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Marieke H. MartensView profile

Professor

• Automated Driving
• Human Factors
• Traffic Safety
• +3 more

Marieke H. Martens is a Full Professor of Automated Driving & Human Interaction at Eindhoven University of Technology (TU/e) and Director of Science at TNO’s Traffic & Transport unit. Her work focuses on human behavior in automated driving systems, addressing challenges like trust, control transitions, and societal acceptance. She holds a PhD in Experimental Psychology from Vrije Universiteit Amsterdam and previously served as a professor at the University of Twente. Education: PhD in Experimental Psychology, Vrije Universiteit Amsterdam (2007) Master’s in Experimental and Cognitive Psychology, Vrije Universiteit Amsterdam Research Interests: Human factors in automated vehicles, human-machine interaction design, road user safety, and the societal implications of smart mobility. Her work bridges psychology, engineering, and design to ensure systems are user-centered and safe. Awards: CHI '20 Best Paper Award (2020) AutomotiveUI '20 Honorable Mention Award (2020) Advising & Grants: Supervises over 10 students, focusing on automated driving’s human aspects. Active in projects like ISA-FIT (Intelligent Speed Assistance) and dynamiC spEed Limits. Collaborates globally with institutions and automotive industries. Labs & Networks: Leads TU/e’s Designing With Intelligence initiative and participates in EU ethics committees for automated mobility. Maintains a global network in academia and industry, fostering interdisciplinary research.

Valentina BreschiView profile

Assistant Professor

• Data-driven control
• Control systems
• Machine learning
• +5 more

Valentina Breschi is an Assistant Professor in the Control Systems Group at the Department of Electrical Engineering, Eindhoven University of Technology (TU/e). She holds a Ph.D. from IMT School for Advanced Studies Lucca, with postdoctoral and junior faculty experience at Politecnico di Milano. Her research focuses on data-driven control, jump model learning, meta-learning for system identification, and human-centered policy design for mobility systems. She contributes to UN Sustainable Development Goals related to sustainable infrastructure and innovation. Education: B.Sc. in Electronic and Telecommunication Engineering (University of Florence, 2011) M.Sc. in Electrical and Automation Engineering (University of Florence, 2014) Ph.D. in Control Systems (IMT School for Advanced Studies Lucca, 2018) Research Interests: Her work spans data-driven control methodologies, including LPV control, predictive control, and ethical frameworks for policy design. She explores applications in sustainable mobility, energy systems, and healthcare, emphasizing fairness and social impact. Labs/Teams: She is part of the Control Systems Group, collaborating on projects like the CONSIDER study and the design of fair-MPC frameworks. Her work integrates theoretical control principles with real-world applications in smart systems and social networks.

• Geotechnical Engineering
• Energy Geotechnics
• Numerical Modeling
• +6 more

Dr David M G Taborda serves as a Reader (equivalent to Associate Professor) in the Geotechnics section of Imperial College London's Department of Civil and Environmental Engineering within the Faculty of Engineering. His research focuses on energy geotechnics, numerical modeling of soil behavior, and offshore wind turbine foundation design, with significant contributions to thermo-active infrastructure systems. Civil Engineering, University of Coimbra, Portugal (2004) PhD in Numerical Modelling of Dynamic Soil Behaviour and Liquefaction, Imperial College London (2010) Dr Taborda's research program spans energy geotechnics , where he pioneered experimental techniques and numerical methods for thermo-active foundations including piles, retaining walls, and tunnels. His work integrates constitutive modeling of soils with applications in offshore wind turbine foundations through the PISA project, developing advanced design methodologies for monopiles in challenging soil conditions. Current investigations address soil liquefaction mechanisms, thermo-hydro-mechanical coupling in granular materials, and machine learning applications for foundation analysis. His research bridges experimental validation with computational innovation to solve complex geotechnical challenges in sustainable energy infrastructure. Analysis of Dr Taborda's 41 publications (2017-2025) reveals three dominant research trajectories: (1) thermo-active infrastructure optimization with increasing focus on thermal interference and machine learning applications; (2) cyclic behavior of offshore foundations using high-cycle accumulation frameworks; and (3) multi-physics modeling of liquefaction and soil-structure interaction. Recent work demonstrates a strong trend toward data-driven approaches and surrogate modeling for complex geotechnical systems, particularly in energy geostructures and offshore wind applications. Dr Taborda actively supports prospective PhD students through Imperial College's President's Scholarships and College funding schemes, and encourages applications for Imperial College Research Fellowships. His research has been advanced through significant industry-academic collaborations including the PISA project for offshore wind foundations, though specific grant details are not provided in the source material. He maintains strong connections with the Geotechnical Consulting Group where he previously worked, facilitating industry-relevant research translation. Affiliated with Imperial's Energy Futures Lab, Dr Taborda collaborates within the Geotechnics section on experimental and computational research. His team develops specialized equipment for thermo-hydro-mechanical testing and contributes to the department's leadership in energy geostructures research, with applications spanning urban infrastructure, renewable energy systems, and offshore engineering projects.

Wout WeijtjensView profile

Research Fellow

• Structural Health Monitoring
• Offshore Wind Turbines
• Fatigue Analysis
• +2 more

Wout Weijtjens is a Research Fellow at Vrije Universiteit Brussel, affiliated with the Acoustics & Vibration Research Group in Applied Mechanics. His research focuses on structural health monitoring (SHM) of offshore wind turbines, fatigue analysis, and vibration-based damage detection using advanced signal processing and machine learning techniques. Current projects include FIRMEST (fatigue assessment of offshore wind turbine substructures) and FOOS (Forced Oscillations in turbines). His research interests span: Operational modal analysis for offshore structures Machine learning applications in SHM Fatigue life prediction under environmental variability Sensor networks for infrastructure monitoring Wind turbine dynamics under harsh conditions Recent publications demonstrate a consistent focus on developing predictive maintenance frameworks through multivariate sensor data analysis, uncertainty quantification in SHM systems, and validation of computational models against full-scale field measurements. Article trends emphasize machine learning integration with physical models for improved fatigue life assessment. Awards and recognitions include: Best Paper Award (2nd place, 2022) Poster Award (2017) Solvay Award (2015) As principal investigator on multiple grants including VLADBC7 and VLADBC9 projects, he supervises PhD candidates in vibration-based SHM and leads experimental validation at OWI-Lab's Large Climate Chamber. His team develops IoT monitoring solutions for civil infrastructure through the SMART TOWERS initiative.

Lorenzo SaniView profile

Researcher

• Federated Learning
• Edge Computing
• Privacy-Preserving Machine Learning
• +3 more

Lorenzo Sani is a PhD student in the Department of Computer Science and Technology at the University of Cambridge, supervised by Prof. Nicholas D. Lane and part of the CaMLSys research group. His work focuses on federated learning, edge computing, and privacy-preserving machine learning algorithms for large-scale distributed systems. Education: He holds a Bachelor's Degree in Physics from the University of Bologna (2019) and a Master's Degree in Applied Physics from the same institution (2021), with a thesis on unsupervised clustering of MDS data using federated learning. During his studies, he contributed to the GenoMed4All project and collaborated with the CaMLSys group on the Flower Framework. Research Interests: Sani's research emphasizes optimizing federated learning efficiency, privacy in distributed machine learning, and the application of federated techniques to large language models. His work addresses challenges in communication efficiency, client collaboration, and ethical data usage in decentralized systems. Teaching: He serves as a Teaching Assistant for the Principles of Machine Learning Systems (L46) and Federated Learning: Theory and Practice (L361) courses, and supervises students at Jesus College for Algorithm and Artificial Intelligence modules. Publications: His recent work includes innovations in federated optimization (DES-LOC, SparsyFed), LLM unlearning (LUNAR), and global federated training systems (Photon, Worldwide federated training). The 2020 Flower Framework paper established a foundational research tool for federated learning experimentation.

Mina Konakovic LukovicView profile

Assistant Professor

• Computer Graphics
• Computational Fabrication
• 3D Geometry Processing
• +4 more

Mina Konaković Luković is an Assistant Professor in the Department of Electrical Engineering and Computer Science at Massachusetts Institute of Technology (MIT) , affiliated with the Computer Science and Artificial Intelligence Laboratory (CSAIL) . She leads the Algorithmic Design Group , focusing on computational design and fabrication, geometry processing, and robotics. Education: PhD in Computer Science (EPFL), MS & BS in Mathematics (University of Belgrade) Research Interests: Spanning computer graphics, computational fabrication, and 3D geometry processing with applications in smart materials, architectural geometry, and physics-based simulations. Her work integrates machine learning and differential geometry to optimize design algorithms for novel materials and deployable structures. Articles Trends: Recent publications emphasize multi-objective optimization for 3D printing materials, graph grammar in robot design, and computational methods for auxetic structures. Themes revolve around data-driven design, deployable shells, and terrain-adaptive robotics. Scientific Awards: Schmidt Science Fellows Additional Study Grant (2020) ACM SIGGRAPH Outstanding Doctoral Dissertation Honorable Mention (2020) Eurographics PhD Award (2020) Patrick Denantes Memorial Prize (2019) Doctoral Program Thesis Distinction from EDIC EPFL (2019) SIAM Early Career Prize (2020) Eurographics Junior Fellows (2021) Advising & Grants: Mentored by Prof. Dr. Wojciech Matusik and Prof. Dr. Mark Pauly, with funding from Swiss National Centre of Competence in Research (NCCR) Digital Fabrication. She actively participates in program committees and summer schools.

Shuangning LiView profile

Assistant Professor

• Econometrics
• Statistics
• Machine Learning
• +5 more

Shuangning Li is an Assistant Professor of Econometrics and Statistics at the University of Chicago's Booth School of Business. He holds a Ph.D. from Stanford University's Department of Statistics, advised by Professors Emmanuel Candès and Stefan Wager, and a Bachelor of Science from the University of Hong Kong. Prior to his current role, he was a postdoctoral fellow in Statistics at Harvard University. His research focuses on causal inference, machine learning, and statistical methodology with applications in econometrics, networks, and genomics. **Education:** Ph.D. in Statistics, Stanford University (Advisors: Emmanuel Candès, Stefan Wager) Bachelor of Science, University of Hong Kong **Research Interests:** Causal inference in complex systems (e.g., networks, high-dimensional data) Statistical methods for experimental design and robustness Machine learning applications in genomics and reinforcement learning Randomization-based testing and knockoff filters **Recent Work Trends:** His articles emphasize methodological innovations in causal effect estimation, network interference modeling, and transfer learning. Recent work addresses challenges in stochastic congestion, multi-environment analysis, and cooperative learning frameworks. His 2024 paper advances covariate shift correction for conditional randomization tests, while his 2023 studies explore robustness in model-X inference and dyadic reinforcement learning dynamics. **Advising & Academic Background:** His doctoral training under Candès and Wager shaped his focus on rigorous statistical foundations. He has not yet listed advising relationships in available materials, but his research collaborations span academia and industry.

Sijia YangView profile

Associate Professor

• Public Health Communication
• Science Communication
• Digital Media Effects
• +3 more

Sijia Yang is an Associate Professor at the School of Journalism and Mass Communication at the University of Wisconsin-Madison . His research focuses on message effects and persuasion in digital media, particularly in public health and science communication. He employs experimental, computational (e.g., causal machine learning, multimodal analysis), and community-engaged methods to address challenges in health communication interventions. Ph.D. in Communication: Annenberg School for Communication, University of Pennsylvania (2019) M.A. in Communication: University of Illinois at Urbana-Champaign (2012) B.A. in English Language and Literature: Renmin University of China (2010) His current research explores three key areas: (1) moralization/politicization of health issues and intervention design, (2) causal machine learning for evaluating multimodal messages, and (3) leveraging AI for pro-social messaging in underserved communities. His work bridges computational methods with societal impact, emphasizing ethical considerations and practical applications. Recent articles highlight his contributions to topics like cannabis warning labels, misinformation correction via TikTok, and AI-driven health chatbots. Despite no explicitly listed awards, his active research portfolio reflects significant scholarly engagement in health communication and digital media studies. His advising and grants focus on collaborative projects with rural communities and under-resourced populations, though specific grants are not detailed here. No lab or team affiliations are mentioned in the provided texts.

Malik BoykinView profile

Assistant Professor

• Social Psychology
• Racial Inequality
• Data Science
• +5 more

Dr. Malik Boykin is an Assistant Professor in the Department of Cognitive, Linguistic, and Psychological Sciences at Brown University. He holds affiliate appointments with the Center for the Study of Race and Ethnicity in America (CSREA) and the Data Science Initiative (DSI). His research focuses on intergroup relations, racial identity, prejudice, mentorship, and the role of technology in societal structures, particularly addressing racial inequality. Boykin received a PhD in Social and Personality Psychology from UC Berkeley, an M.A. from Columbia University, and a B.S. from the University of Maryland. His academic background includes a Presidential Postdoctoral Fellowship at Brown. Notable awards include the Ford Fellowship Foundation grant and funding from the Society for Multivariate Experimental Psychology. Boykin teaches courses such as CLPS 1770 (Psychology of Stigma and Prejudice) and CLPS 2002 (Core Topics in Cognitive Sciences). His work intersects disciplines like social psychology, data science, and ethics, emphasizing emancipatory approaches to technology and systemic justice. Boykin’s research explores topics such as attitudes toward HBCUs, AI ethics in workplaces, and bias in algorithmic decision-making. He advocates for interdisciplinary solutions to fairness in machine learning and equitable educational policies. His contributions span academic publications, policy recommendations, and collaborations with the RISE Lab to advance anti-racist practices in academia and technology.

Robert PeharzView profile

Assistant Professor

• Probabilistic Machine Learning
• Tractable Probabilistic Models
• Artificial Intelligence
• +6 more

Robert Peharz is an Assistant Professor at Graz University of Technology, where he leads research at the Institute of Machine Learning and Neural Computation. His work focuses on probabilistic machine learning, with particular emphasis on tractable probabilistic models, causality, and neurosymbolic AI. Education and Career PhD from TU Graz (Austria) in 2015 Postdoc at Medical University of Graz Postdoc and Marie-Curie Individual Fellow at University of Cambridge (2017-2019) Assistant Professor at Eindhoven University of Technology (2019-2021) Current: Assistant Professor at Graz University of Technology Research Interests Peharz's research spans multiple areas of artificial intelligence with a focus on making probabilistic reasoning both theoretically sound and practically efficient. His work addresses fundamental challenges in tractable probabilistic inference and learning, probabilistic circuits as a unified framework for deep generative models, Bayesian causal inference, and neurosymbolic AI combining sub-symbolic and symbolic approaches. His research has applications in cybersecurity, healthcare, and energy systems. Research Projects VENTUS (2024-present): Physics-informed, probabilistic and causal machine learning for wind energy systems NEO DNA (2023-present): DNA-based data storage systems using computer vision and probabilistic ML VanillaFlow (2023-present): AI-guided development of novel vanillin-based molecules for redox flow batteries Bilateral AI : Cluster of Excellence focused on Broad AI combining sub-symbolic and symbolic AI approaches Awards and Recognition Finalist for TUG's Excellent Teaching Award (2023) for all 3 of his courses Marie-Curie Individual Fellow at University of Cambridge Academic Service Peharz is actively involved in the academic community through conference organization and reviewing: Area Chair: UAI (2022), ECML/PKDD (2022) Senior Committee Member: UAI (2021), IJCAI (2019, 2020) Reviewer for major conferences including ICML, NeurIPS, AAAI, IJCAI-ECAI Teaching and Mentorship Peharz supervises multiple PhD students working on diverse projects at the intersection of machine learning, causality, and neurosymbolic AI. His current advisees include Sepideh Adamiat, Irina Dobrianski, Johannes Exenberger, Giacomo Di Gobbi, Tim d'Hondt, Christian Toth, and Thomas Wedenig. Previous students include Alvaro Correia, Martin Trapp, and David Montalvan.

Sao Mai NguyenView profile

Lecturer

• Robotics
• Artificial Intelligence
• Cognitive Science
• +7 more

Sao Mai Nguyen is an Enseignante-Chercheuse (Lecturer-Researcher) at ENSTA Paris, affiliated with the Unité d'Informatique et d'Ingénierie des Systèmes (U2IS). Her research bridges robotics, artificial intelligence, and cognitive science, focusing on cognitive developmental robotics, intrinsic motivation in learning, and human-robot interaction. She explores how robots can adapt to social and physical environments, particularly in physical rehabilitation and smart home applications. Research Interests: Nguyen’s work integrates machine learning, robotic embodiment, child psychology, and neuroscience. Key areas include human-robot interaction, assistive robotics for chronic low back pain rehabilitation, activity recognition in smart homes using IoT sensors, and intrinsic motivation-driven learning frameworks. Recent Contributions: Her 2024 HDR thesis on reinforcement and imitation learning for sequential tasks underscores her expertise in strategic learning systems. Recent publications address bio-inspired robotics models, hierarchical reinforcement learning, and benchmark environments like 'Open the Chests' for activity recognition. Collaborations include projects like the R-COOL randomized trial for robot-coached physical exercises. Labs & Teams: She contributes to the U2IS lab, advancing interdisciplinary research in AI and robotics. Her work spans experimental platforms for sensorimotor learning and healthcare robotics applications.

Mohamed SolimanView profile

Professor

• Hydraulic Fracturing
• Unconventional Reservoirs
• Well Test Analysis
• +7 more

Dr. Mohamed Soliman is the William C. Miller Endowed Professor at the University of Houston’s Cullen College of Engineering, Department of Petroleum Engineering. He holds a Ph.D. in Petroleum Engineering from Stanford University, complemented by an M.S. and B.S. from Stanford and Cairo University respectively. His research focuses on hydraulic fracturing of unconventional reservoirs, waterless fracturing using shock waves, and advanced numerical simulation techniques. He has authored over 250 technical papers and holds 35 patents, with notable works on shale gas transport, dead oil viscosity modeling, and plasma stimulation technologies. Dr. Soliman is a Distinguished Member of the Society of Petroleum Engineers (SPE) and a Fellow of the National Academy of Inventors. He has received the Gulf Coast 2020 Distinguished Achievement Award for Petroleum Engineering Research. His work bridges theoretical models with practical applications, such as the development of machine learning tools for reservoir analysis and innovative methods for fracture closure detection using wavelet transforms. His teaching spans core petroleum engineering courses including PETR 1111 (Introduction to Petroleum Engineering), advanced production operations (PETR 6372), and well completion stimulation (PETR 5397). He actively mentors graduate students, with current advisees Ibrahim Eltaleb, M. Awad, and Fatmir Likframa. His research group collaborates on projects funded by industry and government agencies, focusing on topics like microwave-assisted heavy oil recovery and geothermal reservoir characterization. Dr. Soliman’s lab develops cutting-edge tools for analyzing fracturing pressure data and interwell connectivity through signal processing. Key collaborations involve experimental validation with institutions like the University of Houston’s Advanced Energy Research Laboratory. His recent work emphasizes sustainable energy solutions, including critiques of carbon capture limitations and innovative plasma-based stimulation techniques to enhance reservoir permeability without water use.

• Plant Genetics
• Crop Biodiversity
• Breeding Informatics
• +5 more

Karl Schmid is a W3 Professor of Crop Plant Biodiversity and Breeding Informatics at the University of Hohenheim's Institute of Plant Breeding, Seed Science and Population Genetics within the College of Agricultural Sciences. His research integrates evolutionary genetics, population genomics, and machine learning to address agricultural challenges. Ph.D. in Biology, University of Munich (1996) Postdoctoral Research, Cornell University (1997-1999) Emmy-Noether Research Group, Max Planck Institute of Chemical Ecology (2000-2006) Group Leader, Leibniz Institute of Plant Genetics (2006-2008) Professor of Genetics, Swedish Agricultural University (2008) His research focuses on crop biodiversity conservation, evolutionary genetics of plant pathogens, and breeding informatics applications. Current work leverages deep learning for phenotyping (quinoa panicles, barley genomics) and analyzes pathogen evolution (Exserohilum turcicum in maize). His team actively develops computational tools like GGoutlieR for geo-genetic pattern detection. Recent publications demonstrate strong trends in applying AI to agricultural genomics, particularly in quinoa improvement and pathogen surveillance. His group leads the EU H2020 INVITE project on molecular markers in plant variety protection and organizes international symposia like the 2024 Quinoa Symposium at Hohenheim. Head of Crop Biodiversity and Breeding Informatics Group Principal Investigator, EU H2020 INVITE project Organizer, International Quinoa Symposium 2024

Hans-Peter PiephoView profile

Professor

• Statistical Methods in Plant Sciences
• Genomic Prediction
• Spatial Modeling for Field Trials
• +5 more

Prof. Dr. Hans-Peter Piepho is a faculty member at the University of Hohenheim , serving as Director of Biostatistics (340c) within the Institute of Crop Science . His research focuses on advanced statistical methods for plant sciences, including genomic prediction, spatial modeling of field trials, and network meta-analysis. Key research areas: Genotype-environment interaction, mixed model applications, and experimental design in agriculture. Collaborations span plant breeding, cultivar testing, and environmental stress analysis. His group emphasizes mixed model procedures as the backbone of methodological innovation. Recent publications highlight climate change adaptation, nitrogen optimization, and agroforestry yield dynamics. Contact: piepho@uni-hohenheim.de | Fruwirthstr. 23, Institute Building, 211, Stuttgart, Germany

Meredith CarrollView profile

Professor

• Aviation Human Factors
• Human-Autonomy Teaming
• Urban/Advanced Air Mobility (AAM/UAM)
• +5 more

Dr. Meredith Carroll is a Professor of Aviation Human Factors and Director of the ATLAS Lab at Florida Institute of Technology's College of Aeronautics. She holds a B.S. in Aerospace Engineering from the University of Virginia, an M.S. in Aviation Science from Florida Tech, and a Ph.D. in Applied Experimental Psychology and Human Factors from the University of Central Florida. Her research focuses on human performance in aviation systems, including decision-making under stress, human-autonomy teaming, advanced air mobility (AAM), and cybersecurity education. Key research initiatives include: Investigating trust dynamics in human-agent teams funded by AFOSR User-centered design of multi-UAV ground control stations for NASA eVTOL battery display design and pilot interface studies Cybersecurity curriculum development for underrepresented high school students Adaptive flight training strategies funded by the Naval Air Warfare Center Her lab facilities include Florida Tech's Center for Aeronautics and Innovation with advanced flight simulators and UAS testing environments. Current projects explore VR training immersion effects, multi-agent team repair strategies, and AAM operational challenges. She advises 12+ graduate and undergraduate researchers and collaborates with industry partners like P17 Solutions and Rockwell Collins. Recent publications emphasize trust calibration in autonomous systems, IoT-based cybersecurity education, and adaptive training efficacy. She has secured grants from NASA, FAA, AFRL, ONR, and NAWC totaling over $5M in external funding.