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Sara Merino-AceitunoView profile

Associate Professor

• Kinetic Theory
• Emergent Phenomena
• Mathematical Biology
• +12 more

Sara Merino-Aceituno is an Associate Professor at the Faculty of Mathematics, University of Vienna, with prior academic roles at the University of Sussex and Imperial College London. Her research focuses on kinetic theory and mathematical modeling of emergent phenomena in biological, medical, and social systems. PhD in Mathematics, University of Cambridge (2015) MSc in Computer Science and Applied Mathematics, INP Grenoble (2010) BSc in Mathematics, Universitat Politècnica de Catalunya (2009) Her research interests center on understanding how macroscopic patterns arise from microscopic interactions, using tools from partial differential equations, probability, and numerical analysis. She specializes in interacting particle systems, collective dynamics, opinion formation, and cell tissue development. She collaborates closely with experimental biologists to validate and refine her models. The recent publications reflect a consistent focus on kinetic modeling of collective behavior, phase transitions, and multiscale analysis. Her work bridges abstract mathematical theory with concrete applications in biology and social sciences, often involving collaboration with interdisciplinary teams. She develops continuum limits of particle systems and investigates stability, alignment, and pattern formation in complex systems. Sara is actively involved in mentoring and science communication. She has co-authored study guides for students, leads outreach initiatives such as exhibitions at the 'Long Night of Research,' and produces educational videos. She also maintains a blog and YouTube channel to share insights on research, learning, and academic life. Co-authored guide: 'GOOD_STUDY_HABITS.pdf' with Amalio Fernández-Pacheco Created educational video 'Describing Patterns' with filmmaker Sameer Patel Regular contributor to public science events Active blogger on topics including coaching, creativity, and academic mindset She leads a research group called 'The HERD,' which investigates emergence in natural domains, focusing on mathematical models of biological and social systems. Her team includes postdoctoral researchers and students working on kinetic theory, numerical simulations, and interdisciplinary modeling projects.

Ricard SoléView profile

Research Professor

• Complex Systems
• Synthetic Biology
• Evolutionary Biology
• +7 more

Ricard Solé is an ICREA Research Professor at the Catalan Institute for Research and Advanced Studies, based at Universitat Pompeu Fabra (UPF), where he leads the Complex Systems Lab within the Department of Experimental and Health Sciences. He is also External Professor at the Santa Fe Institute (USA), fellow of the European Centre for Living Technology (Italy), and external faculty at the Center for Evolution and Cancer at UCSF. He teaches undergraduate courses in Biomathematics, Biological Design, and Complex Diseases. Research Interests: Ricard Solé's work focuses on the emergence of complexity in biological systems. He investigates the evolutionary origins of major transitions—such as the rise of protocells, multicellularity, symbiosis, cognition, and language—through the lens of synthetic biology and mathematical modeling. He introduced the concept of 'synthetic major transitions' to simulate evolutionary innovations in the lab. Another key area involves unstable evolutionary dynamics in RNA viruses and cancer, where high mutation rates drive adaptation. He also explores 'terraforming' ecosystems to prevent catastrophic shifts, advocating for a multiscale synthesis across synthetic biology, ecology, and systems theory. The recent publications highlight his interdisciplinary approach, bridging virology, network theory, and evolutionary dynamics. His 2021 review on phase transitions in virology and 2018 paper on mutualistic networks as evolutionary spandrels reflect his focus on critical transitions and emergent structures in complex biological systems. Scientific Awards and Recognitions: ERC Advanced Grant (2012) Fellow, European Centre for Living Technology Advising and Grants: While specific students are not listed, Ricard Solé leads a research lab and likely mentors graduate students and postdoctoral researchers. His research is supported by major grants, including funding from the European Research Council and Fundación Botin, enabling long-term, high-risk theoretical and experimental work in synthetic and evolutionary systems. Labs and Teams: He heads the Complex Systems Lab at the PRBB in Barcelona, fostering interdisciplinary research at the intersection of physics, biology, and computation.

Utkarsh KapoorView profile

Assistant Professor

• Biophysics
• Biomolecular Engineering
• Self-Assembly
• +7 more

Utkarsh Kapoor is an Assistant Professor in the Department of Chemical and Biomedical Engineering at the University of Wyoming, affiliated with the College of Engineering and Physical Sciences and the Molecular and Cellular Life Sciences Program. He is also an Adjunct Professor in the School of Computing and holds the distinguished title of UW Derecho Professor. Education: Ph.D. in Chemical Engineering, Oklahoma State University, 2018 B.E. (Hons.) in Chemical Engineering, Birla Institute of Technology & Science (BITS)-Pilani, India, 2012 His research centers on computational biophysics and biomolecular engineering, with a focus on self-assembly, phase separation, and polymer nanocomposites. Using multiscale modeling, molecular dynamics, and machine learning, his lab investigates protein-nucleic acid interactions and designs novel biomaterials. His work is highly interdisciplinary, bridging chemical engineering, biophysics, and computational science. The recent publications reflect a strong trend in computational modeling of biomolecular systems, particularly in understanding phase separation phenomena and nanoparticle-polymer interactions. These studies employ advanced simulation techniques to probe molecular mechanisms underlying biological organization and material design. Scientific Awards and Honors: NIH Wyoming INBRE Pilot Project funding 2024 Spring Faculty Fellow Award, School of Computing UW Derecho Professor OSU Graduate Research Excellence Award (2019) OSU Foundation Distinguished Graduate Fellowship (2017) Robert L. Robinson Endowed Fellowship (2017) Halliburton Graduate Fellowship in Engineering (2015) Creativity, Innovation and Entrepreneurship Scholarship (2015) ISA Scholarship (2016–2019) Dr. Kapoor advises graduate students and leads the Kapoor Lab, which has secured competitive funding including NIH support. His collaborative research program integrates theory, simulation, and interdisciplinary science to advance bioengineering applications. He actively participates in academic service, including chairing sessions at AIChE and giving invited seminars. The Kapoor Lab is an active research group focused on computational biophysics, with ongoing projects in protein phase separation, chromatin organization, and polymer nanocomposites. The lab fosters a collaborative environment and is part of broader initiatives at the University of Wyoming, including the Molecular and Cellular Life Sciences Program and the School of Computing.

Hu LiView profile

Professor

• Systems Biology
• Artificial Intelligence in Medicine
• Machine Learning
• +12 more

Hu Li, Ph.D. , is a Professor of Pharmacology and Consultant in the Department of Molecular Pharmacology and Experimental Therapeutics at Mayo Clinic, Rochester, Minnesota. He is affiliated with the Mayo Clinic Comprehensive Cancer Center, Center for Individualized Medicine, and Center for Regenerative Biotherapeutics, where he leads research at the intersection of systems biology, artificial intelligence, and precision medicine. Dr. Li earned his Ph.D. in Computational Biology and Bioinformatics from the National University of Singapore under Dr. Yuzong Chen, followed by postdoctoral training in Systems Biology at the Wyss Institute, Harvard University, and the Broad Institute, MIT, with Dr. Jim Collins. His research focuses on developing unified manifold medicine frameworks using machine learning and AI to model disease at multiple scales. He aims to decode disease heterogeneity, understand tissue spatial architecture, and design personalized treatment regimens. His work leverages systems tools and generative AI to create bodywide tissue maps and improve therapeutic outcomes. His recent publications demonstrate a strong trajectory in iPSC biology, cancer pharmacogenomics, immunology, and treatment response prediction, reflecting a multidisciplinary approach integrating computational and experimental biomedicine. Scientific Awards: The Eric and Wendy Schmidt Fund for AI Research and Innovation (2023) Dr. Li has been a co-investigator on multiple NIH-funded projects, including those from the National Cancer Institute, focusing on pancreatic cancer and melanoma genetics. He collaborates widely across Mayo Clinic and national research networks. His lab produces high-impact research, with over 178 publications, and he actively mentors trainees and leads innovative projects in AI-driven biomedical discovery. His research fingerprint highlights expertise in induced pluripotent stem cells, reprogramming, transcriptomics, and statistical learning methods, underscoring his leadership in computational systems biology.

• plasticity
• material damage
• multiscale simulations (MD, DDD, FEM)
• +4 more

Dr. Yi Hu is a researcher at the Max Planck Institute for Sustainable Materials , affiliated with the Department of Microstructure Physics and Alloy Design within the Theory and Simulation research group. His work focuses on computational materials science and multiscale modeling of structural materials. Research interests include: Plasticity and material damage mechanisms Multiscale simulations (Molecular Dynamics, Discrete Dislocation Dynamics, Finite Element Method) Computational methods like topology optimization, machine learning, and phase field modeling Development of DAMASK (Düsseldorf Advanced MAterial Simulation Kit) Recent publications highlight his expertise in precipitate strengthening of aluminum alloys, dislocation dynamics, and multiscale modeling. He collaborates on code development and has contributed to advancing computational frameworks for material simulations. Key affiliations: Max Planck Institute for Sustainable Materials, Düsseldorf, Germany.

Fabien CrausteView profile

Researcher

• Biomathematics
• Mathematical Immunology
• Delay Differential Equations
• +4 more

Fabien Crauste is a Director of Research at CNRS, affiliated with MAP5 UMR 8541 at the University of Paris Cité. His work bridges applied mathematics and biomedical research through multi-scale modeling. Math Bio Santé Thematic Network Director Organizer of Modeling, Analysis, Simulations working group Co-organizer of mathematical modeling in life sciences seminar Member of PEPR Maths-Vives Program Committee Active in AMIES and MAP1.5 environmental initiatives Research focuses on mathematical immunology , delay differential equations , and cell population dynamics with applications to CD8 T cell responses, B cell differentiation, and diseases like chronic lymphocytic leukemia and atopic dermatitis . Special emphasis on stochastic modeling and inter-individual variability in immune responses. Current projects include AI4scMED (multiscale AI for precision medicine), DCComm (dendritic-T cell communication), and Digi-DermA (dermatitis modeling). Supervises multiple postdocs and PhD students on these topics. Contributions include developing the Simuscale framework for multi-cell modeling and analyzing erythropoiesis dynamics and germinal center B cell differentiation .

• Numerical Analysis
• Partial Differential Equations
• Finite Element Methods
• +5 more

Prof. Dr. Dietmar Gallistl is a faculty member at Friedrich-Schiller-Universität Jena, holding the Professorship for Numerical Mathematics within the Faculty of Mathematics and Computer Science. His research focuses on numerical methods for partial differential equations, including mixed finite elements, multiscale methods, adaptive algorithms, and computational homogenization. He teaches courses such as Theory and Numerics of Partial Differential Equations and Iterative Solvers for Partial Differential Equations . His research interests span various areas including discretization techniques for nonlinear PDEs, error analysis, and computational methods for wave propagation. He has contributed to software tools for finite element mesh refinement and numerical simulations. Recent work includes publications on the Gross-Pitaevskii eigenvalue problem, Monge-Ampère equations, and time-harmonic Maxwell equations. Prof. Gallistl's academic work emphasizes rigorous mathematical analysis alongside practical numerical implementation. His teaching materials include lecture notes on computational PDEs and finite element methods available on his university webpage.

Vivek RaoView profile

Professor

• Engineering Design Theory and Methodology
• Product Design, Development & Innovation
• Emerging Technologies in Design
• +4 more

Vivek Rao serves as Associate Dean for Master's & Professional Programs and Executive Director of the Design and Technology Innovation Master's Program at Duke University's Pratt School of Engineering. He is also an Executive In Residence in Engineering Graduate and Professional Programs, teaching core courses including Design Ethics & Social Innovation and Design Innovation Studio while leading the development of Duke's new Masters of Engineering program. He earned his B.S. (2007), M.S. (2012), and Ph.D. (2018) in Mechanical Engineering from the University of California, Berkeley. His research spans Engineering Design Theory and Methodology , Product Design Innovation , and Emerging Technologies in Design , with significant focus on Sociotechnical Systems , Human-Computer Interaction , and Sustainability applications in water infrastructure. His interdisciplinary work integrates business innovation frameworks with engineering design principles. Analysis of his 15 most recent publications (2021-2024) reveals a dominant trend in AI-enhanced design tools, cybersecurity integration, and human-centered methodologies across disaster response, global health, and virtual collaboration contexts. These works demonstrate consistent interdisciplinary collaboration bridging engineering, business, and social sciences through journals like Design Studies and Journal of Mechanical Design. No scientific awards were documented in the source material. Rao has secured substantial external and internal grant funding during his postdoctoral research at UC Berkeley and maintains an active consulting practice with clients ranging from pre-seed startups to the US Department of Defense. His pedagogical approach emphasizes project-based learning, as evidenced by his development of foundational courses for Berkeley's Masters of Design program and Duke's new initiative. As Executive Director of Duke's Design and Technology Innovation program, Rao leads curriculum development and industry partnerships focused on translating design theory into real-world technological solutions, particularly in infrastructure and sustainability domains.

Tianbai XiaoView profile

Researcher

• Numerical Analysis
• Applied Mathematics
• Kinetic Theory
• +7 more

Tianbai Xiao is a Researcher at the Karlsruhe Institute of Technology (KIT) within the Department of Mathematics and Steinbuch Centre for Computing. His work spans mesoscopic science , uncertainty quantification , and scientific machine learning , focusing on multi-scale, multi-physics problems in flow transport. His research in kinetic theory addresses nonlinear partial differential equations, hyperbolic conservation laws, and the unified modeling of continuum/rarefied flows. He develops high-performance numerical algorithms like the Unified Gas-Kinetic Scheme (UGKS) and Kinetic.jl (a finite volume toolbox for scientific computing). Current projects include mesoscopic science , stochastic data science , and physics-informed neural networks . He contributes to open-source tools including FluxReconstruction.jl for advection-diffusion methods and Langevin.jl for stochastic kinetic modeling. Publications cover Journal of Computational Physics , Engineering Fracture Mechanics , and Entropy , with preprints on arXiv in 2025 addressing force-driven flows and hybrid peridynamics. Teaching activities include the Introduction to Kinetic Theory lecture at KIT, and mentoring in the CAMMP (Computational and Mathematical Modeling Program) to develop problem-solving skills through real-world modeling tasks. He advocates for problem-based learning where students translate non-mathematical problems into mathematical language.

Ilya SafroView profile

Associate Professor

• Quantum Computing
• Machine Learning
• Artificial Intelligence
• +7 more

Ilya Safro is an Associate Professor and Associate Chair for Graduate Studies and Research in the Department of Computer and Information Sciences at the University of Delaware. He holds a joint appointment in the Department of Physics and Astronomy. Previously, he served as a Faculty Scholar at Clemson University's School of Health Research and as a postdoctoral researcher at Argonne National Laboratory. Ph.D. in Applied Mathematics and Computer Science (Weizmann Institute, 2008) MSc in Applied Mathematics and Computer Science (Weizmann Institute, 2004) BSc in Mathematics and Computer Science (Ben-Gurion University, 1998) His research focuses on quantum algorithms, machine learning, and combinatorial scientific computing. He develops hybrid quantum-classical algorithms for graph optimization problems and leads projects in biomedical hypothesis generation systems like AGATHA-C. His work spans quantum computing scalability, tensor network simulations, and multiscale approaches for large-scale optimization. Recent publications highlight advancements in QAOA parameter transferability, fermion-qubit mapping optimization, and quantum-inspired network interdiction algorithms. His lab has received multiple student paper awards at IEEE HPEC and QCE conferences. NSF, DARPA, NIH, DOE, BMW funding recipient Co-Chair of IEEE Quantum Computing and Engineering 2025 and 2024 Quantum Algorithms tracks Editorial board member for IEEE Transactions on Quantum Engineering and SIAM Journal on Scientific Computing Current lab members include Ph.D. students Bao Bach, Cameron Ibrahim, Ilya Tyagin, and alumni working at Fujitsu Research USA and Argonne National Laboratory. His Erdős number is 3 via multiple academic lineages.

• Computational Biology
• Biophysical Modeling
• Cancer Modeling
• +12 more

João Carlos Lopes de Carvalho is a Full Professor at the Faculty of Science and Technology of the University of Coimbra, Portugal, where he has held academic positions since 1987. He was promoted to Associate Professor in 2003 and attained Full Professor status in 2022. His research spans computational biology, biophysical modeling of cancer, and experimental particle physics, with extensive participation in international collaborations including CERN (CPLEAR, ATLAS) and DESY (HERA-B). PhD in Experimental Particle Physics, University of Liverpool (1994) Aggregate Title (Habilitation), University of Coimbra (2003) Bachelor’s Degree in Physics, University of Coimbra (1987) His research focuses on the computational modeling of biological systems, particularly the bioelectric mechanisms underlying cancer initiation and progression. He develops discrete models such as cellular automata and cellular Potts models to simulate tissue dynamics in 2D and 3D, with applications in bladder and prostate cancer. Earlier in his career, he contributed to particle detector development, data acquisition, and physics analysis in high-energy experiments. The recent trends in his publication record show a strong emphasis on multiscale modeling of tumor growth, angiogenesis, and the role of bioelectricity in carcinogenesis. His work integrates computational simulations with biological insights, targeting early cancer detection and therapeutic strategies. Articles are published in high-impact journals such as Scientific Reports , PLOS Computational Biology , and Bulletin of Mathematical Biology . Supervised or co-supervised 4 PhD theses and 19 master’s dissertations Participated in over 50 research projects, coordinating 9 Principal investigator on grants including FCT projects PTDC/EMD-TLM/7289/2020 and PTDC/BIA-CEL/31743/2017 Involved in major collaborations: CERN (ATLAS, CPLEAR), DESY (HERA-B), SNO+ João Carlos Lopes de Carvalho has been a key figure in advancing computational approaches in oncology and biophysics. His work bridges physics, engineering, and life sciences, contributing to both fundamental understanding and potential clinical applications in cancer research.

• Mathematical Physics
• Mathematical Biology
• Biophysics
• +7 more

Annachiara Colombi is a fixed-term researcher at the Department of Mathematical Sciences "GL Lagrange" (DISMA), Politecnico di Torino, Italy. Her work lies at the intersection of mathematical physics, biophysics, and environmental modeling. She is affiliated with multiple academic colleges, including the College of Mathematical Engineering (Member), College of Architecture and Design (Member), and College of Environmental and Land Engineering (Invited member), reflecting her interdisciplinary contributions. Her research focuses on developing and analyzing mathematical models to understand complex biological and environmental systems. Key interests include cell migration under mechanical stimuli , non-local modeling in biological contexts , global sensitivity analysis for model validation , and applications in cancer-on-chip experiments and agricultural system modeling . She employs tools from mathematical physics and computational modeling to address problems in biophysics and sustainability. The recent publications demonstrate a strong trend in applying advanced mathematical techniques—particularly non-local integro-differential equations and stochastic particle models—to real-world biological systems. Her work spans from cellular-level phenomena (e.g., cell reorientation, collective migration) to macro-scale environmental models (e.g., cropping systems), unified by the use of sensitivity analysis and rigorous mathematical frameworks. She has made significant contributions through peer-reviewed journal articles and book chapters in prestigious publications, often in collaboration with leading researchers such as Luigi Preziosi and Marco Scianna. Fixed-term researcher, Law 240/10 art.24-A Scientific disciplinary sector: MATH-04/A - Mathematical Physics Active teaching roles in Aerospace Engineering, Architecture, and Mathematical Sciences Annachiara Colombi actively teaches across various programs, serving as Course Lecturer, Instructor, and Collaborator in courses such as Linear Algebra and Geometry , Mathematical Principles , and Global Sensitivity Analysis at both undergraduate and PhD levels. There is no public information on advised students or research grants. She is a member of the Models and Methods of Mathematical Physics research group within DISMA, which supports her theoretical and applied work.

Robert L. JacksonView profile

Professor

• Tribology
• Contact Mechanics
• Friction
• +10 more

Robert L. Jackson is a Professor in the Department of Mechanical Engineering at Auburn University, College of Engineering. He serves as Editor-in-Chief of the ASME Journal of Tribology, highlighting his leadership in the field. His research focuses on tribology, contact mechanics, friction, wear, lubrication, and electrical contacts, with applications in machine design and electrified systems. His research interests include: Nano, Micro, and Macro Scale Contact Friction, Wear, and Lubrication (Tribology) Electrically Induced Bearing Damage Electrical Contacts and Lubricant Additives Surface Texturing and Nano-lubricants Multiscale/Multiphysics Modeling Design of Machine Components His recent publications reveal a strong trend in the performance of electric vehicle motor greases, electro-pitting, electrical discharge modeling, and mixed lubrication in rolling and sliding contacts. He investigates the role of nanoparticles, surface roughness, and thermal effects in tribological systems. His work spans experimental, analytical, and computational approaches, with a focus on real-world engineering applications such as EVs, connectors, and bearings. He actively mentors graduate students, including current advisees Loren Baugh, Jack Janik, and Sudip Saha. His past students have gone on to careers in industry and academia at organizations like Intel, Amazon, Tesla, and universities in China and India. He has collaborated extensively with researchers on topics ranging from articular cartilage biomechanics to nanocomposite lubricants and electrical connector reliability.

Serveh KamravaView profile

Assistant Professor

• Machine Learning
• Fluid Dynamics
• Chemical Discovery
• +7 more

Serveh Kamrava is an Assistant Professor at the Colorado School of Mines, Department of Chemical and Petroleum Engineering. Her research bridges Chemical, Environmental, and Petroleum Engineering with advanced computational methods, focusing on energy storage, fluid dynamics, and machine learning applications in complex systems. Research Interests: Her work centers on developing physics-guided machine learning models to predict flow and transport in heterogeneous and porous materials. She aims to improve energy storage systems by enhancing efficiency and longevity through data-driven and multiscale modeling techniques. Key areas include deep learning for material reconstruction, fluid flow simulation, and dispersion prediction in geophysical and environmental contexts. Publication Trends: Her recent publications (2019–2021) demonstrate a strong focus on integrating deep learning with physical laws in porous media, particularly in geological and membrane systems. The work spans journals in geophysics, computational materials, environmental science, and chemical engineering, showing interdisciplinary impact. Scientific Awards: 2022 NSF Award 2022 ACS-PRF DNI Award Advising and Grants: Dr. Kamrava is actively recruiting PhD students, indicating ongoing research funding and mentorship activities. She likely holds active grants, including the NSF award, supporting her work in machine learning applications for energy and environmental systems. She advises prospective students with backgrounds in machine learning and computational fluid dynamics. Labs and Teams: She leads a research group focused on the interface of engineering and data science, utilizing state-of-the-art machine learning to solve multiscale problems in energy and environmental engineering. The group emphasizes Python-based computational modeling and interdisciplinary collaboration.

Ingrid DaubechiesView profile

Professor

• Applied Mathematics
• Signal Processing
• Image Compression
• +3 more

Ingrid Daubechies is a prominent mathematician and physicist known for her foundational work in wavelet theory. Born in Belgium, she earned her B.S. and Ph.D. from the Free University of Brussels. She transitioned from a physics background to applied mathematics, becoming a leading authority on wavelets while at AT&T Bell Laboratories. In 1993, she became the first woman tenured professor of mathematics at Princeton University, later joining Duke University as the James B. Duke Professor of Mathematics. Her research spans signal processing, image compression, and interdisciplinary applications in biology and art restoration. Daubechies' academic journey includes roles at Bell Labs, Princeton, and Duke, alongside extensive collaborations in computational biology and mathematics education. She has pioneered wavelet-based algorithms for data compression (JPEG 2000), developed metrics for biological morphology analysis, and contributed to art restoration techniques using mathematical imaging. Her leadership roles include presidency of the International Mathematical Union (2011–2014) and advocacy for women in mathematics. Recognized with prestigious awards like the National Academy of Sciences Award, John von Neumann Lecture Prize, and Nemmers Prize, her work bridges pure mathematics and applied sciences. She has supervised numerous research projects and authored influential texts, including Ten Lectures on Wavelets . Her legacy includes advancing computational tools for diverse fields, from medical imaging to evolutionary biology.