Assoc Prof Ng Teng Yong is an Associate Professor at the School of Mechanical & Aerospace Engineering (NTU), specializing in numerical modeling and simulation. With a background as Research Manager at A*STAR Institute of High Performance Computing, his work spans materials science, nanotechnology, and aerospace engineering. Current focus on graphene-based desalination membranes Expertise in molecular dynamics simulations Investigates nanoscale fluid mechanics and structural dynamics Recent publications highlight advancements in energy-efficient electrodialysis, smart robotics, and nonlinear vibration analysis. His interdisciplinary approach integrates computational methods with experimental validation in additive manufacturing and soft material mechanics.
Olivia Constantin is an Associate Professor in the Department of Mathematics at the Faculty of Mathematics. She has been actively publishing since 2008, with research contributions in functional analysis, complex analysis, and mathematical fluid dynamics. PhD in Mathematics (implied by academic position and research output) Her research primarily focuses on operator theory on function spaces, especially Fock and Bergman spaces, and the application of complex analysis to fluid flows. Key areas include Hankel operators, integral operators, embedding theorems, and the analysis of water waves and oceanic gyres. Her work combines deep analytical techniques with applications in fluid mechanics. Recent publications show a strong trend toward using complex-analytic methods to study irrotational flows, traveling water waves, and gyre dynamics, indicating an interdisciplinary approach bridging pure and applied mathematics. Her 2025 paper on velocity extrema in ocean gyre flows highlights ongoing contributions to geophysical fluid dynamics. She has led two research projects: Operatorenklassen (2012–2016) and Analysis of Operators on Spaces of Holomorphic Functions (2017–2019), demonstrating sustained research leadership. Active speaker at academic conferences, with 18 recorded scientific activities including talks from 2008 to 2023 Olivia Constantin collaborates with prominent mathematicians such as A. Aleman, J. A. Peláez, A.-M. Persson, and D. Kalaj. She has not received any explicitly mentioned scientific awards. There is no information about her advising students or managing labs or research teams.
Mary Lou Zeeman is the R. Wells Johnson Professor of Mathematics at Bowdoin College, specializing in geometric dynamical systems, mathematical biology, and climate modeling. Her work bridges theoretical mathematics with real-world applications in ecology, sustainability, and neuroendocrinology. Education: PhD in Mathematics from the University of California, Berkeley; MA and BA in Mathematics from the University of Oxford. Research focuses on population dynamics, resilience in ecosystems, and interdisciplinary approaches to sustainability. She co-leads initiatives like the Mathematics and Climate Research Network (MCRN) and contributed to the Mathematics of Planet Earth (MPE) 2013 initiative. Her teaching includes Biomathematics (MATH 1758/BIOL 1175) and Multivariate Calculus (MATH 1800). Key contributions include modeling hormone oscillations in the menstrual cycle, climate change impacts, and fisheries management policies. Her work emphasizes decision-support frameworks for environmental challenges. Grants and collaborations span NSF-funded projects on computational sustainability and climate change research, reflecting her commitment to applied interdisciplinary science.
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.
Mikkel N. Schmidt is an Associate Professor in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU). His research focuses on statistical modeling, Bayesian methods, and their applications in science and industry. He has held visiting roles at Columbia University (2007) and Cambridge University (2008-2009). His work integrates probabilistic modeling with computational inference to address complex problems in diverse fields such as molecular discovery, optical communication, and brain connectivity analysis. Education highlights include visiting scholar and postdoctoral experiences at top-tier institutions. Research interests span statistical methodology development, machine learning applications, and interdisciplinary problem-solving. Current projects involve Bayesian neural networks for molecular discovery and federated learning optimization. Advising efforts include supervising multiple PhD students in areas like molecular discovery and denoising diffusion models. Notable collaborations involve work on materials science, quantum communication, and medical signal processing. His contributions bridge theoretical advancements with practical industrial applications, emphasizing interdisciplinary innovation.
Stefano Grivet-Talocia is a Full Professor at the Department of Electronics and Telecommunications at the Polytechnic University of Turin, where he also serves as Director of the Doctoral School and President of the Doctoral School Council. He is a member of the Interdepartmental Center SmartData@PoliTO - Big Data and Data Science Laboratory, the University Committee for Research, Technology Transfer and Services to the Territory, and the Commission for the Promotion of Library, Archive and Museum Heritage. His academic career spans over two decades at Politecnico di Torino, where he has established himself as a leading researcher in electromagnetic modeling and signal integrity. Grivet-Talocia earned his Laurea degree (summa cum laude) in Electronic Engineering in 1994 and his Ph.D. in Electronic and Communication Engineering in 1998, both from the Polytechnic University of Turin. Between 1994 and 1996, he conducted research at NASA/Goddard Space Flight Center in Greenbelt, Maryland. His educational background laid the foundation for his expertise in electromagnetic modeling, wavelet analysis, and signal processing. His research focuses on behavioral modeling, electromagnetic compatibility, macromodeling, model order reduction, numerical modeling, passivity, power integrity, signal integrity, transmission lines, and wavelets . Grivet-Talocia is particularly renowned for his work on passive macromodeling of interconnect structures, development of the TOPLine technique for transmission line simulation, and pioneering contributions to passivity enforcement algorithms. He has co-authored the first book entirely dedicated to Macromodeling (2016) and developed innovative approaches to waveform relaxation and wavelet-based signal processing. His recent publications (2024-2025) demonstrate continued leadership in model order reduction, with significant contributions to data-driven modeling of linear and nonlinear systems, power integrity analysis, and electromagnetic compatibility. His work spans both theoretical advances in numerical methods and practical applications in circuit design, with strong industry relevance particularly for semiconductor and electronic design automation companies. IEEE Fellow (2018-present) Three Intel SRS Grants (2022-2024) Three IBM SUR Grant Awards (2007-2009) Best Associate Editor Award - IEEE Transactions on Components, Packaging and Manufacturing Technology (2020) Multiple Best Conference Paper Awards (2006-2020) URSI Young Scientist Awards (1999) Ranked among the "top 2% worldwide researchers" (Stanford) since 2019 Grivet-Talocia actively supervises doctoral students including Michele Cusano, Sara Paknezhad Panahi, Antonio Carlucci, and Kun Zhao. He has secured numerous research grants from competitive national calls (PRIN) and commercial contracts with industry partners including Intel, IBM, Nokia, Hitachi, Infineon, and Cadence. His technology transfer activities include co-founding the spin-off IdemWorks (2007-2016), which was acquired by CST in 2016. He also developed the autoCircuits web service for automated circuit problem generation, widely used in electrical engineering education. He leads the EMC Group (Electromagnetic Compatibility) at DET and has been instrumental in establishing the Compact Dynamical Modeling research area. His work has practical applications in high-speed electronics design, with algorithms embedded in commercial tools like IBM PowerSPICE. Grivet-Talocia maintains strong industry connections through his research projects and serves as Associate Editor for IEEE Transactions on Components, Packaging and Manufacturing Technology.
Guillermo Ferreyra is a Professor in the Department of Mathematics at Louisiana State University (LSU), where he has served since 1996. He holds a Ph.D. from Rutgers University (1983) and a Licenciado from the Universidad de Córdoba, Argentina (1977). His research focuses on Deterministic and Stochastic Control Theory, Partial Differential Equations, Probability Theory, and Financial Mathematics. Ferreyra has held significant administrative roles, including Associate Dean for Science Education (2012–present), Deputy Superintendent of the Office of STEM at the Louisiana Department of Education (2010–2012), and Dean of the College of Arts and Sciences at LSU (2004–2009). He has led initiatives to enhance STEM education, including professional development programs for K-12 teachers and strategies to improve 8th-grade math achievement. Ferreyra’s academic contributions include over 30 journal articles and co-edited volumes on evolution equations and control theory. He has advised five Ph.D. students and secured millions in grants for interdisciplinary research and educational initiatives. His administrative achievements at LSU include expanding faculty diversity, improving student advising, and fostering interdisciplinary programs such as the China Initiative. He has also contributed to statewide education reforms, including planning for the Common Core State Standards. Ferreyra is a member of the American Mathematical Society and has served on national evaluation committees and grant review panels. Publications highlight his work in stochastic control, free boundary problems, and applications to finance and advertising models. His research bridges theoretical mathematics with practical applications, such as the mathematical underpinnings of the 1997 Nobel Prize in Economics for the Black-Scholes formula.
Professor Quanmin Zhu is a Professor in Control Systems at the School of Engineering, University of the West of England (UWE), Bristol, UK, holding this position since 2004. His academic career spans over four decades, including roles as Lecturer at Qiqihar University (China, 1983-1986), Post-doctoral Researcher at University of Sheffield (UK, 1989-1994), Lecturer at University of Brighton (UK, 1994-1997), and Lecturer/Reader at Aston University (UK, 1997-2004). His educational background includes: MSc in Engineering from Harbin Institute of Technology, China (1980-1983) PhD from University of Warwick, UK (1986-1989) Professor Zhu's research centers on dynamic system modeling, identification, control, and simulation, with pioneering contributions to nonlinear control systems, robust control methodologies, and U-model based control frameworks. His work bridges theoretical advances with practical applications in robotics, renewable energy systems, and industrial automation, emphasizing model-free and adaptive control solutions for complex nonlinear dynamics. Analysis of his 2021-2025 publications reveals a dominant focus on robust control for uncertain nonlinear systems, with significant contributions to sliding mode control, multi-agent coordination, and cyber-physical security. His research increasingly integrates machine learning techniques (e.g., actor-critic reinforcement learning) while maintaining core expertise in optimization-based control algorithms applied to UAVs, robotic manipulators, and wind energy systems. His professional honors include: Chartered Engineer (CEng) Fellow of the Institution of Engineering and Technology (FIET) Fellow of the Higher Education Academy (FHEA) As an academic leader, Professor Zhu serves as President/Founder of the International Conference on Modelling, Identification and Control (ICMIC), Editor/Founder of Elsevier's Book Series on Emerging Methodologies in Modelling and Control, and University Ambassador for UK-China educational collaboration. His research group secures substantial grants in control theory applications, with ongoing projects in U-model control platforms and international partnerships. He leads the Control Systems research group at UWE, driving innovation in the U-control platform and its industrial applications. His team maintains strong international collaborations, particularly with Chinese institutions, and actively develops the Elsevier Book Series as a key publication channel for emerging control methodologies.
Govind Sharma is a Professor in the Department of Electrical Engineering at the Indian Institute of Technology Kanpur. He holds a PhD from the University of Southern California, Los Angeles, and completed both his M.Tech. (1984) and B.Tech. (1979) in Electrical Engineering from IIT Kanpur. His research interests span multiple areas of signal processing and communications, with a focus on: Signal Processing Communication Systems Video signal processing Medical image processing Professor Sharma has published numerous research papers in prestigious journals and conferences. His work primarily focuses on signal processing techniques, including time delay estimation in acoustic channels, direction of arrival estimation, adaptive filtering algorithms, wavelet transforms, and spectrum estimation. His research has contributed significantly to both theoretical foundations and practical applications in these fields, with publications spanning from 1986 to 2011. He can be reached at his office in ACES-205A, Department of Electrical Engineering, Indian Institute of Technology, Kanpur, UP, India-208016, or by phone at 0512-259-7922.
Dr. Zhen Peng is a Research Fellow at Curtin University's School of Civil and Mechanical Engineering, part of the Faculty of Science and Engineering. He holds an ARC Early Career Industry Fellowship (2025–2028), focusing on developing cost-effective bridge monitoring systems using computer vision and edge computing in collaboration with Main Roads WA. His work bridges structural engineering, IoT/edge computing, and machine learning to enhance infrastructure safety. Dr. Peng earned his PhD from Curtin University (Chancellor's Commendation, 2022). His research emphasizes structural dynamics, nonlinear damage detection, and mobile crowdsensing frameworks for infrastructure monitoring. He has published extensively in top journals like Engineering Structures and Structural Control and Health Monitoring , receiving notable awards such as the 2023 Best Paper Award and a Gold Medal in the China Postdoctoral Innovation Competition. His current projects include deploying IoT-driven systems for real-time bridge condition assessment and training students via available 2025 PhD scholarships. Dr. Peng teaches courses in civil engineering and structural analysis, contributing to both academia and industry through innovation in smart infrastructure technologies.
Weining Kang is an Associate Professor in the Department of Mathematics and Statistics at the University of Maryland, Baltimore County (UMBC). Her research focuses on probability theory, stochastic processes, stochastic networks, and queueing systems. She holds a Ph.D. in Mathematics from the University of California, San Diego (2005). Her work emphasizes fluid models for many-server queues, stochastic networks with abandonment, and reflected diffusions. Notable contributions include analyzing nonlinear Volterra equations in queueing systems, equivalence of fluid models for Gt/GI/N+GI queues, and stationary distribution characterizations for reflected diffusions. She collaborates frequently with experts like K. Ramanan and G. Pang on stochastic network dynamics and performance analysis. Recent publications (2023-2007) explore long-time limits of measure-valued equations, submartingale problems for diffusions, and diffusion approximations for input-queued switches. Her work bridges theoretical stochastic analysis with practical applications in operations research and network engineering. While no formal awards are listed, her extensive peer-reviewed publications and collaborative research highlight her contributions to stochastic systems analysis. She advises on fluid model methodologies and has contributed to ACM Sigmetrics and SIAM journals.
Christa Cuchiero is a Professor at the Department of Statistics and Operations Research , University of Vienna , and an elected member of the Austrian Young Academy (Junge Akademie) since 2020. Her research bridges rigorous mathematics and cutting-edge applications in finance, machine learning, and stochastic analysis. Education: Christa earned her M.Sc. in 2006 from TU Wien with a thesis on affine interest-rate models, her Ph.D. in 2011 from ETH Zürich on affine and polynomial processes, and completed her Habilitation at the University of Vienna in 2018 on high-dimensional finance beyond classical paradigms. Research Interests: Her work centers on affine and polynomial processes , stochastic portfolio theory , signature methods , and infinite-dimensional stochastic analysis . Recent projects explore signature-based neural SDEs for option calibration, measure-valued diffusions for energy markets, and universal approximation properties of signature transforms. Awards & Recognition: Among her accolades are the FWF START Award 2019 , the Bruti-Liberati Visiting Fellowship 2018 , the ETH Medal 2012 for an outstanding Ph.D. dissertation, and the Prix de l’Institut Europlace de Finance 2017 for the best paper in finance. Contact: christa.cuchiero@univie.ac.at , Kolingasse 14-16, 05.47, 1090 Wien, Austria.
Inbar Fijalkow is a Full Professor at the National School of Electronics and Computer Science (ENSEA) within CY Cergy Paris University. She is a member of the ETIS Research Unit (UMR 8051), focusing on signal processing for wireless communications, optimization, and machine learning applications. Her research bridges theoretical advancements with practical implementation in emerging communication systems. Education & Career: PhD in Signal Processing from TelecomParisTech (1993) Postdoctoral Fellow at Cornell University (1994–1995) Professor at ENSEA since 1999 Former Head of ETIS Research Unit (2004–2013) Research Interests: Signal processing for wireless communications Optimization techniques in massive MIMO and NOMA systems Machine learning applications in communication systems Nonlinear effects mitigation in high-power amplifiers Community & Awards: Member of CoNRS Section 7 (National Committee for Scientific Research) Chevalier de l’Ordre National du Mérite (2015) Founder of the CY Alliance Women in Science Prize (2017) Recent Projects: Active in ANR-funded initiatives (e.g., EcoBioH2, AI4code) and EU projects (e.g., PERSEUS). Her work emphasizes sustainability and AI-driven communication systems. Teaching: Teaches signal processing and wireless communications at ENSEA. Supervises PhD students and master’s theses in communication systems and signal processing.
Lacra Pavel is a Professor in the Edward S. Rogers Sr. Department of Electrical and Computer Engineering at the University of Toronto, Faculty of Applied Science and Engineering. She joined the department in August 2002 after industry experience at Nortel Networks and Solinet Systems, and remains active in the System Control Group and Photonics Group. Her educational background includes: Diploma of Engineering (with distinction) in Automatic Control, Technical University Gh. Asachi of Iasi, Romania (1989) PhD in Electrical and Computer Engineering, Queen's University at Kingston (1996) Research focuses on integrating game theory, control theory, and optimization within networked systems. She pioneered applications in noncooperative/evolutionary game theory for network control, nonlinear/robust control frameworks, and energy-efficient optical/transportation networks. Current work develops mathematical foundations for learning in games via control-theoretic approaches to enable autonomous multi-agent network optimization. Recent publications (2019-2013) reveal dominant trends: distributed Nash equilibrium seeking using passivity-based and operator-splitting methods, stability analysis for optical network power control with time-delays, and extensions to transportation systems like railway timetabling. Key subfields include graphical games, ADMM algorithms, and Lyapunov-based boundary control for distributed parameter systems. Scientific recognition includes: Fellow of the IEEE (2025) for contributions to game theory, control, and optimization for network systems Connaught New Staff Award, University of Toronto (2003) New Opportunities Infrastructure Award, CFI/OIT (2003) Award for Innovation, Solinet Systems (2001, 2002) Inventor Recognition Award, Nortel Networks (2000) She has advised over 20 graduate students including current PhD candidates and former students now at MIT, Princeton, Amazon, and Ciena. Major grants include the CFI/OIT New Opportunities Infrastructure Award (2003). Her research bridges theoretical game control with practical implementations in optical and transportation networks. As co-director of the System Control Group and member of the Photonics Group, she leads teams developing algorithms for autonomous network optimization. Current projects focus on stochastic approximation methods for multi-agent learning and energy-efficient network design.
Nils-Christian Detering is an Associate Professor in the Department of Statistics & Applied Probability at the University of California, Santa Barbara (UCSB). He also serves as the Undergraduate Diversity, Equity, and Inclusion Officer. His research focuses on financial mathematics, probability theory, and their applications in systemic risk analysis, energy markets, and machine learning. Detering has contributed to understanding default contagion in financial systems, stochastic processes in energy derivatives, and neural network applications in functional data analysis. Research Interests : Financial systemic risk: Analyzing default contagion using random graphs and systemic stability metrics Infinite-dimensional stochastic analysis: Modeling energy markets via stochastic PDEs and forward curves Machine learning: Developing neural network frameworks for functional spaces and financial applications Teaching includes courses on stochastic processes, mathematical finance, and probability theory at both undergraduate and graduate levels. His work has been recognized with awards such as the Best Paper Award at the ACM International Conference on AI in Finance (2023). Key Publications address topics like reinforcement learning in banking networks, neural network calibration of energy curves, and integrated fire sales models. His research bridges theoretical probability with applied financial engineering challenges.
