Günter Rote is a Professor in the Department of Computer Science at Freie Universität Berlin, specifically within the Theoretical Computer Science group (Arbeitsgruppe Theoretische Informatik). He holds a formal academic title of Professor Dr. and is affiliated with the Faculty of Mathematics and Computer Science. His research focuses on theoretical computer science, computational geometry, algorithms, and discrete mathematics. Key research interests include geometric algorithms, optimization problems (e.g., shortest paths, traveling salesman problems), and algorithm design for parallel computing systems. His work spans topics such as systolic arrays, convex hulls, and combinatorial optimization. Rote’s contributions include foundational studies on computational geometry problems, algorithmic complexity, and practical applications in energy equity and infrastructure design. Publications highlight contributions to solving extremal equations, polygon transformations, and the quadratic assignment problem. He has been active in academic leadership, mentoring students, and contributing to computational science communities. His email is rote@inf.fu-berlin.de, and his office is located at Takustraße 9 in Berlin.
Jiannong Cao is a Chair Professor and Director of the University Research Facility in Big Data Analytics at the Department of Computing, Hong Kong Polytechnic University. He has held various academic roles since 1990, including Assistant Professor at City University of Hong Kong and Lecturer at Australian universities. PhD in Computer Science, Washington State University (1990) MSc in Computer Science, Washington State University (1986) BSc in Computer Science, Nanjing University (1982) His research focuses on cloud and edge computing , parallel and distributed computing , and mobile computing , with significant contributions to wireless sensor networks (WSN) for structural health monitoring (SHM) and software-defined networking (SDN) for vehicular communications. Recent work includes WiFi-based non-invasive health monitoring systems and multi-user computation partitioning in mobile cloud environments. Dr. Cao’s publications demonstrate trends in WSN optimization , SDN architectures , and cognitive modeling for network embedding , with applications in smart healthcare , transportation systems , and industrial IoT . Ministry of Education Natural Science Award (2018) ACM Distinguished Member (2017) IEEE Fellow (2014) Best Paper Awards at IEEE DSAA, SMARTCOMP, WCNC He has mentored numerous researchers, including Linchuan Xu , Xuefeng Liu , and Weigang Wu , who have authored key publications in top venues like ACM WSDM and IEEE INFOCOM . His professional roles include chairing IEEE committees and serving on grant panels for the Hong Kong Research Grant Council.
J. Eric Bickel is a Professor at The University of Texas at Austin, serving as Director of the Operations Research & Industrial Engineering (ORIE) and Engineering Management programs. He holds a courtesy appointment in the Department of Petroleum and Geosystems Engineering and directs the Center for Engineering & Decision Analytics (CEDA). His academic background includes a PhD and MS in Engineering-Economic Systems from Stanford University and a BS in Mechanical Engineering from New Mexico State University. His research focuses on decision analysis under uncertainty, addressing topics like probabilistic modeling, climate engineering, risk management, and applications in sports and energy sectors. His work has been featured in major media including The New York Times and Wall Street Journal , and his climate engineering research was endorsed by Nobel Laureates as a top climate change response strategy. Professor Bickel has extensive industry experience, having previously served as Senior Engagement Manager and Co-Director of Client Education at Strategic Decisions Group (SDG), where he remains on the Board of Directors. His consulting spans oil/gas, energy trading, and financial services sectors. He has received recognition as a Fellow of the Society of Decision Professionals and contributed to the Copenhagen Consensus on Climate Project. His teaching extends to executive education through Texas Executive Education and McCombs School of Business. Research highlights include novel methods for probabilistic dependence modeling, value-of-information analysis in shale reservoirs, and critiques of risk assessment tools like heat maps. His climate engineering work emphasizes economically viable solar radiation management strategies.
Michalis Vazirgiannis is a Professor at LIX, École Polytechnique (France) leading the Data Science and Mining (DaSciM) group. With academic backgrounds in Physics (Athens University), AI (Heriot-Watt University), and Informatics (Athens University), he has conducted research at Fraunhofer, Max Planck MPI, and INRIA/FUTURS while teaching at institutions across Greece, France, China, and Spain. His research spans Machine/Deep Learning for Graphs (GNNs, graph kernels, embeddings) Text Mining & NLP (Graph-of-Words, biomedical text analysis) Combinatorial Optimization for pandemic forecasting and energy systems Event/Anomaly Detection in time series and sensory data Industrial collaborations with Airbus, Google, Tencent, and BNP . He has supervised 29 completed PhD theses, published over 250 papers, and received prestigious awards including Marie Curie and Tencent Rhino-Bird Fellowships. His team leads the ANR-HELAS Chair (2020-2025) focusing on heterogeneous data deep learning.
Davide Di Blasio is a Research Associate in the Department of Mechanical Engineering at the University of Bath. His work focuses on hydrogen fuel cell systems, air path optimization, and control mechanisms for vehicle applications. He actively contributes to sustainability research aligned with UN Sustainable Development Goals. Research interests: Hydrogen fuel cell systems Variable-geometry turbocharger control Air loop optimization for vehicles Thermal management in powertrains Proton-exchange membrane fuel cells His recent publications explore advancements in hydrogen fuel cell efficiency and air path dynamics. Collaborative activities include participation in the CENEX Expo 2024 conference.
Dominic Liao-McPherson serves as an Assistant Professor in the Department of Mechanical Engineering within the Faculty of Applied Science at the University of British Columbia. His research bridges algorithmic control, optimization theory, and computational engineering with practical applications across robotics, energy systems, and aerospace domains. His academic background includes a BASc from the University of Toronto, PhD from the University of Michigan, and postdoctoral training at ETH Zürich: BASc (University of Toronto) PhD (University of Michigan) Postdoc (ETH Zürich) Dr. Liao-McPherson's research centers on developing real-time computational decision-making algorithms for physical systems. His work spans predictive and constrained control (including model predictive control and reference governors), real-time embedded optimization, and game-theoretic coordination mechanisms for multi-agent systems. Key application areas include energy grids, autonomous vehicles, additive manufacturing, and aerospace systems, with past projects covering spacecraft landing, engine emissions control, and aircraft upset recovery. His methodology emphasizes rigorous stability analysis, constraint satisfaction, and practical implementation on resource-constrained hardware. Analysis of his 2020-2022 publications reveals a strong focus on advancing optimization-based control frameworks. His work consistently addresses stability guarantees and constraint handling in real-time systems, with increasing emphasis on distributed algorithms for multi-agent coordination. The research demonstrates a clear trajectory from theoretical algorithm development (e.g., FBstab solver) toward experimental validation in complex engineering systems like diesel engines and autonomous networks. No scientific awards are documented in the provided materials. Regarding academic advising and research funding, the source text contains no information about current students, grant awards, or sponsored research projects. He directs the Algorithmic Optimization and Control Lab (AOCL) at UBC, as evidenced by his research website (aocl.mech.ubc.ca). The lab specializes in developing computationally efficient control algorithms for embedded systems, with particular expertise in handling physical constraints and coordination challenges in multi-agent environments across energy, manufacturing, and robotics applications.
Panruo Wu is an Associate Professor in the Department of Computer Science at the University of Houston (UH). He joined UH in 2018 as an Assistant Professor, transitioning to his current rank. His research focuses on high-performance computing, numerical algorithms, parallel and distributed systems, and fault tolerance. He holds a Ph.D. in Computer Science from the University of California, Riverside (2016), advised by Zizhong Chen, and a B.S. in Mathematics from the University of Science and Technology of China (USTC). Research Interests: His work spans high-performance computing, numerical linear algebra, GPU acceleration, fault-tolerant systems, and scalable machine learning. Key projects include LATER (Linear Algebra on Tensor Cores), LibKernel (a scalable kernel machine framework), and Wukong (a serverless parallel computing framework). He emphasizes energy-efficient and hardware-aware algorithms. Publications: Dr. Wu's recent work includes advancements in QR factorization using tensor cores, symmetric eigenvalue decomposition optimizations, and fault-tolerant algorithms for heterogeneous systems. His research often addresses computational challenges in big data and exascale computing. Awards & Grants: Received NSF Grant No. 2146509. His work on high-accuracy matrix computations was a Best Paper Nominee at HPDC'20. He has authored over 30 peer-reviewed publications in top venues like SC, ICS, and IEEE TPDS. Students & Advising: Advises PhD students including Shaoshuai Zhang, Ruchi Shah, Benjamin Carver, and Ao Wang. His students have contributed to projects like LibKernel and fault-tolerant linear algebra libraries. Labs & Collaborations: Leads research in UH's high-performance computing group, collaborating with institutions like Jack Dongarra's Innovative Computing Lab (University of Tennessee) and industry partners on exascale computing initiatives.
Andrew Rowe is a Professor in Mechanical Engineering at the University of Victoria (UVic), with affiliations to the Institute for Integrated Energy Systems (IESVic) and the Advanced Mechanical Research Laboratory (AMRLab). He holds a BEng from the Royal Military College of Canada, MASc and PhD from UVic, and is a licensed Professional Engineer (P.Eng). His research focuses on thermodynamics, energy systems, cryogenics, and heat transfer, with particular expertise in caloric cycles, hydrogen systems, and energy systems analysis. His research emphasizes decarbonization strategies, including hydrogen integration into natural gas networks, grid flexibility under electrification scenarios, and the optimization of magnetocaloric materials for energy-efficient cooling. AMRLab, led by Rowe, develops technologies for energy conversion, storage, and system optimization, addressing challenges in low-temperature thermal systems and integrated energy networks. Recent work explores the systemic impacts of electrification in transportation and buildings, leveraging open-source tools like the NExus Solutions Tool (NEST) for multi-scale energy-water-land system modeling. His projects often address regional energy challenges, such as British Columbia’s transition to renewable energy and grid infrastructure resilience. Key contributions include studies on gas system decarbonization via hydrogen blending, thermal-hydraulic modeling of energy systems, and experimental validation of magnetocaloric materials. Rowe’s publications highlight interdisciplinary approaches to climate mitigation, emphasizing the interplay between technological innovation and systemic policy frameworks.
Eugene Tang is an Assistant Professor in the Department of Mathematics and Physics at Northeastern University. His research focuses on quantum information theory and the theoretical limitations of quantum computing, particularly quantum error correction and efficient protocols using high-rate codes. He received his PhD from the California Institute of Technology in 2021. Dr. Tang's research interests include quantum error correction, the development of efficient quantum protocols surpassing conventional schemes, and the study of quantum algorithms such as QAOA. He explores the theoretical boundaries of quantum computing, with a focus on optimizing error detection and decoding methods for quantum LDPC codes and subsystem codes. His work also intersects with quantum gravity, particularly in the context of black hole interiors and bulk geometry construction through tensor methods. His recent publications highlight advancements in quantum error correction, including optimal locality in subsystem codes and efficient decoding strategies for quantum LDPC codes. His work on variational quantum optimization addresses challenges in scalability, such as QAOA's performance at large qubit scales and symmetry-related obstacles. Earlier contributions include research on superoscillations and hybrid quantum-classical algorithms for graph coloring. No scientific awards or grants are explicitly mentioned in the provided information. No specific labs or teams are associated with his work in the given data.
Dr. King Man Siu is an Assistant Professor in the Department of Electrical Engineering at the University of North Texas, College of Engineering. He established the Power Electronics and Renewable Energy (PERE) Lab in February 2022, focusing on power electronics technologies for renewable energy, smart grids, and electric vehicle applications. University: University of North Texas School: College of Engineering Department: Electrical Engineering Research Interests: Dr. Siu specializes in power electronics, renewable energy systems, and smart grid technologies. His work addresses challenges in: Efficient energy conversion for solar and battery systems Grid integration of electric vehicles and renewable sources Advanced inverter design for residential and industrial applications Reduction of magnetic components in power converters Reactive power management and circuit breaker development Modular solutions for DC distribution and rural electrification Publication Trends: His research emphasizes optimizing power electronics through innovative topologies (e.g., Manitoba inverters, interleaved totem-pole converters) and materials (e.g., SiC MOSFETs). Key areas include energy efficiency in photovoltaic systems, smart grid stability, and DC microgrid interconnection strategies. Contact: Email: Kingman.Siu@unt.edu Office: Discovery Park B233
Prof. Michael Beigl is a faculty member at Karlsruhe Institute of Technology (KIT), serving as Professor of Pervasive Computing Systems (PCS) and head of the Telecooperation Office (TECO). He is a spokesperson for the KIT Center for Health Technologies (KITHealthTech) and coordinator of the Smart Data Innovation Lab (SDIL), a federally funded big data center. His work focuses on developing wearable sensor systems and AI-driven diagnostics for healthcare and industrial applications, collaborating across disciplines with medical experts and technology partners. Research interests include digital health technologies (e.g., gas sensors in headbands for respiratory monitoring), ubiquitous computing for remote patient tracking, and Smart Data solutions in medicine, energy, and Industry 4.0. His team integrates machine learning for optimized diagnostics and real-time data analysis.
Henry F. (Hank) Korth is a Professor of Computer Science and Engineering at Lehigh University, with a courtesy appointment in the Department of Decision and Technology Analytics in the College of Business. He serves as Director of the Blockchain Lab in the Center for Financial Services and Co-Director of the Computer Science and Business Program. Korth is a Fellow of the ACM and IEEE, and a recipient of the VLDB 10-Year Award and Bell Labs President's Silver Award for contributions to database technologies. PhD in Computer Science from Princeton University MA, MSE in Computer Science from Princeton University BA in Mathematics from Williams College Korth's research spans database systems, blockchain systems, distributed systems, and real-time systems. He has pioneered transaction management in parallel and distributed systems, query processing, and the impact of modern computing architectures on database performance. His recent work focuses on blockchain applications in enterprise databases, including acceleration of zero-knowledge proofs, benchmarking frameworks, central-bank digital currencies, and private-yet-provable accounting systems. His contributions are rooted in both theoretical advancements and practical implementations, such as the QTM™ aggregation engine and the DataBlitz™ main-memory storage manager. Scientific awards include: ACM Fellow IEEE Fellow 10-Year Award at the VLDB Conference Bell Labs President's Silver Award Korth actively supervises research within the Blockchain Lab and is affiliated with the Scalable Software Systems Research Group at Lehigh. His scholarly output reflects a deep engagement with blockchain benchmarking, concurrency control, verifiable databases, and the evolution of database systems in response to technological shifts.
Georg Fantner is an Associate Professor at the Swiss Federal Institute of Technology Lausanne (EPFL) with dual appointments in the School of Engineering (STI) within the Institute of Bioengineering and the School of Life Sciences (SV) for teaching. He directs the Laboratory for Bio- and Nano-Instrumentation (LBNI) and holds leadership roles including President of the Open Science Strategic Committee and the Association des Professeurs de l'EPFL. Research Focus: Bioinstrumentation, Nanotechnology, Scanning Probe Microscopy, and Metrology Teaching: Structural Mechanics for Life Sciences, Metrology, and Metrology Practicals His research pioneers advanced instrumentation for nanoscale characterization, emphasizing data-driven approaches to enhance microscopy techniques. Recent work integrates deep learning with scanning probe microscopy for real-time biological imaging and develops novel MEMS devices for fluid-compatible nanoscale manipulation. Key innovations include hermetically sealed sample chambers for pathogen studies and deterministic nanotopography engineering. Professor Fantner actively mentors 7 current PhD students and has supervised 14 graduates. His laboratory fosters interdisciplinary collaboration across engineering, physics, and life sciences to advance nanoscale measurement technologies and instrumentation development.
Professor Bing-Jie (Bruce) Ni is an Adjunct Professor at the University of Technology Sydney (UTS) within the School of Civil and Environmental Engineering and a full Professor at UNSW Sydney. He is an internationally recognised leader in environmental engineering, wastewater treatment, greenhouse-gas mitigation, microplastics fate, electrocatalysis and sustainable energy systems. Education PhD in Environmental Engineering, University of Science and Technology of China, Hefei (2005–2009) Research Interests Professor Ni’s research integrates process engineering, microbial biotechnology, materials science and mathematical modelling to develop sustainable technologies for high-efficiency pollutant removal, minimal carbon footprint and maximal energy recovery from wastewater. He is a global pioneer in: Modelling and control of nitrous oxide (N₂O) and methane (CH₄) emissions from wastewater systems, Micro- and nano-plastics ecotoxicity and mitigation in anaerobic digestion, Transforming sewage sludge into high-value liquid bio-energy (medium-chain fatty acids and long-chain alcohols), Designing cost-effective electrocatalysts from natural minerals for green hydrogen production and wastewater electrolysis. Research Output & Impact Over the last decade he has published 2 research books, 30 book chapters and >400 refereed journal papers , including 35 in Environmental Science & Technology and 85 in Water Research . His work has influenced global policy: the IPCC adopted his nitrous-oxide-emission model in 2019 to revise national greenhouse-gas inventories for the first time in 13 years. Awards & Recognition ARC Future Fellowship & ARC DECRA Fellowship Clarivate Analytics Highly Cited Researcher (Web of Science) Royal Society of Chemistry Highly Cited Researcher (2020–present) Mendeley Data Top 2 % Cited Researchers worldwide Listed among “Australia’s Most Innovative Engineers” (Engineers Australia, 2018) 50+ additional awards including Scopus Young Researcher Award, South Australian Water Awards, UQ Research Excellence Awards, and Outstanding Doctoral Dissertation Awards. Research Funding & Leadership He has secured ≈ AUD $10 million in competitive funding (six major ARC grants plus >20 government, university and industry projects). He serves as: Lead Guest Editor, Water Research Editorial Advisory Board, Environmental Science & Technology Associate Editor for Journal of Cleaner Production , Environmental Chemistry Letters , Environmental Research , Journal of Environmental Management Editorial Board member for five additional high-impact journals. Teaching & Supervision At UTS he teaches Renewable Energy Technologies , Environmental and Sanitation Engineering , Process Dynamics and Control , and Water and Wastewater Treatment . He is available to supervise Masters and PhD students in environmental biotechnology, process modelling and sustainable energy systems. Laboratory & Commercial Translation He heads active research teams at both UNSW and UTS and is the inventor of >10 granted patents , some of which are currently being commercialised to deliver real-world impacts in greenhouse-gas-neutral wastewater treatment and renewable energy production.
Jesse Jenkins is an Assistant Professor at Princeton University, jointly appointed in the Department of Mechanical and Aerospace Engineering and the Andlinger Center for Energy and Environment, with courtesy affiliations in the School of Public and International Affairs and the High Meadows Environmental Institute. He leads the Princeton ZERO Lab and specializes in macro-scale energy systems engineering. Education: PhD and SM – Massachusetts Institute of Technology (MIT) Postdoctoral Fellow – Harvard Kennedy School and Harvard University Center for the Environment Research Interests: Jenkins focuses on the transition to zero-carbon energy systems . His work spans the integration of distributed energy resources, optimization of national and sub-national energy systems, and the role of electricity in economy-wide decarbonization. He applies advanced modeling techniques to inform policy and planning decisions. Publications Focus: His recent work emphasizes the intersection of decarbonization strategies and nature-based climate solutions , exploring how ecosystem-based interventions can complement technological pathways to net-zero emissions. These publications underscore the importance of policy frameworks and risk assessments in scaling sustainable climate interventions. Labs & Teams: Jenkins leads the Princeton ZERO Lab (Zero-carbon Energy systems Research and Optimization Laboratory) , which develops and applies optimization-based models to evaluate low-carbon energy technologies and support policy decisions in transitioning to net-zero emissions systems.
