Find Academic Supervisors

• Distributed control
• Autonomy and Control
• Aerospace Systems
• +6 more

Dengfeng Sun is a Professor and Associate Head of the Gambaro Graduate Program in the School of Aeronautics and Astronautics at Purdue University. His research focuses on distributed control systems, autonomy, resilient networks, and air traffic management. Sun holds a B.Eng. from Tsinghua University, an M.S. from The Ohio State University, and a Ph.D. from UC Berkeley. His work spans advanced air mobility, UAV trajectory planning, and stochastic optimization for large-scale systems. Key contributions include resilient UAV traffic control, distributed state estimation algorithms, and fault detection methods for navigation systems. Sun's research has been published in top journals like IEEE Transactions on Intelligent Transportation Systems and Transportation Research Part E. Education: B.Eng., Tsinghua University (2000) M.S., Ohio State University (2002) Ph.D., UC Berkeley (2008) He advises on cutting-edge projects integrating robotics, autonomous systems, and cloud-based traffic modeling. His lab develops solutions for urban air mobility, emergency medical UAV networks, and next-generation air traffic control systems. Notable collaborations include work with NASA and industry partners on continuous descent approach procedures and metroplex routing paradigms. Sun's work bridges theoretical control systems with practical applications in aviation and infrastructure optimization.

Yongle ZhangView profile

Assistant Professor

• Software Engineering
• Networking and Operating Systems
• Distributed Systems
• +3 more

Yongle Zhang is an Assistant Professor in the Department of Computer Science at Purdue University, joining in Spring 2021. His research focuses on systems software, particularly improving reliability and availability in complex distributed systems through failure detection and diagnosis. He holds a Ph.D. from the University of Toronto and has prior degrees from Shandong University and the Chinese Academy of Sciences. **Education:** Ph.D., University of Toronto, Computer Engineering (2020) Master, Institute of Computing Technology, Chinese Academy of Sciences (2013) Bachelor, Shandong University, Computer Science (2010) **Research Interests:** His work addresses challenges in distributed systems, including root cause diagnosis in cloud environments, diagnosable software design, and concurrency bugs in persistent memory applications. Recent projects include analyzing live debugging activities in production systems and detecting cross-system interaction failures. **Awards & Grants:** SIGOPS Dennis M. Ritchie Thesis Award (2021) Meta 2022 Systems Research Award NSF Core Grant (2021) **Advising & Labs:** Advises PhD and Master’s students in distributed systems research (e.g., Shangshu Qian, Panchapakesan Chitra Sruthi). Leads a lab focused on production system reliability, with collaborations on cloud infrastructure and failure analysis tools.

John BaslView profile

Associate Professor

• Applied Ethics
• AI Ethics
• Machine Learning Ethics
• +3 more

John Basl is an Associate Professor of Philosophy at Northeastern University and Associate Director of the Northeastern Ethics Institute, where he leads initiatives in AI and data ethics. He holds affiliate roles at Khoury College of Computer Science, the Institute for Experiential AI, and Harvard's Edmond and Lily Safra Center for Ethics. His research focuses on applied ethics, particularly in AI, machine learning, data ethics, and environmental ethics. He is the author of The Death of the Ethic of Life (Oxford UP) and has contributed to interdisciplinary projects on AI governance, including the NSF-funded AI & Data Ethics (AIDE) Graduate Training Program and the National Internet Observatory. His work explores ethical challenges posed by emerging technologies, such as moral obligations to AI systems, transparency in automated decision-making, and the ethical implications of autonomous vehicles. Basl’s research also engages with environmental ethics, addressing questions about species partiality, ecological justice, and the moral status of non-human entities. He actively collaborates with technologists, policymakers, and ethicists to ensure ethical considerations are integrated into technological development. His media engagements include talks on AI ethics at institutions like the University of Toronto and the Web Summit, and articles in outlets like Aeon Magazine. Current projects include grants focused on training the next generation of AI ethics scholars and analyzing the societal impacts of generative AI. Basl advocates for accessible, interdisciplinary approaches to ethics in technology, emphasizing practical solutions to real-world challenges.

Ben GreenView profile

Assistant Professor

• Algorithmic Fairness
• AI Ethics
• Government Algorithms
• +3 more

Ben Green is an Assistant Professor at the University of Michigan School of Information and holds a courtesy appointment at the Gerald R. Ford School of Public Policy. He is also a Faculty Associate at Harvard’s Berkman Klein Center for Internet & Society and a Fellow at the Center for Democracy & Technology. Green earned his PhD in Applied Mathematics from Harvard University with a secondary field in Science, Technology, and Society. His research focuses on the ethics of government algorithms, emphasizing algorithmic fairness, human-algorithm interactions, and AI regulation. He advocates for design and governance practices that prevent algorithmic harms and advance social justice. His work includes critiques of overreliance on technology in urban planning, as highlighted in his book The Smart Enough City , and ongoing projects on equitable AI policies. Green’s contributions extend to publications analyzing human oversight in AI systems, privacy in public health contexts, and the societal impacts of smart city technologies. His interdisciplinary approach bridges computer science, law, and public policy to address real-world challenges in technology governance. As a Fellow at the Center for Democracy & Technology and through his academic roles, Green collaborates on initiatives promoting ethical technology use in government and urban environments. His research has informed policy discussions on data privacy, algorithmic accountability, and equitable technology deployment.

• Wireless Communication Systems
• Satellite Communication Networks
• Spectrum Sharing
• +3 more

Dr. Ying He is a Senior Lecturer at the School of Electrical and Data Engineering, University of Technology Sydney (UTS). Her research focuses on wireless communication networks, particularly integrating machine learning with satellite and terrestrial systems. She holds a BEng from Beijing University of Posts and Telecommunications (2009) and a PhD from UTS (2017). Prior to her academic role, she worked on TD-LTE chip design at the Chinese Academy of Sciences. Affiliations : Faculty of Engineering and Information Technology Global Big Data Technologies Centre (GBDTC) Education : BEng in Telecommunications Engineering, Beijing University of Posts and Telecommunications (2009) PhD in Engineering (Telecommunications), UTS (2017) Her research interests include satellite communication (GEO-LEO integration), spectrum sharing, vehicular communication, and applying machine learning to physical layer algorithms. Notable contributions include optimizing beam design in LEO networks and developing secure IoT systems. She supervises PhD/Master’s students and teaches courses like CCNA and capstone projects. Funded projects span satellite networks, IoT security, and supply chain tracking. Recent grants include SmartSat CRC initiatives and collaborations with industry partners like Intel and Ericsson. Her work addresses challenges in 6G, UAV-enabled computing, and resilient quantum algorithms.

• Uncertainty Quantification
• Civil Engineering Systems
• Resilience Engineering
• +5 more

Rui Teixeira is an Assistant Professor in the School of Civil Engineering at University College Dublin (UCD). He leads UCD's Centre for Critical Infrastructure Research (CCIR) and focuses on Uncertainty Quantification, Safety, and Risk in civil engineering systems, with applications to infrastructure resilience. His research emphasizes reliability analysis, multi-fidelity modeling, and AI-driven risk assessment. Education: MSc in Civil Engineering, University of Porto, Portugal PhD in Civil Engineering, Trinity College Dublin Professional Certificate in University Teaching and Learning, UCD Research Interests: Development of novel reliability analysis techniques Resilience of infrastructure systems Artificial intelligence applications for risk assessment Probabilistic system evaluation and safety standards Grants & Projects: Smart Enforcement of Transport Operations (SETO), Horizon Europe (2023–2026) Optimality-Tracking Civil Engineering Systems, Enterprise Ireland (2023–2025) Floating Offshore Wind Dynamic Cables (FlOWDyn), Sustainable Energy Authority of Ireland (2024–2027) Teaching: Coordinates courses such as 'Civil Engineering Systems' and 'Design of Structures 1'. Labs/Teams: Director of the Centre for Critical Infrastructure Research (CCIR), focusing on interdisciplinary approaches to infrastructure resilience.

Anne LauscherView profile

Associate Professor

• Conversational Artificial Intelligence
• Fairness in AI
• Natural Language Processing
• +3 more

Prof. Dr. Anne Lauscher is an Associate Professor of Data Science at the University of Hamburg Business School, specializing in fair, inclusive, and sustainable conversational AI systems. Her research focuses on improving algorithmic fairness through demographic factors in NLP systems and exploring ethical implications of large language models. She holds a PhD from the University of Mannheim, where her work on computational argumentation was awarded summa cum laude, and has conducted research at Grammarly and the Allen Institute for AI. Key contributions include gender-fair machine translation datasets (e.g., Building Bridges), bias detection frameworks (e.g., SHADES), and multilingual benchmarking tools like MultiQ. Her work has been recognized with the Maria Gräfin von Linden-Award and inclusion in the '100 Brilliant Women in AI Ethics' list. Research spans ethical NLP, multilingual AI, and societal impacts of AI technologies. Education: PhD in Data and Web Science (University of Mannheim, 2021), Postdoc at Bocconi University's NLP group (2021-2022). Academic roles include adjunct positions and international collaborations across Europe and the US. Research Interests: Conversational AI fairness, multilingual NLP systems, ethical AI evaluation, bias mitigation in LLMs, and interdisciplinary applications of machine learning in scientific discovery. Publications (select highlights): Over 55 peer-reviewed works in top-tier venues like ACL, EMNLP, and AAAI. Recent focus on LLM hallucination analysis, cross-cultural NLP benchmarks, and gender-neutral language resources. Awards: 2021 Maria Gräfin von Linden-Award (Baden-Württemberg), 2023 '100 Brilliant Women in AI Ethics', 2022 Dissertation Award Nominee (GI). Labs/Teams: Leads the UHH Data Science Research Group, collaborating with industry partners like Grammarly and academic institutions worldwide. Active in initiatives promoting gender equity in STEM and sustainable AI development.

Jackie ChaView profile

Assistant Professor

• Human Factors and Ergonomics
• Healthcare Systems
• Surgical Robotics
• +4 more

Dr. Jackie Cha serves as an Assistant Professor in the Department of Industrial Engineering within Clemson University's College of Engineering, Computing and Applied Sciences. Her research bridges human factors engineering with healthcare innovation, focusing on surgical robotics, physiological signal analysis, and wearable medical technologies to enhance clinical performance and safety. Her academic foundation includes advanced degrees from leading institutions: Ph.D. in Industrial Engineering from Purdue University M.S.E. in Biomedical Engineering from the University of Michigan B.S.E. in Biomedical Engineering from the University of Michigan Cha's research program centers on quantifying human performance in high-stakes medical environments through sensor-based metrics. She investigates nontechnical skills in surgical teams, mental workload during robotic procedures, and ergonomics of exoskeleton implementation in operating rooms. Her work integrates physiological signals, eye-tracking, and proximity sensors to develop objective assessment tools for surgical proficiency and team dynamics. Analysis of her 2023-2025 publications reveals consistent thematic focus on human-robot collaboration in surgery, with emerging trends in AI-driven workload detection (s-DResNet), neural correlates of surgical expertise, and environmental factors affecting robotic surgery outcomes. Key methodological approaches include scoping reviews of human-robot interaction metrics, mixed-methods evaluations of exoskeleton efficacy, and extended reality applications for nontechnical skills training. She leads the ECHO Lab (Engineering for Clinical and Human Outcomes) at Clemson, which develops translational solutions for healthcare human factors challenges. Her lab's work spans from fundamental physiological signal analysis to applied interventions in operating rooms and emergency medical settings.

M.Z. NaserView profile

Assistant Professor

• Machine Learning
• Structural Engineering
• Fire Engineering
• +5 more

Dr. M.Z. Naser is an Assistant Professor in the Glenn Department of Civil Engineering at Clemson University. His research focuses on causal and explainable machine learning methodologies applied to structural engineering, materials science, and fire safety. He holds a PhD from Michigan State University and an M.S. from the American University of Sharjah. Naser teaches courses such as Machine Learning for Civil Engineers and Structural Fire Engineering, emphasizing interdisciplinary innovation. His work bridges data-driven analysis with domain-specific knowledge to address challenges in resilient infrastructure design, including fire-resistant materials, structural retrofits, and AI-driven decision-making. Education: PhD, Michigan State University; M.S., American University of Sharjah Research Themes: Explainable AI, Fire Engineering, Structural Materials, Causal Inference Key Projects: Developing SPINEX framework, wildfire classification models, and cognitive infrastructure systems Recent publications analyze over 1000 fire tests to uncover spalling mechanisms, explore synthetic fire tests via GANs, and benchmark automated ML platforms. His work on causal diagrams for civil engineers and firefighter algorithms highlights contributions to both theory and practical applications. Naser also advocates for integrating AI into engineering education, emphasizing ethical and transparent model deployment.

• Maritime safety management and leadership
• Maritime accident and casualty analysis
• Maritime Autonomous Surface Ships (MASS)
• +3 more

Tae Eun Kim is an Associate Professor in Maritime Safety Management at UiT The Arctic University of Norway, working within the Department of Technology and Security. Her research, teaching, and industrial collaboration focus on maritime safety and human factors, with particular expertise in maritime safety management, accident analysis, Maritime Autonomous Surface Ships (MASS), and human factors in maritime operations. Dr. Kim's research spans four interconnected domains: maritime safety management and leadership, maritime accident and casualty analysis, Maritime Autonomous Surface Ships (MASS), and human factors in maritime operations. She has developed assessment instruments like the Safety Leadership Self-Efficacy Scale (SLSES) and conducted STAMP-based causal analyses of maritime accidents. Her work on MASS addresses safety challenges in mixed navigational environments and examines leadership competencies for autonomous shipping operations. Her human factors research explores how technological advancements impact navigators' performance, crew dynamics, and safety outcomes, including gender parity issues in the maritime industry. Dr. Kim's publication record reveals a strong focus on the intersection of maritime safety, technology, and human performance. Her recent work increasingly addresses autonomous shipping technologies, with numerous publications on AI decision transparency, learning analytics in maritime simulator training, and multi-modal data analysis for nautical skill development. She has conducted systematic reviews on simulator training approaches and scenario design, contributing significantly to methodology development in maritime education and training. Her research demonstrates a clear trajectory toward integrating emerging technologies with traditional maritime safety practices as the industry transitions toward greater automation. Dr. Kim is actively involved in several significant research projects, including the i-MASTER EU Horizon Europe Research and Innovation Project, the REFRAME project, and the SPRICE project (Multidisciplinary approach for spray icing modelling). She is a member of both the Advanced Maritime Ship Operations research group and the Maritime Safety Science (MARSCI) Research Group, demonstrating her commitment to collaborative research in maritime safety science. Dr. Kim teaches several specialized courses at UiT, including SVF-3206 Safety Management and Accident Investigation, TEK-3014 Navigation Technology, MFA-2100 Maritime Digitalization, MFA-8010 Maritime HTO (Human-Technology-Organisation) and Innovation, and MFA-2018 Maritime Administration and Leadership. Her teaching portfolio reflects the interdisciplinary nature of her expertise, bridging engineering, safety science, and organizational behavior in maritime contexts.

Erik SchemeView profile

Associate Professor

• Biomedical Engineering
• Machine Learning
• Neuroprosthetics
• +5 more

Erik Scheme is an Associate Professor in the Department of Electrical and Computer Engineering at the University of New Brunswick (UNB), and serves as Associate Director of the Institute of Biomedical Engineering (IBME). He holds a PhD and is a Professional Engineer (PEng). His roles include advising the Dr. J. Herbert Smith Centre for Technology Management and Entrepreneurship, emphasizing innovation in biomedical technologies and healthcare systems. His research focuses on advanced human-machine interaction through biomedical engineering, with a strong emphasis on myoelectric prosthetics, wearable sensors, and machine learning applications. Key areas include improving neuroprosthetic control via incremental learning, gait analysis using underfoot pressure sensors, and developing robust EMG-based gesture recognition systems. His work bridges clinical needs with technological innovation, addressing challenges in rehabilitation, activity monitoring, and user-centric design. Recent publications highlight advancements in adaptive control systems, sensor fusion, and ethical data practices in healthcare. His contributions span both theoretical frameworks (e.g., self-supervised learning models) and applied technologies (e.g., gold-plated 3D-printed electrodes). Dr. Scheme collaborates across disciplines, integrating robotics, signal processing, and clinical validation to create impactful solutions. His lab, affiliated with IBME, actively explores emerging areas like exhaled breath analysis for disease detection and federated learning in healthcare data analytics.

Marina Freire-GormalyView profile

Assistant Professor

• Renewable Energy Systems
• Water Treatment Technologies
• Machine Learning Applications
• +5 more

Marina Freire-Gormaly is an Assistant Professor in the Mechanical Engineering Department at York University's Lassonde School of Engineering. Her research focuses on renewable energy-powered water treatment systems, machine learning for smart design, advanced manufacturing, and sustainable engineering solutions for remote communities. She holds a PhD and M.A.Sc. from the University of Toronto, specializing in carbon capture and storage technologies. She has worked on nuclear energy projects at Ontario Power Generation and contributed to World Bank sustainability assessments. She currently chairs the Canadian Society of Mechanical Engineers' Student and Young Professional Affairs committee. Education: PhD in Mechanical Engineering, University of Toronto M.A.Sc. in Mechanical Engineering, University of Toronto Research Interests: She pioneers solar-powered reverse osmosis systems, energy recovery mechanisms, and IoT-driven smart systems. Her lab explores nanotechnology applications in environmental sustainability, including carbon capture and aquatic remediation. She integrates machine learning for optimizing energy-water nexus challenges in off-grid regions. Key Contributions: Developed models for membrane fouling in desalination systems, advanced pore network characterization for geologic CO2 storage, and designed automated renewable energy systems. Her work bridges engineering innovation with global sustainability goals. Grants & Collaborations: Engages with industries like Honda Canada and Trane Canada on sustainability initiatives. Supervises graduate students in emerging areas like nanobubble technology and direct air capture systems. Lab Activities: The Freire-Gormaly Lab focuses on clean energy-water systems, with current projects involving nano-technology for space applications (Canadian Space Agency collaboration) and life cycle assessments of carbon storage technologies.

Victor BecerraView profile

Professor

• Power Systems Engineering
• Renewable Energy
• Control Systems
• +4 more

Victor Becerra is a Professor of Power Systems Engineering at the University of Portsmouth, affiliated with the School of Electrical and Mechanical Engineering within the Faculty of Technology. He is also associated with the Centre for Environmental and Renewable Energy Solutions and the Agile Centre For Equitable Sustainability. His research focuses on advanced control systems, renewable energy integration, smart grids, battery management, and nuclear power plant control. He actively supervises PhD students in topics like optimal control of battery systems and microgrid optimization. His academic work spans over 193 publications, with recent contributions emphasizing techno-economic analysis of green hydrogen, modular energy storage solutions, and optimal control strategies for batteries and nuclear reactors. His research often addresses real-world applications such as grid flexibility at ports, peer-to-peer energy trading platforms, and drone-based inspection of offshore wind turbines. Becerra's expertise includes developing control algorithms for marine structures, sodium-cooled reactors, and unmanned aerial vehicles. He has contributed to books on solar energy engineering and applications, highlighting interdisciplinary approaches to sustainability challenges. His work integrates theoretical models with practical implementations, such as energy management systems for compressed air and advanced BMS for drones. Key themes in his research include renewable energy systems optimization, fault-tolerant control mechanisms, and adaptive control strategies for dynamic environments. He collaborates internationally, addressing global energy challenges through innovative control methodologies and sustainable technologies.

Lei LiuView profile

Professor

• Biostatistics
• Data Science
• Survival Analysis
• +5 more

Lei Liu, PhD, is a Professor of Biostatistics, Medicine, and Statistics and Data Science at Washington University in St. Louis. He holds positions in the Roy and Diana Vagelos Division of Biology & Biomedical Sciences (DBBS), the Institute for Informatics, Data Science and Biostatistics (I2DB), and the Center for Biostatistics and Data Science (CBDS). His research focuses on biostatistical and data science methods, including survival analysis, longitudinal data modeling, and machine learning applications in healthcare. He collaborates with clinicians across disciplines like cardiology, ophthalmology, and addiction medicine. Dr. Liu’s work emphasizes high-dimensional omics data analysis, medical cost modeling, and joint multi-outcome models. He is an Associate Editor of Biometrics and a former member of the NIH Biostatistical Methods and Research Design Study Section. He mentors underrepresented minority researchers through the NHLBI PRIDE program.

• Security
• Privacy
• Cybersecurity
• +4 more

Frank Stajano is a Full Professor of Security and Privacy at the University of Cambridge, leading the Academic Centre of Excellence in Cyber Security Research. He is a Fellow of Trinity College, where he collaborates with Nobel laureates. His research focuses on security, privacy, and usability in ubiquitous computing, IoT, and digital finance. He holds a PhD from Cambridge and a Laurea in Electronic Engineering from La Sapienza University. Frank's work includes the Pico project (ERC-funded), aiming to replace passwords with secure, usable alternatives. He co-founded Cambridge Cyber, offering cybersecurity consulting. Notable achievements include the Resurrecting Duckling authentication protocol and contributions to location privacy (Mix Zones). He has authored over 50 refereed publications and a research monograph on ubiquitous computing security. His awards include the Toshiba Fellowship (2000) and an ERC Starting Grant (2017). Frank teaches cybersecurity and lectures globally via his YouTube channel. Beyond academia, he is a 5th-dan kendo practitioner and a scholar of Disney comics.