Ye (Sarah) Sun is an Associate Professor in the Department of Mechanical Engineering at the University of Virginia (UVA), part of the School of Engineering and Applied Science. She joined UVA in 2021 after serving as an Associate Professor at Michigan Technological University. Her work focuses on wearable sensors, robotics, smart health systems, and cyber-physical systems. She leads the WEARLab research group. Education: Ph.D. in Electrical Engineering from Case Western Reserve University (2021), B.S. in Instrumentation Engineering from Tianjin University (not specified). Research interests include wearable electronics, health monitoring, and human-technology interaction. Her interdisciplinary approach integrates engineering innovations with healthcare applications. Notable projects involve self-powered triboelectric sensors and optical fiber-based health monitoring systems. Recent publications highlight advancements in photodiode technologies for high-frequency applications, including millimeter-wave generation and photonic integrated circuits. Awards include the NSF CAREER Award (2018) and NSF BRITE Award (2022). She has organized major conferences and holds editorial roles in health technology journals. Grants include NSF funding for cyber-physical systems and smart health initiatives. Her lab collaborates on projects involving wearable robotics and connected health solutions, with a focus on real-world applications in healthcare and IoT.
Prof. Dr. Martin Burger is a leading scientist at DESY and a Full Professor in the Department of Mathematics at Universität Hamburg, where he leads the Computational Imaging Group. His research bridges applied mathematics, imaging sciences, and machine learning, with a focus on inverse problems, mathematical modeling, and partial differential equations. He has held professorial positions at Universität Münster and FAU Erlangen-Nürnberg prior to his current dual appointment. Full Professor, Universität Hamburg (2023–present) Leading Scientist, DESY, Hamburg (2023–present) Full Professor, FAU Erlangen-Nürnberg (2018–2023) Full Professor, Universität Münster (2006–2018) His research interests include inverse problems, variational regularization, optimal transport, kinetic models, and mathematical modeling in biology and social sciences. He has made significant contributions to imaging reconstruction, sparse neural networks, and the analysis of transformer architectures. His work often integrates theoretical analysis with computational methods, influencing both pure and applied mathematics. The most recent articles reflect a strong trend toward interdisciplinary applications, combining deep learning with PDE-based modeling, analyzing social and biological systems via kinetic and mean-field models, and advancing mathematical imaging through graph-based and optimal transport methods. His publications span high-impact venues in applied mathematics and computational science. Calderon Prize, Inverse Problems International Association (IPIA) ERC Consolidator Grant (2014) Invited speaker at ECM (2021), ICM (2022), and ICIAM (2023) Editor-in-Chief, European Journal of Applied Mathematics (since 2017) Prof. Burger has supervised numerous PhD students and postdoctoral researchers, many of whom appear as co-authors in his publications. His research is supported by major grants, including funding from the German Federal Ministry of Education and Research (BMBF). He is actively involved in collaborative projects across mathematics, physics, and engineering disciplines. He leads the Computational Imaging Group at DESY, fostering a collaborative environment for developing novel mathematical tools in imaging science. The group works on both theoretical foundations and practical implementations, contributing to advancements in tomography, machine learning, and data analysis.
Professor Moncef Gabbouj is a distinguished academic and researcher currently serving as Professor of Signal Processing at the Department of Computing Sciences, Faculty of Information Technology and Communication Sciences, Tampere University, Finland. Previously, he held the same position at Tampere University of Technology before the merger in 2019. He has also held visiting professorships at prestigious institutions including Hong Kong University of Technology and Science, University of Southern California, and Purdue University. Ph.D. and MSc. in Electrical Engineering from Purdue University, USA (1989 and 1986) B.Sc. in Electrical Engineering from Oklahoma State University, USA (1985) Prof. Gabbouj's research spans multiple domains within signal and image processing, with a strong focus on machine learning applications. His primary research interests include artificial intelligence, machine learning, Big Data analytics, multimedia content-based analysis, indexing and retrieval, nonlinear signal and image processing, voice conversion, and video processing and coding. His work bridges theoretical advancements with practical applications across various industries, particularly in multimedia communications and biomedical applications. His extensive publication record demonstrates a clear evolution from traditional signal processing techniques toward more sophisticated machine learning and deep learning approaches. Recent work shows increasing focus on convolutional neural networks for various applications including ECG classification, video processing, financial time-series analysis, and image recognition tasks, reflecting the broader trend in the field toward deep learning methodologies while maintaining strong foundations in signal processing theory. IEEE Fellow (2011) Member, Finnish Academy of Science and Letters (2014) Knight, First Class, of the Order of the White Rose of Finland (2006) Nokia Foundation Recognition Award (2005) Nokia Foundation Visiting Professor Award (2012) Finnish Cultural Foundation for Art and Science Award (2017) TUT Foundation Grand Award (2015) Prof. Gabbouj has supervised 64 doctoral and 72 Master's theses, demonstrating his significant contribution to academic mentoring. His research has been supported by substantial funding, including research grants totaling 8.5 million Euro (2001-2015). He has served as Academy of Finland Professor during 2011-2015 and has been involved in numerous EU research projects including Horizon, ESPRIT, HCM, IST, COST, Tempus and Erasmus programs. As Editor, Guest Editor or member of the Editorial Board of 6 international scientific journals, he has significantly influenced the academic discourse in his field. He leads the Signal Analysis and Machine Intelligence (SAMI) research group at Tampere University and serves as the Finland Site Director of the NSF IUCRC funded Center for Visual and Decision Informatics. His research unit focuses on applying advanced machine learning techniques to solve complex problems in signal processing, computer vision, and multimedia analytics, with applications ranging from healthcare to multimedia communications and financial analysis.
Bahaa E. A. Saleh is a UCF Distinguished Professor of Optics and Photonics at CREOL, The College of Optics and Photonics, University of Central Florida, and former Dean of CREOL (2009–2019). He holds a B.S. from Cairo University (1966) and a Ph.D. from Johns Hopkins University (1971), both in Electrical Engineering. His career includes roles as Chair of the Department of Electrical and Computer Engineering at Boston University (1994–2007) and Deputy Director of the NSF-funded Gordon Center for Subsurface Sensing and Imaging Systems (2000–2008). His research spans quantum optics, statistical optics, nonlinear optics, and optical communication. He authored three influential books, including Fundamentals of Photonics , and has published over 600 papers. He founded the Optical Society (OSA) Advances in Optics and Photonics and held editorial leadership roles in major optics journals. Awards include the 2013 C.E.K. Mees Medal, 2006 Kuwait Prize, and multiple fellowships from SPIE, IEEE, and OSA. Current research focuses on quantum information applications, such as entangled photon generation and quantum imaging. He advises students in optics and photonics, with notable alumni including Seth Smith-Dryden and Walker Larson. His work emphasizes interdisciplinary contributions to optics education and technology.
Robert M. Weikle, II is a Professor in the Charles L. Brown Department of Electrical and Computer Engineering at the University of Virginia, with a courtesy appointment in the Department of Physics. He earned his B.S. from Rice University (1986), M.S. (1987), and Ph.D. (1992) in Electrical Engineering from Caltech, followed by postdoctoral work at Chalmers University of Technology (1992). His research focuses on millimeter-wave and terahertz electronics , applied electromagnetics, integrated antennas, low-noise sensors, and heterogeneous integration of compound semiconductors. His work bridges electronics and photonics for spectrum access, with applications in astronomy, spectroscopy, and metrology. He has published extensively on micromachined silicon substrates, superconducting materials, and emerging technologies. Scientific Awards: IEEE Microwave Prize (1993) David A. Harrison III Award (1999) University of Virginia All-University Outstanding Teaching Award (2000) Edlich-Henderson Innovator of the Year (2016) Fulbright Scholar (2001) As Chief Technology Officer and co-founder of Dominion Microprobes, Inc., he commercializes micromachined wafer probes for high-frequency metrology. His lab, located in E220 Thornton Hall and the Jesse W. Beams Physics Building, has produced 15+ recent publications on submillimeter-wave devices, THz probes, and calibration techniques.
Paras N. Prasad is a SUNY Distinguished Professor with joint appointments in Physics, Chemistry, Medicine, and Electrical Engineering at the University at Buffalo. He serves as Executive Director of the Institute for Lasers, Photonics and Biophotonics (ILPB), which he founded in 1999. Dr. Prasad holds the Samuel P. Capen Chair of Chemistry and has pioneered interdisciplinary research at the interface of photonics, nanotechnology, and biomedicine. Education: BSc, Bihar University, India (1964) MSc, Bihar University, India (1966) PhD, University of Pennsylvania (1971) Postdoctoral Fellow, University of Michigan (1971-74) Research Focus: Dr. Prasad's multidisciplinary research spans photonics, nanophotonics, and biophotonics, with emphasis on nonlinear optical processes in nanostructured materials. His work develops photonic technologies for information processing, medical imaging, and cancer therapy through nanoparticle-based drug delivery systems and diagnostic platforms. The ILPB laboratory features state-of-the-art instrumentation for advanced optical research. Publication Trends: Recent articles demonstrate strong focus on nanomedicine applications, particularly cancer theranostics using functional nanoparticles. Key themes include drug delivery systems, chiral photonic materials, bioimaging technologies, and nanoparticle synthesis techniques. The research consistently bridges fundamental materials science with translational medical applications. Honors and Awards: SPIE Gold Medal (2016) IEEE Photonics Society William Streifer Award (2021) American Chemical Society Peter Debye Award (2018) OSA Michael Feld Biophotonics Award (2017) IEEE Pioneer Award in Nanotechnology (2017) Fellow of National Academy of Inventors (2016) Guggenheim Fellowship (1997) Leadership: As ILPB Executive Director, Dr. Prasad leads multidisciplinary teams developing photonic technologies with applications in healthcare, energy, and communications. His research has generated nine spin-off companies, including Nanobiotix currently in advanced cancer therapy trials.
Zenghu Chang is a Distinguished Professor of Physics and Optics at the University of Central Florida, leading the Institute for the Frontier of Attosecond Science and Technology. He holds the UCF Trustee Chair and Pegasus Professor titles. His research focuses on ultrafast laser science, attosecond phenomena, and high-order harmonic generation. Chang earned his PhD from the Chinese Academy of Sciences and has held academic positions at the University of Michigan and Kansas State University. Education: BSc from Xi’an Jiaotong University (1982), PhD from Xi’an Institute of Optics and Precision Mechanics (1988). Postdoctoral research at the Rutherford Appleton Laboratory (1991-1993) and the University of Michigan (post-1996). Research Interests: Attosecond science, terawatt femtosecond lasers, ultrafast atomic physics, coherent XUV/X-ray sources, high-order harmonic generation, and advanced laser technology. His work includes pioneering contributions like the Double Optical Gating technique and generating the world-record 67-attosecond laser pulse. Awards: APS Fellow, Pegasus Professor (2016), Mercator Professorship (2007), and multiple national and international honors. His publications exceed 150 articles in top journals like Optics Letters and Nature Communications . Advising & Grants: Supervised numerous PhD students in optics and physics. Active in securing research grants for attosecond science and laser development. Collaborates internationally on projects involving ultrafast X-ray sources and advanced laser systems. Labs/Teams: Directs the Florida Attosecond Science and Technology group and leads the Institute for the Frontier of Attosecond Science and Technology, advancing cutting-edge laser and X-ray technologies.
Amy C. Foster is an Associate Professor in the Department of Electrical and Computer Engineering at Johns Hopkins University, affiliated with the Whiting School of Engineering. She leads the Integrated Photonics Laboratory, focusing on nanoscale design of silicon-based photonic devices for optical communication systems and security applications. Her work emphasizes CMOS-compatible fabrication techniques for integrated photonic devices with applications in sensing, imaging, and high-speed processing. Education: BS (Electrical Engineering, University at Buffalo, 2003); MS & PhD (Electrical and Computer Engineering, Cornell University, 2007 & 2009) Postdoctoral Research: Cornell University (2009–2010) Professional Roles: Associate Editor of Optics Express (OSA), Chair of OSA Frontiers in Optics Committee, IEEE Photonics Conference Committee Member Her research interests center on silicon photonics, nonlinear optics, and photonic physical unclonable functions (PUFs). Key areas include developing secure authentication systems using chaotic microcavities, optimizing high-index materials like NbTiOx for visible light photonics, and advancing integrated photonic interconnects for multi-layer systems. Recent work explores machine learning-resistant PUFs and parametric nonlinear effects in sputtered metal oxides. Foster's publications highlight advancements in optical frequency combs, autofluorescence analysis of waveguides, and GHz-rate optical parametric amplifiers. Her lab’s innovations address challenges in quantum photonics, secure communications, and ultra-low-power signal processing. Awards: 2016 Johns Hopkins Catalyst Award, 2012 DARPA Young Faculty Award Grants: IARPA, NSF, APL, DARPA Her lab develops cutting-edge photonic devices for applications in space communications, neural stimulation, and security. Current projects aim to enhance multi-layer photonic integration and leverage nonlinear effects for novel signal processing architectures.
Hamid Krim is a Professor in the Department of Electrical and Computer Engineering at North Carolina State University. He leads the Vision, Information and Statistical Signal Theories and Applications (VISSTA) group, focusing on statistical signal/image analysis, data science, and machine learning. His prior roles include Research Scientist at MIT’s Laboratory for Information and Decision Systems and Member of Technical Staff at AT&T Bell Labs. He holds a Ph.D. in Electrical Engineering from Northeastern University, and degrees from the University of Washington and University of Southern California. Education: Ph.D., Electrical Engineering, Northeastern University (MA), 1990s Master's, Electrical Engineering, University of Washington Bachelor's, Electrical Engineering, University of Southern California and University of Washington Research Interests: Machine Learning, AI, Signal Processing, Communications, and Control Systems . His work bridges formal mathematical frameworks with applied problems, emphasizing generative AI, adversarial robustness, and subspace-driven data analysis. Recent innovations include Volterra neural networks and expansive synthesis techniques for data generation. Awards & Recognition: 2000 NSF CAREER Award 2008 IEEE Fellow 2019 IEEE SPS Sustained Impact Paper Award Multiple extended research invitations at top institutions globally Grants & Advising: Leads the VISSTA Lab, collaborating on projects like medical algorithm development (e.g., lung wheeze analysis) and hurricane activity prediction. His work spans interdisciplinary applications in healthcare, robotics, and defense systems. Labs & Teams: Director of the VISSTA Lab, fostering research in signal theory and machine intelligence. Collaborates with academia and industry on cutting-edge AI and sensor fusion technologies.
Jonathan Hauenstein is the Robert and Sara Lumpkins Collegiate Professor in the Department of Applied and Computational Mathematics and Statistics at the University of Notre Dame, serving as Department Chair. He holds a Ph.D. from Notre Dame (2009) and M.S. from Miami University (2005). His research focuses on numerical algebraic geometry and computational methods for solving nonlinear equations, implemented in the Bertini software package. Applications span engineering, ecology, sports science, and machine learning. Education: Ph.D., Applied and Computational Mathematics, University of Notre Dame (2009) M.S., Mathematics, Miami University (2005) Research Interests: Development of numerical algorithms for polynomial systems, real algebraic geometry, and scientific computing. Key areas include homotopy continuation methods, parameter space decomposition, and applications in mechanism design, ecological modeling, and sports biomechanics. His work bridges theoretical mathematics with practical computational tools. Awards: Sloan Research Fellowship DARPA Young Faculty Award Army Research Office Young Investigator Award Office of Naval Research Young Investigator Award College of Science Research Award Advising & Grants: Advised numerous undergraduates, graduate students, and postdoctoral researchers. Active in securing grants for computational mathematics projects, including NSF-funded initiatives. His work emphasizes interdisciplinary collaboration between mathematics and engineering. Labs/Teams: Leads computational algebraic geometry research groups at Notre Dame, focusing on software development (e.g., Bertini) and numerical methods innovation.
Mohd Fikree Hassan is a Lecturer at the School of Information Technology, Monash University Malaysia, joining in June 2023. He holds a Ph.D. and Master's from the University of Malaya, and a B.Eng. in Electronics Engineering from Multimedia University. With over 14 years of academic experience, he is actively engaged in research, teaching, and supervision. B.Eng. in Electronics Engineering (Telecommunications), Multimedia University, 2004 M.Eng. in Engineering (Telecommunications), University of Malaya, 2015 Ph.D. in Signal and Systems, University of Malaya, 2018 His research focuses on image and signal processing , particularly in image enhancement, restoration, computer vision, and human color vision . His work contributes to improving image visibility, removing color casts, and developing algorithms for noisy or degraded images. He applies mathematical and computational techniques to solve real-world imaging challenges. The recent publication trends (2021–2025) show a strong focus on image restoration using variational methods (e.g., total variation, ℓ0 regularization), color enhancement in HSI space, and video analysis for sports applications. His work bridges theoretical optimization and practical computer vision systems. He actively contributes to the academic community through peer review for journals such as Neurocomputing , Journal of Imaging , and International Journal of Computational Intelligence Systems , as well as for IEEE conferences. Mohd Fikree is currently accepting PhD students and serves as an external examiner for academic programs. His consistent research output and editorial service reflect a growing impact in the field of image processing and computer vision. While no formal lab or team is mentioned in the text, his collaborations with researchers like R. Paramesran, T. Adam, and G. Krishnasamy suggest active research partnerships in signal and image processing.
Stefanie Jegelka is an Associate Professor (currently on leave) at the Massachusetts Institute of Technology's Department of Electrical Engineering and Computer Science, and a Humboldt Professor at Technical University of Munich. At MIT, she is a member of CSAIL (Computer Science and Artificial Intelligence Laboratory), IDSS (Institute for Data, Systems, and Society), the Center for Statistics and Machine Learning, and is affiliated with the Operations Research Center. Her educational background includes a PhD from ETH Zurich and the Max Planck Institute for Intelligent Systems, followed by postdoctoral research at UC Berkeley's AMPlab and computer vision group. Her research program focuses on algorithmic machine learning, with particular emphasis on exploiting mathematical structure for discrete and combinatorial machine learning problems, robustness in learning systems, and developing methods for scaling machine learning algorithms to large datasets. She has made significant theoretical contributions to submodular optimization and its applications in machine learning. Jegelka's publication record demonstrates a consistent focus on the intersection of discrete mathematics and machine learning. Her work spans theoretical foundations of optimization with discrete structures, applications in computer vision, and practical algorithms for submodular function optimization. Her research has evolved from foundational work on submodular functions to broader applications in deep learning and robust machine learning systems, showing increasing impact through numerous workshop best paper awards and high-impact conference publications. NSF CAREER Award Google Research Award German Pattern Recognition Award (Mustererkennngspreis) ICML Best Paper Award Sloan Research Fellowship DARPA Young Faculty Award NSF BIGDATA Award ONR MURI NSF AI Institute for Optimization Professor Jegelka has advised several successful students including Keyulu (recipient of MIT's George M. Sprowls Ph.D. Thesis Award), Derek (NSF Fellowship recipient), Ching-Yao (IBM Fellowship recipient), and Nisha (now Assistant Professor at Georgia Tech). Her research has been generously supported by multiple NSF grants, DARPA awards, and industry funding from Google, Two Sigma, and Adobe. She has also organized multiple workshops and tutorials on discrete optimization and submodularity in machine learning. At MIT, Jegelka is affiliated with the Center for Statistics and Machine Learning and collaborates with researchers across CSAIL. Her work bridges theoretical computer science, optimization, and practical machine learning applications, with recent focus on high-dimensional learning dynamics and in-context learning as evidenced by her group's multiple papers at leading conferences like ICLR.
Prabir Burman is a Professor in the Department of Statistics at the University of California, Davis, with a career spanning over three decades. His research focuses on nonparametric function estimation, model fitting/selection, image analysis, time series, and discrete data. Education: Ph.D. (1982) and Master of Statistics (1977) from University of California, Berkeley; Bachelor of Statistics (1976) from Indian Statistical Institute, Calcutta. His work bridges theoretical statistics and applied problems, including ecological studies (e.g., coyote parasites, mountain lion tracking), biomedical research (e.g., metabolic syndrome in bipolar patients), and time series forecasting. He has secured multiple NSF and NSA grants for projects on multivariate analysis, shape modeling, and covariance estimation. Recent publications highlight his expertise in predictive model fitting, stock return analysis, and stroke survivor studies. While not explicitly listing awards, his editorial roles (e.g., Journal of Multivariate Analysis) and collaborative grants underscore his academic leadership.
Richard B. Sowers is a Professor at the University of Illinois at Urbana-Champaign, holding joint appointments in the Department of Industrial and Enterprise Systems Engineering, Mathematics, and Statistics (courtesy). He has held faculty positions since 1996, starting as an Assistant Professor in Mathematics and advancing to Professor across multiple departments. His research spans stochastic processes, financial engineering, and data analytics. He also serves as a Research Principal at the Office of Financial Research since 2012. Education: B.S. in Electrical Engineering (Drexel University, 1986), M.S. and Ph.D. in Applied Mathematics (University of Maryland, 1988 and 1991). Research Interests: Financial networks, stochastic systems, and applications in decision-making and control. His work bridges theoretical probability with practical domains like finance and healthcare. Recent articles focus on machine learning applications in gait analysis for neurological disorders and stochastic modeling in financial systems. Professional Contributions: Taught courses in stochastic calculus, deep learning, and financial mathematics. His research often involves interdisciplinary collaboration, including projects on credit risk, algorithmic trading, and wearable technology for health monitoring. Labs/Teams: Active in the Institute for Predictive and Computational Science, focusing on data-driven solutions for complex systems.
Prof. Felix Krahmer is a TUM Tenure Track Assistant Professor of Optimization and Data Analysis at the Department of Mathematics, Technische Universität München (TUM), part of the School of Computation, Information and Technology. Previously, he held a Junior Professorship (W1) for Mathematical Data Analysis at the University of Göttingen (2012–2015). His research focuses on mathematical foundations of data science, including compressed sensing, signal quantization, uncertainty quantification, and optimization algorithms. He has led an Emmy Noether research group and contributed to the ZeMat interdisciplinary project. Education : PhD in Mathematics (2009), New York University, advisors: Percy Deift & Sinan Güntürk MSc in Mathematics (2007), New York University BSc in Mathematics (2004), Jacobs University Bremen Research Interests : Krahmer’s work bridges theoretical mathematics and applied data science. Key areas include compressed sensing algorithms, high-dimensional signal reconstruction, quantization theory for analog-to-digital conversion, and optimization methods for inverse problems. He also explores applications in imaging (e.g., MRI reconstruction) and stochastic processes. Key Contributions : His research on phase retrieval, sigma-delta quantization, and compressed sensing recovery has advanced signal processing techniques. Recent efforts focus on uncertainty quantification for high-dimensional inverse problems and the mathematical analysis of consensus-based optimization. Grants & Awards : Emmy Noether Research Group Grant (DFG) Funding from BMBF (ZeMat project) Teaching & Outreach : Krahmer has taught advanced courses on compressed sensing, functional analysis, and random matrix theory. He co-organized workshops on probabilistic techniques and clinical risk prediction, emphasizing interdisciplinary collaboration. Labs/Teams : Member of the TUM Data Science Research Group, focusing on optimization, imaging, and uncertainty quantification. Active in the Munich Center for Quantum Science and Technology (MCQST).
