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Brian J. SmithView profile

Professor

• Quantum Optics
• Quantum Information Science and Technology
• Optical Physics
• +1 more

Brian J. Smith is a Professor in the Department of Physics at the University of Oregon. His research focuses on quantum optics, quantum information science and technology, optical physics, and nonlinear optics. He serves as Director of the Optical Molecular & Quantum Science research group, which develops advanced quantum-optical tools for probing fundamental quantum physics and enabling quantum-enhanced applications. Smith's recent work emphasizes harnessing the temporal-spectral mode structure of light to construct larger quantum systems. His team has made strides in quantum-enhanced sensing and networking, leveraging non-classical states of light for applications like microarcsecond astronomy and attosecond-synchronized photonic entanglement networks. He was elected Fellow of Optica in 2023, recognizing his contributions to quantum optics. His research group includes graduate students Sarah Ashby, Matthew Brown, Clark Embleton, Amy Soudachanh, and Jack Lichtman. He has contributed to the NSF National Quantum Virtual Laboratory through the ASPEN-Net pilot project, which explores precision synchronization in quantum networks. Fellow of Optica (formerly Optical Society of America), 2023

• Riemannian Optimization
• Matrix Manifolds
• Low-Rank Matrix Completion
• +4 more

Pierre-Antoine Absil is a Full Professor at the Louvain Polytechnic School (EPL) , part of the Université catholique de Louvain (UCLouvain) , affiliated with the Mathematical Engineering Center (INMA) and the Institute of Information and Communication Technologies, Electronics and Applied Mathematics (ICTEAM) . His research spans Riemannian optimization , matrix manifolds , and low-rank approximation with applications in astronomical imaging , signal processing , and environmental data imputation . His work emphasizes Riemannian optimization algorithms matrix and tensor completion geometric data analysis applications in astrophysics and bioinformatics Recent publications focus on Stiefel and Grassmann manifolds , variable projection methods , and exoplanet detection via alternating minimization . While no explicit scientific awards are listed, his collaborations with institutions like IEEE and Springer highlight his impact in computational mathematics and engineering. He has supervised researchers such as Simon Vary , Guillaume Olikier , and Valentin Christiaens through projects in direct imaging , economic dispatch , and graph-based data processing . His laboratory, INMA , drives methodological advances in manifold-valued data analysis and nonlinear optimization .

• Biostatistics
• Statistical Analysis
• Applied Statistics
• +5 more

Professor Ufuk Karadavut serves at Karabük University Faculty of Medicine, Department of Basic Medical Sciences, where he holds the position of Professor and currently serves as Vice Dean. Previously, he was Dean of the Faculty of Medicine from 2021-2022 and has held multiple leadership roles including Head of Department since 2020. With an extensive publication record of 493 works and significant contributions to both medical and agricultural statistics, his career spans multiple Turkish universities including Ahi Evran University and Bingöl University. PhD, Selçuk University, Institute of Science (2001-2005) Bachelor's Degree, Ankara University, Faculty of Agriculture, Department of Animal Science (1987-1991) Professor Karadavut's research spans biostatistics, medical informatics, and agricultural statistics, with particular expertise in statistical experimental design. His work bridges healthcare applications (medical informatics, clinical data analysis) with agricultural science (crop breeding, animal husbandry). He has developed statistical methodologies applicable across diverse fields, from analyzing palliative care outcomes to optimizing chickpea breeding programs for drought tolerance. His research demonstrates a consistent pattern of applying advanced statistical techniques to solve practical problems in both medical and agricultural contexts. Analysis of his recent publications reveals a strong focus on applying statistical methodologies to address contemporary challenges. In medical informatics, he examines healthcare quality, medical education, and pandemic impacts. In agricultural science, his work centers on crop improvement (particularly chickpeas and other legumes) and animal breeding. His statistical approaches include multivariate analysis, QTL mapping, growth modeling, and machine learning techniques, demonstrating methodological versatility across disciplinary boundaries. While specific awards aren't detailed in the available information, his h-index of 11 (UNIS) and 8 (WoS), along with 583 total citations, indicate significant scholarly impact. His research has attracted funding from major Turkish research bodies including TÜBİTAK and university-supported projects. Professor Karadavut has supervised 13 theses across multiple institutions and taught diverse courses ranging from medical bioinformatics to agricultural statistics. His research portfolio includes 31 projects, with recent work focusing on climate change adaptation in livestock, medical informatics applications, and advanced statistical methodologies for agricultural research. He has collaborated extensively with researchers across multiple Turkish institutions, demonstrating strong interdisciplinary networking capabilities.

Hyungsik Roger MoonView profile

Professor

• Econometrics Theory
• Applied Econometrics
• Labor Economics
• +5 more

Hyungsik Roger Moon is Professor of Economics in the Department of Economics at the University of Southern California's Dornsife College of Letters, Arts and Sciences, where he has served since 2000 after beginning his career at UC Santa Barbara. His academic trajectory progressed from Assistant Professor (2000) to Associate Professor (2004) and full Professor (2008), reflecting sustained contributions to econometric methodology. His educational foundation includes: Ph.D. in Economics, Yale University, 1998 M.A. in Economics, Yale University, 1995 B.A. in Economics, Seoul National University, 1989 Moon's research centers on econometric theory development and applied methodology, with particular expertise in panel data analysis, dynamic modeling, and high-dimensional estimation. His theoretical innovations address complex challenges in interactive fixed effects, unit root testing, and heterogeneity modeling, while applied work spans labor economics (income dynamics), health economics (pancreatic cancer trials), and macroeconomics (Covid-19 forecasting). This dual focus bridges rigorous mathematical frameworks with real-world policy applications across multiple economic subfields. Analysis of recent publications reveals an intensifying focus on robust estimation techniques for dyadic data, Bayesian approaches to sparse heterogeneity, and methodological innovations in forecasting with censored panel data. His work increasingly integrates machine learning concepts with traditional econometrics, particularly in high-dimensional seemingly unrelated regression systems and network-based peer effect modeling. His distinguished scientific contributions have been recognized through: Fellow of the Econometric Society (2023) Fellow of the Journal of Econometrics (2019) RK Cho Economics Award (2018) Maekyung/KAEA Economist Award (2012) Econometric Theory Multa Scripsit Award (2006-2007) Korea-America Economic Association Young Scholar Award (2005) Moon has secured significant research funding including an NSF grant of $180,675 for 'Forecasting with Dynamic Panel Data Models' (2016-2020) and $68,000 for 'Asymptotic Analysis of Panel Regression Models' (2009-2010). His academic leadership extends to editorial roles at the Journal of Business and Economic Statistics, Econometric Theory, and Journal of Econometrics, plus administrative service as Director of Graduate Studies for USC's Economics Ph.D. program (2018-2021) and Associate Director of USC Dornsife INET (2015-2017). Through his position at USC Dornsife INET and graduate program leadership, Moon actively shapes research directions in new economic thinking while mentoring future econometricians through advanced courses like Big Data Econometrics.

Zihe GaoView profile

Assistant Professor

• Optics
• Integrated Photonics
• Quantum Optics
• +5 more

Zihe Gao serves as a tenure-track Assistant Professor in the Department of Electrical and Computer Engineering at Auburn University's College of Engineering since August 2023, following postdoctoral research at the University of Pennsylvania and industry experience at Meta (Facebook Reality Labs). His academic foundation includes: PhD in Electrical and Computer Engineering, University of Illinois Urbana-Champaign (2018) MS in Physics, University of Illinois Urbana-Champaign (2012) BS in Physics, Nanjing University (2011) Dr. Gao's research integrates optics, microelectronics, and physics to develop programmable photonic systems. His work focuses on controlling collective behaviors in multi-element photonic systems for scalable integrated chips, with applications spanning dynamically steerable laser sources and reconfigurable quantum optical platforms . Key methodologies include non-Hermitian physics, topological photonics, and spin-orbit coupling engineering. Analysis of his 2023-2025 publications reveals dominant trends in non-Hermitian photonic switching , high-dimensional quantum state manipulation , and topological semiconductor laser arrays . His team pioneers lithography-free reconfigurable photonics and spin-orbit microlasers for quantum key distribution, demonstrating strong industry-academia translation from prior Meta work on AR/VR structured-light systems. His scholarly trajectory shows continuous progression from VCSEL array fundamentals (PhD under Prof. Kent Choquette) to quantum-topological photonics (postdoc with Prof. Liang Feng), now establishing independent research at Auburn with emphasis on integrated quantum-classical hybrid systems.

Jody ReimerView profile

Assistant Professor

• Mathematical Ecology
• Ecological Modeling
• Polar Marine Biology
• +7 more

Jody Reimer is an Assistant Professor jointly appointed in the Department of Mathematics and the School of Biological Sciences at the University of Utah. Her interdisciplinary work bridges mathematical theory with ecological applications, focusing on extreme environments including polar regions and Great Salt Lake. She maintains offices in both the Mathematics building (LCB 301) and South Biology building (227), reflecting her dual departmental affiliation and commitment to collaborative research across disciplines. Dr. Reimer's research explores the intersection of mathematics, ecology, and statistical/computational methods to connect models with data. Her work leverages advances in uncertainty quantification, optimal control theory, and data science to understand ecological phenomena. Much of her research is motivated by questions in polar marine biology and Great Salt Lake ecology, with additional collaborative projects spanning diverse biological systems. She develops mathematical approaches to study sea ice ecosystems, polar bear navigation in warming Arctic, and long transient dynamics in ecological systems. Analysis of Dr. Reimer's recent publications reveals a strong focus on mathematical approaches to pressing ecological questions. Her work spans theoretical developments in uncertainty quantification and transient dynamics, alongside applied research on polar ecosystems, wildlife management, and disease modeling. A recurring theme is the development and application of optimal control theory to ecological problems, particularly in understanding animal behavior under environmental change. Her interdisciplinary approach consistently bridges mathematical theory with empirical ecological challenges. Shortlisted for the Robert May Prize for her paper "Matrix methods for stochastic dynamic programming in ecology and evolutionary biology" Dr. Reimer actively mentors a diverse group of students across multiple disciplines, including PhD candidates in Mathematics and Biology, as well as numerous undergraduate researchers. Her lab recently received a 1U4U Seed Grant to support data collection on Great Salt Lake microbialites. She co-organizes significant research initiatives including the ICERM Workshop on Uncertainty Quantification for Mathematical Biology (May 2025) and the SLMath graduate summer school on Mathematics of Climate, Sea Ice, and Polar Ecosystems (June 2025). Current funding supports research on polar bear navigation, sea ice ecology, and Great Salt Lake microbial communities. The Reimer lab operates at the interface of mathematics and biology, with members working on diverse projects spanning polar ecology, Great Salt Lake research, and theoretical ecology. The lab maintains strong connections with field researchers in the Arctic and collaborates with biologists, statisticians, and applied mathematicians across institutions. Recent work includes the "Mathpedition" project documenting academia in the Arctic and collaborative efforts with Polar Bears International on educational initiatives.

• Intelligent Systems and Control
• Cognitive Robotics
• Nano-robotics
• +9 more

Laxmidhar Behera is a Professor in the Department of Electrical Engineering at the Indian Institute of Technology Kanpur, specializing in Intelligent Systems and Control. With over two decades of academic experience at IIT Kanpur and international research experience at institutions including Fraunhofer Institute of Autonomous Intelligent Systems in Germany, ETH Zurich, and University of Ulster, he has established himself as a leading researcher in cognitive robotics and intelligent control systems. Dr. Behera's research spans multiple cutting-edge domains including Cognitive Robotics, Nano-robotics, Vision based Control, Soft Computing, Information Retrieval in music and language, Semantic Information Processing, Physics of Complex Systems, Cyber Physical Systems, Formation Control of UAVs, Brain-Computer Interface (BCI), and Sanskrit Computational Linguistics. His interdisciplinary approach bridges traditional control theory with modern computational intelligence techniques, creating innovative solutions for complex real-world problems. His extensive publication record in top-tier journals like IEEE Transactions demonstrates his leadership in areas such as brain-computer interfaces, visual servoing, multi-robot systems, and music information retrieval. Notably, his work on quantum neural networks for EEG filtering and multisatellite formation control has received significant attention in the research community. UKIERI Standard Research Award 2008 Best Paper at International Conf. on Intelligent Sensors and Information Processing (ICISIP-2004) Best Paper at WoSco,02, Int. Conf. High-Performance Computing (HiPC, 2002) AICTE career award for young teacher (1997) Senior Member IEEE Multiple IEEE top accessed articles (2009-2010) As an Associate Editor for Autosoft Journal and Technical Committee Member for Intelligent Control at IEEE Control System Society, Dr. Behera actively contributes to the academic community. His laboratory in the Western Lab - 212A of the Department of Electrical Engineering serves as a hub for research in intelligent systems, where he mentors students and collaborates with researchers worldwide on cutting-edge projects in robotics, control systems, and computational intelligence.

Mathukumalli VidyasagarView profile

Professor

• Machine Learning
• Systems and Control Theory
• Computational Biology
• +4 more

Mathukumalli Vidyasagar is a Distinguished Professor at the Indian Institute of Technology Hyderabad and previously held the SERB National Science Chair and Cecil & Ida Green Chair in Systems Biology Science at the University of Texas at Dallas. He earned his Ph.D. from the University of Wisconsin, Madison, and has authored 13 books and over 160 peer-reviewed papers.

Volkert PaulsenView profile

Senior Lecturer

• Financial Mathematics
• Stochastic Analysis
• Optimal Stopping
• +5 more

Volkert Paulsen is a Senior Lecturer at the Institute of Mathematical Stochastics at the University of Münster. His career spans institutions including the University of Kiel, where he completed his Habilitation (2000), Dissertation (1994), and Diplomarbeit (1989). He has taught extensively in Financial Mathematics , Stochastic Analysis , and Mathematical Statistics , supervising over 50 Bachelor, Master, and Diploma theses on topics such as risk modeling, portfolio optimization, and derivative valuation. Research Interests: Paulsen's work focuses on Financial Mathematics (continuous-time models, American options, unit-linked insurance), Stochastic Analysis (optimal stopping, martingale methods), and Risk Modeling (credit risk, extreme value statistics). His publications include foundational studies on nonlinear observation costs in optimal stopping problems and stochastic approaches to portfolio management. Scientific Contributions: His research spans journal articles in Stochastic Processes and their Applications and Journal of Applied Probability , with recent seminar topics covering Lévy Processes , Copula Modeling , and Stochastic Volatility . He employs R for statistical applications and integrates mathematical theory with practical finance and insurance contexts. Contact: Email: Volkert.Paulsen@uni-muenster.de Room: 130.010, Orléans-Ring 10, 48149 Münster Phone: +49 251 83-33771

Andrea CristofaroView profile

Associate Professor

• Robotic Systems
• Multi-Agent Systems
• Hybrid Systems
• +4 more

Andrea Cristofaro is an Associate Professor at the Department of Computer, Control and Management Engineering, Sapienza University of Rome. He holds an M.Sc. in Mathematics (2005) from Sapienza University of Rome and a Ph.D. in Information Science and Complex Systems (2010) from the University of Camerino. His academic trajectory includes postdoctoral positions at INRIA Rhône-Alpes (France) and the Norwegian University of Science and Technology, followed by faculty appointments at the University of Camerino and University of Oslo before joining Sapienza in 2019. His research spans: Robotic systems : Fault-tolerant control, multi-agent coordination, and hybrid dynamics Distributed parameter systems : Stability analysis, observer design, and optimal control of PDEs Hybrid systems : Control synthesis for systems with state-driven jumps and switching logic Recent publications (2024-2025) predominantly explore fault-tolerant control architectures for multi-robot systems, decentralized task allocation, adaptive observer designs for biological and thermal systems, and optimal control of reaction-diffusion equations. This reflects a consistent focus on robustness in complex dynamical systems with applications ranging from industrial robotics to biomedical engineering. He serves as Associate Editor for Automatica and IEEE Transactions on Control Systems Technology , and is a member of the IEEE CSS Conference Editorial Board.

• Switched and hybrid systems
• Nonlinear control theory
• Control with limited information
• +3 more

Daniel Liberzon , the Richard T. Cheng Professor in the Department of Electrical and Computer Engineering at the University of Illinois Urbana-Champaign , is a leading expert in theoretical and applied control systems. He also serves as a professor at the Coordinated Science Laboratory and an affiliate professor in the Department of Mathematics . Ph.D. in Mathematics, Brandeis University (1998) Undergraduate, Mathematics, Moscow State University (1989-1993) His research spans foundational and applied aspects of nonlinear control theory , switched/hybrid systems , and control under limited information . Applications include networked control, power systems synchronization, and robotic coordination. Recent work focuses on topological entropy, almost Lyapunov functions, and robust observer design. Key article trends include: Entropy analysis for interconnected nonlinear systems Stability criteria under switching and disturbances Observer robustness via ISS and qDES frameworks Model reduction techniques for power systems Scientific awards include: 2019 ACM SIGBED HSCC Best Paper Award 2016 IFAC Fellow 2013 IEEE Fellow 2007 AACC Donald P. Eckman Award 2007 UIUC CoE Xerox Award 2002 IFAC Young Author Prize 2002 NSF CAREER Award Liberzon has supervised numerous sponsored projects funded by NSF and AFOSR. He teaches advanced courses in nonlinear control, optimal control, and hybrid systems, appearing on the UIUC List of Teachers Ranked as Excellent multiple times.

Kaiming BiView profile

Assistant Professor

• Infectious Disease Modeling
• Epidemic Forecasting
• Public Health Decision-Making
• +12 more

Kaiming Bi, Ph.D., is an Assistant Professor in the Department of Management, Policy & Community Health at the University of Texas Health Science Center at Houston (UTHealth Houston) School of Public Health . As an affiliated member of the Center for Health Care Data , his research bridges quantitative methods and public health, focusing on infectious disease modeling , epidemic forecasting , and data-driven health policy . B.S. in Mathematics from Northeastern University (2015) Ph.D. in Industrial Engineering from Kansas State University (2020) Postdoctoral training at University of California San Diego School of Medicine (2020-2021) and University of Texas at Austin (2021-2024) His methodological expertise spans mathematical modeling , machine learning , and optimization , applied to diverse public health challenges including respiratory infections , vector-borne diseases , STIs , and the opioid epidemic . Recent work includes modeling SARS-CoV-2 Omicron subvariants , population immunity dynamics , and multi-pathogen burden projections for the 2023-2024 US winter season. Dr. Bi has received prestigious accolades such as the Pencis Best Researcher Award (2021) and IISE Best Paper (2018). He previously taught graduate courses in Integer Programming , Information Systems , and Industrial Simulation at Kansas State University. Currently leading the Big-data and Infectious Disease Modeling Lab (BI Lab) , he seeks STEM-motivated PhD students to develop computational solutions for epidemic control.

• Numerical Analysis
• Partial Differential Equations
• Integral Equations
• +3 more

Mario Annunziato is a Researcher in Mathematics at the Department of Physics, University of Salerno, since 2004. His work focuses on numerical methods for stochastic processes and optimal control. Institution: University of Salerno Department: Department of Physics Academic Rank: Researcher Research Interests include numerical solutions of PDEs and integral equations for stochastic processes, probability density function optimization, and modeling random phenomena. His work addresses positivity, monotonicity, and conservation in discrete PDFs. Article Trends span stochastic control frameworks, computational finance, biophysics applications, and numerical methods for jump-diffusion processes. Key topics involve Fokker-Planck equations, Hamilton-Jacobi-Bellman formulations, and splitting methods. Advising and Grants include teaching Numerical Analysis until 2013 and securing funding from the University of Salerno's FARB program, INdAM-GNCS, and the European Science Foundation's OPTPDE grants. He participated in the STRIKE Marie Curie ITN network. Labs & Teams : Collaborated with Prof. Alfio Borzì at Würzburg University and contributed to open-source tools like MATLAB Central File Exchange for PDP solvers.

Tingshu HuView profile

Professor

• Control Systems
• Power and Energy
• Nonlinear systems theory
• +7 more

Tingshu Hu is a Professor and Department Chair of Electrical and Computer Engineering at the Francis College of Engineering, University of Massachusetts Lowell. He holds a Ph.D. in Electrical & Computer Engineering from the University of Virginia, an M.S. in Electrical Engineering from Tong University in China, and a B.S. in Electrical Engineering from Shanghai Jiao Tong University. His research focuses on Control Systems and Power and Energy, with specific interests in nonlinear systems theory, control systems with saturation, optimal control, robust control, and applications to mechanical, biomedical, and power/energy systems. He has made significant contributions to battery modeling and power electronics, particularly in triboelectric nanogenerators and energy storage systems. His publications show a strong trend toward renewable energy systems, with recent work focusing on power management for triboelectric nanogenerators, battery/supercapacitor hybrid energy storage, and advanced power converter design. His research bridges theoretical control systems with practical power electronics applications. Scientific Awards: Teaching Excellence Award (2007) from the Department of Electrical and Computer Engineering 2023 Reader Garden Award by Garden Gate Magazine Research Funding: National Science Foundation grants as Principal Investigator on multiple projects including Control design of power electronic interfaces for renewable energy systems (2012), Analysis of Nonlinear Oscillations via Lyapunov Approach (2009), and Nonlinear/Switching Control Design for Constrained Control Systems (2006) Utah Artificial Heart Institute grant as Co-Principal Investigator on a Continuous Flow Centrifugal Ventricular Assist Device (2002) Dr. Hu maintains an active research program with over 8000 total citations and an h-index of 39 according to Google Scholar. His work has had significant impact in both control theory and practical power systems applications.

John JunkinsView profile

Professor

• Aerospace Engineering
• Astrodynamics
• Spacecraft Dynamics and Control
• +3 more

John Junkins is a distinguished academic and researcher in aerospace engineering, holding the Royce E. Wisenbaker Chair and serving as Director of the Hagler Institute for Advanced Study at Texas A&M University's College of Engineering. He has been affiliated with the Department of Aerospace Engineering since at least 1993. Junkins' roles include being a Distinguished Professor (mapped to Professor rank), a Regents Professor, and an Honorary Fellow of the American Institute of Aeronautics and Astronautics (AIAA). His educational background includes a B.A.E. from Auburn University (1965), and M.S. and Ph.D. in Aerospace Engineering from UCLA (1967, 1969). His research focuses on spacecraft dynamics and control, guidance systems, analytical methods, and smart sensor technologies. Notable contributions include work on trajectory optimization, orbital mechanics, and nonlinear control systems. Key awards include the Kay Bailey Hutchinson Distinguished Service Award (2022), Robert H. Goddard Astronautics Award (2019), and TYCHO BRAHE Medal (2004). His publications span textbooks like 'Analytical Mechanics of Aerospace Systems' and pioneering research in astrodynamics. Junkins' work emphasizes interdisciplinary collaboration through the Hagler Institute, advancing computational methods for space mission design and autonomous systems.