Find Academic Supervisors

• Agricultural Safety Engineering
• Occupational Health in Agriculture
• Grain Entrapment Prevention
• +3 more

Salah Fuad Issa serves as an Assistant Professor in the Department of Agricultural and Biological Engineering at the University of Illinois Urbana-Champaign, with affiliate appointments at the Center for Digital Agriculture and the National Center for Supercomputing Applications (NCSA). His research program addresses critical safety challenges in agricultural environments through engineering solutions and technology integration, focusing on high-fatality scenarios such as grain entrapment and tractor-related incidents. Dr. Issa's research interests center on agricultural safety engineering, with specialized expertise in grain handling safety, tractor driver protection systems, and robotics applications in farming. His fingerprint analysis reveals dominant themes including Grain Entrapment (100%), Entrapment (95%), and Fatality prevention (76%), reflecting his commitment to reducing occupational hazards in agriculture through systematic literature reviews and engineering interventions. Current projects emphasize confined space safety, extrication techniques, and data-driven risk assessment frameworks. Analysis of his 2025 publications shows a cohesive research trajectory targeting practical safety solutions. Key trends include AI-driven safety frameworks for tractor operations, nozzle effectiveness in grain handling, and robotics safety standards development. His work consistently bridges engineering principles with public health outcomes, demonstrating interdisciplinary collaboration across agricultural safety, human factors, and policy development domains. Dr. Issa maintains active research collaborations through the Center for Digital Agriculture and NCSA, contributing computational expertise to agricultural safety initiatives. While specific student mentorship details are unavailable in the profile, his publication record indicates supervision of graduate-level research in agricultural engineering safety systems.

Olga MulaView profile

Researcher

• Optimal Transport
• Wasserstein Spaces
• Model Reduction
• +4 more

Olga Mula is a researcher at Eindhoven University of Technology (TU Eindhoven) in the Netherlands specializing in optimal transport theory, Wasserstein spaces, and model reduction techniques for partial differential equations. Her work bridges theoretical mathematics with practical applications in state estimation and inverse problems. Her research interests focus on Optimal Transport , Wasserstein Spaces , Model Reduction , and Numerical Analysis of PDEs . She develops algorithms for state estimation in metric spaces, particularly focusing on the Wasserstein space of probability measures. Her work includes developing reduced models using barycentric approximation, analyzing convergence properties, and addressing challenges in sensor placement for optimal data acquisition. Her recent publications demonstrate significant contributions to understanding how to build efficient reduced models in Wasserstein spaces for both forward and inverse problems. She has developed theoretical frameworks for state estimation algorithms, analyzed their performance in terms of Kolmogorov widths, and created practical implementations using sparse Wasserstein barycenters. Her work spans pure mathematical theory to applications in image processing and PDE-constrained optimization. Her scientific contributions include: Development of piecewise-affine algorithms for state estimation Nonlinear model reduction on metric spaces for conservative PDEs Sparse approximation using Wasserstein barycenters Applications to shape reconstruction and line completion Theoretical analysis of approximation rates in Wasserstein spaces She collaborates extensively with leading researchers in the field including Cohen, Dahmen, Feydy, and Rai. Her work demonstrates both theoretical depth and practical relevance, connecting abstract mathematical concepts to real-world problems in data assimilation and inverse modeling.

Gabriel PeyréView profile

Research Professor

• Optimal Transport
• Machine Learning
• Data Science
• +4 more

Gabriel Peyré is a CNRS Research Professor at the Department of Mathematics and Applications (DMA) of École normale supérieure (ENS) in Paris, France. A specialist in data science and artificial intelligence, he is renowned for his work on optimal transportation theory and its applications to imaging, machine learning, and neural network training. His research bridges mathematical theory with computational algorithm design, earning him the CNRS Silver Medal (2021) and multiple European Research Council (ERC) grants, including the 2024 Advanced Grant. Research interests include: Optimal Transport Machine Learning AI Theory Image Processing Computational Mathematics Neural Network Training His recent publications focus on advancing optimal transport methods in AI, with applications in neural network learning, spatial transcriptomics, and unsupervised data analysis. He has developed algorithms for large-scale optimal transport computations and contributed to theoretical understanding of transformer models and residual networks. Scientific awards: CNRS Silver Medal (2021) ERC Advanced Grant (2024) ERC Consolidator Grant (2016) ERC Starting Grant (2011) Blaise-Pascal Prize from the Academy of Sciences (2017) He supervises PhD students and postdoctoral researchers, including Raphaël Barboni, Valérie Castin, and Geert-Jan Huizing. His work involves collaborations with institutions like INRIA, MIT, and Heriot-Watt University. Peyré's affiliations include the Center for Data Sciences at ENS, where he contributes to interdisciplinary projects in biology and physics.

• Electric Drives
• Power Electronics
• Electric Vehicles
• +7 more

Carlo Concari is an Associate Professor in the Department of Engineering and Architecture at the University of Parma, where he actively contributes to teaching and research in electrical and electronic engineering, particularly in the domains of electric drives, automotive systems, and sustainable mobility. He teaches core courses such as Electric Drives , Electrotechnics , and Dynamics and Compliant Design of Road Vehicles across both Bachelor’s and Master’s programs. His research focuses on enhancing the performance and efficiency of electric and hybrid vehicles through advanced control strategies, energy management, and innovative powertrain designs. Key areas include multi-actuated electric vehicles, active vibration control in motors, and hybrid energy storage systems. His recent publications highlight collaborative work on optimizing high-performance hybrid powertrains and improving industrial communication for power electronics. The trends in his research articles reflect a strong emphasis on sustainable urban mobility, energy efficiency, and electromechanical integration. His work bridges theoretical modeling with practical implementation in automotive and industrial applications. While no specific scientific awards are mentioned in the available data, his consistent publication record in recent years demonstrates active engagement in impactful research. Prof. Concari advises students in electronic and automotive engineering programs and contributes to curriculum development in electric vehicle technologies. He is involved in research projects related to electric drivetrains, hybrid systems, and industrial automation, as evidenced by his co-authored technical papers. He is part of research teams focused on advanced automotive electronic systems and power electronics applications, particularly within the context of intelligent and sustainable vehicles. These efforts are aligned with broader initiatives in green transportation and smart energy use in mobility.

Ruhai WangView profile

Professor

• Satellite Networks
• Delay-Tolerant Networks
• Space Communications
• +3 more

Dr. Ruhai Wang is a Professor in the Phillip M. Drayer Department of Electrical and Computer Engineering at Lamar University. He holds a Ph.D. in Electrical/Computer Engineering from New Mexico State University and leads research in space communication networks. His office is located in the Cherry Building (Rm. 2205), and he can be contacted at rwang@lamar.edu or (409) 880-1829. Research Focus: Dr. Wang specializes in Delay-/Disruption-Tolerant Networks (DTN), satellite/space communications, cybersecurity, and wireless ad hoc networks. His work bridges theoretical modeling and practical implementations for space Internet systems, with ongoing projects focused on DTN protocol development for NASA collaborations. Publication Trends: His 15 most recent publications (2016-2020) primarily analyze protocol optimization for space communications, with emphases on Licklider Transmission Protocol (LTP), Bundle Protocol efficiency, deep-space channel modeling, and integrated terrestrial-satellite networks. Key themes include disruption resilience, transmission timing optimization, and network scalability under asymmetric conditions. Awards and Honors: Distinguished Lecturer, IEEE AESS (2025) Teaching Board Member, Ph.D. Program in STIET, University of Genova (2018-Present) Best Associate Editor, IEEE Aerospace & Electronics Systems Magazine (2015) Lamar University Presidential Faculty Fellowship (2015) IEEE ComSoc Best Tutorial Paper Nominee (2009) University Merit Award (2007) Advising and Projects: Currently mentoring doctoral candidates Alaa Sabbagh and Hacer Varol, and MSEE students Qinglin Xie and Arkun Zhuang. His primary project involves developing DTN protocols for space networks through a NASA/JPL-collaborated testbed. Grant-supported research focuses on reliable data delivery in deep-space environments. Professional Service: Senior IEEE member (AESS, ComSoc), Associate Editor for IEEE Transactions on Aerospace and Electronic Systems and IEEE Aerospace & Electronics Systems Magazine. Former TPC co-chair for IEEE ICC and organizer of international workshops on satellite communications.

Müjdat ÇetinView profile

Professor

• Signal Processing
• Image Processing
• Data Science
• +12 more

Müjdat Çetin is a Professor of Electrical and Computer Engineering and serves as the Robin and Tim Wentworth Director of the Goergen Institute for Data Science and Director of the New York State Center of Excellence in Data Science at the University of Rochester. He previously held faculty positions at Sabancı University and was a Research Scientist at MIT, with visiting roles at Boston University, Northeastern University, and MIT. Education: PhD in Electrical Engineering, Boston University, 2001 MS in Electrical Engineering, University of Salford, 1995 BS in Electrical Engineering, Boğaziçi University, 1993 His research lies at the intersection of signal processing, machine learning, and data science, with applications in biomedical imaging, radar, and brain-computer interfaces. He develops probabilistic and deep learning models for robust information extraction from noisy and complex data. His work emphasizes computational imaging, sparse representations, and multimodal data fusion. The recent publications reflect a strong trend toward integrating Bayesian methods and deep learning in imaging sciences, particularly in medical image reconstruction, neuroimaging analysis, and radar systems. His group actively explores transformer architectures, federated learning, and model-based deep learning for solving inverse problems in imaging. Scientific Awards and Honors: IEEE Fellow IEEE Signal Processing Society Best Paper Award IET Radar, Sonar and Navigation Premium Award Elsevier Signal Processing Best Paper Award Turkish Academy of Sciences Distinguished Young Scientist Award (GEBİP) ODTÜ Mustafa Parlar Foundation Research Incentive Award TÜBİTAK Career Award Boston University Best Engineering Research Award Professor Cetin has advised numerous PhD and Master’s students and led significant research grants in data science and imaging. He has served as a Senior Area Editor for IEEE Transactions on Image Processing and IEEE Transactions on Computational Imaging, and held editorial roles in several top journals. He has chaired major conferences including ICASSP, ICIP, and IVMSP workshops. He leads a multidisciplinary research group focused on data science and imaging, collaborating with neuroscientists and medical researchers. The team develops novel algorithms for brain-computer interfaces, medical image analysis, and remote sensing systems, often integrating machine learning with physical models of data acquisition.

Michael MumaView profile

Professor

• Robust Data Science
• Signal Processing
• Machine Learning
• +5 more

Michael Muma is a Professor in the Department of Electrical Engineering and Information Technology at Technische Universität Darmstadt. His research focuses on robust data science theory and methods applied to signal processing and machine learning in biomedicine and engineering. He leads the ERC Starting Grant ScReeningData project, developing methods for reproducible information discovery in biomedical databases, and is a Principal Investigator in the LOEWE center emergenCITY and BMBF cluster curATime. Prior roles include Independent Junior Research Group Leader (Athene Young Investigator) and Lecturer at TU Darmstadt from 2017 to 2022, and Research Associate (Post-Doc since 2014) from 2009 to 2017. His research interests span robust statistical methods, high-dimensional data analysis, emergency response systems, and biomedical signal processing. Notable projects include FDR-controlled portfolio optimization, ECG delineation algorithms, and radar-based vital sign estimation. Muma has contributed to distributed sensor networks, robust clustering, and sparse regression techniques. His work addresses challenges in multi-source detection, financial data analysis, and genomics through interdisciplinary approaches combining signal processing, machine learning, and robust statistics. Recent publications emphasize scalable solutions for high-dimensional problems, including applications in robotics, cardiology, and financial index tracking.

• Numerical Analysis
• Iterative Methods
• Nonlinear Equations
• +7 more

Vicente Fco Candela Pomares is an Associate Professor in the Department of Mathematics at the Faculty of Mathematics, University of Valencia, Spain. His academic career has been centered on numerical analysis and computational mathematics, with a focus on iterative methods for nonlinear equations and multiresolution techniques. His research interests lie primarily in Numerical Analysis , especially iterative root-finding methods such as Halley, Chebyshev, and Steffensen-type algorithms. He has contributed significantly to the convergence analysis of these methods, particularly in Banach spaces and for ill-conditioned problems. His work extends to multiresolution analysis , wavelets , and image restoration , where he applies fractional regularization and nonlinear approximation frameworks. The trends in his recent publications show a sustained focus on derivative-free iterative methods , convergence theory , and applications in image processing . His work often bridges theoretical numerical analysis with practical computational challenges. He earned his PhD from the University of Valencia in 1988 under the supervision of Dr. Antonio Marquina Vila, with a thesis on a priori error estimators for iterative methods. He has collaborated extensively with researchers including Sergio Amat, Sonia Busquier, and Rosa Peris. Notable co-authors include Pantaleón D. Romero and Francesc Aràndiga. His publications appear in high-quality journals such as Journal of Computational and Applied Mathematics , Applied Mathematics and Computation , and SIAM journals. He is actively affiliated with the University of Valencia, as evidenced by his institutional email and ongoing publications. There is no indication of part-time status, retirement, or awards in the available data.

• Applied Mathematics
• Mathematical Modeling
• Multiphase Flow
• +8 more

Steinar Evje is a Professor of Applied Mathematics at the University of Stavanger, affiliated with the Faculty of Science and Technology and the Department of Energy and Petroleum Technology. His research bridges mathematical modeling with applications in petroleum engineering and cancer biology. His research interests include: Mathematical modeling of multiphase flow in porous media Conservation laws and PDEs Tumor cell migration and biomechanics Fluid-structure and cell-fluid interactions Data-driven discovery of PDEs Numerical analysis and scientific computing His recent publications (2023–2024) show a strong trend in combining machine learning with physical modeling, particularly in learning flux and diffusion functions from data, solving conservation laws using graph neural networks, and modeling tumor progression under fluid pressure. These works span disciplines from petroleum engineering to computational oncology, reflecting interdisciplinary impact. Scientific awards are not mentioned in the provided text. He actively collaborates with researchers in both engineering and biomedical fields. While specific students are not listed, his extensive publication record suggests mentorship of graduate students and postdocs. He has contributed to modeling in oil recovery, well control, and tumor microenvironment dynamics, with no mention of external grants but evident sustained research output. He is involved in modeling efforts related to: Two-phase and three-phase flow in porous media Cell migration under interstitial fluid flow Spontaneous imbibition and wettability alteration Gas migration in leaking wells Low salinity flooding in oil recovery

Shuchin AeronView profile

Associate Professor

• Data Science
• Statistical Signal Processing
• Inverse Problems
• +8 more

Shuchin Aeron is an Associate Professor in the Department of Electrical and Computer Engineering at Tufts School of Engineering, with joint appointments in the Departments of Computer Science and Mathematics. He holds a Ph.D. from Boston University (2009) and completed postdoctoral research at Schlumberger Doll Research, focusing on borehole acoustic signal processing. His research spans statistical signal processing, machine learning, compressed sensing, and information theory, with applications in geophysics, bioengineering, and imaging. Aeron has authored over 175 publications and holds patents in acoustic signal processing. He received the NSF CAREER Award (2016) and is a Senior Member of the IEEE. Educations: Ph.D., Electrical Engineering, Boston University, 2009 M.S., Electrical Engineering, Boston University, 2004 B.Tech., Indian Institute of Technology, 2002 Research Interests: Statistical signal processing (SSP), inverse problems, compressed sensing, information theory, convex optimization Machine learning applications in geophysical signal processing, imaging, and bioengineering His work emphasizes optimal sampling and recovery of multidimensional signals, with contributions to compressed sensing architectures and generative models for particle physics experiments. He leads NSF-funded projects on data science and domain generalization, and collaborates with industry partners like Schlumberger and Mitsubishi Electric Research Labs. Awards: NSF CAREER Award (2016) Mitsubishi Electric Research Lab Research Gift (2015) Grants and Funding: NSF HDR TRIPODS (2019–2023) AFOSR: Enabling Trusted Human-Like Artificial Teammates (2018–2023) NSF: Optimal Sampling and Recovery for Multilinear Signals (2013–2016) Aeron teaches advanced courses in probabilistic systems analysis, information theory, and machine learning. He directs the Tufts Data Science undergraduate and graduate programs, and serves on editorial boards of journals including Frontiers in Signal Processing and IEEE Transactions on Geoscience and Remote Sensing .

Abraham EngedaView profile

Professor

• Supercritical CO2 Power Generation
• Turbomachinery Design and Testing
• Centrifugal Compressors
• +5 more

Abraham Engeda is a Professor in the Department of Mechanical Engineering at Michigan State University's College of Engineering. His research focuses on supercritical CO2 power generation, turbomachinery design, and low-head turbine systems. He holds a Ph.D. from the University of Hannover (1987), M.Sc. from the University of Manchester (1974), and B.Sc. from the University of Brighton (1973). Research Interests: Experimental and numerical analysis of centrifugal compressors and radial turbines Design methodologies for low-head hydraulic turbines CFD prediction and optimization of turbomachinery components Integration of solar-biogas hybrid systems for power generation Awards: Fellow, American Society of Mechanical Engineers ASME Fluid Machinery Award & Medal Fulbright Senior Global Award Labs/Teams: Co-leads the Turbomachinery Laboratory, focused on centrifugal compressor design and testing. Facilities include industrial-scale test stands, CFD workstations, and advanced data acquisition systems. Collaborates with industry partners like Elliott Company and NASA. Grants: Supported by U.S. Department of Energy, NASA, and private sector funding.

Erik ElmrothView profile

Professor

• Distributed Systems
• Cloud Computing
• Edge Computing
• +3 more

Erik Elmroth is a Professor at the Department of Computing Science, Umeå University. He leads research in distributed systems, cloud/edge computing, and autonomous resource management, directing a 30+ member research group. His leadership includes transformative roles as department head (2009-2021) and Deputy Director at High Performance Computing Center North (HPC2N). Elmroth serves on executive committees for the SEK 6.2B Wallenberg AI program (WASP) and SEK 390M eSSENCE initiative, and leads multiple Kempe Foundation projects. Research interests center on: Autonomous control of cloud/edge infrastructures Software-defined systems and federated clouds AI-driven resource optimization High-performance computing architectures Robust machine learning for distributed environments His publications emphasize adaptive cloud systems, anomaly detection, and federated learning, with consistent focus on scalability and resilience in edge/cloud deployments. Awards and honors: Member of Royal Swedish Academy of Engineering Sciences (IVA) Nordea Scientific Prize (2011) SIAM Linear Algebra Prize (2000) National HPC Lecturer appointment He has supervised 40+ PhD students and secured major grants including the Swedish Research Council's second-largest award for the Cloud Control project. Elmroth founded the Control Workshops series and co-founded Elastisys AB, a cloud security firm with 50+ employees recognized as Umeå's Spin-off Company of the Year.

Dustin RichmondView profile

Assistant Professor

• Computer Architecture
• FPGA
• RISC-V
• +4 more

Dustin Richmond is an Assistant Professor in the Department of Computer Science and Engineering at the Baskin School of Engineering, University of California, Santa Cruz. His work focuses on secure, usable hardware systems with applications in FPGA acceleration, RISC-V architectures, and side-channel analysis. Email: drichmond@ucsc Office: Engineering 2, Room 221 Research Interests: Secure hardware systems FPGA-based computing Manycore processors High-level synthesis Side-channel vulnerabilities Notable Article Trends: Recent publications emphasize cloud FPGA security, manycore design optimization, and hardware security. Earlier works focus on RISC-V acceleration, OpenCL compiler enhancements, and heterogeneous computing systems. GitHub Contributions: Maintains open-source projects like RISC-V-On-PYNQ and PYNQ-HLS, addressing FPGA programming challenges and RISC-V integration. Active in resolving community issues related to toolchain compatibility and hardware-software interfaces.

Deanna NeedellView profile

Professor

• Numerical Linear Algebra
• Machine Learning
• Signal Processing
• +7 more

Deanna Needell is a Professor of Mathematics at UCLA and holds the Dunn Family Endowed Chair in Data Theory. She serves as Executive Director of the Institute for Digital Research and Education (IDRE). Her work spans numerical linear algebra, machine learning, and signal processing with applications in healthcare, criminal justice, and community projects. Her research interests focus on stochastic iterative methods , compressed sensing , and optimization algorithms , with significant contributions to data completion, deep learning theory, and nonlinear signal recovery. She emphasizes inclusive research practices and community-engaged applications. Awards: Dunn Family Endowed Chair in Data Theory Needell has mentored over 35 postdocs, PhD students, and REU participants since 2012. Her leadership in UCLA's Applied Mathematics REU program has produced projects on AI for Lyme Disease, Holocaust Studies, and Criminal Justice. She maintains an inclusive workspace valuing diversity in race, gender, health status, and disability.

Irena OrovicView profile

Researcher

• Compressive Sensing
• Sparse Signal Processing
• Signal Reconstruction
• +3 more

IRENA OROVIC is a researcher affiliated with COPELABS (Cognitive and People-centric Computing), focusing on signal processing and sparse signal reconstruction. She actively contributes to advanced research in compressive sensing, time-frequency analysis, and machine learning applications. Research Interests: Her work spans Compressive Sensing Sparse Signal Processing Signal Reconstruction Time-Frequency Analysis Watermarking Machine Learning Publications: Recent research outputs include advancements in adaptive compressive sensing, Hopfield network optimization, and Hermite transform applications, reflecting her expertise in sparse signal analysis and multidisciplinary computational methods.