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Sylvia UrbanView profile

Professor

• Natural products chemistry
• Separation science
• Chromatography
• +6 more

Professor Sylvia Urban is a distinguished academic at RMIT University, serving as a Professor of Chemistry in the School of Science. She leads the Marine and Terrestrial Natural Product (MATNAP) research group and is the Program Manager for the Bachelor of Science degree, the largest and flagship program in the School of Science. Professor Urban also holds significant leadership roles including Reconciliation and Responsible Practice Facilitator in the School of Science (STEM College) and member of the Nugulu Committee at RMIT University. Her expertise spans natural products chemistry and separation science, with particular focus on chromatography for purification and instrumental analysis for structural characterisation and elucidation. Professor Urban's research interests encompass natural product chemistry isolation and structural elucidation, NMR spectroscopy and mass spectrometry for characterisation of natural products, High Performance/Pressure Liquid Chromatography (HPLC) and other chromatographic techniques for natural product purification, hyphenated spectroscopic techniques such as HPLC-NMR and HPLC-MS for natural product profiling, and biological evaluation of natural products for drug discovery applications. Her work primarily focuses on exploring the biodiversity of Australian marine and terrestrial organisms including plants, fungi, sponges, and algae to discover new compounds with therapeutic potential. She has developed various dereplication and chemical profiling strategies to expedite the discovery process. Professor Urban's publication record demonstrates a strong focus on natural products derived from Australian flora and marine organisms, with particular emphasis on their chemical characterisation and biological evaluation. Her research spans ethnobotanical studies of Indigenous Australian medicinal plants, phytochemical profiling of Australian species, anthelmintic and antimicrobial assessments of natural compounds, and development of analytical methodologies for natural product research. The interdisciplinary nature of her work connects chemistry with pharmacology, ethnobotany, and sustainable development goals related to health, education, and gender equality. STEM College Learning & Teaching Award (Award for Values in Action) 2024 STEM College Athena Swan Award 2023 Top STEM College Media Star 2022 School of Science Reconciliation Champion Award for 2021 School of Science Associate Dean's Impact Award (Applied Chemistry) for 2021 STEM Female Educator of the Year Award in the STEM College in 2021 Fellow of the Royal Australian Chemical Institute (RACI) in 2020 2019 Australian Award for University Teaching (AAUT) Citation for Outstanding Contributions to Student Learning Professor Urban actively supervises Masters and PhD students, with recent projects focusing on nanoparticle synthesis, natural product evaluation from Australian plants and marine organisms, food science applications, and biomedical imaging agents. She has received numerous teaching grants including the SteLR Grant 2017 for Pen-enabled, Real-time Student Engagement for Teaching in STEM Subjects, SteLR Plus Learning and Teaching Grant 2016 for Contextualizing Learning Chemistry, and Global Learning by Design (GLbD) Learning and Teaching Grant 2014. As the leader of the MATNAP research group, Professor Urban oversees a team focused on exploring Australian biodiversity for drug discovery. She has been instrumental in establishing the VICS Molecular Resolution Facility (chromatography node at RMIT University) as part of "The Pipeline – An Integrated Approach to Drug Design and Development." Her research involves collaborations both within and external to RMIT University, including Australian and international university and industry partners.

Michael FelsbergView profile

Professor

• Artificial Visual Systems
• Computer Vision
• Robot Vision
• +4 more

Michael Felsberg is a Professor and Head of Division at the Department of Electrical Engineering (ISY) at Linköping University, leading the Computer Vision Laboratory (CVL). His research focuses on artificial visual systems (AVS), including 3D computer vision, computational imaging, object tracking, and autonomous systems. He emphasizes HVS-inspired approaches to bridge the gap between human and machine vision capabilities. Notable achievements include over 20,000 citations (h-index 47), leadership roles in the Wallenberg AI, Autonomous Systems and Software Program (WASP), and recognition as Sweden’s top AI researcher by Vinnova. His work spans academic contributions, industry collaborations, and interdisciplinary projects like climate science applications of machine learning. Positions : WASP Executive Committee Member, WASP Area Cluster Leader for Machine Learning, and Vice-Head of Department (Electrical Engineering). Education : Extensive academic background in electrical engineering and computer vision (details not explicitly stated). Research trends in his articles reflect advancements in autonomous systems, multimodal AI, and robust vision models. His teams address challenges like object tracking, generative models for 3D simulation, and culturally diverse AI systems. Awards : Tracking Challenge Winner (OpenCV, 2015) Best Paper Awards (ICPR 2016, VISAPP 2021) Vinnova’s Highest-Ranked Swedish AI Researcher (2018) He advises numerous PhD students and oversees grants in WASP-funded initiatives. CVL collaborates on projects like disaster-response robotics and Berzelius supercomputer utilization for AI.

Vivek SrikumarView profile

Associate Professor

• Machine Learning
• Natural Language Processing
• Artificial Intelligence
• +5 more

Vivek Srikumar is an Associate Professor in the Kahlert School of Computing at the University of Utah, co-leading the Utah NLP group and affiliated with the Utah Center for Data Science. His research focuses on Machine Learning and Natural Language Processing, particularly in structured prediction, bias mitigation, and healthcare NLP applications. He teaches Machine Learning (CS 6350/DS 4350) and has been supported by NSF, NIH, and corporate grants from Intel, Google, and others. Education: Ph.D. in Computer Science, University of Illinois at Urbana-Champaign (2013) Postdoctoral Researcher at Stanford University's NLP Group (2013-2014) Visiting Researcher at Allen Institute for Artificial Intelligence (2022 sabbatical) Research Interests: Srikumar explores text understanding, structured learning, and robust AI systems. His work addresses challenges in table-based reasoning, adversarial robustness, and ethical AI. He develops methods to ensure models use appropriate evidence and mitigate biases in representations. Grants & Collaborations: Supported by NSF, NIH, BSF, and industry partnerships with Intel, Google, Verisk, Bloomberg, and Nvidia. Notable projects include table QA systems (TempTabQA), bias mitigation (OSCaR/VERB), and crisis counseling NLP tools (ClientBot). Advising: Supervised over 30 students, including 15+ Ph.D./M.S. alumni now in academia and industry (e.g., Google, Amazon, Microsoft). Current advisees focus on multimodal reasoning, healthcare NLP, and AI ethics. Labs/Teams: Utah NLP Group and Utah Center for Data Science. Active in reproducibility efforts (LogFlux) and open-source tools (CogCompNLP/Pylon frameworks).

• Mobile Robotics
• Underwater Robotics
• Autonomous Systems
• +4 more

John Folkesson is an Associate Professor at the Department of Robotics, Perception and Learning at KTH Royal Institute of Technology. His research focuses on mobile robotics, underwater autonomous vehicles (AUVs), and Simultaneous Localization and Mapping (SLAM), particularly addressing challenges in dynamic underwater environments. He leads the AUV group within the Swedish Maritime Robotics Centre (SMaRC2.0) and supervises multiple PhD projects, including those funded by Ocean Infinity and Vinnova. Folkesson has pioneered work on sonar-based SLAM, bathymetric mapping, and autonomous underwater navigation without human intervention. He teaches courses such as Probabilistic Graphical Models (DD2420) and Applied Estimation (EL2320). Recent projects include developing neural rendering techniques for sidescan SLAM and automatic launch systems for AUVs in collaboration with Purdue University and SAAB. His research emphasizes long-term autonomy, environmental ambiguity, and sensor data interpretation in unstructured underwater scenarios. Education: PhD in Robotics (2005, KTH Royal Institute of Technology) Recent Funding: 2024 projects include ALARS (Vinnova), WASP WARA-PS, and industrial collaborations. Research Interests Folkesson's work spans underwater robotics, SLAM algorithms, and sensor fusion. Key areas include: Underwater SLAM and sonar modeling Bathymetric reconstruction using neural networks Autonomous decision-making in AUV missions Real-time terrain modeling and localization Articles Trends Recent publications emphasize neural networks for SLAM optimization, sonar data processing, and autonomous underwater systems. Themes include real-time bathymetric mapping, sensor fusion in dynamic environments, and neural rendering techniques for improving navigation accuracy. Folkesson's work bridges theory and practice, with applications in marine robotics and industrial surveys. Advising & Grants PhD supervision: AUV perception (2024), SLAM with Ocean Infinity, event-response AUV systems. Collaborations: Purdue University, SAAB, Ocean Infinity. Course responsibilities: Over 10 advanced robotics and engineering courses at KTH. Labs & Teams Lead of SMaRC2.0, KTH's official research center for maritime robotics. Active in developing AUV systems for long-duration missions, including ice-covered and deep-sea exploration.

• Theoretical Computer Science
• Algorithms
• Convex Optimization
• +4 more

Rasmus Kyng is an Assistant Professor in the Department of Computer Science at ETH Zurich, where he has been since 2019. His research focuses on fast algorithms for graph problems, convex optimization, and their applications in machine learning. He has received grants from the Swiss National Science Foundation, including project grants and a starting grant. Education: B.A. in Computer Science from the University of Cambridge (2011), PhD in Computer Science from Yale University (2017), advised by Daniel A. Spielman. Postdoctoral positions included Harvard University (2018–2019) and a research fellowship at the Simons Institute, UC Berkeley (2017). Research Interests: Development of nearly linear-time algorithms for fundamental graph problems (e.g., maximum flow, minimum-cost flow), dynamic graph algorithms, discrepancy theory, and fine-grained complexity. His work bridges numerical linear algebra and combinatorial optimization, emphasizing practical implementations such as the Laplacians.jl package. Awards: FOCS Best Paper Award (2022), Inaugural ICBS Frontiers of Science Award (2022), Machtey Award (Best Student Paper, FOCS 2017). Teaching: Advanced Graph Algorithms and Optimization (ETH Zurich, 2020–2023), Algorithms, Probability, and Computing (ETH Zurich, 2020–2022). Supervised numerous PhD students and mentored postdocs in theoretical computer science. Labs/Teams: Co-leads a research group with Maximilian Probst Gutenberg, focusing on dynamic graph algorithms and optimization. Collaborations include work on sparsification, spectral graph theory, and machine learning applications.

• Language Acquisition
• Second Language Acquisition (SLA)
• Third Language Acquisition (L3)
• +7 more

Pascale Trevisiol Okamura is an Associate Professor in Language Sciences and Language Teaching at Sorbonne Nouvelle University, affiliated with the DILTEC research team (EA 2288). Her primary role includes teaching language acquisition and foreign language didactics within the UFR of Literature, Linguistics, and Didactics (LLD). She co-heads the Master 2 program in Language Teaching (FLE/FLS) and holds administrative roles in departmental councils and educational committees. Education Background: Lecturer at Sorbonne Nouvelle University since 2015 Previously taught at Université de Poitiers (2010–2015) and Université Paris 8 (2007–2010) French teaching assistant at Hokkaido University, Japan Research Focus: Specializes in third language acquisition (L3), crosslinguistic influence, and the interface between language acquisition and didactics. Key themes include: Discourse construction in L3 French Input processing and initial language exposure Development of teaching materials for FLE Plurilingual practices among language teachers Current projects involve multilingualism in primary education, Tamil-speaking learners' L3 French acquisition, and reflexive training in research. Teaching & Supervision: Co-supervises doctoral research on topics such as L3 French acquisition in Chinese contexts, translinguistic discourse influences, and plurilingual teacher practices. Teaches advanced courses on language acquisition theories and FLE methodology. Labs/Teams: Member of the Second Language Acquisition Network (ReAL2) and part of the DILTEC team, focusing on language didactics and multilingualism research.

Kassem FawazView profile

Assistant Professor

• Security & Privacy
• Machine Learning
• Mobile Computing
• +5 more

Kassem Fawaz is an Assistant Professor in the Department of Electrical & Computer Engineering at the University of Wisconsin-Madison. His research focuses on security, privacy, and mobile computing, with applications in social robotics, generative AI, and adversarial machine learning. He teaches graduate-level courses including Advanced Computer Security, Master's Research, and Independent Study in Electrical & Computer Engineering. Education: PhD (2017) and MS (2011) from the University of Michigan, BE (2009) from the American University of Beirut His work addresses challenges in privacy-preserving analytics, model robustness, and ethical AI, leveraging commodity devices for secure systems. Recent publications explore social media algorithms, black-box attacks, and family dynamics in generative AI use. Key scientific awards include the NSF CAREER Award (2020), Caspar Bowden Award (2019), and multiple student travel grants from ACM, PETS, and USENIX. He has supervised graduate research projects and taught core security courses since 2023.

Somayeh DodgeView profile

Associate Professor

• Spatial Data Science
• GIScience
• Movement Analysis
• +5 more

Somayeh Dodge is an Associate Professor of Spatial Data Science in the Department of Geography at the University of California, Santa Barbara (UCSB). She leads the MOVE Lab and serves as Co-Associate Director of the UCSB Center for Spatial Studies and Data Science. Her research focuses on computational movement analysis, spatiotemporal data science, and the application of these tools to study human and ecological systems. She holds editorial roles in major journals including Journal of Spatial Information Science and Geographical Analysis. Education: PhD in GIScience (University of Zurich, 2011), MS in GIS Engineering (K.N.Toosi University of Technology, 2005), and BS in Geomatics Engineering (2003). Postdoctoral work at The Ohio State University and University of Zurich. Prior faculty positions include University of Minnesota (2016–2019) and University of Colorado, Colorado Springs (2013–2016). Research interests include wildfire impact analysis, mobility patterns during disasters, environmental vulnerability modeling, and geovisualization techniques. Her NSF CAREER project explores movement responses to environmental disruptions. Teaching focuses on GIScience, movement analytics, and spatial modeling courses. Awards: 2021 NSF CAREER Award and 2022 AAG Emerging Scholar Award. Active in editorial boards for multiple journals and serves on the Board of Directors for the University Consortium for Geographic Information Science (UCGIS). Advising: Supervises 7 graduate students in mobility analytics and environmental modeling. Labs/Teams: MOVE Lab (https://move.geog.ucsb.edu/) focuses on computational movement ecology and human mobility studies.

Saeed GazorView profile

Professor

• Signal Processing
• Electrical Energy Systems
• Communications and Networks Technologies
• +7 more

Dr. Saeed Gazor is a full Professor in the Department of Electrical and Computer Engineering at Queen's University. He holds a cross-appointment in the Department of Mathematics and Statistics. His research focuses on signal processing applications in electrical energy systems, communications, and medical imaging. He has supervised postdoctoral fellows Babak Ghaffari and Yaser Esmaeili Salehani. Professional affiliations include Senior Member IEEE and membership in the Institution of Engineering and Technology. Education: PhD (1994) in Signal and Image Processing from Télécom ParisTech; M.Sc. (1989) and B.Sc. (1987) from Isfahan University of Technology with highest honors. Academic roles include former Assistant Professor at Isfahan University of Technology (1995–1998) and research associate at University of Toronto (1999). Research interests span detection theory, smart energy systems, hyperspectral imaging, and medical signal processing. Notable contributions include innovations in radar signal processing, sparse signal reconstruction, and adaptive filtering. Active in academic service, including editorial roles in IEEE journals. Awards: Professional Engineer designation from Professional Engineers Ontario. Over 200 peer-reviewed publications with recent focus on AI-driven hyperspectral analysis, robust beamforming, and energy-efficient communication systems. Labs/Teams: Leads signal processing research initiatives at Queen's, collaborating on projects involving smart energy grids, distributed radar networks, and biomedical signal analysis. Current work emphasizes integrating deep learning with traditional signal processing techniques.

• Robotics
• Autonomous Systems
• Computer Vision
• +4 more

Dr. Stewart Worrall is a Senior Research Fellow at the Australian Centre for Field Robotics (ACFR) within the University of Sydney. His research focuses on autonomous systems, robotics, and intelligent transportation systems, particularly in the areas of autonomous vehicle perception, human-robot interaction, and sensor fusion. He has contributed to numerous high-impact publications on topics such as edge case testing for autonomous vehicles, collaborative perception frameworks, and context-aware human-robot interaction design. His work integrates robotics hardware, computer vision, and machine learning to address challenges in autonomous driving, crowd dynamics, and urban mobility scenarios. Current research students under his supervision explore topics ranging from light field imaging for autonomous driving to human-machine interfaces for vehicles. Worrall has pioneered datasets like the University of Sydney Campus Dataset and the ACFR Five Roundabouts Dataset, which are critical for evaluating autonomous systems. His contributions span academic conferences (e.g., IEEE IV, ICRA) and journals, emphasizing both technical innovation and societal impacts of autonomous technologies. Key labs/teams: Core member of the ACFR, collaborating across disciplines including robotics, computer science, and urban design.

Liang YingyuView profile

Associate Professor

• Machine Learning
• Optimization in Deep Learning
• Generalization in Neural Networks
• +3 more

Dr. Yingyu Liang is an Associate Professor at the University of Hong Kong (HKU), affiliated with the Department of Computer Science and the Musketeers Foundation Institute of Data Science. Previously, he held an Associate Professor position at the University of Wisconsin-Madison and was a postdoc at Princeton University. He earned his PhD from Georgia Tech and holds M.S. and B.S. degrees from Tsinghua University. His research focuses on theoretical foundations of machine learning, particularly optimization and generalization in deep learning, robust machine learning, and practical applications. Key contributions include analyzing neural network feature learning, adversarial robustness, and contrastive learning efficiency. Education: PhD (Georgia Tech, 2014), M.S. (Tsinghua, 2010), B.S. (Tsinghua, 2008) Awards: NSF CAREER Award Labs/Groups: Research Group focused on theoretical ML and algorithm design Publications span top venues like NeurIPS, ICML, and ICLR, addressing topics such as deep learning theory, adversarial robustness, and graph representation learning.

• Zoology
• Entomology
• Applied Entomology
• +13 more

Teresa Bonacci is an Associate Professor in the Department of Biology, Ecology and Earth Sciences at the University of Calabria (UNICAL), where she conducts research and teaches in zoology with a focus on entomology. She is also the Scientific Director of the Zoology Museum within the university's museum system (SIMU). Her academic affiliations include membership in the PhD School of Life Science and Technology and leadership roles in degree program coordination. Laurea in Natural Sciences, University of Calabria PhD in Animal Biology, University of Calabria Socrates/Erasmus Scholarship, University of Kiel, Germany Research Grant (Assegno di ricerca), SSD BIO/05, University of Calabria National Scientific Qualification for Full Professor (I fascia) Her research spans applied and medico-veterinary entomology, forensic entomology, and conservation biology. She investigates saproxylic beetles (e.g., Cucujus spp.), pollinators (bees, flies, beetles), and insect behavior, especially in forensic contexts such as post-mortem interval estimation and myiasis. Her work integrates taxonomy, morphology, ecology, and chemical signaling, often in collaboration with chemists, legal experts, and conservation agencies. She has published extensively in high-impact journals, with over 80 scientific publications and strong citation metrics. The most recent articles reflect a multidisciplinary trend, combining taxonomy, forensic science, biodiversity monitoring, and veterinary entomology. Topics include the discovery of new insect species in Italy, the role of beetles in decomposition, insect damage in cultural heritage, and myiasis in domestic and wild animals. These works highlight her expertise in both fundamental zoological research and practical applications in forensics and conservation. Scientific Contributions: Over 80 scientific publications Scopus h-index: 15; Google Scholar h-index: 20 Key researcher in saproxylic beetle conservation Pioneer in forensic entomology studies in Southern Italy Active collaborator with legal and medical institutions She supervises numerous PhD and master's students and has directed over 90 theses. Her teaching responsibilities include courses in Zoology, Entomology, Forensic Entomology, and Medical-Veterinary Entomology. She is deeply involved in student mentorship, thesis supervision, and academic service, including examination boards and curriculum development. She also leads biodiversity monitoring projects in Calabrian national parks and has contributed to the development of machine learning tools for insect identification. Her research team collaborates with national and international institutions, including the CNR, University of Milan, and forensic pathology units. She leads projects on bioindicators, insect-plant interactions, and the impact of climate change on mountain species. The Zoology Museum under her direction supports both research and public outreach.

Nina BalcanView profile

Professor

• Machine Learning
• Artificial Intelligence
• Theoretical Computer Science
• +5 more

Nina Balcan is the Cadence Design Systems Professor of Computer Science at Carnegie Mellon University's School of Computer Science, with affiliations in both the Machine Learning Department (MLD) and Computer Science Department (CSD). She maintains her office in Gates Hillman Center (GHC) 8205 and is a prominent figure in theoretical machine learning and algorithmic game theory. Her research spans multiple critical areas in computer science, with a strong focus on the theoretical foundations of machine learning, algorithm design and analysis, and computational approaches to game theory and economics. Balcan has made significant contributions to developing principled algorithms for deep learning, learning with limited supervision, representation learning, and life-long learning. Her work uniquely bridges theoretical computer science with practical applications, particularly in the analysis of complex objects and processes, including algorithmic learning and multi-agent systems. Analysis of her recent publications reveals a strong trend toward data-driven algorithm design, with particular emphasis on learning to optimize combinatorial algorithms, revenue-maximizing mechanisms, and robust learning frameworks. Her work consistently demonstrates how theoretical guarantees can inform practical algorithm development across diverse domains from optimization to economics. Major Awards and Honors: ACM Fellow AAAI Fellow Simons Investigator 2019 ACM Grace Murray Hopper Award (awarded to the outstanding young computer professional of the year) Winner of Outstanding Student Paper Award at UAI 2024 Winner of Exemplary Artificial Intelligence Track Paper Award at ACM EC 2019 Runner Up Best Paper Award at COLT 2012 Professor Balcan has served as Program Committee Co-chair for major conferences including NeurIPS 2020, ICML 2016, and COLT 2014, demonstrating her leadership in the machine learning community. Her teaching portfolio at CMU includes foundational courses such as 10-701 Machine Learning, 10-315 Machine Learning, and 10-715 Advanced Introduction to Machine Learning, where she has mentored numerous students in both theoretical and applied aspects of the field. Her research group focuses on developing theoretically sound yet practically applicable machine learning algorithms, with particular attention to algorithm configuration, data-driven optimization, and learning in strategic environments. Current projects involve learning to improve combinatorial algorithms, designing revenue-maximizing mechanisms, and developing robust learning frameworks that can operate effectively in challenging environments.

• Spatio-temporal data modeling
• Mobile and web-based GIS
• Map personalisation
• +6 more

Michela Bertolotto is a Professor in the School of Computer Science at University College Dublin (UCD). Her research focuses on spatio-temporal data modeling, GIScience, and applications of geospatial technologies in fields like urban planning and health informatics. She leads a research group and has supervised 19 PhD and 8 MSc students. Her work includes innovations in LiDAR-based flood risk visualization, semantic web quality assurance, and open-source spatial data analysis. Bertolotto has held roles including College Lecturer at UCD (2000–2006) and postdoctoral research positions at the University of Maine and University of Genoa. Education: BSc and PhD in Computer Science from the University of Genoa (1993, 1998). Professional achievements include over 100 publications, 24 grants (e.g., Science Foundation Ireland-funded Urban ARK project), and editorial roles at journals like the International Journal of Geographical Information Science. Awards include the UCD President's Research Award (2001) and NATO Postdoc Fellowship (1998–1999). Research interests span map personalization, volunteered geographic information (VGI), and geospatial data quality. Her lab develops tools like the LAMSkyCam (low-cost sky imaging system) and dynamic flood risk viewers. She chairs international conferences and serves on program committees for GIScience events.

Kalyan R. PiratlaView profile

Professor

• Underground Construction
• Construction Planning and Management
• Sustainable and Resilient Infrastructure Systems
• +7 more

Dr. Kalyan R. Piratla is a Professor in the Department of Civil Engineering at Clemson University's College of Engineering, Computing and Applied Sciences. His research focuses on underground construction, infrastructure resilience, and sustainable water systems. He leads the Center for Research in Underground Infrastructure Systems Engineering (CRUISE), which develops decision-making models to enhance the sustainability and resilience of underground infrastructure systems. Ph.D. in Construction Management, Arizona State University (2012) Masters in Civil Engineering, Indian Institute of Technology Madras (2008) Bachelors in Civil Engineering, Indian Institute of Technology Madras (2007) Dr. Piratla's research integrates interdisciplinary approaches across water supply systems , power systems engineering , wireless sensing technologies , and graph theory . His work emphasizes: Seismic resilience metrics for pipeline systems Decentralized water reuse planning Vibration-based infrastructure monitoring Interdependencies among lifeline infrastructures Transportation project delivery optimization Research trends include: Application of machine learning to pipeline leakage detection Advanced seismic vulnerability assessment Life cycle cost analysis of water reuse systems Integration of geotechnical and structural monitoring Scientific Awards S.E. Liles, Jr. Distinguished Professor Dr. Piratla actively supervises graduate research and offers assistantships for PhD students. His work benefits water utilities, construction contractors, and emergency response agencies through innovative monitoring techniques and resilience enhancement frameworks . His CRUISE research group explores: Interdependencies among critical infrastructure systems Transportation project delivery efficiency Collaborations spanning power systems and wireless sensing