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Hoai Phuong HaView profile

Professor

• Green and energy-efficient computing
• Parallel and distributed computing systems
• Cyber-physical systems
• +3 more

Professor Hoai Phuong Ha is affiliated with UiT The Arctic University of Norway's Department of Computer Science. A leading expert in green computing and cyber-physical systems, they contribute to Arctic research through the Distributed Arctic Observatory (DAO) and Arctic Green Computing (AGC) group. Founded ARC (Arctic Center for Sustainable Energy) PI in EU FP7 EXCESS and H2020 TAILOR projects WP-leader in EEA POLNOR HAPADS and RCN PREAPP projects Their research focuses on energy-efficient computing, including IoT systems, edge computing, and parallel algorithms. Recent work addresses wireless charging trajectories (eU2U, 2025), smart grid networks (GridWatch, 2024), and pollution monitoring (2024). Publications span cyber-physical observatories, sensor calibration, and distributed systems optimization. Key trends in their 15 most recent articles (2017-2025) include: energy-aware data structures, Arctic-adapted IoT deployments, and sustainable computing methods. Collaborations span EU and Norwegian grants with applications in smart grids, environmental sensing, and high-performance computing. Co-founder of Arctic Center for Sustainable Energy (2017) Active in EEA POLNOR (2019-2023) and RCN eX3 infrastructure project Their lab (Realfagbygget A237) develops systems for Arctic tundra monitoring, including UAV-powered networks and energy-harvesting protocols. Students include researchers from multiple international collaborations.

Naoki YamamotoView profile

Assistant Professor

• Graph Pattern Matching
• Static Type Checking
• Graph Rewriting Languages
• +4 more

Naoki Yamamoto is an Assistant Professor (non-tenure lecturer) at Waseda University's School of Fundamental Science and Engineering, Department of Computer Science and Engineering since April 2025. He also holds a part-time lecturer position at the University of Tokyo's College of Arts and Sciences (Junior Division) since October 2025. Previously, he served as a Research Associate at Waseda University from April 2022 to March 2025. He received his Doctor of Engineering from Waseda University in March 2025, following his Master of Engineering (2021) and Bachelor of Engineering (2019) from the same institution. Dr. Yamamoto's educational background is impressive, having earned his Bachelor's, Master's, and Doctoral degrees consecutively from Waseda University's School of Fundamental Science and Engineering. During his doctoral studies under Professor Kazunori Ueda, he focused on advanced topics in programming languages and graph theory. He was awarded the Outstanding Student Award (Dean's Award) from Waseda University in March 2019. His research interests center around graph-based programming language theory, with particular expertise in Graph Pattern Matching , Static Type Checking for complex data structures , and Graph Rewriting Languages . He has developed innovative approaches to handling graph structures that go beyond traditional algebraic data types like lists and trees. His work bridges theoretical computer science with practical implementation, focusing on how to safely manipulate complex graph structures in programming languages. His publication record demonstrates consistent advancement in graph-based type systems. Starting with foundational work on static type checking for graph operations (2019), he progressed to handling numeric constraints (2020), extending type expression power (2022), and developing grammar-based approaches for difference data structures (2024). His research shows a clear trajectory from theoretical foundations to practical applications, with increasing sophistication in handling complex graph structures. His work on LMNtal ShapeType represents a significant contribution to the field of graph rewriting languages. Outstanding Student Award (Dean's Award), Waseda University (2019) Dr. Yamamoto has been actively involved in teaching and research supervision, having served as a Teaching Assistant for 14 courses between 2018-2021. He currently teaches Introduction to C Programming and C Application Development at Waseda University, and Introduction to Algorithms at the University of Tokyo. He has received research funding through the Japan Society for the Promotion of Science Grants-in-Aid for Scientific Research, collaborating with Professor Kazunori Ueda on projects related to high-level languages with powerful data structures and concurrency. As a member of Ueda Laboratory at Waseda University, Dr. Yamamoto has contributed to the development of tools like Lambda Friends, a web-based interpreter for lambda calculus written in TypeScript. His research group focuses on advancing programming language theory, particularly in the areas of graph rewriting and type systems, with applications in both programming languages and modeling languages. His work demonstrates how graph rewriting languages can unify program execution and model checking functionalities while guaranteeing well-formedness of graph structures.

Roland ReitbergerView profile

Research Fellow

• Life Cycle Assessment
• Building Simulation
• Sustainable Architecture
• +3 more

Roland Reitberger is a Research Fellow at the Chair of Energy Efficient and Sustainable Design and Building at the Technical University of Munich . His work focuses on Life Cycle Assessment , thermal-dynamic building simulation , and parametric methods for analyzing the built environment. He investigates interactions in urban systems and synergy effects in sustainable construction. Research interests include climate-resilient urban planning, integration of green infrastructure with building systems, and data-driven optimization of building energy performance. His recent publications demonstrate expertise in multi-objective decision support for neighborhoods, GIS-based urban tree analysis , and holistic climate assessments of construction projects. Reitberger contributes to urban climate resilience research through parametric modeling of building density-vegetation relationships and develops frameworks for circular economy strategies in building densification and refurbishment. His methodological innovations combine machine learning with building simulation for explainable AI applications in office energy management.

Andreas PavlogiannisView profile

Associate Professor

• formal methods
• algorithmic verification
• automata theory
• +6 more

Andreas Pavlogiannis is an Associate Professor in the Department of Computer Science at Aarhus University. His research focuses on formal methods , algorithmic verification , automata theory , concurrency , static and dynamic program analysis , network diffusion , evolutionary graph theory , and evolutionary game theory . Teaching courses: Programming Languages (Bachelor) , Algorithmic Model Checking (Master) , and Program Analysis (Master) Service: Program committee member for POPL, ESOP, AAAI, IJCAI, CONCUR, OOPSLA, and organizer of CONFEST'25 His research has been supported by the Austrian Science Fund (FWF), VILLUM Foundation, Stibo Foundation, and Danish Council for Independent Research (DFF). He is actively recruiting PhD and PostDoc researchers. Recent publications span quantum computing , concurrent systems , evolutionary dynamics , and network science , with particular emphasis on symbolic algorithms , dynamic analysis , and graph-based models .

Hoa Khanh DamView profile

Professor

• Software Engineering
• Artificial Intelligence
• Software Analytics
• +4 more

Hoa Khanh Dam is Professor and Deputy Head of School (Research) & Head of Postgraduate Studies in the School of Computing and Information Technology at the University of Wollongong, Australia. He serves as Co-Director of the Decision System Lab where he leads research at the intersection of Software Engineering and Artificial Intelligence. His research focuses on developing AI-driven solutions for software quality, cybersecurity, and productivity enhancement. Key interest areas include: AI/IoT autonomous and cyber resilient systems Software Analytics and Mining Software Repositories Large Language Models for software engineering tasks Defect prediction and vulnerability analysis Agile project management optimization Analysis of Dam's 12 publications from 2018-2025 reveals consistent application of machine learning to software engineering challenges. His work shows progressive evolution from traditional ML techniques toward LLM-based frameworks, with major contributions in defect prediction (DeepJIT), vulnerability analysis, microservice recommendation, and agile effort estimation. The research demonstrates strong industry relevance through practical implementations in code review, component prediction, and security systems. Dam co-leads the Decision System Lab at UOW, which develops intelligent decision support systems using AI and data analytics. The lab's work bridges theoretical AI advancements with real-world software engineering applications, particularly in cybersecurity and autonomous systems development.

• Robotics
• Artificial Intelligence
• Control Systems
• +3 more

Ben Riviere is a Postdoctoral Scholar Research Associate in Mechanical and Civil Engineering at the California Institute of Technology (Caltech), part of the Division of Engineering and Applied Science. His research focuses on advanced robotics, artificial intelligence, and control systems, with a particular emphasis on multi-agent systems, motion planning, and optimization. He explores applications in aerospace systems, urban transportation, and distributed learning frameworks. Key research areas include Monte Carlo tree search algorithms, safety-certified motion planning for autonomous systems, and adaptive reinforcement learning for urban logistics. His work bridges theoretical advancements with practical implementations in robotics coordination, fault detection, and resource allocation. No scientific awards are explicitly mentioned in the provided information. Riviere has not listed any advisees or students, and grants or lab affiliations are not detailed in the text.

• Circular Economy
• Business Analytics
• Industrial Ecology
• +7 more

Dr. Mohammad-Sadegh Taskhiri is a Lecturer in Business Analytics at La Trobe University, Australia. He holds a PhD from Georg August Universität Göttingen, Germany, with a focus on logistics networks for wood flows and cascade utilization. His expertise spans circular economy, industrial ecology, and operations research, with a strong emphasis on integrating business analytics into environmental and supply chain challenges. He has secured over $1.2 million in research grants for projects in precision forestry, transportation, waste management, and energy systems. Education: PhD (Dr. rer. pol) in Economic Sciences, Georg August Universität Göttingen, Germany (2012–2016) Research Interests: Dr. Taskhiri's work focuses on circular economy metrics, sustainable forestry management through AI-driven technologies, and optimizing supply chains for decarbonization. He has published in leading journals such as Annals of Operations Research , Journal of Industrial Ecology , and Remote Sensing , emphasizing interdisciplinary approaches to environmental and operational challenges. Grants & Projects: He has led impactful projects, including modeling biomass utilization for energy plants and developing tools for forest structural complexity analysis. His work bridges academic research with practical applications, such as UAV-based forest canopy sampling and discrete-event simulation for port terminal optimization. Labs/Teams: Collaborates with interdisciplinary teams in circular economy, remote sensing, and precision forestry, fostering innovation in sustainable resource management.

Roman ManetschView profile

Professor

• Medicinal Chemistry
• Antimicrobial Agents
• Antiparasitic Drug Development
• +3 more

Roman Manetsch is a Professor of Chemistry and Chemical Biology at Northeastern University, with a joint appointment in the Bouvé College of Health Sciences. His research focuses on medicinal chemistry, particularly developing novel therapies for neglected parasitic diseases like malaria and leishmaniasis, as well as antibacterial agents targeting drug-resistant pathogens. He leads the Manetsch Lab, which integrates mass spectrometry-based approaches with synthetic chemistry to optimize drug candidates. Expertise includes kinetic target-guided synthesis, protein crosslinking strategies for neurodegenerative diseases, and the discovery of compounds targeting Plasmodium species. Notable contributions include advancements in antimalarial drug design, development of streptothricin F as a novel antibiotic, and synthetic methodologies like sulfo-click and seleno-click amidation. Recipient of the 2023 TIER1 Seed Grant for innovative drug discovery research Leading multidisciplinary projects with the Barnett Institute of Chemical and Biological Analysis Collaborations in both biomedical research and pharmaceutical sciences Research trends emphasize translational applications of synthetic chemistry, with recent work exploring multifragment screening for protein interaction modulators and biocompatible reaction systems for targeted drug delivery. His lab’s publications consistently address critical gaps in treating neglected tropical diseases and antibiotic-resistant infections.

Marc MezardView profile

Professor

• Statistical Physics
• Machine Learning
• Information Theory
• +5 more

Marc Mezard is a Professor of Theoretical Physics at Bocconi University, where he leads the newly established Department of Computational Sciences. Previously, he served as Research Director at CNRS in Paris and held roles at Université Paris Sud. He earned his PhD in Physics from École Normale Supérieure in Paris in 1984. His research focuses on statistical physics of disordered systems, with applications to machine learning, information theory, computer science, and biophysics. His work bridges theoretical physics and interdisciplinary fields, including neural networks and deep learning, where he explores the impact of data structure on learning strategies. He teaches undergraduate courses in statistical and quantum physics and a doctoral course on complex systems. Key research themes include emergent phenomena in complex systems, with contributions to spin glass theory, compressed sensing, and algorithmic solutions for random satisfiability problems. His publications span foundational topics in statistical mechanics and modern applications in data science. Marc Mezard has collaborated extensively with institutions and researchers globally, contributing to the theoretical foundations of computational and physical sciences. His academic leadership includes directing École Normale Supérieure from 2012 to 2022, fostering interdisciplinary research initiatives.

Bernd WollscheidView profile

Lecturer

• Molecular Health
• Proteomics
• Cell Signaling
• +3 more

Prof. Dr. Bernd Wollscheid is a Lecturer at the Department of Health Sciences and Technology (D-HEST) at ETH Zurich, Switzerland. He leads the Wollscheid Lab and the Proteomics Plattform D-HEST , focusing on decoding the extracellular interactome and the cell surfaceome's nanoscale organization. His work bridges biology, chemistry, medicine, and bioinformatics, developing cutting-edge technologies like LUX-MS and TRICEPS-based LRC to study cellular communication and signaling pathways. Research interests include understanding how the surfaceome influences cellular functions, particularly in disease contexts such as cancer and metabolic disorders. Key projects involve creating resources like the Cell Surface Protein Atlas and PROTTER , tools for visualizing proteoforms and analyzing multi-omics datasets. His lab also explores precision medicine applications, including biomarker discovery and tumor profiling for clinical decision support. Publications highlight advancements in multi-omics integration, drug repurposing, and functional proteomics. The lab collaborates on initiatives like the Swiss Personalized Health Network (SPHN) and the Personalized Health and related Technologies (PHRT) strategic focus area. Funding comes from public grants and strategic partnerships. Recruitment for motivated researchers is ongoing, emphasizing contributions to molecular health and surfaceome research.

Jörg FrankeView profile

Lecturer

• Climate Modeling
• Paleoclimatology
• Data Assimilation
• +4 more

Jörg Franke is a Lecturer in Climatology and PostDoc researcher at the Institute of Geography, University of Bern, Switzerland. He specializes in paleoclimatology, climate modeling, and data assimilation techniques. His research focuses on reconstructing historical climate patterns using proxy data and climate models, particularly through the development of paleo-reanalysis systems like ModE-RA and EKF400. Educational Background: Franke earned a Diploma in Physical Geography (2003) from the University of Hanover, followed by a PhD in Geosciences (2008) from the University of Bremen, where he studied radiocarbon reservoir ages in marine environments. His postdoctoral work (2008–2011) at the Swiss Federal Research Institute WSL focused on dendrochronology and paleoclimatic reconstructions. Research Interests: His work emphasizes data assimilation of instrumental and proxy records, climatic extremes analysis, and spatial climate reconstructions. He has contributed to understanding the impacts of volcanic eruptions (e.g., Laki 1783) on global and regional climate systems, as well as the role of atmospheric circulation patterns in historical climate variability. Key Projects: Co-developed the ModE-RA global paleo-reanalysis system (1421–2008 CE) and led efforts to integrate satellite and reanalysis data for climate studies in regions like Eritrea and the Hindu Kush Himalaya. His tools, such as ClimeApp, advance climate data processing for historical reconstructions. Professional Roles: In addition to teaching quantitative methods in geography, Franke managed climate data services at the Oeschger Centre for Climate Change Research (2012–2014) and has contributed to interdisciplinary projects linking paleoclimate data with modern climate models. Lab/Team Affiliations: Active in the Climate Research Group and Oeschger Centre at the University of Bern, collaborating with international teams on paleo-reanalysis and climate variability studies.

• Bayesian Analysis
• Approximate Bayesian Computation
• Deep Learning Methods
• +3 more

Yuexi Wang is an Assistant Professor in the Department of Statistics at the University of Illinois. Their research focuses on Bayesian methodology, approximate Bayesian computation, and deep learning applications in statistical inference. Key areas include uncertainty quantification, sparse deep learning, and statistical modeling for count data. Education: Not explicitly stated in text. Research interests span Bayesian analysis, with emphasis on developing scalable methods for posterior approximation, adversarial simulation, and generative models. They have contributed to variable selection via Bayesian forests and uncertainty quantification in sparse neural networks. Recent work explores optimal transport-based methods for posterior sampling and Pochhammer priors in count models. Publications emphasize methodological advances in Bayesian deep learning, including data augmentation techniques and adversarial approaches. Articles often bridge theory and application in machine learning and computational statistics. No scientific awards explicitly mentioned. Advising and grants information unavailable in provided text.

• Medical Imaging
• Positron Emission Tomography (PET)
• Detector Physics
• +12 more

David Schug is a Group Leader in PET detector research at the Chair of Imaging and Image Processing, RWTH Aachen University, Germany. He is also Co-Founder and Co-Director of Hyperion Hybrid Imaging Systems GmbH and serves as a Radiation Safety Officer. His research is centered on advancing positron emission tomography (PET) technology, particularly in detector design, time-of-flight performance, depth-of-interaction encoding, and integration with MRI systems. He is actively involved in both academic and industrial innovation in medical imaging. B.Sc. Physics, RWTH Aachen University (2006–2009) M.Sc. Physics, RWTH Aachen University (2009–2011) Dr.rer.nat., RWTH Aachen University (2012–2015) David Schug's research focuses on medical imaging physics , particularly PET detector development , hybrid PET/MRI systems , and advanced calibration techniques . His work integrates machine learning with physics-based models to enhance timing resolution and image quality. Key areas include ASIC-based readout electronics, RF shielding for MRI compatibility, and real-time data processing. He has made significant contributions to improving coincidence time resolution beyond 200 ps in clinical and preclinical systems. His recent publications demonstrate a strong trend in high-resolution PET detector design , with emphasis on time-of-flight capabilities , depth-of-interaction encoding , and in-system calibration methods . Articles frequently involve machine learning (e.g., gradient tree boosting), ASIC integration (TOFPET), and MRI compatibility. The research spans both preclinical and clinical applications, including breast cancer imaging, brain PET, and proton therapy monitoring. David Schug has not been publicly awarded any scientific prizes or fellowships based on available information. He advises several researchers and students involved in PET detector projects, as evidenced by co-authorship on numerous publications. His team develops cutting-edge detector platforms like the Hyperion series and collaborates on EU projects such as HYPMED. While specific grant details are not listed, his sustained research output and entrepreneurial activity suggest successful funding acquisition. His work bridges academia and industry through Hyperion GmbH. David Schug leads the PET detector research group at the Chair of Imaging and Image Processing, RWTH Aachen. He is part of AG START at Uniklinik RWTH Aachen and collaborates extensively with Prof. Volkmar Schulz. His team focuses on the Hyperion detector platform, developing inserts for simultaneous PET/MRI, with applications in oncology, neurology, and cardiology.

Jessica HemingwayView profile

Researcher

• Environmental Science
• Urban and Regional Planning
• Climate Change Adaptation
• +5 more

Jessica Hemingway is a researcher at the Leibniz Institute of Ecological Urban and Regional Development (IOER) in Dresden, affiliated with the Dresden University of Technology as part of the Landscape Ecosystems and Biodiversity research area. Her work focuses on urban environmental acupuncture, green infrastructure, climate change adaptation, and sustainable urban transformation, with a strong emphasis on integrating ecosystem services and community-driven planning. PhD in Environmental Science, Dresden Leibniz Graduate School (DLGS), 2017 Master of Arts in City and Regional Planning, The Ohio State University, 2008 Bachelor of Arts in Psychology, SUNY College at Buffalo, 2006 Hemingway's research explores how small-scale, targeted urban interventions ("acupuncture") can catalyze broader ecological and social sustainability. Her publications emphasize nature-based solutions for heat stress reduction, citizen participation in green space design, and policy frameworks for climate adaptation. Recent work includes comparative studies between Central Europe and East Asia. Key trends in her 15 most recent articles (2022-2024) revolve around urban environmental acupuncture as a strategy for implementing nature-based solutions, evaluating green infrastructure effectiveness, and developing transnational action planning frameworks. She frequently collaborates with Juliane Mathey and Peter Wirth on guidelines and case studies for small green spaces. Scientific Awards: American Planning Association Clintonville Sustainability Plan, Outstanding Project Award (2008) Ronald E. McNair Scholar Award, Upholding the Ideals of a Scholar (2004) McNair Scholars Program, Research Scholarship (2008) Hemingway holds a full scholarship from the Dresden Leibniz Graduate School (2008-2013) and contributes to projects like CICADA4CE , SALUTE4CE , and SPOT , focusing on ecosystem services, climate resilience, and cultural tourism development.

• Real-Time Systems
• Parallel Computing
• Cyber-Physical Systems
• +3 more

Jing Li is an Assistant Professor at the Department of Computer Science in the Ying Wu College of Computing at New Jersey Institute of Technology. She holds a Ph.D. in Computer Science from Washington University in St. Louis (2017), advised by Chenyang Lu and Kunal Agrawal. Ph.D. in Computer Science, Washington University in St. Louis (2017) M.S. in Computer Science, Washington University in St. Louis (2014) B.S. in Computer Science, Harbin Institute of Technology, China (2011) Her research spans Real-Time Systems , Parallel Computing , Reinforcement Learning for System Design , and Scheduling Theory . Current projects include NSF-funded work on real-time systems with parallel resources and ARPA-E/IBM collaborations on reinforcement learning for converter design. Recent publications focus on AI-driven scheduling , parallel task optimization , and reinforcement learning applications in traffic control and circuit design. Key venues include AAAI, RTSS, PPoPP, and RTAS. Outstanding Achievement in Research (2022) Outstanding Paper Awards at RTSS (2018), RTAS (2016), ECRTS (2013) Turner Dissertation Award (2017) She advises graduate students in real-time systems and parallel computing, mentors NSF/ARPA-E projects, and leads professional services as TPC member and workshop organizer.