Find Academic Supervisors

Nicolas FlammarionView profile

Assistant Professor

• Machine Learning
• Statistics
• Optimization
• +3 more

Nicolas Flammarion is a tenure-track Assistant Professor in Computer Science at the École Polytechnique Fédérale de Lausanne (EPFL). He holds positions in multiple departments including the Theory in Machine Learning (TML) lab under the School of Computer and Communication Sciences (IC). His roles include teaching and doctoral program leadership across disciplines like Communication Systems and Computer Science. Education: PhD in 2017 from École Normale Supérieure (Paris), advised by Alexandre d’Aspremont and Francis Bach. Postdoctoral fellowship at UC Berkeley under Michael I. Jordan. Research focuses on machine learning theory, optimization, and statistical methods. Key areas include adversarial robustness, algorithmic generalization, and optimization dynamics. Notable contributions include work on SGD/GD comparisons, adversarial benchmarking (RobustBench), and safety in AI systems. Awards include the 2018 Fondation Mathématique Jacques Hadamard PhD Prize, 2021 NeurIPS Outstanding Paper Award, and 2024 Trust & Safety Google Research Award. Supervises PhD students in topics like algorithm design and machine learning theory. Labs/Teams: Leads TML lab and contributes to EPFL's doctoral programs in Informatics and Communication Sciences. Active in curriculum development for advanced machine learning topics.

Michael MaireView profile

Associate Professor

• Computer Vision
• Machine Learning
• Deep Learning
• +5 more

Michael Maire is an Associate Professor in the Department of Computer Science at the University of Chicago, with a courtesy appointment at the Toyota Technological Institute at Chicago (TTIC). His research focuses on computer vision, machine learning, and deep learning, particularly in neural network architectures, optimization, and self-supervised learning. Prior to UChicago, he held roles at TTIC and Caltech, and earned his Ph.D. from UC Berkeley in 2009. His work spans perceptual organization, object recognition, and representation learning. Key contributions include the Microsoft COCO dataset, fractalnet architectures, and spectral embedding techniques for segmentation and figure/ground organization. He advises students on topics like efficient network growth, sparsification, and contrastive learning. He co-leads the Machine Learning Group at UChicago and contributes to the CERES Center for Unstoppable Computing. His research has been published in top venues like CVPR, ICCV, and NeurIPS, with over 30 peer-reviewed articles since 2004. Teaching includes courses on computer vision and deep learning fundamentals.

Michel KIEFFERView profile

Professor

• Signal Processing
• Communication Systems
• Networking
• +5 more

Michel KIEFFER is a Professor in Signal Processing for Communications at Paris-Sud University and a researcher at the Laboratoire des Signaux et Systèmes (L2S), Gif-sur-Yvette, France. He holds a Ph.D. (1999) and Habilitation (2005) from Université Paris-Sud. Previously, he served on part-time leave at Laboratoire de Traitement et Communication de l’Information (CNRS-Télécom ParisTech, 2009–2016) and was a junior member of the Institut Universitaire de France (2011–2016). His research focuses on joint source-channel coding, signal processing for communication networks, and robust control systems. Key contributions include works on multimedia transmission reliability, UAV localization, and fault identification in power grids. He co-authored over 100 publications and books like Applied Interval Analysis (Springer, 2001) and Joint Source-Channel Decoding (Academic Press, 2009). He serves as Associate Editor for Signal Processing (since 2008) and IEEE Transactions on Communications (2012–2016). His work spans interdisciplinary areas such as energy systems, robotics, and telecommunications, addressing challenges in real-time data transmission, network slicing, and distributed control.

Swapnil MishraView profile

Assistant Professor

• Global Health
• Machine Learning
• Bayesian Modelling
• +4 more

Dr. Swapnil Mishra is an Assistant Professor at the NUS Saw Swee Hock School of Public Health and the NUS Institute of Data Science. His work bridges global health challenges with machine learning and Bayesian modelling, focusing on spatiotemporal data analysis for epidemics and public policy. He co-founded the Machine Learning & Global Health Network with leading international institutions. Education: B.Eng., University of Pune, 2009 Masters Computing (Hons.), The Australian National University, 2014 Ph.D., The Australian National University, 2019 Research Interests: His research encompasses infectious disease modelling, hierarchical Bayesian approaches, and generative deep learning. He develops scalable models to understand epidemics like malaria and HIV, while also exploring crime patterns and online information diffusion. Key Contributions: Notable work includes SARS-CoV-2 lineage analysis in Brazil, evaluating non-pharmaceutical interventions during the pandemic, and developing the πVAE framework for Bayesian deep learning. His research often addresses real-world policy challenges, such as pandemic preparedness and disease surveillance. Awards: Singapore NRF Fellowship (2023) Queen’s Anniversary Prize (2021) SPI-M-O Award (2022) Blackwell-Rosenbluth Award (2022) Advisory Roles: Advised New York State and Herbert Smith Freehills on pandemic response strategies. His work integrates academic research with practical policy solutions. Labs/Teams: Leads interdisciplinary collaborations through the Machine Learning & Global Health Network, fostering global health science innovation.

Philippe TointView profile

Professor

• Numerical Analysis
• Optimization
• Algorithms
• +3 more

Philippe Toint is a Professor at the University of Namur, specializing in numerical analysis, optimization, algorithms, transportation modeling, and mobility analysis. He holds a Doctor of Science (1978) and Master of Mathematics (1974) from the University of Namur. His research focuses on optimization methods, numerical linear algebra, and applications in transportation and data assimilation. Toint has led numerous projects, including the CUTEr testing environment and GALAHAD optimization suite, and has contributed to advancements in algorithmic complexity and stochastic optimization. He has been awarded the Beale-Orchard-Hayes Prize (1994) and multiple EPSRC Research Fellowships (2002–2009). His work spans theoretical and applied domains, with collaborations in computational efficiency and global optimization challenges. Education: Doctor of Science (1978), Master of Mathematics (1974) – University of Namur Key Research Areas: Nonconvex optimization, first-order methods, stochastic algorithms, and computational complexity Notable Projects: CUTEr, GALAHAD, and multi-level optimization frameworks Awards: Beale-Orchard-Hayes Prize, EPSRC Research Fellowships (2002–2009), Erskine Fellowship (2003) His recent publications emphasize refining complexity bounds for optimization algorithms, adaptive regularization methods, and applications in physics-informed neural networks. Toint actively contributes to the optimization community through editorial roles and international conferences, driving advancements in both theoretical foundations and practical implementations.

• NOMA Systems
• Cooperative Communications
• Relay Networks
• +5 more

İnci Umakoğlu is an Assistant Professor at the Department of Electrical and Electronics Engineering, Faculty of Engineering, Kutahya Dumlupınar University. She holds a PhD in Electrical-Electronics Engineering from Dumlupınar University (2017–2024) and has over 10 years of academic experience as a Research Assistant and faculty member. Her research focuses on NOMA systems, cooperative communications, satellite design, and autonomous UAV development. She actively advises student research projects supported by TÜBİTAK and leads initiatives in analog/digital communications and measurement laboratory operations. Education: Bachelor's in Electronics and Communication Engineering (Kocaeli University, 2009–2014) Master's in Electrical-Electronic Engineering (Dumlupınar University, 2015–2017) PhD in Electrical-Electronics Engineering (Dumlupınar University, 2017–2024) Her research interests include 5G/6G antenna design, deep learning-enhanced signal processing, and IoT integration in smart transportation systems. She has published extensively on NOMA systems, UAV-assisted networks, and OTFS modulation techniques. Currently teaches Analog Communication and Advanced Professional English in the undergraduate curriculum. She leads multiple projects including satellite telemetry systems, autonomous blood delivery UAVs, and low-altitude defense systems. Holds IEEE and ACES memberships, and coordinates Erasmus+ student exchange programs.

Michael Masao MiyamotoView profile

Professor

• Molecular Phylogenetics
• Evolutionary Biology
• Population Genetics
• +2 more

Michael Masao Miyamoto is a Professor at the University of Florida's College of Liberal Arts and Sciences. His research focuses on molecular phylogenetics, evolutionary biology, and bioinformatics with applications in population genetics. He advises current graduate student Fan Qiu. Miyamoto holds a Ph.D. from the University of Southern California (1982). His work integrates genomic data to explore evolutionary mechanisms and organismal history. Key research themes include mutation rate dynamics, viral phylogenetics for human migration studies, and computational modeling of population genetic processes. He has contributed to landmark studies on vertebrate skeletal evolution using lamprey gene analysis. Miyamoto's lab is located in Bartram Hall, Room 311, with a mailing address at PO Box 118525, Gainesville, FL 32611-8525.

Tuna TuğcuView profile

Professor

• NanoNetworking
• Molecular Communications
• 5G Networks
• +3 more

Tuna Tuğcu is a Professor in the Department of Computer Engineering at Bogazici University, where he leads research in nanonetworking and wireless communications. He holds affiliations with the Computer Networks Research Lab (NetLab), Nanonetworking Research Group (NRG), and Telecommunications and Informatics Technologies Research Center (TETAM). He earned his PhD in Computer Engineering from Bogazici University in 2001 and completed postdoctoral research at Georgia Institute of Technology. His research focuses on NanoNetworking/Molecular Communications , 5G Networks , Cognitive Radio , and Wireless Systems . Key areas include diffusion-based signal propagation, modulation techniques for molecular channels, and optimization of nanoscale communication protocols. His recent publications emphasize practical applications in transmitter localization, error minimization, and throughput enhancement for emerging technologies. Awards include: Best Paper Award at ISCC'12 (2012) Best Paper Award at AICT (2010) National Utility Model for disaster coordination systems (TR 2013 11786 Y) He serves as Senior Projects Coordinator and chairs the Information Technologies Committee. He also contributes to ÖBİKAS and TETAM governance. No specific grants or student advisees are detailed in the source text.

Roua YoussefView profile

Assistant Professor

• UWB Signal Processing
• Compressive Sampling
• Full-Duplex Communications
• +4 more

Roua Youssef is an Assistant Professor at the University of Brest, France, and a member of the SI3 (Security, Intelligence and Integrity of Information) research team within Lab-STICC (CNRS UMR 6285). Her work focuses on advanced signal processing techniques for telecommunications, including UWB systems, compressive sampling, full-duplex communications, and drone detection. Education: She earned a Telecommunications Engineering diploma from the Lebanese University (Beirut, Lebanon) and an M.Sc. in Electronic Engineering from IMT Atlantique (Brest, France) in 2007. She completed her Ph.D. in Telecommunications at IMT Atlantique in 2011. Research interests include radar-based sensing for Industry 4.0 applications, robust heart rate detection in vehicular environments, and interference cancellation in full-duplex systems. Her work combines machine learning with UWB radar and compressive sensing to address challenges in indoor localization, collision avoidance, and non-cooperative signal detection. Recent contributions include experimental validation of IR-UWB radar systems for people detection in industrial settings and digital self-interference cancellation techniques for full-duplex transceivers. She has also explored low-complexity drone control signal detection and optimal Fourier coefficient selection for UWB channel estimation. Labs/Teams: Active member of Lab-STICC’s SI3 team, focusing on secure and intelligent information systems. Her research integrates hardware prototyping (e.g., USRP platforms) with algorithm development for real-world wireless communication problems.

Milica StojanovicView profile

Professor

• Wireless Communications
• Underwater Acoustic Systems
• Statistical Signal Processing
• +3 more

Milica Stojanovic is a Professor in the Department of Electrical and Computer Engineering at Northeastern University, with a secondary affiliation as a permanent Guest Investigator at the Woods Hole Oceanographic Institution (WHOI). Her expertise spans wireless communications, underwater acoustic systems, and statistical signal processing. She holds a B.S. from the University of Belgrade and M.S./Ph.D. from Northeastern University. Education: B.S. (1988, University of Belgrade), M.S./Ph.D. (1991/1993, Northeastern University) Postdoctoral Fellowships: Woods Hole Oceanographic Institution (1993), NSF Research Scholarship (1994) Her research focuses on underwater acoustic communications, pioneering advancements in signal processing and network design. Notable contributions include phase-coherent modems and underwater OFDM systems. Awards include IEEE Fellow (2010), Ocean Engineering Society’s Distinguished Technical Achievement (2015), and Aarhus University’s honorary doctorate (2022). She chairs the IEEE Technical Committee on Underwater Communication and serves as an editor for the IEEE Journal of Oceanic Engineering. Recent projects include underwater acoustic networks, software-defined networking for the Internet of Underwater Things, and robotic aquaculture systems. Her work bridges theoretical research with practical applications in marine environments. Grants: U.S. Army Research Lab, NSF, Office of Naval Research Labs: Institute for the Wireless Internet of Things (IoT)

Guillermo AguilarView profile

Researcher

• Visual Perception
• Lightness Perception
• Depth Perception
• +3 more

Guillermo Aguilar is a Researcher at the Department of Computational Psychology at Technische Universität Berlin. He has been a Postdoctoral Researcher since 2017 under Prof. Marianne Maertens, following his doctoral work (2012–2017) on depth and lightness perception using MLDS methods. His research focuses on visual perception, particularly lightness, depth, and transparency, alongside the development of robust psychophysical methods for measuring perceptual phenomena. He holds a Ph.D. from TU Berlin (2017), an M.Sc. in Integrative Neuroscience from Otto-von-Guericke University Magdeburg (2012), and a B.Sc. in Medical Sciences from the University of Chile (2007). His work addresses core questions in visual perception, including lightness constancy mechanisms and the evaluation of experimental techniques like maximum likelihood difference scaling (MLDS) and conjoint measurement. Recent projects emphasize separating perceptual encoding/decoding processes and constraining computational models using psychophysical data. Aguilar’s methodologies have advanced understanding of brightness transfer functions and transparency perception in complex scenes. Key contributions include refining MLDS for cross-context comparisons and analyzing noise contributions in vision. He collaborates actively with the Institute of Computer Engineering and Microelectronics, leveraging computational tools to bridge theoretical and experimental research. His lab focuses on integrating psychophysical experiments with computational modeling to explain human visual behavior.

Jonathan SiegelView profile

Assistant Professor

• Approximation Theory
• Neural Networks
• Numerical PDEs
• +3 more

Jonathan Siegel is Assistant Professor of Mathematics at Texas A&M University. His research bridges approximation theory, neural network mathematics, statistics, and numerical methods for PDEs, with applications to materials science. Funded by NSF and ONR grants, his work develops theoretical foundations for deep learning algorithms. Research areas include: Mathematical theory of neural networks High-order approximation rates for shallow networks Sparse neural network training Optimization on manifolds Structure-informed materials prediction Recent publications analyze spectral bias of neural networks, approximation rates for ReLU networks, and greedy training algorithms.

Andrea BonitoView profile

Professor

• Numerical Analysis
• Partial Differential Equations
• Adaptive Finite Element Methods
• +3 more

Andrea Bonito is a Professor in the Department of Mathematics at Texas A&M University, affiliated with the College of Arts and Sciences. His research focuses on numerical methods for partial differential equations (PDEs), including adaptive finite element methods, free boundary problems, geometric PDEs, and non-Newtonian fluid dynamics. He explores topics like finite element approximations of thin structures, fractional operators, and optimal learning algorithms. His work emphasizes applications in material science and fluid dynamics, such as modeling bilayer plates, folding mechanics, and electroconvection of thin liquid crystals. Recent contributions include advancements in PINNs (Physics-Informed Neural Networks) for elliptic PDEs and gamma-convergent methods for large deformations. Bonito’s research bridges theoretical analysis with computational implementation, addressing challenges in high-dimensional approximation and incomplete information scenarios. His academic output spans over two decades, with notable publications on geometric PDEs, spectral fractional diffusion, and adaptive methods for elliptic problems. Bonito collaborates on interdisciplinary projects, leveraging numerical analysis to solve complex physical phenomena.

Solesne BourguinView profile

Associate Professor

• Stochastic Analysis
• Fractional Brownian Motion
• Malliavin Calculus
• +6 more

Solesne Bourguin is an Associate Professor in the Department of Mathematics and Statistics at Boston University. She is a member of the Probability and Statistics research group. Her research focuses on advanced topics in stochastic analysis, including stochastic dynamical systems, fractional Brownian motion, Malliavin calculus, Stein's method, and free probability theory. She teaches graduate and undergraduate courses such as Probability Theory II and Linear Algebra. Her research interests emphasize the theoretical foundations of stochastic processes, with applications to nonlinear statistics and random matrices. Recent work explores quantitative analysis of stochastic iterative algorithms and Gaussian approximations in high-dimensional settings. She has contributed to understanding fluctuation dynamics in multiscale systems driven by fractional Brownian motion and has published in leading journals like the Electronic Journal of Probability and Stochastic Processes and their Applications. Bourguin's recent publications highlight her expertise in topics like functional Gaussian approximations, moderate deviation principles, and spherical Poisson waves. These studies often combine techniques from Malliavin calculus and Stein's method to derive precise probabilistic estimates. She also investigates the limiting behavior of correlated Wishart matrices in high-dimensional regimes, contributing to random matrix theory. Scientific awards and grants are not explicitly listed in the provided information. She actively participates in academic activities, including organizing Boston University's Statistics and Probability Seminar Series. Her teaching reflects her commitment to both foundational and advanced mathematical education, spanning topics from linear algebra to stochastic processes.

Yehia ElkhatibView profile

Associate Professor

• Distributed Systems
• Cloud Computing
• Edge Computing
• +7 more

Yehia Elkhatib is a Reader (Associate Professor) in Computing Science at the University of Glasgow, where he leads research in distributed systems that traverse infrastructural boundaries. His work focuses on developing data-driven approaches to improve performance and programmability of distributed systems, with particular emphasis on cloud interoperability, intent-driven networking, systems of systems composition, and edge computing. Dr. Elkhatib earned his PhD in Network Measurement & Grid Computing from Lancaster University in 2011, following an MSc with Distinction in Networking and Internet Systems from the same institution. His academic journey demonstrates a consistent focus on network systems and distributed computing. His research interests center on developing data-driven solutions to optimize complex systems, with particular emphasis on addressing resource utilization challenges from a user-centric perspective. His work spans decision-support systems for cost-effective cloud deployment, memory-aware ML scheduling to reduce training errors and GPU resource requirements, energy-efficient scheduling of scientific workflows, enabling seamless interoperability between services, developing systems of systems in IoT environments, designing intent-driven network architectures, and caching in information-centric networks. Analysis of his recent publications reveals a strong focus on sustainability in computing, with multiple papers on energy efficiency, carbon footprint estimation, and carbon-aware execution of scientific workflows. His research also shows significant contributions to IoT systems, cloud computing, and quality of experience in multimedia systems, demonstrating a diverse yet cohesive research portfolio addressing critical challenges in distributed computing. Dr. Elkhatib has secured substantial research funding including AI4ME (EPSRC) £3.8m, ABC (EPSRC) £775k, and DIONASYS (CHIST-ERA) €1.03m, demonstrating the significance and impact of his research. He actively supervises PhD students working on cutting-edge topics including DNS infrastructure placement, memory-aware scheduling, and carbon-aware execution of scientific workflows. His teaching responsibilities include Cloud Systems, Systems Programming, Advanced Programming, Building Big Data Systems, Distributed Systems, and Data Science Fundamentals, reflecting his expertise across multiple domains of computing science. His office is located in The Sir Alwyn Williams Building, room 322a at the University of Glasgow.