Dr. Christopher S. Hlas is a Professor of Mathematics Education at the University of Wisconsin-Eau Claire within the College of Arts and Sciences Department of Mathematics. With a doctorate from the University of Iowa, he specializes in mathematics pedagogy, technology integration, and cross-disciplinary connections between math and foreign languages. Ph.D. in Mathematics Education (University of Iowa, 2005) B.S. in Mathematics and Computer Science (University of Iowa, 2001) His research focuses on teaching through problem solving , student motivation , and technology-enhanced mathematics instruction . He has developed innovative formative assessment probes and conducted extensive studies on homework design, flow theory, and creativity in K-12 education through grants like the ESEA Title II Mathematics and Science Partnerships. His work spans curriculum development, professional development for teachers, and mathematical modeling. Recent publications highlight his exploration of creativity assessment in language education and the application of game mechanics to classroom engagement. He serves as an AP Calculus Reader and Table Leader, while maintaining active roles in the Wisconsin Mathematics Council and National Council of Teachers of Mathematics. Bilingual education initiatives GeoGebra integration in geometry instruction Formative assessment frameworks Game-based learning strategies Scientific Awards CARE Award (2015) ACTFL Research Priority Grant (2010) Multiple teaching scholarships UWEC student-nominated award (2007) Outstanding teaching assistant recognition (2004) Mentoring over a dozen student research projects and securing more than $4 million in federal and state grant funding, Dr. Hlas has supervised numerous collaborative research initiatives. He maintains open-source educational tools like interactive Pascal's Triangle and rational functions resources at math.hlasnet.com while serving on multiple university committees and grant review panels.
Dr. Leila Moslemi Naeni is a Senior Lecturer at the University of Technology Sydney (UTS), School of Built Environment, with a dual appointment in the Faculty of Design, Architecture and Building. She previously served as a Lecturer at UTS (2016-2018) and Sessional Lecturer at Curtin University (2015-2016). PhD in Computer Science (University of Newcastle, 2017) MSc in Industrial Engineering (Sharif University of Technology, 2007) BSc in Industrial Engineering (Iran University of Science and Technology, 2004) Her research focuses on project management under uncertainty, integrating fuzzy systems and mathematical modeling with applications in construction, ESG reporting, and disruptive technologies. She developed innovative methods for statistical control charts in project monitoring and leads research on leveraging blockchain and digital tools for sustainable infrastructure. Recent publications highlight her work on resource-constrained scheduling algorithms, ESG integration in megaprojects, and gamification in project management education. She serves as Review Editor for Frontiers in Environmental Science and on the Editorial Board of Smart and Sustainable Built Environment . 2016 PMI NSW Research Award 2022 Walt Lipke Award 2013 FEBE Postgraduate Research Prize As an active research mentor, she supervises PhD students in machine learning applications, disaster management technologies, and social infrastructure investments. Her teaching emphasizes simulation-based learning, collaborating with Oulo University (Finland) to quantify educational value.
Lina von Sydow is a Professor in Computational Science at Uppsala University's Department of Information Technology. She serves as Section Dean for the Mathematical-Computer Science Section since July 2023. Her academic journey includes becoming an Associate Professor in 2000, Senior Lecturer since 1997, and leading the Department of Information Technology from 2018 to 2023. PhD in Domain Decomposition Methods (1995, Uppsala University) Postdoctoral Fellow at Oxford University (1996-1997) Her research spans computational science with dual focuses on Computational Finance and Ice Sheet Modeling . In finance, she develops numerical methods for option pricing using PDEs, radial basis functions, and stochastic volatility models. In climate science, she contributes to ice sheet dynamics through full Stokes models and adaptive time-stepping approaches, particularly in simulating grounding line migration. Recent publications (2025) address gender disparities in IT education, including comparative analysis of admission trends and intervention studies to boost female enrollment. Earlier works (2020-2015) focus on high-order finite difference methods for financial derivatives, BENCHOP benchmarking projects, and preconditioning techniques for PDEs. Scientific awards include Excellent Teacher (2013) She actively collaborates on educational reforms, co-authoring studies like Gender-aware course reform in Scientific Computing (2013). Her leadership roles include Head of Department (2018-2023) and Section Dean (2023-present), influencing academic governance and interdisciplinary research. Labs and teams: Works with Uppsala University's Computational Science group, Elmer/ICE project collaborators (e.g., Per Lötstedt, Gong Cheng), and international partners in numerical finance and climate modeling.
Pengyu Qian serves as an Assistant Professor in the Department of Operations and Technology Management at Boston University's Questrom School of Business, where he conducts research at the intersection of operations research, economics, and algorithmic systems design. Education PhD, Graduate School of Business, Columbia University, 2021 B.Sc., Peking University, 2015 Research Interests Dr. Qian specializes in theoretical and applied problems within matching markets, dynamic resource allocation, and queueing network optimization. His work develops algorithmic solutions for market inefficiencies using tools from game theory, stochastic modeling, and mechanism design. Current investigations focus on partner competition dynamics in two-sided markets, incentive structures for resource pooling, and blind control policies in closed networks where system states remain partially observable. Publication Trends His 2018-2024 publications demonstrate consistent output in top-tier venues including Management Science and ACM Economics & Computation conferences, with increasing emphasis on real-world market applications. The research trajectory shows progression from foundational queueing network control to sophisticated matching market analyses, maintaining strong theoretical rigor while addressing practical constraints like information asymmetry and dynamic partner competition. Scientific Awards No scientific awards, fellowships, or major honors are documented in the available profile information. Advising and Grants While specific student advisees and grant funding details are not disclosed in the current materials, Dr. Qian's publication record suggests active collaboration with leading researchers including Yash Kanoria and Itai Ashlagi, indicating participation in significant research initiatives within operations management and market design. Labs and Teams Though no dedicated laboratory is specified, his research profile aligns with Boston University's operations management research groups at Questrom, particularly those focused on algorithmic market design and stochastic optimization within business contexts.
Sophie H. Yu is an Assistant Professor of Operations, Information and Decisions at the Wharton School of Business, University of Pennsylvania. She completed her postdoctoral work in the Department of Management Science and Engineering at Stanford University before joining Wharton. Her academic journey includes a Ph.D. in Decision Sciences from the Fuqua School of Business at Duke University (2023), an M.S. in statistical and economic modeling from Duke University (2017), and a B.S. in Economics from Renmin University of China (2015). Ph.D. in Decision Sciences, Fuqua School of Business, Duke University (2023) M.S. in Statistical and Economic Modeling, Duke University (2017) B.S. in Economics, Renmin University of China (2015) Sophie's research focuses on high-dimensional statistics, algorithm design, and performance evaluation in large-scale networks and stochastic systems . Her work draws inspiration from real-world business, engineering, and natural sciences problems that can be modeled into large and complex networks. She has explored fundamental limits and efficient algorithms on graph matching, online platform policy design with bounded regret, and data confidentiality protection. Her research spans the intersection of operations research, applied probability, statistics, and computer science, with particular emphasis on network science and information theory. Sophie's publications demonstrate a strong focus on matching problems in networks, with significant contributions to understanding random graph matching, network correlation testing, and online matching algorithms. Her work shows a progression from fundamental theoretical questions to practical applications in resource allocation and market design. Recent papers indicate increasing focus on practical implementations of theoretical concepts in real-world matching markets. Thomas M. Cover Dissertation Award from IEEE Information Theory Society (2024) Best Dissertation Award from Fuqua George Nicholson Student Paper Competition finalist, INFORMS 2022 Sophie has been actively involved in academic service, presenting her work at numerous prestigious institutions including University of Texas at Austin, University of Toronto, London School of Business, and MIT. She has taught graduate courses in decision modeling and served as a teaching assistant for various statistics and operations courses during her doctoral studies at Duke University. Her research has been supported through academic appointments and likely research grants related to her work in network science and matching algorithms.
Serban Raianu is a Professor in the Department of Mathematics at California State University Dominguez Hills since 2004. His research focuses on Hopf algebras , quantum groups , and their applications to algebraic structures. He has published extensively on topics such as graded rings , crossed coproducts , and co-Frobenius Hopf algebras , often collaborating with leading mathematicians like S. Dăscălescu and C. Năstăsescu. His work in number theory includes recent studies on arithmetic properties of 3-cycles in quadratic maps (2022), extending the abc conjecture and exploring connections to Diophantine equations . He has also contributed to linear algebra with a 2005 paper on Jordan forms and matrix optimization . Key research contributions: Hopf algebras acting on algebras and coalgebras Quantum groups and their representations Duality theories for finite Hopf algebras Dr. Raianu has received notable scientific awards , including the Gheorghe Titeica Prize from the Romanian Academy (2001) and the first prize in the annual scientific contest for students at the University of Bucharest (1981). He has advised undergraduate research projects on partition problems , harmonic number differences , and Green's theorem applications , supported by grants such as the PUMP Undergraduate Research Grant (2016-2017) and NSF-Cal State grant (2003-2006).
Patrick Jaillet is the Dugald C. Jackson Professor in the Department of Electrical Engineering and Computer Science at MIT's School of Engineering. He holds joint appointments with the Laboratory for Information and Decision Systems (LIDS), the Operations Research Center (ORC), the Operations Research and Statistics Group at MIT Sloan, and the Department of Civil and Environmental Engineering. Previously, he served as Head of Civil and Environmental Engineering at MIT (2002-2009) and Chair of the Department of Management Science and Information Systems at UT Austin (1997-2002). Dr. Jaillet's research focuses on online optimization and learning, sequential decision-making under uncertainty, and security and resilience in complex networks. His work spans theoretical foundations in optimization and machine learning with applications in transportation, online market analytics, and network security. He has developed mathematical frameworks for problems involving uncertainty, dynamic resource allocation, and strategic behavior in complex systems. His recent publications reveal strong trends in bridging theoretical optimization with practical machine learning applications. Key themes include Bayesian optimization for black-box functions, online learning with limited information, mechanism design for resource allocation, and network security applications. His work increasingly integrates large language models with traditional optimization techniques, reflecting the evolving landscape of AI-driven decision-making systems. Fulbright Scholar (1990) Fellow of the Institute for Operations Research and Management Science (INFORMS) Best Applications Paper Award at ICAPS 2019 Long-standing Associate Editor for top journals including Operations Research and Transportation Science Dr. Jaillet has advised over 40 doctoral students who now hold prominent positions in academia and industry, including faculty positions at MIT, Georgia Tech, and ETH Zurich, and research scientist roles at Amazon, Microsoft Research, and Google. His research has been consistently funded by major agencies including NSF, ONR, AFOSR, and international partners like Singapore NRF, with current projects focusing on learning algorithms for autonomous security and fundamental tradeoffs in optimization. He leads a vibrant research group spanning MIT's EECS department and ORC, with current funding supporting work on neural bandits, federated optimization, and network security applications. His research group operates at the intersection of theory and practice, with strong connections to industry through collaborations with IBM, Microsoft, Google, and various transportation and technology companies. The group maintains active partnerships with international institutions, particularly through SMART in Singapore, reflecting Dr. Jaillet's global research impact.
François Goulette is a Professor and Deputy Director of the Computer Science and Systems Engineering Unit (U2IS) at ENSTA Paris, part of Institut Polytechnique de Paris. His research focuses on 3D point cloud processing, LiDAR perception, and autonomous systems within the Robotics Center (CAOR). His primary research interests lie in 3D point cloud processing , LiDAR perception , and autonomous systems . His work spans fundamental algorithm development to practical applications in autonomous driving, cultural heritage digitization, and robotics. He has made significant contributions to domain generalization of LiDAR perception, semantic segmentation of 3D point clouds, and point cloud registration techniques. The analysis of his recent publications reveals a strong focus on domain generalization for LiDAR perception systems, with multiple papers addressing challenges in 3D semantic segmentation across different environments. His work combines multi-scale architectures , unsupervised learning , and dataset creation to advance the state-of-the-art in autonomous systems perception. The research spans both theoretical algorithm development and practical applications in urban environments. François Goulette leads research activities within the Robotics Center (CAOR) at ENSTA Paris. His team develops advanced techniques for 3D environment understanding, with applications in autonomous vehicles, cultural heritage preservation, and industrial robotics. The research combines computer vision, machine learning, and robotics to solve challenging problems in 3D perception and scene understanding.
Dr Joshua Alcock is a Lecturer at the University of Liverpool, actively involved in teaching and research. He contributes to modules such as Cloud Computing for E-Commerce (COMP315), High Performance Computing (COMP328), and Multi-Core and Multi-Processor Programming (COMP528), where he serves as Module Co-ordinator. His research focuses on computational operations research and optimization, particularly in heuristic approaches for the Periodic Multiple Maintenance Person Problem (2023). This work addresses dynamic scheduling challenges in industrial maintenance, leveraging algorithmic design and stochastic optimization techniques.
Charith Mendis is an Assistant Professor in the Siebel School of Computing and Data Science at the University of Illinois at Urbana-Champaign, with joint appointments in the Department of Computer Science, Electrical and Computer Engineering, and the Coordinated Science Lab. His research focuses on the intersection of compilers, program optimization, and machine learning systems. Dr. Mendis received his educational background from prestigious institutions: Ph.D. in Computer Science from Massachusetts Institute of Technology (2020) S.M. in Computer Science from Massachusetts Institute of Technology (2015) B.Sc. in Electronics and Telecommunication Engineering from University of Moratuwa (2013) His primary research interests center around compiler technology and machine learning systems. Mendis leads the ADAPT lab at UIUC, where his team works on creating high-performance ML optimization techniques and automated compiler construction using machine learning and formal methods. His work bridges the gap between traditional compiler design and modern machine learning approaches, with applications in tensor compilers, graph neural networks, and sparse computation. He has developed novel frameworks for optimizing deep learning workloads, verification of compiler transformations, and performance modeling for emerging hardware architectures. Mendis has established himself as a leading researcher in compiler optimization for machine learning systems, with a particular focus on tensor compilers, graph neural networks, and performance modeling. His recent publications demonstrate increasing sophistication in combining formal methods with machine learning techniques to solve challenging problems in compiler optimization and verification, with multiple papers accepted at top-tier conferences including OOPSLA, PLDI, POPL, and SIGMOD. His notable scientific achievements include: Google ML and Systems Junior Faculty Award (2025) DARPA Young Faculty Award (2024) NSF CAREER Award (2024) Distinguished Paper Award at POPL (2025) William A. Martin Thesis Award for Outstanding SM thesis, MIT (2015) Multiple teaching excellence awards at UIUC (2021-2023) Dr. Mendis actively mentors students through the ADAPT lab, offering research opportunities for undergraduates, master's students, and PhD candidates interested in compiler technology and machine learning systems. His research is supported by significant funding from the ACE center (part of JUMP 2.0), National Science Foundation (NSF), DARPA, IIDAI, and industry partners including Google, Intel, Amazon, and Qualcomm. He teaches advanced courses in compiler construction and machine learning for compilers. He leads the ADAPT lab at UIUC, which focuses on developing advanced compiler technologies for modern machine learning workloads. The lab maintains active collaborations with industry partners and has established itself as a leading research group in compiler optimization for AI systems. Current projects include tensor compilers, graph neural network optimization, and automated verification of deep learning systems.
Yikun Ban is a tenure-track Associate Professor in the School of Computer Science and Engineering at Beihang University, where he is a member of the State Key Laboratory of Software Development Environment. He earned his PhD in Computer Science from the University of Illinois Urbana-Champaign (2023), MS in Computer Science from Peking University (2019), and BS in Software Engineering from Wuhan University (2016). PhD: University of Illinois Urbana-Champaign (2023) MS: Peking University (2019) BS: Wuhan University (2016) His research focuses on principled algorithms for reinforcement learning with human feedback, neural contextual bandits, and exploration-exploitation problems. He develops frameworks combining deep learning with bandit theory for applications in recommendation systems, disinformation detection, and dynamic graph learning. Recent publications address: Robust neural contextual bandits (NeurIPS 2024) Graph neural bandits (KDD 2023) Meta-learning for bandit scheduling (NeurIPS 2023) Clustering in contextual bandits (WWW 2021, AAAI 2021) Honors include the NeurIPS Scholar Award and recognition as ICML Outstanding Reviewer . His open-source LOCB repository provides Python implementations for contextual multi-armed bandit algorithms with local clustering, supporting applications in recommendation systems and online learning.
Malte Laurens Kampschulte serves as Assistant Professor at the Department of Mathematical Analysis, Faculty of Mathematics and Physics, Charles University in Prague. He leads research within S. Schwarzacher's fluid structure interaction group and the OP JAK project FerrMion, following his role as Substitute Professor at the University of Leipzig during Summer 2024. His academic credentials include: B.Sc in Mathematics (2009) and Computer Science (2010) from RWTH Aachen M.Sc in Mathematics (2012) from RWTH Aachen Ph.D. in Mathematics (2018) with thesis "Gradient flows and a generalized Wasserstein distance in the space of Cartesian currents" Dr. Kampschulte's research centers on fluid structure interaction, calculus of variations, partial differential equations, and geometric measure theory. His work examines variational aspects of Eulerian-Lagrangian frameworks, relaxation methods for generalized solutions, topological invariants in PDEs, and current transport on manifolds. This integrated approach bridges theoretical analysis with physical applications in continuum mechanics. Analysis of his 2023-2024 publications reveals concentrated focus on three-dimensional fluid-structure systems with viscoelastic solids, compressible fluids, and self-collision phenomena. Key contributions include global weak solution frameworks for contact problems, variational approaches to hyperbolic evolutions, and regularity analysis for free surface dynamics—demonstrating both mathematical rigor and physical relevance. As Principal Investigator for the PRIMUS grant "Qualitative and quantitative Analysis for non-linear non-uniformly elliptic models" (previously held by Anna Balci), he oversees active research funding while mentoring through an open PostDoc position. His leadership extends to the FerrMion project where he develops mathematical frameworks for fluid-matter interactions. Based in the Department of Mathematical Analysis at Charles University, Dr. Kampschulte collaborates within S. Schwarzacher's research group to advance mathematical understanding of fluid-structure systems through both theoretical innovation and computational modeling.
Anne-Laure Dalibard is a Professor at Sorbonne University's Faculty of Science and Engineering, affiliated with the Jacques-Louis Lions Laboratory (UMR CNRS 7598). She also serves as a Junior member of the Institut Universitaire de France (2020-2025) and was previously a part-time professor at the École Normale Supérieure in Paris (2021-2024). Her research focuses on mathematical analysis of fluid mechanics with applications to geophysical and oceanographic phenomena. Education: Student at ENS Ulm (2001-2005) PhD at CEREMADE, Paris-Dauphine University (defended October 8, 2007) Dalibard's research centers on geophysical fluids, boundary layers in fluid mechanics, congestion models, roughness models, scalar conservation laws, and homogenization theory. She specializes in asymptotic analysis of fluid equations relevant to oceanographic models, particularly those involving rotating fluids and boundary layer phenomena. Her work bridges rigorous mathematical analysis with practical applications in environmental fluid dynamics. Her recent publications demonstrate a consistent focus on boundary layer phenomena in fluid mechanics, with particular emphasis on geophysical applications. She has made significant contributions to understanding boundary layers in rotating fluids, congestion models in Navier-Stokes systems, and wave phenomena in stratified fluids. Her mathematical approach typically involves rigorous analysis of partial differential equations with singular perturbations, often using asymptotic methods, homogenization theory, and kinetic formulations. Scientific Awards: Junior member of the Institut Universitaire de France (2020-2025) Principal Investigator for ERC Starting grant BLOC (2015-2020) Leader of ANR BOURGEONS project (2023-2027) Dalibard leads substantial research initiatives including the ANR BOURGEONS project (2023-2027), which involves over 30 researchers, PhD students, and post-docs working on fluid dynamics aspects relevant to geophysical flows. She has supervised several PhD students including Jean Rax and Gabriela Lopez-Ruiz, and mentored post-doctoral researchers such as Frédéric Marbach, Marc Briant, and Matthew Paddick. Her research has been supported by prestigious grants from the European Research Council and the French National Research Agency. She is actively involved with the Jacques-Louis Lions Laboratory at Sorbonne University and collaborates extensively with researchers across France and internationally. Her work often intersects with oceanographic applications, connecting mathematical theory with environmental fluid dynamics problems.
Ryozo Nagamune is a Professor in the Department of Mechanical Engineering within the Faculty of Applied Science at the University of British Columbia (UBC). His research focuses on control engineering with specific expertise in floating offshore wind turbines, integrated solar thermal systems, and metal additive manufacturing processes. He maintains active collaborations with NSERC, MITACS, and industry partners including Ascent Systems Technologies. Dr. Nagamune received his B.Sc. and M.Sc. degrees from Osaka University, followed by a Ph.D. from the Royal Institute of Technology in Stockholm, Sweden. His educational background laid the foundation for his expertise in control systems theory and applications. His primary research interests center on control engineering, with particular emphasis on the control of floating offshore wind turbines and wind farms, integrated solar thermal systems, directed energy deposition metal additive manufacturing processes, engine aftertreatment systems, and data-driven modeling and control of dynamical systems. His work addresses critical challenges in renewable energy, manufacturing, and automotive applications, focusing on optimization, robustness, and efficiency improvements. The research spans theoretical developments in control algorithms to practical implementation in real-world systems. Analysis of Dr. Nagamune's recent publications reveals a strong focus on floating offshore wind turbine control, which constitutes approximately 40% of his recent work. Another significant portion (30%) addresses automotive control systems, particularly selective catalytic reduction for emissions control. The remaining publications cover diverse applications including haptic interfaces, spacecraft control, and precision manufacturing systems. His research demonstrates a consistent pattern of applying advanced control methodologies to solve practical engineering problems across multiple domains. Dr. Nagamune leads the Control Engineering Laboratory at UBC (located in KAIS 3104) and actively seeks collaborations with industry partners, research clusters, and interdisciplinary teams. His research is supported by major funding agencies including NSERC and MITACS, as well as industry partnerships. He is available for supervision of graduate students and expresses interest in working with undergraduate students on research projects. Dr. Nagamune welcomes interdisciplinary research opportunities and is particularly interested in collaborations that bridge multiple engineering domains.
Georgios Arvanitidis is an Associate Professor at the Technical University of Denmark (DTU) in the Department of Applied Mathematics and Computer Science, specifically within the Section for Cognitive Systems (CogSys). He has established himself as a leading researcher in geometric machine learning, focusing on the application of differential geometry principles to enhance machine learning models. His work bridges theoretical mathematics with practical applications in artificial intelligence, with particular emphasis on understanding the geometric structure of data manifolds and latent spaces. Dr. Arvanitidis completed his educational journey with a Bachelor's degree from the Department of Informatics at the Aristotle University of Thessaloniki, followed by a Master's degree in Computer Science from Saarland University supported by the Max Planck Institute for Informatics. He earned his PhD at DTU's Cognitive Systems section under the supervision of Søren Hauberg, with additional research experience at Philipp Hennig's Probabilistic Numerics group. Prior to his current position as associate professor, he was a PostDoc at the Max Planck Institute for Intelligent Systems working with Bernhard Schölkopf. Dr. Arvanitidis's research primarily focuses on differential geometry in machine learning , where he explores how geometric structures can enhance representation learning and statistical modeling. His work in generative models investigates how learning the geometry of data manifolds can improve deep learning architectures. In the domain of deep learning theory , he examines why deep learning models generalize effectively on unseen data, with particular attention to the curvature properties of loss landscapes. His research in approximate Bayesian inference applies geometric principles to improve uncertainty quantification in neural networks. Through his innovative approaches, Dr. Arvanitidis has established himself as a leading researcher in geometric machine learning, contributing to both theoretical foundations and practical applications across various domains including robotics and life sciences. The publication trends of Dr. Arvanitidis reveal a consistent and evolving focus on geometric approaches to machine learning problems. His recent work (2023-2025) demonstrates increasing sophistication in applying Riemannian geometry to deep learning architectures, with particular emphasis on latent space geometry, optimization on manifolds, and geometric interpretations of neural network behavior. A notable pattern is the progression from foundational work on geometric representations to more applied research in areas like robotics and causal inference. His publications span top-tier conferences including NeurIPS, ICML, ICLR, and AISTATS, reflecting the high impact of his research. The interdisciplinary nature of his work is evident in collaborations across mathematics, computer science, and robotics domains, with recent papers addressing challenges in multimodal sampling, safety guarantees for dynamical systems, and counterfactual explanations. Dr. Arvanitidis has received several notable scientific awards and recognitions: Sapere Aude starting grant from the Independent Research Fund Denmark (DFF) GADL funding i-Rase, Pathfinder, and EIC (European Innovation Council) funding Best reviewer award for NeurIPS 2019 Best reviewer award for NeurIPS 2018 Best student paper award at Robotics: Science and Systems (R:SS) 2021 Dr. Arvanitidis actively mentors PhD students and researchers, currently supervising Alejandro Valverde, Johanna Gegenfurtner, and Albert Kjøller Jacobsen. He has previously co-supervised Alison Pouplin's PhD and worked with research assistant Georgios Pantis. His group receives substantial funding through multiple prestigious grants including the Sapere Aude starting grant from the Independent Research Fund Denmark, as well as European Innovation Council funding. He has been instrumental in creating opportunities for students interested in geometric machine learning, offering BSc and MSc thesis projects focused on generative models, deep learning theory, and optimization techniques. Dr. Arvanitidis also contributes significantly to the academic community as a reviewer for top conferences including ICLR and TMLR, and as an area chair for NeurIPS, ICML, AISTATS, and UAI. He co-organized the Machine Learning Summer School 2020 in Tübingen, further demonstrating his commitment to education and community building. Dr. Arvanitidis leads a vibrant research group focused on geometric machine learning within the Cognitive Systems section at DTU. His team includes multiple PhD students working on cutting-edge research at the intersection of differential geometry and artificial intelligence. The group has developed notable software tools, including the "geometric_ml" GitHub repository with over 70 stars, which contains implementations for applying Riemannian geometry in machine learning. His research has practical applications in robotics, where geometric approaches enable more robust motion planning, as evidenced by his work on "Reactive Motion Generation on Learned Riemannian Manifolds" which received a best student paper award. Additionally, his methodologies have found applications in life sciences, as mentioned in his 2022 AISTATS paper. The collaborative nature of his work is evident through extensive partnerships with researchers at institutions including the Max Planck Institute for Intelligent Systems, University of Cambridge, and various European universities. His recent news items indicate active engagement with the academic community through talks, conference presentations, and ongoing supervision of new PhD students joining his group.
