants, and industry contracts Teaching: Courses include Circuits I , Electric Power Transmission & Distribution , and Advanced Power Systems . She also mentors students through co-op programs and independent studies. Labs/Teams: Leads the Intelligent Lab of Power and Manufacturing (ILPM), focusing on multidisciplinary solutions for energy and manufacturing systems. The lab emphasizes hands-on training and innovation in smart grid technologies and sustainable energy systems.

• Bayesian Statistics
• Machine Learning
• Health Informatics
• +3 more

Sara Wade is a Lecturer in Statistics and Machine Learning at the University of Edinburgh , within the School of Mathematics . Her research focuses on Bayesian statistics, machine learning, and their applications in health sciences, particularly in dementia diagnosis and predictive modeling. She holds a PhD from the University of Milan and has held academic positions at the University of Cambridge and University of Warwick before joining Edinburgh. She teaches a popular Machine Learning and Python course for Master’s and final-year undergraduate students, attracting nearly 200 enrollments annually. Her work integrates Bayesian methods with modern machine learning, emphasizing interdisciplinary applications such as scalar-on-image regression and biomarker analysis. Notable contributions include developing hierarchical Dirichlet processes for clustering and uncertainty quantification in RNA velocity studies. She secured a Royal Society of Edinburgh grant for her dementia research project, which aims to improve early diagnosis through statistical modeling. Education: PhD in Statistics, University of Milan Bachelor’s in Mathematics, University of Maryland Wade advocates for diversity in STEM, actively participating in the Women in Machine Learning community. Her research bridges statistical rigor and computational tools, fostering collaborations across academia and healthcare sectors.