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Craig J. FennieView profile

Professor

• Computational Materials Physics
• Condensed Matter Physics
• Solid State Chemistry
• +4 more

Craig J. Fennie is Professor in the College of Engineering at Cornell University , where he has been on the faculty since July 2008. His appointment spans the interdisciplinary space between physics, materials science, and solid-state chemistry. Education: Ph.D. in Physics, Rutgers University (2006) M.S. in Electrical Engineering, Villanova University (1996) Research Focus: Fennie’s group pursues computational and theoretical materials physics , with a long-term goal of realizing a first-principles “materials-by-design” paradigm. By integrating microscopic Hamiltonians, symmetry analysis, and density-functional theory, the team explores how crystalline motifs—composition, symmetry, geometry, and topology—govern emergent phenomena such as multiferroicity, magnetoelectric coupling, and tunable dielectric response. Particular attention is paid to complex oxides, layered heterostructures, and materials under extreme conditions where chemical intuition can fail. Publication Trends: From 2011 to 2016, Fennie co-authored a series of high-impact papers in Nature family journals and Physical Review Letters . These works collectively advance the understanding and design of room-temperature magnetoelectric multiferroics, bulk magnetoelectric effects in hexagonal manganites, tunable microwave dielectrics through defect mitigation, and the fundamental scarcity of ferroelectric perovskites. The studies span synthesis, characterization, and theory, underscoring a collaborative, interdisciplinary approach. Awards & Honors: MacArthur Fellowship, John D. and Catherine T. MacArthur Foundation (2013) Presidential Early Career Award for Scientists and Engineers (PECASE, 2012) NSF CAREER Award (2011) Army Research Office Young Investigator Award (2010) Fellow of the American Physical Society (2015) Advising & Funding: While specific student names are not listed, Fennie leads a vibrant research program supported by NSF, ARO, and other agencies. His group trains graduate and post-doctoral researchers at the intersection of physics, materials science, and chemistry, preparing them for careers in both academia and industry. Labs & Teams: Work is conducted within Cornell’s shared high-performance computing ecosystem and leverages national user facilities such as the Cornell High Energy Synchrotron Source (CHESS) and collaborations with experimental groups worldwide.

Kai S. ExnerView profile

Professor

• Electrocatalysis
• Inorganic Chemistry
• Materials Science
• +7 more

Prof. Kai S. Exner is a Professor of Theoretical Inorganic Chemistry at the University of Duisburg-Essen's Faculty of Chemistry and a member of the Zentrum für Medizinische Biotechnologie (ZMB). His research focuses on electrocatalysis , particularly optimizing electrode materials for energy applications such as batteries, electrolyzers, and fuel cells. He employs computational methods like ab initio calculations and microkinetic modeling to bridge theoretical insights with experimental and industrial applications, emphasizing sustainable catalyst design without relying on noble metals. His work integrates basic research with applied medical biotechnology through ZMB collaborations, though his primary affiliation lies in the Faculty of Chemistry. Exner has pioneered concepts like the electrochemical-step asymmetry index and free-energy span model, which are critical for screening electrocatalysts. His research group, 'Theoretical Inorganic Chemistry,' actively explores mechanisms behind oxygen and chlorine evolution reactions, emphasizing selectivity control and reaction pathway complexity. Exner received the Gottschalk-Diederich-Baedeker-Preis (2024) for advancing the energy transition through foundational electrocatalysis research. He leads Aktuelles Projekt funded by third-party grants and collaborates with facilities like the Analytics Core Facility Essen (ACE) and Imaging Center Campus Essen (ICCE). His advisory role focuses on guiding theoretical and computational approaches in electrochemistry, and he has established a reputation for interdisciplinary research connecting computational chemistry with practical energy solutions.

• Data Science
• Machine Learning
• Computational Harmonic Analysis
• +6 more

Pierre Vandergheynst is a Full Professor at the Swiss Federal Institute of Technology Lausanne (EPFL) in the Department of Electrical Engineering, with a courtesy appointment in Computer and Communication Sciences. He serves as EPFL’s Vice-Provost for Education since 2015 and leads the Signal Processing Laboratory 2 (LTS2). His research spans harmonic analysis, sparse approximations, mathematical data processing, and applications in signal/image processing, computer vision, machine learning, and graph-based data analysis. PhD in Mathematical Physics (1998), Université catholique de Louvain Postdoctoral Researcher at EPFL (1998-2001) Assistant Professor at EPFL (2002-2007) His research explores geometry/symmetry in high-dimensional data, redundant dictionaries for dimensionality reduction, and computational harmonic analysis on manifolds. Recent work focuses on protein structure modeling, geometric deep learning, and graph-based signal processing. Key article trends include graph neural networks for protein analysis, geometric deep learning in neuroscience, and structured knowledge priors in neural models. His 2023-2025 publications emphasize interpretable AI, long-range dependencies in graphs, and molecular representation learning. Scientific Awards: IEEE Signal Processing Magazine Best Paper Award (2023) Signal Processing Society Best Paper Award (2022) Apple ARTS Award (2007) De Boelpaepe Prize, Royal Academy of Sciences of Belgium (2009-2010) He has supervised over 30 PhD theses and contributed to foundational work in graph signal processing, compressive sensing, and geometric deep learning. His lab develops tools for data science on non-Euclidean structures, with applications in medicine, astronomy, and wireless systems.

Zhenman FangView profile

Associate Professor

• Customizable Computing
• Heterogeneous Computing
• FPGA Acceleration
• +8 more

Dr. Zhenman Fang is an Associate Professor at the School of Engineering Science , Simon Fraser University (SFU) , where he founded and directs the HiAccel Lab . He also holds an associate membership in the School of Computing Science at SFU. His research focuses on customizable computing with software-defined hardware acceleration , addressing performance, energy-efficiency, and reliability in post-Moore’s law computing across domains like machine learning , big data analytics , quantum chemistry , and precision medicine . Education: Ph.D. in Computer Science from Fudan University (2014), with a visit to University of Minnesota during his studies. Postdoctoral Work: University of California, Los Angeles (UCLA) (2014-2017). Industry Experience: Staff Software Engineer at Xilinx (2017-2019). Dr. Fang’s research spans the entire computing stack , including application characterization , accelerator-rich architecture design , and programming/tool support . He has developed frameworks like HiSpMV , SyncNN , and SQL2FPGA , emphasizing FPGA acceleration for vision transformers , quantum chemistry , and spiking neural networks . His work has been recognized with 3 best paper awards (FPL 2024, TCAD 2019, MEMSYS 2017) and 3 best paper nominees (FCCM 2025, HPCA 2017, ISPASS 2018). Recent publications highlight trends in low-precision machine learning ( ShiftQuant , ESRU ), quantum chemistry acceleration ( SERI ), and vision transformer optimization ( Quasar-ViT ). His HiAccel Lab actively mentors PhD and MASc students , with notable graduates like Alec Lu (PhD 2024, now at Meta) and Philip Stachura (MASc, now with BC Graduate Scholarship). Scientific Awards: Inaugural SFU Research Excellence Award - Horizon Award (2025) FPL 2024 Stamatis Vassiliadis Best Paper NSERC Alliance Award (2020) CFI JELF Award (2019) Xilinx University Program Award (2019) IEEE Senior Member (2023) Grants: NSERC Discovery Grant (2019) CFI JELF Funding (2019) Huawei and Xilinx sponsorships Dr. Fang leads open-source initiatives like SyncNN , PASTA , and SQL2FPGA , and serves as General Chair for ASAP 2025 and Program Co-Chair for RAW 2025 . His lab collaborates globally with institutions such as UCLA , Northeastern University , and Xidian University .

Samir H. MushrifView profile

Professor

• Computational Catalysis
• Molecular Modeling
• Reaction Engineering
• +7 more

Dr. Samir H. Mushrif is a Professor in the Department of Chemical and Materials Engineering at the University of Alberta . Prior to this role, he served as faculty at the School of Chemical and Biomedical Engineering at Nanyang Technological University (NTU), Singapore . He holds a PhD in Chemical Engineering from McGill University and completed postdoctoral research at the University of Delaware, USA . Education : PhD (Chemical Engineering, McGill University), Postdoc (University of Delaware) His research focuses on computational catalysis , molecular modeling , and reaction engineering for biomass conversion and CO2 reduction . He develops novel catalysts, solvents, and reactor systems using integrated quantum mechanical and classical molecular simulations , synergized with experimental data to enable sustainable energy and chemical production . Recent publications highlight trends in condensed phase chemistry for biomass reactions, machine learning applications in solvent configuration prediction, and mechanistic studies of lignin-carbohydrate complex deconstruction. His work bridges methane activation on metal oxides, hydrodeoxygenation of bio-oil compounds, and polymerization pathways in lignin structures. Scientific Awards include: NSERC Doctoral and Post-doctoral Fellowships Discovery International Award 2017 (Australian Research Council) NANYANG EDUCATION AWARD 2016 (Singapore) SCBE Teaching Excellence Awards (Silver 2015, Gold 2016) Bharat Gaurav (Pride of India) Award 2014 Dr. Mushrif's NSERC Discovery Grant (2018), CFI John R. Evans Leaders Fund Grant (2022), and AcRF Tier-2 Grant (Singapore, 2015) have advanced his work. Current PhD and Master's students include José Carlos Velasco Calderón , Arul Mozhi Devan Padmanathan , and Sagar Bathla , among others. The CARES Lab (Catalysis Research for Sustainability) under his leadership combines ab initio molecular dynamics , machine learning potentials , and Density Functional Theory to design materials for renewable energy . Collaborations span institutions in France , Canada , India , and the UK .

Alexander HolleitnerView profile

Professor

• Nanotechnology
• Quantum Electronics
• Optoelectronics
• +8 more

Prof. Alexander Holleitner leads the Chair of Nanotechnology and Nanomaterials at the Department of Physics, Technical University of Munich , under the Walter Schottky Institute. His research focuses on ultrafast optoelectronics, quantum optoelectronics, and excitonic systems in nanoscale circuits. Research Directions : Ultrafast optoelectronics, quantum optoelectronics, excitonic systems, THz time-domain spectroscopy, and nanofabrication of mixed organic/inorganic systems. Publications : Recent work spans hyperbolic polaritons, interlayer excitons, graphene nano-gap dynamics, and defect engineering in 2D materials. Collaborations include interdisciplinary projects with groups studying semiconductor heterostructures and quantum technologies. His lab welcomes students and researchers interested in experimental physics, quantum electronics, and nanofabrication.

Qian YangView profile

Assistant Professor

• Human-AI Interaction
• User Experience (UX) Design
• Healthcare Technology
• +4 more

Qian Yang is an Assistant Professor in Information Science at Cornell University, with a faculty appointment in Computer Science. Her research focuses on human-AI interaction, designing AI applications for healthcare, autonomous systems, and UX design. She holds a PhD in Human-Computer Interaction from Carnegie Mellon University and has industry experience in design consultancy (2007–2014). Yang co-directs the Cornell Digital and AI Literacy Initiative and is a Senior Fellow at the Cornell Brooks Tech Policy Institute. Her work emphasizes bridging AI technologies with societal needs, supported by awards like the Schmidt Futures AI2050 Fellowship. She leads the DesignAI research group, focusing on AI-driven design methods and tools for practitioners. Education: PhD in HCI (Carnegie Mellon), M.S. in HCI (CMU), M.Des in Design, B.Eng in Industrial Design (Shanghai Jiao Tong University). Research interests include AI ethics, healthcare decision support systems, and context-aware mobile services. Notable contributions include: Developing AI systems for life-critical healthcare decisions (e.g., artificial heart implants) Innovating UX design methods for integrating AI into practice Advancing human-centered AI evaluation frameworks Awards include the SIGCHI Outstanding Dissertation Award and AI2050 Fellowship. She actively collaborates across disciplines, with work published in top HCI venues like CHI and ACM Transactions.

Quek Su YingView profile

Associate Professor

• Materials Science
• Condensed Matter Physics
• Theoretical Physics
• +5 more

Associate Professor Quek Su Ying is affiliated with the Department of Physics at the National University of Singapore (NUS) and serves as Assistant Dean (Special Duties). Her research focuses on theoretical and computational approaches to understanding the electronic, vibrational, and transport properties of emerging materials, particularly 2D and organic systems. Affiliations : Institute of High Performance Computing, Centre for Advanced 2D Materials, NUS Research Interests : First principles calculations (mean field and many-electron perturbation theory), interface science, electronic energy level alignment, and transport in emerging materials. Her work includes studies of exciton condensation, quantum emitters, and valleytronic control via magnetic fields. Article Trends : Recent publications highlight investigations into 2D materials, organic-inorganic interfaces, and quantum phenomena. Topics include exciton dynamics, defect engineering, charge density waves, and spin-dependent transport, employing advanced ab initio methods like GW theory. Scientific Awards : Singapore NRF Fellowship Advising & Collaborations : Her group develops state-of-the-art computational methods and collaborates with experimental teams. Notable affiliations include Google Scholar Profile and partnerships with institutions like the Institute of High Performance Computing. Labs & Teams : Associated with the Centre for Advanced 2D Materials at NUS, which supports interdisciplinary research on graphene and related 2D systems. Her work bridges theoretical modeling and experimental validation.

Dane MorganView profile

Professor

• Computational materials science
• Ab initio electronic structure
• Multiscale methods
• +4 more

Dane Morgan is a Professor in the Department of Materials Science & Engineering at the University of Wisconsin-Madison, College of Engineering. His research focuses on computational materials science for materials design, including ab initio electronic structure modeling, multiscale methods, and machine learning applications in materials discovery. His work spans nuclear materials, battery and fuel cell electrodes, and electronic materials. Education : PhD, 1998, University of California, Berkeley MS, 1994, University of California, Berkeley BA, 1992, Swarthmore College Research Interests : Computational materials science, ab initio methods for electronic structure and thermokinetics, machine learning for materials discovery, electrochemical systems modeling, and applications in nuclear materials, batteries, and electronic materials. His work integrates advanced computational techniques with experimental validation. Scientific Awards : 2024 APL Materials, Editors Pick 2023 Microscopy and Microanalysis Best Paper Award (Instrumentation and Software category) 2023 IEEE Transactions on Plasma Science Best Paper Award 2023 Kellet Mid-Career Award 2015 TMS Materials Genome Initiative Ambassador 2006 3M Technical Nontenured Faculty Grant

Amit SingerView profile

Professor

• Computational Mathematics
• Structural Biology
• Cryo-Electron Microscopy
• +3 more

Amit Singer is a Professor of Mathematics at Princeton University, specializing in computational methods for structural biology and cryo-electron microscopy (cryo-EM). His work focuses on developing mathematical frameworks and algorithms for analyzing large-scale microscopy datasets, particularly in 3D reconstruction and heterogeneity analysis of molecular structures. He leads research in manifold learning, optimal transport, and harmonic analysis, with applications to cryo-EM, signal processing, and inverse problems. Research interests include: (1) Mathematical methods for cryo-EM, including particle alignment, density map analysis, and subspace-based reconstruction techniques; (2) Development of rotation-invariant representations for imaging problems; (3) Application of machine learning and optimization to biomedical imaging challenges. His contributions bridge pure mathematics (e.g., harmonic analysis, manifold theory) with applied computational techniques for real-world microscopy data. Key trends in his recent articles (2023–2025) include advancements in multi-reference alignment methods, Wasserstein distance-based image registration, and overcoming particle detection limitations in cryo-EM. He also explores sparsity constraints, autocorrelation analysis, and novel algorithms for handling heterogeneous datasets. These methods improve resolution and reduce computational costs in analyzing molecular structures at atomic scales. Notable contributions include the ASPiRE software package for steerable PCA, and foundational work on synchronization problems in cryo-EM orientation estimation. His research often addresses algorithmic scalability and robustness to noise in experimental setups.

Steven A. CorcelliView profile

Professor

• Theoretical Chemistry
• Computational Molecular Science
• Biomolecular Binding
• +5 more

Steven A. Corcelli is a Professor and Interim Dean of the College of Science at the University of Notre Dame, with a research focus on Theoretical Chemistry and Molecular Dynamics Simulations . His work bridges Physical Chemistry and Biochemistry , targeting Energy Applications and Biomolecular Binding Mechanisms . He leads the Computational Molecular Science & Engineering Laboratory (CoMSEL). Ph.D., Chemistry, Yale University (2001) Sc.B., Chemistry, Brown University (1997) Research interests span ionic liquids for Carbon Capture , aqueous electrolytes in battery technologies , and molecular binding processes in immunology and DNA interactions . His group employs GPU-accelerated simulations and weighted ensemble methods to uncover structural and dynamic motifs. Recent publications highlight trends in vibrational spectroscopy , TCR-MHC binding , and CO2 solvation mechanisms . Awards include the Thomas P. Madden Award (2020) , ACS Fellowship (2016) , and NSF CAREER Award (2009) . Staff: Erin Brossard (Ph.D.), Nell Karpinski, Shuang Wu, Noah Vasconez, Kaitlyn Handy, Isabel Thompson

ZHANG YangView profile

Professor

• Computational Biology
• Bioinformatics
• Algorithms
• +6 more

Dr. Yang Zhang is a Professor at the National University of Singapore (NUS), holding appointments in the Department of Computer Science (School of Computing) and the Department of Biochemistry (Yong Loo Lin School of Medicine). He also leads the Zhang Lab, which focuses on AI-driven computational methods for protein structure prediction and design. Previously, he was a Professor at the University of Michigan. His research integrates artificial intelligence, deep learning, and physics-based models to address challenges in computational biology. Affiliations: School of Computing; Yong Loo Lin School of Medicine; Cancer Science Institute of Singapore Key Roles: Principal Investigator of Zhang Lab; Developer of I-TASSER algorithm Research interests span AI-driven protein design, deep learning for RNA structure prediction, and drug discovery. Projects include the EvoDesign server for protein interaction design and TripletRes for coevolution-based contact prediction. Major contributions include the I-TASSER algorithm, ranked top in CASP experiments for protein structure prediction. Awards: Alfred P. Sloan Award, NSF CAREER Award, and seven-time Highly Cited Researcher (2015–2021).

OU PengfeiView profile

Assistant Professor

• Computational Catalysis
• Electrochemical Reactions
• Ab Initio Calculations
• +3 more

Asst. Prof. OU Pengfei is an Assistant Professor and NUS Presidential Young Professor in the Department of Chemistry at the National University of Singapore, Faculty of Science. He leads the AI for Chemistry (AI4Chem) research group, focusing on computational catalysis, machine learning, and materials science. Previously, he was a Research Associate at Northwestern University and a Postdoctoral Fellow at the University of Toronto under Prof. Edward H. Sargent, and earned his Ph.D. from McGill University. Education: Ph.D., McGill University, 2020 M.Eng., Central South University, 2015 B.Eng., Central South University, 2012 Research interests include catalyst design for electrochemical reactions using ab initio DFT, molecular dynamics simulations, and AI-driven methods. He develops dynamic simulations of chemical processes under reaction conditions and machine learning tools for accelerated catalyst discovery. His work addresses challenges in energy and environmental applications such as CO2 reduction and hydrogen evolution. Notable awards include the NUS Presidential Young Professorship (2024), Climate Positive Energy Postdoctoral Fellowship (2021), and Chinese Government Award for Outstanding Self-Financed Students Abroad (2020). Labs/Teams: The AI4Chem group integrates theory-guided and data-driven approaches to advance computational catalysis, with three core research directions: (1) reaction mechanism exploration and catalyst optimization, (2) dynamic structure-performance relationships under reaction conditions, and (3) machine learning algorithms for high-throughput screening.

Xiaofeng QianView profile

Associate Professor

• Materials Theory
• Quantum Materials
• 2D Materials
• +7 more

Dr. Xiaofeng Qian is an Associate Professor in the Department of Materials Science & Engineering at Texas A&M University, with joint appointments in Physics and Astronomy, and Electrical & Computer Engineering. His research focuses on materials theory , quantum materials design , and high-throughput computational discovery , particularly for 2D materials and energy applications . Educational Background: Ph.D., Nuclear Science and Engineering, Massachusetts Institute of Technology (2008) B.S., Engineering Physics, Tsinghua University (2001) Research spans first-principles electronic structure methods , nonlinear optical responses , and multiscale modeling of electronic, thermal, and ionic transport. Key areas include quantum spin Hall effect , ferroelectric switching , and machine learning for materials prediction . Notable Awards: Dean of Engineering Excellence Award (2024) Engineering Genesis Multidisciplinary Award (2024) AZZ Faculty Fellow (2021) NSF CAREER Award (2018) Manson Benedict Fellowship (2006) Actively recruiting PhD, MS, and UG researchers with backgrounds in physics, materials science, or computational methods. Collaborates extensively on hybrid AI-materials projects and topological device concepts .

• Contact Mechanics
• Tribology
• Advanced Manufacturing
• +5 more

Hongtao Zhu is an Associate Professor at the University of Wollongong (UOW), Australia, affiliated with the School of Mechanical, Materials, Mechatronic and Biomedical Engineering. He holds a PhD from Northeastern University (China) and has held academic positions at UOW since 2002, progressing from Research Fellow (2002–2013) to Senior Lecturer/Lecturer (2013–2022) before becoming an Associate Professor in 2022. His research focuses on Contact Mechanics , Tribology , Advanced Manufacturing , and Computational Material Science . Notable contributions include studies on rail-wheel contact systems, high-entropy alloys, and friction reduction in manufacturing processes. He has authored/co-authored over 230 publications with an H-index of 38 (Scopus) and secured $2.2M in industry grants. Zhu leads the 'Wheel and Rail Contact System' research group, collaborating with major railway industries. He has supervised 22 PhD students (6 as principal supervisor) and currently oversees 5 PhD candidates. His professional roles include editorial board memberships for journals like Advances in Materials Science and Engineering and service as an assessor for funding bodies in Australia, Chile, Poland, and the Czech Republic. Key achievements include securing 5 ARC Discovery Projects, 3 ARC Linkage Projects, and industry collaborations with organizations like Sydney Trains and Comsteel. His work addresses critical challenges in rail infrastructure durability, material optimization, and sustainable manufacturing.