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Antonella BogoniView profile

Professor

• Photonics
• Optical Communications
• Sensing
• +6 more

Antonella Bogoni serves as Professor at Sant'Anna School of Advanced Studies in Pisa, Italy, and directs the CNIT National Laboratory on Photonic Networks and Technologies (PNTLab). She previously led the Integrated Photonic Technologies Center (INPHOTEC) as technical responsible from 2020-2021 and pioneered the Integrated Research Center for Photonic Networks established in 2001. Her research spans photonics technologies for optical communication and sensing, with specialized applications in security, space systems, automotive safety, and precision agriculture. Notable achievements include developing the photonic-based fully digital radar system published in Nature (2014), securing three ERC grants, and advancing distributed radar architectures for space applications through the SPACEBEAM project. Recent 2024 publications reveal intense focus on integrated photonics for radar systems, particularly space-based implementations. Key advancements include micro-transfer-printed multi-band transceivers, spectrally pure W-band RF generation, and machine learning-enhanced target modeling. Her work demonstrates convergence of silicon photonics, microwave engineering, and AI-driven signal processing across automotive, space, and agricultural domains. Scientific Awards: Fulbright Fellowship 9 Best Paper Awards Professor Bogoni has secured over 48 national and international grants exceeding 10M€, including ERC Starting and Proof of Concept grants. Her leadership in major projects like INPHOTEC and PNTLab indicates significant mentorship activities despite unlisted student names. She contributes to research strategy through technical committee roles at international conferences and editorial work for Optics Letters. She directs the CNIT National Laboratory on Photonic Networks and Technologies (PNTLab) and previously led INPHOTEC—a technology center for integrated photonic circuit fabrication. Her research group collaborates globally, including with USC under Fulbright sponsorship, driving innovations in photonic radar systems and space applications through the Integrated Research Center established in 2001.

• Biomedical Engineering
• Signal Processing
• Artificial Intelligence
• +4 more

Dr. Rubén Martín Clemente is a Professor in the Department of Signal Theory and Communications at the University of Seville, Spain. His research focuses on biomedical engineering, signal processing, and artificial intelligence, with significant contributions to blind source separation (BSS), fetal electrocardiogram (ECG) extraction, and EEG analysis for cognitive neuroscience. He leads the Biomedical Engineering research group and has participated in numerous national and European projects, including IAREPRO II (AI in assisted reproduction), MARCO-BOLO (coastal biodiversity monitoring), and PID2022-138590OB-C22 (wind turbine structural monitoring). Research Themes : Machine learning for biomedical signal processing, ICA algorithms, cognitive neuroscience applications, and IoT systems for infrastructure monitoring Technical Leadership : Developed RFID-based traffic sign inventory systems and adaptive algorithms for nonlinear source separation His work spans neuroimaging data analysis (e.g., Scientific Reports 2022), energy prediction models (IEEE Access 2023), and smart city applications . Key publications include foundational work on ICA in Springer (2014) and CRC Press (2013). He holds patents for wireless highway inventory systems and has mentored doctoral theses on ICA and image processing. Selected Scientific Contributions 2011: Fetal ECG extraction techniques (IEEE Transactions on Biomedical Engineering) 2022: Reliability analysis of visual processing EEG (Behavioral Brain Research) 2017: L1-PCA/ICA connections (IEEE TPAMI)

• Signal Processing
• Communications Engineering
• Artificial Intelligence
• +2 more

Francisco José Simois Tirado is an Assistant Professor in the Department of Signal Theory and Communications at the University of Seville. He actively participates in multiple research projects funded by the State Research Agency (AEI) and the Andalusian Government, including "IAREPRO II" (AI for assisted reproduction), "MISION ALPHA" (CubeSat development), and IoT-based structural health monitoring. His work bridges signal processing, communications engineering, and interdisciplinary applications in art conservation and agriculture. His research interests center on signal processing techniques (Fourier transforms, convolutions, spectral analysis) and communications systems (OFDM, channel estimation, precoding). Recently, he has extended these methods to art canvas analysis and dairy farm biosecurity, showcasing adaptability across diverse fields while maintaining core signal processing expertise. An analysis of his publications reveals a trajectory from foundational communications research to impactful interdisciplinary applications. His recent work applies signal processing to art authentication and veterinary science, demonstrating the versatility of his core methodologies in solving real-world problems beyond traditional engineering domains. Dr. Simois Tirado has contributed to over 15 research projects since 2002, spanning wireless communications, IoT, and AI applications. He is a long-standing member of Research Group TIC-155, which has received multiple consolidation incentives from the Andalusian Government, reflecting sustained research excellence. He is affiliated with the Signal Processing and Communications Research Group at the University of Seville, a team focused on advancing signal processing theory and its applications in communications, biomedical engineering, and cultural heritage preservation.

Zhao ChenView profile

Associate Professor

• Symbolic Computation
• Numerical Computation
• Matrix Computations
• +3 more

Dr. Zhao Chen serves as an Associate Professor in the Department of Mathematics within the School of Arts and Sciences at New York City College of Technology, City University of New York (CUNY). He holds a Ph.D. in Mathematics from the CUNY Graduate Center (2000) and teaches a comprehensive range of courses from developmental mathematics (MAT063) to advanced topics including cryptography research projects in upper-level classes. His scholarly work centers on symbolic and numerical computations, with deep specialization in structured matrix algorithms, displacement structures, and efficient polynomial root-finding methods. Chen's research bridges theoretical mathematics and practical computational applications, consistently focusing on accelerating solutions for linear systems and matrix operations through innovative preprocessing techniques and displacement-based frameworks. Analysis of his publication history (1998-2011) reveals sustained contributions to structured matrix theory, particularly Cauchy-like and Toeplitz systems. His work demonstrates progressive refinement from foundational displacement structures to practical algorithmic implementations, predominantly published in numerical analysis journals and high-performance computing conferences. Key themes include recursive factorization methods, operator theory applications, and polynomial division approximations. Chen actively contributes to educational infrastructure through co-development of campus-wide software systems: the MMT scheduling tool (with Alexander Rozenblyum), Advisement Registrations Schedule (with Rozenblyum, Sandie Han, and Henry Africk), and Random Generating Exams software (with multiple collaborators). These projects showcase his commitment to integrating computational research with institutional service while supporting student learning through applied cryptography projects.

• Numerical Fourier Analysis
• Wavelet Theory
• Signal Processing
• +3 more

Gerlind Plonka-Hoch is a Professor of Applied Mathematics and Deputy Director of the Institute for Numerical and Applied Mathematics (NAM) at the University of Göttingen. She leads research in numerical analysis and signal processing, with a focus on mathematical methods for image reconstruction and analysis. Her work bridges theoretical mathematics with practical applications in medical imaging and data science. Professor Plonka-Hoch's research spans several key areas in applied mathematics: Numerical Fourier analysis and fast algorithms Wavelet theory and sparse signal representation Regularization methods and nonlinear diffusion Applications in signal and image processing, particularly medical imaging Phase retrieval and parameter estimation problems Her recent publications demonstrate a strong focus on medical imaging applications, particularly using Optical Coherence Tomography (OCT) data. She has developed innovative approaches combining wavelet analysis, deep learning, and sparse representation techniques for image reconstruction and classification. Her work on ESPIRA (Estimation of Signal Parameters by Iterative Rational Approximation) has provided new methods for reconstructing exponential sums from limited data, with applications across multiple scientific domains. Professor Plonka-Hoch leads an active research group at the University of Göttingen, mentoring several doctoral students including Dr. Yurii Kolomoitsev, M.Sc. Benjamin Kocurov, M.Sc. Anahita Riahi, M.Sc. Yannick Nicola Riebe, and M.Sc. Janina Schmidt. Her working group has produced numerous publications on numerical methods and their applications, with a strong emphasis on both theoretical foundations and practical implementation.

• Telecommunications Engineering
• Wireless Communications
• Signal Processing
• +5 more

Slavko Šajić serves as an Associate Professor in the Department of Telecommunications at the Faculty of Electrical Engineering, University of Banja Luka. His academic career spans over a decade with continuous research contributions in wireless communications and signal processing. He teaches telecommunications courses at both undergraduate and graduate levels while leading research initiatives in advanced communication systems. His research interests focus on telecommunications engineering with specialization in wireless communication systems , speech recognition technologies , and secure communication protocols . Current work emphasizes practical implementations of frequency hopping spread spectrum, visible light communication, and data augmentation techniques for speech processing. His departmental role includes developing curriculum for telecommunications engineering programs. Analysis of his 15 most recent publications reveals strong trends in real-time communication systems (60% of works), signal processing innovations (35%), and security applications (25%). Notable thematic clusters include VHF synthesizer optimization (2025), audio quality assessment in visible light communication (2024), and FPGA-based synchronization for FH-SS systems (2022). His work consistently bridges theoretical algorithms with hardware implementations. Professor Šajić actively participates in research funding initiatives including the national Smart City project (2018) addressing Banja Luka's infrastructure, and the Erasmus+ project (2017-2021) focused on modernizing telecommunications engineering education. He mentors graduate students through thesis supervision and research projects within the Department of Telecommunications laboratory environment.

Liu ZhangView profile

Research Fellow

• Symmetric-key Cryptography
• Artificial Intelligence
• Cryptanalysis
• +2 more

Liu Zhang is a Research Fellow at Nanyang Technological University's SYmmetric cryptography and machine Learning Lab (SyLLab) since February 2025. His work bridges symmetric-key cryptography with advanced machine learning techniques. Educational Background : PhD in Cyber Engineering (2024) from Xidian University Visiting PhD Student (2022-2024) at NTU's School of Physical and Mathematical Sciences Master's in Engineering (2017-2020) from Shantou University Bachelor's in Computer Science (2013-2016) from Xinyang Normal University Research focuses on symmetric-key cryptography , cryptanalysis , and artificial intelligence , with particular emphasis on deep learning-aided cryptanalysis. His publications demonstrate expertise in AES, DES, and block cipher vulnerabilities using neural networks. Recent publications highlight trends in neural network integration for cryptanalysis, including Differential-Neural Cryptanalysis on AES and Neural-Inspired Advances in Integral Cryptanalysis . He explores theoretical explanations for machine learning applications in cryptographic attacks. Scientific Awards : 2019 - National Scholarship (Master) 2018 - National Scholarship (Master) 2015 - National Inspirational Scholarship (Undergraduate) Supported by the China Scholarship Council for joint training at NTU (2022-2024). His academic journey shows strong ties to both Chinese and Singaporean institutions.

Håkan JohanssonView profile

Professor

• Signal Processing
• Communications Engineering
• Digital Filter Design
• +3 more

Håkan Johansson is a Professor at the Department of Electrical Engineering (ISY) at Linköping University, Sweden, affiliated with the Division of Communication Systems (KS). This division conducts research and education in communications engineering, statistical signal processing, and network science, emphasizing strong industrial collaborations and practical research applications. His research spans Signal Processing , Communications Engineering , and Digital Filter Design , focusing on low-complexity and reconfigurable solutions for communication systems. Key areas include power amplifier linearization, advanced sampling techniques, and equalization algorithms for 5G/6G and massive MIMO systems, with strong emphasis on implementation efficiency and real-world applicability. Recent publications (2024-2025) reveal a concentrated effort on energy-efficient signal processing for next-generation wireless infrastructure. Dominant themes include linearization of power amplifiers in multi-antenna systems, reconstruction of complex signals from nonuniform sampling, and machine learning integration for UAV communications, reflecting industry-driven priorities for 6G development. No scientific awards were documented in the provided materials. Professor Johansson operates within a robust research ecosystem at ISY, supervising PhD students in the Communication Systems division alongside colleagues like Erik G. Larsson (Head of Division). While specific grant details are absent, his extensive publication record indicates sustained funding for high-impact telecommunications research. He contributes to Linköping University's Communication Systems division, a collaborative hub bridging theoretical signal processing with industrial applications. The team specializes in end-to-end communication solutions from algorithm design to hardware implementation, maintaining strong ties with Sweden's telecommunications sector for rapid technology transfer.

James SmithView profile

Associate Professor

• Male Reproductive Health
• Fertility Preservation
• Urology
• +3 more

Dr. James Smith is an Associate Professor at the UCSF School of Medicine and a urologist at the University of California San Francisco (UCSF) Center for Reproductive Health. As Director of the Male Reproductive Health Center, he specializes in fertility preservation counseling, sperm retrieval techniques, microsurgical vasectomy reversal, and management of male reproductive disorders like Peyronie's disease and hypogonadism. UCSF School of Medicine graduate (MD/MS 2002) Completed surgery/urology residency at University of Utah (2007) Board-certified by the American Board of Urology His research focuses on advancing male reproductive biology understanding through clinical studies on semen analysis methodologies, fertility preservation strategies, and biomarker development for conditions like prostate cancer. Recent publications examine mail-in fertility testing compliance, sperm cryopreservation techniques, and environmental impacts on reproductive health. Dr. Smith maintains professional affiliations with six key medical societies including the American Urological Association and Sexual Medicine Society of North America. As a clinical innovator, he advocates for expanded access to fertility care through remote testing solutions while balancing clinical practice with research leadership at UCSF's pioneering reproductive health program.

Alexander KorotinView profile

Assistant Professor

• Generative Modeling
• Unpaired Learning
• Optimal Transport
• +2 more

Alexander Korotin is an Assistant Professor at the Skolkovo Institute of Science and Technology (Skoltech) where he heads the Generative AI research group. He is also a senior research scientist at the Artificial Intelligence Research Institute (AIRI), leading the "Foundations of Generative AI" group. His academic journey includes a PhD in Math & Physics from Skoltech (2023), an MSc in Computer Science from the Higher School of Economics (HSE), and a BSc in Mathematics also from HSE. Dr. Korotin's research focuses on generative modeling, with particular emphasis on developing novel algorithms based on Optimal Transport and Schrodinger Bridges. His work bridges theoretical mathematics with practical machine learning applications, contributing significantly to the field of generative artificial intelligence. He has pioneered approaches to make Schrodinger Bridge solvers more efficient and practical, most notably with his "Light Schrödinger Bridge" framework that simplifies complex computational procedures while maintaining theoretical rigor. His publication record shows a clear progression toward making advanced generative modeling techniques more accessible and computationally efficient. Recent work demonstrates increasing sophistication in handling complex distribution matching problems through physics-inspired approaches (like electrostatic field matching) and novel distillation techniques that accelerate inference. The research spans from theoretical foundations to practical applications in image processing, semi-supervised learning, and reinforcement learning. Dr. Korotin has received recognition for his contributions to neural optimal transport and Schrodinger Bridges, with his papers frequently appearing in premier machine learning venues. His work on efficient computational methods for optimal transport has established him as a rising expert in these specialized areas of machine learning. As an academic leader, Dr. Korotin advises research students and collaborates extensively with colleagues across institutions, contributing to the advancement of generative AI through both theoretical developments and practical implementations. His work continues to push the boundaries of what's possible in generative modeling, with recent publications focusing on making advanced mathematical approaches more computationally efficient for real-world AI applications.

Abe DavisView profile

Assistant Professor

• Computer Graphics
• Computer Vision
• Human-Computer Interaction (HCI)
• +3 more

Abe Davis is an Assistant Professor of Computer Science at Cornell University in Ithaca, NY, with a research focus at the intersection of Computer Graphics , Computer Vision , and Human-Computer Interaction (HCI) . He leads a research group that develops innovative computational methods for video analysis, augmented reality (AR), and visual editing. Davis previously held postdoctoral positions at Stanford University and Cornell Tech , following his Ph.D. in Electrical Engineering and Computer Science from MIT (2016) and undergraduate degree in Computer Science at Stanford. Education Ph.D. in Electrical Engineering and Computer Science, MIT M.A. in Electrical Engineering and Computer Science, MIT B.S. in Computer Science, Stanford University Davis' research addresses novel problems in computational video editing, AR applications, and structural health monitoring. His group has made significant contributions to visual vibration analysis , light field reconstruction , and video-based sound recovery . Recent work includes Noise-Coded Illumination for fake video detection and MeCapture for mobile AR body change visualization. His publications span top-tier conferences like SIGGRAPH, CVPR, and CHI. Scientific recognition includes being named to Forbes 30 Under 30 (Science, 2015), 5 TED 2015 Technologists , and recognition as one of The 8 most innovative scientists in tech and engineering (2015). His TED 2015 talk on visual vibration analysis gained international attention. The group's work has been featured in Ars Technica , Engadget , and Cornell Chronicle .

• Environmental Fluid Mechanics
• Wildfire Propagation Modeling
• Computational Wind Dynamics
• +5 more

Professor Rob Stoll at the University of Utah's Mechanical Engineering Department specializes in environmental fluid dynamics, wildfire modeling, and urban/agricultural canopy flows. His work bridges computational modeling with field measurements across diverse domains. Develops GPU-accelerated QES simulation tools Focuses on wildfire-atmosphere coupling Advances low-cost sensor networks for environmental monitoring Research Trends revealed through 15 recent publications include: Wildfire propagation under heterogeneous terrain/fuel 3D wind modeling in vineyards and urban environments Particle transport in sparse and row-organized canopies Turbulence characterization in stable boundary layers Open-source hardware for atmospheric sensing Scientific Contributions 2008: Best PhD Dissertation (University of Minnesota) 2017-2022: NSF Grant for wildfire modeling 2020-2025: Systems approach for carrot blight management 2023-2026: Vineyard smoke exposure modeling grants Teaching Activities include graduate courses in Numerical Methods for Engineering Systems and advising PhD/Master's theses since 2015.

• Geometric and probabilistic functional analysis
• Random matrices, random graphs, and random tensors
• Free probability and high dimensional phenomena
• +10 more

Djalil CHAFAÏ is a University Professor of Mathematics at Université Paris-Dauphine - PSL, with dual affiliation at CEREMADE (Centre de Recherche en Mathématiques de la Décision) and DMA (Département de Mathématiques et Applications) at École normale supérieure (Paris) - PSL. He currently serves as Directeur des études du DMA (2021-2026) and Directeur scientifique du RNBM (2021-2025). His extensive research spans multiple areas of probability theory, mathematical physics, and applied mathematics. CHAFAÏ's research interests center around geometric and probabilistic functional analysis, random matrices, random graphs, free probability, and high-dimensional phenomena. His work connects mathematical theory with applications in biology, physics, and data science. He has made significant contributions to understanding cutoff phenomena in high-dimensional diffusions, Riesz energy problems, and Coulomb gases. His research often combines theoretical analysis with visual illustrations created using computational tools like Octave, Python, and Julia. Analysis of his recent publications reveals a strong focus on cutoff phenomena in various stochastic processes, equilibrium measures in potential theory, and the mathematical properties of random matrix ensembles. His work demonstrates a consistent pattern of bridging abstract mathematical concepts with concrete physical phenomena, particularly in statistical physics. The interdisciplinary nature of his research is evident in the diverse applications ranging from mathematical biology to data science. CHAFAÏ has successfully advised numerous doctoral students whose work continues to influence the field. His current doctoral students include Samuel Chan-ashing, Rémi Bonnin, and Kewei Pan, while his former students have gone on to positions at prestigious institutions worldwide. He is actively involved in the mathematical community through organizing conferences, seminars, and workshops, including the Matrices Et Graphes Aléatoires (MEGA) project and the Conviviality project funded by ANR.

Yoann Le HénaffView profile

Researcher

• Numerical analysis
• Mathematical physics
• Partial differential equations
• +2 more

Yoann Le Hénaff is a postdoctoral researcher at the Numerical Analysis Department of the University of Tübingen, Germany. He works within the CRC TRR 352's B4 project and collaborates with C. Lubich's group. His research focuses on computational mathematics and physical modeling through numerical analysis. Education: PhD in Mathematics (2024), University of Rennes, France Master's degree in Mathematics, University of Rennes, France Engineering degree in applied mathematics, INSA Rennes, France Research Interests: Specializing in partial differential equations and numerical simulations, Le Hénaff develops grid-free particle methods for the Vlasov-Poisson system and studies modulated soliton solutions for nonlinear Schrödinger equations. Key subfields include spectral concentration problems, Dirac-Frenkel variational principles, and computational complexity optimization. Publication Trends: Recent work demonstrates expertise in hybrid numerical methods combining classical PDE approaches with modern computational techniques. 2023-2025 publications show progression from particle method convergence proofs to advanced soliton-based algorithms and spectral concentration generalizations across multiple dimensions. Collaborations: Actively involved in the CRC TRR 352 consortium, participating in annual retreats and contributing to the B4 project's objectives. Regularly presents at international seminars including the NAG seminar in Tübingen and IRMAR doctoral meetings in Rennes.

Jeremy HoskinsView profile

Assistant Professor

• Numerical Analysis
• Fast Algorithms
• Quantum Optics
• +4 more

Jeremy Hoskins is an Assistant Professor in the Department of Statistics at the University of Chicago. His research focuses on fast algorithms and numerical analysis with diverse applications including quantum optics, lens design, glaciology, computational biology, and inverse problems. Research Interests: Specializing in Numerical Analysis and Fast Algorithms , he applies computational methods to cross-disciplinary challenges in Quantum Optics , Lens Design , Glaciology , and Computational Biology . His work addresses Inverse Problems requiring high-performance computing solutions. Contact: Email: jeremyhoskins@uchicago.edu Institution: University of Chicago Department: Department of Statistics