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Francesco GaluppiView profile

Assistant Professor

• Complex Algebraic Geometry
• Tensor Decompositions
• Applied Algebraic Geometry
• +3 more

Francesco Galuppi is an Assistant Professor at the University of Warsaw, within the Faculty of Mathematics, Informatics and Mechanics. His research focuses on complex algebraic geometry, tensor decompositions, and their applications to signature tensors of paths, bridging theoretical mathematics with applied sciences like data analysis and machine learning. He leads the Sonata grant 'Tensor rank and its applications to signature tensors of paths' funded by the National Science Center, Poland. His work explores geometric approaches to tensor structure, emphasizing interactions across disciplines. Key topics include secant varieties, tensor rank, and symmetry analysis of signature tensors. Galuppi has organized workshops such as 'Apolarity in Varsavia' and co-organized conferences like the SIAM minisymposium 'Tensors in Applications.' His recent activities include talks on path signatures, eigenpoint schemes, and defectivity of Segre-Veronese varieties, reflecting his commitment to advancing algebraic geometry’s practical applications. He actively engages in academic communities, delivering seminars and collaborating internationally. His research narrative includes grants, interdisciplinary collaborations, and contributions to thematic semesters like AGATES, focusing on tensors in diverse fields from quantum physics to optimization.

Alberto Maydeu OlivaresView profile

Professor

• Psychometrics
• Structural Equation Modeling
• Item Response Theory
• +3 more

Alberto Maydeu Olivares is a Professor at the Department of Clinical Psychology and Psychobiology, Universitat de Barcelona, within the Faculty of Psychology. He leads the Violence, Behavior, Individual differences and Technology Studies (WITS) research group. His expertise spans psychometrics, structural equation modeling (SEM), and item response theory (IRT). He holds a Licenciatura in Psychology from the Universitat de Barcelona (1988), a Doctorat in Quantitative Psychology from the same institution (1991), and advanced degrees from the University of Illinois, including a Ph.D. in Quantitative Psychology (1997) and masters in Mathematical Statistics and Quantitative Psychology. His research focuses on improving psychological measurement methods, evaluating model fit in SEM/IRT, and addressing methodological challenges in behavioral data. Notable projects include NSF-funded work on SEM fit indices (2017–2020) and AGAUR-supported studies on IRT applications (2014–2017). He teaches advanced courses like Multivariate Analysis and Structural Equation Models at the master’s level. Maydeu Olivares has authored influential papers on goodness-of-fit assessment, causal effect estimation via instrumental variables, and forced-choice test design. His work bridges statistical rigor with practical applications in psychology, addressing issues like faking in assessments and common method bias. He actively contributes to methodological advancements, emphasizing transparent and robust statistical practices.

Hannah ClaphamView profile

Assistant Professor

• Infectious Disease Dynamics and Control
• Vaccination Impact Analysis
• Dengue Epidemiology
• +3 more

Hannah Clapham is an Assistant Professor and Programme Leader (Infectious Diseases) at the Saw Swee Hock School of Public Health, National University of Singapore. Her research focuses on infectious disease dynamics, vaccination impact, and epidemiology of dengue and Japanese encephalitis. She holds a PhD in Disease Dynamics from Imperial College London and has held roles including Head of the Mathematical Modelling Group at the Oxford University Clinical Research Unit in Vietnam. Her work integrates mathematical models with public health policy to address global health challenges, particularly in resource-limited settings. Education: BA Hons Mathematics, University of Oxford (2004-2007) MSc Modern Epidemiology, Imperial College London (2008-2009) PhD Disease Dynamics, Imperial College London (2009-2013) Research Interests: Her research emphasizes quantitative approaches to understand disease transmission, assess vaccine effectiveness, and inform policy. Key themes include: Mathematical modeling of dengue and arbovirus outbreaks Impact of vaccination strategies on disease control Serological data analysis to infer infection history Public health responses to pandemics (e.g., SARS-CoV-2) Media Contributions: Dr. Clapham frequently engages with media to explain public health issues, including commentaries on Singapore’s dengue outbreaks, pandemic preparedness, and vaccination strategies for Channel NewsAsia , The Straits Times , and others. Grants & Collaborations: Her career includes leadership in interdisciplinary projects, such as the Vaccine Impact Modelling Consortium and studies on pathogen genomics in Southeast Asia. She collaborates with global health organizations to translate research into actionable policies.

Larry GoldsteinView profile

Professor

• Distributional approximation
• Stein’s method
• High dimensional statistics
• +4 more

Larry Goldstein is a Professor of Mathematics at the University of Southern California, specializing in probability theory, mathematical statistics, and their applications. He holds a Ph.D. in Mathematics from the University of California, San Diego (1984). His research focuses on distributional approximation via Stein’s method, high-dimensional statistics, concentration inequalities, and statistical efficiency, with applications in epidemiology and biomedical monitoring. He has organized and participated in numerous conferences, including the 'Stein’s Method: The Golden Anniversary' in Singapore (2022) and the 'BIRS Stein Conference' in Banff (2022). Goldstein teaches advanced courses such as Probability Theory, Statistical Consulting, and Mathematical Statistics, often incorporating modern computational tools like R. He has led international summer programs at the University of Perugia, Italy, on topics including mathematical statistics and high-dimensional probability. His work bridges theoretical foundations with practical applications, including modeling transdermal alcohol concentration and analyzing complex sampling designs in cohort studies. His contributions to Stein’s method include developing couplings for distributional approximation and concentration inequalities. Goldstein’s teaching emphasizes statistical inference, machine learning, and data analysis, reflecting his dual focus on rigorous theory and real-world problem-solving.

Lasse HeikkinenView profile

Senior Lecturer

• Physics
• Applied Physics
• Process Tomography
• +5 more

Lasse Heikkinen is a Senior Lecturer at the Department of Technical Physics, Faculty of Science, Forestry and Technology, University of Eastern Finland. His work spans applied physics, process tomography, and educational technology, with a focus on innovative teaching methods like flipped classrooms during the pandemic. Current Role: Deputy Head of Department, Senior University Lecturer Research Themes: Electrical impedance tomography for industrial processes, flipped teaching frameworks, and physics education adaptation to remote learning. Recent publications highlight his dual expertise in process tomography (gas-solid flows, pharmaceutical monitoring) and pedagogical innovation (learning analytics, flipped classrooms). He has contributed to educational manuals and toolkits for teacher training. Key Projects: Technology education infrastructure development (2015–2024), pandemic-era teaching adjustments (2022). Collaborations: Active in international process tomography conferences and multidisciplinary teams like the Ameba project.

Raaz DwivediView profile

Assistant Professor

• Causal Inference
• Reinforcement Learning
• Personalized Decision-Making
• +2 more

Raaz Dwivedi is Assistant Professor in Operations Research and Information Engineering at Cornell University and Cornell Tech. His research develops statistical and computational methods for personalized decision-making, focusing on causal inference, reinforcement learning, and distribution compression. Recent publications advance kernel thinning techniques, counterfactual inference methods, and adaptive nearest-neighbor algorithms with applications in healthcare and recommendation systems. Research appears in top venues with 15+ publications since 2022. Awards and honors: Blackwell-Rosenbluth Award (2024) ASA Best Student Paper Award (2022) MIT LIDS Best Presentation Award Harvard Teaching Excellence Award FODSI Postdoctoral Fellowship Holds PhD in EECS from UC Berkeley and BTech from IIT Bombay.

Martin AlbrechtView profile

Professor

• Lattice-based cryptography
• Post-quantum cryptography
• Applied cryptography
• +3 more

Martin Albrecht is a Professor of Cybersecurity and Chair of Cryptography in the Department of Informatics at King's College London, Faculty of Natural, Mathematical & Engineering Sciences. He is also a Principal Research Scientist for SandboxAQ, demonstrating his strong industry-academia connections in the cybersecurity field. His research focuses on lattice-based cryptography, post-quantum cryptography, applied cryptography, social foundations of cryptography and information security, and computational mathematics. Albrecht's work bridges theoretical cryptography with practical security concerns, examining cryptographic implementations 'in the wild' and addressing vulnerabilities in widely used communication platforms like WhatsApp, Telegram, and Matrix. His Erdős–Bacon Number is 6, reflecting his interdisciplinary connections. Recent publications reveal a strong trend toward practical cryptographic analysis of real-world systems while advancing theoretical foundations of post-quantum cryptography. His work spans from breaking end-to-end encryption implementations to developing new lattice-based cryptographic primitives resistant to quantum computing threats. Much of his recent research addresses the urgent need for quantum-resistant cryptography as demonstrated by his European-funded project developing algorithms to protect data encryption against future quantum computers. IEEE Symposium on Security and Privacy distinguished paper award (2023) for work revealing security vulnerabilities in popular chat platforms Major European funding (2023) for advancing encryption technology against quantum computing threats Professor Albrecht leads the Cybersecurity research group at King's which studies design, modeling, analysis, verification and testing of networks and systems, and is part of the Security Hub that consolidates security-related research. His work has demonstrated critical vulnerabilities in widely used platforms including WhatsApp, Matrix, and Nextcloud, with findings reported as recently as July 2025 regarding WhatsApp's persistent vulnerabilities. He has supervised numerous PhD students and maintains active collaborations across the cryptographic research community. Through his open-source projects including FPLLL, FPyLLL, G6K, Lattice Estimator, M4RI, and M4RIE, Albrecht has made significant contributions to the cryptographic tools ecosystem, providing researchers and practitioners with essential resources for lattice-based cryptography and computational mathematics.

Fabio NobileView profile

Professor

• Scientific Computing
• Uncertainty Quantification
• Numerical Analysis
• +5 more

Fabio Nobile is a Full Professor at the École Polytechnique Fédérale de Lausanne (EPFL) in the School of Basic Sciences (SB), Department of Mathematics (MATH), holding the CADMOS Chair in Scientific Computing and Uncertainty Quantification. He leads the CSQI (Chair of Scientific Computing and Uncertainty Quantification) group. His work focuses on numerical methods for partial differential equations (PDEs), uncertainty quantification, stochastic modeling, and computational fluid dynamics. He is involved in collaborative projects involving fluid-structure interaction, cardiac electro-mechanics, and energy systems. Professor Nobile has extensive teaching experience, including courses on advanced analysis, stochastic simulation, and numerical integration of stochastic differential equations. He supervises numerous PhD students and has contributed to over 200 peer-reviewed publications, covering topics such as low-rank approximation methods, multilevel Monte Carlo techniques, and optimal control under uncertainty. His research emphasizes interdisciplinary applications, including biomedical engineering (e.g., personalized cardiac simulations) and renewable energy (e.g., probabilistic load forecasting). He collaborates with industries and academic institutions globally, advancing computational methodologies for engineering and scientific challenges.

Maxime Ferreira Da CostaView profile

Associate Professor

• Signal Processing
• Data Science
• Inverse Problems
• +6 more

Maxime Ferreira Da Costa is an Associate Professor at CentraleSupélec, Université Paris-Saclay, affiliated with the Laboratory of Signals and Systems (L2S). His research focuses on theoretical and algorithmic foundations of data science, particularly in inverse problems, structured signal processing, and physical layer security. Key research areas include super-resolution, system calibration, and privacy-enhancing communication schemes. He holds a Ph.D. from Imperial College London (2018), and previously worked at USC and Carnegie Mellon University. Notable achievements include an ANR Young Researcher Grant (2023) for projects on data science and physical layer security. He actively contributes to conferences and workshops, with recent presentations at Institut Henri Poincaré and Université Paris-Saclay. Education: Ph.D. in Electrical Engineering, Imperial College London (2018) M.Sc. Electrical Engineering, Imperial College London (2012) Engineer Diploma, CentraleSupélec (2012) Research interests span continuous inverse problems, off-the-grid methods, wireless security, and sensing systems. Current projects explore goal-oriented resource allocation, fake path injection for privacy, and preconditioned optimization techniques. His work bridges signal processing theory with practical applications in imaging, telecommunications, and sensing.

• Educational Measurement
• Large-Scale Assessment
• Hierarchical Linear Modeling
• +5 more

Meltem ACAR GÜVENDİR is an Associate Professor in the Department of Educational Sciences at Trakya University's Faculty of Education. She holds a PhD in Measurement and Evaluation in Education from Ankara University (2013) and has conducted postdoctoral research at the University of California, Los Angeles (UCLA). Her research focuses on large-scale educational assessment, including analyses of PISA, TIMSS, and OBBS datasets using hierarchical linear modeling. She specializes in value-added assessment, meta-analyses of study abroad impacts, and psychometric evaluation of educational policies. Academic roles include serving as Head of the Measurement and Evaluation Commission (since 2018) and Deputy Head of the Department of Educational Sciences (2016–2018). She co-edits the International Journal of Assessment Tools in Education and has contributed to over 60 peer-reviewed articles and book chapters. Her work explores student achievement predictors, test anxiety measurement, and international student integration. Key projects include a TÜBİTAK-funded study on scale development processes and participation in the EU-funded CA19103 initiative addressing LGBTI+ inequalities. She has supervised numerous research projects and delivered keynotes on educational measurement methodologies.

Kasper Green LarsenView profile

Professor

• Machine Learning
• Theoretical Computer Science
• Algorithms and Data Structures
• +3 more

Kasper Green Larsen is a Professor in the Department of Computer Science at Aarhus University. His research focuses on theoretical computer science, machine learning, algorithms, and data structures. He has made significant contributions to boosting algorithms, PAC learning theory, and computational geometry. His work often bridges algorithm design with complexity theory, addressing challenges in optimization, memory efficiency, and lower bounds analysis. Key research areas include: Algorithmic Learning Theory (e.g., boosting, bagging, and PAC learners) Data Structure Design (e.g., invertible Bloom tables, succinct representations) Computational Complexity (e.g., lower bounds for dynamic and oblivious algorithms) Geometric Algorithms (e.g., hierarchical searching, range queries) Recent publications emphasize foundational advancements in learning theory (e.g., optimal weak-to-strong learning) and data efficiency (e.g., memory-reduced Bloom filters). His work frequently appears in top conferences like IJCAI, ICALP, and SODA, reflecting rigorous theoretical contributions with practical implications.

En-Hui YangView profile

Professor

• Information Theory
• Multimedia Compression
• Digital Communications
• +3 more

Dr. En-Hui Yang is University Professor in Electrical and Computer Engineering at the University of Waterloo and founding Director of the Leitch-University of Waterloo Multimedia Communications Lab. A world-renowned expert in information theory, he co-developed the Yang-Kieffer algorithm for lossless compression and invented soft decision quantization technology used in smartphones and web browsers. His research spans multimedia compression, digital communications, and deep learning. Education: Ph.D. Electrical Engineering, University of Southern California (1996) Ph.D. Probability and Statistics, Nankai University (1991) B.Sc. Applied Mathematics, HuaQiao University (1986) His transformative work in data compression has impacted millions globally through technologies accelerating data transmission efficiency. Articles focus on optimization of video/image compression standards (HEVC/H.264), channel coding theorems, and novel compression algorithms. Scientific Awards: IEEE Eric E. Sumner Award (2021) Canada Research Chair - Tier 1 (2010, 2017) Fellow of the Royal Society of Canada (2009)

Ferit ÖztürkView profile

Professor

• Singularity Theory
• Contact Topology
• Symplectic Topology
• +1 more

Ferit Öztürk is a Professor in the Mathematics Department at Bogazici University, specializing in singularity theory, contact topology, and symplectic topology. He leads the Geometry/Topology Seminar and supervises numerous graduate students in these fields. His research bridges topological methods with applications in robotics and dynamical systems. Recent work includes exotic manifold construction, real contact structures on 3-manifolds, and Morse-Bott theory on Lie groups. Dr. Öztürk holds a Ph.D. from Middle East Technical University (2001) and has developed course materials on Morse Theory and Projective Geometry. Student Supervision: 12+ M.S./Ph.D. students in topology and geometry Research Tools: Contact branched covers, real algebraic models, geometric surgery

Michelle ZhuView profile

Professor

• Parallel and Distributed Computing
• Big Data Analytics
• Cloud Computing
• +4 more

Dr. Michelle Zhu is a Professor and Associate Director for Faculty and Academic Affairs at the School of Computing, Montclair State University. She previously held roles as Associate Professor and Director of Undergraduate Programs at Southern Illinois University Carbondale. Dr. Zhu holds a Ph.D. in Computer Science from Louisiana State University and a B.S. in Biomedical Engineering from Zhejiang University. Her research focuses on parallel/distributed computing, big data analytics, and high-performance networking, supported by grants from NSF, DOE, and NVIDIA. She has authored over 150 peer-reviewed publications. Education: Ph.D., Computer Science, Louisiana State University (2005) M.Sc., Computer Science, Louisiana State University (2002) B.S., Biomedical Engineering, Zhejiang University (1996) Her research interests span parallel computing architectures, cloud workflow scheduling, and cybersecurity. She has led initiatives integrating computational thinking into STEM education and developed robotics-based learning tools. Her work has been funded through NSF grants such as the $1.1M "Assimilating Computational and Mathematical Thinking into Earth and Environmental Science" project (2017–2022). Dr. Zhu’s articles explore topics like blockchain-based cloud security, GPU-accelerated Gibbs sampling, and edge computing deployment strategies. She actively contributes to academic governance, serving on Montclair State’s Middle States accreditation committee and the University Academic Assessment Council. Key Grants: NSF MRI: Multimodal Collaborative Robot System (MCROS), $321,737 (2021–2024) DOE: Scalable Application Support Platform for E-Sciences, $389,398 (2009–2013) Service Roles: Curriculum Committee Chair, Computer Science Department Blue Ribbon Task Force for Gen Ed Redesign (2019–2020) She collaborates on robotics projects like MCROS and leads outreach efforts to engage pre-university communities in AI and robotics education.

Alain TrouvéView profile

Professor

• Shape Spaces and Applications
• Imaging Processing and Computational Vision
• Computational Biology
• +3 more

Alain Trouvé is a Professor at the Center for Mathematics and Their Applications (CMLA) within the Ecole Normale Supérieure de Cachan , France. His research focuses on Shape Spaces , Computational Anatomy , Imaging Processing , and Biological Imaging , with applications in medical and computational fields. He directs the Mathematics Department at ENS Cachan and contributes to neuroanatomical studies through diffeomorphometry techniques. Key roles include membership in the Conseil National des Universités (Section 26) and teaching responsibilities such as courses on Geometry and Shape Spaces and Probability Theory . His work spans from theoretical frameworks (e.g., Hamiltonian modeling of shape evolution) to practical applications like 3D cell imaging and white matter fiber analysis. Publications emphasize interdisciplinary methods, including diffeomorphic registration, varifold-based image analysis, and stochastic shape evolutions. Current projects explore multi-scale modeling of biological systems and AI-driven medical diagnostics. Key Research Themes: Diffeomorphic mappings, computational vision, and functional shape analysis. Teaching: Courses on geometric modeling and probability at undergraduate and graduate levels. Tools Developed: xIV-LDDMM Toolkit for multi-modal biomedical data analysis.