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Steven CollinsView profile

Academic

• Timber Engineering
• Structural Reliability
• Sustainability
• +7 more

Steven Collins is a Professor of Practice in Civil Engineering at Aalto University , specializing in industrial wood construction. His work bridges scientific research and industry applications to enhance timber's role in sustainable structural engineering. Research Interests : Species-specific timber behavior (particularly birch), focusing on modeling, testing, and variance analysis. Non-destructive testing techniques like laser scattering, grain orientation analysis, and digital image correlation. Statistical and Bayesian approaches for probabilistic modeling of material properties and uncertainty quantification. Timber finger joint mechanics and wood processing techniques (e.g., densification, chemical treatment). Scientific Contributions : His recent publications (2022–2025) emphasize mechanical behavior of timber , predictive modeling , and innovative processing methods . Key trends include optimizing birch timber for structural applications, advancing Bayesian statistical models for wood mechanics, and improving non-destructive testing protocols using laser technologies and digital image correlation. Laboratory & Collaborations : Actively involved in experimental testing (destructive and non-destructive) and statistical analysis of wood properties. Collaborates with researchers like Gerhard Fink , Lauri Rautkari , and Farid Vafadar on timber mechanics and reliability studies. Part of the Structures – Structural Engineering, Mechanics and Computation research group at Aalto University.

Joel SokolView profile

Professor

• Sports Analytics
• Applied Operations Research
• Health Informatics
• +7 more

Joel Sokol is the Harold E. Smalley Professor in the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Tech. He serves as Director of the interdisciplinary Master of Science in Analytics (MSA) degree, offered both on-campus and online. His academic journey began with a Ph.D. in Operations Research from MIT (1999), followed by bachelor's degrees in Mathematics, Computer Science, and Applied Sciences in Engineering from Rutgers University (1994). Education Ph.D. in Operations Research (MIT, 1999) B.S./B.A. in Mathematics, Computer Science, Applied Sciences in Engineering (Rutgers, 1994) Dr. Sokol's research focuses on Sports Analytics , Health Informatics , and Supply Chain Optimization . He pioneered the LRMC (Logistic Regression/Markov Chain) method for NCAA basketball tournament predictions, which has become an industry standard. His work extends to organ transplantation logistics, maritime shipping networks, and semiconductor manufacturing optimization, blending machine learning with traditional operations research techniques. The articles reflect his interdisciplinary expertise: 2025 introduced a Smart Stadium Testbed for real-time sports analytics, while 2024 addressed Language Model Safety . Earlier publications (2023–2018) focused on transplant survival modeling, vaccine scheduling, and maritime logistics, showcasing his ability to apply analytics to diverse domains. Scientific Awards EURO Management Science Strategic Innovation Prize (2008) Cozzarelli Prize finalist (non-sports research) Georgia Tech's highest teaching awards (multiple years) INFORMS and IISE recognitions for curriculum development As a leader in analytics education, Sokol co-founded the INFORMS Sports Operations Research section and served as INFORMS Vice President of Education. His work has practical applications in professional sports, healthcare, and industry, with methodologies adopted by teams, medical institutions, and global logistics networks.

John DolbowView profile

Professor

• Computational Mechanics
• Fracture Mechanics
• Numerical Methods
• +5 more

John Dolbow is a Professor in the Thomas Lord Department of Mechanical Engineering and Materials Science at Duke University, with secondary appointments in Civil and Environmental Engineering and Mathematics. He is a Bass Fellow and holds leadership roles as Associate Vice President for Research & Innovation since 2024. Education: B.S.M.E. (University of New Hampshire, 1995), M.S. (Northwestern, 1998), Ph.D. (Northwestern, 1999) Research Focus: Computational fracture mechanics, phase-field modeling, hydrogels, and multiphysics problems in geomechanics and biomedical engineering His recent work advances phase-field methods for fracture nucleation, hydraulic fracturing in geothermal systems, and laser lithotripsy simulations. Dolbow leads Duke's Computational Mechanics Laboratory, integrating civil, mechanical, and materials science approaches. Key contributions include: Erratum corrections for computational mechanics frameworks Nitsche-stabilized methods for interface constraints Phase-field models for surfactant-driven particle raft fracture Multi-resolution approaches for hydraulic fracture simulation Embedded FEM techniques for moving boundary problems Scientific Recognition: R. H. Gallagher Young Investigator Award (2005) Robert J. Melosh Medal for Finite Element Analysis (1999) DOE Computational Science Graduate Fellowship (1997) DOE CSGF Steering Committee Chair

Andrew ElliottView profile

Senior Lecturer

• Statistics
• Data Analytics
• Modeling in Space and Time
• +8 more

Dr Andrew Elliott is a Senior Lecturer in the School of Statistics at the University of Glasgow. His research bridges Statistics , Computational Statistics , and Machine Learning & AI , with applications in Social & Urban Studies and Imaging, Image Processing & Image Analysis . Education: Not explicitly detailed in the text. Dr Elliott's work focuses on modeling in space and time , synthetic data generation , and network analysis . His recent publications explore fairness constraints in AI , agent swarms , and temporal network modeling , reflecting interdisciplinary applications in urban analytics and environmental science. His publications from 2014–2024 span network science , machine learning , image analysis , and geospatial studies . Key trends include privacy auditing , counterfactual explanations , and deep learning for network time series . Dr Elliott supervises doctoral students and has advised Zhengduo Zhao and Weiyue Zheng . He actively contributes to research groups in Statistics & Data Analytics and Machine Learning . Notable collaborations include work with Mihai Cucuringu and Gesine Reinert .

• Trustworthy Machine Learning
• Algorithmic Fairness
• Robust Distributed Learning
• +5 more

Sanmi (Oluwasanmi) Koyejo is an Assistant Professor in the Department of Computer Science at Stanford University and an adjunct Associate Professor at the University of Illinois at Urbana-Champaign. He leads Stanford Trustworthy AI Research (STAIR), working to develop the principles and practice of trustworthy machine learning with applications to neuroscience and healthcare. Koyejo holds affiliations with multiple Stanford institutes including SAIL, HAI, CRFM, AIMI, AI Safety, Machine Learning Group, and Bio-X. Koyejo completed his Ph.D. at the University of Texas at Austin followed by postdoctoral research at Stanford University. His research bridges theoretical machine learning with practical healthcare applications, focusing on developing robust and fair AI systems that can be trusted in critical domains. His work spans algorithmic fairness, robust distributed learning, metric elicitation, and applications to medical imaging and neuroscience. His recent publications demonstrate a strong focus on emerging challenges in AI including emergent abilities in large language models, fairness in medical AI, federated learning, and robustness against adversarial attacks. His work has increasingly addressed real-world healthcare challenges through deep learning applications to medical imaging, particularly chest radiographs for disease detection. Scientific Awards: NSF CAREER Award 2021 Skip Ellis Early Career Award Sloan Research Fellowship Frederick E. Terman Faculty Fellow (2022) Best Paper Award from UAI Kavli Fellowship IJCAI Early Career Spotlight Koyejo actively mentors a large research group with numerous PhD students and postdocs. His research has been supported by significant grants including NSF funding for projects like 'Fair Federated Representation Learning for Breast Cancer Risk Scoring.' He serves in leadership roles including as General Co-chair for NeurIPS 2022 and President of the Black in AI organization. His STAIR research group focuses on developing trustworthy AI principles and practices, with applications to healthcare and neuroimaging. The group collaborates extensively with healthcare institutions including OSF Healthcare and participates in major initiatives like the NIH-funded MIDRC and the NSF AI research institute AIFARMS.

• Forecasting
• Time Series Analysis
• Inventory Management
• +5 more

Nikolaos Kourentzes is a Professor of Informatics at the University of Skövde , specializing in forecasting and operations research. His work bridges theoretical advancements in time series analysis with practical applications in supply chain management, tourism demand, and renewable energy forecasting. Academic Rank: Professor Department: Department of Information Technology Research Interests: His research focuses on hierarchical and temporal forecasting methodologies, integrating macroeconomic indicators into demand planning, inventory optimization, and machine learning applications. He explores forecast reconciliation, shrinkage estimators, and the role of expert judgment in predictive analytics. Recent Publications: Highlights include advances in hierarchical forecasting with leading indicators, probabilistic forecasts during crises like the pandemic, and complex smoothing techniques. His work spans journals such as Omega , International Journal of Forecasting , and European Journal of Operational Research . Collaborations: Kourentzes collaborates with researchers globally, including George Athanasopoulos, Rob Hyndman, and Robert Fildes, across domains like tourism analytics, tire industry forecasting, and public health modeling.

Abolfazl SimorghView profile

Researcher

• Aircraft Trajectory Optimization
• Climate Change Mitigation in Aviation
• Robust Control
• +3 more

Abolfazl Simorgh is a researcher at Charles III University of Madrid's Department of Aerospace Engineering, specializing in climate-optimized aviation systems. His work bridges mathematical control theory with practical climate impact mitigation, focusing on robust trajectory optimization under environmental and operational uncertainties. He leads development of open-source tools for sustainable flight planning while contributing to major European aviation initiatives. Education: B.Sc. in Control Engineering (2017) M.Sc. in Control Engineering (2020) Ph.D. in Aerospace Engineering from Charles III University of Madrid Dr. Simorgh's research centers on developing mathematical frameworks that reconcile aircraft trajectory optimization with climate impact reduction. His expertise spans robust control systems, optimization under uncertainty, and climate modeling integration, with particular emphasis on non-CO₂ emissions. His methodology addresses both CO₂ and non-CO₂ climate forcing mechanisms through computationally efficient algorithms that account for weather variability and climate metric uncertainties. This work directly supports aviation's decarbonization by providing operational strategies that reduce environmental footprint without prohibitive cost increases. Analysis of his 15 most recent publications reveals a cohesive research trajectory focused on operationalizing climate-optimal flight planning. His work consistently integrates climate science with aerospace engineering through robust optimization frameworks, demonstrating particular innovation in handling multiple uncertainty sources (weather, climate models, emissions). The publications cluster around three interconnected themes: 1) Development of open-source computational tools (ROOST, CLIMaCCF), 2) Network-scale implementation of climate-aware air traffic management, and 3) Risk analysis of climate mitigation strategies. This body of work establishes new methodological standards for quantifying and minimizing aviation's total climate impact. Scientific Awards: Luis Azcárraga Aeronautical Innovation Award for collaborative research impact Best Paper Award (2022) from a high-impact aerospace journal Dr. Simorgh secures significant research funding through European Commission projects including FlyATM4E (climate-optimized flight planning), ALARM (aviation emissions reduction), and RefMAP (sustainable aviation pathways). His grant portfolio emphasizes practical implementation of climate mitigation strategies, with strong industry-academia collaboration. He mentors junior researchers through project teams and has developed three major open-source Python libraries (CLIMaCCF, ROOST, ROC) that have become community standards for climate impact assessment in aviation research. His current work focuses on scaling climate-optimized trajectories to continental airspace while addressing operational constraints and economic viability. He leads a research group focused on climate-aware air traffic management, developing the ROOST simulation framework for GPU-accelerated trajectory optimization and the CLIMaCCF library for standardized climate metric calculations. His team collaborates with European air navigation service providers and aircraft manufacturers to transition research into operational practice, with current projects emphasizing real-time implementation and regulatory compliance frameworks.

Venkat N. KroviView profile

Professor

• Robotics
• Autonomous Vehicles
• Human-Robot Interaction
• +5 more

Prof. Venkat N. Krovi serves as the Michelin Endowed Chair Professor of Vehicle Automation in the Departments of Automotive Engineering and Mechanical Engineering at Clemson University's College of Engineering, Computing and Applied Sciences (CECAS). He directs the Automation, Robotics and Mechatronics Laboratory (ARMLab) at the International Center for Automotive Research (CU-ICAR), focusing on smart embedded systems for autonomy in challenging environments. He earned his Ph.D. in Mechanical Engineering and Applied Mechanics from the University of Pennsylvania in 1998. His research leverages distributed autonomy and human-robot synergy to extend human capabilities, with applications spanning plant automation, consumer electronics, automobile, defense, and healthcare. The work emphasizes lifecycle treatment (design through verification) of robotic systems under uncertainty. Recent publications (2024-2025) demonstrate strong trends in digital twin frameworks for autonomous vehicle validation, sim2real transfer via reinforcement learning, and integration of large language models for editable simulations. Key themes include scalable cloud-based architectures, Koopman operator theory for robustness, and containerization for reproducible robotics development. His accolades include: National Science Foundation (NSF) CAREER Award Petro-Canada Young Innovator Award Multiple best paper awards at conferences and journals ASME Dedicated Service Award (2024) Prof. Krovi has advised doctoral students including Dr. Srivatsan Srinivasan (2024). His research receives substantial funding from NSF, DARPA, ARO, and industrial partners like Michelin. He leads the NSF I/UCRC RoSeHuB center and the AutoDRIVE ecosystem for autonomous driving education. As ARMLab director, he oversees projects including OpenCAV, the Robotics for AV Systems Bootcamp, and containerized terramechanics simulations. The lab specializes in mechatronic design, verification/validation frameworks, and human-autonomy coexistence studies for next-generation mobility solutions.

• Computational Neuroscience
• Neural Networks
• Deep Learning
• +4 more

Prof. Sander M. Bohte holds a part-time appointment as a Professor of Computational Neuroscience at the Swammerdam Institute for Life Sciences (SILS), University of Amsterdam, and is a researcher at the CWI Machine Learning group. His research focuses on computational models of neural information processing, emphasizing spiking neural networks, predictive coding, and reinforcement learning. He bridges computational neuroscience and machine learning, exploring how biological insights can improve neural network designs and vice versa. Key collaborations include work with Cyriel Pennartz (UvA), Pieter Roelfsema (NIN), and Steven Scholte (B&C). His applied research spans scientific machine learning applications in finance and genomics. He actively supervises MSc thesis students, prioritizing those from UvA, with projects ranging from biologically inspired neural architectures to efficient spiking network simulations. Research highlights include developing biologically plausible learning rules for deep networks, predictive coding models for sensory data, and spiking network models for working memory tasks. His work also addresses challenges in temporal dynamics and scalable neural computation, leveraging both theoretical and applied perspectives.

Michael DeWeeseView profile

Associate Professor

• Nonequilibrium Statistical Mechanics
• Machine Learning Theory
• Systems Neuroscience
• +3 more

Michael DeWeese is an Associate Professor of Physics and Neuroscience at the University of California, Berkeley. His research spans nonequilibrium statistical mechanics, machine learning theory, and systems neuroscience. He holds a BA in Physics from UC Santa Cruz (1988) and a PhD in Physics from Princeton (1995). Before joining UC Berkeley in 2007, he held postdoctoral positions at the Salk Institute and Cold Spring Harbor Laboratory. His work integrates principles from physics, neuroscience, and machine learning to address fundamental questions in theoretical and experimental biology, computation, and statistical mechanics. DeWeese Lab Website provides further details on ongoing projects and collaborations. Education: BA in Physics, UC Santa Cruz (1988) PhD in Physics, Princeton University (1995) Research Interests: Nonequilibrium Statistical Mechanics: Focuses on thermodynamic optimization, active matter, and non-equilibrium processes. Machine Learning Theory: Develops first-principles models to explain neural network performance and efficient algorithms for probabilistic models. Systems Neuroscience: Uses biologically inspired models to understand neural coding, sensory processing, and computational roles of neural networks. Advising & Grants: While no formal student advisees are listed, his lab actively collaborates across disciplines. Funding sources are not explicitly mentioned but likely involve NSF, NIH, or DOE grants based on research themes. His work on quantum control and neural networks suggests potential ties to interdisciplinary funding initiatives. Labs & Teams: Directs the DeWeese Lab, which bridges physics, neuroscience, and machine learning. Collaborations include institutions like the Helen Wills Neuroscience Institute (UC Berkeley).

Daniel RomeroView profile

Professor

• UAV Communications
• Wireless Communications
• Time-Series Analysis
• +5 more

**Daniel Romero** is a **Professor** in the **Department of Information and Communication Technology** at the **University of Agder**, Norway. His research focuses on UAV communications, time-series analysis using machine learning and network science, and decentralized processing for sensor networks. He holds a Ph.D. in Signal Theory and Communications from the University of Vigo (2015), an M.Sc. in Signal Theory (2011), and a Telecommunication Engineering degree (2009). **Education**: Ph.D. in Signal Theory and Communications, University of Vigo (2015) M.Sc. in Signal Theory and Communications, University of Vigo (2011) Telecommunication Engineering, University of Vigo (2009) **Research Interests**: His work spans UAV communication systems (focusing on low-latency, high-reliability networks), time-series analysis for complex systems (using ML and network science), and decentralized computation in sensor networks to improve robustness and hardware efficiency. Recent projects include radio map estimation for mmWave beam alignment, spoofing detection via graph neural networks, and aerial base station placement optimization. **Publications**: Over 30+ peer-reviewed articles in top venues like IEEE Transactions on Wireless Communications and ICC. Recent trends emphasize radio map estimation (2023–2024), UAV-enabled spectrum surveying (2022), and robust D2D communications (2022). **Advising & Grants**: Teaches PhD courses (Statistical Signal Processing, Advanced Optimization) and leads the **Advanced Signal Processing Lab (ASL)**. Collaborates with the **CIEM (Center for Integrated Emergency Management)** on crisis-related communication systems. **Labs/Teams**: Directs the Advanced Signal Processing Lab (ASL.uia.no) and contributes to CIEM, applying ML and signal processing to emergency management challenges.

Kuldeep S. MeelView profile

Associate Professor

• Automated Reasoning
• Formal Methods
• Artificial Intelligence
• +3 more

Kuldeep S. Meel is the Stephen Fleming Early-Career Associate Professor at Georgia Institute of Technology's School of Computer Science and an Associate Professor at the University of Toronto (currently on leave). His research focuses on the intersection of Formal Methods and Artificial Intelligence, emphasizing scalable automated reasoning techniques. He holds prestigious awards including the 2022 ACP Early Career Researcher Award and the 2019 NRF Fellowship for AI. His work has been recognized with multiple best paper awards at conferences like ICLP, CAV, and IJCAI. Meel's research spans automated reasoning, formal methods, and their applications in AI. He has developed influential tools like ApproxMC and UniGen, advancing model counting and uniform sampling. His academic journey includes roles at NUS and collaborations with institutions globally. Teaching excellence is highlighted by NUS Annual Teaching Awards (2022, 2023). Awards include Distinguished Paper Awards at CAV-23 and CAV-24, and 1st place in Model Counting Competitions. His lab has produced notable advisees securing tenure-track positions worldwide. Current projects explore distribution testing, probabilistic reasoning, and AI verification.

Adel AbdelnabyView profile

Associate Professor

• Structural dynamics
• Earthquake engineering
• Structural health monitoring
• +6 more

Dr. Adel Abdelnaby is an Associate Professor in the Department of Civil, Construction, and Environmental Engineering at The University of Memphis College of Engineering. He holds a Ph.D. from the University of Illinois at Urbana-Champaign (2012) and has been on the faculty since fall 2012. Dr. Abdelnaby is a licensed Professional Engineer (P.E.) in multiple states and a licensed Structural Engineer (S.E.) in Illinois. His research interests span structural dynamics, earthquake engineering, structural health monitoring, life-cycle analysis of structures, application of innovative materials, and nonlinear finite element methods. He specializes in analyzing structures subjected to multiple hazards and developing methods for structural assessment and improvement. Dr. Abdelnaby's publications reveal a strong focus on earthquake engineering, particularly the effects of multiple earthquakes on reinforced concrete structures. His work includes fragility analysis, hybrid simulation techniques, and vulnerability assessment of bridges and buildings. He has established the Multi-Axial Testing and Simulation (MAT-SIM) Facility at the University of Memphis for advanced structural testing. Engaged Learning Fellowship for redesigning undergraduate steel design courses American Institute of Steel Construction Educator Workshop participant Dr. Abdelnaby has advised numerous graduate students on topics including semi-rigid steel connections, multiple earthquake effects, fragility analysis, and structural health monitoring. He directs the MAT-SIM facility, which includes sophisticated equipment for structural testing under complex loading conditions. His professional affiliations include ASCE, AISC, ACI, EERI, and ASEE, and he serves as a reviewer for several structural engineering journals.

• Algorithm Design
• Discrete Optimization
• Approximation Algorithms
• +2 more

Nikhil Bansal is Patrick C. Fischer Professor of Theoretical Computer Science at the University of Michigan. His research focuses on algorithms for discrete optimization problems, discrepancy theory, and scheduling. Research Contributions: Developed foundational techniques in randomized rounding and algorithmic discrepancy minimization. Current investigations include online algorithms, approximation methods for scheduling, and quantum-classical complexity separations. Teaching: Offers courses on randomized algorithms, linear algebra, and approximation algorithms. Created instructional materials on discrepancy theory and semidefinite programming. Professional Service: Serves on editorial boards for Journal of the ACM and Theory of Computing. Organized international workshops on discrepancy theory and optimization.

Borut TrpinView profile

Researcher

• Formal Epistemology
• Philosophy of Science
• Models of Human Reasoning
• +3 more

Borut Trpin is a postdoctoral fellow at the Munich Center for Mathematical Philosophy (MCMP) at Ludwig-Maximilians-Universität München and a principal investigator at the University of Maribor's Department of Philosophy. Starting October 2025, he will assume the role of Assistant Professor at the University of Ljubljana. His research focuses on formal epistemology and philosophy of science, with emphases on models of human reasoning, epistemic justification, and scientific disagreements. He employs computer simulations to explore these topics and serves as an associate editor for Acta Analytica . Trpin holds a PhD in Philosophy from the University of Ljubljana (2018) and previously served as a postdoctoral fellow at the University of Salzburg (2018-2019). His work bridges philosophical inquiry with computational methods, examining issues like coherence theory, Bayesian epistemology, and the dynamics of trust in social contexts. Recent research includes studies on scientific disagreements, conjunction fallacies, and the role of coherence in epistemic justification. His publications span journals such as Philosophical Studies , Erkenntnis , and Synthese , reflecting his interdisciplinary approach. Key themes include methodological critiques (e.g., 'Against Methodological Gambling'), coherence metrics, and simulation-based analyses of deception and trust. Trpin’s future work includes assuming a permanent academic role at the University of Ljubljana while continuing collaborative research projects in mathematical philosophy.