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Hamed DalirView profile

Associate Professor

• Mid-IR Photonics
• On-chip Light Sources
• Classical & Quantum Sensors
• +3 more

Hamed Dalir is an Associate Professor of Electrical and Computer Engineering at the University of Florida, serving as Area Chair for Electronics since 2025. His research focuses on Mid-IR Photonics, On-chip Light Sources, Classical & Quantum Sensors, Ising Machine, AI Accelerators, and Photonics Integrated Circuits. PD, University of California – Berkeley, USA (2016) Ph.D., Institute of Science Tokyo, Japan (2014) M.Sc., Institute of Science Tokyo, Japan (2011) PD, University of Florida (2025) Visiting Scholar, University of Texas at Austin (2025) PD, The George Washington University (2023-2025) Dr. Dalir's research bridges photonic computing, semiconductor lasers, AI hardware, and integrated sensing systems. He has over 300 peer-reviewed publications and 20 patents, with industry collaborations from TSMC, Broadcom, and Fuji Xerox. His work includes photonic neural networks, orbital angular momentum beams, and quantum sensing. The 15 most recent publications highlight trends in AI acceleration, quantum optics, and semiconductor packaging. Key themes include photonic convolutional neural networks, orbital angular momentum beam processing, phase-change materials, and advanced packaging techniques for reliability and security. His lab develops hardware-efficient algorithms and hybrid optical-electronic systems for computing and sensing. ECE Teaching Excellence Award, 2025 Best Project Collaboration Award, TSMC, 2025 ECE Faculty of the Year (Student Choice), 2024–25 Rising Star in Photonics, ACS, 2025 Stanford–Elsevier Top 2% Scientists Ranking (top 0.3% in field), 2024 IAAM Scientist Medal & Fellow, 2024 SPIE Senior Member, 2023 AFWERX Commercialization Solutions, 2022 SPIE Community Champion, 2020 IEEE/Optica Senior Member, 2018 JSPS Fellow, 2015 KAKENHI Early Investigator Award, 2014 JSPS Young Scientist Award, 2014 GCOE Golden Award, Tokyo, 2012 Yoshida Foundation Award, 2009 Dr. Dalir advises current Ph.D. students Sydney Mutchnick, Salem Altaleb, Bel Jahannia, Joseph Crandall, Abdulaziz Bazammul, and Vahid Najmi. Former advisees include Dr. Jiachi Ye (Ph.D. 2025) and Dr. Haoyan Kang (Ph.D. 2023). His lab (CHIP Lab) collaborates with institutions like TSMC and Volkswagen Group, focusing on wafer-scale fabrication, photonic neural networks, and quantum sensing.

Erik SorensenView profile

Professor

• Condensed Matter Physics
• Quantum Mechanics
• Superconductivity
• +3 more

Erik Sorensen is a Professor of Physics & Astronomy at McMaster University, Canada. His research focuses on quantum spin systems, superconductivity, and entanglement in condensed matter systems. He earned his PhD in Theoretical Physics from the University of California, Santa Cruz (1992), and holds MSc and BSc degrees from Aarhus University (Denmark). Education: PhD, Theoretical Physics, UC Santa Cruz (1988–1992) MSc, Physics, Aarhus University (1985–1987) BSc (Honors), Physics & Mathematics, Aarhus University (1982–1985) BSc (Honors), Computer Science, Aarhus University (1981–1985) His research interests span quantum spin systems, superconductivity, entanglement entropy, and quantum Monte Carlo methods. Notable work includes studies on Kondo screening clouds, topological superconductivity in noncentrosymmetric materials, and the dynamics of quantum criticality. He has authored over 150 peer-reviewed articles in journals like Physical Review B , Nature Communications , and New Journal of Physics . Teaching activities include courses on Computational Physics, Condensed Matter Physics, and Quantum Information at both undergraduate and graduate levels.

Andrew A. BETTIOLView profile

Associate Professor

• Quantum Materials
• Radiobiology
• Ion Beam Technology
• +3 more

Andrew A. Bettiol is an Associate Professor and Deputy Head (Resources & Exec/Admin Staff) at the National University of Singapore (NUS), affiliated with the Centre for Ion Beam Applications (CIBA). He holds a PhD from the University of Melbourne (1999). His research focuses on advanced ion beam technologies for quantum materials and biomedicine, including quantum defect engineering in diamond and 2D materials, radiobiology studies using proton beams, and novel detector systems. Key research areas include creating spin defects in materials like hBN and SiC for quantum sensing, developing single-proton dosimetry for targeted cellular studies, and optimizing proton beam applications in medicine and materials science. His work bridges fundamental physics with applied technologies, such as diamond-based photon emitters and proton therapy improvements. Notable contributions include high-resolution ion beam microscopy of whole cells, single-proton counting scintillators, and defect-engineered quantum systems. His research emphasizes interdisciplinary approaches in quantum technologies and biomedical imaging. Dr. Bettiol leads projects at CIBA, collaborating on quantum communication, radiation biology, and accelerator-driven materials innovations. His studies span from nanoscale defect characterization to large-scale biomedical and industrial applications.

Carl FranckView profile

Associate Professor

• Biological Physics
• Experimental Condensed Matter Physics
• Atomic Physics
• +3 more

Carl Franck is an Associate Professor of Physics at Cornell University, affiliated with the College of Arts and Sciences. He has maintained a continuous faculty position since 1982, progressing from Assistant Professor (1982-1988) to Associate Professor (1988-present), with a Visiting Professorship at the University of Bristol in 1991. His research bridges experimental physics and biological systems, employing quantitative approaches to complex phenomena. Dr. Franck's academic foundation includes: A.B. from Harvard College (1974) Ph.D. from Princeton University (1978) His research program focuses on biological physics and experimental condensed matter physics. Dr. Franck's group has pioneered the application of x-ray techniques to study photon-electron interactions and correlated electron dynamics, while simultaneously exploring microbial collective behavior—particularly Dictyostelium discoideum's transition from unicellular to multicellular life. His laboratory employs microfluidic technology, light scattering experiments, and advanced microscopy to quantify cellular signaling processes and population dynamics. The group has developed innovative automated cell counting systems and specialized equipment for measuring cell growth kinetics, contributing significantly to understanding how cells process information through chemical signaling. Dr. Franck's publication record shows consistent output across two major research domains, with recent work (2020-2022) continuing to advance both x-ray physics and microbial behavior studies. His research demonstrates a distinctive pattern of maintaining parallel investigations in condensed matter and biological physics while identifying common principles of collective behavior across different scales. His scientific contributions have been recognized through membership in the American Physical Society: Member, American Physical Society Dr. Franck has mentored an extensive network of students across multiple generations, currently advising Christopher Donohue, Rowan Hess, Yasmine Meziani, Gwendolyn Parks, and Daren Chen. His former students include Igor Segota, Elijah Bogart, Kayvon Daie, Albert Bae, and numerous undergraduate researchers who have contributed to his work on microbial signaling and condensed matter systems. His mentoring approach emphasizes interdisciplinary collaboration and the development of novel experimental techniques. The Franck Group operates at the physics-biology-engineering interface, maintaining specialized facilities for light scattering experiments and microfluidic research. The laboratory has developed unique methodologies for measuring cell population dynamics at low densities using laser-based detection systems, and continues to explore the quantitative bases of communication and computation in living matter through both experimental and theoretical approaches.

Ken MaiView profile

Research Professor

• High-Performance Circuit Design
• Secure IC Design
• Radiation Hardening
• +4 more

Ken Mai is a Principal Systems Scientist in the Department of Electrical and Computer Engineering at Carnegie Mellon University (CMU), part of the College of Engineering. He holds a B.S., M.S., and Ph.D. in Electrical Engineering from Stanford University. His research focuses on high-performance circuit design, secure IC design, radiation hardening, reconfigurable computing, and computer architecture. He has received prestigious awards including the NSF CAREER Award and the George Tallman Ladd Research Award. Education: Ph.D., Electrical Engineering, Stanford University, 2005 M.S., Electrical Engineering, Stanford University, 1997 B.S., Electrical Engineering, Stanford University, 1993 Research Interests: Dr. Mai’s work addresses challenges in nanometer-scale CMOS technology, including interconnect delay, leakage, and soft errors. His projects include secure IC design against hardware attacks, robust memory techniques using digital communications, 100GHz logic via cryogenic cooling, and bio-implantable computing platforms. He collaborates with neurosurgeons at the University of Pittsburgh for medical applications. Key Projects: Secure IC Design: Countermeasures against invasive/non-invasive attacks Robust Memory Design: Error correction codes for resilience 100GHz Logic: Cryogenically cooled CMOS for high-speed applications Bio-Implantable Systems: Low-power, high-density computing for medical devices Awards: NSF CAREER Award George Tallman Ladd Research Award Eta Kappa Nu Excellence in Teaching Award Teaching and Advising: He teaches courses like 18-322 (Digital Circuits), 18-617 (Memory Systems), and advises students including Mudit Bhargava and Mark McCartney. His lab, the VLSI Design Group, develops tools and methodologies linking circuit design to architectural design. Contact: Email: kenmai@ece.cmu.edu Website: Carnegie Mellon VLSI Design Group

Zubair Abdullah-VetterView profile

Research Fellow

• Machine Learning
• Renewable Energy Engineering
• Solar Cell Technology
• +3 more

Dr. Zubair Abdullah-Vetter is a Research Fellow at the University of New South Wales (UNSW) , affiliated with the College of Engineering and the Photovoltaic and Renewable Energy Engineering department. His work focuses on integrating advanced machine learning techniques with photovoltaic device analysis to enhance solar energy system quality and reliability. PhD in Photovoltaic Engineering Specializes in AI-driven solar cell diagnostics Active in both journal publications and conference presentations Research interests center on machine learning for renewable energy systems , particularly: Deep learning for defect analysis in solar cells Predictive modeling of photovoltaic degradation Accelerated testing protocols using AI Quantum efficiency measurement automation Luminescence image interpretation Time-series analysis of PV performance Recent publications demonstrate expertise in applying neural networks and regression techniques to photovoltaic diagnostics. The work has significant implications for climate change mitigation through improved solar technology efficiency. Scientific recognition includes: BT Imaging Photovoltaic Thesis Prize (2020) Best Poster Award at IEEE PVSC (2022) ACAP Best Student Paper Award (2024) Collaborates with leading researchers in photovoltaic materials and device reliability while contributing to practical applications in Australian solar infrastructure projects.

• Quantum Chemistry
• Materials Science
• Mathematics
• +12 more

Michael F. Herbst is a tenure-track Assistant Professor at the Swiss Federal Institute of Technology Lausanne (EPFL) with joint appointments in Mathematics and Materials Science. He leads the Mathematics for Materials Modelling (MatMat) research group, focusing on algorithm development for quantum-chemical simulations of solids and error control in computational modeling. His work bridges mathematics, solid-state physics, and computer science through interdisciplinary research. His research interests include: Density-functional theory (DFT) and Kohn-Sham equations Error propagation in materials property predictions High-throughput screening algorithms Julia programming for scientific computing Tensor networks and reduced basis modeling Self-consistent field convergence methods Recent publications highlight his contributions to: Efficient response property calculations in DFT Rotationally equivariant machine learning operators GPU-accelerated electronic structure methods Polarizable continuum solvation models Robust black-box quantum chemistry algorithms Open-source software development Teaching activities span mathematics, computer science, and chemistry curricula, including interdisciplinary workshops on electronic structure numerics and Julia programming for materials science. He has mentored PhD students Bruno Ploumhans and Niklas Frederik Schmitz.

Gordon BelotView profile

Professor

• Philosophy of Physics
• Philosophy of Science
• Metaphysics
• +3 more

Gordon Belot is the Lawrence Sklar Collegiate Professor of Philosophy at the University of Michigan, Ann Arbor. He has held academic positions at Princeton University, New York University, and the University of Pittsburgh. His research focuses on philosophy of physics, philosophy of science, metaphysics, and epistemology, with recent work exploring cosmological implications of the accelerating universe and geometric possibility. Belot holds a Ph.D. in Philosophy from the University of Pittsburgh (1996), M.Sc. in Mathematics (1993), and B.Sc. in Mathematics and Philosophy (1991) from the University of Toronto. Belot's key awards include the 2014 Lakatos Award for his book Geometric Possibility and fellowships from the National Science Foundation, ACLS, and Stanford’s Center for Advanced Study. His current projects address general relativity, cosmology boundary conditions, and computable epistemology. He is a Fellow of the American Association for the Advancement of Science. His work bridges foundational physics and philosophy, analyzing symmetry principles, inter-theory relations, and confirmation theory. Notable contributions include critiques of classical electromagnetism’s consistency, relationalist space frameworks, and Bayesian reasoning challenges.

• Quantum Computing
• Quantum Thermodynamics
• Machine Learning
• +3 more

Natalia Ares is an Associate Professor in the Materials Department at the University of Oxford and a Tutorial Fellow at New College. She holds a Royal Society University Research Fellowship and joined Oxford in 2013. Her research focuses on advancing quantum technologies through machine learning for device control and quantum thermodynamics. Education: PhD in quantum computing (silicon-based devices) from CEA Grenoble, France Undergraduate and Master's in Physics with specialization in quantum chaos theory from University of Buenos Aires, Argentina Research Focus: Dr. Ares develops machine learning algorithms to control quantum circuits in real-time, addressing device variability challenges. Her group explores quantum thermodynamics at nanoscales, fabricating devices cooled to millikelvin temperatures to study information-thermodynamics relationships, aiming to build autonomous quantum machines like quantum engines and learning circuits. Current projects include machine learning for quantum control and thermodynamics of quantum information processing. Publication Trends (2024-2025): Recent works demonstrate strong integration of machine learning with quantum physics, focusing on quantum-dot systems, thermodynamic costs of quantum operations, symplectic learning frameworks, and high-fidelity qubit control. Publications consistently bridge theoretical quantum physics with practical engineering solutions. Awards and Recognition: Marie Skłodowska-Curie Fellowship Royal Society University Research Fellowship European Research Council Starting Grant (2020) Research Leadership: Leads the Machine Learning research group developing algorithms for quantum control. Manages experimental labs fabricating nanoscale quantum devices operating at cryogenic temperatures. Secured significant funding including ERC Starting Grant for quantum thermodynamics research.

• Quantum Information
• Quantum Cryptography
• Post-Quantum Cryptography
• +7 more

Carlo Ottaviani is a Senior Lecturer in the Department of Computer Science at the University of York and a member of the York Centre for Quantum Technologies. His academic qualifications include a PhD in Physics & Computer Science, and Fellowships from the Advance HE (HEA) in 2023 and 2013. He has held roles such as Senior Research Fellow and Research Associate within the department, contributing to quantum information, post-quantum cryptography, and quantum networks. Education: PhD in Physics & Computer Science PGCAP & Fellow of Advance HE (2023) YLTA & Associate Fellow of Advance HE (2013) Research Interests: Quantum Information and Quantum Technologies Quantum Cryptography (QKD), Post-Quantum Cryptography Quantum Optics and Quantum Atom-Optics Security, Continuous-Variable Quantum Systems Dr. Ottaviani has contributed to major projects, including the UK Quantum Technology Hub for Quantum Communications Technologies. His work focuses on developing secure quantum communication protocols and advancing theoretical frameworks for quantum systems. He actively participates in academic collaborations and has authored over 45 publications in peer-reviewed journals and conferences. Grants and Projects: Principal Investigator for projects on Coherent Control of Quantum Optical Systems and Quantum Non-Linear Optics Contributor to the EPSRC-funded UK Quantum Communications Hub Labs/Teams: York Centre for Quantum Technologies (YCQT) Quantum Information and Cyber Security & Privacy Research Groups

Kenneth JordanView profile

Professor

• Theoretical Chemistry
• Computational Chemistry
• Materials Science
• +5 more

Kenneth Jordan is the Richard King Mellon Professor of Computational Chemistry and Distinguished Professor at the University of Pittsburgh's Department of Chemistry, within the Dietrich School of Arts and Sciences. He also serves as Associate Director of the Center for Research Computing. His research focuses on theoretical and computational studies of molecular clusters, surface reactions, quantum Monte Carlo methods, and electronic-molecule interactions. Key interests include water cluster structures, proton accommodation, and non-valence correlation-bound anions of polycyclic aromatic hydrocarbons. Jordan's work leverages high-performance computing resources to tackle complex systems. Education: Not explicitly stated in text, but inferred from his academic rank and publications as having earned a PhD in Chemistry. Research interests span water clusters (melting transitions, hydrated proton dynamics), electron and proton localization in polyatomic molecules, and quantum mechanical simulations of chemical reactions on surfaces. His group employs ab initio quantum mechanical techniques, Monte Carlo, and molecular dynamics methods. Notable collaborations include work with Yale University's Mark Johnson group and the University of Washington's Anne McCoy group. Recent articles highlight advancements in quantum computing applications (e.g., LiH n complexes), non-valence anion characterization (pyridine, diazines), and graphene-H interactions. His work on proton transfer mechanisms in water clusters (e.g., Grotthuss relay) bridges theory and experimental spectroscopy. Awards: ACS Physical Chemistry Award (2009), Fellow of the Royal Society of Chemistry (2009), AAAS Fellow (2006), and multiple honor symposia at national conferences. Jordan mentors a diverse group of PhD students and postdocs, with recent advisees focusing on quantum computing algorithms, metastable states, and PAH anion modeling. His lab also explores computational tools like Q-GPU for GPU-accelerated quantum simulations. Current research themes include many-body force fields, dispersion interactions, and the role of mechanical strain in graphene electrocatalysis. He leads collaborative projects at national labs (e.g., PNNL, NETL) and international institutions, contributing to both foundational theory and applied materials science. His group's findings frequently appear in top journals like PNAS , Journal of Physical Chemistry , and ACS Applied Materials & Interfaces .

• Theoretical Chemistry
• Computational Science
• Physical Chemistry
• +3 more

Prof. dr. H.B. (Ria) Braam is a Professor in Theoretical Chemistry at the University of Groningen, affiliated with the Zernike Institute for Advanced Materials within the Faculty of Science and Engineering. Her research focuses on computational and theoretical methods to study molecular electronic structure, with a particular emphasis on non-orthogonal configuration interaction (NOCI) for large molecular systems. She leads projects on singlet fission, exciton dynamics, and electronic coupling in organic materials. Her expertise includes quantum chemistry, molecular modeling, and high-performance computing. Key contributions include the development of the GronOR software for NOCI calculations and studies on dielectric properties of organic semiconductors. Braam holds additional roles such as Member of the FWO Chemistry Committee and Editorial Board of the International Journal of Quantum Chemistry. Recent research highlights include advancements in NOCI methodologies for singlet fission mechanisms, triplet diffusion in molecular materials, and computational strategies for energy transport. Her work bridges fundamental theory with applications in materials science and photovoltaics.

Ivy PengView profile

Associate Professor

• High-Performance Computing (HPC)
• Cloud Computing
• Parallel Computing
• +6 more

Ivy Peng is an Associate Professor of Computer Science at KTH Royal Institute of Technology’s School of Electrical Engineering and Computer Science (EECS), where she leads the Scalable Computing Laboratory (ScaLab). She also holds a Docent title in Parallel Computing and is affiliated with the Digital Futures Faculty and the Cooperate Working Group. Prior to Sweden, she worked at the Lawrence Livermore National Laboratory (LLNL) as a Computer Scientist and as a post-doctoral fellow at Oak Ridge National Laboratory (ORNL) in the USA. Her research focuses on large-scale parallel systems, system-level optimization through workload and architecture awareness, performance modeling and analysis, and memory subsystem optimization. Key areas include converged HPC and cloud computing, heterogeneous accelerators (e.g., GPUs, RISC-V Vector units, QPUs, DPUs), and heterogeneous memories (e.g., persistent memory, HBM). She also explores disaggregated resources to enhance computing efficiency and scalability. Ivy Peng coordinates the EU Horizon 2022 Project OpenCUBE, which develops open-source cloud-based services for EPI systems, and leads the Swedish Research Council (VR) Grant on Disaggregated Memory for Emerging Parallel Systems. She actively contributes to technical committees and review boards for journals like IEEE TPDS and conferences including SC, ICS, HPDC, and IPDPS. She advises Ph.D. students Jacob Wahlgren, Gabin Schieffer, Ruimin Shi, and Daniel Medeiros, while co-supervising Jeremy Williams and Måns Andersson. Her academic leadership extends to multiple courses at KTH, including Applied GPU Programming, Introduction to High Performance Computing, and Quantum Computing for Computer Scientists.

Rampi RamprasadView profile

Professor

• Materials Informatics
• Polymer Design
• Machine Learning
• +3 more

Rampi Ramprasad is Professor and Michael E. Tennenbaum Family Chair at Georgia Tech, holding joint appointments as Georgia Research Alliance Eminent Scholar in Energy Sustainability. His research develops machine learning methods for accelerated materials discovery. Education: B.Tech. in Metallurgical Engineering, Indian Institute of Technology Madras M.S. in Materials Science & Engineering, Washington State University Ph.D. in Materials Science & Engineering, University of Illinois Urbana-Champaign Research Leadership: Leads multi-institution projects including ONR MURI programs on polymeric dielectrics. Research areas: Machine learning for polymer, metal, and dielectric design High-performance materials for energy storage applications Computational frameworks for predicting material properties Data-driven discovery of sustainable materials Recognition: Fellow of multiple scientific societies including American Physical Society and recipient of Max Planck Society Fellowship. Current research demonstrates strong focus on AI-augmented materials design, particularly for energy storage polymers and recyclable materials.

• Theoretical Physics
• Particle Cosmology
• Geometric Phases
• +3 more

Michael Morgan is a Professor in the School of Physics and Astronomy at Monash University. His research focuses on theoretical physics, including topological defects, particle cosmology, geometric phases, and X-ray imaging techniques. He has contributed to advancements in electron microscopy and phase contrast imaging, particularly in nanoscale field mapping and vector tomography. His teaching interests include electrodynamics, scattering theory, and quantum mechanics. Professor Morgan has led or participated in multiple research projects funded by organizations like the Australian Research Council (ARC). Notable projects include the 'Nanoscale field mapping in functional materials' and 'Electron Tomography of Electromagnetic Fields'. He has also been recognized with the Vice-Chancellor's Diversity and Inclusion Award in 2020. His work spans interdisciplinary collaborations, addressing challenges in materials science, medical imaging, and computational physics. Key contributions include developing novel microscopy techniques and applying mathematical models to reconstruct physical phenomena. His research has been published in high-impact journals, with a focus on enhancing precision and resolving power in imaging technologies.