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Jacob TsaoView profile

Professor

• Design of Statistical Experiment
• Statistical Quality Control and Reliability Theory
• Operations Research
• +2 more

Jacob Tsao is a Professor in the Department of Industrial and Systems Engineering at San Jose State University's Charles W. Davidson College of Engineering, where he has served since 2001. He currently holds the position of Professor and Graduate Advisor, and previously served as Associate Dean for Extended Studies (2018-2020) and Acting Chair of the Civil and Environmental Engineering Department (2022). He also maintains an adjunct professorship at SP Jain Institute of Management and Research in Mumbai, India since 2009. Dr. Tsao earned his Ph.D. in Operations Research from the University of California, Berkeley, an M.S. in Mathematical Statistics from the University of Texas, Dallas, and a B.S. in Applied Mathematics from National Chiao-Tung University in Taiwan. His educational background established the foundation for his distinguished career spanning academic research, industry applications, and leadership positions. Tsao's research spans several interconnected domains with primary focus on experimental design methodologies, statistical quality control, and transportation systems engineering. His work in factorial experiment design has produced innovative approaches to minimizing test conditions while maintaining statistical validity. In transportation, he has made significant contributions to intelligent transportation systems, particularly in bus rapid transit optimization, automated highway systems, and large vehicle operations. His research bridges theoretical statistical methods with practical engineering applications, often addressing real-world transportation challenges through mathematical modeling and optimization techniques. His publication record demonstrates consistent scholarly output with recent work focusing on political districting models, advanced experimental design patterns, and efficient transit system configurations. The research shows a clear trajectory from foundational work in entropy optimization and mathematical programming toward increasingly applied transportation and supply chain problems, while maintaining strong methodological rigor in statistical and operations research approaches. The Newnan Brothers Award for Faculty Excellence (2017) Charles W. Davidson College of Engineering Faculty Award for Excellence in Scholarship (2016) Teacher-Scholar designation (2011-2012) The Applied Materials Award for Excellence in Teaching (2011) Donald Newnan Teaching Excellence Award (2010) The McCoy Family Faculty Award for Excellence in Service (2005) Dr. Tsao has secured over $1.9 million in externally funded research as PI or co-PI, including significant grants from the National Science Foundation, NASA, and the California Department of Transportation. His major projects include the Silicon Valley Innovation and Entrepreneurship Scholarships program ($599,642), NASA's Integrated Approaches for Surface Traffic Optimization ($1M), and multiple Caltrans-funded research initiatives on bus rapid transit systems. He has served as Area Editor for Statistics, Quality and Reliability for Computers and Industrial Engineering journal (2010-2015) and held leadership roles in professional conferences including Program Co-Chair for the International Conference on Computer and Industrial Engineering.

David BuscherView profile

Professor

• Optical Interferometry
• Astronomical Instrumentation
• High-Resolution Imaging
• +3 more

Prof David Buscher is a Professor of Astrophysics at the University of Cambridge, affiliated with the Cavendish Laboratory. His work focuses on advancing optical/infrared interferometry through the Magdalena Ridge Observatory Interferometer (MROI) project and the ANDES spectrograph for the Extremely Large Telescope (ELT). He leads efforts in instrument design, automation, and data analysis, with a focus on high-resolution imaging of celestial objects. Education details are not explicitly stated in the text, though his affiliation with the Cavendish Laboratory implies a strong academic background in physics or astronomy. Research Interests: Optical interferometry techniques, interferometric imaging of distant objects (e.g., AGN dust tori, geosynchronous satellites), and development of advanced astronomical instruments. His work bridges theoretical frameworks with practical engineering challenges, emphasizing thermal stability, precision alignment, and real-time data processing. Articles from 2025 to 2020 reflect a progression toward achieving first fringes with MROI, optimizing fringe tracking systems, and exploring applications in space surveillance and exoplanet studies. Collaborations include the European Interferometry Initiative, emphasizing community-driven advancements and diversity in science. Scientific Awards: None explicitly mentioned in the provided texts. Advising & Grants: No explicit student names or grant details are listed. However, his role in MROI and ANDES suggests significant institutional and collaborative project funding. His work involves interdisciplinary teams across mechanical engineering, software development, and astrophysical theory. Labs/Teams: Lead investigator for MROI projects at the Cavendish Laboratory. Collaborates with international teams on ELT instrumentation and interferometry education initiatives.

Ross McAreeView profile

Professor

• Mining Automation
• Robotics
• Machinery Dynamics
• +2 more

Ross McAree is Professor and Head of the School of Mechanical and Mining Engineering at the University of Queensland, and Fellow of the Australian Academy of Technology and Engineering. His research focuses on mining automation, including pioneering work on autonomous excavators and bulldozers. Research encompasses machinery dynamics, sensor fusion for terrain mapping, and collision avoidance systems. Recent publications address point cloud processing for mining applications, predictive maintenance using machine learning, and hyperspectral ore classification techniques. Publication themes show progression from fundamental sensing/control algorithms toward integrated autonomous mining systems, with increasing emphasis on AI applications in harsh environments. Work consistently addresses industrial challenges through rigorous theoretical development and field validation. Awards & Honors: Fellow of the Australian Academy of Technology and Engineering (ATSE)

Ozgur AkgunView profile

Senior Lecturer

• Constraint Programming
• Data Mining
• Cloud Computing
• +8 more

Dr Ozgur Akgun is a Senior Lecturer and Director of Impact at the School of Computer Science, University of St Andrews. His research focuses on Constraint Programming (CP), including the development of the automated constraint modelling tool Conjure. He explores applications of CP in areas like data mining, cloud computing, network science, logistics, and economics. Akgun teaches core CS modules for undergraduates and advanced courses in constraint programming, discrete optimization, and video games. He supervises PhD students and leads the VIP project 'Artificial Intelligence for Decision Making'. His work also spans functional programming, data linkage, and the intersection of machine learning with CP. Research interests include automated constraint modelling, algorithm selection/configuration, and data linkage using graph databases. Current projects involve cost-efficient algorithm training and frugal algorithm selection. He collaborates on interdisciplinary projects such as the ADR UK Programme and Modelling and Optimisation with Graphs. His teaching includes supervising undergraduate, master's, and PhD students, contributing to both academic and applied computer science education.

• Artificial Intelligence
• Evolutionary Computation
• Neural Networks
• +3 more

Professor Larry Bull is a faculty member at the University of the West of England (UWE Bristol), affiliated with the School of Computing & Creative Technologies and the Computer Science Research Centre . His academic rank is Professor of Artificial Intelligence , and he teaches and researches in AI, focusing on evolutionary systems in both natural and artificial contexts. His work bridges computational intelligence, biological systems, and engineering applications. Research Interests: Prof. Bull's research explores evolution-inspired algorithms, neural networks, and bio-inspired computing. He investigates topics such as evolutionary optimization (e.g., cancer treatment design via nanoparticles), coevolutionary dynamics, and the application of AI to complex systems like anaerobic digestion and medical image classification. Recent work includes studies on neural network architectures, genetic recombination mechanisms, and the integration of biological principles into machine learning frameworks. Publications: His 279+ publications reflect contributions to evolutionary computation, neural networks, and interdisciplinary applications. Recent highlights include advancements in ResNet-based image classification architectures, haploid-diploid evolutionary algorithms for medical treatments, and theoretical studies on recombination's evolutionary role. Labs & Teams: Active in the Computer Science Research Centre , he collaborates on projects spanning computational biology, AI-driven healthcare, and sustainable engineering solutions. His work often employs unconventional computing paradigms and evolutionary modeling techniques.

Qiyu ZhouView profile

Assistant Professor

• Precision Agriculture
• Turfgrass Management
• Remote Sensing
• +5 more

Dr. Qiyu Zhou is an Assistant Professor of Turfgrass Management at North Carolina State University, holding a 60% teaching and 40% research appointment. She directs the Lab for Precision Turfgrass Management, which develops sustainable management strategies through data-driven approaches. Her research integrates precision agriculture technologies including remote sensing and machine learning to optimize resource efficiency in turf systems. Primary focus areas include: Machine learning models for nitrogen management and yield prediction Robotic weed control systems and precision spraying Carbon sequestration via enhanced weathering techniques Irrigation optimization and water quality management Research spans multiple environments including the Lake Wheeler Turfgrass Research Field, greenhouse studies, and growth chamber experiments. Recent publications demonstrate cross-disciplinary work combining turfgrass science with environmental engineering, data analytics, and climate mitigation strategies.

Nicolas DurandView profile

Professor

• Metaheuristics
• Combinatorial Optimization
• Interval Methods
• +5 more

Nicolas Durand is a Professor at the National School of Civil Aviation (ENAC), specializing in Air Traffic Management through the integration of Metaheuristics , Combinatorial Optimization , and Interval Methods . His work focuses on hybridizing Evolutionary Algorithms with Machine Learning to enhance Conflict Resolution and Trajectory Prediction in aviation systems. His research spans Unmanned Aerial Systems (UAS) , ADS-B Data Analysis , and 3D Collision Avoidance . Recent projects include Behavior Cloning for conformal automation and Median Regression for lateral deconfliction. Collaborations with institutions like ONERA and ISAE-SUPAERO highlight his interdisciplinary approach. Publications reveal a trend toward Explainable AI in ATM, with applications in Dynamic Conflict Resolution , Mass Estimation , and UAS Integration . His work bridges theoretical optimization (e.g., Global Optimization , Interval CP ) with operational needs, emphasizing Controller Expertise and Uncertainty Quantification .

• Machine Learning
• Statistical Methods
• Data Science
• +3 more

Dr. Daniel Horn is affiliated with the Department of Statistics at the Technical University of Dortmund, specifically within the Faculty of Statistics. He is part of the working group led by Prof. Dr. Andreas Groll and serves as the Study Coordinator for the B.Sc. and M.Sc. Data Science programs. His research focuses on machine learning algorithms, statistical methods for big data, and optimization techniques, with a particular emphasis on hyperparameter tuning, ensemble methods, and robust outlier detection. Key research areas include the development of efficient algorithms for high-dimensional data analysis, such as tree ensembles for ordinal prediction, kernelized support vector machines, and model-based optimization frameworks like mlrMBO. His work also addresses industrial applications of machine learning, emphasizing practical qualification concepts for data-driven production processes. Dr. Horn's publications span topics such as multi-objective optimization, robust outlier detection (RODD), and the Contextual Shift Method (CSM). His contributions highlight advancements in both theoretical methodologies and applied computational tools, supporting data science education and industrial innovation. Contact: dhorn@statistik.tu-dortmund.de

Ran JinView profile

Professor

• Manufacturing Systems Engineering
• System Informatics and Control
• Data Science and Visualization
• +6 more

Ran Jin is a Professor in the Department of Industrial and Systems Engineering at Virginia Tech, leading the Data Science and Visualization Lab. He holds academic appointments in the College of Engineering. His research focuses on integrating data science with manufacturing systems engineering, emphasizing system informatics, quality control, and process optimization. He has contributed to advancements in profile modeling, defect detection, and statistical process monitoring in industries like additive manufacturing. Education : Ph.D., Georgia Institute of Technology, 2011 M.A. and M.S., University of Michigan, 2009 and 2007 B.Eng., Tsinghua University, 2005 Research Interests : Jin's work bridges statistical methodologies with engineering systems, particularly in high-definition profile data analysis, ensemble modeling for manufacturing scale-up, and cybermanufacturing challenges. He develops algorithms for process variation reduction and real-time monitoring. His recent focus includes AI-driven solutions for manufacturing resilience and distributed data systems. Recognition : Jin has received awards such as the Best Applied Paper Award (IIE Transactions) and NSF grants for projects like 'EAGER: AM: CoFedAI' and 'QQ Models' for mixed quality responses. His contributions span academic journals and industry partnerships, including collaborations with JSME Co. Ltd. and the Commonwealth Center for Advanced Manufacturing. Grants & Projects : Key initiatives include 'Distributed Data Filtering for Fog Manufacturing' and 'AI Incubation in Manufacturing.' He has served as PI or Co-PI on NSF-funded projects addressing experimental design, anomaly detection, and thermal spray process modeling. Labs & Teams : Directs the Data Science and Visualization Lab, fostering innovation in AI applications for smart manufacturing and system informatics. Engages in interdisciplinary research through roles like Co-Chair of the Commission on Research at Virginia Tech.

Shangyan ZouView profile

Assistant Professor

• Dynamics and Control
• Ocean Renewable Energy
• Multi-Agent Systems
• +5 more

Dr. Shangyan Zou is an Assistant Professor in the Department of Mechanical and Aerospace Engineering at Michigan Technological University (MTU). He holds a PhD from MTU's Mechanical Engineering-Engineering Mechanics program (2018) and has held postdoctoral positions at Oregon State University (2021-2022) and Iowa State University (2018-2021). His research focuses on dynamics and control systems applied to ocean renewable energy, multi-agent systems, machine learning, and state estimation. He is actively advancing marine energy technologies to improve economic viability through optimized modeling, control, and networking of wave energy systems. Dr. Zou's educational background includes: PhD in Mechanical Engineering-Engineering Mechanics, Michigan Technological University (2018) Postdoctoral Researcher at Oregon State University (2021-2022) Postdoctoral Researcher at Iowa State University (2018-2021) His research interests emphasize: Swarm behavior of networked marine energy systems Fluid-structure interaction in soft ocean energy converters Control of unmanned underwater vehicles Deep reinforcement learning applications for wave energy optimization His recent work demonstrates trends in: Integration of machine learning for real-time system adaptation Optimization of WEC array configurations Data-driven fault diagnosis for underwater systems Hybrid control strategies for multi-agent marine systems Dr. Zou currently oversees a research group investigating marine renewable energy systems and is recruiting PhD candidates for funded positions in this domain.

Monica NicolescuView profile

Professor

• Human-Robot Interaction
• Social Robotics
• Multi-Robot Systems
• +5 more

Monica Nicolescu is a Professor in the Department of Computer Science at the University of Nevada, Reno, and Director of the Robotics Research Lab. Her research focuses on human-robot interaction, social robotics, and multi-robot systems, emphasizing communication, learning, and teamwork in dynamic environments. She develops methodologies for integrating robots into human society, with a focus on autonomous control systems and adaptive learning capabilities. Her work explores heterogeneous human-robot teams, emphasizing social norms and collaboration in service robotics applications. She also investigates maritime safety through AI-driven solutions and multi-modal interaction frameworks combining gestures, speech, and visual cues for reliable human-robot task configuration. Recent publications highlight advancements in medical imaging segmentation, cryptocurrency forecasting, and socially-aware navigation systems. Her research integrates deep learning, computer vision, and behavioral algorithms to enhance robotic functionality and safety in complex environments. Dr. Nicolescu leads the Robotics Research Lab, fostering interdisciplinary collaboration in robotics, AI, and human-robot interaction. Her research addresses both technical and societal challenges, aiming to create robots that adaptively and ethically integrate into diverse human contexts.

Heike TrautmannView profile

Professor

• Data Science
• Artificial Intelligence
• Multiobjective Optimization
• +4 more

Prof. Heike Trautmann is a Professor of Data Science: Statistics and Optimization at the University of Münster, holding the Chair of Statistics and Optimization within the Department of Information Systems and Statistics, School of Business & Economics. She also serves as Vice Dean for Internationalization at the School. Her academic career includes roles such as Pascal Professor at Leiden University (2017) and a visiting position at the University of Twente (2021–2026). She earned her PhD (2004) and Habilitation (2013) in Statistics from TU Dortmund, focusing on optimization methodologies. Her research interests span Multiobjective Optimization, Evolutionary Algorithms, Automated Algorithm Selection, Data Stream Mining, and Social Media Analytics. Notable projects include the COSEAL consortium for algorithm selection, the Benchmarking Network for optimization heuristics, and the ERCIS Social Media Analytics Competence Center addressing online disinformation. She has led initiatives like MODERAT! (automated comment moderation) and Algorithmisierung und gesellschaftliche Interaktion (societal impact of algorithms). Awarded multiple best paper prizes, including at CBI 2019 and PPSN XIV 2016, she emphasizes interdisciplinary collaboration. Her work bridges technical advancements with societal implications, reflected in contributions to AI ethics and platform regulation. Active in international conferences (e.g., EMO, GECCO), she maintains roles in program committees and advisory boards such as CLAIRE and ACM SIGEVO. Funded projects include EU initiatives on algorithm configuration and DAAD collaborations on platform regulation. Her research outputs span over 50 peer-reviewed articles since 2012, focusing on algorithmic innovation and real-world applications in optimization and digital media.

Jan JanssenView profile

Researcher

• Materials Informatics
• Computational Materials Science
• Machine Learning
• +3 more

Jan Janssen is the Group Leader for Materials Informatics at the Max-Planck Institute for Sustainable Materials, leading research in applying computational and machine learning methods to materials discovery. He previously served as a Director's Postdoctoral Fellow at Los Alamos National Laboratory and earned his PhD from the MPIE Düsseldorf. His work focuses on ab-initio thermodynamics, machine-learned interatomic potentials, and high-performance computing workflows. Education: PhD in Physics, Max Planck Institute for Iron Research (MPIE), Düsseldorf (2015–2021) MSc/BSc in Physics, Technical University of Kaiserslautern (2008–2015) Research Interests: Janssen’s research integrates computational materials science with machine learning, emphasizing automated workflows, open-source software (e.g., pyiron), and Bayesian optimization for materials discovery. His team develops tools like Executorlib for scaling Python workflows on HPC systems and leverages LLMs for scientific automation. Contributions: Lead developer of pyiron, an open-source computational materials science framework Maintainer of conda-forge packages for materials informatics Contributor to projects like FitSNAP and LangSim Labs/Teams: He leads the Materials Informatics group, advancing methods for high-throughput materials exploration and AI-driven scientific workflows.

• Epilepsy Research
• Neurostimulation
• Deep Brain Stimulation (DBS)
• +3 more

Dr. Daria Anderson is a Lecturer in the School of Biomedical Engineering at The University of Sydney. Her work focuses on epilepsy, neurostimulation technologies, and computational models of neural activity. She advises three current PhD students: Josh MOSSE-ROBINSON, Michael SUN, and Joshua WOOLEY. Her research integrates engineering principles with clinical neuroscience to improve seizure prediction, neurostimulation efficacy, and patient-specific treatment outcomes. Key projects include analyzing circadian rhythms' impact on epilepsy therapies and developing automated tools for intracranial electrode localization. Education details are not explicitly provided, but her work spans computational neuroscience, biomedical device development, and clinical neurology. Research grants include a 2024 project on structural-functional decoupling in epilepsy, aiming to identify biomarkers for network restructuring. Anderson’s publications emphasize DBS optimization, seizure propagation pathways, and machine learning applications in neuroimaging. Recent work highlights closed-loop stimulation strategies and the role of structural connectivity in treatment outcomes for epilepsy and Tourette syndrome. No scientific awards are listed, though her contributions are significant in neuroengineering and clinical neuroscience. Labs/Teams: Her research aligns with the School of Biomedical Engineering and collaborations with the Brain and Mind Centre. Future directions include predictive modeling of seizure dynamics and advancing personalized neurostimulation therapies.

Shengchao QinView profile

Professor

• Formal Methods
• Programming Languages
• Software Engineering
• +3 more

Dr. Shengchao Qin is a Professor of Computer Science at Teesside University's School of Computing & Digital Technologies, specializing in formal methods and software verification. He holds a BSc and PhD from Peking University, with postdoctoral experience at the Singapore-MIT Alliance. As Associate Dean for Research & Innovation (2016-2019), he led strategic research initiatives. His research focuses on automated verification of software safety/security, program analysis, and cyber-physical systems. Major funded projects include EPSRC grants for resource analysis of embedded software (£403k) and inference mechanisms for separation logic domains (£213k). He actively contributes to academic service as Program Committee Chair for ICFEM/TASE conferences, steering committee member for formal methods symposiums, and editorial roles for IEEE Access and Science of Computer Programming. Collaborations span top institutions including National University of Singapore and Peking University. Research Supervision: Current PhD advisees: Chris Curry, Yang Liu, Colin Joy, Ndidi Ogbo Former supervisees: Zhang Zhang (PhD '20), Florin Craciun (Postdoc), Ryuta Arisaka (PhD '13)