Christopher Re is a Professor in the Department of Computer Science at Stanford University, affiliated with the Stanford AI Lab, Machine Learning Group, and Center for Research on Foundation Models. His research focuses on the intersection of machine learning, database systems, and scientific computing, with applications in humanitarian efforts, scientific discovery (e.g., extrasolar neutrinos, DNA foundation model Evo), and industry partnerships with companies like Apple and Google. He has been recognized with prestigious awards, including the MacArthur Foundation Fellowship and multiple test-of-time awards. His work emphasizes advancing thermal materials, phase-change memory, and ultrafast electron microscopy technologies. Re's research contributions span database theory, systems, and machine learning, with best papers at PODS 2012, SIGMOD 2014, and ICML 2016. His lab’s innovations have been incorporated into products globally, and he actively invests in technology startups. Key projects include developing thermal interface materials for 3D integrated circuits and exploring energy-efficient neuro-inspired memory systems. His awards reflect sustained excellence: NeurIPS 2020 and PODS 2022 test-of-time awards, along with recent accolades for student-led initiatives at MIDL 2022 and ICLR22. Re’s interdisciplinary approach bridges academia and industry, driving both scientific and humanitarian impact.
Karthik Dantu is an Associate Professor in the Department of Computer Science and Engineering at the University at Buffalo, State University of New York, within the School of Engineering and Applied Sciences. His research focuses on mobile sensor networks, robot networks, networked embedded systems, mobile computing, wireless networks, and embedded operating systems. He leads the Distributed Robotics and Networked Embedded Sensing (DRONES) Lab and has received significant funding including an NSF CAREER Award. Dr. Dantu's educational background includes: PhD in Computer Science from University of Southern California (2009) BE in Computer Science from Sri Jayachamarajendra College of Engineering (1999) His research interests center on algorithmic and systems challenges in Edge Computing Systems, with particular focus on enabling seamless vision sensing in cloud-edge environments. Dantu's work bridges mobile systems and robotics, developing novel approaches for UAV software, visual SLAM, and distributed sensing. His research addresses critical challenges in resource-constrained environments, security, and real-time performance for mobile and robotic systems, with emphasis on practical implementations that solve real-world problems in autonomous systems. Dr. Dantu's publication record shows a strong trajectory in mobile systems and robotics research, with increasing focus on edge computing applications for visual sensing. His recent work demonstrates expertise in adapting visual SLAM to edge environments, securing mobile systems through technologies like Rushmore, and developing novel approaches for UAV software reliability and depth sensing. The research spans theoretical algorithms and practical system implementations, with particular strength in bringing academic research to practical applications in robotics and mobile computing. Dr. Dantu has received several scientific honors: NSF CAREER Award on Enabling Seamless Vision Sensing in Cloud-Edge Systems Outstanding service award from the Office of International Services NSF Travel Grant for SenSys 2005 Conference Travel Grant for SIGCOMM 2002 As an advisor, Dr. Dantu has mentored numerous PhD students to completion, with graduates now working at companies like Samsung Research and Zoox Inc., or continuing academic careers as Assistant Professors. His research is supported by substantial grants including a DARPA OFFSET Sprint 4 award ($470k), an NSF CAREER award ($550k), and multiple NSF collaborative grants totaling over $1.5 million. He serves on numerous conference committees including Mobicom, MobiSys, and ICRA, demonstrating leadership in the mobile systems and robotics research communities. Dr. Dantu leads the Distributed Robotics and Networked Embedded Sensing (DRONES) Lab at UB, which focuses on developing algorithms and systems for mobile sensor networks, robot networks, and embedded sensing applications. The lab's work spans theoretical foundations to practical implementations, with particular expertise in UAV systems, visual SLAM, and edge computing for robotics, maintaining strong collaborations with industry partners and other academic institutions to advance the state of the art in mobile and robotic systems.
Sean Z. Qian is a Professor at Carnegie Mellon University with joint appointments in the Department of Civil and Environmental Engineering (College of Engineering), Heinz College of Information Systems and Public Policy, and the Department of Electrical and Computer Engineering. He directs the Mobility Data Analytics Center (MAC) and founded the spinoff firm TraffiQure Technologies in 2020 to commercialize AI/ML technologies in infrastructure and mobility services. His academic credentials include: 2012: MS in Statistics, Stanford University 2011: Ph.D. in Civil Engineering, University of California, Davis 2006: MS in Civil Engineering, Tsinghua University 2004: BS in Civil Engineering, Tsinghua University Qian's research centers on large-scale dynamic network modeling and data analytics for multi-modal transportation systems, applying AI, network flow theory, and economics to address aging infrastructure challenges. His work spans Infrastructure Resilience under climate stress, Urban Systems Interdependency , and Intelligent Transportation Systems , with emphasis on sustainable network optimization and cyber-physical-social system integration. Key methodologies include remote sensing, transportation economics, and digital twin technologies for infrastructure management. His notable scientific awards include: NSF CAREER Award (2018) Greenshields Prize, Transportation Research Board (2017) Research funding has been secured from diverse sources: Federal Agencies: National Science Foundation (NSF), U.S. Department of Energy (DOE), U.S. Department of Transportation (DOT) State Agencies: Pennsylvania Department of Transportation (PennDOT), Maryland Department of Transportation (MDOT), Pennsylvania Department of Community and Economic Development (DCED) Industry Partners: IBM, Honda Research Institute, Fujitsu Inc. Foundations: Benedum Foundation, Hillman Foundation As Director of the Mobility Data Analytics Center (MAC), Qian leads collaborations with Fujitsu on digital twin technologies for infrastructure management in southwestern Pennsylvania, while actively mentoring graduate students and recruiting Ph.D. candidates with strong quantitative backgrounds for Fall 2025.
Ana Inés Torres is an Associate Professor in the Department of Chemical Engineering at Carnegie Mellon University's College of Engineering. She leads an active research group focused on sustainable process systems engineering, with affiliations at the Center for Advanced Process Decision-Making and the Wilton E. Scott Institute for Energy Innovation. Her work bridges chemical engineering with sustainability challenges, particularly in decarbonization and circular economy applications. Dr. Torres earned her educational credentials from Universidad de la República Oriental del Uruguay and the University of Minnesota: Ph.D. in Chemical Engineering, University of Minnesota (2013) Diploma in Chemical Engineering, Universidad de la República Oriental del Uruguay (2005) B.S. in Chemistry, Universidad de la República Oriental del Uruguay (2003) Her research interests span process systems engineering with a sustainability focus, particularly in chemical industry decarbonization through electrification and biomass utilization, circular economy network analysis, and environmentally-friendly rare earth element recovery processes. She integrates modeling, analysis, and optimization to design clean and sustainable chemical processes, with growing emphasis on machine learning applications in process optimization. Analyzing her recent publications reveals a strong focus on decarbonization strategies for existing industrial infrastructure, particularly oil refineries, and circular economy network design. Her work demonstrates increasing integration of machine learning with traditional process systems engineering approaches to tackle complex sustainability challenges across multiple scales, from molecular recovery processes to entire supply chain networks. Dr. Torres has received several prestigious recognitions: NSF CAREER award (2024) Dean's Early Career Fellowships award (2025) Consultant for United Nations Industrial Development Organization (UNIDO) (2024) Associate editor of Clean Technologies and Environmental Policy She actively mentors a diverse group of graduate students working on cutting-edge sustainability challenges, with recent projects focusing on circular economy networks, rare earth element recovery, and bio-refinery design. Her research has attracted significant funding, including the NSF CAREER award, and she participates in multiple collaborative initiatives through CMU's energy research centers. Dr. Torres also serves as an invited speaker at major conferences including FOCAPD and FOCAPO/CPC. Dr. Torres leads the Torres Research Group at CMU, which maintains strong connections with industry partners and international organizations including UNIDO. The group operates within CMU's robust energy research ecosystem, collaborating with the Wilton E. Scott Institute for Energy Innovation and the Center for Advanced Process Decision-Making to address complex sustainability challenges through interdisciplinary approaches.
Martin Sposato is a Professor at Zayed University whose research focuses on the intersection of artificial intelligence, educational leadership, and organizational development. His work spans multiple disciplines including education, business, and human resources management, with particular emphasis on how AI technologies transform leadership practices and institutional operations. Dr. Sposato's research interests center on Artificial Intelligence in Education, Educational Leadership, Digital Transformation, and Human Resources Management. His work explores how AI can be effectively integrated into educational leadership practices while addressing ethical considerations and equity issues. He has developed frameworks for understanding AI applications across ten distinct domains in higher education leadership, including Administrative Efficiency, Personalized Learning, and Ethical AI Leadership. His publication record demonstrates significant contributions to understanding AI implementation in educational contexts, with a focus on creating structured frameworks for evaluation and adoption. His research shows a clear trajectory toward developing practical tools for educational leaders to navigate the complex landscape of AI integration while maintaining educational integrity and addressing potential risks. Artificial intelligence in educational leadership: a comprehensive taxonomy and future directions (2025) Transforming Corporate Social Responsibility and Business Ethics With AI (2025) Leadership strategies for implementing environmental management systems (2025) Bias and its impact on hiring and promotion (2025) Artificial intelligence in modern human resources practice (2025) Dr. Sposato's work on leadership extends beyond technology to explore fundamental leadership concepts, including followership theory, global leadership dynamics, and the balance between leader-centric and more distributed leadership models. His research provides valuable insights for organizations navigating digital transformation while maintaining human-centered values and ethical practices.
Dr. Brett J. Borghetti is a Professor of Computer Science in the Department of Electrical and Computer Engineering at the Air Force Institute of Technology (AFIT), Graduate School of Engineering and Management, Wright-Patterson AFB, OH. He was promoted to Professor in July 2022, following prior appointments as Associate Professor (2017) and Assistant Professor (2008/2013). His expertise lies in artificial intelligence, machine learning, deep learning, cybersecurity, and human-machine teaming. Education: Ph.D. in Computer Science, University of Minnesota, Twin Cities (2008) M.S. in Computer Systems, Air Force Institute of Technology (1996) B.S. in Electrical Engineering, Worcester Polytechnic Institute (1992) Dr. Borghetti's research focuses on applying machine learning to physical science sensors (hyperspectral, seismic, RF), cybersecurity, and enhancing human-machine team performance. He teaches graduate courses in machine learning, AI, data security, and algorithm design, and advises numerous MS and PhD students in areas such as sensor exploitation, cognitive workload, and cyber situational awareness. His recent publications demonstrate strong trends in deep learning for multimodal sensor fusion, nuclear security, and neuroergonomics. Scientific Awards: AETC Educator of the Year (2021, Civilian) AFIT Ezra Kotcher Teaching Award (2021) AFIT Teaching Excellence Award (2019) AF STEM Outstanding Science and Educator Award (2015) Multiple Eta Kappa Nu Outstanding Instructor Awards Air Force Meritorious Service Medal and other military honors Dr. Borghetti has advised numerous graduate students and led research projects with significant funding and applications in defense and national security. He has directed research in AI-driven sensor analysis, cyber defense systems, and adaptive automation. His work often involves collaboration with national labs and DoD agencies. He has contributed to major research initiatives in human factors, cyber intruder detection, and machine learning for operational environments. Labs and Research Teams: His work is associated with AFIT's research in cyber security, sensor exploitation, and human-machine systems. He collaborates with teams working on the Cyber Intruder Alert Testbed (CIAT), neuroergonomic modeling, and machine learning for defense applications.
Professor Mohamed Naim is a distinguished academic at Cardiff Business School, Cardiff University, where he holds the position of Professor in Logistics and Operations Management. He has served in several leadership roles, including former Head of the Logistics and Operations Management Section and former Deputy Dean at the Business School. He was also the founding director of the Centre for Advanced Manufacturing Systems at Cardiff (CAMSAC). He is actively engaged in high-impact research and industrial collaboration, currently leading a Knowledge Transfer Partnership (KTP) with Tower Cold Chain Solutions Ltd. and contributing to major research programs such as ASTUTE, ASTUTE2020, and AFAIR. His research interests center on the development of business systems engineering approaches to resilient supply chains, with a focus on sustainability, circularity, and flexibility in lean and agile systems. He has made significant contributions to supply chain dynamics, particularly in engineer-to-order (ETO) sectors such as construction and shipbuilding. His recent work explores digital transformation in logistics, including 5G-enabled smart ports, building information modelling (BIM), and AI-driven decision-making. The analysis of his recent publications reveals a strong trend toward interdisciplinary research, integrating operations management with healthcare (trauma systems), digital technologies, and sustainable industrial practices. His work frequently employs system dynamics, simulation, and empirical case studies to address complex supply chain challenges. Scientific Awards: Institution of Electrical Engineers' Manufacturing Division Premium (1996) Royal Academy of Engineering Global Research Award (2003) Professor Naim is actively involved in research supervision and has contributed to two Research Excellence Framework (REF2021) impact cases on innovative procurement and financially sustainable supply chains. He is a founding member of the Logistics Systems Dynamics Group (LSDG) and has led and co-directed multiple large-scale research initiatives. His work is characterized by strong industry engagement, particularly through Knowledge Transfer Partnerships and collaborations with public sector bodies like the Welsh European Funding Office (WEFO). Research Centers and Teams: Logistics and Operations Management Section, Cardiff Business School Centre for Advanced Manufacturing Systems at Cardiff (CAMSAC) Logistics Systems Dynamics Group (LSDG) ASTUTE / ASTUTE2020 / ASTUTE East programmes AFAIR project (Expert Advisor)
Chris Volinsky is a Clinical Professor of Technology, Operations, and Statistics at the Leonard N. Stern School of Business, New York University, joining in September 2023. His work bridges industry-scale data science and academic research, focusing on practical applications of machine learning and statistical modeling in business contexts. Education: PhD in Statistics, University of Washington BA in Statistics and Mathematics, University of Buffalo His research interests lie at the intersection of data science and business operations, with a focus on recommender systems , personalization , social network analysis , and mitigating bias in machine learning models . He also emphasizes data visualization and the ethical implications of data usage, particularly in balancing innovation with privacy concerns and regulatory compliance. While no specific publications are listed in the provided text, his career has been defined by high-impact, real-world applications of data science, particularly in telecommunications and entertainment industries. Scientific Awards: $1M Netflix Prize (2009) as member of BellKor's Pragmatic Chaos team Volinsky has extensive experience in advising and leading data science teams. He led a team of 40 data scientists at AT&T, where he oversaw projects with significant business impact, including fraud detection, customer complaint prediction, and computer vision applications. Although formal student advising is not detailed, his leadership roles imply substantial mentorship and team development. He has not disclosed specific grants, but his work at AT&T and NYU suggests engagement with large-scale, industry-funded research initiatives. He was instrumental in pioneering work on large-scale recommender systems and continues to contribute to the evolution of data-driven decision-making in enterprise settings.
Katrina M. Groth is a Professor and Director of the Reliability Engineering Program at the University of Maryland, affiliated with the A. James Clark School of Engineering. She also serves as Associate Director for Research at the Center for Risk and Reliability and is part of the Maryland Energy Innovation Institute. Her expertise spans risk analysis, hydrogen safety, and nuclear safety, with notable contributions to probabilistic risk assessment (PRA) and human reliability analysis (HRA). Groth holds a Ph.D., M.S., and B.S. in Reliability Engineering from the University of Maryland (2009, 2008, 2004). Her research focuses on advancing safety practices for energy systems, including hydrogen technologies, nuclear power plants, and pipelines. Key innovations include the HyRAM toolkit for hydrogen risk assessment and the HyCReD database for reliability data. Groth has published over 175 papers and secured funding from DOE, NRC, and industry partners. She advocates for educational equity, mentoring women and first-generation engineering students. Award highlights include the NSF CAREER Award, USM Board of Regents Faculty Award, and ASME Rising Star of Mechanical Engineering. She leads initiatives such as the SyRRA Lab and serves on editorial boards for journals like Reliability Engineering & System Safety . Groth teaches graduate and undergraduate courses on reliability engineering and risk analysis, emphasizing practical applications in infrastructure and energy systems. Education: Ph.D., Reliability Engineering, University of Maryland, 2009 M.S., Reliability Engineering, University of Maryland, 2008 B.S., Engineering, University of Maryland, 2004 Professional Service: Board of Trustees, National Museum of Nuclear Science & History Associate Editor, ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems Her work integrates Bayesian methods, deep learning, and causal reasoning to address complex system safety challenges, with global impact on engineering standards and practices.
Joe Devanny is a Senior Lecturer in National Security Studies at the War Studies department of King's College London. His research focuses on cyber security, international cyber policy, and national defense strategies. He contributes to understanding the evolving role of cyber power in global politics, particularly in the UK, Brazil, India, and Mexico. His work spans cybersecurity policy analysis, AI implications in intelligence, and the intersection of cyber operations with traditional statecraft. Recent publications address the UK's Strategic Defence Review, India's cyber statecraft, and Brazil's cyber strategy under shifting political leadership. He has co-edited major works like the Research Handbook on Cyberwarfare and authored reports on Mexico's stalled cybersecurity progress and South Africa's limited cyber strategy under Ramaphosa. His research emphasizes ethical dimensions of cyber operations, the impact of AI on cyber power dynamics, and the need for responsible democratic cyber strategies. Collaboration with institutions like the Carnegie Endowment and NATO CCD COE highlights his international engagement in shaping cyber policy discourse.
Christopher Ames is a Clinical Professor in the Departments of Neurological Surgery and Orthopaedic Surgery at the University of California San Francisco (UCSF). He serves as Director of Spinal Deformity & Spine Tumor Surgery, Co-Director of the UCSF Spine Center, Director of the California Deformity Institute, and Director of the Spinal Biomechanics Laboratory. With over 200 annual spinal deformity cases, he specializes in complex procedures for scoliosis, kyphosis, and spinal tumors, pioneering innovative techniques including the transpedicular approach and AI decision support tools. Developed first cervical spine deformity classification Created Adult Deformity Frailty Index and Invasiveness Index Recipient of multiple Scoliosis Research Society awards His research, funded through studies like the ROSE Study and Telomere Study, focuses on spinal biomechanics, surgical outcomes, and AI integration in spine surgery. He has published over 600 peer-reviewed articles and serves as Spine Section Lead Editor for Operative Neurosurgery . Scientific Awards: Hibbs Award (3×) Goldstein Award Whitecloud Award Top Doctors (Neurosurgery & Cancer) US News Top 1% Neurosurgeons
Michel Gendreau is a Full Professor in the Department of Mathematics and Industrial Engineering at Polytechnique Montréal . His research focuses on the application of Operations Research to Transportation , Telecommunications , and Energy Systems , with an emphasis on Stochastic Optimization and Real-time Planning . He co-directs the Intelligent Transportation Systems Laboratory and is affiliated with the CIRRELT , IVADO , and Trottier Energy Institute . Education : Ph.D. in Computer Science (1984), Université de Montréal His work includes developing metaheuristics for complex optimization problems and dynamic transportation systems . Recent projects address smart supply chains and real-time logistics . He has supervised over 40 doctoral and master's students, including notable graduates like Sanchez-Martinez, Guillen Reyes, and Parada Pradenas. Dr. Gendreau has been recognized with prestigious fellowships from IFORS (2022) and INFORMS (2010). His academic contributions span 420 publications, with recent studies appearing in Reliability Engineering and System Safety and Networks , focusing on stochastic programming , multiperiod routing , and UAV network design . He collaborates extensively with industry partners and has secured grants from organizations like FRQNT and CIRRELT . His research integrates machine learning with operations research to solve real-world challenges in transportation , energy , and logistics .
Brian Uzzi holds the Richard L. Thomas Professorship of Leadership and Organizational Change at Northwestern University's Kellogg School of Management. He serves as Co-Director of the Northwestern Institute on Complex Systems (NICO) and The Ryan Institute on Complexity (RIC), with additional appointments in Sociology at Weinberg College of Arts and Sciences and Industrial Engineering and Management Sciences at McCormick School of Engineering. His educational background includes a PhD in Sociology (1994) from State University of New York, Stony Brook, an MS in Organizational Psychology (1989) from Carnegie Mellon University, and a BA in Business Economics (1982) from Hofstra University. Prior to academia, he worked as a carpenter and musician. Research Focus: Dr. Uzzi's work centers on social networks, complexity theory, and the concept of embeddedness—the idea that individuals and organizations operate within social networks that significantly influence their achievements, economic activity, and creative output. His research examines how AI facilitates mind-machine partnerships and how network structures affect scientific collaboration, innovation, and leadership. His work spans sociology, management science, computer science, and ecology, with practical applications in business and government. His recent publications reveal a strong focus on the science of science, exploring topics like innovation abandonment, social media's role in political violence, promotional language in scientific grants, and gender diversity's impact on scientific creativity. The research consistently applies network science to understand patterns of human achievement and organizational performance. Euler Award recipient (2022) from the Network Science Society Member of the American Academy of Arts and Sciences Network Science Society Fellow Multiple 'Professor of the Year' awards at Kellogg World Wide Web Best Paper Prize (2016-2017) As an educator, Dr. Uzzi has developed innovative courses on network science for managers and executives. His consulting work extends to companies and governments worldwide, applying network science principles to real-world challenges in leadership, organizational design, and AI strategy. His research has been funded by DARPA, NSF, and other foundations, demonstrating its significance across multiple disciplines.
Erhan Bayraktar is a Professor of Mathematics at the University of Michigan, holding the Susan Smith Chair. He serves as Director of the Quantitative Finance and Risk Management Masters Program, which he established in 2015. His academic career at the University of Michigan spans since 2004, progressing from T. H. Hildebrandt Research Assistant Professor to his current full professorship. Professor Bayraktar earned his Ph.D. from Princeton University in 2004, following dual Bachelor's degrees in Electrical Engineering and Mathematics from Middle East Technical University in Turkey. His academic journey reflects a strong foundation in both theoretical and applied mathematical disciplines. Bayraktar's research focuses on mathematical finance, applied probability, machine learning, mean field games, stochastic analysis, stochastic control, and optimal stopping. His work bridges theoretical mathematics with practical applications in finance and risk management. He has developed sophisticated mathematical frameworks for analyzing complex financial systems, market behaviors, and optimal decision-making under uncertainty. His contributions to mean field games have provided new insights into large-scale interacting systems, while his work on stochastic control has advanced methodologies for optimal decision processes. His publication record demonstrates a consistent trajectory of high-impact research, with recent work focusing on Wasserstein space analysis, graphon particle systems, and applications of machine learning to financial mathematics. His research shows increasing interdisciplinary connections between traditional mathematical finance and modern computational approaches. Susan M. Smith Professorship (2010-present) National Science Foundation CAREER Grant (2010-2016) SIAM Activity Group on Financial Mathematics and Engineering Early Career Prize (2010) Professor Bayraktar has mentored 14 Ph.D. students (13 graduated) and approximately 40 post-doctoral researchers. His students hold prestigious positions in academia and industry, including tenure-track positions at Boston University, University of Colorado, University of Sydney, and University of Toronto. He has secured continuous funding from the National Science Foundation, including the current grant DMS-2507940 (2025-2028) and previous grants totaling over 15 years of continuous NSF support. As Director of the Quantitative Finance and Risk Management Masters Program, Bayraktar has built a robust academic community through the Financial/Actuarial Math seminar series, which hosts about 10 outside speakers annually, and by organizing international workshops in stochastic analysis for finance and insurance in Ann Arbor.
Professor Sara Bernardini is a leading academic in Artificial Intelligence at the University of Oxford's Department of Computer Science, where she holds a joint appointment as a Tutorial Fellow at Mansfield College. Her research specializes in decision-making for autonomous systems, automated planning, and robotics, with applications in extreme environments like space missions, nuclear decommissioning, and offshore energy. She bridges theoretical AI with real-world challenges through projects funded by Innovate UK, EPSRC, NERC, and the Alan Turing Institute. Her research interests span: Autonomous Systems : Developing agents that support humans in complex cognitive tasks. Automated Planning : Algorithms for goal recognition, pathfinding, and multi-agent coordination. Robotics : Solutions for subterranean exploration, offshore wind farms, and UAV operations. AI Safety : Risk-aware autonomous systems and interpretable decision-making. Bernardini's publications emphasize algorithmic robustness in path planning, multi-agent coordination , and real-world AI deployments . Recent work explores goal legibility in uncertain environments, energy-efficient robotics, and AI education tools. Her 65+ papers in top venues (e.g., AIJ, JAIR, ICAPS) show a trend toward safety-critical applications and human-AI collaboration. Awards & Leadership: ICAPS-2020 Best Paper Honorable Mention Executive Council Member, Association for the Advancement of Artificial Intelligence (AAAI) Program Chair, International Conference on Automated Planning and Scheduling (ICAPS 2024) Associate Editor, Artificial Intelligence Journal She leads interdisciplinary teams for projects like autonomous offshore wind farm maintenance and modular robots for extreme environments. As Principal Scientist at the UK National Oceanography Centre, she advanced marine robotics. She mentors PhD candidates and collaborates globally (e.g., NASA Ames, MIT).
