Prof. Maryna Viazovska is a Full Professor and holds the Chair of Arithmetics at the Institute of Mathematics (EPFL SB MATH TN) at the École Polytechnique Fédérale de Lausanne (EPFL). She also serves as a Full Professor in the SMA-ENS division, part of the School of Basic Sciences (SB). Additionally, she leads the Theoretical Number Theory (TN) group , directs the MATH-GE unit, and is a member of the Bernoulli Center's Executive Committee (CIB) and the SB Faculty Board. Her roles reflect her leadership in both research and academic governance. Her research focuses on sphere packing , lattice theory , modular forms , and Fourier analysis . She is celebrated for solving the sphere packing problem in dimensions 8 and 24, a breakthrough integrating modular forms and harmonic analysis. Her work bridges number theory, discrete geometry, and mathematical physics, with implications for coding and cryptography. Prof. Viazovska has advised doctoral students including Matthias Gröbner Nihar Prakash Gargava Gauthier Leterrier Martin Peter Stoller Her teaching includes courses such as Discrete Mathematics , Riemann Surfaces , and Number Theory II.b - Modular Forms . She actively contributes to EPFL's academic infrastructure, including the Institute of Mathematics and the Bernoulli Center .
Dr. Lucas Slot is a Lecturer at the Department of Computer Science, ETH Zurich, specializing in theoretical computer science, computational complexity, and optimization algorithms. His research focuses on polynomial optimization, sum-of-squares hierarchies, and semidefinite programming, with applications to algorithmic design and complexity analysis. Recent work includes studies on computational thresholds in stochastic block models, convergence rates of optimization hierarchies, and kernel-based methods for high-dimensional inference. His contributions span theoretical foundations and algorithmic advancements in mathematical programming and geometric data analysis. Lacking explicit mentions of academic awards or grants, Dr. Slot’s scholarly activities emphasize computational and mathematical challenges in optimization and discrete geometry. No student advisees are listed in the provided materials.
Ola Svensson is an Associate Professor at the School of Computer and Communication Sciences, EPFL. His research focuses on approximation algorithms, combinatorial optimization, computational complexity, and scheduling. He has been supported by grants including the ERC Starting Grant "OptApprox" (2014-2019), SNF grants, and the ERC Consolidator Grant "POTCO" (2023-). He teaches courses such as Advanced Algorithms and Approximation Algorithms and Hardness of Approximation. Education: PhD from IDSIA - Universita della Svizzera italiana (2009) and Master's from Uppsala University (2005). Research Interests: Design and analysis of approximation algorithms for NP-hard problems, scheduling, and computational complexity. He explores limitations of approximation techniques through hardness results and contributes to theoretical computer science. Publications span clustering, scheduling, and graph problems like the Traveling Salesman Problem. Recent work includes learning-augmented algorithms and robust optimization. Awards: I&C teaching award and best paper awards at FOCS (2017) and STOC (2018). Over a dozen PhD students advised, many entering postdocs or industry roles. Labs/Teams: Part of the theory group at EPFL, collaborating on academic projects and course development.
Daniele Zambon is a postdoctoral researcher at the Dalle Molle Institute for Artificial Intelligence (IDSIA), affiliated with Università della Svizzera italiana (USI) in Lugano, Switzerland. He is a member of the Faculty of Computer Science and the Graph Machine Learning Group, as well as the IEEE Task Force on Learning for Graphs. PhD : Informatics, Università della Svizzera italiana (USI), 2022 Master’s & Bachelor’s : Mathematics, University of Milan, Italy Visiting Researcher : University of Florida, University of Exeter Internship : STMicroelectronics, Italy His research lies at the intersection of machine learning and graph-structured data, with a strong emphasis on graph representation learning , learning in non-stationary environments , and time series analysis . He explores how to model dynamic graphs, detect anomalies and changes over time, and develop deep learning methods for spatiotemporal forecasting. His work integrates statistical testing, geometric deep learning, and neural architectures like Graph Neural Networks (GNNs) and Neural ODEs. The recent publications highlight a clear trend toward temporal and dynamic graph modeling , especially for time series forecasting and irregularly sampled data . There is a growing focus on generative and foundation models for graphs , uncertainty-aware learning , and the creation of benchmark datasets like PeakWeather. His work bridges theoretical contributions (e.g., statistical tests, Kalman filters on graphs) with practical applications in sensing, environmental modeling, and system monitoring. Co-author of patent: Method for the Detecting Electrocardiogram Anomalies and Corresponding System (US10610162B2) PhD thesis featured in D22 Excellent Computer Science Dissertations (2022) Associate Editor, IEEE Transactions on Neural Networks and Learning Systems (IEEE TNNLS) Organizer of tutorials and special sessions at ICML, LoG, KDD, and ESANN Daniele actively contributes to the academic community through advising and teaching at USI’s Bachelor’s and Master’s programs. He has co-supervised research projects and co-organized educational initiatives such as tutorials on graph deep learning. His collaborative work involves grants and partnerships with institutions like MeteoSwiss, leading to impactful datasets and applied research. He is deeply involved in building research capacity through workshops and community engagement in the graph learning field. He is a core member of the Graph Machine Learning Group at IDSIA and contributes to the IEEE Task Force on Learning for Graphs , fostering international collaboration and setting research agendas in the domain of graph-based AI.
Anders Karlsson is an Associate Professor at the University of Geneva since 2010 and holds a concurrent professorship at Uppsala University since 2013. His academic journey began with engineering physics studies at KTH, followed by a mathematics PhD from Yale University in 2000, with subsequent positions at ETH Zurich, Neuchâtel, Bielefeld, Yale, and KTH. His research spans multiple mathematical domains with significant interdisciplinary applications: Ergodic theory and random walks Metric geometry and group actions Spectral invariants Deep learning and neural networks Karlsson's theoretical work focuses on noncommuting random products and metric functional analysis, applied to random walks on groups, operator theory, complex variables, stochastic game theory, and machine learning. He also investigates connections between zeta functions of graphs, spaces, and numbers using heat kernel analysis. He leads the Algebra and Geometry research group, mentoring PhD students Kamila Kashaeva and Dylan Mueller, and supervising postdoctoral researcher Tsviqa Lakrec. His teaching portfolio includes various mathematics courses documented in the university database, reflecting his expertise across theoretical and applied mathematics domains.
