Find Academic Supervisors

• Lithium-ion battery modeling
• Electrochemical-mechanical modeling
• Battery diagnostics
• +3 more

Davide Clerici is a Fixed-term Assistant Professor at the Department of Mechanical and Aerospace Engineering (DIMEAS) at Politecnico di Torino. He serves as an invited member of both the College of Chemical and Materials Engineering and the College of Mechanical, Aerospace, and Automotive Engineering. His teaching responsibilities span multiple academic years (2021/22 through 2025/26), including courses on Materials and Production Process Simulation for the Materials Engineering for Industry 4.0 program and Product and Process Design with Numerical Methods for the Mechanical Engineering program. Dr. Clerici's research focuses on the intersection of mechanical engineering and electrochemistry, particularly in lithium-ion battery technology. His work centers on developing advanced models that couple electrochemical processes with mechanical behavior to understand battery degradation mechanisms. He investigates how mechanical stresses develop during battery operation and contribute to performance degradation, employing both analytical solutions and multi-scale modeling approaches. His research spans from fundamental fracture mechanics in battery materials to practical applications in battery diagnostics and state-of-charge estimation. His publication record reveals a strong trend toward increasingly sophisticated multi-physics modeling, with recent work (2023-2025) developing diagnostic methodologies based on mechanical measurements, innovative modeling frameworks like Polidemo for predicting battery degradation, and hybrid state-of-charge estimation algorithms that leverage both electrical and mechanical measurements. These advancements demonstrate a clear progression from theoretical modeling toward practical engineering applications in energy storage technology. Dr. Clerici actively participates in significant research projects, including the EU-funded LIFE LAERTHES project (2025-2027) focused on sustainable non-road mobile machinery, where he contributes as a research group member. He also leads commercial research initiatives, serving as Scientific Director for the 'Functional and performance evaluation of a sensorized battery pack' project (2025) and participating in MEMS sensor applications research. He currently supervises PhD student Salvatore Scalzo in the Mechanical Engineering Program (40th cycle, 2024-present), guiding research in the mechanical aspects of battery technology. His work bridges theoretical modeling and practical applications, contributing significantly to the advancement of safer, more reliable, and longer-lasting lithium-ion battery systems.

Michele SerpilliView profile

Associate Professor

• Structural Engineering
• Composite Materials
• Mechanics of Materials
• +6 more

Prof. Michele Serpilli is an Associate Professor at the Department of Civil, Building and Architectural Engineering within the College of Engineering at Università Politecnica delle Marche (UNIVPM). His research focuses on structural mechanics and composite materials, particularly imperfections in interfaces, strain gradient elasticity, and multiphysics modeling. University: Università Politecnica delle Marche (UNIVPM) Department: Department of Civil, Building and Architectural Engineering Academic Rank: Associate Professor Research Contact Email: m.serpilli@univpm.it Research Interests: Michele Serpilli specializes in the mechanics of composite materials, developing advanced models for imperfect interfaces in multiphysics systems. His work bridges classical elasticity, micropolar theory, and strain gradient approaches to analyze micro- and macro-scale structural behaviors. He also explores sustainable engineering solutions, such as hybrid glass-wood bracing systems and thermal-performance masonry. Publication Trends: Over the past five years, Serpilli has published extensively on imperfect interface modeling (12 articles), strain gradient elasticity (5 articles), piezoelectric composites (6 articles), and thermoelasticity (3 articles). His methodologies emphasize asymptotic analysis, homogenization, and finite element validation, addressing both theoretical and applied challenges in materials science.

• Coal Reservoir Characterization
• Methane Diffusion in Coals
• Geophysical Logging
• +3 more

Dr. Zhentao Li is a Postdoctoral Research Fellow at the School of Chemical Engineering , University of Queensland , focusing on coal reservoir characterization and unconventional gas recovery . His work integrates geophysical logging , machine learning inversion , and multiscale pore structure analysis to understand methane diffusion dynamics and reservoir productivity in coal seams. Current research explores natural fracture development , adsorption mechanisms , and 3D pore-fracture network modeling using advanced imaging techniques like FIB-SEM tomography and low-field NMR . Notable methodologies include: Hybrid geophysical logging approaches for gas evaluation Machine learning applications in logging inversion Nanoindentation for micromechanical heterogeneity Fractal analysis of pore structures 3D tomographic reconstruction of coal fractures His recent publications address challenges in coalbed methane recovery , gas diffusion pathways , and reservoir parameter estimation . Despite no formal awards listed, his collaborative work appears in journals like Fuel , Energy and Fuels , and Marine and Petroleum Geology . Affiliations include the University of Queensland and collaborations with institutions in China and Australia.

Lingzhong GuoView profile

Lecturer

• Signal Processing
• Biomedical Engineering
• Nonlinear Systems Identification
• +3 more

Lingzhong Guo is a Lecturer in the Department of Automatic Control and Systems Engineering at the University of Sheffield , affiliated with the Insigneo Institute for in silico Medicine and Neuroscience Institute . His research bridges signal processing, nonlinear systems identification, and biomedical applications. PhD from Bristol Robotics Laboratory (UWE Bristol) Specializes in spatio-temporal system identification, PDE control, and machine learning for medical imaging Recent work focuses on deep learning applications for muscle segmentation in MR images and EIS analysis for oral disorder detection. Collaborations with Zilico Ltd. and participation in Horizon2020 projects highlight his translational research. Scientific Recognition SANO Centre grant (H2020, £2.5M) for computational diagnostics SPINe: Numerical and Experimental Repair strategies (H2020 European program, €1.58M) Innovate UK KTP for EIS-based medical diagnosis (£190K) His publications span nonlinear dynamics , multiscale modeling , and biomedical signal processing , with methodological contributions to Volterra series and coupled PDE-ODE systems.

• Numerical Simulation of Fluid Behavior
• Rarefied Gas Flows
• Multiscale Hybrid Coupling Methodologies
• +4 more

Stephanie Docherty is an Assistant Professor at the School of Engineering & Physical Sciences and the Institute of Mechanical, Process & Energy Engineering at Heriot-Watt University. She specializes in numerical simulation of fluid behavior, with a focus on rarefied gas flows, multiscale hybrid coupling methodologies, and thermal management of low-carbon solutions. Her work contributes to UN Sustainable Development Goals related to sustainable energy and climate action. Research interests include: Rarefied gas dynamics for aerospace applications Hybrid simulation techniques for microscale and low-speed flows Multiphase flow analysis in thermal systems Publications span from 2012 to 2025, addressing topics such as DSMC-CFD coupling, methane transport in shale, and molecular dynamics in nanotubes. She is actively accepting PhD students and holds an ORCID: 0000-0001-7627-2692 . Her research bridges computational methods and practical engineering challenges, with a focus on advancing fluid dynamics simulations for low-carbon technologies.

Montserrat Galceran MestresView profile

Researcher

• Electrochemical Energy Storage
• Materials Science
• Nanomaterials
• +11 more

Dr. Montserrat Galceran Mestres is a Senior Researcher in Electrochemical Energy Storage at CIC energiGUNE, specializing in advanced electrode materials for sodium-ion batteries. She holds a PhD in Materials Science from Universitat Rovira i Virgili (URV), where her work on nanomaterials earned the URV's Extraordinary PhD Award . Education : MSc in Materials Science and Engineering; PhD in Chemistry (URV, Spain) International Experience : Research stays at Polish Academy of Sciences, Institute of Materials Science (Madrid), and Université Libre de Bruxelles (Belgium) Her research focuses on low-cost, environmentally friendly electrode materials for next-generation intercalation systems, combining multi-technique characterization (X-ray diffraction, electron microscopy) with material development. She leads CIC energiGUNE's Na-ion battery research line since 2016, emphasizing industrial knowledge transfer and multidisciplinary collaboration. Scientific awards include AGAUR PhD mobility fellowship , José Castillejo and Ayudas Formación Post-doctoral grants from Spain's Ministry of Economy. She actively evaluates national and EU research projects and serves as a member of RSEQ's GE3C and QES groups . Her recent publications address polyanionic compounds , ionogel electrolytes , and SEI-free anodes , while her 2013 work on Automated Crystallographic Orientation Mapping in TEM remains influential. Conference contributions span plenary talks at ENSTIN 2019 to posters at ICNaB-2018 and 65th ISE Annual Meeting.

• Aeroelasticity
• Aircraft design
• Composite structures
• +4 more

Enrico Cestino is an Associate Professor at the Department of Mechanical and Aerospace Engineering (DIMEAS) at the Polytechnic University of Turin, where he has been actively involved in teaching and research for over a decade. His professional activities span across multiple academic cycles, with consistent contributions to the Aerospace Engineering doctoral program since the 28th cycle (2012-2013) through the current 40th cycle (2024-2025). Professor Cestino's research interests focus on advanced aerospace engineering topics including aeroelasticity, aircraft design, composite structures, fuel cells, and lightweight design. His work bridges theoretical and applied aspects of aerospace engineering, with particular emphasis on computational engineering approaches to structural analysis. His research aligns with key European Research Council sectors including aerospace engineering, computational engineering, lightweight construction, and materials engineering. Analysis of his recent publications reveals a strong thematic focus on composite materials and structures, with significant attention to morphing wings, nanotechnology applications in aerospace, and advanced manufacturing processes. His work demonstrates a consistent integration of computational methods with experimental validation across multiple domains including structural analysis, impact dynamics, and thermal management. Notably, there's a growing trend toward interdisciplinary applications such as medical logistics using eVTOL aircraft and space technology for CubeSat applications. Professor Cestino serves as scientific supervisor for the student team "Team S55" (http://www.teams55.polito.it/) and has led several significant research projects including MechaTwing (2024-2027), REDI (2022-2027), and A2-NET-TEAM (2012-2014). His current research portfolio includes both competitively funded EU projects and commercial consultancy work such as the EVTOL Modeling project (2023-2024). He currently supervises multiple PhD students including Valerio Romano (40th cycle, 2024-present) researching computational solid mechanics and lightweight structures, Davide Angelini (39th cycle, 2023-present) investigating nanotechnology applications in composite materials, and has previously supervised students including Cesare Patuelli and Alessandro Polla who completed their theses on advanced structural concepts and progressive damage analysis for composite structures. Professor Cestino teaches across multiple academic levels, from undergraduate courses in Aeronautical Construction to specialized graduate courses including Aerospace Manufacturing Processes using Advanced Composite Materials and Physically Similar Models in Aerospace Design. He has also participated in educational innovation projects including the Mentoring Polito Project (M2P) and Learning to Teach with Innovative Teaching Project (L2T_ITP).

Changho KimView profile

Assistant Professor

• Stochastic Modeling
• Multi-physics Phenomena
• Soft Matter Physics
• +5 more

Changho Kim is an Assistant Professor in the Department of Applied Mathematics at the University of California, Merced. His academic journey includes dual Ph.D. degrees in Applied Mathematics from Brown University (advisor: George Em Karniadakis) and Chemistry from KAIST, South Korea. Prior to his current position, he worked as a postdoctoral researcher at Lawrence Berkeley National Laboratory under John B. Bell. University: University of California, Merced School: School of Natural Sciences Department: Department of Applied Mathematics Previous Affiliation: Lawrence Berkeley National Laboratory Kim's research focuses on stochastic modeling of multi-physics phenomena in fluids and soft matter at small scales. He develops stochastic multiscale simulation methodologies, combining computational fluid dynamics, molecular dynamics, kinetic Monte Carlo methods, and machine learning to study systems with multiple time and length scales. His work aims to bridge gaps at the physics-chemistry-biology interface through a unifying computational framework. Research Keywords: Stochastic Modeling, Multi-physics Systems, Soft Matter, Computational Fluid Dynamics, Molecular Dynamics, Machine Learning Methodologies: Stochastic Processes, Multi-scale Simulations, Particle-Based Methods, High-Performance Computing Kim actively contributes to academic outreach through initiatives like the Math Problem of the Month, annual MathMagic event, weekly Math Club for undergraduates, and the Merced Math Teachers’ Circle for local educators. Contact: Email: ckim103@ucmerced.edu Office: ACS 351, 5200 N Lake Road, School of Natural Sciences, Merced, CA 95343

• Computational Physics
• Molecular Dynamics
• Tribology
• +7 more

Mauro Ferrario is a Full Professor at the Department of Physical, Computer and Mathematical Sciences (former Physics campus) within the University of Modena and Reggio Emilia . His academic leadership is evident through dual roles in Computational Physics Laboratory (Physics degree program) and Physics teaching for Biological Sciences . Research Pillars: Non-Equilibrium Molecular Dynamics (NEMD/D-NEMD) Quantum-informed machine learning potentials Green lubricant additive development 2D material tribology (graphene, phosphorene) Multi-scale modeling from atomistic to continuum Surface passivation and chemical reactivity under stress Methodological Expertise: Ab initio/DFT simulations QM/MM hybrid approaches Constraint dynamics and thermostat algorithms High-throughput interface characterization Transient transport phenomena analysis The 2025-2024 publications reveal cutting-edge work on machine learning-enhanced simulations for green lubricants, NEMD melt-front modeling, and superlubricity mechanisms. His 2023-2022 studies on phosphorene oxidation, tribochemical constraints, and friction reduction through nanoscale patterning demonstrate sustained innovation across 15 recent articles . Teaching Impact: He develops computational physics curricula emphasizing Python programming, numerical algorithms, and interdisciplinary applications. His Computational Physics Laboratory course integrates DataCamp tools, while Physics for Biological Sciences bridges classical mechanics with life science applications through digital whiteboard instruction. Contact: Office MO-17-04-011 , Via Campi 213/A. Reception: Mondays 9:00-10:00 or by email appointment.

Markus ReiherView profile

Professor

• Theoretical Chemistry
• Quantum Chemistry
• Electronic Structure Calculation
• +7 more

Markus Reiher is a Full Professor at the Department of Chemistry and Applied Biosciences, ETH Zurich, where he leads a research group focused on theoretical chemistry since 2006. His work bridges quantum mechanics and computational chemistry, emphasizing electronic structure theory and algorithm development for chemical reaction exploration. BSc in Chemistry (University of Bielefeld, 1995) PhD in Chemistry (University of Bielefeld, 1998) Habilitation in Chemistry (University of Erlangen, 2002) His research spans quantum computing applications in chemistry, machine learning potentials, and advanced methods for reaction networks. Recent publications highlight quantum-classical hybrid models, vibronic structure simulations, and solvent effects in chemical processes. Scientific awards include the ADUC Annual Prize (2004), Emmy Noether Habilitation Award (2003), Golden Owl Award for Teaching (2010), Löwdin Lecture (2018), and Heilbronn-Hückel Lectureship (2023). He was elected to the International Academy of Quantum Molecular Science in 2022.

• Photochemistry
• Molecular Dynamics
• Quantum Mechanics
• +3 more

Dr. Marcus Boeckmann is a researcher at the Institute of Solid State Theory within the University of Münster , Germany. His work focuses on photochemistry , molecular dynamics , and computational chemistry of azobenzene-based photoswitches and quantum-materials . He is part of the Doltsinis AG research group. Research Themes : Photoisomerization dynamics in organic systems Multiscale simulations of light-responsive materials QM/MM hybrid approaches for excited state modeling Surface functionalization for switchable wettability Photomigration mechanisms in polymers Optoelectronic molecular devices His publication trends (2015-2023) emphasize computational and experimental studies of photoswitchable molecules like arylazopyrazoles, with applications in nanotechnology and smart materials . Key methodologies include molecular dynamics , ab initio simulations , and UV/vis spectroscopy . Dr. Boeckmann collaborates closely with the Doltsinis AG group and has contributed to understanding azobenzene derivatives , helical foldamers , and optoelectronic molecular switches . No scientific awards or teaching roles are mentioned in the provided text.

Jingzhi PuView profile

Associate Professor

• Computational Chemistry
• Biophysical Chemistry
• Molecular Dynamics Simulations
• +5 more

Jingzhi Pu serves as Associate Professor and Director of Graduate Studies at Indiana University, conducting interdisciplinary research at the nexus of computational chemistry and biophysics. His work focuses on deciphering biomolecular motor mechanisms and developing advanced simulation methodologies for biological electron transfer processes. His academic foundation includes: B.S. in Chemistry from Peking University (1999) Ph.D. in Chemistry from the University of Minnesota (2004) Postdoctoral research at University of Minnesota (2004-2005) Postdoctoral fellowship at Harvard University (2005-2010) Research centers on biomolecular dynamics and computational methodology development , with dual emphases on ABC transporter mechanisms (linked to cystic fibrosis and cancer drug resistance) and electron transfer in DNA photolyases . His lab pioneers multiscale approaches integrating coarse-grained modeling, QM/MM, and free energy calculations to bridge structural biology with functional dynamics. Publication analysis reveals sustained expertise in enzyme catalysis (particularly hydride transfer in dihydrofolate reductase) and molecular motor mechanics (F1-ATPase, ABC transporters). His methodological innovations span polarizable force fields, tunneling corrections, and hybrid quantum-classical simulations, consistently addressing fundamental questions in biomolecular energetics and kinetics. He directs the Pu Group laboratory, which employs computational frameworks to investigate conformational dynamics in membrane transporters and develop simulation tools for biological electron transfer systems. Current projects focus on chemomechanical coupling in ABC transporters and quantum mechanical aspects of DNA repair enzymes.

Orçun Koray ÇelebiView profile

Assistant Professor

• Materials Science
• Computational Mechanics
• Quantum Mechanics Simulations
• +4 more

Dr. Orçun Koray Çelebi is an Assistant Professor in the Department of Mechanical Engineering at Bilkent University, Ankara. He leads the Atomistic Mechanics & Materials Modeling Laboratory (A3ML), which focuses on computational and theoretical modeling of metallic materials' deformation mechanics using quantum mechanics-based methods, molecular dynamics, and machine learning. His academic journey includes a B.S. from Boğaziçi University (2018) and a Ph.D. from the University of Illinois Urbana-Champaign (2024), followed by a postdoctoral position there before joining Bilkent. Research interests center on multiscale modeling of plastic deformation in metals, alloys, and high-entropy alloys, with a focus on crystal defects (point, line, interface defects) and their role in material behavior. Techniques employed include Density Functional Theory, Molecular Dynamics, and machine learning approaches. His work aims to develop predictive models for high-performance material design. Recent publications emphasize dislocation dynamics, CRSS prediction, and machine learning applications in materials science. He actively contributes to academic networks like CPERN but has no listed scientific awards. The A3ML lab collaborates on projects across scales, integrating quantum-to-mesoscale modeling for practical material solutions in aerospace, automotive, and defense sectors.

BERTAN BEYLERGİLView profile

Associate Professor

• Composite Materials
• Material Design and Behaviors
• Solid Mechanics
• +5 more

Bertan Beylergil is an Associate Professor at Alanya Aladdin Keykubat University, Faculty of Engineering, Department of Mechanical Engineering , with a career spanning 20 years in composite materials research. His work focuses on enhancing mechanical and thermal properties of fiber-reinforced polymers for aerospace and automotive applications. Education: PhD in Mechanical Engineering (2017), Izmir Institute of High Technology Master in Mechanical Engineering (2010), Niğde University BSc in Mechanical Engineering (2006), Dokuz Eylul University Research Interests include: Design of composite materials with tailored thermal conductivity Fracture toughness enhancement via nanofibers and 3D printing Surface modification techniques for adhesive bonding Multi-scale integration in thermoplastic and thermoset composites His work addresses critical challenges in aerospace structural repair and hydrogen storage systems through innovative nanoparticle and interlayer technologies. Scientific Contributions span 15 recent publications analyzing: Hexagonal boron nitride (h-BN) reinforced epoxy composites (2025) Mechanical behavior of nano-calcite particle composites (2024) Plasma-activated composite bonding systems (2023) Key methodologies involve experimental validation, finite element modeling, and advanced manufacturing techniques like fused deposition modeling.

Bill CannonView profile

Researcher

• Computational Biophysics
• Biochemistry
• Proteomics
• +6 more

Bill Cannon is a Computational Scientist at Pacific Northwest National Laboratory (PNNL) and affiliated with the Department of Mathematics at the University of California, Riverside. His research focuses on computational biophysics, biochemical systems modeling, and high-performance computing. He holds a B.A. in Chemistry from the University of California, Santa Cruz, a Ph.D. in Biochemistry and Biophysics from the University of Houston, and a postdoctoral fellowship in enzymology at Pennsylvania State University. Dr. Cannon’s work bridges experimental and computational approaches, with significant contributions to metabolic pathway modeling, proteomics data analysis, and thermodynamic frameworks for biochemical reactions. His research includes simulating microbial metabolism, optimizing enzyme activities, and leveraging cloud computing for large-scale biological simulations. He has pioneered methods for integrating proteomics data with computational models and advancing algorithms for peptide identification in mass spectrometry. His awards include multiple Outstanding Performance Awards from PNNL’s Computational and Information Sciences Directorate, NIH fellowships, and the Welch Foundation Graduate Award. Cannon’s interdisciplinary projects span systems biology, metabolic engineering, and computational infrastructure for biological research. He collaborates widely, contributing to initiatives like the Biological Modelling and Interface Exchange (BMX) framework and advancing novel approaches to chemical reaction network modeling.