Find Academic Supervisors

• Nonlinear Analysis
• Calculus of Variations
• Partial Differential Equations
• +5 more

Dr. Joan Duran Grimalt is an Associate Professor of Applied Mathematics in the Department of Mathematics and Computer Science at the University of the Balearic Islands (UIB). He serves as a member of the Mathematical Image Processing (TAMI) research group and the Institute of Applied Computing and Community Code (IAC3). From July 2021 to June 2024, he held the position of deputy director of the Higher Polytechnic School and head of studies for the Degree in Mathematics program. His academic career at UIB began in 2015 following the completion of his PhD. Dr. Duran Grimalt earned his academic credentials at prestigious institutions: BSc in Mathematics (2010) from the University of the Balearic Islands (UIB) MSc in Advanced Mathematics and Mathematical Engineering (2011) from the Polytechnic University of Catalonia PhD in Mathematics (2016) from UIB with thesis on variational models for ill-posed inverse problems in digital imaging His research spans the intersection of mathematical theory and practical applications in imaging science. Dr. Duran Grimalt specializes in nonlinear analysis, calculus of variations, partial differential equations, and deep unfolding architectures. His work focuses on developing mathematical frameworks that bridge traditional variational methods with modern deep learning approaches, particularly for image processing and computer vision applications. This hybrid methodology allows for both the interpretability of model-based approaches and the performance benefits of data-driven techniques. Analysis of his recent publications reveals a clear research trajectory toward integrating classical mathematical models with deep learning architectures, particularly through the technique of deep unfolding. His work consistently addresses challenging problems in satellite image processing, pansharpening, hypersharpening, and low-light image enhancement. The publications demonstrate a progression from purely variational approaches to increasingly sophisticated hybrid models that incorporate attention mechanisms, nonlocal operations, and specialized network architectures designed specifically for imaging problems. His notable scientific recognition includes: Fellowship from the Govern de les Illes Balears for PhD research (2011-2015) Dr. Duran Grimalt has secured research funding for multiple projects, leading two major initiatives. He has established significant international collaborations with the National Centre for Space Studies (CNES) in France, where he contributed to the image restoration chain for Earth observation satellites, and with the Oceanographic Centre of the Balearic Islands, focusing on deep unfolding architectures for remote sensing data fusion and marine object detection. His academic mentorship includes supervising PhD candidates M. Francesc Alcover (working on nonlocal theory for variational problems) and Daniel Torres (researching the combination of variational models and deep learning for image processing). He has also been a visiting researcher at leading institutions including the Technical University of Munich, ENS Paris-Saclay, and New York University. His research is conducted through the Mathematics, Imaging and Learning (MIA) Consolidated R+D+I Group, where he is an active member, and leverages resources from the Institute of Applied Computing and Community Code (IAC3). These research structures provide the computational infrastructure and collaborative environment necessary for his work on advanced image processing algorithms and their applications in satellite imaging and computer vision.

Ralf HielscherView profile

Professor

• Mathematical Imaging
• Crystallography
• Texture Analysis
• +7 more

Ralf Hielscher is a Professor at the Institute of Applied Analysis within the Faculty of Mathematics and Computer Science at the Technical University of Freiberg, Germany. His research lies at the intersection of applied mathematics, materials science, and imaging, with a strong focus on crystallographic texture analysis and electron backscatter diffraction (EBSD). He is a core developer and leading figure behind MTEX, a widely used open-source MATLAB toolbox for texture and orientation data analysis. Research Interests: His work centers on mathematical methods for analyzing crystallographic orientations, including spherical harmonic transforms, kernel density estimation on rotation groups, manifold-valued data processing, and inverse problems in tomography and texture reconstruction. He develops algorithms for parent grain reconstruction, orientation mapping, denoising, and visualization of microstructures. The recent publications reveal a consistent trend in advancing computational techniques for EBSD and texture analysis, particularly through the MTEX platform. His work bridges theoretical mathematics with practical materials characterization, enabling more accurate and efficient analysis of polycrystalline materials across geology, metallurgy, and engineering. Email: ralf.hielscher@math.tu-freiberg.de Scientific Contributions: While no formal awards are listed, his extensive publication record in high-impact journals such as SIAM Journal on Imaging Sciences , Journal of Applied Crystallography , and Inverse Problems underscores his significant contributions to the field. He has developed foundational algorithms now embedded in MTEX, which is used globally by researchers in materials science and geology. Teaching and Advising: He teaches courses such as Function Theory, Analysis 3, and Mathematics for Engineers. Although specific students are not mentioned, his leadership in MTEX and numerous collaborative publications suggest he mentors researchers and contributes to training the next generation of scientists in computational materials analysis. Labs and Teams: He is part of the team at the Institute of Applied Analysis and leads research efforts related to signal and image processing in crystallography. The MTEX project serves as a virtual research platform involving international collaborators in Germany, France, the UK, and beyond, facilitating open science in texture analysis.

Susanta GhoshView profile

Assistant Professor

• Bayesian Machine Learning
• Scientific Machine Learning
• Computational Solid Mechanics
• +8 more

Susanta Ghosh is an Assistant Professor in the Department of Mechanical and Aerospace Engineering at Michigan Technological University, where he is also a faculty member of the Center for Data Sciences at the Institute of Computing and Cybersystems. His research integrates Bayesian Machine Learning , Scientific Machine Learning , and Computational Mechanics to address uncertainty quantification in materials design, fracture modeling, and biomedical imaging. Education : Ph.D. and M.S. from Indian Institute of Science (IISc), Bangalore; B.S. from Indian Institute of Engineering Science and Technology, Shibpur. Research Expertise : Bayesian Neural Networks, Uncertainty Quantification, Computational Inverse Problems, Electronic Structure Prediction, and Nonlocal Elasticity. Funding : NSF CAREER Award (2025-2030, $669,490), DOE grants for chiral nanomaterials (2022-2025, $321,941; 2025-2028, $396,528), and NSF EAGER Award (2019-2022, $170,604). Teaching : Undergraduate and graduate courses in mechanics, finite element methods, and applied machine learning. Selected Awards : National Science Foundation (NSF) CAREER Award (2025)

• Face Recognition
• Biometrics
• Computer Vision
• +6 more

Anjith George is a researcher at École Polytechnique Fédérale de Lausanne (EPFL) in the School of Computer and Communication Sciences, working within the Biometrics Security and Privacy Laboratory. His research focuses on advancing face recognition systems with particular emphasis on security, efficiency, and cross-domain applications. He maintains a strong collaborative relationship with Professor Sébastien Marcel's research group at EPFL. George's research interests span multiple critical areas in modern biometrics including face recognition systems, face anti-spoofing techniques, heterogeneous face recognition across different modalities (such as visible to infrared), and efficient model deployment for edge devices. His work addresses fundamental challenges in biometric security by developing robust systems that can withstand presentation attacks while maintaining high accuracy across diverse conditions. He has made significant contributions to the field of synthetic data generation and utilization for improving face recognition systems, exploring how knowledge can be effectively transferred from synthetic to real-world domains. Analysis of George's recent publications reveals a clear research trajectory focused on solving practical challenges in face recognition. His work has evolved from fundamental eye tracking and gaze direction research in the early 2010s toward increasingly sophisticated face recognition systems addressing security vulnerabilities, efficiency constraints, and domain adaptation problems. A significant portion of his recent work explores the potential of synthetic data to overcome limitations in real-world training data, while also investigating how to bridge the gap between different face recognition modalities. His research demonstrates a strong emphasis on practical applications, particularly in resource-constrained environments where edge deployment is necessary. George has been actively involved in major biometrics competitions and challenges, including the FRCSyn Challenge and EFaR (Efficient Face Recognition) competition, contributing to community benchmarking efforts and advancing state-of-the-art solutions. His collaborative work spans multiple institutions globally, reflecting the international nature of biometrics research.

• Computer Vision
• Imaging
• Optimization Algorithms
• +3 more

Michael Möller is a Professor for Computer Vision at the University of Siegen, Germany. His research focuses on integrating model-based and learning-based techniques in imaging and vision, with an emphasis on efficient optimization algorithms for solving high-dimensional minimization problems. Research Interests: Combining classical model-based methods with deep learning Optimization algorithms for inverse problems Applications in CT reconstruction, image segmentation, and quantum computing Development of energy-dissipating neural networks and non-smooth regularization techniques Scientific Awards: Best Paper Award at ISWC 2021 Best Paper Honorable Mention at GCPR 2020 Best Paper Award at VMV 2015 Collaborations and Grants: Active collaborations with institutions like IEEE, Springer, and universities across Europe. Publications in top-tier venues such as NeurIPS, CVPR, ICCV, and ICLR. His work spans theoretical frameworks (e.g., convex relaxations, spectral methods) and practical applications (e.g., medical imaging, THz defect detection).

Tobias LasserView profile

Adjunct Professor

• Computational Imaging
• Inverse Problems in Tomographic Reconstruction
• X-ray Phase-Contrast and Dark-field Imaging
• +2 more

Prof. Dr. Tobias Lasser is an Adjunct Professor at the Technical University of Munich (TUM) since 2024, leading the Computational Imaging and Inverse Problems research group. He holds affiliations with the TUM School of Computation, Information and Technology and the Munich Institute of Biomedical Engineering. His academic career includes a PhD (2011) and habilitation (2017) in Computer Science from TUM, along with prior roles as a Postdoctoral Fellow and Akademischer Rat at TUM's Chair for Computer Aided Medical Procedures. His research focuses on computational imaging , inverse problems in tomography , and clinical decision support systems . Key areas include X-ray phase-contrast/dark-field imaging, light field microscopy, and multi-modal medical data analysis. Notable contributions include advancements in sparse-view CT reconstruction, artifact-free deconvolution techniques, and AI-driven diagnostic tools. Recent work emphasizes integrating deep learning with traditional imaging modalities, such as encoder-decoder architectures for anomaly detection and attention-based models for skin lesion classification. His team also explores robotic sample holders for advanced CT setups and open-source frameworks like elsa for tomographic reconstruction. Educations: Diplom-Informatiker (2006), Diplom-Mathematiker (2008), Dr. rer. nat. (2011, summa cum laude), Habilitation (2017) Awards: IEEE editorial award (2023), Best Poster (2021), Supervisory Excellence (2021), Teaching Award (2021) Labs/Teams: Munich Institute of Biomedical Engineering, Computational Imaging Group (TUM)

Edwin A. MarengoView profile

Associate Professor

• Electromagnetic Imaging
• Inverse Scattering
• Wavefield Imaging
• +14 more

Edwin A. Marengo is an Associate Professor in the Department of Electrical and Computer Engineering at Northeastern University, Boston, Massachusetts. He holds a Ph.D. in Electrical Engineering from Northeastern University (1997), where he is currently a tenured faculty member. His academic journey began with a B.S. in Electromechanical Engineering from the Technological University of Panama (1990), where he graduated as valedictorian and Summa Cum Laude. His research is centered on electromagnetic imaging, inverse scattering, physics-based signal processing, and information theory. He has made significant contributions to time-reversal imaging, compressive sensing, optical theorem applications, and radar signal processing. His work bridges theoretical electromagnetics with practical applications in secure communications, medical imaging, and distributed sensing. The trends in his recent publications reflect a strong focus on coherent change detection , scattering-based encryption , data-driven imaging , and the application of the optical theorem in radar and optical systems. His work increasingly integrates compressive sensing and nonlinear inverse methods to solve complex imaging problems in noisy and multipath environments. His scientific honors include: National Science Foundation (NSF) CAREER Award (2008) Fulbright Scholar (1992–1994) Technological University of Panama Most Distinguished Alumnus (2008) Elected Member of URSI Commission B (2006) Senior Member of IEEE and member of OSA, Phi Kappa Phi, Eta Kappa Nu, and Tau Beta Pi Dr. Marengo has advised students and supervised research at both Northeastern and the Technological University of Panama. He has served as an Associate Editor for IEEE Transactions on Computational Imaging and IEEE Transactions on Image Processing, and is currently on the editorial board of SpringerPlus. He has led major research grants from the NSF, AFOSR, and U.S. Army Research Laboratory. He is actively involved in professional service, including organizing conference sessions, reviewing for top journals, and mentoring student honor societies. He has also held visiting positions at Boston University (2013) and Tufts University (2020), and has been an invited speaker at institutions in the U.S., Panama, Italy, Sweden, Finland, Chile, and the U.K.

Knarik TunyanView profile

Visiting Assistant Professor

• Linear Algebra
• Numerical Analysis
• Linear Programming
• +11 more

Knarik Tunyan is a Visiting Assistant Professor in the Department of Mathematics/Computer Science at Purchase College, State University of New York, within the School of Natural and Social Sciences. She contributes to undergraduate education through courses in Linear Algebra, Calculus, Differential Equations, and advanced topics in mathematics. Education: PhD in Technology, Tampere University of Technology, Finland MS in Mathematics, Yerevan State University, Armenia Her research integrates applied mathematics with computational science, focusing on Linear Algebra, Numerical Analysis, Linear Programming, and Computational Inverse Problems . She explores mathematical methods in signal and image processing, optimization, network theory, economics, and biostatistics. Recently, she has been investigating the use of technology in mathematics education, including 3D printing and software tools like Mathematica and OpenSCAD. The 15 most recent publications highlight a consistent trajectory in matrix computations, optimization algorithms, and interdisciplinary applications . Early work centered on pivot rules and interior-point methods, particularly their behavior on challenging problems like the Klee-Minty cube. Later contributions expanded into image compression using QkRk factorization, Bayesian metabolic modeling, and educational innovations involving STEAM and 3D visualization. The articles reflect a blend of theoretical rigor and real-world application, spanning fields from computational biology to educational technology. Scientific Contributions and Recognition: Active presenter at international conferences including ICTCM and WCGO Published in reputable journals such as SIAM Journal on Matrix Analysis and Applications , Journal of Theoretical Biology , and Inverse Problems and Imaging Contributions to algorithmic efficiency in least squares and optimization She advises on curriculum development and student learning, particularly in integrating technology into mathematical instruction. Though no formal students are listed, she mentors through senior seminars and research-oriented courses. She has not received explicitly mentioned awards or grants in the provided text. Her work is supported by institutional resources and collaborative networks in computational mathematics and STEM education. Dr. Tunyan is involved in academic outreach and technological experimentation, especially through her presentations on 3D printing and mathematical software. She maintains regular office hours and engages students through digital platforms like Starfish, emphasizing accessibility and active learning.

• Inverse Problems
• Ill-Posed Problems
• Microwaves
• +4 more

Mlkhail Klibanov is a Professor in the Department of Mathematics and Statistics at the University of North Carolina at Charlotte , where he has served since 1990. His research sits at the intersection of pure mathematics and applied science, focusing on inverse problems for PDEs , ill-posed problems , and interdisciplinary applications in microwaves and nano-science . Dr. Klibanov's work revolutionized the field through his introduction of Carleman estimates (1981), enabling global uniqueness and stability theorems for multidimensional inverse problems. He later developed globally convergent numerical methods —including the convexification method —which solve inverse problems without prior knowledge of solution neighborhoods , unlike conventional approaches. His 2014-2016 work on phaseless inverse scattering resolved a 40-year-old open problem from Chadan and Sabatier's quantum scattering theory textbook, with applications to live cell imaging and nanotechnology . His publications demonstrate remarkable disciplinary reach, spanning mathematical finance (Black-Scholes equation), control theory (1991 stability analysis), and computational methods verified on experimental data. With over 8,800 Google Scholar citations and $3.3 million in federal grants , he ranks among the most highly funded mathematicians in the US. Scientific Recognition: Laudation in Journal of Inverse and Ill-Posed Problems (2022) celebrating his 70th birthday Malta Conference Medal (May 2022) for distinguished contributions Klein College of Science Excellence in Research Award (May 2025) for ongoing impact He serves on editorial boards of Communications in Analysis and Computations , Numerical Methods and Programming , and Journal of Applied and Industrial Mathematics .

Thomas HumphriesView profile

Associate Professor

• Tomographic Image Reconstruction
• Iterative Methods (CT/SPECT Imaging)
• Dynamic SPECT Imaging
• +5 more

Dr. Thomas Humphries is an Associate Professor in the Division of Engineering and Mathematics at the University of Washington Bothell since 2022. He earned his Ph.D. in Applied and Computational Mathematics from Simon Fraser University and holds a B.Math from the University of Waterloo. His research focuses on tomographic image reconstruction and mathematical optimization techniques. Ph.D., Applied and Computational Mathematics, Simon Fraser University (2011) M.Sc., Applied and Computational Mathematics, Simon Fraser University (2007) B.Math, Joint Honours Applied Math and Computer Science, University of Waterloo (2005) His work in Medical Imaging addresses challenges in CT and SPECT reconstruction, particularly for polyenergetic/sparse data. He also explores derivative-free optimization in oil field operations and has developed open-source MATLAB code for polyenergetic CT reconstruction available on GitHub. Recent publications focus on superiorization methodology and machine learning integration. Key research trends include iterative reconstruction algorithms, metal artifact reduction, dynamic SPECT imaging, and regularization techniques. No formal scientific awards are listed in the provided text. Dr. Humphries teaches mathematics courses including calculus, linear algebra, and numerical analysis. His professional journey includes postdoctoral work at Memorial University (2011-2013) and Oregon State University (2013-2015) before joining UW Bothell in 2015.

Melina FreitagView profile

Professor

• Numerical Linear Algebra
• Data Assimilation
• Inverse Problems
• +6 more

Melina Freitag is Professor for Data Assimilation at the Institute for Mathematics, University of Potsdam. Her research spans numerical linear algebra, inverse problems, and model order reduction, with applications in geophysics, image processing, and machine learning. She leads the Data Assimilation Group and contributes to the SFB 1294 Collaborative Research Center. Education : Prof. Freitag earned her Diplom in Mathematics at TU Chemnitz (2004) and PhD in Mathematical Sciences from University of Bath (2007). Research Themes : Her work focuses on Krylov subspace methods, low-rank approximations, and preconditioning for large-scale systems. She bridges classical numerical analysis with modern data assimilation, addressing challenges in: Bayesian inverse problems with unstable systems Optimized spectral sampling in X-ray imaging Physics-informed neural networks for Navier-Stokes inversion Parameter-dependent eigenvalue analysis Collaborations & Grants : She collaborates with institutions like KTH Stockholm and Arizona State University. Her group secures funding through SFB 1294 and participates in INI Cambridge networks. Leadership : Co-Chair of GAMM Activity Group on Applied Numerical Linear Algebra SIAM Activity Group on Linear Algebra Chair (2022) Teaching : Delivers courses on numerical optimization, matrix methods in data science, and inverse problems, integrating computational theory with real-world applications.

Julie A. JacksonView profile

Professor

• Radar Systems
• Synthetic Aperture Radar (SAR)
• Polarimetric SAR
• +7 more

Dr. Julie A. Jackson is a Professor of Electrical Engineering in the Department of Electrical and Computer Engineering at the Air Force Institute of Technology (AFIT), part of the Graduate School of Engineering and Management. She holds a Ph.D. in Electrical Engineering from The Ohio State University (2009), an M.S. (2004), and a B.S. (2002) from Wright State University. Her work bridges theoretical and applied radar systems, with emphasis on synthetic aperture radar, signal processing, and electromagnetics. Ph.D., Electrical Engineering, The Ohio State University, 2009 M.S., Electrical Engineering, The Ohio State University, 2004 B.S., Electrical Engineering, Wright State University, 2002 Dr. Jackson's research focuses on radar systems , particularly synthetic aperture radar (SAR) , compressive sensing , polarimetric imaging , and passive radar using commercial signals . She develops advanced signal processing algorithms for feature extraction, autofocus, and clutter modeling. Her work enables high-resolution imaging in challenging environments and has been applied to both military and civilian remote sensing. Her recent publications show a strong trend in exploiting sparsity and crosstalk in polarimetric SAR, autofocus algorithms using convex optimization, and passive radar using LTE and WiMAX signals. These efforts reflect a deep integration of electromagnetic theory, statistical signal processing, and computational imaging. Dr. Jackson has received numerous accolades, including: 2023 EN Distinguished Teaching Professor Award 2022 John L. McLucas Basic Research Award 2020 Air Force Outstanding Scientist/Engineer Award (Mid-Career) 2019 IEEE Fred Nathanson Memorial Radar Award 2019 Gage H. Crocker Outstanding Professor Award 2016 SOCHE Faculty Excellence Award She has advised multiple graduate students and led collaborative research with faculty and laboratory technicians at AFIT. Her work has been supported by internal Air Force research funding and has resulted in peer-reviewed publications, patents, and real-world radar applications. Dr. Jackson also holds a U.S. patent on polarization recovery in SAR systems, highlighting the translational impact of her research. She is actively involved in the IEEE Aerospace and Electronic Systems Society and contributes to the advancement of radar technology through publications, conferences, and mentorship. Her laboratory work emphasizes experimental validation of theoretical models, particularly in heterogeneous clutter environments and multistatic configurations.

Diego CaccavoView profile

Assistant Professor

• Mathematical Modeling
• Hydrogels
• Drug Delivery Systems
• +12 more

Diego Caccavo is a researcher at the Department of Industrial Engineering (DIIN) , University of Salerno , Italy. He holds a PhD in Industrial Engineering and has been actively contributing to research and teaching since 2017, transitioning from a research fellow to a permanent researcher in 2022. His academic work spans interdisciplinary domains at the intersection of chemical engineering, pharmaceutical sciences, and materials science. Education: He earned his Bachelor's degree with honors in Chemical Engineering from the University of Salerno in 2013, with part of his thesis work conducted at Chalmers University of Technology, Sweden. He completed his PhD in 2017 at the University of Salerno. Dr. Caccavo’s research is centered on hydrogel-based systems for biomedical and agro-food applications, with a strong emphasis on mathematical modeling of drug release mechanisms. His work explores the development of innovative nanoparticle production methods, such as coaxial jet mixers and microfluidic analogs, with a focus on liposome synthesis. He also investigates process control and optimization, particularly in wet granulation and rapid prototyping of pharmaceutical devices. His research integrates transport phenomena , biomaterials science , and process engineering to design advanced drug delivery systems. The analysis of his recent publications reveals a consistent focus on modeling hydrogel behavior , controlled drug release , and nanoparticle synthesis . His work often combines experimental data with computational models, particularly using population balance equations and viscoelastic frameworks. There is a strong trend toward translational research, evident in his spin-off ventures and patent filings. Topics such as in vitro stomach simulation, USP II testing, and pharmacokinetic modeling indicate a growing interest in bridging laboratory findings with clinical outcomes. Scientific Awards and Recognition: Recognized as a Top Scientist 2019–2020 (top 2% globally, PLOS Biology) Editorial Board Member of European Journal of Pharmaceutics and Biopharmaceutics (Elsevier) Active peer reviewer for multiple ISI-indexed journals Dr. Caccavo has supervised over 40 thesis projects in Chemical Engineering, including numerous master’s theses. He has contributed to research grants and collaborative projects, though specific grant names are not mentioned. His entrepreneurial activities include co-founding two spin-offs: Eng4Life S.r.L. and EST S.r.L. , which likely commercialize technologies in drug delivery and process instrumentation. He holds two Italian patent applications related to beverage alcohol measurement and gastric pH monitoring, highlighting his innovation in analytical and biomedical devices. He is actively involved in laboratory research, with affiliations to the T5a Laboratory (DIIN) and the Plant and Process Laboratory (DIFARMA) . These labs support his work in process development, formulation, and characterization of advanced materials.

Nathan JeongView profile

Associate Professor

• Artificial Intelligence
• Wireless Power Transfer
• Millimeter-Wave Antenna and Systems
• +9 more

Nathan Jeong is an Associate Professor in the Department of Electrical and Computer Engineering at The University of Alabama, College of Engineering. He leads the Intelligent Sensor and Wireless System Lab (ISWS), where he conducts cutting-edge research at the intersection of artificial intelligence, wireless systems, and sensor technologies. His work is supported by major federal grants, including a $3 million project from the Federal Transit Administration and the U.S. Department of Transportation for autonomous bus safety. Ph.D., Electrical and Computer Engineering, Purdue University, 2010 Visiting Scholar, Georgia Institute of Technology, 2010 M.S., Electrical and Electronic Engineering, Yonsei University, 2002 B.S., Radio Sciences and Engineering, Korea Maritime University, 2000 Dr. Jeong's research spans artificial intelligence, wireless power transfer, millimeter-wave systems, vehicle-to-everything (V2X) communication, adaptive RF front-ends, and biomedical electronics. His lab focuses on developing intelligent sensor systems for applications in healthcare, agriculture, transportation, and public safety. He integrates machine learning with electromagnetic and RF technologies to create innovative solutions for real-world challenges. The recent publications from Dr. Jeong and his team reflect a strong trend toward intelligent sensing systems using microwave and millimeter-wave technologies. These works span domains such as non-invasive food quality inspection, wearable biomechanical monitoring, autonomous vehicle safety, and UAV-based remote sensing. The integration of machine learning with electromagnetic wave-based sensing is a unifying theme, demonstrating a multidisciplinary approach to solving complex engineering problems. National Academy of Inventors Inductee Award Faculty and Staff Innovation Pitch Competition Award IEEE Distinguished Microwave Instructor Ambassador Alabama Society of Professional Engineers Graduate Student Engineer of the Year (advised student) Randall Outstanding Undergraduate Research Award (multiple students) Most Innovative Award, Crimson Startup Academy (student award) Dr. Jeong has successfully advised numerous graduate and undergraduate students, many of whom have gone on to win prestigious awards and publish high-impact research. He has secured significant external funding, including a $3 million federal grant for autonomous bus safety systems, demonstrating strong grant-writing capabilities and leadership in large-scale research initiatives. His work bridges industry and academia, leveraging over eleven years of industrial experience at Samsung, BlackBerry, and Qualcomm. The Intelligent Sensor and Wireless System Lab (ISWS) is a vibrant research group under Dr. Jeong’s leadership, actively engaged in projects involving AI-driven sensor networks, wireless power, V2X communication, and biomedical electronics. The lab fosters innovation through hands-on research and collaboration, welcoming motivated students at all levels.

Mathews JacobView profile

Professor

• Medical Imaging
• Machine Learning
• Image Reconstruction
• +5 more

Mathews Jacob is a Professor of Electrical and Computer Engineering at the University of Virginia’s School of Engineering and Applied Science, leading the Computational Biomedical Imaging Group (CBIG). His research focuses on image reconstruction, analysis, and quantification in MRI, particularly in ultrahigh-resolution brain MRI, cardiac/pulmonary MRI, and metabolic imaging. He holds a B.Tech from National Institute of Technology, M.E. from Indian Institute of Science, and Ph.D. from Swiss Federal Institute of Technology. Research Contributions: Dr. Jacob pioneered model-based deep learning frameworks like MoDL and MOL, advancing accelerated MRI techniques. His work bridges machine learning and medical imaging, with applications in dynamic speech MRI, motion-compensated cardiac imaging, and compressed sensing. Professional Roles: He serves as Associate Editor for IEEE Transactions on Medical Imaging and was General Chair of IEEE International Symposium on Biomedical Imaging (2020). A Fellow of IEEE (2022), he has received NSF CAREER (2009), ACS Research Scholar (2011), and Eminent Researcher (2024) awards. Grants & Projects: His NIH-funded projects include ultra-high-resolution multi-contrast MRI, free-breathing dynamic MRI, and MR spectroscopic imaging for Alzheimer’s research. Current grants emphasize computational MRI innovations. Labs & Impact: The CBIG lab develops cutting-edge imaging algorithms, collaborating on clinical applications like pulmonary hypertension detection via cardiac MRI radiomics. His work impacts radiology, oncology, and neurology.