Find Academic Supervisors

• Light Scattering
• Radiative Transfer
• Optical Modeling
• +3 more

Per Edström is a Professor at the Department of Engineering, Mathematics and Science Education (IMD) within Mid Sweden University . His research focuses on mathematical modeling of light scattering in paper and print , particularly addressing challenges in optical interactions and radiative transfer . Education: Doctoral thesis (2007) and Licentiate thesis (2004) on light scattering in paper, both from Mid Sweden University. Research Interests: Edström’s work spans radiative transfer theory , fluorescence modeling , and computational methods for simulating optical properties in turbid media. He has developed advanced models like DORT2002 to improve accuracy in paper industry applications , focusing on phenomena such as dot gain , goniochromism , and edge effects in halftone printing . Recent Publications: His studies (2013–2015) explore Monte Carlo simulations , particle dynamics , and high-speed nanofilm deposition , contributing to fields like optical measurement techniques and color prediction models . Labs and Teams: Affiliated with the FSCN (Fibre Science and Communication Network) at Mid Sweden University, he collaborates on projects involving microroughness analysis , inverse radiative transfer problems , and open-source simulation platforms .

• Colloid Science
• Biophysics
• Nanotechnology
• +5 more

Peter van Oostrum is a Privatdozent (Priv.Doz.Dr.) at the Institute of Colloid and Biointerface Science , Department of Biotechnology and Food Science, University of Natural Resources and Life Sciences Vienna (BOKU). His research integrates advanced optical techniques and biophysical modeling to study colloidal systems. Projects : Led key initiatives like AI-supported Holographic Environmental Water Monitoring (2025–2029) and Self-folding particle chains (2015–2020), funded by Austrian Science Fund, City of Vienna, and European Commission. Research Focus : Self-assembly of colloids, holographic microscopy, microfluidics, and force-modulated multivalent binding in bacterial systems. His work bridges nanotechnology, biophysics, and environmental monitoring. Publications : 32 peer-reviewed works, emphasizing colloidal interactions, inverse patchy particles, and holographic cytometry applications. Community Services : Active reviewer for journals like Physical Review Letters , Chemistry of Materials , and Soft Matter . Theses Supervised : Guided Bernhard Pichler’s diploma thesis on polymer-functionalized nanopores (2020).

Iftach HaitnerView profile

Professor

• Cryptography
• Computational Complexity
• Secure Computation
• +3 more

Iftach Haitner is a Professor at Tel Aviv University's School of Computer Science, currently on leave while serving as Principal Researcher at the Stellar Development Foundation. His primary academic affiliation remains with Tel Aviv University where he maintains an active research group and supervises multiple PhD students. His educational background includes a PhD (2008) and Master's degree from the Weizmann Institute of Science under Omer Reingold and Oded Goldreich respectively, and undergraduate studies in Mathematics and Computer Science at Tel Aviv University. His research focuses on Cryptography and Computational Complexity , with significant contributions to coin-flipping protocols, one-way functions, differential privacy, and secure computation. Analysis of his 15 most recent publications reveals consistent focus on foundational cryptographic problems. His work demonstrates strong emphasis on computational entropy concepts (inaccessible entropy, next-block pseudoentropy), protocol security against adaptive adversaries, and tight complexity bounds for cryptographic primitives. The publications span top venues including STOC, FOCS, Crypto, and Eurocrypt, showing sustained high-impact research output. The Kadar Family Award for Outstanding Research (2018) Tel Aviv University Rector's Awards for Excellence in Teaching (2017, 2018) SIAM Outstanding Paper Prize (2011) Intel Israel award for outstanding PhD students (2008) Multiple best paper awards (CRYPTO 2006, ICALP 2006) Haitner has advised numerous PhD students including Noam Mazor, Jad Silbak, and Eliad Tsfadia. His research has been supported by multiple Israel Science Foundation grants (2011-2023), an ERC Starting Grant (2015-2020), and Blavatnik ICRC grants. He also maintains industry connections through roles at Coinbase (2022-2024) and previous consulting positions at Unbound Security and Team8. His professional service includes editorial work for SIAM Journal on Computing and program committee memberships for major cryptography conferences including Crypto, Eurocrypt, and TCC. He co-organizes the Greater Tel Aviv Area Cryptography Seminar and other specialized workshops.

Ru-Yu LaiView profile

Associate Professor

• Inverse Problems
• Partial Differential Equations
• Kinetic Theory
• +5 more

Ru-Yu Lai is an Associate Professor at the School of Mathematics, University of Minnesota-Twin Cities, specializing in inverse problems and partial differential equations. Their research bridges theoretical mathematics with applications in medical imaging, plasma physics, and optical tomography. Current affiliations include active roles in graduate student supervision and research leadership within the mathematics department. Dr. Lai obtained their PhD in 2015 from the University of Washington under Professor Gunther Uhlmann. Their educational background forms the foundation for their expertise in inverse problems and mathematical analysis. Research focuses on inverse problems for kinetic equations (Boltzmann equation, transport systems) and elliptic boundary value problems , with direct applications to Electrical impedance tomography (EIT) Hybrid medical imaging techniques Plasma physics and semiconductor modeling Photoacoustic imaging with nonlinear absorption Recent work emphasizes partial data reconstruction, nonlinear systems, and fractional calculus applications, demonstrating increasing mathematical sophistication in handling real-world imaging constraints. Recent publications (2021-2024) reveal strong trends in fractional PDEs, nonlinear transport equations, and magnetic Schrödinger systems. Over 60% of recent work focuses on medical imaging applications, particularly electrical impedance tomography and photoacoustic methods, while 30% addresses fundamental kinetic theory problems. The research consistently combines rigorous mathematical analysis with practical implementation considerations. Dr. Lai actively recruits graduate students for research positions, offering Summer Research Assistantships and travel support. Current projects involve developing novel mathematical frameworks for inverse problems in emerging imaging modalities, with emphasis on stability analysis and partial data reconstruction.

• Structural Health Monitoring
• Wave Propagation
• Structural Dynamics
• +9 more

Fabio Semperlotti is a Professor of Mechanical Engineering at Purdue University's School of Mechanical Engineering. His research focuses on advanced materials, structural health monitoring, wave propagation, and vibration control. He holds M.S. degrees in Aerospace and Astronautic Engineering from the University of Rome 'La Sapienza' (2000, 2002) and a Ph.D. from The Pennsylvania State University (2009). His work spans topics such as acoustic metamaterials, topological elastic systems, fractional-order elasticity, and machine learning applications in engineering. Recent contributions include studies on non-Abelian topological behavior in elastic waveguides and reinforcement learning frameworks for microelectronic component design. Semperlotti’s research also explores vibration attenuation via metastructures and deep learning-based inverse scattering solutions. Selected recent projects involve developing physics-informed neural networks for acoustic scattering, multimesh finite element methods for nonlocal elasticity, and geometric phase analysis in elastic systems. His work frequently bridges fundamental theory with practical applications in smart materials, structural optimization, and energy harvesting.

Mrinal K SenView profile

Professor

• Seismic Wave Propagation
• Geophysical Inverse Problems
• Seismic Anisotropy
• +7 more

Mrinal K Sen is a Professor and Morgan J. Davis Centennial Chair in Geosciences at the University of Texas at Austin's Jackson School of Geosciences. He leads the Sen Research Group and holds affiliations with the Department of Earth and Planetary Sciences and the Institute for Geophysics. His work focuses on seismic wave propagation, anisotropy, and geophysical inverse problems, with applications in exploration seismology, reservoir characterization, and whole-Earth seismology. Sen pioneered methods combining Bayesian statistics, machine learning, and advanced numerical techniques to address uncertainty quantification and improve seismic imaging. Education: Ph.D., University of Hawaii at Manoa M.S., Indian School of Mines (now IIT) B.S., Indian School of Mines (now IIT) Research Interests: Seismic Inversion and FWI Bayesian Methods and Uncertainty Analysis Machine Learning in Geophysics Anisotropy and Wave Propagation Reservoir and CO2 Storage Modeling Recent work emphasizes hybrid quantum-neural networks for image generation, trans-dimensional MCMC for FWI, and physics-guided deep learning. His articles highlight advancements in stochastic inversion, fluid saturation modeling, and ambient noise monitoring for groundwater studies. Labs/Teams: Director of the Sen Research Group and former Interim Director (2017-2018) and Associate Director (2016-2020) of the UT Institute for Geophysics.

Arvind KrishnaView profile

Assistant Professor

• Data Science Applications
• Educational Technology
• Statistical Methodologies
• +3 more

Arvind Krishna is an Assistant Professor of Instruction in the Department of Statistics and Data Science at Northwestern University’s Weinberg College of Arts & Sciences. He serves as the advisor for Data Science Majors and Minors. His teaching philosophy centers on student motivation, engagement, and feedback, employing modern examples and active learning methods both inside and outside the classroom. He holds a PhD from Georgia Institute of Technology (2021). Research Interests : Applying data science to real-world problems, with a current focus on education. His work bridges statistical methodologies and practical challenges in educational technology and pedagogical innovation. Recent Research Contributions : Recent publications span adaptive materials design optimization, robust experimental modeling, and novel clustering techniques. His work integrates statistical rigor with interdisciplinary applications in acoustics and materials science. Affiliations : Office located at 2010 Sheridan Rd, Room 208. Contact via krish@northwestern.edu or visit his departmental website.

• Causal Inference
• Machine Learning
• Nonparametric Methods
• +3 more

Edward H. Kennedy is an Associate Professor in the Department of Statistics & Data Science at Carnegie Mellon University (CMU), affiliated with the Dietrich College of Humanities and Social Sciences. He joined CMU in 2016 as an Assistant Professor after earning his PhD in Biostatistics from the University of Pennsylvania, alongside an MA in Statistics from the Wharton School, an MS in Biostatistics from the University of Michigan, and a BA in Mathematics from the University of Pennsylvania. His research focuses on causal inference, missing data problems, nonparametric methods, and machine learning, with applications in criminal justice, health services, medicine, and public policy. Key areas include incremental treatment effects, counterfactual analysis, and semiparametric sensitivity frameworks. He has developed methodologies for causal clustering, functional average treatment effects, and robust population size estimation. Edward's work bridges theoretical statistics and practical applications, addressing challenges in high-dimensional and complex data. His contributions span methodological advancements in instrumental variables, doubly robust estimation, and counterfactual fairness in algorithmic decision-making. His research also extends to policy-relevant domains such as healthcare resource allocation, criminal justice reform, and pandemic response analysis. Edward is actively involved in interdisciplinary collaborations, mentoring students in statistical theory and applied data science. His lab emphasizes rigorous methodological innovation while addressing real-world societal challenges through data-driven approaches.

• Magnetic Particle Imaging (MPI)
• Tomographic Imaging
• Image Reconstruction
• +3 more

Prof. Tobias Knopp is a Professor of Experimental Biomedical Imaging at Hamburg University of Technology (TUHH) and the University Medical Center Hamburg-Eppendorf (UKE). He leads the Institute for Biomedical Imaging and serves as Editor-in-Chief of the International Journal on Magnetic Particle Imaging (IJMPI). His expertise spans tomographic imaging, image reconstruction, and signal processing with a focus on Magnetic Particle Imaging (MPI). Education: Diplom in Computer Science (University of Lübeck, 2007) PhD in Biomedical Imaging (University of Lübeck, 2010) awarded the Klee Prize (2011) Research Interests: Tomographic imaging methods, MPI system development, model-based reconstruction, and hardware optimization. His work emphasizes MPI applications in clinical imaging and tracer development. Awards: Klee Prize from DGBMT (2011) for groundbreaking MPI research. Key Contributions: Pioneered the first commercial MPI system, developed open-source reconstruction frameworks (e.g., MRIReco.jl), and advanced MPI for real-time clinical applications. His research bridges theoretical models with practical hardware implementations.

Jelena SchmalzView profile

Lecturer

• Applied Mathematics
• X-ray Imaging
• Tomography
• +2 more

Jelena Schmalz is a Lecturer in Mathematics at the School of Science and Technology, University of New England. She holds an M.Sc. in Mathematics from Moscow State Lomonossov University and a Ph.D. in Physics from UNE. Her background includes extensive teaching experience in first-year mathematics, number theory, and cryptography courses. Dr. Schmalz's research focuses on applied mathematics with applications to X-ray imaging, tomography, sport science, cryptography, and tertiary education. Her doctoral work investigated direct and inverse problems in X-ray phase-contrast imaging. Her publications demonstrate consistent focus on mathematical applications and educational innovation, with recent work exploring mathematical education during COVID-19 and security protocols in banking systems. Earlier research established foundations in X-ray physics and tomography techniques.

Brady CoxView profile

Professor

• Geotechnical Earthquake Engineering
• Subsurface Imaging
• Soil Dynamics
• +2 more

Dr. Brady Cox is a Professor in Civil and Environmental Engineering at Utah State University, with previous appointments at University of Texas and University of Arkansas. He holds a Ph.D. in Geotechnical Engineering from University of Texas (2006). Research specializes in geotechnical earthquake engineering, subsurface imaging, and in-situ site characterization for seismic design. Teaching includes foundation design, soil mechanics, soil/rock dynamics, and geotechnical earthquake engineering. Research focuses on experimental field testing combined with computational analyses for subsurface imaging, with applications in nuclear facilities, transportation infrastructure, and earthquake reconnaissance. Major awards include Presidential Early Career Award for Scientists and Engineers (2012), ASTM Geotechnical Testing Journal Award (2019), and ISSMGE Young Researcher Award (2017) Directs field deployments for seismic characterization globally, including Ecuador, Haiti, Japan, New Zealand, Peru, and Turkey. Manages NHERI@UTexas facility with large-scale mobile shakers. Recent publications advance surface wave inversion methods, uncertainty quantification, and 3D ambient noise tomography.

Sarah J. HamiltonView profile

Associate Professor

• Inverse Problems
• Medical Imaging
• Electrical Impedance Tomography
• +3 more

Dr. Sarah J. Hamilton is an Associate Professor and Graduate Chair in the Department of Mathematical and Statistical Sciences at Marquette University. She specializes in inverse problems, nonlinear solution methods, and medical imaging, particularly in Electrical Impedance Tomography (EIT). Her research integrates deep learning and computational techniques to advance imaging technologies. Education: Holds a BA in Mathematics from Saint Michael's College and a Ph.D. in Mathematics from Colorado State University. Previously, she was a Postdoctoral Researcher at the University of Helsinki's Centre of Excellence in Inverse Problems Research. Research interests include developing algorithms for medical imaging systems like the ACE1 EIT system, which focuses on thoracic imaging applications. Her work emphasizes real-time imaging and model-based learning, leveraging graph neural networks and D-bar methods for improved diagnostic capabilities. Publications span experimental and theoretical advancements in EIT, including 3D reconstructions, domain-independent deep learning frameworks, and comparative studies of inversion methods. While no specific awards are listed, her contributions highlight innovation in medical imaging and computational mathematics. Advising focuses on training students in inverse problems and imaging sciences. She collaborates on research laboratories advancing EIT systems and computational imaging tools, contributing to interdisciplinary projects in biomedicine and engineering.

Scott KovaleskiView profile

Professor

• Electromagnetics
• Pulsed Power
• Nuclear Engineering
• +2 more

Scott Kovaleski is Professor of Electrical Engineering and Computer Science at the University of Missouri. He holds a Ph.D. and M.S.E. from the University of Michigan and a B.S. from Purdue University. His research develops charged particle sources, electromagnetic systems, and nanofabrication methods using pulsed power and computational techniques. Current projects focus on piezoelectric-driven particle accelerators, metamaterial design optimization, and carbon nanotube electron sources. His laboratory advances compact radiation sources and computational methods for electromagnetic simulations. Recent publications demonstrate growing integration of deep learning in optical metasurface design and electromagnetic modeling. Key themes include physics-informed neural networks for inverse design, nanofabrication techniques, and vacuum electronics applications. His federally funded projects include research in charged particle generation, electromagnetics simulation, and pulsed power systems. Laboratory capabilities include computational modeling and experimental validation of particle acceleration systems.

Alexandru TamasanView profile

Professor

• Inverse Problems
• Partial Differential Equations
• Computational Mathematics

Dr. Alexandru Tamasan is a Professor in the Department of Mathematics at the University of Central Florida. His research focuses on theoretical and computational aspects of inverse problems for partial differential equations. Research Interests: Specializes in inverse problems for partial differential equations, particularly reconstruction methods for radiative transport, ray transforms, and X-ray tomography. Recent work develops inversion techniques for momentum ray transforms, attenuated Doppler transforms, and radiative source reconstruction from partial boundary measurements. Publication Trends: Current research (2022-2024) emphasizes inversion formulas for tensor field tomography and momentum ray transforms. Earlier work established numerical methods for radiative source reconstruction (2020-2021) and characterization of transform ranges (2022). Techniques combine functional analysis, integral geometry, and numerical computation for solving ill-posed inverse problems. Teaching: Instructs courses in Partial Differential Equations and Complex Variables. Maintains active research program in mathematical methods for tomography and transport theory.

Dan EdidinView profile

Professor

• Algebraic Geometry
• Machine Learning
• Topology
• +1 more

Dr. Dan Edidin is Professor of Mathematics at the University of Missouri's College of Arts and Science, holding a Ph.D. from MIT (1991). His research spans algebraic geometry, machine learning, and topological methods in data science. Current investigations address phase retrieval problems over Lie groups, moduli space invariants, and orbit recovery algorithms. His work connects abstract algebraic geometry with applications in signal processing and cryo-electron microscopy reconstruction. Recent publications develop theoretical frameworks for signal recovery from invariant moments, K-theoretic analyses of moduli stacks, and sample complexity bounds for cryo-EM imaging. This interdisciplinary research bridges pure mathematics with computational imaging challenges.