Find Academic Supervisors

• Stochastic Calculus
• Numerical Analysis
• Scientific Computing
• +8 more

Antoine Tambue is a Full Professor in Mathematics at the Department of Computer Science, Electrical Engineering and Mathematical Sciences, Western Norway University of Applied Sciences (HVL), Norway, and an Honorary Professor at the University of Cape Town, South Africa. His research focuses on stochastic calculus, numerical analysis, computational finance, and AI, with a vision to develop efficient numerical algorithms for high-dimensional problems in energy, finance, and data science. He holds a PhD from Heriot-Watt University (UK), funded by the ORS Awards Scheme, and has held postdoctoral roles at the University of Bergen and NTNU. He also served as the AIMS ARETÉ junior research Chair, supervising 5 PhD and 12 master’s students across multiple institutions. Education: Bachelor in Mathematics – University of Dschang, Cameroon Master in Mathematics – University of Yaounde I, Cameroon Professional Master in Mathematics Education – Ecole Normale Supérieure de Yaoundé Postgraduate Diploma in Mathematical Sciences – AIMS South Africa & University of Cape Town PhD in Mathematics – Heriot-Watt University (UK) Research Interests: Tambue’s work bridges stochastic analysis, numerical methods, and AI. Key areas include: - Developing scalable algorithms for high-dimensional PDEs and uncertainty quantification - Stochastic optimal control and Bayesian inference in computational finance and engineering - Machine learning applications in glaciology and energy systems. Scientific Awards: ORS Awards Scheme PhD Scholarship AIMS ARETÉ Junior Research Chair (Robert Bosch Stiftung) Advising & Grants: Supervised 5 PhD and 12 MSc students across Africa and Europe. Research funded by interdisciplinary projects linking applied mathematics to energy and environmental challenges.

Lizhong ChenView profile

Professor

• Computer Architecture
• Machine Learning Accelerators
• GPU Architecture
• +6 more

Lizhong Chen is a Professor in the School of Electrical Engineering and Computer Science at Oregon State University and a core AI faculty member in the Collaborative Robotics and Intelligent Systems (CoRIS) Institute. He leads the STAR Lab which focuses on computing systems and AI applications with emphasis on computing efficiency across various computing platforms from embedded devices to supercomputers. Ph.D., Computer Engineering, University of Southern California, 2014 M.S., Electrical Engineering, University of Southern California, 2011 B.S., Electrical Engineering, Zhejiang University, 2009 Chen's research focuses on efficient computer systems (GPUs, accelerators, HPCs, IoT devices) and their applications in machine learning and natural language processing, especially large language models. His work spans machine learning accelerators, GPU architecture, AI-assisted design for computer architecture, and energy-efficient computing systems. He has made significant contributions to NoC (Network-on-Chip) power-gating research and developed the Agate simulator for simulating NoC power-gating. His recent publications (2023-2025) show a strong focus on large language models, particularly for simultaneous translation tasks, Kolmogorov-Arnold networks, and efficient model architectures. His work bridges computer architecture design with AI applications, creating synergies between hardware efficiency and machine learning performance. Scientific Awards: NSF CRII Award (2016) NSF CAREER Award (2018) Best Paper Nomination at IEEE NAS (2018) Best Paper Runner-up Award at HPCA (2020) Chu Kochen Award from Zhejiang University IEEE HPCA Hall of Fame (2020) Chen has served as an Associate Editor of IEEE Transactions on Computers and as program committee member for top computer system and machine learning conferences. He is the founder and organizer of the Annual International Workshop on AIDArc (AI-assisted Design for Architecture). His research is supported by multiple grants from NSF, NIH, Department of Energy, and the Northwest-AI-Hub supported by the CHIPS and Science Act. He teaches courses in computer architecture, high-performance computing, and specialized topics in AI accelerators and GPU architecture. As director of the STAR Lab, Chen leads research on computing efficiency across the spectrum from embedded and mobile devices to supercomputers and data centers. The lab's recent focuses include machine learning accelerators, GPU architecture, applications of AI in architecture designs, and improving the computing efficiency of machine learning and natural language processing models.

Jaya BishwalView profile

Associate Professor

• Probability
• Stochastic Processes
• Statistical Inference
• +2 more

Dr. Jaya Bishwal is an Associate Professor in the Mathematics & Statistics Department at the University of North Carolina at Charlotte. Her research expertise centers on Probability and Stochastic Processes, with applications in mathematical finance and statistical inference. Her extensive publication record demonstrates deep specialization in fractional processes, stochastic differential equations, and statistical estimation methods. Recent work focuses on advanced econometric applications including Levy process modeling, fractional Ornstein-Uhlenbeck systems, and volatility estimation in financial mathematics. Methodological innovations include developments in quasi-likelihood estimation, Kolmogorov distance analysis for estimators, bootstrap methods for financial derivatives, and asymptotic theory for nonergodic systems. Her research bridges theoretical probability and practical applications in quantitative finance.

Lennart HoogerheideView profile

Associate Professor

• Econometrics
• Bayesian Analysis
• Monte Carlo Methods
• +6 more

Lennart Hoogerheide is an Associate Professor at the Department of Econometrics, School of Business and Economics, Vrije Universiteit Amsterdam, and a Research Fellow at the Tinbergen Institute. He earned his PhD from Erasmus University Rotterdam in 2006. His research focuses on Bayesian econometrics, Monte Carlo integration methods, and computational statistics, with applications in financial econometrics and instrumental variable models. Hoogerheide has developed influential R packages like MitISEM and AdMit for Bayesian analysis and importance sampling. He has received awards including the 'Best Senior Lecturer' (2015-2016) and 'Lecturer of the Year' (2009-2010). His work explores complex posterior distributions, non-elliptical contours, and robust inference techniques in econometric models. His recent research includes time-varying factor models, Bayesian mode inference for discrete distributions, and adaptive mixture methods. He has been awarded the VENI grant (NWO, 2009) and is recognized for contributions to computational Bayesian methods in economics and finance.

• Theoretical Computer Science
• Statistics
• Probability
• +5 more

Alex Wein is an Assistant Professor of Mathematics at the University of California, Davis. His research focuses on theoretical computer science, statistics, probability, and data science, with an emphasis on computational complexity of statistical inference, particularly in low-degree polynomial models. He explores challenges in tensor analysis, Bayesian inference, and group actions such as cryo-electron microscopy. His work bridges statistical physics and algorithmic hardness, contributing to foundational understanding of estimation and recovery thresholds. Education: Ph.D. in Mathematics, Massachusetts Institute of Technology (2018) Research Interests: Optimal algorithms for hidden structure detection in noisy data Low-degree polynomial framework for algorithmic hardness predictions Tensors and their computational challenges Connections between Bayesian methods and statistical physics Recent Talks: 'Sharp Phase Transitions in Estimation with Low-Degree Polynomials' at SLMath (2025) 'Unifying Statistical Physics and Low-Degree Polynomials?' at Les Houches School of Physics (2025)

• Computer Architecture
• Systems & Networking
• Approximate Computing
• +6 more

Weng Fai WONG is an Associate Professor and Deputy Head of the Department of Computer Science at the School of Computing, National University of Singapore (NUS). With over three decades of academic experience at NUS, he has established himself as a leading researcher in computer systems, with particular expertise in the interface between hardware and software stacks. Dr. Wong received his B.Sc. (First Class Honors) and M.Sc. from the National University of Singapore in 1989 and 1991 respectively, followed by a Dr.Eng.Sc. from the University of Tsukuba in 1993. His academic journey began at NUS (then DISCS) in 1985, where he progressed from student to Senior Tutor in 1989, and later returned from Japan as a Lecturer in 1993. Dr. Wong's research focuses on systems and networking, with special emphasis on hardware-software co-optimization. His current research interests include approximate computing , neuromorphic computing , and hardware acceleration for deep learning. His work spans computer architecture , embedded systems , compilers and runtime systems , and programming languages . He has made significant contributions to optimizing software for novel hardware including FPGAs, GPUs, and non-volatile memory technologies. His recent publications (2023-2025) demonstrate a strong focus on energy-efficient AI computing, with particular emphasis on spiking neural networks, large language model acceleration, and FPGA-based solutions for graph processing and machine learning workloads. His research shows a clear trajectory toward green AI through hardware-software co-design that minimizes energy consumption while maintaining computational effectiveness. Dr. Wong is a Member of ACM and a Senior Member of IEEE. His paper "Exploiting half precision arithmetic in Nvidia GPUs" was a Best Paper Finalist at the IEEE High Performance Extreme Computing Conference (HPEC 2017). As Deputy Head of the Department of Computer Science at NUS, Dr. Wong plays a key leadership role in academic administration while maintaining an active research program. His work has been supported by numerous research grants, though specific details are not provided in the available information. Dr. Wong leads research in the Systems & Networking area at NUS, with particular focus on the Hardware-Software Interface Laboratory. His team explores innovative approaches to bridge the gap between theoretical computer science and practical hardware implementation, with applications spanning from edge computing to large-scale data centers.

• Convex Optimization
• Quantum Computing
• Quantum Information Theory
• +7 more

Sander Gribling is an Assistant Professor in the Department of Econometrics and Operations Research at Tilburg School of Economics and Management, Tilburg University. His research bridges convex optimization, quantum computing, and quantum information theory, with a focus on quantum algorithms for classical optimization problems, semidefinite and polynomial optimization, and quantum graph parameters. PhD in Optimization and Quantum Information, Tilburg University (2019) MSc and BSc in Applied Mathematics, TU Delft His research interests include: Quantum algorithms for linear programming, semidefinite programming, and matrix scaling Polynomial and sum-of-squares optimization Quantum query complexity and the polynomial method Applications of optimization to quantum information and noncommutative graphs His recent publications reveal a strong focus on quantum algorithms, theoretical foundations of optimization, and sampling methods. Key trends include the development of optimal quantum linear solvers, complexity analysis of the Lasserre hierarchy, and the interplay between Grothendieck inequalities and quantum query models. Notable scientific roles: Associate Editor, INFORMS Journal on Computing Sander Gribling has advised or collaborated with numerous researchers including Simon Apers, Joran van Apeldoorn, Monique Laurent, and Ronald de Wolf. He has received no explicitly listed awards in the provided text, but his publications in high-impact journals such as Nature Reviews Physics and Quantum indicate strong recognition. He is involved in the Operations Research research group and contributes to advancing theoretical and applied quantum optimization.

Andrea TancrediView profile

Professor

• Bayesian Inference
• Bayesian Computational Methods
• Biostatistics
• +9 more

Andrea Tancredi is a Professor at the Department of Methods and Models for Economy, Territory, and Finance, Sapienza University of Rome. His research focuses on Bayesian inference, computational statistics, and their applications in biostatistics, population size estimation, and health economics. He has coordinated multiple research projects funded by the University, including studies on record linkage, approximate Bayesian computation (ABC), and stochastic frontier models. Research Interests: Bayesian Inference Computational Methods (MCMC, ABC) Population Size Estimation Record Linkage and Data Integration Extreme Value Theory Biostatistics and Health Economics Teaching: He teaches courses such as Basic Statistics, Advanced Statistical Methods, and Extreme Value Theory at the Faculty of Economics and doctoral programs in Economics. Publications and Citations: With 47 publications (23 journal articles), his work spans Bayesian modeling for microbial diversity, climate-conflict risk forecasting, and multi-state health models. His research is cited 682 times on Google Scholar (h-index 12) and 401 times in Scopus (h-index 9).

Dominik BaumannView profile

Assistant Professor

• Systems and Control Theory
• Machine Learning
• Communication Networks
• +3 more

Dominik Baumann is an Assistant Professor at the Department of Electrical Engineering and Automation at Aalto University in Espoo, Finland. His research focuses on the interplay of systems and control theory with machine learning and communication networks, with a current emphasis on causal inference in control systems. Education: Diploma in Electrical Engineering from TU Dresden, Germany (2016) PhD from KTH Stockholm, Sweden (2020), supervised by Sebastian Trimpe (Max Planck Institute) and Karl H. Johansson Postdoctoral positions at RWTH Aachen University (1 year) and Uppsala University with Thomas Schön (1 year) Dr. Baumann's research bridges theoretical foundations with practical applications in robotics, wireless networks, and decision-making systems. His work spans safe reinforcement learning, event-triggered control systems, causal inference in dynamical systems, and ergodicity economics perspectives on long-term decision-making. He applies mathematical rigor to address challenges in resource-constrained environments, particularly focusing on safety guarantees and computational efficiency for real-world implementation. His recent publication record demonstrates a strong trajectory in safe learning-based control, with increasing focus on ergodicity economics in reinforcement learning, multi-agent coordination, and human-robot interaction. The research consistently balances theoretical guarantees with practical implementation constraints, particularly in bandwidth-limited wireless control systems and robotics applications. Scientific Awards: Best Paper Award for 'Feedback control goes wireless: Guaranteed stability over low-power multi-hop networks' at ACM/IEEE International Conference on Cyber-Physical Systems (2019) Dr. Baumann maintains extensive international collaborations, evidenced by numerous seminar invitations worldwide including Oxford University, ETH Zürich, University College London, and institutions across Asia. His research program addresses fundamental challenges in cyber-physical systems with applications in industrial automation, robotics, and the Internet of Things, securing research funding for projects focused on safe learning in control systems and wireless cyber-physical systems. His research group at Aalto University develops both theoretical foundations of learning-based control and practical algorithms for real-world deployment, with active software repositories on GitHub related to predictive triggering, causal structure identification, and ergodic reinforcement learning.

Michael ShieldsView profile

Professor

• Uncertainty quantification
• Machine learning
• Stochastic simulation
• +7 more

Michael Shields is a Professor in the Department of Civil and Systems Engineering at Johns Hopkins University, with secondary appointments in Materials Science and Engineering and affiliations with the Hopkins Extreme Materials Institute (HEMI) and the Data Science and AI Institute. He directs the Center on High-Throughput Materials Discovery for Extremes and co-leads the Center on Artificial Intelligence for Materials in Extreme Environments. Shields chairs the Whiting School of Engineering Faculty Senate and is an Ivy+ Provost Leadership Fellow. His research focuses on uncertainty quantification, machine learning, stochastic simulation, and reliability analysis for engineering systems. Key applications include extreme event modeling (earthquakes, blasts, impacts), materials science, and computational mechanics. His group develops open-source tools like UQpy for uncertainty quantification in physical systems. Recent publications (2024-2025) demonstrate strong trends in physics-informed machine learning, with neural operators and Bayesian methods dominating 80% of works. Surrogate modeling techniques appear in 60% of articles, while materials science applications feature in 40%. Natural hazards and computational efficiency are recurring themes. Awards & Honors: 2025 Early Achievement Research Award (IASSAR) DOE Early Career Award NSF CAREER Award ONR Young Investigator Award Johns Hopkins Catalyst Award He leads the Shields Uncertainty Research Group (SURG), funded by NSF, ONR, ARL, and national laboratories. Current projects emphasize real-time prediction of complex systems and reliability analysis under uncertainty.

Ilse C.F. IpsenView profile

Professor

• Numerical Linear Algebra
• Randomized Algorithms
• Probabilistic Numerical Analysis
• +8 more

Ilse C.F. Ipsen is a Distinguished Professor in the Department of Mathematics at North Carolina State University, with a joint appointment in the Department of Statistics. She is a Fellow of the American Mathematical Society and the Society for Industrial and Applied Mathematics (SIAM), and delivered the Olga Taussky-Todd Lecture at ICIAM 2023. Institution: North Carolina State University School: College of Sciences Department: Department of Mathematics Rank: Professor Email: ipsen@ncsu.edu Her research centers on numerical linear algebra, randomized algorithms, and probabilistic numerical analysis. She has made significant contributions to error analysis in floating-point computation, matrix perturbation theory, and the development of randomized methods for large-scale data problems. Her work bridges theoretical foundations with practical applications in data science, quantum physics, and computational statistics. She is the author of the textbook Numerical Matrix Analysis (SIAM, 2009) and serves as Editor-in-Chief of the SIAM Book Series on Data Science. The recent publications highlight a strong trend toward probabilistic numerical methods, uncertainty quantification, and randomized matrix algorithms. Themes include error analysis for inner products and summation, Bayesian solvers for linear systems, and scalable methods for trace and diagonal estimation. Applications span biobank data analysis, quantum physics, and PageRank computation. Scientific Awards: Fellow of the American Mathematical Society (AMS) Fellow of the Society for Industrial and Applied Mathematics (SIAM) Olga Taussky-Todd Lecture, ICIAM 2023 She has advised multiple graduate and undergraduate students, including J.T. Holodnak, T. Wentworth, and R. Rehman. Her collaborative research has been supported by interdisciplinary grants, particularly in quantum physics and data-intensive computing. She has served on editorial boards of leading journals such as SIAM Review , Acta Numerica , and Numerische Mathematik , and chaired SIAM activity groups, reflecting her leadership in the applied mathematics community. She is actively involved in the development of software and algorithms, including the MATLAB package kappa_SQ for randomized sampling, and has contributed to discussions on communicating applied mathematics through case studies.

Claudio LuccheseView profile

Professor

• Machine Learning
• Information Retrieval
• Data Mining
• +4 more

Claudio Lucchese is a Full Professor in the Department of Environmental Sciences, Computer Science and Statistics at Ca' Foscari University of Venice. His research focuses on Machine Learning in Information Retrieval, specifically Learning to Rank, Adversarial ML, and Explainable AI. He has published over 100 articles and won awards including the 2015 ACM SIGIR Best Paper Award. He coordinates the Hospitality Innovation and e-Tourism degree program and leads the Data Mining and Information Retrieval Lab. Research Interests: Efficiency-Effectiveness Trade-offs in IR Adversarial Machine Learning Explainable AI Large-Scale Data Mining Recent Projects: Data Science for Mobility (Humco S.r.l., 2020) Flexymob (Currant s.r.l., 2022) Lucchese serves on editorial boards for ACM Transactions on Information Systems and Data Mining and Knowledge Discovery. His teaching spans Computer Science and Engineering Physics programs, emphasizing Massive Data Learning and High-Performance Computing.

• Signal Processing
• Telecommunications
• Quantization
• +12 more

Jelena Nikolic is an Associate Professor in the Department of Telecommunications at the Faculty of Electronics, University of Niš, Serbia. She has been a core faculty member since earning her PhD in 2011 and was promoted to Assistant Professor in 2014 and Associate Professor in 2020. Her academic work is deeply rooted in signal processing, quantization theory, and data compression, with expanding interests in machine learning applications for communications. Research Interests: Her primary research areas include quantization , data and signal compression , approximations of the Q-function , and neural network weight compression . She applies these to telecommunications, digital signal processing, and emerging AI-driven communication systems. Her work bridges theoretical analysis with practical implementation, particularly in companding quantizers, polar quantization, and adaptive coding schemes. Publication Trends: Her recent publications (2020–2022) show a clear evolution toward integrating machine learning with classical signal processing, especially in post-training quantization of neural networks and AI-based compression. Earlier works (2013–2018) focus on theoretical foundations of quantization for Gaussian and Laplacian sources, with rigorous mathematical modeling and performance analysis. The consistent use of terms like 'companding', 'scalar quantizer', 'Gaussian source', and 'SQNR' reflects a strong foundation in information theory and digital communications. Scientific Awards: Prof. Dr. Ilija Stojanović Award (2017) – Best professional work in telecommunications Advising and Grants: She has mentored 2 master’s and 2 diploma students. She has participated in 10 major research projects funded by the Ministry of Education and Science of Serbia, Erasmus+, Norwegian Ministry of Foreign Affairs, and the Science Fund of Serbia, including projects on software-defined radio, dialogic systems, and AI-based quantization (Com-in-AI). She has also served on numerous academic committees for thesis defense, hiring, and academic evaluation across multiple institutions. Labs and Teams: While no specific lab name is mentioned, her involvement in experimental and computational work—such as laboratory exercises in acoustics, signal processing, and VoIP, as well as MATLAB-based practicals—indicates active participation in teaching and research labs at the Faculty of Electronics. She has collaborated extensively with Prof. Zoran Perić and others in the telecommunications group, forming a strong research team focused on quantization and compression.

• Econometrics
• Time Series Analysis
• Bootstrap Inference
• +3 more

Iliyan Georgiev is an Associate Professor of Econometrics at the Faculty of Economics, University of Bologna, since 2018. Previously, he held roles at Nova School of Business and Economics (2011–2015) and the Agency for Economic Analysis and Forecasting in Sofia (1995–1998). He obtained a PhD in Economics from the European University Institute (2004), and degrees in Mathematics and International Economic Relations from Sofia University and Karl Marx Institute of Economics. His research focuses on econometric theory, time series analysis, bootstrap inference, and spurious correlations. He teaches Econometrics and Mathematics, including differential calculus and stochastic calculus. Georgiev’s work emphasizes robust statistical methods for non-stationary processes and rare events, with applications in financial and economic forecasting. His recent publications explore predictive regression, canonical correlation analysis, and unit root testing under heavy-tailed distributions. He has contributed to methodologies addressing parameter instability and spurious correlations in time series data. No explicit scientific awards are listed, but he achieved National Scientific Qualification for Full Professor in Econometrics (MIUR, 2018). No grants or lab affiliations are mentioned in the provided texts.

Hoi NguyenView profile

Professor

• Combinatorics
• Probability
• Random Matrices
• +3 more

Hoi Nguyen is a Professor of Mathematics at The Ohio State University (OSU), specializing in Combinatorics, Probability, and Random Matrices. His research explores areas such as inverse Littlewood-Offord problems, random matrix universality, and stochastic processes. He holds a PhD from Rutgers University (2010) and has held postdoctoral positions at the University of Pennsylvania, Yale University, and the Institute for Advanced Study. His work is supported by NSF grants and a Simons Foundation Fellowship. Recent projects include studies on random matrix cokernels, eigenvalue gaps, and trigonometric polynomials' real roots. He teaches graduate courses in Probability and Combinatorics at OSU. Education: PhD in Mathematics, Rutgers University, 2010. Research interests focus on probabilistic combinatorics, random matrix theory, and stochastic models. Key themes include universality phenomena in random structures, anti-concentration inequalities, and applications to algebraic problems. His work bridges combinatorial methods with probabilistic analysis, addressing foundational questions in matrix theory and polynomial root distribution. Grants include NSF CAREER Award (2018-2024) and Simons Fellowship (2023-2024). His recent articles investigate universality in random matrix cokernels, rank fluctuations in matrix products, and exponential concentration in root counts. He organizes workshops on random polynomials and matrices, reflecting his leadership in these fields.