Helen Oleynikova is a Lecturer at the Department of Mechanical and Process Engineering at ETH Zürich, where she is part of the Autonomous Systems Lab. She works on the intersection of perception and planning, particularly for micro-aerial vehicles. Her research focuses on real-time onboard mapping, planning, and localization using visual-inertial systems and signed distance fields. Research Interests: Helen's work spans robotics, autonomous systems, and computer vision, with a focus on enabling safe and efficient navigation in complex environments. She specializes in visual-inertial odometry, SLAM, 3D mapping using signed distance fields, and real-time path planning for MAVs. Her projects often involve embedded systems and FPGA-based high-speed vision for obstacle avoidance. Publication Trends: Her recent publications (2023–2019) show a consistent focus on real-time, onboard algorithms for autonomous navigation. Key themes include signed distance function maps, collision-free motion generation, global localization, and efficient exploration. She frequently publishes in top-tier robotics conferences such as ICRA and IROS, and journals like IEEE RA-L and Journal of Field Robotics. Professional Experience: Senior Researcher, Autonomous Systems Lab, ETH Zürich Senior Software Engineer, Isaac 3D Perception, Nvidia Senior Scientist, Microsoft Mixed Reality and AI Lab, Zürich Software Engineer, Google (StreetView) Contributor, Willow Garage (ROS, TurtleBot Arm) Education: PhD in Robotics, ETH Zürich (2019) MSc in Robotics, ETH Zürich BSc in Robotics, Olin College of Engineering (2011) Advising and Grants: While no formal students are listed, she has collaborated extensively with researchers at ETH Zürich and industry labs. Her work has been supported through institutional affiliations and industry research roles. She has contributed to open-source robotics software, particularly in ROS-based systems for manipulation and navigation. Labs and Teams: Helen is a key member of the Mobile Manipulation team at the Autonomous Systems Lab at ETH Zürich. She has also been involved in projects at Nvidia, Microsoft, Google, and Willow Garage, focusing on real-world deployment of autonomous systems.
Dr. Jianbing Li is a Professor and Professional Engineer (P.Eng.) in the Environmental Engineering Program at the University of Northern British Columbia (UNBC), holding prestigious fellowships from CSCE, CSSE, EIC, and Engineers Canada. His research program addresses critical environmental challenges with significant real-world impact, particularly in northern and remote communities of British Columbia. Education: PhD in Environmental Systems Engineering, University of Regina Research Focus: Dr. Li's work centers on environmental pollution control , petroleum waste management , soil and groundwater remediation , environmental modeling , risk assessment , and oil spill response . His innovative approaches integrate machine learning, advanced materials, and sustainable engineering principles to develop practical solutions for complex environmental problems, with particular emphasis on resource recovery from waste streams. Publication Trends: Analysis of his 15 most recent publications (2023-2025) reveals a strategic focus on oil spill response technologies, wastewater treatment innovations, and waste valorization. Key advancements include nano/micro bubble flotation systems, chitosan-based adsorbents, and machine learning models for pyrolysis optimization, demonstrating his leadership in translating laboratory research to field applications. Scientific Recognition: 2024 Fellow of Engineers Canada and Engineering Institute of Canada 2023 CSCE Dr. Albert E. Berry Medal (Canada's top environmental engineering award) Multiple UNBC Research Excellence Awards (2010, 2014, 2019, 2023) 2013 Northern BC Business and Technology Award with Husky Energy Best paper awards from International Academy of Science and Environmental Geotechnology Society Research Leadership: Dr. Li has secured over $800,000 in 2023 and $1.9 million in 2020 for oil spill response research through NSERC, DFO, and NRCan. His current portfolio includes groundwater protection for Indigenous communities, next-generation decanting technologies, and water security for remote regions. He actively mentors PhD, MSc, and MASc students while serving on NSERC evaluation committees and co-directing the UNBC/UBC environmental engineering program (2013-2017). Collaborative Networks: Dr. Li leads multi-institutional partnerships with UBC, government agencies, industry (including Husky Energy), and Indigenous communities like Lheidli T'enneh First Nation. His work through the Multi-Partner Research Initiative addresses practical challenges in rural British Columbia while advancing fundamental knowledge in environmental systems engineering.
Enrico Macii is a Full Professor at the Politecnico di Torino, affiliated with the Interuniversity Department of Regional and Urban Studies and Planning (DIST) and the Department of Control and Computer Engineering (DAUIN). He leads the Electronic Design Automation (EDA) research group and holds key roles as Scientific Advisor for the Politecnico-STMicroelectronics partnership and Scientific Contact for the European Chips Joint Undertaking. Research Interests: His work spans digital circuits and systems, energy efficiency, smart cities, Industry 4.0, and smart manufacturing. He focuses on embedded and cyber-physical systems, low-power design, neuromorphic computing, AIoT, and sustainable urban development. Recent Publications: His recent research demonstrates strong trends in edge AI, neuromorphic computing, and smart energy systems. Articles highlight innovations in low-power hardware acceleration, federated learning, physics-informed AI, and digital twin applications for urban and industrial systems. There is a clear emphasis on deploying AI efficiently on constrained devices and integrating physical models with machine learning. J. William Fullbright Fellowship (1993) Best paper award IEEE European Design Automation Conference (1996) Best paper award ACM/IEEE Great Lakes Symposium on VLSI (2008) DAC Service Award (2014) IEEE Fellow (2006) DATE Fellow (2014) Advising and Grants: He has supervised over 25 PhD students in computer engineering, AI, and urban systems. His research is funded by major EU programs (Horizon 2020, PNRR, KDT JU), national (PRIN, FAR), and regional grants, as well as industrial contracts with STMicroelectronics, Michelin, and Cefriel. He leads numerous high-impact projects in smart manufacturing, energy efficiency, and digital twins. Labs and Teams: He is a core member of the EDA Group, an interdepartmental research team at Politecnico di Torino focusing on VLSI-CAD, bioinformatics, smart cities, and Industry 4.0. He also contributes to IAM@PoliTo (Integrated Additive Manufacturing) and leads multiple EU and national research consortia.
Jetmir Haxhibeqiri is a Postdoctoral Researcher at Ghent University 's Faculty of Engineering and Architecture , Department of Information Technology. His work focuses on Time-Sensitive Networking (TSN) over wireless systems, WiFi optimization, and Industrial IoT solutions. Key projects: IMEC Postdoctoral Fellowship Collaborations: Jeroen Hoebeke (UGent), Ingrid Moerman (UGent), Xianjun Jiao (UGent) Research Interests : Wireless network coordination, SDN integration for heterogeneous networks, low-latency communication, and machine learning applications in network optimization. Specialized in WiFi-LPWAN coexistence , In-Band Network Telemetry , and Cross-Technology Synchronization . Recent Publications : 2025 work on Wi-Fi-UWB synchronization, 2024 studies on coordinated spatial reuse in WiFi 7, and 2022 research on hardware-efficient PTP clock synchronization. Contributions span from theoretical models to practical implementations in industrial environments. Technical Expertise : Network densification strategies, interference management, and performance evaluation of large-scale wireless deployments. Developed simulation frameworks for LoRaWAN and WiFi TSN, with a focus on ns-3 validation. Education : PhD in Industrial Wireless Communication (2019, Ghent University).
Dr. Hong Ming Tan is a Senior Lecturer at the Department of Analytics and Operations, NUS Business School, and a Research Fellow at the Institute of Operations Research and Analytics (IORA) at National University of Singapore. He holds a PhD in Operations Research and Analytics (2021), MSc in Mathematics (2017), and BSc (Hons) in Applied Mathematics and Economics (2013) from NUS. Doctor of Philosophy, Operations Research and Analytics (2021) Master of Science, Mathematics (2017) Bachelor of Science (Hons), Applied Mathematics and Economics (2013) His research spans Business Analytics , Machine Learning , Operations Research , and Pharmacogenetics . Recent work includes: EcoVal Framework for efficient data valuation in ML Personalized Mental Health through adaptive testing and clustering Antibiotic Resistance modeling using antiresistic strategies CYP2D6 Methylation prediction for precision medicine His publications demonstrate expertise in ML optimization , healthcare informatics , behavioral analytics , and decision science . Current projects include AI-led Smart Data Centre Management and SIA Corp Lab research. He serves as Chair of the Department Finance Committee and advisor to student clubs like Business Analytics Consulting Team. His work addresses real-world challenges in industrial operations, healthcare diagnostics, and educational innovation.
Ramazan Yeniçeri is a Lecturer at Istanbul Technical University's Department of Aeronautical Engineering. His research focuses on Unmanned Aerial Vehicles (UAVs), Field Programmable Gate Arrays (FPGAs), and computational fluid dynamics, with applications in hardware acceleration and autonomous flight systems. Academic Rank: Lecturer University: Istanbul Technical University Department: Aeronautical Engineering Research Interests: Yeniçeri's work bridges aerospace engineering and computer science, emphasizing: FPGA-based hardware acceleration for aerospace systems UAV communication networks (FANETs) and formation flight Dynamical modeling for 6-DoF systems Autopilot software and real-time operating systems Scientific Awards: He has received the BOEING Academic Work Encouragement Award (2017) and the Best Doctoral Thesis Award (2015) . Project Leadership: As Principal Investigator (PI), he leads projects like: "IHA Kayıt, Takip, Kontrol ve Hava Trafik Yönetim Sistemi" (2024–2025) "FPGA Tabanlı 6DoF Dinamik Hızlandırıcı Tasarımı" (2024) "EU Sürü İHA" (2020–2022) His recent publications highlight trends in UAV communication, FPGA acceleration, and multi-sensor tracking.
Volker Markl is a Professor at Technische Universität Berlin in the Institute of Software Engineering and Theoretical Computer Science, with additional affiliations at the Berlin Institute for the Foundations of Learning and Data (BIFOLD) and the German Research Center for Artificial Intelligence (DFKI). His research spans database systems, stream processing, and distributed data management with significant contributions to both theoretical foundations and practical implementations. Markl's research interests focus on next-generation data management systems, particularly for streaming and IoT environments. His work addresses critical challenges in distributed query processing, system integration, and performance optimization. He has pioneered approaches for stream processing in volatile infrastructures and developed innovative techniques for GPU-accelerated database operations. His NebulaStream project represents a major contribution to distributed stream processing systems. His publication record demonstrates consistent impact across top database venues including VLDB, SIGMOD, and ICDE. Recent work shows increasing focus on machine learning integration with database systems, privacy-preserving query processing, and educational approaches for teaching large-scale data management. Markl has mentored numerous researchers who have become prominent in the database community, with frequent collaborators including Steffen Zeuch, Tilmann Rabl, and Philipp Grulich. His leadership extends to major research initiatives and collaborations across European institutions.
Peter K. Allen is a Professor of Computer Science at Columbia University's School of Engineering and Applied Science, with a career spanning over three decades in robotics research. His work focuses on robotic grasping , 3D vision and modeling , and medical robotics , where he has made significant contributions to autonomous manipulation and sensor integration. Current affiliation: Columbia University Robotics Lab Academic rank: Professor Key research areas: Robotics, Computer Vision, Artificial Intelligence Education A.B. in Mathematics-Economics from Brown University M.S. in Computer Science from University of Oregon Ph.D. in Computer Science from University of Pennsylvania (recipient of CBS Foundation Fellowship, Army Research Office Fellowship) Research Interests Allen's research bridges fundamental robotics challenges with applied domains. His work on robotic grasping explores low-dimensional subspaces and semantic task suitability, while 3D vision contributions include illumination coherence and texture registration methods. In medical robotics , he develops surgical imaging tools and BCI-enabled grasping systems. Recent publications show trends in: Deep learning for robotic manipulation (2017-2022) Human-robot interaction through BCI and augmented reality Deformable object manipulation (garments, thin shells) Multi-modal sensing (vision-tactile fusion) Scientific Recognition NSF Presidential Young Investigator Award Best Student Paper Award (2007) for collaborative work Over 30 years of continuous funding from NSF, Army Research Office, and medical grants Teaching and Mentorship He has taught graduate courses in robotics (COMS 4733/6731) since 2010, emphasizing hands-on projects with advanced platforms like Baxter, PR2, and Fetch robots. His lab provides immersive training in: 3D photography Humanoid robotics Autonomous navigation Grasp planning
R.K. Shyamasundar is a Professor at the Indian Institute of Technology Bombay , with a focus on Real-Time and Reactive Programming, Logic Programming, Pi-Calculus, and Parallel Programs. Research spans formal verification, concurrency, and distributed systems. Key contributions include RT-CDL semantics, Esterel language extensions, and hybrid system controller synthesis. Scientific awards include JC Bose National Fellow, Fellowships at Indian Academy of Sciences and Indian National Science Academy, and Senior Membership in IEEE. His work involves collaborations with institutions like TCS Group and researchers such as Basant Rajan, N. Raja, and Deepak Kapur.
Guillermo Gallego is a Professor of Robotic Interactive Perception at the Faculty of Electrical Engineering and Computer Science , Technische Universität Berlin , holding the Einstein Center Digital Future (ECDF) Professorship since 2019. His research bridges robotics , computer vision , and applied mathematics , focusing on optimization methods for interdisciplinary imaging and control problems. Education : PhD in Electrical and Computer Engineering (Georgia Tech, 2011), MS in Mathematics (Georgia Tech, 2009), MS in Electrical Engineering (Georgia Tech, 2007), MS in Mathematical Engineering (Universidad Complutense de Madrid, 2005). Gallego's work explores event-based vision to enhance robot perception through low-latency sensing and real-time 3D reconstruction . He previously held postdoctoral positions at the Institute of Neuroinformatics (University of Zurich/ETH Zurich) and Technical University of Madrid (Marie Curie Experienced Researcher). His interdisciplinary projects span applications in ocean remote sensing , autonomous driving , and space exploration . Key scientific awards include the Fulbright Fellowship (2005-2010) and Marie Curie Experienced Researcher (2011-2014). His recent publications focus on event camera algorithms for optical flow , SLAM , and noise estimation , reflecting his leadership in event-based vision research. Collaborations include institutions like University of Zurich , Georgia Tech , and University of Pennsylvania . Research Grants : Funded through ECDF and Marie Curie programs. Labs : Affiliated with the Einstein Center Digital Future and Institute of Neuroinformatics (Zurich/ETH Zurich).
Dr. Xiong Yi is an Assistant Professor at the School of System Design and Intelligent Manufacturing (SDIM) at Southern University of Science and Technology (SUSTech) in Shenzhen, China. He leads the Computational Design and Fabrication (CoDeFab) research group, focusing on the integration of computational design methods with advanced manufacturing technologies, particularly in the field of additive manufacturing. Dr. Xiong has established himself as a leading researcher in computational design for additive manufacturing, with a strong international research background spanning Europe and Asia. Dr. Xiong's educational journey includes: Doctor of Science (DSc) in Engineering Design and Production from Aalto University, Finland (2012-2016) Master of Science (MSc) in Machine Automation from Tampere University of Technology, Finland (2010-2012) Bachelor of Engineering (BEng) in Mechanical Engineering from Hubei University of Technology, China (2006-2010) Dr. Xiong's research primarily focuses on computational design and fabrication methodologies, with particular emphasis on design for additive manufacturing (DfAM), intelligent manufacturing systems, and smart materials. His work bridges the gap between theoretical design principles and practical manufacturing constraints, developing novel approaches for the production of complex engineered products. He has pioneered research in continuous fiber-reinforced composite additive manufacturing, developing innovative process planning and optimization techniques that enable the production of high-performance structural components. His research in electrothermally controlled origami and 4D printing of smart materials represents cutting-edge work at the intersection of materials science, mechanical engineering, and computational design. Dr. Xiong's recent publications reveal a strong focus on continuous fiber-reinforced composites, with significant contributions to 4D printing, metamaterials, and intelligent process planning. His work integrates computational design with manufacturing constraints, creating novel approaches for topology optimization, toolpath planning, and structural design that consider both performance requirements and manufacturability limitations. The research demonstrates increasing sophistication in materials science applications, particularly in programmable materials and multi-functional structures. Dr. Xiong has received multiple prestigious awards for his research contributions, including: Best Presentation Award at the 24th Chinese Conference on Mechanisms and Machine Science (IFToMM CCMMS2024) Best Presentation Award at the International Conference on Frontiers of Additive Manufacturing Research (RAAM 2024) Best Paper Award at the International Conference on Design for 3D Printing (ICD3DP 2023) PhD Scholarship from Aalto University (2016) Research Travel Grant from the International Association for Vehicle System Dynamics (IAVSD) (2013) National Scholarship from the Ministry of Education (2008) As a dedicated educator and mentor, Dr. Xiong serves as a PhD supervisor at SUSTech and has successfully guided students who have gone on to pursue advanced studies and careers at prestigious institutions including Hong Kong Polytechnic University, Beihang University, DJI Innovations, and Singapore's A*STAR research institute. His research is supported by multiple competitive grants, including key projects from the National Key R&D Program of China, the National Natural Science Foundation of China, and provincial and municipal funding agencies. Dr. Xiong also serves on the editorial board of the Journal of Engineering Design and as a guest editor for Composites Communications, contributing to the advancement of his field through scholarly service. Dr. Xiong leads the CoDeFab research group, which maintains a strong collaborative culture focused on 'design leading manufacturing, manufacturing driving design, and digital-intelligent integration.' The group has developed several advanced manufacturing platforms, including multi-axis continuous fiber-reinforced composite additive manufacturing systems, smart composite additive manufacturing platforms, and multifunctional soft matter open manufacturing platforms. With a focus on practical applications and innovation, the CoDeFab group actively collaborates with industry partners and has established a joint laboratory to bridge academic research with industrial implementation.
Dr Francesca Pianosi is an Associate Professor in Water & Environmental Engineering at the University of Bristol 's School of Civil, Aerospace and Design Engineering. She contributes to the Cabot Institute for the Environment and leads research on data analysis, mathematical modelling, and uncertainty quantification for hydrology and water engineering. Specialises in simulation and optimisation methods for water resource management Focuses on uncertainty propagation in natural hazard models Developed the open-source SAFE Toolbox for sensitivity analysis Research Trends Her recent publications (2023-2025) demonstrate expertise in: Groundwater flow and recharge in data-scarce regions Digital Twin applications for watershed management Climate change impact on landslides and droughts Multi-objective optimisation for reservoir operations Integration of machine learning with hydrological models Scientific Awards Arne Richter Award for Outstanding Young Scientists (2015) Best Research Oriented Paper - Journal of Water Resources Planning and Management (2024) Early Career Research Excellence (ECRE) award (2014) Francesca leads the Water Management and Adaptation based on Watershed Digital Twins project (2024-2027) and contributes to the USARIS project on uncertainty quantification for infrastructure systems (2023-2025).
Vlahogianni Eleni is a Professor and Dean of the Department of Transportation Planning and Engineering at the National Technical University of Athens (NTUA). Her research focuses on integrating machine learning , quantum computing , and reinforcement learning with urban mobility and traffic engineering , addressing challenges in eco-routing , congestion pricing , and autonomous vehicle interactions . Her work emphasizes data-driven approaches to traffic forecasting, including quantum neural networks and theory-aware unsupervised learning . Recent publications explore mixed traffic environments , shared space modeling , and parking occupancy prediction , highlighting her commitment to advancing intelligent transportation systems . Professor Vlahogianni leads the Traffic Engineering Laboratory at NTUA and contributes to policy frameworks for connected and automated transport , wildfire resilience , and dynamic mobility solutions . She is actively involved in the LEVITATE project and advocates for explainable AI in transportation applications.
Eilif Pedersen is a Professor and Program Leader for Marine Technology at the Norwegian University of Science and Technology (NTNU). He leads the Department of Marine Technology under the Faculty of Engineering. His research focuses on mathematical modeling, simulation of machinery systems, and energy-efficient solutions in marine and offshore contexts. He is actively involved in projects such as SEACo, SFI Smart Maritime, and ViProMa, emphasizing virtual prototyping and hybrid power systems. Key research areas include bond graph methodology, thermodynamic system modeling, and dynamic analysis of marine systems. Pedersen supervises numerous PhD and industrial projects, including studies on hybrid propulsion, wind turbine dynamics, and fuel cell integration. He has contributed to over 50 publications, with recent work addressing co-simulation techniques, energy conservation in marine systems, and emission reduction strategies. His expertise spans marine engines, fluid dynamics, and renewable energy applications. Collaborations with industry partners like Rolls-Royce and Kongsberg Digital highlight his commitment to bridging academic research with practical maritime challenges.
Evan Franklin is an Associate Professor in Energy and Power Systems within the School of Engineering at the University of Tasmania. He also serves as Associate Head of Research, reflecting his leadership in advancing engineering research at the institution. His academic work is centered on modern power systems with a strong emphasis on renewable integration, grid stability, and sustainable energy technologies. His primary research interests include energy and power systems, renewable energy integration, grid frequency control, harmonic analysis, distributed energy resources (DER), battery and compressed air energy storage, agrivoltaics, and hydrogen integration. His work bridges engineering fundamentals with real-world applications in sustainable energy systems, contributing to Australia's transition toward clean energy. The recent publications of Dr. Franklin span high-impact journals such as Energies , IEEE Transactions on Industry Applications , Renewable and Sustainable Energy Reviews , and Journal of Energy Storage . The research trends reflect a strong focus on power system stability, microgrid control, harmonic mitigation, and innovative energy storage solutions. His work increasingly integrates AI and machine learning techniques for power quality and system monitoring, while also exploring interdisciplinary applications like agrivoltaics and offshore energy systems. Dr. Franklin has successfully supervised both PhD and Master’s students, including Ahmadreza Eslami and Md Ruhul Amin, with research topics ranging from harmonic analysis to frequency control using battery storage. He has secured substantial research funding from major national and international bodies, including the Australian Research Council (ARC), Australian Renewable Energy Agency (ARENA), CSIRO, and the Blue Economy CRC. Notable projects include the ARC Training Centre in Energy Technologies for Future Grids, MoorPower wave energy projects, and studies on hydrogen integration and black-start capabilities. He leads and participates in research teams focused on renewable energy systems, including the Centre for Renewable Energy and Power Systems at UTAS. His collaborative network includes key researchers such as Professor Michael Negnevitsky, industry partners like Carnegie Clean Energy and TasNetworks, and government agencies including Hydro Tasmania and Aurora Energy. His work is instrumental in shaping resilient, sustainable, and intelligent power systems for the future.
