Thomas Berger is a Professor at the University of Hohenheim , affiliated with the Faculty of Agricultural Sciences and leading the Department of Economics of Land Use . He also contributes to the Computational Science Hub and Hohenheim Tropics initiatives. Focus Areas: Climate change adaptation, land-use modeling, biodiversity-productivity trade-offs, agent-based simulation, and machine learning in agricultural systems. Key Projects: Simulation frameworks for smallholder resilience in Ethiopia, bioeconomic modeling in the Amazon, and hybrid intelligence applications in European agricultural policy. Recent Publications: 2025 study on climate change effects on insecticide reduction in Germany, 2024 work on reconciling biodiversity with productivity via hybrid models, and 2023 methodological contributions to surrogate modeling and seasonal forecast integration. Research Trends: Interdisciplinary integration of climate science, agricultural economics, and computational modeling, with increasing emphasis on AI-assisted decision support systems and sustainability policy validation. Teaching & Outreach: Offers Agricultural Economics seminars and Hohenheim Tropics discussions, requiring advance email registration for office hours.
Dr. XiaoYue Cathy Liu is an Associate Professor in the Department of Civil & Environmental Engineering at the University of Utah. She holds a PhD in Transportation Engineering from the University of Washington and advanced degrees in Transportation Planning and Electronics Engineering. Her research focuses on sustainable transportation systems, shared mobility, public transit optimization, managed lanes, and intelligent transportation systems. She actively serves on committees including the Transportation Research Board's Highway Capacity Quality of Service (HCQS) Committee and chairs its Technology Transfer Subcommittee. She also advises the Utah Model Advisory Committee and previously served on Salt Lake City’s Transportation Advisory Board. Dr. Liu is a licensed professional engineer in Utah. Education: PhD, Transportation Engineering, University of Washington (2013) MA, Transportation Planning & Management, Texas Southern University BS, Electronics & Electrical Engineering, Beijing Jiaotong University Graduate Certificate, Global Trade & Logistics, University of Washington (2011) Research Interests: Electric vehicle infrastructure and charging networks Smart transportation systems and agent-based modeling GIS-based asset management for transportation infrastructure Snowplowing operations optimization and winter maintenance Managed lanes and high-occupancy toll (HOT) lane analysis Public transit equity and accessibility Her recent publications emphasize data-driven approaches to transportation challenges, including EV demand forecasting, drone delivery networks, and resilience in interdependent transit systems. She collaborates on projects addressing Utah’s unique transportation needs, such as optimizing snowplow routes and integrating renewable energy into public transport systems. Professional Contributions: Board member, Utah Model Advisory Committee Former Chair, Salt Lake City Transportation Advisory Board (2013-2016) TRB Managed Lane Committee member TRB Transit Capacity & Quality of Service (TCQS) Committee member (2016-2019) Dr. Liu’s work bridges transportation engineering with computational methods, focusing on equity, sustainability, and operational efficiency. She leads research initiatives involving large-scale modeling, geospatial analytics, and interdisciplinary solutions for modern mobility challenges.
Nebojša Bačanin Džakula is an academic affiliated with Singidunum University's Faculty of Mathematics, specializing in Computer Science. He earned his PhD in 2015 with a thesis on improving swarm intelligence metaheuristics for global optimization. His research focuses on AI-driven solutions for cybersecurity, energy forecasting, and optimization algorithms. He has authored/co-authored books on cloud computing and web programming. His work bridges metaheuristics with machine learning, addressing challenges in IoT security, renewable energy prediction, and healthcare diagnostics. He actively contributes to conferences like Sinteza and IEEE events, emphasizing practical applications of AI and optimization in real-world scenarios. Education: Completed doctoral studies at the Faculty of Mathematics (2009–2015). Extensive industry certifications include Microsoft, CompTIA, and Oracle credentials, enhancing his technical expertise. Research Interests: Develops hybrid models combining metaheuristics (e.g., PSO, GA) with deep learning for tasks like intrusion detection, price forecasting, and medical diagnostics. Specializes in optimizing neural networks and feature selection using advanced algorithms. His work often addresses societal challenges in sustainability, cybersecurity, and healthcare. Recent Publications: Focus on AI-driven solutions for IoT security, renewable energy prediction, and medical diagnostics (e.g., Parkinson’s detection via LSTM networks). His articles appear in prestigious journals like Engineering Applications of Artificial Intelligence and Applied Soft Computing.
Ying-Cheng Lai is an ISS Endowed Professor of Electrical Engineering and Affiliated Professor of Physics at Arizona State University, with a 20-year track record in academic leadership. His research bridges classical and quantum dynamics, pioneering fields like Relativistic Quantum Chaos (RQC) and transient chaos theory, impacting materials science, nanotechnology, and medicine. Developed RQC to study chaos in relativistic quantum systems Authored Transient Chaos (Springer, 2011), redefining natural system dynamics Advanced seizure prediction through nonlinear dynamics Innovated compressive-sensing applications in network science Awarded PECASE, NSF Career, and Vannevar Bush Fellowships, Lai collaborates globally across Europe, Asia, and China, generating 80+ high-impact publications in five years. His lab trains influential researchers and hosts visiting scholars worldwide.
Chee-Ming Ting is an Associate Professor in the School of Information Technology at Monash University Malaysia. His expertise lies in machine learning, data science, and biomedical engineering, with a focus on signal processing, computational neuroimaging, and computer-aided detection. Previously, he held positions at King Abdullah University of Science and Technology (Research Scientist) and Universiti Teknologi Malaysia (Senior Lecturer). He has authored over 26 journal papers and 43 conference papers, and has secured research grants totaling RM2.5 million as PI/Co-PI. Education: PhD in Mathematics - Statistics, Master of Engineering in Electrical Engineering, and Bachelor of Engineering (Hons.) in Electrical & Electronics Engineering. Research interests include biomedical signal/image analysis, deep learning, spatio-temporal modeling, and neuroimaging applications for disease prediction and patient monitoring. He has supervised 9 graduate students (4 PhD, 5 Masters) and currently oversees 10 PhD candidates. Awards include the IEEE Signal Processing Society Malaysia's Research Excellence Award (2019, 2022) and several national/international innovation awards. His work contributes to UN Sustainable Development Goals related to health and technological advancement. Key projects include frameworks for neurological disease prediction using brain networks and generative adversarial networks for medical imaging enhancement.
Jon McCormack is a Professor jointly appointed in Monash University's Faculty of Art, Design & Architecture (MADA) and Faculty of Information Technology. He founded and directs SensiLab, a research facility focusing on computational creativity, human-machine interfaces, and generative systems. His work spans electronic media art, evolutionary music, and artificial life. McCormack holds a PhD in Computer Science from Monash University, along with degrees in Computer Science, Applied Mathematics, and Film/Television. Research interests include computational creativity, tangible interfaces, and cybernetic systems. Notable projects include 'Explainable Artificial Creativity' (ARC-funded) and 'Building 4.0 CRC,' addressing architectural innovation through AI. He has been recognized with awards for collaborative projects like the Blundstone Intelligent Footwear for Healthcare. McCormack's recent articles explore AI-driven art, generative systems, and interdisciplinary design. His work bridges artistic practice with technical innovation, emphasizing ethical and creative dimensions of human-AI collaboration. SensiLab serves as a hub for practice-based research in digital media and interactive systems. Education: PhD in Computer Science, Monash University (2004) Bachelor of Science (Honours), Computer Science/Applied Mathematics, Monash University (1987) Graduate Diploma in Film/TV, Swinburne University (1986) Bachelor of Science, Computer Science/Applied Mathematics, Monash University (1985) Key Projects: Lead investigator on 'Explainable Artificial Creativity' (2022–2026) Co-investigator in 'Building 4.0 CRC' (2020–2027), exploring AI-driven architectural design Awards: 2022 Designers Australia Award for Blundstone Footwear 2020 'On the Machine Condition' Prize McCormack's lab, SensiLab, fosters collaborations across disciplines, producing exhibitions, software, and theoretical frameworks for computational creativity. He actively supervises PhD students in practice-based research, emphasizing the intersection of art and technology.
Professor Steffen Dereich is a leading researcher in mathematical stochastics at the University of Münster's Faculty of Mathematics and Computer Science, where he serves as Professor at the Institute of Mathematical Stochastics. He is an active investigator in the Mathematics Münster cluster of excellence, contributing significantly to the fields of stochastic processes and machine learning theory. His primary research interests span Stochastic Processes , Machine Learning , Deep Learning , Complex Networks , and Stochastic Analysis . Dereich has developed a unique research program that bridges classical probability theory with modern machine learning challenges, particularly focusing on the mathematical foundations of optimization algorithms used in deep learning. His work on stochastic gradient descent methods, especially the Adam optimizer, has provided crucial theoretical insights into convergence properties and optimization landscapes. The 15 most recent publications reveal a strong trend toward mathematical analysis of deep learning, with approximately 70% of his work focusing on neural network optimization, convergence analysis, and theoretical foundations of machine learning algorithms. The remaining publications continue his earlier work on complex networks, stochastic processes, and branching structures, demonstrating how he has successfully connected his foundational work in probability with cutting-edge machine learning research. Professor Dereich actively supervises PhD students and maintains productive collaborations, particularly with Arnulf Jentzen and Sebastian Kassing. His research group at Münster has secured significant funding through the Mathematics Münster cluster, supporting multiple projects including T8: Random discrete structures and their limits, and T10: Deep learning and surrogate methods. His teaching portfolio includes advanced courses on Probability Theory, Stochastic Analysis, Markov Chains, and specialized seminars on Machine Learning and Financial Mathematics, reflecting his dual expertise in theoretical mathematics and applied data science.
Aniket 'Niki' Kittur is a Professor in the Human-Computer Interaction Institute at Carnegie Mellon University's School of Computer Science. His research focuses on AI-augmented cognition, exploring how human and machine intelligence can collaborate to enhance creativity, decision-making, and innovation. He leads projects like the Semantic Reader and Skeema browser extension, aiming to reduce cognitive overload through intelligent systems. Education: BA in Psychology & Computer Science from Princeton University; PhD in Cognitive Psychology from UCLA. His work bridges HCI, crowdsourcing, and cognitive science, with 100+ publications and 17 best paper awards. He advises industry partners including Google, Microsoft, and Toyota while maintaining a lab focused on real-world impact. Research interests center on accelerating knowledge acquisition via systems that scaffold sensemaking (e.g., Selenite for web exploration) and fostering analogical innovation through crowdsourced/AI hybrid approaches. Notable contributions include CrowdForge (human-machine workflows) and Kinetica (touch-based data visualization). Awards include NSF CAREER Award, Allen Newell Award, and CHI Academy membership. His lab's Skeema tool has achieved 79% 30-day retention in beta, reflecting impactful user-centered design principles. Current projects emphasize LLM integration for composite cognition, aiming to create systems where 'LLMs + Humans > Either Alone.' Funding来自NSF, NIH, ONR, and industry partners like Bosch and Wikimedia. Teaching includes PhD bootcamps and user-centered research courses. Over 100 students have contributed to his projects, many advancing to tech leadership roles.
Julia Camps is a postdoctoral research associate at the University of Oxford, Department of Computer Science. Her work bridges Computational Biology and Health Informatics, focusing on cardiac digital twin development for precision medicine applications. She specializes in combining data-driven and mechanistic approaches for in silico clinical trials, particularly through Purkinje network modeling and ECG-based calibration. Education: Informatics Engineer (2014) and Master's in Artificial Intelligence (2015-2017) from Universitat Politècnica de Catalunya PhD in Computer Science (2017-2021) at Oxford, completed within the Computational Cardiovascular Science research group under Prof Blanca Rodriguez Current role: postdoc in Prof Rodriguez's group since 2021, focusing on post-myocardial infarction disease progression Software development: open-source cardiac digital twin tools available on GitHub Her research interests center on creating patient-specific cardiac digital twins using multimodal clinical data. This work enables virtual therapy evaluation and in silico clinical trials through: Integration of statistical inference and machine learning techniques Development of Purkinje network models from clinical ECG data Electrophysiological and repolarization sequence modeling Gait detection algorithms for Parkinson's disease applications Recent publications (2024-2025) demonstrate trends in: GPU-accelerated cardiac electrophysiology simulations (MonoAlg3D) Topology-informed ECG electrode localization Sex-specific electromechanical cardiac modeling Multi-modal characterisation of diabetic cardiac deterioration Pro-arrhythmic risk assessment for stem cell therapies
Kurt Maute is a Professor and Palmer Engineering Chair at the University of Colorado Boulder’s College of Engineering and Applied Science (CEAS). He currently serves as Associate Dean for Undergraduate Education. His academic journey includes a PhD in Civil Engineering (University of Stuttgart, 1998) and a Dipl.-Ing. in Aerospace Engineering (University of Stuttgart, 1992). He has held progressively senior roles at CU Boulder, including Associate Dean for Research (2012–2014), Associate Professor (2006–2012), and Assistant Professor (2000–2006). Maute’s research focuses on structural topology optimization, multi-disciplinary optimization, and aeroelastic systems. He has pioneered methods integrating XFEM, level-set techniques, and isogeometric analysis for complex engineering problems. His work spans fluid-structure interaction, hypersonic vehicle design, and additive manufacturing. His notable contributions include advancements in immersed boundary methods, multi-material optimization, and uncertainty quantification. Awards include the NSF Career Award (2004) and Palmer Endowed Chair (2016–present). Maute’s lab (Aerospace Mechanics Research Center, AMREC) addresses challenges in computational mechanics and multi-physics systems. He has advised numerous students and led grants in battery modeling, topology optimization, and aerospace systems. His research bridges theory and application, emphasizing industrial relevance and computational innovation.
Dr. Shirley Coleman is a distinguished Professor at Newcastle University Business School, specializing in the application of statistical methods to business and industrial problems. With over two decades of academic contributions, she has established herself as a leading expert in statistics, data science, and quality management within industrial contexts. Her research interests span several interconnected domains: Statistics, Data Science, Business Analytics, Quality Management, Six Sigma methodologies, Kansei Engineering (which integrates emotional design with product development), Industrial Statistics, Design of Experiments, Predictive Maintenance, and Customer Lifetime Value analysis. Coleman's work consistently bridges theoretical statistical concepts with practical business applications across diverse sectors including healthcare, manufacturing, facilities management, and digital marketing. Analysis of her recent publications reveals a strong focus on the evolving role of statistics in the digital age, particularly examining how statistical expertise contributes to AI development, Industry 4.0 initiatives, and data-driven business transformation. Her work demonstrates increasing emphasis on customer analytics, predictive maintenance modeling, and the strategic implementation of data science in small and medium enterprises. Coleman's publications frequently address methodological challenges while maintaining strong practical relevance for industry practitioners. Throughout her career, Coleman has been actively involved with the European Network for Business and Industrial Statistics (ENBIS), contributing to the development and dissemination of statistical methods in business contexts. Her collaborative approach is evident in numerous co-authored publications across disciplines, demonstrating her ability to work effectively with researchers from diverse fields including engineering, healthcare, and business management. Her advisory work appears focused on helping organizations implement statistical thinking in business processes, with particular attention to small and medium enterprises seeking to leverage data analytics for competitive advantage. Though specific grant information isn't detailed in the available publications, her extensive industry-focused research suggests significant engagement with practical business problems and industry partnerships. Dr. Coleman has made substantial contributions to the field through her leadership in professional organizations, particularly ENBIS, where she has helped shape the discourse around industrial statistics and their business applications. Her work on Kansei Engineering demonstrates innovative approaches to integrating human factors with statistical methods for product development.
Jun.-Prof. Dr. Annette Rudolph is an Assistant Professor leading the AI and (Climate-Induced) Land Use Change research group at TU Berlin's Institute of Landscape Architecture and Environmental Planning. She holds a Diplom in Mathematics (TU Berlin, 2011) and a PhD in Meteorology (FU Berlin, 2018), with a habilitation thesis on geophysical fluid dynamics and data-driven methods (2023). Her research integrates AI, climate science, and geophysical fluid dynamics. Academic Roles: Head of FG KI und Landnutzungswandel (since 2023), Postdoc in SFB 1114 (2014–2022) Research interests focus on AI applications in environmental sciences, clouds-climate interactions, and fluid dynamics. Her work bridges theoretical meteorology with data science, including machine learning for precipitation modeling and climate analysis. Publications emphasize AI-driven climate modeling, geostatistical methods, and atmospheric dynamics. Notable contributions include a 2024 paper on deep learning for precipitation nowcasting and a 2023 study on CAPE-precipitation relationships using machine learning. She developed e-learning resources on geodata analysis using Python and R, and led DAAD-funded research in Oslo (2022). Current projects involve AI-driven land-use change analysis and climate impact modeling.
Jun Li is a Full Professor in the Department of Applied and Computational Mathematics and Statistics at the University of Notre Dame's College of Science. He specializes in developing statistical and computational methods for big data, with a focus on interdisciplinary applications in bioinformatics, machine learning, and data mining. His career includes tenure as an Assistant Professor (2012–2017) and promotion to Associate Professor (2017) before becoming Full Professor (2020). Dr. Li holds a Ph.D. in Statistics from Stanford University (2012), supervised by Robert Tibshirani, and earlier degrees from Tsinghua University: a B.E. in Automation (2004) and an M.S. in Pattern Recognition and Intelligent Systems (2007). Research Interests : Dr. Li’s work centers on advancing computational frameworks for handling large-scale datasets, integrating statistical rigor with algorithmic innovation. Recent themes include AI-driven code improvement, ethical LLM applications in HCI, and GUI automation. His methodologies emphasize human-AI collaboration and transparency in algorithmic systems. Publications : His 2025 work explores LLM vulnerabilities in GUI agents, AI-assisted education tools like GLITTER, and ethical challenges in HCI research. Earlier studies (2024–2023) address topics such as natural language database queries, privacy-preserving app promotion analysis, and multimodal task learning. Lab/Teams : Affiliated with Notre Dame’s computational statistics research groups, focusing on interdisciplinary projects bridging statistics, computer science, and applied mathematics. His work often involves collaborations with industry and academic partners to translate theoretical advancements into practical applications.
David M. Higdon is a Professor and Department Head of the Department of Statistics at Virginia Tech within the College of Science. He specializes in Bayesian statistical modeling of environmental and physical systems, focusing on integrating physical observations with computer simulations for prediction and inference. Previously, he spent 14 years at Los Alamos National Laboratory as a scientist and group leader in the Statistical Sciences Group. Education: Ph.D. in Statistics, University of Washington, 1994 M.A. in Mathematics, University of California San Diego, 1989 B.A. in Mathematics, University of California San Diego, 1987 Research Interests: Higdon’s work spans space-time modeling , inverse problems in hydrology and imaging , statistical modeling in ecology and environmental science , and multiscale models . He develops methods for parallel processing in posterior exploration , statistical computing , and Monte Carlo simulations . His research addresses critical challenges in uncertainty quantification (UQ), including climate modeling, nuclear density functional theory, and geophysical imaging. Publications Trends: His recent articles emphasize Bayesian methodologies applied to complex systems, such as climate forecasting, materials science, and cosmology. A recurring theme is the development of emulators and surrogate models to handle computationally intensive simulations. Awards: Fellow of the American Statistical Association Advising & Grants: While no specific advisees are listed, Higdon has contributed to interdisciplinary collaborations in UQ and statistical modeling. His work has been supported by grants from agencies such as the National Science Foundation and Department of Energy. Labs/Teams: He leads the Statistics Department’s efforts in UQ and computational statistics, fostering collaborations across engineering, environmental science, and physics.
Professor Nir Oren is a faculty member at the School of Natural and Computing Sciences , University of Aberdeen. His research focuses on multi-agent systems , formal argumentation , computational trust theory , and norm-based reasoning . He currently supervises PhD students in Computing Science and serves as Dean for Research Performance. Research Specialisms: Artificial Intelligence, Operational Research Contact: n.oren@abdn.ac.uk Research Trends (2022–2025): Nir Oren's publications span argumentation theory , BDI agent modeling , resilience in autonomous systems , and human-machine collaboration . His recent work addresses responsibility-aware AI , medical explainability , and environmental sensor networks . Key methods include probabilistic reasoning , game theory , and logical formalisms .
