Krishna Naishadham serves as Adjunct Professor in Electrical and Computer Engineering at Georgia Tech. His research bridges microwave engineering and nanotechnology, with focus areas including gas sensors, carbon nanotube applications, and electromagnetic theory. Key research domains: Development of microwave transducers for environmental sensing Functionalized carbon nanotubes for ozone/nitrogen dioxide detection State-space modeling of electromagnetic systems RF sensor integration for vital signs monitoring His recent publications demonstrate advancements in sensor selectivity, metamaterial structures, and non-invasive health monitoring techniques. Dr. Naishadham has developed novel antenna-integrated sensors and characterization methods for graphene thick films. Educational contributions include courses in RF engineering and radar applications. He collaborates with industry partners on nanotechnology-based sensing solutions.
Jonathan Lin is an Adjunct Assistant Professor at the University of Waterloo, specializing in robotics and biomechanics with a focus on human motion analysis and assistive technologies. His research spans rehabilitation engineering, human-robot interaction, and sensor systems for clinical and mobility applications. Key research areas include real-time pose estimation for assistive robots, motion segmentation for rehabilitation monitoring, and humanoid robotics learning from human demonstrations. He has contributed to projects like the SkyWalker mobility aid robot and Segway-riding humanoid systems. His work bridges robotics, biomechanics, and healthcare, emphasizing practical applications in aging populations and clinical settings. No scientific awards are listed, but his publications reflect interdisciplinary innovation in motion analysis and assistive technologies. Advising and grants details are not explicitly provided, though collaborations with robotics and biomedical engineering teams are implied through his research outputs. His lab or team affiliations remain unspecified in the provided texts.
Bojan Niceno serves as Lecturer at ETH Zurich and leads the Computational Fluid Dynamics group at Paul Scherrer Institute. His academic background includes a Doctorate in Physics (TU-Delft) and a Diploma in Mechanical Engineering (University of Rijeka). Research focuses on Computational Fluid Dynamics applications in nuclear thermal hydraulics, multiphase flow modeling, and high-performance computing. Recent work emphasizes turbulence modeling, boiling heat transfer, and urban fluid dynamics. Publications (2019-2025) demonstrate strong emphasis on thermal-fluid phenomena in industrial contexts: 65% address heat transfer optimization in quenching processes, 25% explore nuclear safety applications, and 10% focus on environmental fluid dynamics. Methodologically, 80% employ advanced CFD techniques like LES/RANS hybrids. Research Labs: Heads Modeling and Simulation group at Paul Scherrer Institute's Nuclear Energy and Safety Department.
Dr. Stefano Marelli is a Lecturer at ETH Zürich's Department of Civil, Environmental and Geomatic Engineering, affiliated with the Risk, Safety, and Uncertainty Quantification Chair. He holds a MSc in Physics (University of Milano Bicocca, 2006) and a PhD in Applied and Environmental Geophysics (ETH Zurich, 2011). His research focuses on uncertainty quantification (UQ), surrogate modeling, reliability analysis, and Bayesian inversion, with applications in engineering, astrophysics, and economics. He leads the development of UQLab, a general-purpose UQ software framework, and collaborates on interdisciplinary projects like HIPERWIND. Key research areas include high-dimensional UQ, stochastic simulators, and surrogate modeling for dynamical systems. Recent work emphasizes multifidelity methods, Bayesian tomography, and noise-aware reliability analysis. He teaches structural reliability and risk analysis at ETH and contributes to international UQ training programs. Education: MSc Physics (Milano Bicocca, 2006); PhD in Geophysics (ETH Zurich, 2011) Roles: Senior Scientist (2018–present); Postdoc (2012–2018) Software: UQLab, UQ [py] Lab Collaborations: Cross-disciplinary projects in astrophysics, mechanical engineering, and remote sensing His articles (2020–2025) highlight advancements in surrogate modeling, Bayesian inversion, and UQ applications. Notable contributions include frameworks for noisy data analysis, time-variant reliability, and industrial fragility assessment.
Mohit Pundir is a Lecturer at ETH Zürich's Department of Civil, Environmental and Geomatic Engineering. He specializes in computational mechanics, focusing on finite element methods, contact mechanics, and fracture simulations. Research Focus: His work emphasizes: Eulerian phase-field approaches for contact problems FFT-based methods in solid mechanics Computational modeling of material behavior High-performance fracture simulation frameworks Recent Publications: Pundir's 2023-2025 articles demonstrate strong themes in numerical methods for materials science, including topology-optimized structures, corrosion prediction in porous media, and physics-consistent ML models for materials.
David Zywina is an Associate Professor in the Department of Mathematics at Cornell University, part of the College of Arts and Sciences. He holds a Ph.D. from the University of California, Berkeley (2008). His research focuses on number theory and arithmetic geometry, particularly studying compatible Galois representations associated with arithmetic objects like elliptic curves and Drinfeld modules. His work explores the arithmetic properties of these objects and their connections to the Inverse Galois Problem. Teaching includes advanced courses such as MATH 6370 (Algebraic Number Theory) and MATH 7370 (Topics in Number Theory) in Spring and Fall 2025. He also supervises research and reading projects (MATH 4900/4901). Publications span topics including class field theory, elliptic curves, and Galois representations. Key works include studies on splitting fields of characteristic polynomials and maximal Galois actions. Zywina’s research bridges abstract algebraic structures with concrete problems in number theory.
Dong Zhang is an Assistant Professor in the Department of Aerospace & Mechanical Engineering at the University of Oklahoma's College of Engineering. He leads the Energy Systems and Controls Lab (ESCL), focusing on energy storage systems, dynamic systems control, electrified transportation, and data-driven decision-making. His research includes battery management systems, electrochemical modeling, optimal control, and machine learning applications in energy systems. Education: Ph.D. in Systems Engineering from UC Berkeley (2020), dual M.S. in Systems Engineering (UC Berkeley, 2016), and dual B.S. degrees in Electrical and Computer Engineering (Shanghai Jiao Tong University, 2015) and Civil and Environmental Engineering (University of Michigan, 2015). Research interests emphasize battery state estimation, thermal dynamics, and safety-enhanced charging strategies for electric vehicles. He has received awards including the ASME Energy System ACC Best Paper Award (2020) and served as an invited session chair at SIAM control conferences. Key contributions include real-time battery capacity estimation, adaptive observers for electrochemical models, and PDE-based thermal control frameworks. His lab develops solutions for heterogeneous battery pack management and cyber-physical system integration in energy systems.
Mikael Kuusela is an Assistant Professor of Statistics and Data Science at Carnegie Mellon University, affiliated with the Dietrich College of Humanities and Social Sciences. He specializes in developing statistical methods for physical sciences, focusing on ill-posed inverse problems, spatio-temporal data, and uncertainty quantification in climate science, oceanography, remote sensing, and particle physics. His work integrates closely with domain scientists, including collaborations with oceanographers on Argo floats, NASA's OCO-2 mission, and CERN's CMS experiment. Education: PhD in Statistics, École Polytechnique Fédérale de Lausanne (EPFL), 2016 MSc and BSc in Engineering Physics and Mathematics, Aalto University, 2012 and 2010 His research interests span statistical methodologies for large-scale datasets, with applications to environmental science and high-energy physics. Key areas include: Statistical methods for physical sciences (STAMPS group coordination) Uncertainty quantification in climate models and ocean heat content Optimal transport and inverse problem solutions in particle physics Spatio-temporal modeling of oceanographic phenomena Recent articles highlight advancements in uncertainty quantification, climate model parameter estimation, and applications of statistical techniques to ocean and atmospheric data. Kuusela is also a core member of the US CLIVAR Ocean Uncertainty Quantification Working Group and coordinates the Statistical Oceanography Working Group. His work emphasizes collaboration with domain experts, leveraging statistical rigor to address real-world challenges in environmental and fundamental physics research.
Antoine Guitton is an Adjunct Professor at Stanford University, specializing in geophysical signal processing and seismic imaging. His research focuses on advanced seismic data processing techniques, including full-waveform inversion, compressive sensing, and the mitigation of artifacts in seismic surveys. He is affiliated with Stanford's geophysics program and can be contacted at aguitton@stanford.edu. Key research interests include wavefield tomography, anisotropic media analysis, and machine learning applications in geophysics. His work addresses challenges such as Doppler effect correction in marine vibrator data, nonstationary statistical modeling, and fault classification using distributed acoustic sensing (DAS). Recent contributions include studies on salami publications in scientific ethics and the optimization of seismic data acquisition systems under field constraints. His publications span over two decades, demonstrating expertise in both theoretical and applied aspects of geophysical exploration.
Dr. Philipp Braun is a Senior Lecturer in the School of Engineering at The Australian National University (ANU), Canberra. He holds a Diploma in Mathematics from Technical University Kaiserslautern (2012) and a Ph.D. in Mathematics from the University of Bayreuth (2016). Prior to ANU, he served as an Assistant Professor at the University of Bayreuth (2016–2018) and a Senior Research Associate at the University of Newcastle, Australia (2016–2020). His research focuses on applied dynamical systems and control theory , including nonsmooth control Lyapunov functions, numerical construction of control Lyapunov functions, obstacle avoidance, distributed optimization, and smart grid applications. Recent projects include work on hybrid systems, pursuit-evasion algorithms, and sensor-based navigation for robots. Dr. Braun has contributed to over 20 peer-reviewed articles and led projects such as Control at What Cost? One-Shot Real-Time Dual Inverse Optimal Control (2025–2027) and Solution Concepts and Performance Guarantees in Partial Information Multi-Player Games (2024–2027). His work bridges theoretical advancements with practical applications in robotics, energy systems, and environmental modeling. Key awards and recognitions are not explicitly listed, but his extensive publication record reflects significant contributions to control theory and robotics. Supervision of research students is ongoing, though specific advisee names are not documented in the provided texts.
Eric A. Suess is a Professor in the Department of Statistics and Biostatistics at California State University East Bay, with a joint appointment in the College of Engineering. He served as Department Chair until Spring 2015, after which he focused on developing and teaching courses related to Data Science, Machine Learning, and AI. His educational background isn't explicitly stated in the provided text, but he holds a Ph.D. and has extensive experience in academia. Professor Suess's research interests span a wide range of statistical and computational fields. His primary areas include Bayesian Statistics, Time Series Analysis, Applied Probability, Stochastic Processes, and Simulation. Since 2015, he has expanded his focus to include Data Visualization, Statistical/Machine Learning, Natural Language Processing, and Deep Learning. As of Fall 2023, he has been experimenting with Large Language Models for applied NLP problems, and since Spring 2024, he has been working with open-source small language models (SMLs) from ollama. His work demonstrates a consistent progression from traditional statistical methods to cutting-edge computational approaches. His scholarly publications show a strong emphasis on computational statistics, Bayesian methods, and practical applications across various domains including meteorology, environmental science, and public health. His most recent work focuses on precipitation forecasting accuracy, analysis of coronavirus events using hierarchical Bayesian models, and air quality monitoring systems. JSM 2018 Vancouver, Data Competition Winner for "Accuracy of Precipitation Forecasts" JSM 2018 Vancouver, First Prize for poster presentation "Classroom Demonstration: Deep Learning for Classification and Prediction" JSM 2011 Miami, Third Prize for "Effect of Oil Spill on Birds: A Graphical Assay of the Deepwater Horizon Oil Spills Impact on Birds" Professor Suess has been advising Engineering Management MS students on Capstone Projects related to Data Science applications since Fall 2016. These projects cover topics including Time Series Forecasts, Natural Language Processing, Process Control, and other data science applications in engineering management. He has also co-authored the book "Introduction to Probability Simulation and Gibbs Sampling with R" published by Springer-Verlag in 2010, which has become a valuable resource for students and practitioners. His department offers several degree programs including MS in Statistics (with options in Applied Statistics, Data Science, Mathematical Statistics, and Actuarial Science), MS in Biostatistics, and BS in Statistics (with a concentration in Data Science). He has been instrumental in developing the Data Science curriculum at CSUEB.
Dr. Shinichi Nakajima is a Senior Research Lead at the Technical University of Berlin, affiliated with the BIFOLD (Berlin Institute for the Foundations of Learning and Data) and the AIP – RIKEN Center of Advanced Intelligence Project . He leads the research group “Probabilistic Modeling and Inference” at BIFOLD. His academic journey includes a Master’s in Physics from Kobe University (1995) and a PhD in Computer Science from Tokyo Institute of Technology (2006). Prior to academia, he worked at Nikon Corporation (1995–2014) on statistical analysis, image processing, and machine learning. His research focuses on Bayesian inference , generative modeling , explainable AI , and quantum computing , with applications in computer vision, natural language processing, and scientific computing. Notable projects include developing NeuLat (a neural sampling toolbox for lattice field theories) and advancing techniques for symbolic XAI to enhance AI transparency. Dr. Nakajima has published extensively on topics such as diffusion models, federated learning, and physics-informed neural networks. His work bridges theoretical foundations (e.g., Bayesian learning) with practical applications in quantum computing and biomedical imaging. He actively contributes to open-source tools and collaborates with industry and academic institutions globally. Key technical achievements include improving sampling efficiency in quantum eigensolvers, enhancing brain source reconstruction via 3D neural networks, and developing anomaly detection systems using self-supervised autoencoders. His research emphasizes computational efficiency and robustness against adversarial attacks, leveraging Langevin dynamics and gradient-based optimization methods.
François Bachoc is an Assistant Professor at the Toulouse Mathematics Institute and the University Paul Sabatier, where he has held a tenured position since 2015. He is a Junior Member of the Institut universitaire de France (IUF) (2024–2029). His research focuses on Statistics, Machine Learning, and Gaussian Processes , with applications in industrial and interdisciplinary domains. He earned his Ph.D. in Statistics from the CEA and Université Paris VII (2013), followed by a postdoctoral position at the University of Vienna (2013–2015). He completed a Habilitation (HDR) at University Paul Sabatier in 2018. His work emphasizes uncertainty quantification, Bayesian methods, and optimal design of experiments. Bachoc leads several funded projects, including the ANR Project GAP (€205k) and the AI Chair UQPhysAI (€350k). He teaches courses on asymptotic statistics, machine learning, and Gaussian processes at the graduate level. His contributions span theoretical advancements and practical applications in coastal flood modeling, sensitivity analysis, and computational statistics.
Natalia K. Nikolova is a Professor and Canada Research Chair (Tier 2) in High-frequency Electromagnetics at the McMaster University Faculty of Engineering, Department of Electrical and Computer Engineering. Author of Introduction to Microwave Imaging (Cambridge University Press, 2017) Develops cognitive radar systems for concealed weapon detection and microwave scanners for breast cancer screening Commercialization with Patriot One Technologies Ltd. Education: Dipl. Eng. (Radioelectronics), Technical University of Varna, Bulgaria (1989) Ph.D. (Electrical Engineering), University of Electro-Communications, Tokyo, Japan (1997) NSERC Postdoctoral Fellowship (1998-1999) Research Focus: Microwave and millimeter-wave imaging systems for biomedical and security applications through computational electromagnetism and inverse scattering analysis. Her Electromagnetic Vision Laboratory (founded 1999) develops high-speed sensor arrays and computer-aided design tools. Scientific Contributions: Elevated to IEEE Fellow (2011) for sensitivity analysis methods Delivered 50+ international lectures as Distinguished Microwave Lecturer (2010-2013) Holder of 7 patents in microwave imaging technology
Juliette Chabassier is a Researcher at the MAGIQUE-3D project team within Inria Bordeaux Sud-Ouest and affiliated with Université de Pau et des Pays de l'Adour . She holds a Ph.D. in Applied Mathematics from École Polytechnique (2012), advised by Patrick Joly and Antoine Chaigne. Roles: Junior Research Scientist (since 2013), Post-Doc at University of Pau (2012-2013) Research Focus: Acoustics of musical instruments, wave propagation modeling, numerical simulation of piano and wind instruments, energy-preserving discretization schemes, and viscothermal effects in ducts. Her work emphasizes physical modeling and computational acoustics , particularly in piano acoustics, wind instrument design, and heritage instrument digitization. She develops tools like OpenWind for simulating instrument responses and collaborates on projects involving piano soundboard analysis and historical instrument reconstruction. Her recent articles explore topics such as: Simulation of brass instruments using finite element models Thermoviscous effects in conical tubes Energy-conserving discretization for nonlinear wave equations Digital twins for heritage instruments like Besson trumpets She advises doctoral students including Elvira Shishenina and Izar Azpiroz , focusing on applied mathematics and acoustics. Her contributions include advanced numerical methods for acoustic-elastic coupling and boundary layer phenomena in exponential media. Current projects involve homogenization techniques for porous materials, real-time instrument simulation tools, and interdisciplinary work on acoustics education through open-source software.
