Prof. Dr. Barbara Kraus is the Chair of Quantum Algorithms and Applications at the Technical University of Munich (TUM), affiliated with the TUM School of Natural Sciences. She previously held academic positions at the University of Innsbruck, where she founded her research group in 2010. Education : Physics and Mathematics at the University of Innsbruck; Post-doctoral work at MPI for Quantum Optics and University of Geneva. Her research focuses on foundational problems in quantum information theory, particularly entanglement in multipartite systems, quantum simulation, and verification of quantum processors. She develops theoretical tools for quantum many-body systems and explores applications in quantum computing, emphasizing error characterization and experimental validation. Recent publications highlight advancements in Hamiltonian learning, symmetry-resolved entanglement detection, and multipartite state transformations. Her work bridges theoretical quantum physics with practical implementations, including Rydberg platforms and quantum metrology. Key Awards : START Prize (2010), Ignaz L. Lieben Award (2013), Boltzmann Prize (2011), Südtiroler Sparkasse Research Prize (2019). She supervises doctoral students and postdocs in quantum information theory, with a focus on stabilizer states, quantum networks, and entanglement measures. Her courses at TUM include Quantum Information , Quantum Algorithms , and workshops on entanglement manipulation.
Daniel Gottesman is the Brin Family Endowed Professor in Theoretical Computer Science at the University of Maryland, affiliated with the Department of Computer Science, Institute for Advanced Computer Studies (UMIACS), and the Joint Center for Quantum Information and Computer Science (QuICS). He holds a Ph.D. in Physics from Caltech (1997) and has held positions at institutions like the Perimeter Institute and Quantum Benchmark. His research focuses on quantum computing, quantum error correction, and fault-tolerant systems, with contributions to stabilizer codes and quantum teleportation-based gates. Education: Bachelor's in Physics, Harvard University (1992) Ph.D. in Physics, California Institute of Technology (1997) Research Interests: Quantum error correction and fault-tolerant architectures Quantum cryptography and secure communication protocols Quantum complexity theory and algorithm design Applications of stabilizer codes and topological quantum computing Scientific Awards: Fellow of the American Physical Society CIFAR Senior Fellow in Quantum Information Science Three U.S. Patents (e.g., quantum key distribution systems) Advising & Grants: Supervised over 30 students/postdocs and served on numerous thesis committees. Active in securing funding for quantum research through endowed professorships and industry partnerships (e.g., Quantum Benchmark). Labs/Teams: Member of QuICS and UMIACS, collaborating on quantum hardware-software integration and error correction challenges.
Jason Ritt is an Associate Professor of Brain Science (Research) and Scientific Director of Quantitative Neuroscience at the Robert J. and Nancy D. Carney Institute for Brain Science, Brown University. He holds affiliations with the Data Science Institute and collaborates across disciplines on quantitative research methods. Education : B.S., M.A., and Ph.D. in Neuroscience from Boston University (1997–2003). Research : Focuses on neural processing during active sensing and neuroengineering for neurostimulation. Combines electrophysiology, optogenetics, and theoretical approaches in rodent models. Develops closed-loop systems for studying sensory neural prosthetics and brain-machine interfaces. Key areas include synaptic diversity, neurocontrol algorithms, and sensory restoration. Teaching : Instructs NEUR 2100 NeuroPracticum, integrating hands-on neuroscience research training.
Alexis Berne is an Associate Professor at the Environmental Remote Sensing Laboratory (LTE) within the School of Architecture, Civil and Environmental Engineering (ENAC) at École Polytechnique Fédérale de Lausanne (EPFL). He co-directs the SSIE-GE program and serves as a member of the CDS (Commission for Doctoral Studies) . His research spans radar meteorology, precipitation microphysics, polar precipitation, and geostatistics, with a focus on mountainous and polar regions. Current Positions Associate Professor, LTE, EPFL (2013–present) Co-Director, SSIE-GE, EPFL PhD Program Committee Member, EDCE-GE, EPFL Research Interests include the remote sensing of precipitation, particularly snowfall and ice production mechanisms, using radar and geostatistical methods. His work addresses atmospheric processes in extreme environments like Antarctica and the Swiss Alps, leveraging machine learning and numerical modeling for climate analysis. Teaching encompasses courses on remote sensing, atmospheric processes, and climate change, emphasizing interdisciplinary approaches and spatiotemporal variability. He advises current PhD students such as Heather Anne Corden and Gionata Ghiggi, alongside mentoring past students like Jacopo Grazioli and Timothy Hugh Raupach.
Aaron Tohuvavohu is a Research Fellow in the Division of Physics, Mathematics, and Astronomy at the California Institute of Technology. His work focuses on high-energy astrophysics, particularly gamma-ray bursts (GRBs) and multi-messenger astronomy. He is deeply involved in the Neil Gehrels Swift Observatory mission, specializing in real-time localization of transient events using the BAT-GUANO pipeline and collaborating with gravitational-wave detectors like LIGO/Virgo/KAGRA. His research emphasizes rapid-response observations of GRBs and gravitational-wave events, leveraging the Interplanetary Network (IPN) for precise localization. He has contributed to studies of short-hard GRBs associated with compact object mergers and long-duration GRBs linked to hypernovae. Notable projects include the CASTOR mission concept for UV photometry and detector characterization for next-generation astronomical instruments. Aaron's recent work includes analyzing Swift/XRT and UVOT observations of GRB afterglows, setting upper limits for electromagnetic counterparts to gravitational-wave triggers, and improving IPN triangulation algorithms. His publications reflect a systematic approach to transient astronomy, integrating data from multiple observatories for comprehensive event characterization.
Oscar P. Bruno is a Professor of Applied and Computational Mathematics at the California Institute of Technology (Caltech). He holds a Licenciado from the University of Buenos Aires (1982) and a Ph.D. in Mathematics from New York University's Courant Institute (1989). Since 1998, he has been a Professor at Caltech, previously serving as Associate Professor (1995–98) and Executive Officer for Applied Mathematics (1998–2000). His research focuses on developing high-performance numerical methods for solving partial differential equations (PDEs), addressing challenges in complex geometries, singularities, and high-frequency phenomena. Key contributions include the Fourier Continuation (FC) method and integral-equation techniques, enabling solutions to previously intractable PDE problems in science and engineering. Prof. Bruno's expertise spans computational electromagnetics, computational fluid dynamics (CFD), solid mechanics, and mathematical physics. His work integrates numerical analysis, multiphysics modeling, and computational science to solve real-world problems in geophysics, optics, and fluid dynamics. He has received numerous awards, including membership in the National Academy of Sciences of Argentina (2020), the Vannevar Bush National Security Science and Engineering Fellowship (2016), and SIAM Fellow (2013). Bruno serves on editorial boards for journals like SIAM Journal on Scientific Computing and SIAM Journal on Applied Mathematics, and participates in national science advisory roles. His teaching includes advanced courses on applied mathematics methods (ACM/IDS 101 ab), emphasizing theoretical foundations and numerical techniques for PDEs. His research group develops cutting-edge solvers with applications in shock dynamics, optical tomography, and geophysical fluid dynamics.
Assoc Prof Ng Teng Yong is an Associate Professor at the School of Mechanical & Aerospace Engineering (NTU), specializing in numerical modeling and simulation. With a background as Research Manager at A*STAR Institute of High Performance Computing, his work spans materials science, nanotechnology, and aerospace engineering. Current focus on graphene-based desalination membranes Expertise in molecular dynamics simulations Investigates nanoscale fluid mechanics and structural dynamics Recent publications highlight advancements in energy-efficient electrodialysis, smart robotics, and nonlinear vibration analysis. His interdisciplinary approach integrates computational methods with experimental validation in additive manufacturing and soft material mechanics.
Dr. Abdullah Bal is a researcher at Georgia State University's College of Arts & Sciences, Department of Computer Science, with over 25 years of academic experience. He holds a Ph.D. in Electrical Engineering from Yildiz Technical University (2002) and has taught graduate and undergraduate courses in algorithms, machine learning, and optical pattern recognition. B.Sc., Electronics and Communication Engineering, Istanbul Technical University (1993) M.Sc., Electrical Engineering, Yildiz Technical University (1997) Ph.D., Electrical Engineering, Yildiz Technical University (2002) His research focuses on data science, machine learning, and hyperspectral imaging applications in fields ranging from forensic analysis to historical structure preservation. He has led projects funded by the U.S. Army Research Office and the Scientific and Technological Research Council of Turkey, including real-time target detection systems and digital imaging for historical structures. Recent publications demonstrate his expertise in kernel-based transforms, ensemble learning, and hyperspectral data analysis. His work spans food safety inspection, infrared target tracking, and biometric verification systems. Faculty Outstanding Research Publication Award (2006) Turkish Air Force Academy Science Competition Winner (2009) Best Paper Award at ICFCT (2016) Previously, he chaired YTU's Informatics Department (2009-2016) and participated in academic governance through the Electrical and Electronics College Executive Committee (2012-2015). You can contact him at abal@gsu.edu in room 739, 25 Park Place.
Jeremy Dahl is a Professor of Radiology (Pediatric Radiology) at Stanford University School of Medicine. He directs the Ultrasound Imaging & Instrumentation Lab and serves as Director of Research Academic Affairs in the Department of Radiology since 2020. He holds multiple affiliations across Stanford including Bio-X, the Cardiovascular Institute, Wu Tsai Human Performance Alliance, Maternal & Child Health Research Institute, Stanford Cancer Institute, and Wu Tsai Neurosciences Institute. Dr. Dahl received his B.S. in Electrical Engineering from the University of Cincinnati (1999) and Ph.D. in Biomedical Engineering from Duke University (2004). His research focuses on developing ultrasonic beamforming and image reconstruction methods for diagnostic imaging applications, particularly techniques that generate high-quality images in difficult-to-image patients. His laboratory specializes in B-mode and Doppler imaging techniques that utilize additional information from ultrasonic wavefields to improve image quality and develop real-time imaging systems for clinical applications including cardiac, liver, and fetal imaging. Dr. Dahl's research has led to significant advancements in ultrasound molecular imaging platforms, sound speed estimation, aberration correction, and reverberation noise suppression. His work often bridges engineering innovation with clinical applications for cancer detection and other diseases. His recent publications demonstrate strong focus on machine learning applications in ultrasound, distributed aberration correction, and molecular imaging techniques. Fellow, American Institute of Ultrasound in Medicine (2021) Senior Member, Institute of Electrical and Electronics Engineers (2020) Distinguished Investigator Award, The Academy for Radiology & Biomedical Imaging Research (2018) Outstanding Paper Award, IEEE Ultrasonics, Ferroelectrics, and Frequency Control Society (2011) Dr. Dahl serves in editorial roles for major journals including IEEE Transactions on Medical Imaging (2017-2024) and IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control (2013-Present). His laboratory has successfully translated numerous innovations into clinical applications, with multiple patents including recent developments in pulsed focused ultrasound therapy and speed of sound quantification.
R. Edwin García is a Professor at the School of Materials Engineering at Purdue University, where he has been faculty since 2005. He holds appointments in the Materials Engineering department within Purdue's College of Engineering, specifically in the School of Materials Engineering located in the Neil Armstrong Hall of Engineering at Purdue's West Lafayette campus. His educational background includes: B.S. in Physics from the National University of Mexico (1996) M.S. in Materials Science and Engineering from Massachusetts Institute of Technology (2000) Ph.D. in Materials Science and Engineering with a minor in Applied Mathematics from Massachusetts Institute of Technology (2003) Professor García's research focuses on the design of materials and devices through the development of a fundamental understanding of the solid state physics of individual phases, their short and long range interactions, and associated microstructural properties and time evolution. His current research emphasizes establishing relationships between material properties and resultant performance and degradation in electrochemical systems. He integrates computational approaches ranging from kinetic Monte Carlo, phase field and level set methods, to finite elements, finite volumes, and symbolic computing. His work particularly addresses microstructure design, crystallographic texture, and grain boundary science and engineering to control the topology of underlying phases and establish practical relations between processing, microstructure, and material properties. His recent publications demonstrate a strong focus on lithium-ion battery technology, ferroelectric materials, and computational modeling of material behaviors. The research trends show increasing integration of machine learning with traditional computational methods, exploration of novel sintering techniques like flash sintering, and deeper investigation into the fundamental mechanisms of material degradation in energy storage systems. His work spans multiple length scales from atomistic to continuum modeling, reflecting a comprehensive approach to materials design and analysis. Professor García teaches several courses including MSE 230 (Structure and Properties of Materials), MSE 350 (Thermodynamics of Materials), MSE 597G (Modeling and Simulation of Materials), MSE 597I (Introduction to Computational Materials), and MSE 597N (Physical Properties of Crystals). He mentors graduate students in areas related to computational materials science, battery technology, and microstructural evolution. His research group, the Laboratory of Computational Microstructures, focuses on developing home-grown analytical theories and algorithms to resolve relevant time and length scales in materials systems. The group's work has significant implications for portable power sources, including rechargeable batteries and fuel cells, as well as for ferroelectric ceramic applications.
Damek Davis serves as an Associate Professor of Statistics and Data Science and Co-Academic Director of the Dual Master's Degree in Statistics at the Wharton School, University of Pennsylvania. His academic base is the Department of Statistics and Data Science within the Wharton School, with his office located at the Academic Research Building in Philadelphia, PA. His research expertise centers on optimization theory for data science, with deep specialization in nonsmooth and stochastic optimization problems. Key focus areas include convergence analysis of first-order methods, variance reduction techniques, and theoretical guarantees for algorithms in nonconvex settings. His work bridges mathematical rigor with practical applications in machine learning and statistical inference, particularly in developing efficient computational frameworks for large-scale data analysis. Analysis of his 2022-2024 publications reveals dominant themes in optimization for modern data challenges: nonsmooth stochastic approximation, linear convergence under sharpness conditions, and global optimality in mixture models. His research consistently appears in premier venues across optimization (Mathematical Programming, SIAM Journal), statistics (The Annals of Statistics), and machine learning (IEEE Transactions), demonstrating cross-disciplinary impact in both theoretical foundations and computational methodologies.
Prof. Norbert Lütkenhaus is a Professor and Executive Director of the Institute for Quantum Computing (IQC) at the University of Waterloo, cross-appointed to the Department of Applied Mathematics. He holds affiliations with Perimeter Institute and the Centre for Applied Cryptographic Research. His research focuses on quantum communication theory, particularly quantum key distribution (QKD) and quantum repeaters. He has pioneered methods to bridge abstract quantum protocols with practical optical implementations, emphasizing secure key rate calculations and overcoming quantum channel limitations. Education: PhD (2003) in Physics from University Erlangen-Nürnberg, MSc (1993) and BSc (1990) from Ludwig-Maximilians-Universität München and RWTH Aachen, respectively. Awards include the 2015 American Physical Society Outstanding Referee Award and a 2009 University of Waterloo Excellence Award. Research interests span QKD protocols (e.g., decoy-state BB84, phase-error mitigation), quantum repeater architectures, and entanglement verification. He develops numerical tools for key rate analysis and addresses implementation security loopholes. His work includes theoretical frameworks for long-distance quantum communication and practical QKD system optimizations. Teaching includes courses on quantum information processing (PHYS 768/QIC 890) and mechanics (PHYS 115). He contributes to international standards via ETSI’s QKD-ISG and the QCrypt steering committee. His patents cover QKD system designs and phase-randomization techniques.
Yongmin Liu is a Professor in Mechanical and Industrial Engineering and Electrical & Computer Engineering at Northeastern University, and a member of the Cross-College Magnetics Center. He holds a PhD in Applied Science and Technology from UC Berkeley (2009), with earlier degrees from Nanjing University. His research focuses on nano-optics, metamaterials, plasmonics, and their applications in optical devices and systems. His interdisciplinary work bridges engineering, physics, and AI, with notable contributions to metasurface design and optical neural networks. Education: PhD, Applied Science and Technology, UC Berkeley (2009) M.S. and B.S., Physics, Nanjing University (2003, 2000) Research Interests: Nano-optics, nanoscale materials engineering, metamaterials, plasmonics, and applied physics. His group develops novel optical materials and devices for applications like super-resolution imaging, efficient light harvesting, and biomedical detection. Recent projects include AI-driven photonic materials design and meta-optical neural networks. Key Achievements: Recipient of the Søren Buus Outstanding Research Award (2024) NSF CAREER Award (2017) and ONR Young Investigator Award (2016) Elected SPIE Fellow (2023) and Optica Fellow (2023) Lab & Collaborations: Head of the Yongmin Liu Research Group, collaborating with institutions like Georgia Tech and Purdue University. Recent grants include a $1.5M NSF DMREF grant for AI-driven photonic materials and a $468K NSF grant for meta-optical neural networks.
Elaine M. Huang is an Associate Professor of Human-Computer Interaction at the Department of Informatics, University of Zurich, where she has served since 2010. She also leads the People and Computing Lab, focusing on the dynamic interplay between human practices and technological advancements. Her academic background includes: PhD in Computer Science, Georgia Institute of Technology (2006) Dr. Huang's research centers on human-computer interaction, examining the bidirectional relationship between technology and human practices. She is particularly known for her work on gender equality in technology, challenging assumptions about innate gender differences and investigating how AI systems may perpetuate biases. Her research also spans sustainable interaction design, mental health technologies, and chronic disease management, always emphasizing empirical data over intuition. Analysis of her recent publications (2023-2025) reveals a strong trajectory toward socially impactful HCI, with significant focus on health technologies (diabetes management, mental health), cultural sensitivity in design, and the ethical challenges of AI. Her methodology often involves field studies to understand real-world technology use, countering the industry's reliance on intuition. As head of the People and Computing Lab, Dr. Huang oversees a research group dedicated to designing and evaluating technologies that address complex human needs. The lab's work frequently involves co-design with diverse user communities to ensure relevance and inclusivity in technological solutions.
Kevin C. Zhou is an Assistant Professor in the Department of Biomedical Engineering at the University of Michigan. His research focuses on developing high-performance computational optical imaging systems with unprecedented spatiotemporal throughput, integrating advanced optical instrumentation with machine learning-driven algorithms to analyze big data in biology and medicine. His lab specializes in creating imaging systems capable of capturing high-resolution, high-speed, and high-dimensional datasets. Dr. Zhou holds a Ph.D. in Biomedical Engineering from Duke University (NSF GRFP Fellow) and a B.S. in Biomedical Engineering from Yale University (Barry Goldwater Scholar). Prior to joining U-M, he was a Schmidt Science Fellow and postdoctoral researcher at UC Berkeley. Key research areas include: High-throughput microscopy (gigapixel-scale systems) 3D tomographic imaging Light field and Fourier-based imaging modalities Machine learning for image reconstruction and analysis Biomedical applications in cellular/molecular imaging His recent work has advanced technologies like multi-camera array microscopes (MCAM/MCAS) and Fourier light field mesoscopes, achieving video-rate 3D imaging of freely moving organisms. These innovations enable applications in digital cytopathology, behavioral tracking, and high-content biological studies. Notable awards include the NSF Graduate Research Fellowship and Barry Goldwater Scholarship. His research has been featured in top journals and conferences with a focus on advancing optical imaging hardware and computational pipelines.
