Find Academic Supervisors

Wenjun ZhengView profile

Professor

• Computational Biophysics
• Structural Biology
• Molecular Simulation
• +3 more

Wenjun Zheng is a Professor in the Department of Physics at the University at Buffalo (College of Arts and Sciences). His research focuses on computational biophysics and structural biology, specifically investigating biomolecular functions via multi-scale modeling of molecular dynamics. He employs coarse-grained and atomistic models to study biomolecular 'nanomachines' like motor proteins, ion channels, and ryanodine receptors. His work integrates experimental data with computational models to predict molecular behaviors and guide future experiments. Education: BS in Electrical Engineering, Zhejiang University, China (1995) MS in Physics, Chinese Academy of Sciences (1998) PhD in Physics, Stanford University (2003) Research Interests: Zheng’s long-term goal is to computationally decrypt biomolecular dynamics, particularly in systems like TRPV1, NMDA receptors, and myosin. His methodologies include molecular dynamics simulations, elastic network models, and flexible fitting of experimental data (e.g., cryo-EM, SAXS). He emphasizes multi-scale approaches to balance accuracy and computational efficiency. Publications: Over 100 peer-reviewed articles span topics like ion channel gating, helicase mechanisms, and protein conformational transitions. Recent work explores allosteric modulation of NMDA receptors and cryptic ligand-binding sites prediction using machine learning. Awards: NSF CAREER Award (2010) Advising/Grants: Active in mentoring graduate students and securing funding for computational biophysics projects. Collaborates with experimentalists to validate and refine models. Labs/Teams: Leads a lab focused on computational structural biology, emphasizing interdisciplinary approaches to biomolecular systems.

Zafer DoganView profile

Assistant Professor

• Machine Learning
• Data Analytics
• Signal Processing
• +5 more

Zafer Dogan is an Assistant Professor in the Department of Electrical and Electronics Engineering at Koç University, where he leads the Machine Learning Information Processing (MLIP) research group and serves as Deputy Director of the Koç University İşbank AI (KUIS AI) Research Center. He holds a Ph.D. from École Polytechnique Fédérale de Lausanne (EPFL) and a B.Sc. from Middle East Technical University (METU). Before joining Koç University, he worked as a postdoctoral researcher at Harvard University's John A. Paulson School of Engineering and Applied Sciences (2016–2019). His research bridges theoretical advancements in machine learning, optimization, and signal processing with practical applications in high-dimensional data mining, AI-driven imaging (e.g., biomedical imaging), and inverse problem solutions. Key areas include generative models, image super-resolution, neural network dynamics, and domain-specific interpolation techniques. His work emphasizes trustworthy AI, perceptual-quality image enhancement, and biomedical applications like neuroimaging analysis. Notable contributions include developing the MMSR framework for class-specific image super-resolution, wavelet-domain losses for artifact reduction, and leveraging vision-language models for trustworthy super-resolution. His articles span domains from theoretical ML to applied biomedical imaging solutions. Dr. Dogan’s academic journey includes postdoctoral training at Harvard and a strong focus on interdisciplinary research at the intersection of engineering and AI. He currently leads initiatives at KUIS AI, advancing applied AI research and innovation.

• Consumer Behaviour
• Marketing Technology
• Pricing Strategies
• +7 more

Dr. Rhys Ashby is a Lecturer in Marketing at Swinburne University of Technology's Department of Management and Marketing within the School of Business, Law and Entrepreneurship. He holds a PhD from Macquarie University and multiple advanced degrees in finance and commerce. His research focuses on how buy-now-pay-later (BNPL) systems, pricing strategies, and marketing technologies influence consumer decision-making, with particular emphasis on financial behavior and judgment biases. Education background includes: PhD in Marketing, Macquarie University MRes in Marketing, Macquarie University MAppFin from Financial Services Institute of Australasia MCom and BSc(InfoSys) from UNSW Sydney Research interests span consumer behavior, marketing technology applications, pricing mechanisms, household finance literacy, and experimental research design. His work examines how contextual cues like installment options and payment modes affect consumer perceptions of value and spending patterns. Recent studies emphasize BNPL's role in shaping purchasing decisions and word-of-mouth dynamics. Key contributions include analyses published in the Journal of Retailing and presentations at ANZMAC and the Association for Consumer Research conferences. His work has been recognized with the Executive Dean's Commendation for Academic Excellence. Advisory services include PhD supervision and industry collaboration in product development, pricing strategies, and customer experience optimization. Active in Swinburne's Digital Capability and Innovative Society initiatives, he explores fintech innovations' societal impacts through Sustainable Development Goals 8 and 10.

Laleh AlisaraieView profile

Associate Professor

• Chemical Biology
• Computational Structural Biology
• Medicinal Chemistry
• +7 more

Dr. Laleh Alisaraie is an Associate Professor at the School of Pharmacy, Memorial University of Newfoundland, where she leads a research group focused on computational approaches in drug discovery and structural biology. Her interdisciplinary work bridges chemistry, biochemistry, biophysics, and pharmaceutical sciences, with a strong emphasis on understanding molecular mechanisms in disease and drug delivery. Her educational background includes a Dr. rer. nat. in Chemistry from Universität Paderborn, Germany, an M.Sc. in Organic Chemistry, and a B.Sc. in Applied Chemistry. She completed postdoctoral training in protein crystallography at McGill University and in computational chemical biology at the University of Alberta. Dr. Alisaraie's research interests center on chemical biology, computational structural biology, and pharmaceutical nanoscience. She investigates the structure and function of cytoskeletal motor proteins like dynein, the effects of chemotherapeutic agents on microtubules, and the development of biomimetic nanocarriers for targeted drug delivery. Her lab uses advanced computational tools such as molecular dynamics simulations, normal mode analysis, and structural bioinformatics to explore protein-ligand interactions and nanomaterial behavior. The trends in her recent publications highlight a strong focus on motor protein regulation, tubulin-targeting drugs, and functionalized carbon nanotubes for improved drug delivery. Her work integrates structural insights with therapeutic applications, particularly in cancer treatment and reducing drug toxicity. Recruitment of Dynein and Kinesin to Viral Particles (2022) Microtubule-Inspired Functionalization of Carbon Nanotubes (2022) Metabolites of Vinca Alkaloid Vinblastine (2019) Dr. Alisaraie advises multiple graduate and undergraduate researchers, including Ph.D. and M.Sc. candidates, and has mentored numerous former lab members across disciplines such as biochemistry, pharmacy, computer science, and engineering. While no specific grants are named, her research is supported by technical and funding partners, indicating active project funding. She teaches key courses including Medicinal Chemistry, Drug Design, and Proteins Structure and Function at both undergraduate and graduate levels. Her lab, located in the Health Sciences Center (H-3421-B), conducts fundamental science using computational methodologies to address challenges in drug discovery, nanomedicine, and cellular transport mechanisms. The team explores dynein’s motor domain, microtubule-binding dynamics, and peptide-functionalized carbon nanotubes as innovative drug carriers.

Marc DelarueView profile

Professor

• Structural Biology
• Computational Structural Biology
• DNA Repair
• +12 more

Marc Delarue is a Professor and Head of Structure at the Laboratory of Architecture and Dynamics of Biological Macromolecules, part of the Department of Structural Biology and Chemistry at the Institut Pasteur in Paris, France. He leads multiple research projects focused on DNA repair, DNA replication, ion channels, and computational structural biology. Institution: Institut Pasteur School: Department of Structural Biology and Chemistry Department: Architecture and Dynamics of Biological Macromolecules Role: Head of Structure, Principal Investigator His research integrates experimental and computational approaches, including X-ray crystallography, cryo-EM, molecular dynamics, and electrostatic modeling, to understand macromolecular structure-function relationships. Key interests include DNA polymerases, NHEJ repair in cancer, ligand-gated ion channels, and the development of simulation software such as MinActionPath and NOMAD-Ref. The recent publications highlight a strong trend in structural mechanisms of DNA-processing enzymes, ion channel pharmacology, and computational modeling of macromolecular transitions. These works frequently appear in high-impact journals and involve collaborations across disciplines and institutions. Marc Delarue has mentored numerous PhD students and postdoctoral researchers, including Jérémie Piton, Jerome Gouge, Frederic Poitevin, and Leonardo Betancurt Anzola. His projects are supported by internal and transversal funding initiatives at the Institut Pasteur, such as the Cancer Initiative and INCEPTION convergence program, though specific grant names are not detailed. He is actively involved in software development for structural bioinformatics, leading projects like MinActionPath and NOMAD-Ref, which are used to simulate conformational changes and analyze dynamics in large macromolecular complexes.

• Computational Materials Science
• First-Principles Calculations
• Anharmonicity in Solids
• +7 more

Johan Klarbring is a researcher in the Department of Physics, Chemistry and Biology at Linköping University, within the Faculty of Science & Engineering. His work is centered on theoretical and computational materials science, particularly focusing on complex atomic dynamics in functional materials. His research interests include: Anharmonicity in crystalline solids Phase transformations in perovskites Ion conduction in solid electrolytes Thermochromic and optoelectronic properties of lead-free perovskites Machine learning force fields for molecular dynamics First-principles modeling of dynamically disordered systems His recent publications (2022–2024) reveal a strong focus on halide perovskites and double perovskites, examining phenomena such as thermochromism, antiferromagnetic coupling, and fast ion conduction. He employs advanced computational techniques, including machine learning-accelerated molecular dynamics and ab initio methods, to uncover atomic-scale mechanisms behind macroscopic material properties. His work bridges fundamental physics with applications in energy materials, solid-state batteries, and optoelectronics. Notable scientific contributions include: Development of models for Na vacancy-driven phase stabilization in sodium ion conductors Elucidation of electron-phonon coupling as the origin of thermochromism in Cs2NaFeCl6 Analysis of diverging anharmonic behaviors in lead-based vs. lead-free perovskites Application of the TDEP method to study temperature-dependent effective potentials Investigation of ionic conductivity in doped ceria using nonequilibrium molecular dynamics Klarbring completed his doctoral thesis in 2020 titled A First-Principles Study of Highly Anharmonic and Dynamically Disordered Solids , which laid the foundation for his current research. He has collaborated with prominent researchers and institutions, and his work is supported by major funding agencies such as the Swedish Research Council, Knut and Alice Wallenberg Foundation, and the European Research Council. He is actively involved in developing and applying cutting-edge computational methodologies to address challenges in materials science. He is affiliated with the Theoretical Physics division at Linköping University and contributes to open-source scientific software development, as evidenced by his involvement in the TDEP package. There is no mention of advising students or teaching responsibilities in the provided text.

Steven G. JohnsonView profile

Associate Professor

• Photonic crystals
• Electromagnetism
• Structured media
• +6 more

Steven G. Johnson is an Associate Professor of Applied Mathematics and Physics at the Massachusetts Institute of Technology (MIT), affiliated with the Research Laboratory of Electronics (RLE). He holds a B.S. in physics, mathematics, and computer science (1995) and a Ph.D. in physics (2001), both from MIT. His research spans the influence of complex geometries on wave phenomena, high-performance computation (including FFTW library development), and nanoscale electromagnetism in structured media. Johnson's work focuses on two primary areas: photonic crystals and high-performance computing. He co-developed the FFTW fast-Fourier transform library, earning the 1999 J.H. Wilkinson Prize. His research group explores nanostructure effects on partial differential equations, numerical electromagnetism, and device design. He teaches courses such as Mathematical Methods in Nanophotonics and Numerical Methods , reflecting his expertise in computational physics. Key contributions include advancing surface-enhanced Raman scattering substrates, inverse design of meta-optics, and quasi-normal mode theory. Collaborations span experimental and theoretical groups, including the Ab Initio Physics Group at MIT. His team's work bridges fundamental theory with applied photonics, yielding innovations in imaging, lasers, and computational tools. Recent projects highlight end-to-end metasurface design for imaging, physics-enhanced machine learning models, and tunable terahertz lasers. Johnson's interdisciplinary approach integrates nanophotonics, optimization, and AI, driving advancements in light-matter interaction and computational methods.

• Cosmology
• Early Universe Studies
• Cosmic Microwave Background (CMB)
• +3 more

Kimmy Wu is a Visiting Professor in the Department of Physics at the California Institute of Technology (Caltech), part of the Division of Physics, Mathematics and Astronomy. She holds a B.S. from the University of Michigan (2008) and a Ph.D. from Stanford University (2015). Her research focuses on observational cosmology, leveraging large-scale surveys across multiple wavelengths to study the early universe, cosmic microwave background (CMB), and gravitational lensing. She develops modern statistical and machine learning tools to address cosmological questions such as the nature of primordial gravitational waves and the consistency of the ΛCDM model. Research interests include delensing techniques, cross-correlations with galaxy surveys, and constraints on cosmological parameters like Hubble constant (H₀) and matter density (σ₈). She collaborates with major projects like the South Pole Telescope (SPT-3G), BICEP/Keck Array, and CMB-S4. Her work emphasizes interdisciplinary approaches, integrating machine learning with traditional cosmological methods. Kimmy advises current students from institutions such as Stanford University and collaborates with postdocs on projects involving CMB observations and data analysis. She emphasizes a supportive and inclusive research environment, aligning with Caltech’s commitment to diversity and excellence in science.

François LevrierView profile

Associate Professor

• Astrophysics
• Interstellar medium
• Magnetic fields
• +5 more

François Levrier is an Associate Professor in the Physics Department at École Normale Supérieure (ENS) Paris, where he conducts research on astrophysics with a focus on the interstellar medium, magnetic fields, and data analysis techniques. He is affiliated with the Laboratoire de Physique de l'ENS (LPENS) and has made significant contributions to the Planck collaboration as well as other major astrophysical projects. His educational background includes: Graduate from ENS Paris (1996-2000) "Agrégé" in Physics (2000) Ph.D. in Astrophysics (2004) HDR in Astrophysics (2016) Levrier's research focuses on the structure and dynamics of the interstellar medium, new techniques in radio-interferometry and astronomical data analysis, and databases and virtual observatory initiatives. His work particularly examines the relationship between magnetic fields and interstellar structures, using data from missions like Planck to study polarized dust emission and its implications for understanding the Galactic magnetic field. His recent research analyzes the statistical properties of interstellar turbulence and develops new methods for characterizing complex structures in observational data. His research output shows a consistent focus on polarized submillimeter emission from thermal dust in turbulent, magnetized interstellar media, with applications to understanding cosmic microwave background foregrounds and the formation of molecular clouds. His work combines observational data with sophisticated statistical methods and simulations to extract physical insights from complex datasets. Among his notable achievements are: Leading the "Planck 2018 results. XII. Galactic astrophysics using polarized dust emission" paper Co-leading the MIST project (2017-2022), which received a 2.5 M€ Advanced Grant from the European Research Council Coordinating the BxB project, funded by the Agence Nationale de la Recherche with 500 k€ Developing the S3-Tools for astronomical data analysis Levrier actively supervises PhD and Master's students, with current projects focusing on polarized CMB foregrounds and primordial B-modes in CMB data. He has secured significant research funding through competitive grants and collaborates extensively with international research teams. He is also involved in the ORION-B project, which has conducted comprehensive observations of the Orion B Giant Molecular Cloud, creating one of the most detailed radio-domain datasets of this region.

Igor YakushevView profile

Associate Professor

• Molecular Imaging
• Multimodal Neuroimaging
• Neurodegenerative Disorders
• +7 more

Igor Yakushev is a Privatdozent (PD) and Senior Consultant in Nuclear Medicine at the Technical University of Munich (TUM), Department of Nuclear Medicine. He is the Head of Neuroimaging and a full member of the Graduate School of Systemic Neurosciences (GSN), as well as an associate member of MCN. His work bridges clinical nuclear medicine and advanced neuroimaging research, with a focus on understanding cognition in health and disease. Research Interests: Dr. Yakushev leads the research group multimodal imaging of normal and pathological cognition , centered on brain connectivity. His team employs positron emission tomography (PET), structural and functional MRI, and diffusion-weighted imaging to explore the neural mechanisms underlying cognitive function and decline. A key focus is the integration of molecular imaging into connectomics, notably through the Molecular Connectivity consortium. The recent publications highlight a strong trend in simultaneous multimodal imaging , particularly PET/fMRI, to dissect neurocognitive networks in dementia and working memory. His work emphasizes the development and application of imaging biomarkers for neurodegenerative disorders, with significant contributions to understanding amyloid dynamics and brain network integrity. Scientific Awards: Featured article in Alzheimer’s & Dementia (2018) Advising and Grants: Dr. Yakushev actively supervises several students, including Dr. Isabelle Ripp, Yifan Mayr, Shuqi Xie, and Dilara Steenken. While specific grants are not listed, his leadership of a dedicated research group and participation in a major consortium suggest active grant funding in neuroimaging and dementia research. Labs and Teams: He leads the multimodal imaging of normal and pathological cognition research group and is central to the Molecular Connectivity consortium, which aims to advance the field of molecular connectomics by integrating PET with other neuroimaging modalities.

Michael S. ChapmanView profile

Professor

• Structural Biology
• Viral-Host Interactions
• Enzyme Dynamics
• +3 more

Michael S. Chapman is a Professor and Division Director/Chair in the Department of Biochemistry at the University of Missouri, jointly appointed in the School of Medicine and College of Agriculture, Food and Natural Resources . He specializes in structural biology , focusing on Adeno-Associated Virus (AAV) interactions with host proteins for gene therapy applications. Research Focus: Structural virology, enzyme dynamics, cryo-EM, and computational methods development Key Contributions: Discovery of the AAV receptor (AAVR), high-resolution AAV capsid structures, and advanced EM refinement algorithms His work combines biophysical and molecular approaches with cryo-EM, X-ray crystallography, and NMR. Recent projects include elucidating AAV-2 and AAV-5 receptor-binding modes , redefining antibody neutralization mechanisms , and developing resolution-dependent EM refinement tools . While no formal student list is provided, his lab accepts graduate trainees from diverse backgrounds.

Ivan Fernandez-CorbatonView profile

Researcher

• Electromagnetic radiation
• Light-matter interactions
• Symmetries and conservation laws
• +5 more

Ivan Fernandez-Corbaton is a Project Leader and Staff Scientist at the Institute of Nanotechnology, Karlsruhe Institute of Technology (KIT), where he leads research in the Theoretical Nanooptics unit. His position focuses on fundamental electromagnetic theory with applications in nanophotonics and metamaterials. His research centers on the systematic use of electromagnetic duality symmetry and its conserved quantity, electromagnetic helicity, to treat electromagnetic polarization degrees of freedom. This approach provides insights and design guidelines across diverse areas including zero backscattering, optical activity, metamaterials for transformation optics, and nanophotonic phenomena involving electromagnetic angular momentum. His work bridges theoretical physics with practical applications in chiral sensing, thermal radiation control, and light structuring technologies. Analysis of his recent publications reveals a strong focus on computational methods for electromagnetic problems, particularly the T-matrix formalism extended to polychromatic fields and complex geometries. His research increasingly integrates multi-scale approaches connecting quantum chemical simulations with classical electromagnetic modeling, especially for molecular nanomaterials and chiral structures. The work shows consistent theoretical depth with practical applications in sensing, light manipulation, and thermal radiation control. As a Staff Scientist, Fernandez-Corbaton collaborates extensively within KIT's Carsten Rockstuhl's Lab and with international partners. His research has significant implications for chiral molecule detection, photonic device design, and fundamental electromagnetic theory. He maintains active publication output with numerous preprints available on arXiv and ResearchGate, reflecting his commitment to open scientific communication.

Florian SchuberthView profile

Associate Professor

• Structural Equation Modeling
• Partial Least Squares
• Composite-based Methods
• +7 more

Florian Schuberth is an Associate Professor at the Chair of Product–Market Relations within the Faculty of Engineering Technology at the University of Twente. His research focuses on composite-based structural equation modeling (SEM), particularly Partial Least Squares (PLS) methods, with applications across Information Systems, Social Sciences, and Computer Science. He actively contributes to quantitative research methodology and open science. Bachelor's and Master's in Business Administration and Economics, University of Würzburg PhD in Econometrics, summa cum laude, University of Würzburg (2017) His research interests center on advancing composite-based SEM, including model specification, fit assessment, higher-order constructs, and robust estimation techniques. He emphasizes methodological rigor and reproducibility in social science research. His recent work, reflected in publications and software development, demonstrates a strong trend toward improving the transparency, validity, and accessibility of composite modeling techniques, particularly through R-based tools. He has made significant contributions to model evaluation, measurement invariance, and endogeneity correction in PLS-SEM. Florian Schuberth is a passionate educator, coordinating and teaching courses on quantitative research methods. He is also deeply committed to open science, serving as coordinator of the Open Science Community Twente. Notable contributions include the development and maintenance of the cSEM R package, which enables researchers to perform composite-based SEM in R, promoting open, reproducible research practices. Maintainer of the R package cSEM Coordinator, Open Science Community Twente (since 2022) Active contributor to methodological software and open science infrastructure

• Machine Learning
• Natural Language Processing
• Language Modelling
• +7 more

Wilker Ferreira Aziz is an Assistant Professor at the Institute for Logic, Language and Computation (ILLC) within the Faculty of Science at the University of Amsterdam, where he leads the Probabilistic Language Learning group. His primary affiliation is with the Natural Language Processing & Digital Humanities research unit. His research focuses on the intersection of machine learning, natural language processing, and probabilistic modeling. Key areas of interest include language modeling, machine translation, syntactic parsing, text classification, and question answering. He develops techniques for probabilistic inference, gradient estimation, and uncertainty quantification in neural language models. Dr. Aziz's recent publications demonstrate a strong focus on uncertainty in natural language generation, with multiple papers at top-tier conferences like EACL, EMNLP, and ICLR. His work examines how language models represent uncertainty compared to humans, calibration issues when humans disagree on labels, and methods for more robust decision-making in text generation. Best Paper Award at Coling 2020 He actively supervises both PhD and MSc students, with several ongoing PhD projects focusing on uncertainty in language models and neural text generation. Dr. Aziz serves on program committees for major ML and NLP conferences including ACL, EMNLP, NeurIPS, and ICLR, and has acted as area chair for several of these venues. His research has been supported through positions at the Mercury Machine Learning Lab, a collaboration between Booking.com, TU Delft, and the University of Amsterdam.

Dan RenView profile

Associate Professor

• Financial Mathematics
• Stochastic Control
• Optimal Stopping
• +5 more

Dan Ren is an Associate Professor in the Department of Mathematics at the University of Dayton, College of Arts and Sciences. She joined the university in 2013 as a full-time faculty member and has since been actively contributing to research in financial mathematics and stochastic analysis. Education: Ph.D. in Mathematics, Boston University, 2013 M.S. in Mathematics, New Mexico State University, 2008 B.S. in Mathematics, Beijing Normal University, 2004 Her research focuses on Financial Mathematics , Stochastic Control and Analysis , and Optimal Stopping . These areas involve modeling uncertainty in financial markets, optimizing investment strategies under risk, and determining optimal decision times in dynamic environments. Her work often employs regime-switching models, stochastic volatility, and transaction cost frameworks to reflect real-world market complexities. The recent publications (2012–2025) demonstrate a strong focus on portfolio optimization, consumption-investment problems, shortfall aversion, and optimal stopping under stochastic models. These works frequently apply advanced techniques from stochastic differential equations, Markov processes, and dynamic programming, indicating a deep integration of theoretical mathematics with financial applications. Scientific Awards: Dan Ren advises students and contributes to academic mentoring, though specific names of advisees are not listed. She has not received any externally mentioned grants in the provided text. Her academic service includes active research and teaching within the mathematics department, contributing to both graduate and undergraduate education in quantitative finance and applied probability. There is no mention of specific labs or research teams led by Dr. Ren in the provided information. However, her publication record suggests collaboration within mathematical finance circles and possible affiliation with research groups focused on stochastic modeling and financial engineering at the University of Dayton.