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Benedikt WirthView profile

Professor

• Optimisation and calculus of variations
• image processing
• scientific computing
• +9 more

Prof. Dr. Benedikt Wirth is a Professor of Mathematics at the University of Münster, Germany, affiliated with the Institute for Analysis and Numerics within the Department of Mathematics and Computer Science. He is an active researcher and educator specializing in optimization and calculus of variations, with significant contributions to mathematical imaging and shape analysis. His research interests include image processing, scientific computing, numerical analysis, optimization, shape spaces, geodesics in shape space, variational methods, elastic deformation, and optimal transport. Wirth has developed innovative mathematical frameworks for shape analysis, particularly focusing on Riemannian metrics for shape spaces and variational approaches to shape comparison and optimization. His recent publications (2023-2025) demonstrate continued leadership in mathematical optimization, with particular focus on PET reconstruction, dimension reduction techniques, manifold embeddings, and branched transport theory. His work bridges theoretical mathematics with practical applications in medical imaging and computer vision, showing particular strength in connecting geometric analysis with computational methods. CRC 1450 - A05: Targeting immune cell dynamics by longitudinal whole-body imaging and mathematical modelling CRC 1450 - A06: Improving intravital microscopy of inflammatory cell response by active motion compensation EXC 2044 - C1: Evolution and asymptotics EXC 2044 - C2: Multi-scale phenomena and macroscopic structures EXC 2044 - C3: Interacting particle systems and phase transitions EXC 2044 - C4: Geometry-based modelling, approximation, and reduction Prof. Wirth actively supervises numerous bachelor's and master's students, with over 40 theses completed under his guidance since 2015. His teaching portfolio includes courses on inverse problems, numerical methods for partial differential equations, shape spaces, optimization, and optimal transport. He has consistently maintained an active research program while contributing significantly to the education of the next generation of mathematicians.

Jens TetensView profile

Professor

• Animal Genetics
• Genomics
• Breeding Improvement
• +5 more

Prof. Dr. Jens Tetens is a Professor at the University of Göttingen, where he heads the Department of Genetics and Breeding Improvement of Functional Traits at the School of Veterinary Medicine. He studied veterinary medicine at the University of Veterinary Medicine Hannover Foundation from 1999 to 2005, receiving his doctorate in 2006. After a postdoc period in Hannover and a stay in Switzerland, he joined the Christian-Albrechts-University of Kiel in 2007, where he headed the molecular genetics laboratory for 10 years and completed his habilitation in 2015 with the thesis 'Recent advances in cattle genomics and beyond,' which earned him the H. Wilhelm Schaumann Foundation's award in 2013. His research spans multiple areas of animal genetics and genomics, with particular emphasis on: Genomic selection methodologies and breeding value estimation Genetic analysis of production, health, and functional traits Identification of causal genetic variants through advanced genomic approaches Application of genomic technologies to improve animal breeding programs Mendelian randomization to establish causal relationships between traits Prof. Tetens' publication record shows a strong evolution from traditional quantitative genetics to cutting-edge genomic analyses using sequence-level data. His recent work has increasingly focused on understanding genetic correlations between milk production and health traits in dairy cattle, structural variation in livestock genomes, and the genetic basis of behavioral traits in poultry. His research encompasses multiple livestock species including dairy cattle, beef cattle, horses, and poultry. His notable achievements include the H. Wilhelm Schaumann Foundation's award in 2013 and his leadership in developing single-step genomic evaluation methods that have advanced genomic selection practices across multiple livestock species. Prof. Tetens maintains active collaborations with researchers across Europe and has contributed significantly to both fundamental research in animal genomics and its practical implementation in breeding programs worldwide. Prof. Tetens has supervised numerous graduate students and postdoctoral researchers, contributing to the training of the next generation of animal geneticists. His department at Göttingen focuses on integrating genomic technologies with traditional breeding approaches to develop more effective and sustainable animal breeding strategies that balance production with animal health and welfare considerations.

Yang LiView profile

Associate Professor

• Statistical Machine Learning
• Bayesian Nonparametrics
• Scalable Optimization
• +5 more

Yang Li serves as Associate Professor of Marketing and Associate Dean for the MBA Program at Cheung Kong Graduate School of Business (CKGSB). Holding a PhD in Marketing from Columbia Business School alongside dual master's and bachelor's degrees from Columbia and Peking University respectively, he bridges advanced statistical methodologies with practical business applications. His research centers on statistical machine learning and Bayesian nonparametrics applied to consumer behavior analysis, with specialization in online personalization, text mining, and choice modeling. Recent work demonstrates significant focus on fragmented attention economies, ethical AI frameworks, and NFT network dynamics, reflecting contemporary digital market challenges. Management Science Marketing Science Journal of Marketing Research Journal of Consumer Research Harvard Business Review Professor Li's publications reveal evolving expertise from foundational pricing elasticity studies toward cutting-edge AI applications in consumer contexts. His work increasingly integrates generative models and graph neural networks to decode complex consumer collection behaviors and digital ecosystem dynamics. Scientific recognition includes being a Finalist for the 2021 Paul E. Green Best Paper Award. Industry impact is demonstrated through executive education programs and strategic consultancies with Tencent, Haier, and Tmall. As Associate Dean for MBA Programs, he oversees curriculum development while maintaining active corporate governance roles on boards of publicly traded companies across China and Hong Kong, directly applying his research insights to strategic decision-making in digital transformation initiatives.

Smita KrishnaswamyView profile

Associate Professor

• Unsupervised Machine Learning
• Manifold Learning
• Deep Learning
• +7 more

Smita Krishnaswamy is an Associate Professor of Genetics and Computer Science at Yale University with joint appointments in both departments. She is affiliated with multiple interdisciplinary programs including the Applied Mathematics Program, Computational Biology and Bioinformatics Program, Yale Center for Biomedical Data Science, Yale Cancer Center, and the Wu Tsai Institute. Her research bridges computational methods development with biomedical applications, focusing on unsupervised machine learning approaches for high-dimensional data analysis. Associate Professor of Genetics, Yale School of Medicine Associate Professor of Computer Science, Yale University Affiliated Faculty, Applied Mathematics Program Affiliated Faculty, Computational Biology and Bioinformatics Member, Yale Center for Biomedical Data Science Member, Yale Cancer Center Member, Wu Tsai Institute Dr. Krishnaswamy's research focuses on developing unsupervised machine learning techniques, particularly manifold learning and deep learning methods, to analyze high-dimensional biomedical data. Her lab creates algorithms for non-linear dimensionality reduction, data geometry learning, denoising, imputation, and inference of multi-granular structures from complex datasets. These methods are applied to diverse data types including single-cell RNA-sequencing, mass cytometry, electronic health records, and connectomic data across multiple biological systems. Her work spans several key application areas including immunology and immunotherapy, cancer research, neuroscience, developmental biology, and health outcomes analysis. The lab employs approaches from geometric deep learning, multiscale graph signal processing, and topological data analysis to extract meaningful biological insights from complex datasets. Recent publications demonstrate the lab's leadership in developing methods for spatial transcriptomics, brain-state trajectory modeling, and organ donation prediction. Excellence in Science Early-Career Investigator Award from FASEB (2022) Yale Cancer Center Class of '61 Cancer Research Award (2025) Dr. Krishnaswamy maintains active collaborations across Yale and secures research funding supporting her work in computational biomedicine. She advises students through multiple programs including Genetics, Computer Science, and the Biological and Biomedical Sciences Graduate Program, fostering interdisciplinary training at the intersection of computation and biomedicine. The Krishnaswamy Lab operates at the forefront of computational biomedicine, developing mathematical approaches that enable new biological discoveries from complex datasets.

• Quantitative Linguistics
• Speech Production
• Articulatory Speech Synthesis
• +4 more

Konstantin Sering is a researcher at the University of Tübingen within the Faculty of Philosophy, specifically working at the Seminar for Linguistics as part of the Quantitative Linguistics group. His academic journey bridges physics and psychology with computational linguistics, culminating in his 2023 PhD thesis on the PAULE (Predictive Articulatory speech synthesis Utilizing Lexical Embeddings) model. He maintains active collaborations with the Cognitive Modeling Group led by Martin V. Butz and the Quantitative Linguistics Group led by Harald Baayen. Sering's research interests center on the computational modeling of human speech production, with particular focus on the articulatory aspects of language. He approaches spoken language from the perspective of computer-aided modeling and simulation, extending the VocalTractLab model to produce spontaneous human speech. His work emphasizes the information-bearing nature of language over its meaning-bearing aspects, investigating how language signals contextual states and conditions. He combines artificial neural networks with predictive forward approaches to model vocal tract control parameters across multiple target spaces, including meaning and acoustic representations. Analysis of Sering's publication record reveals a consistent trajectory in articulatory speech synthesis and quantitative linguistics. His work demonstrates increasing sophistication in modeling the human speech apparatus, with recent publications focusing on improving the PAULE model through speech/non-speech classification, somatosensory feedback integration, and benchmarking methodologies. His research spans both theoretical computational linguistics and practical applications, with strong connections to cognitive science and psycholinguistics. The publications show interdisciplinary collaboration across linguistics, computer science, and cognitive psychology. Sering actively contributes to the academic community through teaching and software development. He has taught courses including Introduction to Psycholinguistics, Physics of Human Speech Production, and seminars on Predictive Articulatory Speech Synthesis. As software author and maintainer, he has developed several important tools for the field: Articubench (an articulatory speech synthesis benchmark), PAULE (his namesake speech synthesis model), create_vtl_corpus, and contributions to pyndl and other packages. His technical expertise spans Python, R, and specialized instrumentation for speech analysis. Based in room 188 at Keplerstr. 2 in Tübingen, Sering maintains an active research program with regular publications and software releases since 2013. His work environment benefits from the resources of the University of Tübingen's linguistics infrastructure and collaborative opportunities across disciplines.