Find Academic Supervisors

Amie J GoodinView profile

Assistant Professor

• Health Policy
• Health Disparities
• Maternal and Child Health
• +3 more

Dr. Amie J Goodin is an Assistant Professor in the Department of Pharmaceutical Outcomes and Policy at the University of Florida's College of Pharmacy. She holds dual affiliations with the Center for Drug Evaluation and Safety (CoDES) and leads the Health Services Research track in her department's graduate program. Her research focuses on policy impacts on treatment access for Substance Use Disorders (SUDs), particularly among Medicaid enrollees and pregnant women, integrating mixed-methods approaches. Dr. Goodin completed her PhD at the University of Kentucky and a postdoctoral fellowship at UF, specializing in pharmacoepidemiology and health services research. Education Ph.D. in Public Policy, University of Kentucky (2015) M.P.P., University of Kentucky (2009) B.S., University of Kentucky (2007) Research Interests Dr. Goodin examines how policy changes affect healthcare utilization and disparities, with emphasis on SUD treatment, maternal health, and cannabis use. She develops predictive models using machine learning and electronic health records (EHRs) to address gaps in mental health monitoring and opioid-related outcomes. Her work bridges epidemiology, informatics, and policy analysis to improve health equity. Recent Article Trends Her 2025 publications emphasize cannabinoid analysis in maternal health, suicide risk in opioid discontinuation, and cannabis contamination perceptions. 2024 work explores fall risks with depression treatments and machine learning for buprenorphine adherence. These reflect her focus on policy-driven research and translational informatics. Awards/Grants Lead PI for Gator MOMitor maternal health project (Florida Department of Health, 2022–2024) NIH-funded study on mental health outcomes of restless legs syndrome treatments (2021–2022) Pharmacist opioid disposal system development (Pfizer-funded, 2021) Advising & Teams Advisor to graduate students in pharmaceutical outcomes research, with courses on patient safety, research design, and health policy. Leads the Consortium for Medical Marijuana Clinical Outcomes Research's strategy initiatives. Labs/Teams Active in the UF Center for Drug Evaluation and Safety, focusing on cannabis clinical outcomes. Collaborates with the Florida Medical Marijuana Use Registry and recovery community centers on implementation science projects.

Mazdak GhajariView profile

Associate Professor

• Biomechanics
• Traumatic Brain Injury (TBI)
• Computational Modeling
• +5 more

Dr. Mazdak Ghajari is a Reader in Brain Biomechanics at Imperial College London’s Dyson School of Design Engineering, part of the Faculty of Engineering. He leads research on mechanical loading effects on the brain in contexts like sports collisions, traffic incidents, and surgical interventions. His work bridges computational modeling, impact testing, and AI to develop injury prevention solutions. Key affiliations include the Centre for Blast Injury Studies, TBI-REPORTER, and the RAIDS advisory panel. Research contributions include HIPER (a helmet rating system), EN17950 headform standards, and policy-influencing studies on e-scooter falls. Current projects focus on children’s helmets, industrial safety, motorcycle facial impact protection, and wearable brain injury monitoring via instrumented mouthguards. He directs the HEAD lab, mentoring PhD students and postdocs. Teaching responsibilities include Solid Mechanics I and Finite Element Analysis modules in the MEng Design Engineering program, emphasizing student engagement through lab-based learning. Admin roles include Director of Postgraduate Studies (PhD) and former Senior Tutor. His work spans biomechanical modeling, material science, and interdisciplinary collaborations across neurotechnology and additive manufacturing. Ongoing projects aim to integrate real-time brain injury data into clinical and safety systems.

Nicholas C. SchmerrView profile

Associate Professor

• Planetary Seismology
• Solid Earth Geophysics
• Planetary Geology
• +5 more

Nicholas C. Schmerr is an Associate Professor in the Department of Geology at the University of Maryland, College Park. He holds affiliations with NASA Goddard Space Flight Center’s Planetary Geodynamics Laboratory and the Carnegie Institution for Science’s Department of Terrestrial Magnetism. His research focuses on planetary seismology, geodynamics, and the evolution of terrestrial bodies, with a particular emphasis on Mars, the Moon, and icy ocean worlds. Schmerr leads the GEODES SSERVI NASA project, which integrates seismology for planetary exploration. He earned his Ph.D. in Geology from Arizona State University in 2008. Research Interests: Schmerr’s work spans planetary seismology, mantle dynamics, and cryospheric processes. Key projects include analyzing data from the InSight Mars mission, studying lunar seismicity, and investigating subsurface structures on icy moons like Europa and Enceladus. His lab develops advanced seismological tools and collaborates with institutions such as the USGS, Smithsonian, and JPL. Publications: Over 100 peer-reviewed articles, including breakthroughs in Martian core detection, lunar crustal thickness, and subglacial lake characterization. Recent work focuses on tidal seismicity on icy worlds and mantle transition zone anisotropy. Grants & Awards: Not explicitly listed, but his leadership of NASA-funded projects indicates significant grant support. His research is featured in venues like Nature Geoscience and Science. Labs & Teams: Directs the UMD Planetary Geophysics Lab and collaborates internationally to advance planetary exploration technologies. Current projects include the Lunar Geophysical Network and Arctic analog studies for icy ocean seismology.

Laura CrocettiView profile

Lecturer

• Space Geodesy
• Machine Learning Applications
• GNSS Time Series Analysis
• +4 more

Dr. Laura Crocetti is a Lecturer at the Department of Civil, Environmental and Geomatic Engineering at ETH Zurich. She joined ETH Zurich in 2020 as a Ph.D. student and currently holds a postdoctoral researcher position (2025-present) while serving as faculty. Her research focuses on advanced applications of machine learning in space geodesy, including GNSS data analysis and geophysical pattern detection. Research Interests: Dr. Crocetti develops computational methods for analyzing GNSS time-series data, models environmental impacts on geodetic measurements, and creates data-driven pipelines for detecting anomalies in geophysical datasets. Her work bridges geodesy with AI methodologies. Education: Ph.D. in Space Geodesy, ETH Zurich (2020-2024) MSc in Geodesy and Geoinformation, TU Vienna (2016-2019) BSc in Geodesy and Geoinformatics, TU Vienna (2012-2016) Awards: Best Presentation at ETH IGP Doctoral Day (2022) Best Presentation at D-BAUG Research Day (2021) Early Adopter for Earth System Data Lab (2019) EC Student Grant for ESA Symposium (2019) Excellence Scholarship, TU Vienna (2018)

Marc BoxbergView profile

Researcher

• Geophysical Imaging
• Radioactive Waste Disposal
• Ice Data Analysis
• +3 more

Dr. Marc Boxberg is a researcher at RWTH Aachen University's Department of Geophysical Imaging and Process Observation, affiliated with the Faculty of Georesources and Materials Engineering. His work focuses on geophysical imaging, environmental risk assessment for radioactive waste disposal, and cryosphere-related planetary exploration. He leads research into seismic wave propagation, data integration frameworks, and robotic exploration systems. Key research interests include icy moon mission preparation, subglacial access technologies, and data-driven approaches for radioactive waste management. He contributes to interdisciplinary projects like the Ice Data Hub and Cryotwin digital infrastructure. Dr. Boxberg's expertise spans geophysical modeling, mission simulation tools (e.g., NEXD software), and field-testing of cryogenic exploration hardware. His recent publications highlight advancements in multi-physical data fusion, cryobot performance analysis, and uncertainty quantification in waste repository safety. Ongoing work includes astrobiology research for icy ocean worlds and development of actionable data hubs for long-term environmental monitoring.

• Applied Geophysics
• Seismic Hazard
• Seismic Surface Waves
• +10 more

Laura Socco is a Full Professor at the Department of Environmental, Land and Infrastructure Engineering (DIATI) at the Polytechnic University of Turin , with membership in the SmartData@PoliTO Big Data and Data Science Laboratory. Her career spans over two decades, marked by leadership in applied geophysics and seismic hazard assessment. Scientific Branch: GEOS-04/B - Applied Geophysics ERC Skills: PE8_3 (Civil Engineering, Geotechnics) , PE10_7 (Seismology) SDG Focus: Climate Action , Life on Land , Quality Education Laura’s research focuses on data-driven geophysical processing , leveraging seismic surface waves , seismic tomography , and Monte Carlo methods for site characterization and subsurface modeling. Her work bridges geophysics with mineral exploration , infrastructure monitoring , and climate-resilient engineering . Recent publications highlight innovations in UAV-based geophysical surveys and fiber-optic sensing for mining and environmental applications. Scientific Awards include the SEG Outstanding Educator Award (2019) , SEG Honorable Mentions for Best Paper (2012, 2013, 2016) , and the Conrad Schlumberger Award (EAGE, 2014) . She has served as an Assistant Editor of GEOPHYSICS and held leadership roles in EAGE and SEG committees. Laura supervises PhD students in Mathematical Sciences and Civil and Environmental Engineering , while leading research projects like GEOCHARME (2023-2025) and FUTURE (2022-2024) funded by national and EU grants. Her editorial contributions and collaborative agreements with institutions like AIPO and University of Turin underscore her interdisciplinary impact.

• Precision Medicine
• Functional Data Analysis
• High-Dimensional Regression
• +5 more

Xinyi Li is an Assistant Professor in the School of Mathematical and Statistical Sciences at Clemson University. She holds a Ph.D. in Statistics from Iowa State University, an M.S. in Statistics from the University of Georgia, and a B.S. in Mathematical Statistics from Beijing Normal University. Prior to joining Clemson, she was a postdoctoral fellow at SAMSI and UNC Chapel Hill. Her research focuses on precision medicine, functional data analysis, high-dimensional regression, spatio-temporal analysis, and reinforcement learning, with applications in statistical genetics, neuroimaging, and public health. She teaches courses in probability theory, data analysis, machine learning, and mathematical statistics. Dr. Li's recent publications demonstrate strong emphasis on machine learning applications in statistical modeling, with innovations in nonparametric regression, dynamic treatment regimes, and high-dimensional data analysis. Her work frequently appears in top-tier journals including the Journal of Machine Learning Research and Journal of the American Statistical Association. National Science Foundation DMS-2210658 (2022-2025) IMS New Researcher Travel Award (2019) ASA GA Chapter Best Student Poster Award (2017) 2nd Place in 2016 Data Mining Cup She maintains active collaborations through affiliations with the Center for Human Genetics and the Institute for Engaged Aging, and serves as an AI advocate for the Clemson-MUSC AI Hub.

Scott FieldView profile

Associate Professor

• Gravitational Wave Data Science
• Discontinuous Galerkin Methods
• Computational Relativity
• +2 more

Scott Field is an Associate Professor in the Department of Mathematics at the University of Massachusetts Dartmouth, where he co-directs the Engineering & Applied Science PhD program. His research integrates gravitational wave data science, computational relativity, and high-performance computing to model astrophysical phenomena like black hole mergers. His primary research areas include developing discontinuous Galerkin methods for efficient waveform computation, creating surrogate models to accelerate gravitational wave predictions, and applying machine learning to astrophysical inverse problems. His work supports gravitational wave observatories like LIGO and LISA. He has secured substantial research funding, including NSF and ONR grants exceeding $1.6 million. Notable projects focus on neural ODE dynamics for orbital modeling and high-order methods for long-duration waveform simulations. As co-developer of the GWSurrogate Python package, he enables efficient gravitational wave data analysis. He collaborates with the Simulating eXtreme Spacetimes (SXS) consortium and contributes to the LISA mission through waveform modeling.

Pablo TarazagaView profile

Professor

• Structural Dynamics
• Vibration Analysis
• Smart Structures
• +3 more

Pablo Tarazaga is a Professor in the Department of Mechanical Engineering at Texas A&M University and holds the James J. Cain '51 Faculty Fellowship. He earned his PhD (2009) and MS (2004) from Virginia Tech, following a BS from the University of Puerto Rico at Mayagüez (2002). His research encompasses structural dynamics, smart infrastructure, and bio-inspired systems. Primary research areas include: Vibration analysis and modal testing Structural health monitoring and nondestructive evaluation Smart building technologies and IoT applications Energy harvesting from mechanical vibrations Biomedical device development Recent publications focus on wave propagation control, structural health monitoring using electromechanical impedance, and bio-inspired sensory systems. Research trends include advanced wave manipulation techniques for vibration control and particle manipulation, and data-driven modeling approaches for mechanical systems. Significant honors include ASME Fellowship (2022), AFOSR Young Investigator Award (2015), and multiple faculty fellowships at Virginia Tech and Texas A&M. He directs the Fusion of Analysis, Simulation & Testing (FAST) Laboratory, which develops integrated approaches for structural dynamics validation. Educational initiatives include developing curriculum in structural dynamics and mentoring students in smart infrastructure research. Research collaborations span aerospace, civil infrastructure, and biomedical engineering domains.

• Underground Mining Methods
• Green Mining Process
• Coal Mine Safety
• +7 more

Professor Ting Ren is a Professor at the University of Wollongong's School of Civil, Mining and Environmental Engineering. He specializes in mining safety, coal mine gas management, and dust control. His research focuses on underground mining methods, computational modeling, and geomechanics. Ren has overseen over 35 higher-degree research students, contributing to advancements in mine safety technologies. He leads industry-funded projects totaling over A$4 million, addressing challenges in dust mitigation, gas drainage, and explosion-resistant materials. Ren has been recognized with awards such as the 2009 BMA Health & Safety Award and the 2012 Australian Bulk Handling Review Award. His work bridges academic research and industrial applications, with collaborations spanning global mining companies. Education PhD in Mining Engineering from the University of Nottingham, UK Research Interests Professor Ren's expertise spans coal mine safety, gas management, mine fire prevention, and computational fluid dynamics (CFD) applications. His work emphasizes practical solutions for dust control, gas drainage optimization, and rock burst prevention. Recent projects include VR-based training tools for dust mitigation and advanced models for predicting infrastructure damage from explosions. Awards & Grants CSIRO Partnership Excellence Award (2006) ARC Training Centre for Innovative Composites in Mining (A$5M, 2023-2028) UOW-ANSTO Joint Project for Dust Control (2025-2026) Advising & Grants Ren supervises over 35 PhD/Master’s students, focusing on topics like silica dust monitoring and composite materials for explosion resistance. His grants include industry partnerships with BHP, Anglo American, and ACARP. Labs & Teams He leads the Green Mining and Safety Research Group , collaborating with academia and industry to develop sustainable mining solutions. His work integrates advanced modeling tools and experimental validation for real-world mining hazards.

Paweł OlejnikView profile

Professor

• Dynamical Systems
• Numerical Computations
• Nonlinear Dynamics
• +12 more

Prof. Paweł Olejnik is a full Professor at the Lodz University of Technology (TUL), Faculty of Mechanical Engineering, where he serves as Head of the Department of Automation, Biomechanics and Mechatronics since 2023. He holds the official title of Professor in Mechanical Engineering, conferred by the President of Poland in 2025. His academic career at TUL spans over two decades, with continuous involvement in research, teaching, and leadership roles. Education: M.Sc. Eng. in Computer Physics, TUL (2000) Ph.D. in Applied Mechanics, TUL (2002) D.Sc. in Applied Mechanics, TUL (2013) Research Interests: Prof. Olejnik specializes in nonlinear and discontinuous dynamical systems, with a focus on friction-induced vibrations, chaos, bifurcation, numerical modeling, control, and optimization in mechatronic and mechanical systems. He combines theoretical analysis with experimental validation in laboratory settings. Publication Trends: His recent work (2021–2025) emphasizes the integration of machine learning and physics-informed models for friction and system identification, energy harvesting via variable-length pendulums, stick-slip dynamics in electromechanical systems, and advanced signal processing for fault detection. His publications appear in high-impact journals such as Nonlinear Dynamics , Energies , and Mechanical Systems and Signal Processing . Scientific Awards: SPRINGER NATURE Editorial Contribution Award (2025) TUL Rector’s Award for Scientific Excellence (2021–2024) FME Dean’s Award (2021, 2023) Ministry of Science and Higher Education Team Award for textbook (2018) Doctoral Thesis Distinction (2003) FNP Fellowship (2010) Grants and Supervision: He has led and participated in multiple national and international research projects funded by the National Science Centre and National Centre for Research and Development. He actively supervises PhD and Master’s students, with several graduates and ongoing advisees. He also mentors numerous engineering theses in mechatronics and control systems. Laboratory and Editorial Leadership: He leads the 'Control Laboratory in Mechatronics' and has developed multiple experimental rigs. He is an editor for International Journal of Dynamics and Control and Journal of Vibration Testing and System Dynamics , and has co-edited over a dozen conference proceedings and special journal issues.

Celso GrebogiView profile

Professor

• Nonlinear Dynamics
• Chaos Theory
• Complex Systems
• +11 more

Professor Celso Grebogi is the Sixth Century Chair in Nonlinear & Complex Systems at the School of Natural and Computing Sciences, University of Aberdeen, UK. He is the Founding Director of the Institute for Complex Systems and Mathematical Biology and Co-founder of the Aberdeen-Lanzhou-Tempe Research Centre, advancing interdisciplinary research in relativistic quantum chaos. He has been an External Scientific Member of the Max-Planck-Society since 1998 and maintains extensive international collaborations. BSc, Chemical Engineering, Federal University of Parana, 1970 MS, Physics, University of Maryland, 1975 PhD, Physics, University of Maryland, 1978 Post-doctoral Research Fellow, University of California at Berkeley, 1978–1981 Professor Grebogi is a leading expert in nonlinear and complex dynamics, with research spanning chaotic dynamics, fractal geometry, systems biology, neurodynamics, fluid advection, relativistic quantum chaos, and nanosystems. His work has profoundly influenced the understanding and control of chaotic systems, most notably through the OGY method for chaos control. He has pioneered research in strange nonchaotic attractors, multistability, and dynamical transitions in complex systems. The recent publications highlight a strong trend toward interdisciplinary applications, integrating nonlinear dynamics with machine learning, neuroscience, biomedical engineering, and quantum systems. His team applies advanced mathematical frameworks to real-world problems such as motor imagery recognition, brain dynamics in mental disorders, fatigue detection, and quantum scarring. The integration of data-driven methods with classical dynamical systems theory underscores a modern, hybrid approach to complexity science. Fulbright Fellowship Award, 1974 Senior Humboldt Prize, 1996 Doctor Honoris Causa, University of Potsdam, 1997 Controlling Chaos paper selected as milestone by Physical Review Letters, 2008 Citation Laureate - Researcher of Nobel Class, 2016 Lagrange Award for Lifetime Achievement, 2023 James Yorke Award, 2024 Professor Grebogi has delivered over 500 invited talks and authored more than 500 publications. He has supervised numerous PhD students and postdoctoral researchers, though specific names are not listed in the provided texts. His research has been supported by major international grants and collaborations, including partnerships with institutions in China, Brazil, and the US. He serves on multiple editorial boards and has held visiting professorships worldwide. He leads the Institute for Complex Systems and Mathematical Biology at Aberdeen, fostering interdisciplinary research in nonlinear science. The institute collaborates with global partners, including Lanzhou University and Arizona State University, and supports research in relativistic quantum chaos, systems biology, and complex network dynamics. His group integrates theoretical modeling, computational simulations, and data analysis to explore emergent behaviors in complex systems.

Wolfgang HaerdleView profile

Professor

• Smoothing methods
• Discrete choice models
• Statistical modelling of financial markets
• +7 more

Professor Wolfgang Haerdle is the Ladislaus von Bortkiewicz Professor of Statistics at Humboldt University of Berlin's School of Business and Economics, where he has served as Professor of Statistics since 1992. He maintains an active international presence with visiting appointments at institutions including Xiamen University in China and National Jiaotong University in Taiwan. His research spans smoothing methods, discrete choice models, statistical modeling of financial markets, and computer-aided statistics, with recent work focusing on high-dimensional nonstationary time series, cryptocurrency analytics, and machine learning applications in finance. Professor Haerdle has authored numerous influential textbooks including 'Applied Multivariate Statistical Analysis' and 'Statistics of Financial Markets', which have become standard references in their fields. His publication record shows a clear evolution toward contemporary challenges in data science and finance, with recent work heavily focused on cryptocurrency markets, machine learning applications, and high-dimensional statistical methods. The trend indicates increasing interdisciplinary work bridging statistics, finance, and computer science, particularly in analyzing complex financial data streams and developing robust risk management frameworks. 2019-2023: YuShan Scholar, Taiwan 2017: Charter Fellow, INDI Institute Nonlinear Dynamics, RUDN University 2015: Foreign Expert Professor, Xiamen University, China 2013: Honorary Member, Romanian Academy of Science 2012: Multa Scripsit Award 'Econometric Theory' 2003: 'Highly cited scientist' by ISI 1997: Fellow, International Statistical Institute 1992: Fellow, Institute of Mathematical Statistics Professor Haerdle has supervised over 50 PhD students whose work spans financial statistics, risk management, machine learning, and econometrics. His research has been supported by significant grants including the IRTG 1792 'High Dimensional Nonstationary Time Series' funded by the DFG. He founded and maintains the Quantlet platform for reproducible research and the Quantinar ecosystem for data analytics.

• Computational Mechanics
• Machine Learning
• Inverse Problems
• +7 more

Assad Anshuman Oberai is a Professor at the University of Southern California's Viterbi School of Engineering . His research spans computational mechanics, inverse problems, and machine learning applications in nuclear engineering and biomedical imaging. Key Research Areas: Multi-fidelity modeling, Bayesian inference in mechanics, conditional GANs for posterior estimation, ultrasonic sensing of nuclear fuel canisters, mechano-microscopy for tumor elasticity imaging Methodological Contributions: Novel operator network architectures, dimension-reduced Bayesian frameworks, active noise cancellation techniques, and error estimation in variational multiscale methods Recent work focuses on non-invasive nuclear fuel inspection using ultrasonic sensing and machine learning, achieving significant signal-to-noise ratio improvements with active noise cancellation. In biomedical domains, his team develops cGAN-enhanced elasticity imaging for cancer differentiation, outperforming traditional algebraic methods. Methodologically, he pioneered VarMiON (Variationally Mimetic Operator Networks) with rigorous error analysis, demonstrating superior performance over DeepONet in partial differential equation approximations. His publications address high-dimensional inverse problems through generative models, with applications in epidemic modeling (capturing algebraic decay via behavioral feedback), 3D traction microscopy accounting for cell-induced matrix degradation, and compressible phase change simulations using discontinuous finite element methods. Co-authors include researchers from mechanical engineering, biomedical informatics, and nuclear safety domains.

Yacine Ait-SahaliaView profile

Professor

• High-Frequency Financial Econometrics
• Stochastic Volatility Models
• Jump Processes
• +4 more

Yacine Ait-Sahalia is the Otto A. Hack 1903 Professor of Finance and Economics at Princeton University , affiliated with the Department of Economics and the Bendheim Center for Finance. His research focuses on high-frequency financial econometrics, stochastic volatility modeling, jump processes, and continuous-time asset pricing models. Research Interests include: High-Frequency Data Analysis Stochastic Volatility and Jump-Diffusion Models Nonparametric and Parametric Estimation Financial Market Microstructure Portfolio Optimization Under Jumps Term Structure of Interest Rates Recent Articles explore volatility-uncertainty disconnects, non-standard errors in high-frequency data, market making dynamics, and spectral analysis of asset returns. His work often bridges statistical theory with financial applications. Scientific Awards include: 1998 Cornerstone Research Award 2003 Dennis J. Aigner Award Michael J. Brennan Award for Best Paper in Review of Financial Studies Grants from the National Science Foundation (NSF) have supported his research. He has also authored influential books like High Frequency Financial Econometrics and co-edited the Handbook of Financial Econometrics .