Find Academic Supervisors

• Data Science
• Machine Learning
• Transportation Systems
• +4 more

Yu Liu is an Assistant Research Professor in the Department of Physics at The Pennsylvania State University, affiliated with the Eberly College of Science. His research focuses on applying data-driven methodologies to complex systems, including transportation networks, urban mobility, financial markets, and social behavior analysis. He holds a primary appointment in the Department of Physics, where he contributes to interdisciplinary research initiatives. Education details are not explicitly provided in the text. Dr. Liu’s research interests span several key areas, including data science, machine learning, and their applications in transportation systems, urban planning, and financial analysis. He explores topics such as anomaly detection, predictive modeling, and the analysis of large-scale spatiotemporal datasets. His work often integrates interdisciplinary approaches, leveraging tools from physics, computer science, and economics to address real-world challenges in mobility, insurance, and cybersecurity. His recent publications highlight advancements in machine learning applications across diverse fields. Recent work includes studies on telematics-driven risk classification, exchange rate predictability, and semi-supervised anomaly detection. Liu’s research also delves into urban mobility patterns, cryptocurrency price forecasting, and social network cascade analysis, demonstrating a commitment to both theoretical and applied data science. No scientific awards or fellowships are explicitly mentioned in the provided information. Advising and grant details are not detailed in the text. His involvement in research projects is reflected through his extensive publication record. While specific laboratory affiliations are not mentioned, Dr. Liu’s work is likely conducted within the Department of Physics at Penn State and through interdisciplinary collaborations.

Ao WangView profile

Assistant Professor

• Empirical Industrial Organization
• Structural Econometrics
• Demand Analysis
• +3 more

Ao Wang is an Assistant Professor in the Department of Economics at the University of Warwick, affiliated with the CAGE Research Centre (Public Policy and Data Group). His research focuses on Empirical Industrial Organization (e.g., demand systems, revenue management) and Structural Econometrics (e.g., identification of unobserved preferences, large choice sets). He holds a PhD in Economics from CREST, France, and a Master’s from Ecole Polytechnique/ENSAE. His work has been published in journals such as Econometric Theory and Journal of Econometrics . Education: PhD in Economics, CREST, France (2016–2020) Master in Economics (summa cum laude), Ecole Polytechnique/ENSAE, France (2014–2016) Bachelor’s in Probability and Statistics, Peking University, China (2008–2012) Awards & Funding: ESRC New Investigator Grant (£298,944) for research on quantity discounts in retail Recipient of multiple grants from Research England, BA/Leverhulme, and Labex programs Teaching: Courses include Industrial Economics 2 and Topics in Industrial Organisation and Data Science. He advises students and offers regular office hours.

• Time Series Econometrics
• Forecast Evaluation
• Economics of Education
• +2 more

Gianna Boero serves as a Teaching Professor in the Department of Economics at the University of Warwick, where she has been a continuous faculty member since joining as a Lecturer in 1997. Her academic career includes prior appointments as Associate Professor at the University of Cagliari and Lecturer positions at the Universities of Birmingham, Nottingham, and Cagliari. Her educational credentials include a PhD in Econometrics from the University of Naples and Cagliari, a Master in Economics from the University of Warwick, and a BSc in Political Science (majoring in Economics) from the University of Cagliari. Professor Boero's research centers on time series econometrics with emphasis on nonlinear modeling, forecast evaluation criteria , macroeconomic forecast uncertainty , and economics of higher education outcomes . Her empirical work bridges theoretical econometric methods with applications in educational disparities and macroeconomic forecasting. Analysis of her 15 most recent publications (2004-2025) reveals consistent focus on applied time series methods, forecast uncertainty measurement, and educational economics. Key trends include methodological innovations in nonlinear modeling, analysis of exchange rate dynamics, and investigation of awarding gaps in UK higher education. No scientific awards are documented in the provided materials. As an educator, Professor Boero has led core econometrics modules for MSc Finance and Economics (1997-2023) and MSc Economics programs (1997-2022), while directing the undergraduate dissertation module Research in Applied Economics since 2006. She has supervised extensive undergraduate and MSc dissertations alongside several PhD students, demonstrating significant commitment to student development. No specific research laboratories or collaborative teams are mentioned in the source materials.

• Industrial Organization
• Environmental Economics
• Applied Microeconomics
• +4 more

Frank Verboven is a full Professor at the Department of Economics within the Faculty of Economics and Business at KU Leuven. His research focuses on empirical industrial organization, competition policy, and applied microeconomics, with methodological expertise in structural demand modeling and bargaining power analysis. KU Leuven, Faculty of Economics and Business Economics Research Group Research Interests: Verboven’s work bridges industrial organization, environmental economics, and microeconomic policy. He analyzes globalizing consumer markets, location decisions in regulated sectors, and develops structural econometric models to assess geographic entry restrictions in professions like notaries. His sabbatical research explores competition dynamics in consumer goods markets and regulatory trade-offs in semi-public sectors such as education and healthcare. Scientific Awards: No specific awards are listed in the provided data. Advising & Grants: He supervises PhD students including De Cannière, C., Burton, W.H., and Hidalgo Rodriguez, J.F. His projects are funded by KU Leuven’s Bijzonder Onderzoeksfonds (BOF) and focus on competition, decarbonization, and data infrastructure.

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.

• Experimental Nuclear Physics
• Quantum Chromodynamics (QCD)
• Nucleon Structure
• +12 more

Chao Peng is an experimental physicist in the Physics Division at Argonne National Laboratory, where he serves as an Assistant Physicist since 2021, following a postdoctoral appointment from 2019 to 2021. His research is centered on probing the internal structure of nucleons and their emergent properties within the framework of Quantum Chromodynamics (QCD), with a focus on spin, mass, size, and polarizabilities. He is actively involved in major electron scattering experiments at Jefferson Lab, including PRad-II, X17, and SoLID, and is preparing for future research using the ePIC detector at the Electron-Ion Collider (EIC). Education: Ph.D. in Experimental Nuclear Physics, Duke University, 2018 B.E. in Engineering Physics, Tsinghua University, China, 2009 Chao Peng’s research interests lie at the intersection of nuclear physics, particle physics, and advanced instrumentation. He specializes in understanding how quark and gluon distributions change when nucleons are embedded in light nuclei such as Deuterium, Helium-3, and Lithium-6. His work combines precision electron scattering with cutting-edge detector development, particularly in calorimetry, Cherenkov detectors, and polarized lithium sources. He leads an Argonne LDRD project focused on polarized 6 Li and 7 Li sources, highlighting his leadership in experimental innovation. The 15 most recent publications reflect a strong emphasis on nucleon structure, precision measurements, and detector R&D. Key themes include the proton radius puzzle, spin structure functions (g₂, d₂), searches for new bosons (X17), and instrumentation for the EIC. The articles span subfields such as transverse momentum dependent parton distributions (TMDs), generalized parton distributions (GPDs), dark photon searches, and Monte Carlo simulation frameworks, indicating a broad and impactful research portfolio. Scientific Awards: Argonne Impact Award (2023): Development of a Novel Imaging Calorimeter for EIC Argonne Impact Award (2023): Experimental Determination of the Proton Mass Density JSA Graduate Fellowship (2014–2015): Hardware Preparation of the PRad Experiment JSA 2014 Poster Competition – 1st Place JSA Graduate Fellowship (2013–2014): Simulation/Analysis Development for the PRad Experiment Nuclear Power Award, Tsinghua University (2008) Chao Peng has played a significant role in advising and leading research projects, serving as Lead PI on an Argonne LDRD project and as Co-spokesperson for multiple high-profile experiments (SoLID-SIDIS, Neutron g₂/d₂, PRad-II, X17). While no formal students are listed, his leadership in collaborative teams suggests mentorship roles within large experimental groups. He has secured competitive fellowships and internal funding, demonstrating strong grant acquisition capabilities. His work is supported by national user facilities such as Jefferson Lab and Argonne’s own advanced research infrastructure. Labs and Teams: He is deeply integrated into major experimental collaborations including the SoLID, PRad, and future ePIC collaborations. His work leverages national user facilities such as the Jefferson Lab Continuous Electron Beam Accelerator Facility (CEBAF) and is aligned with the scientific goals of the Electron-Ion Collider. At Argonne, he contributes to the Physics Division's mission in discovery science and detector innovation, working within multidisciplinary teams focused on advancing the frontiers of nuclear and particle physics.

Sanjeev KhudanpurView profile

Associate Professor

• Information Theory
• Speech Recognition
• Machine Translation
• +3 more

Sanjeev Khudanpur is an Associate Professor at Johns Hopkins University (JHU), holding dual appointments in the Department of Electrical and Computer Engineering and the Department of Computer Science. He is affiliated with the Center for Language and Speech Processing (CLSP) and the Human Language Technology Center of Excellence (HLTCOE). Khudanpur received his B.Tech. from IIT Bombay (1988) and his Ph.D. from the University of Maryland, College Park (1997), under Prof. Prakash Narayan, focusing on information theory and data compression. His research integrates information theory with human language technologies, including speech recognition, machine translation, and natural language processing. Key contributions include statistical modeling of language structure, cross-lingual adaptation techniques, and sparse recovery algorithms. He organizes the CLSP Summer Workshops to advance the field and has advised numerous PhD and MSE students. Khudanpur’s work spans academic roles since 1996, including visiting appointments at the IMA (University of Minnesota) and editorial roles in journals. His research has been published in top-tier venues such as IEEE Transactions on Information Theory, Speech Communication, and Computer Speech & Language.

• Signal Processing
• Radar Systems
• Electromagnetic Modelling
• +3 more

Javier Rosado Sanz is an academic at the Universidad de Alcalá, holding the position of Profesor Ayudante Doctor in the Signal Theory and Communications Department. His research focuses on advanced radar systems, particularly passive radar technologies leveraging existing broadcast signals (DVB-T/S, GPS, etc.) for target detection and tracking. He leads work in the AES3 group, developing smart sensor networks for coastal and urban surveillance, emphasizing acoustic and electromagnetic sensing. He earned his PhD from Universidad de Alcalá in 2021 with a thesis on antenna array configurations and electromagnetic target modeling for passive radar enhancement. His technical expertise spans antenna design optimization, clutter mitigation algorithms, and multi-static radar architectures. Recent research emphasizes UAV/drone detection, 2D/4D tracking systems, and adaptive beamforming techniques. His work bridges theoretical signal processing with practical implementations in maritime/coastal surveillance and smart city sensor networks. Key innovations include reflectarray antenna designs and decentralized detection frameworks for distributed sensor arrays. Publications highlight contributions to passive radar performance under challenging environments (e.g., wind farms, urban clutter) and novel applications like oil spill classification using SAR data. Collaborations involve experimental validations of DVB-based radar systems and conductivity analysis of composite materials used in UAVs.

Thorsten RaaschView profile

Professor

• Numerical Mathematics
• Adaptive Discretization Methods
• Partial Differential Equations
• +7 more

Thorsten Raasch is a Professor of Numerical Mathematics at the Institute of Mathematics, Johannes Gutenberg University Mainz, Germany. He is affiliated with the Department of Physics, Mathematics, and Computer Science, and leads research in numerical analysis, particularly adaptive methods for PDEs and inverse problems. Research Interests: His work focuses on adaptive discretization methods for partial differential equations and inverse problems, non-smooth numerical optimization , wavelet systems , multilevel frames , and GPGPU computing . These interests are deeply rooted in applied and computational mathematics, with applications in scientific computing and engineering. The most recent articles show a strong trend in developing adaptive wavelet-based numerical schemes for solving inverse and stochastic PDEs, with a particular emphasis on sparsity, convergence analysis, and preconditioning. His publications span high-impact journals in numerical analysis and inverse problems, indicating sustained scholarly activity. Teaching: He has taught a variety of courses including Fundamentals of Numerics, Convex Optimization, Numerical PDEs, Spectral Methods, and advanced seminars on topics like semi-smooth Newton methods and reduced basis methods. Thorsten Raasch earned his doctorate in 2007 from Philipps University of Marburg and has held academic positions at JGU Mainz, the University of Siegen, and the University of Rostock. He was appointed W2 Professor at JGU Mainz in 2015 and previously held a junior professorship there. No scientific awards are mentioned in the provided texts. Advising and Grants: While no students or grants are explicitly listed, his role as a professor and active researcher suggests involvement in supervising graduate students and participating in research projects, possibly within the 'Mathematics of Computation' research network. Labs and Teams: He is part of the Numerical Mathematics Working Group at JGU Mainz and contributes to the 'Mathematics of Computation' research network, indicating collaboration within a structured research environment.

John MerrimanView profile

Assistant Professor

• Oncology Nursing
• Cognitive Function in Cancer Survivors
• Stress Management Interventions
• +12 more

John Merriman, PhD, RN, AOCNS, is an Assistant Professor at NYU Rory Meyers College of Nursing. His research focuses on improving cognitive function in postmenopausal women with breast cancer through stress management interventions, particularly Mindfulness-Based Stress Reduction. He investigates the impact of biobehavioral and social determinants on intervention efficacy and is funded by a K99/R00 award from the National Institute of Nursing Research (NR015473). PhD, Nursing – University of California, San Francisco MS, Nursing – University of California, San Francisco BS, Communication – Mississippi College Dr. Merriman’s research spans oncology nursing, symptom science, palliative care, and genetics. He explores cognitive function, work and quality of life in young adult cancer survivors, financial toxicity, and symptom burden in patients with multiple chronic conditions. His work often employs advanced statistical methods such as latent class analysis and mixed-effects modeling. His recent publications reveal a strong trend in interdisciplinary research combining nursing, psychology, genetics, and public health. Themes include cognitive and emotional functioning during cancer treatment, work reintegration, financial distress, and end-of-life care complexity. His studies frequently use mixed methods and secondary data analysis of clinical trials. Postdoctoral Alumni Award, University of Pittsburgh Postdoctoral Association (2016) International inductee, Sigma Theta Tau (2006) Dr. Merriman is actively involved in mentoring, as he is accepting PhD students. He leads research on stress management and cognitive outcomes, supported by NIH funding. He is President of the International Society of Nurses in Genetics, underscoring his leadership in nursing science and genetics. He has no disclosed grants listed beyond the K99/R00, but his publication record suggests ongoing funded research. He is affiliated with multiple professional organizations, including the American Nurses Association, Eastern Nursing Research Society, Oncology Nursing Society, and Sigma Theta Tau International. His leadership role in the International Society of Nurses in Genetics highlights his contribution to advancing genetic nursing practice and research.

Bo ZhangView profile

Associate Professor

• Seismic data processing
• Seismic interpretation
• Machine learning
• +3 more

Bo Zhang is an Associate Professor in the Department of Geological Sciences at the University of Alabama. He holds a PhD in Geophysics from the University of Oklahoma (2014), an MS from the Institute of Geology and Geophysics, Chinese Academy of Sciences (2009), and a BS from China University of Petroleum (2006). His research focuses on seismic data processing, interpretation, and machine learning applications in geophysics. Key areas include fault attribute estimation, seismic noise reduction, and reservoir characterization through integrated multidiscipline data analysis. He is affiliated with SEG and AAPG. Recent work emphasizes machine learning-driven solutions for seismic challenges, such as horizon extraction, first-arrival picking, and fault detection. His publications span high-impact journals like Geophysics , Interpretation , and IEEE Transactions . Collaborations include projects on shale brittleness evaluation, microseismic event analysis, and 3D seismic attribute integration. Ongoing research explores deep learning applications in groundwater modeling and self-supervised denoising techniques. Bo Zhang’s contributions bridge traditional seismic methods with modern AI tools, advancing exploration geology and resource evaluation. His workflow innovations, such as semi-automated horizon interpretation and VMD-based decomposition, enhance data fidelity and interpretation efficiency.

Sheng-I YangView profile

Assistant Professor

• Forest Biometrics
• Applied Statistics
• Forest Inventory
• +3 more

Sheng-I Yang is an Assistant Professor of Forest Biometrics at the University of Georgia. He specializes in quantitative methods for forest growth modeling, stand dynamics, and inventory systems. His work integrates applied statistics, remote sensing, and geospatial technologies to inform sustainable forest management. He currently coordinates IUFRO's 4.01.03 working party and serves as an Associate Editor for the Journal of Forestry and Forest Ecosystems . Education: B.S. Forestry, National Taiwan University (2013) M.S./Ph.D. Forest Biometrics & Statistics, Virginia Tech (2016-2019) Affiliations: Plantation Management Research Cooperative (PMRC) His research focuses on forest biometrics , including stem taper modeling, stand carrying capacity estimation, and tree allometry. Recent projects involve predicting Caribbean tree growth via machine learning and optimizing pine plantation management in Turkey. He develops novel statistical frameworks for integrating Sentinel satellite data with ground-based inventories. His 2023-2025 publications emphasize tropical forest modeling, bark volume quantification, and elk population dynamics collaborations. Major funders include USDA Forest Service and the NCASI Foundation. Awards: 2024 Early Career Achievements Award (Northeastern/Southern Mensurationists Conference) 2023 Early Career Investigator Award (Forests Journal) Teaching responsibilities include Timber Management and Statistical Software for Natural Resources . His lab actively collaborates with IUFRO networks to advance global forest mensuration standards.

Yuhang XuView profile

Associate Professor

• Functional data analysis
• Multivariate statistics
• Measurement error models
• +4 more

Yuhang Xu is an Associate Professor in the Department of Applied Statistics and Operations Research at Bowling Green State University (BGSU), part of the Schmidthorst College of Business. He holds a Ph.D. in Statistics from Iowa State University (2016) and previously served as an Assistant Professor at the University of Nebraska-Lincoln (2016–2019). His research focuses on functional data analysis, measurement error models, and collaborative projects in agriculture and biological sciences. Education: Ph.D. in Statistics, Iowa State University (2012–2016) M.S. in Statistics, East China Normal University (2007–2010) B.S. in Mathematics, East China University of Science and Technology (2003–2007) Research Interests: Dr. Xu specializes in statistical methodologies for complex data structures, including functional data analysis (FDA) and high-dimensional data. He applies these techniques to problems in plant phenotyping, genomics, and logistics. His work bridges theoretical statistics with practical applications in agriculture, environmental science, and business analytics. Awards: Alumni Undergraduate Teaching Award (2022) Outstanding Paper Award, The Plant Phenome Journal (2020) Advising & Grants: Collaborates with departments across disciplines on grants involving statistical modeling. Previously coordinated the MSAS graduate program (2022–2024) and currently co-coordinates Data Science programs. Labs/Teams: Engaged in interdisciplinary research teams focused on agricultural phenomics and business analytics through BGSU’s Center for Business Analytics.

Jalal AshayeriView profile

Professor

• Health Care Management
• Business Intelligence
• Operations Research
• +3 more

Dr. Jalal Ashayeri is a Professor of Quantitative Supply Chain Management at TIAS Business School, part of Utrecht University. Previously, he held a position in Tilburg University's School of Economics and Management (Department of Econometrics and Operations Research) until 2016. With over 35 years of experience, he focuses on advancing supply chain design, facility automation, and operational efficiency in manufacturing and service industries. His work bridges academic research with practical applications, addressing challenges like supply chain flexibility, quality management, and sustainable development. Research Interests : He specializes in Health Care Management, Business Intelligence, Operations Research, and Sustainable Supply Chains . Recent projects include optimizing pharmaceutical R&D portfolios, designing smart supply chain systems, and improving manufacturing line efficiency. His contributions span over 110 refereed articles, emphasizing robust optimization and closed-loop logistics. Professional Contributions : Dr. Ashayeri has led 120+ industry projects across manufacturing and service sectors. He actively engages in academic leadership, including designing MScBA specialization tracks at TIAS and maintaining collaborations with global research institutes. His expertise in Big Data analytics and disruptive innovation is highlighted in his articles on strategic decision-making and project management.

Hélène HALCONRUYView profile

Assistant Professor

• Probability Theory
• Mathematical Statistics
• Stochastic Analysis
• +4 more

Hélène Halconruy is an Assistant Professor at Télécom SudParis since September 2023. She previously served as a Lecturer at ESILV (2022–2023), Postdoctoral Researcher at the University of Luxembourg (2020–2022), and held roles as Pedagogical Manager and Lecturer at ESME Sudria (2013–2020). Her doctoral work (PhD 2020) focused on probability under Prof. Laurent Decreusefond at Télécom Paris. Research Interests : Probability theory and mathematical statistics, including stochastic analysis, Malliavin calculus for discrete-time processes, Stein's method applications, financial market models (e.g., Greeks computation, portfolio management), and non-parametric statistical inference (e.g., density estimation under shape constraints, robust methods). Recent Work Trends : Her articles address advanced topics like wavelet leaders distribution, privacy-aware parameter estimation, stochastic process generators, and jump-diffusion models. These contributions bridge theoretical probability with practical applications in finance and data science. Grants & Teams : Affiliated with the SAMOVAR research group at Télécom SudParis. No explicit grants or formal advising roles listed.