جست‌وجوی استاد و پژوهشگر

Laura Fumagalliمشاهده پروفایل

دانشیار

• Condensed Matter Physics
• Nanotechnology
• Biophysics
• +۴ مورد دیگر

Laura Fumagalli is an Associate Professor (Reader) in Condensed Matter Physics at the University of Manchester's Department of Physics & Astronomy and a staff researcher at the National Graphene Institute. She holds a M.Sc. in Electronic Engineering (2002) from the Polytechnic University of Milan and École Supérieure d'Electricité, and a PhD in Physics (2006) from the Polytechnic University of Milan. Her research focuses on dielectric properties of nanoscale systems, including confined water, biomolecules, 2D materials, and the development of scanning dielectric microscopy (SDM). Key achievements include an ERC Consolidator Grant (2018) for the Liquid2DM project and a Royal Microscopical Society Medal (2021). Current research positions include leadership in the ERC-funded Liquid2DM project and supervision of postdoctoral researchers like Dr. Simone Benaglia. Ongoing funding includes the UKRI-MSCA fellowship (2023-2024) and Royal Society instrumentation grants. Past positions include lecturer roles at the University of Barcelona, where she earned prestigious fellowships (Juan de la Cierva, 2009; Ramon y Cajal, 2014). Teaching responsibilities include leading the Frontiers of Solid-State Physics course and tutoring foundational physics modules. Her lab (Schuster Building Lab 1.09 and NGI) hosts a dynamic team of PhD students and postdocs investigating confined liquids, biomolecules, and 2D heterostructures. Notable students include M. Souilamas (2020-2024) and H. Read (2019-2023), whose theses focused on confined water and biological surfaces. Publications span topics like interfacial water dynamics, lipid membrane structures, and ferroelectricity in 2D materials. Her work bridges materials science and biophysics, addressing fundamental questions in nanoscale dielectric behavior with applications in both fields.

Xifan Wuمشاهده پروفایل

استاد

• Computational Physics
• Materials Science
• Condensed Matter Physics
• +۳ مورد دیگر

Xifan Wu is a Professor of Physics at Temple University, specializing in computational methods and materials science. His research focuses on first-principles computational approaches, particularly exploring the locality of Wannier orbitals to address physical problems in solids and liquids. Key interests include superlattice design and applications of order-N exact exchange functionals like PBE0 and GW quasi-particle approximations. He has authored numerous high-impact publications in journals such as Physical Review Letters and Physical Review B , covering topics like ferroelectric superlattices, X-ray absorption spectroscopy, and the dielectric properties of electrolyte solutions. His work bridges quantum mechanical models with machine learning potentials, advancing large-scale simulations of complex materials. Education/Background: Not explicitly detailed in the provided text. Grants/Awards: No specific awards listed, but his research is supported by Temple University’s Center for the Computational Design of Functional Layered Materials (CCDM). Labs/Teams: Collaborates with teams focused on computational design and materials modeling, possibly through Temple’s physics department and affiliated research centers. His recent work explores molecular-scale insights into electrical double layers at oxide-electrolyte interfaces and the impact of ions on X-ray spectra, demonstrating expertise in linking theoretical models with experimental phenomena.

Igor Di Marcoمشاهده پروفایل

پژوهشگر

• Computational Physics
• Condensed Matter Theory
• Strongly Correlated Materials
• +۵ مورد دیگر

Igor Di Marco is a Researcher at Uppsala University's Department of Physics and Astronomy, specializing in Materials Theory. He has maintained continuous research activity at Uppsala since 2009, initially as a postdoctoral fellow and subsequently as a researcher, with a temporary leave in 2017 to lead a group at the Asia-Pacific Center for Theoretical Physics in South Korea. Dr. Di Marco earned his PhD in condensed matter theory from Radboud University of Nijmegen in 2009. His academic trajectory has focused on computational approaches to understanding complex quantum materials, particularly those exhibiting strong electron correlations. His research centers on computational physics and condensed matter theory , with emphasis on developing methods to determine electronic and magnetic properties of strongly correlated materials . Dr. Di Marco is one of the principal developers of the all-electron DFT code RSPt (a Sweden-USA-France collaboration), which utilizes the full-potential linearized muffin-tin orbitals method. His expertise spans density-functional theory (DFT) , dynamical mean-field theory (DMFT) , and their integration (DFT+DMFT). Current research extends to X-ray absorption spectroscopy (XAS) and resonant inelastic X-ray scattering (RIXS) . Analysis of his recent publications reveals a consistent focus on electronic correlations in quantum materials, particularly in kagome metals, van der Waals magnets, and complex alloys. His work bridges theoretical method development with practical materials applications, frequently examining magnetic properties and electronic structure calculations across diverse material systems. Dr. Di Marco has made significant contributions to computational methodologies for strongly correlated electron systems, including the development of the DFT+DMFT framework within RSPt featuring full self-consistency over electron density and self-energy. His research projects have addressed magnetic properties of transition metals, excitation spectra of metal oxides, theoretical frameworks for lanthanides, and prediction of novel 2D materials.

• Condensed Matter Physics
• Electronic and Thermal Transport
• Time-Dependent Density Functional Theory (TDDFT)
• +۶ مورد دیگر

Angel Rubio is a distinguished Professor of Physics at the University of Hamburg and holds concurrent roles as Director of the Max Planck Institute for the Structure and Dynamics of Matter, and Distinguished Professor at the University of the Basque Country (UPV/EHU). He leads the Wolfgang Pauli Centre and the Nano-bio Spectroscopy group. His academic journey includes Full Professorships at UPV/EHU (2001–2014) and positions at the Simons Foundation’s Flatiron Institute, UC Berkeley, and the Fritz Haber Institute. Rubio is a global leader in computational quantum physics, particularly in TDDFT and nanomaterials. Education : Ph.D. in Physics, University of Valladolid, 1991 (Summa Cum Laude) B.S. in Physics, University of Valladolid, 1988 (Summa Cum Laude) Research Focus : Rubio’s work centers on electronic structure methods, time-resolved spectroscopy, and quantum many-body theory. He pioneered the open-source octopus code for ab initio simulations, widely used globally. His contributions span nanocapillarity, nanoplasmonics, and strong light-matter interactions, with applications in novel materials and energy systems. Grants & Leadership : Holder of two ERC Advanced Grants (DYNamo and QSpec-NewMat), Rubio directs the European Theoretical Spectroscopy Facility (ETSF). His research has garnered over 28,000 citations (H-index 82) and 65 papers with >100 citations. He is a vocal advocate for computational physics, organizing >50 international workshops and advising major institutes like the Psi-k Network. Recognition : Top 0.3% in the American Physical Society’s Author Rank Honor Prize for Best Ph.D. Thesis (1992) Member of multiple national academies and advisory boards

Xing Wangمشاهده پروفایل

پژوهشگر

• High-throughput Computing
• Nanoscale Simulation
• Heterogeneous Catalysis
• +۳ مورد دیگر

Xing Wang is a Scientist at the Laboratory for Materials Simulations, Paul Scherrer Institute. Research focuses on computational materials science including high-throughput computing, first-principles calculations, nanoscale simulations, and heterogeneous catalysis. Specializes in phase stability and transformations in transition metals and oxides. Develops scientific data management platforms for materials simulations. Contributes to understanding atomic-scale processes in catalytic systems and nanomaterial behavior under various conditions.

Geoffroy Hautierمشاهده پروفایل

دانشیار مدعو

• Computational Materials Design
• Ab Initio Computing
• High-Throughput Computing
• +۸ مورد دیگر

Geoffroy Hautier serves as Adjunct Associate Professor of Engineering at Dartmouth College's Thayer School of Engineering within the Physics and Astronomy Department. His research bridges computational materials science with practical energy applications through high-throughput methodologies and machine learning integration. Previously holding positions at MIT and ULB, he maintains active collaborations across international research networks including the psi-k society. His research expertise spans computational materials design with emphasis on ab initio and high-throughput computing approaches. Key focus areas include opto-electronic properties of materials, transparent conducting oxides, thermoelectrics, photovoltaics, and high entropy alloys for energy applications. His group develops advanced computational frameworks for materials discovery, particularly targeting energy production and storage solutions through quantum mechanical simulations. Analysis of recent publications reveals strong trends in quantum materials discovery (2022-2024), sustainable magnet development (2023), and hydrogen storage technologies (2024). His work consistently integrates machine learning with first-principles calculations to accelerate materials design cycles across photovoltaics, quantum information science, and sustainable energy systems. Scientific recognition includes: Chemistry of Materials Reviewer Excellence Award (2018, 2019) Finalist for Rising Star in Computational Materials Science (2018) Marie Curie Fellowship (2012) As group leader, Hautier mentors multiple PhD students and postdoctoral researchers while directing projects funded by DOE and other agencies. His research portfolio includes significant grants for quantum materials ($2.7M DOE grant), sustainable magnets, and hydrogen storage frameworks. The Hautier Research Group maintains active collaborations with national laboratories and industry partners through the Materials Project initiative. The group operates within Dartmouth's advanced computational infrastructure, focusing on quantum defect engineering for silicon photonics, 2D material design for electrocatalysis, and rare-earth-free permanent magnet development. Current efforts emphasize scalable quantum technologies and decarbonization pathways through advanced materials discovery.

Souvik Paulمشاهده پروفایل

استادیار

• Magnetism in two and three dimensions
• Topological magnetic quasiparticles
• Surface physics
• +۵ مورد دیگر

Souvik Paul is an Assistant Professor in the School of Physics at Indian Institute of Science Education and Research Thiruvananthapuram, where he leads the Computational Materials Science (CMS) Laboratory established in 2023. His research employs advanced computational techniques to investigate fundamental properties of magnetic materials and topological phenomena. Education: Ph.D. (2015), Indian Institute of Technology Guwahati, India M.Sc. (2008), Presidency College, Kolkata, India B.Sc. (2006), University of Calcutta, India Dr. Paul's research focuses on computational materials science with particular emphasis on magnetism in two and three dimensions, topological magnetic quasiparticles like skyrmions, surface physics, strongly correlated systems, and multifunctional materials including Heusler alloys. His work primarily utilizes Density Functional Theory (DFT) to predict and explain material properties at the atomic scale, bridging computational predictions with experimental observations through international collaborations. His publication record reveals a consistent trajectory in magnetic skyrmions research, transition metal systems, and Heusler alloys, with significant contributions to understanding spin interactions, stability mechanisms, and electronic properties. His work frequently appears in high-impact journals including Physical Review Letters, Nature Communications, and npj Computational Materials, demonstrating both theoretical depth and practical relevance to materials design. Scientific Awards: Prime Minister Early Career Research Grant (2025) from Anusandhan National Research Foundation Departmental Postdoctoral Fellowship (2015), Uppsala University Doctoral fellowship (2009), IIT Guwahati Graduate Aptitude Test in Engineering (GATE) (2009), MHRD, India Dr. Paul actively mentors graduate students including Moinak Ghosh and Bipin Babu. Through the International PhD Program, he has established formal collaborations with Prof. Stefan Heinze at CAU Kiel, Germany, providing students with international research opportunities, access to high-performance computing facilities, and extended research stays at partner institutions. His recently awarded Prime Minister Early Career Research Grant supports innovative work on antiferromagnetic skyrmions. The Computational Materials Science Laboratory employs Density Functional Theory to investigate structural, electronic, magnetic, and optical properties of materials at the atomic level. The lab maintains strong international collaborations with research groups at CAU Kiel and Forschungszentrum Jülich in Germany, focusing on discovering novel materials, explaining fundamental material behaviors, and developing predictive materials theory with applications in electronics and energy technologies.

Shabnam Sadeghi Esfahlaniمشاهده پروفایل

دانشیار

• Robotics
• Mechatronics
• Artificial Intelligence
• +۳ مورد دیگر

Dr. Shabnam Sadeghi Esfahlani is an Associate Professor in Robotics at the School of Engineering and the Built Environment, Anglia Ruskin University , where she serves as Deputy Leader of the BORI research group and leads the Automation & Robotics MSc program. Her interdisciplinary expertise spans mechatronics, artificial intelligence, virtual reality, and serious games , with a focus on applications for rehabilitation, medical training, and autonomous systems . As a Chartered Engineer and Senior Fellow of the Higher Education Academy , she has secured significant funding from Innovate UK, Horizon 2020, and GCRF , with grants exceeding £3 million. Education PhD in Mechanical Engineering, Anglia Ruskin University BSc (First Class) in Statistics & Mathematical Science, Shahid Beheshty University Her research integrates AI with robotics for societal impact, exemplified by the open-source SROBO ground robot and projects like Rehabgame and the Assistive Feeding Robot . She has published over 45 peer-reviewed articles and contributes to academic communities as a journal guest editor and conference organizer . Key collaborations include IET, IMechE, and the Nuffield Foundation as a mentor for young students. Scientific Awards & Recognitions: Chartered Engineer (CEng), Engineering Council UK Senior Fellow (SFHEA), Higher Education Academy Student-Voted 'Made a Difference Award' (2018) Post-Graduate Certificate in Higher Education

Greis Julieth Kim Reyesمشاهده پروفایل

استادیار

• Computational Materials Science
• Density Functional Theory
• Machine Learning in Materials Science
• +۵ مورد دیگر

Dr. Greis Julieth Kim Reyes serves as Assistant Professor of Physics in the Department of Physics and Astronomy at SUNY New Paltz, where she conducts computational research on semiconductor materials and defects. Her work bridges theoretical physics and practical materials design for energy applications. Her educational journey includes a Ph.D. in Physics from University at Buffalo (2024), Master's in Physics from Universidad Nacional de Colombia (2014), and Bachelor's in Physics-Education from Universidad Distrital Francisco José de Caldas (2010). This international background informs her interdisciplinary approach to materials science. Dr. Reyes specializes in computational exploration of intermediate band semiconductors, defect engineering, and magnetic materials using density functional theory (DFT) and machine learning. Her research reveals how atomic-scale defects create novel electronic properties, particularly in 2D materials like C 3 N/C 3 B bilayers and perovskite oxides. She employs iterative Kohn-Sham methods to simulate electronic behavior and optical responses, with recent work focusing on excitonic effects for solar energy applications. Analysis of her 15 most recent publications shows consistent emphasis on computational discovery of materials with tailored optical and electronic properties. Key trends include defect-enabled photocatalysis, interlayer exciton engineering in van der Waals heterostructures, and Jahn-Teller effects in doped semiconductors - all targeting next-generation energy technologies. Her scholarly recognition includes: Bahethi Scholarship (SUNY Buffalo, 2022) Silvestro Scholarship (SUNY Buffalo, 2022) Marshall Plan Foundation grant (Johannes Keppler Universität, 2018) As an educator, Dr. Reyes develops interactive quantum mechanics curricula using Mathematica simulations, as evidenced by her GitHub repository. She teaches General Physics and Quantum Physics courses while integrating computational tools to build student intuition for quantum materials. Though specific research students aren't listed, her teaching philosophy emphasizes critical thinking through problem-solving sessions and real-world applications. Her computational laboratory work focuses on first-principles simulations of materials, with active development of educational resources for quantum mechanics instruction. Current projects explore machine learning pipelines for materials discovery and defect-property relationships in emerging semiconductor systems.

Qiang Cuiمشاهده پروفایل

استاد

• Computational Chemistry
• Biophysics
• Molecular Biology
• +۳ مورد دیگر

Qiang Cui is a Professor of Computational Chemistry at Boston University, specializing in developing and applying advanced computational methods to study complex biomolecular systems. His research focuses on understanding mechanisms of enzymes, biomolecular machines, and bio-material interactions through multi-scale simulations, including quantum mechanical/molecular mechanical (QM/MM) approaches and coarse-grained models. Education: B.S., Chemical Physics, University of Science & Technology of China (1993) Ph.D., Physical Chemistry, Emory University (1997) Postdoctoral Associate, Harvard University (1998-2001) Research Interests: Development of novel computational techniques for simulating complex systems Study of energy transduction in molecular machines (e.g., myosin, DNA repair enzymes) Investigation of biomaterial interfaces and nanotechnology applications Protein allostery and mutational effects using machine learning Labs/Teams: The Cui Group at Boston University advances computational methodologies and collaborates on projects spanning biophysics, material science, and molecular biology.

Shuxia Taoمشاهده پروفایل

دانشیار

• Computational Materials Physics
• Machine Learning for Materials Design
• Semiconducting Materials
• +۵ مورد دیگر

Shuxia Tao is an Associate Professor at the Department of Applied Physics, Eindhoven University of Technology (TU/e), leading the Computational Materials Physics Group. Her research bridges quantum mechanics and macroscopic material performance through first-principles simulations and machine learning. Education: MSc in Physical Chemistry (Nankai University, China) and PhD (TU/e, 2011) in computational materials design. Roles: Affiliated with Materials Simulation & Modelling (MSM) division, Computational Center for Energy Research (CCER), and Eindhoven Institute for Renewable Energy Systems (EIRES). Her work focuses on chiral-induced spin selectivity (CISS), spin electrochemistry (SEC), and quantum phenomena in halide, oxide, and nitride materials. Recent publications analyze temperature-dependent chirality in perovskites, circular dichroism modeling, and band gap engineering for optoelectronic applications. Scientific awards include: 2024 NWO Open Competition M Grant 2024 ERC Consolidator Grant 2023 Aspasia Award 2022 NWO VIDI 2019 NWO START-UP 2016 CSER Tenure Track Fellowship She serves as Associate Editor for AI for Science (IOP Publishing) and on editorial boards of Applied Physics Letters . Her group collaborates with national and international research communities in Nano, Quantum, and Materials Physics.

John Kiefferمشاهده پروفایل

استاد

• Materials Simulation
• Energy Materials
• Molecular Dynamics
• +۶ مورد دیگر

John Kieffer is a Professor in the Department of Materials Science & Engineering at the University of Michigan College of Engineering. His research focuses on simulation-based predictive design of materials for energy applications, combining computational methods with experimental validation. M.S. in Metallurgy from Clausthal Technical University (1980) Ph.D. in Materials Science from Clausthal Technical University (1985) His research integrates first-principles quantum mechanical calculations , reactive molecular dynamics simulations , and hybrid Monte Carlo/molecular dynamics techniques to study materials for photovoltaics, batteries, and composite materials. Experimental validation includes dielectric impedance spectroscopy and inelastic light scattering , complemented by sol-gel synthesis of organic-inorganic hybrids. Current projects sponsored by NASA and the National Science Foundation explore amorphous systems , self-healing polymers , Li-ion battery electrolytes , and high-pressure materials . His work also involves multiscale simulation of nucleation and crystal growth. Morey Award Lecture (2002) Kieffer's facilities include a unique tandem Fabry-Perot interferometer for concurrent Raman and Brillouin light scattering measurements, enabling non-contact nano-scale monitoring of reacting systems.

Inna Ponomarevaمشاهده پروفایل

استاد

• Computational Materials Science
• Ferroic Materials
• Phase Transitions
• +۲ مورد دیگر

Inna Ponomareva is Professor and Director of Graduate Admissions in Physics at the University of South Florida. She leads the Computational Nanoscience Lab, specializing in ferroic materials using atomistic simulations and machine learning. Research explores phase transitions, nanoscale phenomena, and caloric effects in functional materials. Current group includes 4 researchers focusing on: Halide perovskite spin physics Ultra-thin ferroelectric behavior Multicaloric effects Recent publications demonstrate advances in controlling spin textures via strain and intercalation in 2D materials. Teaches quantum mechanics and computational physics courses. Recognized with SIGMOD Distinguished Reviewer Award and ELIDEK grants.

Anirban Chakrabortiمشاهده پروفایل

استاد

• Econophysics
• Sociophysics
• Complex Systems
• +۱۴ مورد دیگر

Anirban Chakraborti is a Professor at the School of Computational and Integrative Sciences, Jawaharlal Nehru University (JNU), New Delhi, India, where he has been a faculty member since 2014. He previously held academic positions at École Centrale Paris (France) as Chercheur Senior (Associate Professor) and Chargé de Recherche (Assistant Professor), and earlier roles at Banaras Hindu University, Brookhaven National Laboratory (USA), and Helsinki University of Technology (Finland). He is a leading figure in the interdisciplinary field of econophysics and complex systems. Education: Diplôme d’Habilitation à Diriger des Recherches (2013), Université Pierre et Marie Curie – Paris VI, France (Physics) Ph.D. in Physics (2003), Saha Institute of Nuclear Physics, Jadavpur University, India Post-M.Sc. in Physics (1999), Saha Institute of Nuclear Physics, Jadavpur University, India (Ranked First) M.Sc. in Physics (1998), University of Calcutta, India (Ranked First) B.Sc. in Physics (1996), Scottish Church College, University of Calcutta, India His research interests lie at the intersection of physics, economics, and data science. He is particularly known for pioneering work in econophysics , including the statistical mechanics of money, wealth distribution, agent-based market models, and network-based analysis of financial and social systems. He also works on complex systems , computational finance , statistical physics , and nanosciences , with applications in sensing and imaging. His work often involves modeling socio-economic phenomena using tools from statistical physics. His recent publications span topics such as financial fluctuations, wealth inequality, network analysis of conflicts, order book dynamics, and nanomaterial characterization. These works reflect a strong trend toward interdisciplinary research combining physics, economics, and data analytics, with a focus on real-world applications in finance, inequality, and social systems. Scientific Awards: Indian National Science Academy Young Scientist Medal (2009) He has advised Ph.D. students such as Kiran Sharma and leads the ETC (Experimental-Theoretical-Computational) Lab at JNU, which brings together physicists, computer scientists, and mathematicians. The lab has been involved in international collaborations, including projects funded by the Estonian Ministry of Education and Research and consultancies with TCS Innovation Labs and DONO Consulting. His research has been supported through grants and collaborative projects, reflecting strong industry and global academic engagement.

Austin Sendekمشاهده پروفایل

استاد مدعو

• Materials Science
• Machine Learning
• Artificial Intelligence
• +۳ مورد دیگر

Austin Sendek serves as an Adjunct Professor in the Department of Materials Science and Engineering at Stanford University, where he applies machine learning and AI to accelerate materials discovery for decarbonization. He is the Founder and CEO of Aionics, Inc., a company specializing in AI-driven battery design using high-performance computing. At Stanford, he collaborates on research initiatives spanning fundamental science to entrepreneurship mentoring. His academic background includes: B.S. in Applied Physics, UC Davis Ph.D. in Applied Physics, Stanford University Sendek's research focuses on computational materials science for battery technologies. He develops machine learning models to predict material properties, design solid-state electrolytes, and optimize battery performance. His work integrates quantum mechanical simulations with experimental data to overcome challenges in the small data regime, aiming to expedite next-generation energy storage solutions. His publication record demonstrates a strong commitment to advancing battery materials through interdisciplinary approaches. Recent work emphasizes machine learning applications for solid electrolyte discovery, interface engineering, and cathode material design. The research consistently addresses critical barriers in solid-state batteries, such as ionic conductivity and stability, while leveraging data-driven methods to navigate vast materials spaces. Awards: Forbes 30 Under 30 in Energy (2019) Sendek has mentored students as a Guest Lecturer at Columbia University and through Stanford affiliations. His leadership at Aionics, Inc., fosters a collaborative environment for AI and battery research, facilitating industry-academia partnerships and grant-funded projects. He directs Aionics, Inc., which operates at the intersection of AI and battery innovation. At Stanford, he engages with research teams to bridge computational discovery and practical battery applications.