Find Academic Supervisors

Michael EverettView profile

Assistant Professor

• Robotics
• Motion Planning
• Control Theory
• +6 more

Michael Everett is an Assistant Professor at Northeastern University with a joint appointment in the Department of Electrical & Computer Engineering and the Khoury College of Computer Sciences. He directs the Autonomy & Intelligence Laboratory, focusing on certifiable learning machines at the intersection of robotics, deep learning, and control theory. His research emphasizes safety, reliability, and efficiency in robotics applications like off-road navigation and social environments. Education: PhD in Mechanical Engineering, Massachusetts Institute of Technology (2020) SM in Mechanical Engineering, MIT (2017) SB in Mechanical Engineering, MIT (2015) Research Interests: Robotics and motion planning Control theory and neural network verification Reinforcement learning applications Certifiable safety guarantees for autonomous systems Navigation in dynamic/human environments Awards: Runner-Up: Best Paper Award (ICML 2022) Winner: Best Student Paper (IROS 2017/2023) Editors’ Top 5 Published Articles (IEEE Access 2021) Lab & Contributions: The Autonomy & Intelligence Lab develops algorithms for high-speed off-road autonomy, socially aware navigation, and neural feedback verification. His work includes the RAMP planning pipeline and Evora traversability learning framework. He collaborates with Google’s PAIR team on trustworthy AI.

Chris LeeView profile

Professor

• Transportation Engineering
• Traffic Safety
• Driver Behavior Analysis
• +5 more

Professor Chris Lee is a faculty member in the Department of Transportation Science and Engineering at the University of Windsor's Faculty of Engineering. His research focuses on advancing transportation safety through the analysis of driver behavior, traffic flow dynamics, and the integration of emerging technologies like autonomous vehicles and machine learning. Key areas include collision risk prediction, driver vigilance assessment, and the development of advanced car-following models. He has contributed to initiatives such as the Transportation Science and Engineering scholarship program, supporting student research in innovative technologies like driving simulators for lane change behavior studies. His work bridges engineering and human factors, addressing challenges such as driver response to autonomous systems, heavy vehicle traffic management, and cross-cultural automotive design. Lee's interdisciplinary approach leverages data analytics, physiological signals, and machine learning to solve real-world transportation problems. His research has implications for policy-making, infrastructure design, and vehicle safety standards. Lee has collaborated extensively on projects analyzing crash precursors, variable speed limits, and the impact of ITS (Intelligent Transportation Systems) on safety. His publications span over two decades, demonstrating a commitment to both academic rigor and practical applications in transportation engineering. Notable contributions include refining car-following models, studying driver aggression, and evaluating the effectiveness of traffic management strategies.

• Animal Communication
• Social Cognition
• Anthropogenic Impacts on Animal Behavior
• +3 more

Çağlar Akçay is a Senior Lecturer in the School of Life Sciences at Anglia Ruskin University, part of the Faculty of Science and Engineering. He holds a PhD in Animal Behavior from the University of Washington, alongside an MA in Cognition and Perception and a BS in Biology and Psychology from Middle East Technical University. His research focuses on behavioral ecology, particularly the evolution of animal communication and social cognition in songbirds. He investigates how animals adapt to anthropogenic environments, including effects of urbanization on signaling and aggression. Key research areas include animal communication systems, social interactions mediated by vocal signals, and the impact of environmental changes on behavior. He leads the Bird Lab, studying the interplay between communication and social behavior in birds, with projects on Darwin’s finches and song sparrow territorial dynamics. His work emphasizes field studies combined with experimental approaches, such as acoustic manipulations and observational tracking. Member of the Behavioral Ecology Research Group Recipient of Turkish Science Academy Early Career Fellowship (2019-2021) Teaching responsibilities include BSc programs in Animal Behaviour and Zoology, and an MSc in Animal Behaviour Applications for Conservation Recent publications highlight topics like survival processing effects in memory, avian behavioral plasticity in urban settings, and the role of acoustic signals in territorial disputes. His interdisciplinary work bridges ecology, cognition, and conservation, with a focus on translating findings into wildlife management strategies.

Klaus MoeltnerView profile

Professor

• Environmental Economics
• Natural Resource Valuation
• Applied Econometrics
• +3 more

Klaus Moeltner is a Professor in the Department of Agricultural and Applied Economics at Virginia Tech since 2015. He previously held positions at the University of Nevada, Reno, including Associate and Assistant Professor roles. His expertise lies in environmental economics, natural resource valuation, and applied econometrics. He has secured over $6 million in grants as PI or Co-PI and contributes to interdisciplinary research through the Global Change Center and Remote Sensing Program at Virginia Tech. Education: Ph.D., Economics, University of Washington, 2000 M.A., Economics, University of Washington, 1998 M.A., International Policy Studies, Monterey Institute of International Studies, 1994 M.S., Environmental Planning & Engineering, University of Agriculture and Forestry, Vienna, Austria, 1990 Research Interests: Dr. Moeltner’s work focuses on environmental and natural resource economics, including water quality valuation, wildfire health impacts, urban water use, and coastal flooding risks. His research employs advanced econometric methods, such as Bayesian modeling and meta-regression, to inform policy decisions. Current projects include analyzing the economic benefits of stream restoration, harmful algal bloom forecasts, and offshore wind farm co-location opportunities. Awards: 2023: Distinguished Graduate Teaching Award (AAEA) 2014: Best Paper in Environmental and Resource Economics (EAERE) 2004: CABNR Outstanding Instructor Award Advising & Grants: Advised 6 Ph.D. students and secured over $6 million in grants. Research collaborations include USDA-NIFA, EPA, and the European Union. Labs & Teams: Active in interdisciplinary groups like the Global Change Center and Remote Sensing Program at Virginia Tech.

Eric FrewView profile

Professor

• Autonomous Systems
• Robotics
• Unmanned Aircraft Systems
• +3 more

Eric Frew is a Professor in the Department of Aerospace Engineering Sciences at the University of Colorado Boulder. He holds leadership roles including Director of the Autonomous Systems Interdisciplinary Research Theme (ASIRT) and former Director of the Research and Engineering Center for Unmanned Vehicles (RECUV). His research focuses on autonomous systems, heterogeneous unmanned aircraft systems, and optimal distributed sensing. He earned his PhD from Stanford University in 2003, and has been a faculty member at CU Boulder since 2004. Education: PhD, Aeronautics and Astronautics, Stanford University, 2003 MS, Aeronautics and Astronautics, Stanford University, 1996 BS, Mechanical Engineering, Cornell University, 1995 Research Interests: Networked unmanned systems Optimal distributed sensing Controlled mobility in sensor networks Miniature self-deploying systems Guidance and control of unmanned aircraft in complex atmospheric phenomena Notable Awards: Outstanding Mentor Award (2023) AIAA Associate Fellow (2013) NSF CAREER Award (2009) Grants and Labs: Leads the Center for Autonomous Air Mobility and Sensing (CAAMS), and has conducted field campaigns such as TORUS (Targeted Observation by Radars and UAS of Supercells). His work integrates theoretical research with practical deployment of autonomous systems for environmental monitoring and severe weather studies. Labs/Teams: Active in CAAMS and RECUV, collaborating with industry/government on pre-competitive research in autonomous air mobility and sensing.

Fernando Sánchez-FigueroaView profile

Professor

• Software Engineering
• Big Data
• Business Intelligence
• +3 more

Fernando Sánchez-Figueroa is a Full Professor at the University of Extremadura's Department of Computer Systems Engineering and Telematics. He is a co-founder of Homeria Open Solutions, a spin-off engaged in R&D projects under EU frameworks. His research focuses on Software Engineering, Machine Learning, Data Visualization, and Ambient Intelligence. He has authored over 50 scientific articles and led numerous R&D contracts with public and private entities. Key roles include: Academic: Full Professor at University of Extremadura Entrepreneur: Co-founder of Homeria Open Solutions Research: Participation in EU-funded projects and development of AI-driven solutions for healthcare, smart cities, and education Research Interests: Machine Learning applications in healthcare, predictive analytics for education, and sustainable smart city technologies. His work bridges theoretical advancements with practical implementations, such as medical image segmentation using SAM models and cost-efficient UAV systems. Publications: Recent works include decision support systems for employability analysis, zero-shot learning in medical imaging, and recommender systems for education. He emphasizes data-driven approaches and model-driven engineering in software development. Impact: Developed tools like CompareML for preliminary data analysis and LiveSankey for advanced web visualization. His contributions span academia and industry, addressing challenges in healthcare, urban sustainability, and educational technology.

Zhiyu QuanView profile

Assistant Professor

• Actuarial Science
• Data Science
• Computational Statistics
• +3 more

Zhiyu (Frank) Quan is an Assistant Professor at the University of Illinois at Urbana-Champaign (UIUC), holding positions in the Department of Mathematics, Department of Statistics, and National Center for Supercomputing Applications (NCSA). He is also an affiliate faculty member at Discovery Partners Institute as InsurTech Lead and serves as an ORMI Faculty Fellow in Finance. His research focuses on data science applications in actuarial science, including tree-based models, natural language processing, and deep learning for insurance risk modeling, predictive analytics, and InsurTech innovation. Education: Ph.D. in Actuarial Science (University of Connecticut, 2019), MS in Applied Statistics (Michigan State University, 2014), and BS in Mathematics and Applied Mathematics (Xiamen University, 2012). Research interests include computational statistics, insurance analytics, and leveraging machine learning for actuarial challenges such as claim prediction, rate-making, and cyber risk modeling. He leads the Illinois Risk Lab, bridging academic research with industry needs, and has pioneered hybrid tree-based models to address imbalanced data in insurance. Notable achievements include the Arnold O. Beckman Research Award and Society of Actuaries Research Institute recognition. He advises two doctoral students and teaches advanced predictive analytics courses, emphasizing practical applications in actuarial science and data ethics. Key collaborations involve InsurTech companies and NCSA, focusing on NLP-driven academic paper repositories (CyLit) and federated learning for privacy-preserving insurance data sharing. His work addresses real-world challenges in cyber insurance and automated machine learning systems.

David GarlanView profile

Professor

• Software Engineering
• Software Architecture
• Self-Adaptive Systems
• +4 more

David Garlan is a Professor at the Software and Societal Systems Department within the School of Computer Science at Carnegie Mellon University , where he also serves as Associate Dean for Master’s Programs . He received his Ph.D. from Carnegie Mellon in 1987 after working in industry as a software architect. His research focuses on controlling complexity in large software systems through formalized architectural design, self-adaptive systems, and cyber-physical systems. He developed AcmeStudio , a widely used architecture design environment, and pioneered formal representation and analysis of software architecture. Education : Ph.D. in Computer Science (Carnegie Mellon, 1987) Research Interests include: Software Architecture: Formal methods for architectural design, end-user composition, and architectural styles Self-Adaptive Systems: Stochastic planning, model checking, security adaptation, and uncertainty reduction Cyber-Physical Systems: Multi-view design methods, consistency checking, and automotive systems Recent Article Trends address microservice resiliency, hybrid planning (combining formal methods and ML), simulation-augmented robotics, and sustainable machine translation. Themes include stochastic modeling , probabilistic verification , and adaptive decision-making . Scientific Awards : Stevens Award Citation (2005) ACM SIGSOFT Outstanding Research Award (2011) Allen Newell Award for Research Excellence (2016) IEEE TCSE Distinguished Education Award (2017) Nancy Mead Award (2017) Fellow of IEEE and ACM Advising and Grants : He has advised 25+ graduate students and collaborated on projects with Toyota and the Software Engineering Institute. His work includes model-based adaptation, automated planning, and formal verification of adaptive systems. Labs & Teams : Affiliated with the Institute for Software Research and works on tools like AcmeStudio, Rainbow, and IPL for architectural modeling and self-adaptation.

Christian SeilerView profile

Assistant Professor

• Climate Change
• Terrestrial Carbon Cycle
• Earth System Modeling
• +3 more

Dr. Christian Seiler is an Assistant Professor at Queen's University, holding cross-appointments in the School of Environmental Studies and the School of Computing. His research focuses on the role of the land surface in the global climate system, particularly the terrestrial carbon cycle's response to anthropogenic CO₂ emissions. He specializes in numerical modeling, global earth observations, and evaluating climate models like the Canadian Earth System Model (CanESM). His work addresses critical questions about forest dieback, model biases, and carbon dynamics under future climate scenarios. Education: Ph.D. Earth System Science, Wageningen University (2009-2014) M.Sc. Earth System Science, Wageningen University (2006-2008) B.Sc. Forest and Environment, University of Eberswalde (2001-2003) Research Interests: Dr. Seiler’s research integrates climate modeling, biogeochemical cycles, and earth system dynamics to understand tipping points in the land carbon sink. He leverages machine learning and dynamical systems theory to advance model evaluation and climate projections. His work supports initiatives like the Canadian Land Surface Scheme Including Biogeochemical Cycles (CLASSIC) and the CanESM. Publications Trends: His articles emphasize model benchmarking, sensitivity analysis, and climate forcing biases. Recent work explores nitrogen cycle disconnects in terrestrial models and the impacts of dynamic land cover changes. His contributions span climate extremes, decadal predictions, and earth observation applications. Awards/Grants: 2023 Environment Canada Grant (CAD 70,000, co-applicant) 2022 Queen's University Research Initiation Grant (CAD 50,000) 2018 NSERC Discovery Grant (CAD 162,500) Advising & Teaching: He supervises PhD students like Florence Kayode and Raj Deepak. Courses include Climate Change Science and Policy , Land-Atmosphere Interactions , and The Climate System . His teaching integrates R programming, dynamical systems theory, and climate model analysis. Labs/Teams: Collaborates with Environment and Climate Change Canada, the Pacific Climate Impacts Consortium, and international modeling teams to advance earth system science and climate policy.

Ram BajpaiView profile

Lecturer

• Epidemiology
• Biostatistics
• Survival Analysis
• +7 more

Dr. Ram Bajpai is a Lecturer in Epidemiology/Applied Statistics at Keele University's School of Medicine. He joined in 2019 as part of the Research Institute for Primary Care and Health Sciences, combining active research and teaching roles. Previously, he worked at the Lee Kong Chian School of Medicine (Nanyang Technological University, Singapore) and the Army College of Medical Sciences (India). Education: BSc in Statistics/Mathematics (University of Lucknow), MSc Health Statistics (Banaras Hindu University), PhD in Medical Statistics (Guru Gobind Singh Indraprastha University). Research focuses on cross-domain applications of statistical/epidemiological methods, including survival analysis, Bayesian methods, risk prediction modelling, and evidence synthesis. Teaching experience includes biostatistics modules for medical students at multiple institutions. Current research interests span prognostic studies, meta-analysis, complex data analysis, and design of epidemiological studies. Key contributions include systematic reviews on gout prophylaxis safety, dementia prognostic factors, and long-term outcomes of pediatric COVID-19. Active in collaborative projects on aging populations, musculoskeletal health, and public health interventions.

Seth KaplanView profile

Professor

• Industrial/Organizational Psychology
• Employee Well-being
• Emotions at Work
• +4 more

Seth Kaplan is a Professor in the Department of Psychology at George Mason University. His research focuses on employee well-being, team effectiveness, virtual work, and occupational health through projects like NSF-funded emotion regulation interventions and Army Research Institute collaborations on affective forecasting. He directs the KA-Lab, studying team resilience, metaperceptions, and statistical methodologies. Recent publications analyze job boredom, cognitive reappraisal interventions, and personality measurement innovations. His book Crisis-Ready Teams (2024) synthesizes data from high-risk environments. Courses taught include Occupational Health, Organizational Change, Psychometrics, and Multivariate Statistics. Grants: National Science Foundation (Co-PI): Just-in-Time Adaptive Interventions for Emotion Regulation Army Research Institute (PI): Affective Forecasting Errors Recent Presentations: Personality and generative AI use at work (SIOP 2025) Work situation identification via NLP (SIOP 2025) Helicopter helping in teams (SIOP 2025)

Mourad ZeghalView profile

Professor

• Geotechnical Engineering
• Soil Mechanics
• Seismic Response Analysis
• +3 more

Dr. Mourad Zeghal is a Professor in the Department of Civil and Environmental Engineering at Rensselaer Polytechnic Institute (RPI). His research focuses on computational geomechanics, seismic response monitoring, and geotechnical system identification. He leads projects addressing liquefaction mitigation, multiscale modeling of geosystems, and development of advanced computational tools for geotechnical analysis. Dr. Zeghal collaborates with RPI's Center for Network for Earthquake Engineering Simulation (CEES), Scientific Computation Research Center (SCOREC), and Inverse Problems Center (IPRPI). His work emphasizes reducing risks from natural hazards through improved design tools and model validation. Key projects include the Liquefaction Experiments and Analysis Projects (LEAP), which use centrifuge testing and machine learning to analyze soil behavior under seismic loads. Dr. Zeghal's research integrates experimental data with numerical simulations to enhance understanding of soil-structure interaction and lateral spreading during earthquakes. Research Interests: Soil liquefaction, multiscale modeling, inverse problem methods, computational geomechanics Key Collaborations: CEES, SCOREC, IPRPI, and global research networks Focus Areas: Centrifuge testing, model validation, seismic hazard mitigation His recent publications (2023–2025) highlight advancements in quantifying uncertainty in soil response, analyzing LEAP centrifuge experiments, and developing machine learning approaches for model calibration. Dr. Zeghal actively engages with industry and government labs to translate research into practical engineering solutions.

Jianxi GaoView profile

Associate Professor

• Network Science
• Artificial Intelligence
• Control Theory
• +3 more

Jianxi Gao is an Associate Professor in the Department of Computer Science at Rensselaer Polytechnic Institute (RPI). His research focuses on network science, particularly network resilience, robustness, and control, integrating network theory, control theory, statistical physics, and operations research. He also explores the intersection of network science and AI, including applications of AI to network analysis and vice versa. His work aims to understand, predict, and control the resilience of complex systems against cascading failures. Key research areas include network resilience in transportation systems, quantum networks, and biological systems, with applications to pandemic response and infrastructure optimization. Gao's contributions span theoretical frameworks and computational tools, such as the NuRsE MATLAB package for network resilience analysis. His GitHub repositories (e.g., NuRsE and NON) showcase his open-source contributions to network science and computational methods. His recent publications address topics like AI-driven network analysis, quantum network percolation, and pandemic-induced healthcare system stress. He actively collaborates on interdisciplinary projects, emphasizing real-world applications of network science principles.

Alexandru G. BardasView profile

Associate Professor

• Cybersecurity
• Systems Security
• Moving Target Defenses
• +9 more

Alexandru G. Bardas is an Associate Professor at the University of Kansas in the Department of Electrical Engineering & Computer Science (EECS) and the Institute for Information Sciences (I2S) . He received his PhD from Kansas State University under advisors Xinming (Simon) Ou and Scott A. DeLoach. His research focuses on cybersecurity from a systems perspective , including moving target defenses, security operations center (SOC) metrics, DevOps security, power grid cybersecurity, and defensive technologies for political activists. He explores UDP-based DDoS detection, DNS traffic analysis, and the intersection of AI with cybersecurity, emphasizing foundational knowledge over tool-specific training. Key research areas: Cybersecurity, Systems Security, Moving Target Defenses, SOC Metrics, DevOps Security Recent publications in ACSAC 2024 , USENIX Security 2024/2023 , and IEEE Security & Privacy 2022 Dr. Bardas has received significant recognition including: NSF CAREER Award (2022) for SOC automation Bellows Scholar (2021) at KU NSA SoS Honorable Mention (2023) He actively advises students across disciplines, with graduates now at Sandia National Laboratories , Blue Cross Blue Shield , and Pacific Northwest National Laboratory . Dr. Bardas participates in NSF grant reviews , serves on program committees for SOUPS and MILCOM , and leads outreach initiatives like the GenCyber Summer Camp .

Ali NazemiView profile

Associate Professor

• Hydrology
• Water Security
• Climate Change
• +3 more

Dr. Ali Nazemi is an Associate Professor in the Department of Building, Civil, and Environmental Engineering at Concordia University, where he joined in 2015 as a Strategic Hire in Water Resources. He holds adjunct appointments at the University of Saskatchewan's School of Environment and Sustainability and is an Associate Member of the Global Institute for Water Security. His foundational education includes a PhD from the University of Birmingham (UK), an MSc from Ferdowsi University of Mashhad (Iran), and a BSc from KNT University of Technology (Iran). Dr. Nazemi's research advances methodologies for water security challenges under climate change, focusing on: Hydrological modeling and algorithm development Climate change vulnerability assessments Coupled human-water systems Hydroclimatic data diagnostics His recent publications predominantly explore climate model integration, uncertainty quantification in water systems, and hybrid modeling approaches, with consistent themes of climate adaptation and risk management across Canadian and international contexts. He has been recognized with several awards, including: Dorothy Hodgkin Postgraduate Award (UK government, 2005–2009) Best Presentation Award at IEEE World Congress on Computational Intelligence (2006) Post Graduate Teaching Award (University of Birmingham, 2004–2007) Dr. Nazemi actively supervises graduate students through his Water Security and Climate Change (WSCC) lab, with projects on climate downscaling, vulnerability assessment tools, and coupled human-water systems. He leads research collaborations with the Saskatchewan Water Security Agency, NSERC's Changing Cold Region Network, and the Global Water and Energy Cycle Experiment.