Prof. Dr. Tobias Gemmeke is a University Professor at RWTH Aachen University's Faculty of Electrical Engineering and Information Technology, leading the Chair of Integrated Digital Systems and Circuit Design. His work focuses on neuromorphic computing, hardware accelerators, and energy-efficient electronics. He has pioneered advancements in FPGA-based computational neuroscience simulators, neuromorphic processor architectures, and sensor integration for industrial and medical applications. Research interests include time-domain computing, ReRAM reliability, and co-optimization of neural networks with hardware. Notable contributions include the neuroAIx framework for accelerated neuroscience simulations and energy-efficient ASIC designs for post-quantum cryptography. He actively explores memristive devices and domain generalization techniques for edge computing. Recent publications highlight innovations in spiking neural networks, sensor systems for plain bearings, and time-domain compute-in-memory engines. His work bridges theoretical neuroscience with practical hardware implementations, emphasizing scalability and real-time performance.
Michael Knap is an Associate Professor of Collective Quantum Dynamics at the Technical University of Munich (TUM), within the Department of Physics at the TUM School of Natural Sciences. His research group focuses on condensed matter theory, quantum many-body systems, and quantum simulation. Knap holds office in room 5101.01.037 at James-Franck-Str. 1, 85748 Garching b. München, and can be reached at michael.knap@ph.tum.de or +49 (89) 289 - 53777. Prof. Knap's research delves into the rich physics of quantum many-body systems, particularly exploring non-equilibrium dynamics and transport phenomena in ultracold quantum gases, interacting light-matter systems, and correlated quantum materials. His work spans multiple subfields including topological phases of matter, quantum simulation with trapped ions, fracton physics, and quantum computation. He develops novel numerical approaches based on quantum information theory and utilizes artificial intelligence and machine learning to tackle challenging problems in condensed matter physics. His group's research connects fundamental theoretical questions with experimental implementations in quantum simulators. The analysis of Prof. Knap's recent publications (2023-2025) reveals a strong focus on topological quantum matter, quantum simulation, and emergent phenomena in constrained quantum systems. His work frequently bridges condensed matter theory with quantum information science, as evidenced by publications on fracton hydrodynamics, higher-form symmetries, and quantum error correction. There's a clear progression toward increasingly complex quantum systems and connections to experimental implementations on quantum processors. His research shows significant interdisciplinary reach, connecting condensed matter physics with quantum computing and quantum information theory. ERC Consolidator Grant (2025) ERC Starting Grant (2019) Supervisory Award, TUM Department of Physics (2018) Promotio sub auspiciis Praesidentis rei publicae, Austria (2013) Prof. Knap has established a robust research program supported by prestigious European Research Council grants. His group actively collaborates with both theoretical and experimental groups worldwide, particularly in the quantum simulation community. He has supervised numerous students through Master's Seminars on Collective Quantum Dynamics covering topics like quantum simulation with trapped ions and theoretical quantum computation. His research has received significant attention, with several publications featured as Editors' suggestions and Research Highlights in leading journals. The Collective Quantum Dynamics group maintains strong connections with experimental quantum simulation efforts, particularly in the areas of ultracold atoms and trapped ion systems. Knap's theoretical work often provides frameworks for interpreting experimental results in quantum simulators, creating a productive feedback loop between theory and experiment. His group participates in collaborative research networks focused on advancing quantum simulation capabilities and understanding fundamental aspects of quantum many-body physics.
Jacob P. Covey is an Assistant Professor in the Department of Physics at the University of Illinois at Urbana-Champaign (UIUC). He holds a Ph.D. in Physics from the University of Colorado Boulder (2017) and B.S. in Engineering Physics from the University of Wisconsin-Madison (2011). His research focuses on quantum optics, atomic physics, and quantum information science, particularly in quantum control of ultracold atoms and molecules, superradiance phenomena, and quantum networking. He leads the Covey Lab, which explores topics such as neutral atom quantum processors, Rydberg atom interactions, and precision measurement with optical clocks. Academic Positions: Assistant Professor at UIUC (2020–present); Richard Chace Tolman Postdoctoral Scholar at Caltech (2017–2020). Research highlights include pioneering work on Dicke superradiance in ordered atomic arrays and telecom-band quantum networking with Yb-171 atom arrays. He teaches undergraduate courses in mechanics, electromagnetism, thermodynamics, quantum physics, and quantum information. Scientific Awards: NSF CAREER Award (2024), Young Investigator Awards from AFOSR (2023) and ONR (2022), Springer Thesis Award (2018), and multiple fellowships including the Richard Chace Tolman Postdoctoral Fellowship (2017). His work bridges experimental and theoretical advances in quantum technologies, with contributions to quantum state control, precision metrology, and many-body quantum systems.
Michela Becchi is an Associate Professor in the Department of Electrical and Computer Engineering at North Carolina State University. She specializes in computer architecture, systems software, and applications, with a focus on heterogeneous systems, parallel algorithms, and acceleration techniques for bioinformatics, pattern recognition, and quantum computing. Her work spans multi-core CPUs, GPUs, FPGAs, and distributed clusters, emphasizing the boundary between hardware and software design. Dr. Becchi holds a Ph.D. and Master’s degree in Computer Engineering from Washington University in St. Louis (2009) and a Bachelor’s degree in Computer Engineering from Politecnico di Milano, Italy (2000). Her research has been recognized with prestigious awards, including the NSF CAREER Award (2015) and the University of Missouri System President Award for Early Career Excellence (2016). Her research interests include compiler and runtime techniques for heterogeneous systems, acceleration of bioinformatics algorithms, and high-speed networking applications. She has pioneered frameworks for efficient data transformation, GPU-accelerated compression, and memory-efficient graph algorithms for quantum computing. Her work also explores thread coarsening, mixed-precision auto-tuning, and secure multi-core processor design. Key contributions include the PILOT runtime system for GPU memory management, the GPU-FPtuner auto-tuner for floating-point applications, and innovative approaches to automata processors for genomic analysis. Her publications emphasize reproducible accuracy in scientific simulations and the optimization of irregular applications on many-core platforms.
Prof. Dr. Nabeel Aslam is a Full (W3) Professor in Physics at the Felix Bloch Institute for Solid State Physics , Leipzig University, Germany, since September 2023. He previously held a Tenure Track W1 Juniorprofessor position at TU Braunschweig (2022–23) and was a Feodor Lynen Fellow at Harvard University (2018–22). His research focuses on quantum sensing, spin qubits, and nanoscale nuclear magnetic resonance (NMR). Education: Dr. rer. nat. in Physics (2018), University of Stuttgart Diplom in Physics (2012), Johannes Gutenberg University Mainz Bachelor of Science in Economics (2012), Johannes Gutenberg University Mainz Research Interests span quantum information, solid-state physics, and nanotechnology. His work leverages nitrogen-vacancy (NV) centers in diamond for high-resolution quantum sensing, probing spin dynamics in 2D materials, and developing programmable quantum processors with mechanically mediated interactions. Recent efforts include biomedical applications of quantum sensors and enhancing NMR capabilities at the nanoscale. Publication Trends highlight advancements in quantum sensing technologies, spin-mechanical systems, and nanoscale spectroscopy. Key themes include NV center optimization, 2D material analysis, and quantum memory engineering for biomedical and quantum computing applications. Scientific Awards Quantum Futur group funding (2022) Bruker Thesis Prize (2020) Finalist in Quantum Futur Award (2019) Feodor Lynen Fellowship (2019) Exchange Program Fellowship by SFB/TRR 21 (2017) Advising & Grants include mentorship under Prof. Mikhail Lukin and Prof. Hongkun Park during his postdoc at Harvard. His current lab at Leipzig University investigates quantum information processing and biomedical sensing, supported by the Quantum Futur grant. Labs & Teams involve the Quantum Information Group at Leipzig University, focusing on quantum sensors, spin qubits, and related technologies.
Kevin Singh is an Assistant Professor at The Ohio State University in the Department of Physics, holding the John W. Wilkins Endowed Professorship. He established the Singh Group in the Physics Research Building on January 1, 2025, focusing on building quantum devices and information processors using individually controlled single atoms. Education: B.S. in Physics from Massachusetts Institute of Technology (2013) M.A. in Physics from University of California, Santa Barbara (2016) Ph.D. in Physics from University of California, Santa Barbara (2019) Dr. Singh's research spans quantum information science, atomic physics, and quantum optics with emphasis on neutral atom quantum computing. His group develops dual-species Rydberg atom arrays (rubidium and cesium) for quantum error correction, mid-circuit measurement, and quantum simulation. Key methodologies include optical tweezers for atom rearrangement and Floquet engineering for non-equilibrium quantum dynamics. This work bridges fundamental quantum phenomena with transformative device technologies. His publication record (2018-2024) shows consistent advancement in neutral-atom quantum computing, featuring dual-species systems for error mitigation and studies of Floquet-engineered quantum matter. Articles appear in premier journals including Nature Physics, Science, and Physical Review X, demonstrating high-impact contributions to quantum processor architecture and non-equilibrium dynamics. Awards: Boeing Quantum Creators Prize (Chicago Quantum Exchange), 2023 The Maria Lastra Excellence in Mentoring Award (PME, University of Chicago), 2021 Dr. Singh actively recruits undergraduate students, PhD candidates, and postdocs for his laboratory. His prior mentoring at the University of Chicago earned institutional recognition. Research is supported by the John W. Wilkins Endowed Professorship and likely external quantum initiative funding, though specific grants aren't detailed in the source material. The Singh Group operates from OSU's Physics Research Building, utilizing optical tweezers to create programmable atom arrays. Current work focuses on scaling quantum processors through dual-species architectures and developing real-time feedback protocols for error correction in neutral-atom systems.
Jonas Bylander is a Professor at Chalmers University of Technology in the Department of Microtechnology and Nanoscience, specifically within the Quantum Technology division. He leads a research group focused on developing quantum computers using superconducting circuits.
Na Young Kim is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Waterloo with affiliations at the Institute for Quantum Computing (IQC) and Waterloo Institute for Nanotechnology. She holds cross-appointments in the Departments of Physics and Astronomy and Chemistry. Her research focuses on developing large-scale quantum processors using novel materials and advanced technologies, including semiconductor quantum processors and multi-functional nanoscale devices. Dr. Kim leads the Quantum Innovation (QuIN) laboratory, pioneering projects in planar architecture design for quantum devices integrating electrical, optical, thermal, and mechanical functionalities. Prior to academia, she worked at Apple Inc. on small display technologies. She earned a BS in Physics from Seoul National University and a PhD in Applied Physics from Stanford University, where she specialized in mesoscopic transport in nanostructures. Her postdoctoral work expanded into quantum optics and nanophotonics through collaborations with international researchers. Current teaching includes courses on quantum mechanics, quantum computing algorithms, quantum information processing devices, and photonic systems. She actively supervises graduate students in quantum technology development and is accepting new applications. Research activities span quantum artificial intelligence, quantum security protocols, and nanotechnology applications. Her work bridges theoretical frameworks with experimental implementations in solid-state quantum systems.
Gavin Brennen is a Professor in Quantum Information Science (Core) at Macquarie University's School of Mathematical and Physical Sciences. He leads the Macquarie Centre for Quantum Engineering (MQCQE) and serves as a Chief Investigator at the Australian Research Council (ARC) Centre of Excellence for Engineered Quantum Systems (EQUS). He is also an Executive Board Member of the Sydney Quantum Academy (SQA). His research focuses on quantum computing, quantum sensing, and atomic physics, with a particular emphasis on quantum error correction and quantum LDPC codes. Key roles and affiliations include directorship of MQCQE, leadership in ARC EQUS, and SQA board membership. He has secured funding for multiple research projects, including Sydney Quantum Academy scholarships (e.g., Brennen/Gharat and Brennen/Vedl) and the Engineered Quantum Matter initiative. His work addresses quantum technologies' applications in sensing, computing, and communication. Research interests span quantum computing architectures, quantum error correction protocols, and atomic systems. Notable projects include high-rate quantum LDPC codes for neutral atom registers, cavity-based quantum gates, and quantum internet protocols. His contributions to quantum crypto-economics and blockchain security further highlight his interdisciplinary impact. He has advised on projects such as the Australian Dark Matter Detector for High-Mass Axions and collaborates internationally. Current efforts prioritize scalable quantum systems, fault-tolerant protocols, and quantum networking. His lab and teams drive innovation in quantum hardware and theoretical frameworks for emerging technologies.
Dr. Scott Chen is an Assistant Professor in the Department of Electrical & Computer Engineering at McMaster University, where he focuses on teaching and research in embedded systems, RF technologies, and biomedical sensors. He previously held roles as a lecturer at the University of Waterloo and program coordinator at Conestoga College, alongside industry experience in embedded systems engineering and sensor development. Education: B.A.Sc. (Simon Fraser University, 2007) and Ph.D. (University of Waterloo, 2015), followed by a MITAC postdoctoral fellowship. His industry experience includes roles at Thalmic Labs/North, Sober Steering Sensors, and Equustek Solutions. Research interests span embedded systems for IoT, RF biomedical sensors, cleanroom micro/nano-fabrication, and game design in Unity. Notable achievements include a 2018 US patent for ethanol sensing technologies and a 2021 teaching award nomination. Current courses taught include Principles of Programming (COMPENG 2SH4), Data Structures and Algorithms (COMPENG 2SI3), and Introduction to Electrical Engineering (ELECENG 2CI4). Awards: US Patent 9,958,444B2 (2018), nominated for Aubrey Hagar Distinguished Teaching Award (2021). His work bridges academia and industry, emphasizing practical applications in wearable sensors, quantum computing components, and interdisciplinary engineering solutions.
Kevin P. O'Brien is an Associate Professor in the Department of Electrical Engineering and Computer Science (EECS) at the Massachusetts Institute of Technology (MIT), affiliated with the Research Laboratory of Electronics (RLE). He leads the Quantum Coherent Electronics (QCE) group, focusing on advancing superconducting quantum computing, microwave quantum optics, and quantum metamaterials. His research explores nonlinear and quantum-mechanical light-matter interactions using superconducting circuits, aiming to improve quantum technologies like qubits and amplifiers. Education: B.S. in Physics from Purdue University, Ph.D. in Physics from UC Berkeley, and postdoctoral research at UC Berkeley developing superconducting quantum processors. His group collaborates with MIT Lincoln Laboratory and institutions nationwide. Research Interests: Quantum computing hardware, superconducting circuits, parametric amplifiers, qubit measurement systems, and metamaterials for quantum applications. His work emphasizes scalable architecture design, noise reduction, and novel device concepts. Key projects include directional qubit readout resonators, Floquet-mode amplifiers, and quarton couplers for ultrafast readout. The group actively engages in training graduate students and postdocs, emphasizing open collaboration and problem-solving in quantum technologies. Advising & Grants: Supervises a dynamic team of graduate students and postdocs. Students like Bright Ye and Kaidong Peng have contributed to award-winning projects. The group receives support through fellowships (e.g., Jin Au Kong, NSF GRFP) and industry partnerships. Labs/Teams: Quantum Coherent Electronics Group at MIT, collaborating on quantum device fabrication, theoretical modeling, and experimental validation of quantum systems.
Luca Carloni is a Professor of Computer Science and Department Chair at Columbia University's Columbia Engineering. He leads the System-Level Design Group, focusing on heterogeneous system-on-chip (SoC) architectures, networks-on-chip (NoC), and embedded systems. Carloni holds a Laurea Summa Cum Laude in Electronics Engineering from the University of Bologna and a PhD in Electrical Engineering and Computer Sciences from UC Berkeley. His work emphasizes specialized hardware design, energy-efficient computing, and FPGA-based prototyping. Research interests include system-level design methodologies for SoCs, embedded accelerators, and quantum computing hardware. He has pioneered frameworks like Embedded Scalable Platforms (ESP) and tools like MosaicSim for rapid SoC prototyping. Carloni has received numerous awards, including the NSF CAREER Award (2006), IEEE Fellow (2017), and multiple best paper awards at DATE and CloudCom conferences. He has served on editorial boards of IEEE Transactions on CAD and ACM Transactions on Embedded Computing , and chaired key conferences like EMSOFT and ESWeek. His research addresses challenges in heterogeneous architectures, power management, and the intersection of machine learning with embedded systems. Current projects explore quantum control systems, brain-computer interfaces, and energy-efficient datacenter computing.
Professor Vedran Dunjko is a faculty member at the Leiden Institute of Advanced Computer Science (LIACS), Leiden University, with affiliations to the Leiden Institute of Physics (LION). He leads the Applied Quantum Algorithms group and co-founded the Quantum@LIACS initiative, focusing on the intersection of quantum computing, machine learning, and artificial intelligence. His research interests include quantum machine learning, quantum-enhanced reinforcement learning, quantum heuristics, and the application of AI to quantum computing challenges. Dunjko's work bridges theoretical foundations with experimental implementations on near-term quantum devices, exploring both quantum advantages in learning and the use of classical AI for quantum system design. The recent publications show a strong trend toward proving quantum advantages in learning tasks, optimization, and topological data analysis, with publications in Nature , Nature Communications , and NeurIPS . Key themes include quantum policy gradients, quantum TDA, and reinforcement learning for quantum circuit optimization. ERC Consolidator Grant (2024) PNAS Cozzarelli Prize (2018) Editor’s Suggestion in Physical Review Letters (2014, 2018) Featured in Physics (American Physical Society) (2014, 2018) Dunjko advises several PhD candidates and postdocs, including Rahul Bandyopadhyay, Sofiene Jerbi, and Lea Trenkwalder. He has received competitive grants, most notably the ERC Consolidator Grant in 2024. His group fosters international collaborations with institutions across Europe and industry partners. The Applied Quantum Algorithms group and the Quantum@LIACS team combine theoretical investigations with practical implementations on quantum hardware, focusing on scalable quantum algorithms and AI-driven quantum discovery.
Garnet K. Chan is the Bren Professor of Chemistry and Director of the Rudolph A. Marcus Center for Theoretical Chemistry at the California Institute of Technology. He received his B.S. from the University of Cambridge in 1996 and his M.A. and Ph.D. from the University of Cambridge in 2000. Dr. Chan's research lies at the interface of theoretical chemistry, condensed matter physics, and quantum information theory, focusing on quantum many-particle phenomena and the numerical methods to simulate them. His group has developed numerous methodologies including density matrix renormalization and tensor network algorithms, canonical transformation-based down-foldings, local quantum chemistry methods, quantum embeddings, and new quantum Monte Carlo algorithms. His work addresses problems that appear naively exponentially hard but where understanding of physics, particularly entanglement structure, allows for calculations of polynomial cost. Analysis of his recent publications reveals a strong focus on quantum simulation techniques, particularly tensor network methods applied to strongly correlated systems. His research spans fundamental theoretical developments to practical applications in quantum computing, molecular simulation, and materials science, with increasing integration of machine learning techniques and GPU acceleration in computational chemistry frameworks. Dr. Chan leads an active research group at Caltech dedicated to simulating chemical and physical systems at the level of many-particle quantum mechanics. His group has welcomed numerous researchers including Kasra Hejazi, Zuxin Jin, Zhihao Cui, Ke Liao, Henrik Larsson, and Wenyuan Liu. He teaches courses in Physical Chemistry (Ch 21 abc) and Advanced Quantum Chemistry (Ch 225), contributing significantly to theoretical chemistry education at Caltech.
Dr. Gushu Li is an Assistant Professor at the University of Pennsylvania's School of Engineering and Applied Science, affiliated with the Computer and Information Science Department (primary) and Electrical and Systems Engineering Department (secondary). He leads the Penn Quantum System Lab, focusing on quantum computing software-hardware co-design. University: University of Pennsylvania School: School of Engineering and Applied Science Department: Computer and Information Science Academic Rank: Assistant Professor His research spans quantum compilers, programming languages, algorithm optimization, computer architecture, and electronic design automation. He develops techniques for quantum error correction verification, qubit mapping, and hybrid quantum-classical systems, with work integrated into IBM's Qiskit and Quantinuum's TKET frameworks. The 15 most recent publications highlight advancements in quantum simulation , bosonic quantum computing , fermion-to-qubit mapping , and NISQ-era architectures . Key methodologies include symbolic Hamiltonian compilation, adaptive tree structures, and runtime assertions for quantum program testing. 2024: NSF CAREER Award, NVIDIA Academic Grant Program Award, Intel Rising Star Faculty Award 2021-2022: QISE-NET Triplet Fellow, ACM SIGPLAN Distinguished Paper Award, multiple travel grants 2015-2017: Fellowships from UCSB, UChicago, and DAC Dr. Li advises four PhD students and actively recruits candidates with FPGA/digital design skills for 2025. His lab emphasizes interdisciplinary backgrounds to tackle quantum system challenges.
