Holger Wittges is the Managing Director of the SAP University Competence Center (UCC) at the Technische Universität München (TUM) . His work focuses on Digital Transformation , Next Generation ERP , and Hybrid Cloud infrastructure. He is affiliated with the KrcmarLab and collaborates with IBM via the OpenPOWER@TUM initiative. Educational Background: 2004: Dr. rer. oec. (Promotion), Universität Hohenheim 1996: Diplom Wirtschaftsinformatiker, Universität Bamberg Research Interests include Digital Transformation, Cloud Computing, Enterprise Resource Planning (ERP), XaaS (Everything as a Service), and Service-Oriented Architecture (SOA). His work bridges academic innovation with industry needs through SAP UCC TUM, which provides 40+ educational service bundles like SAP HANA and S/4HANA for teaching and research. Recent Publications highlight advancements in machine learning for ERP support ticket systems, energy efficiency in SAP S/4HANA, and educational frameworks for cloud-based enterprise software. Articles emphasize collaboration with institutions across Europe and contributions to digital ecosystems like the SAP University Alliances. Key Projects include the OpenPOWER@TUM initiative with IBM, focusing on accessible AI/ML infrastructure for academia, and the SAP UCC TUM, which drives Education as a Service (EaaS) strategies for digital business ecosystems.
Dr. Sheila Castilho is an Assistant Professor at the School of Applied Language & Intercultural Studies, Dublin City University (DCU). She holds a PhD from DCU (2016) and a Master's from the University of Wolverhampton and University of Algarve. Her expertise lies in machine translation (MT), post-editing, and translation technology evaluation. She co-leads the New Trends in Translation Technology (NeTTT’22) conference and chairs DCU's Master in Translation Studies and Master in Translation Technology programs. Education: Licenciatura em Letras Inglês/Português (UNIOESTE University, Brazil) Master in Natural Language Processing (University of Wolverhampton & University of Algarve) PhD in Translation Technologies (Dublin City University) Research: Focuses on document-level MT evaluation, post-editing strategies, and user-centric MT assessment. Leads the DELA project and contributed to TraMOOC/iADAATPA initiatives. Published over 40 articles and co-edited 'Translation Quality Assessment: From Principles to Practice' (Springer, 2018). Grants & Projects: DCU PI for DELA (Document-level Evaluation) PRINCIPLE project (EU Low-resource MT) ELE (European Language Equality) initiative Labs/Teams: Active in ADAPT Centre (DCU) and collaborates with international NLP/MT communities (ACL, EMNLP, WMT).
Ueli Grossniklaus is an Ordinary Professor at the University of Zurich within the Faculty of Mathematical and Natural Sciences , affiliated with the Department of Plant and Microbiology . His work focuses on plant developmental biology, particularly epigenetic and genetic mechanisms governing reproduction and adaptation. Key Courses: Epigenetics, Plant Biology Workshop, Group Seminars on Current Research Laboratory Techniques: Advanced methods in plant cell mechanics, transcriptomics, and genome editing Research Interests span plant epigenetics, reproductive biology, and the interplay between environmental stress and genetic regulation. He investigates: Mechanistic control of gametogenesis and fertilization Epigenetic contributions to plant adaptation Evolutionary implications of asexual reproduction Biophysical forces in plant cell growth Publication Trends (2025–2018) reveal expertise in: Arabidopsis and fern model systems Epigenetic regulation (DNA methylation, histone dynamics) Apomixis and hybrid seed failure mechanisms Biomechanics of pollen tubes and carnivorous plants Genome editing tools (CRISPR) and long-read sequencing Scientific Collaborations include interdisciplinary projects on: Microfluidic devices for plant cell analysis Gene drive ecology and ethics 3D imaging of plant reproductive structures Advising and Grants focus on mentoring through research internships in developmental biology, genetics, and systems biology. His lab engages in: Epigenetic response to environmental stress Cell wall mechanics in reproduction Computational modeling of plant growth Laboratory Teams integrate plant biologists, bioengineers, and computational scientists to study: Mechanistic gene regulation Evolutionary developmental biology Microrobotics for cellular force measurement
Gerard D Schellenberg is a Professor of Pathology and Laboratory Medicine at the University of Pennsylvania Perelman School of Medicine, with graduate affiliations in Genomics and Computational Biology and Neuroscience. His research focuses on the genetic architecture of Alzheimer's disease and related neurodegenerative disorders, particularly through large-scale genomic studies and neuropathological correlations. Education: B.S. in Biochemistry (minor: Cell Biology), University of California at Riverside, 1973 Ph.D. in Biochemistry (minor: Cell Biology), University of California at Riverside, 1978 NIH Post Doctoral Fellowship, National Institute of Health, 1980-1982 Senior Research Fellow positions at University of Washington departments (1978-1983) Dr. Schellenberg's research program centers on identifying genetic risk factors for Alzheimer's disease through genome-wide association studies (GWAS), whole-genome sequencing, and multi-omics integration. His work emphasizes population diversity, with significant contributions to understanding genetic risk in African American cohorts and sex-specific effects in neurodegeneration. Key focus areas include tauopathies, TDP-43 pathology, and the role of immune-related genes in disease progression. He has pioneered studies on progranulin mutations and their variable phenotypic expression across neurodegenerative conditions. Recent publications reveal a strong trajectory toward multi-ethnic genetic studies, with emphasis on Alzheimer's disease genetics in underrepresented populations , sex differences in cognitive resilience , and novel risk genes like MGMT and DCDC2. His team integrates neuropathological data with genomic findings to establish causal mechanisms, frequently publishing in high-impact journals like Nature Genetics and Alzheimer's & Dementia . Key Scientific Contributions: Leadership in the Alzheimer's Disease Sequencing Project (ADSP) expanding ethnic diversity in genetics research Development of the Alzheimer's Disease Variant Portal (ADVP) for harmonized genetic data Pioneering work on APOE-ε4 modifying loci in African ancestry populations Identification of sex-specific genetic predictors for memory maintenance Dr. Schellenberg directs genomic research initiatives that bridge basic science with clinical neuropathology. His laboratory maintains extensive collaborations with neuropathology cores for autopsy-confirmed diagnoses and leverages multi-ethnic cohorts to address health disparities in dementia research. Current work focuses on elucidating how genetic variants influence tau and TDP-43 pathology across diverse populations, with implications for precision medicine approaches to neurodegenerative diseases.
Gül Varol is a permanent researcher at École des Ponts ParisTech's IMAGINE group, an ELLIS Scholar, and Guest Scientist at Max Planck Institute. She holds a PhD from Inria Paris/ENS with awards from ELLIS and AFRIF. Her academic service includes Program Chair at ECCV'24 and Area Chair roles at major conferences. Current affiliations: IMAGINE group (École des Ponts ParisTech), Max Planck Institute Previous roles: Postdoctoral researcher at University of Oxford Her research focuses on vision-language applications, particularly in 3D human motion synthesis, sign language technology, and audio description generation. Key techniques include text-conditioned diffusion models, temporal context modeling, and synthetic data utilization. Scientific contributions recognized through: Google Research Scholar award (2023) ELLIS PhD Award (2020) AFRIF PhD thesis award (2020) Best application paper at ACCV'20 Recent publications demonstrate expertise in: Text-driven 3D motion editing (MotionFix, 2024) Cross-dataset generalization studies (TMR++, 2024) Temporal action composition frameworks (TEACH, 2022) Sign language dense annotation methods (BOBSL, 2022) Zero-shot audio description generation (AutoAD-Zero, 2024) She actively contributes to dataset development including BOBSL (British Sign Language corpus) and SURREACT synthetic action dataset, while pioneering new evaluation metrics for audio description quality and motion retrieval benchmarks.
Dr. Shawn Gomez is a Professor in the Lampe Joint Department of Biomedical Engineering at UNC-Chapel Hill and North Carolina State University and in the Department of Pharmacology at UNC-Chapel Hill. He serves as the Executive Director of FastTraCS, a component of the NC TraCS Institute funded through the NIH CTSA Program, and is a UNC Lineberger Comprehensive Cancer Center member. His educational background includes a PhD in Biomedical Engineering from Columbia University (1999), an MS in Aerospace Engineering Sciences from the University of Colorado, Boulder (1993), and a BS in Aerospace Engineering Sciences from the same institution (1990). He completed postdoctoral training in Bioinformatics and Computational Biology at the Judith P. Sulzberger Columbia Genome Center and Institut Pasteur in Paris. Dr. Gomez's research spans systems biology , network pharmacology , and translational AI , with a focus on understanding cell signaling architecture in human disease. His lab develops computational approaches for network pharmacology and targeted cancer therapies, along with machine learning methodologies to address clinical needs and enhance clinical decision making. The research integrates computational modeling with experimental approaches to improve diagnostic and therapeutic interventions. His recent publications reveal a strong focus on kinome research, particularly in pancreatic cancer and understudied kinases, with increasing integration of machine learning techniques for predicting clinical outcomes and kinase-substrate relationships. The work spans from fundamental systems biology to translational applications in cancer therapeutics and surgical outcomes prediction. Scientific Awards: Leadership Advanced Program, UNC-Chapel Hill 2017 Chancellor's Entrepreneurship Boot Camp 2015 ACCLAIM Scholar (Academic Career Leadership Academy in Medicine) 2013-2014 UNC Research Council Award 2011 Carl Storm URM Fellowship 2008 UNC Junior Faculty Development Award 2006 Florence Gould Scholar 2005 Pasteur Foundation Fellow 2002-2005 Dr. Gomez directs the Gomez Lab, which focuses on systems biology, network pharmacology, and translational AI. The lab maintains several key resources including Darkkinome.org, FAAS, and IAS servers for kinome research. His work bridges computational biology with clinical applications, particularly in cancer therapeutics and surgical outcomes prediction through machine learning approaches.
Benoît Sagot is a Senior Researcher in Natural Language Processing and Computational Linguistics at Inria , currently holding the 2023-2024 Informatics and Digital Sciences Annual Chair at Collège de France. He directs the ALMAnaCH research team and contributes to the PRAIRIE Institute for AI research. Research Focus: His work spans neural language models, machine translation, text simplification, multimodal NLP, and lexical resource development for French and low-resource languages. He explores computational morphology, etymology, and historical linguistics, with applications in opinion mining and computational oenology. Recent Articles emphasize language model interpretability, cross-lingual transfer, and multimodal integration (speech, image). Tools & Resources: He has developed morphological lexicons (Le fff, Alexina), corpora (OSCAR, CAMEMBERT), and parsing pipelines (SxPipe). Projects: Involved in initiatives like ANR BASNUM (Furetière's dictionary digitization) and 3IA PRAIRIE (AI research). His career combines foundational work in syntactic analysis with evolving deep learning approaches.
Mahadev Satyanarayanan is the Jaime Carbonell University Professor of Computer Science at Carnegie Mellon University. His multi-decade research focuses on performance, scalability, availability, and trust in distributed systems spanning cloud to mobile edge computing. He pioneered foundational concepts in mobile computing and Edge Computing through his seminal work on VM-based cloudlets. His current research explores cloudlet-based Edge Computing for latency-sensitive applications, wearable cognitive assistance systems integrating augmented reality, and edge-based machine learning frameworks for efficient training data discovery. He collaborates with Dan Siewiorek, Martial Hebert, and Bobby Klatzky on transformative applications. Dr. Satyanarayanan received his PhD from Carnegie Mellon University after completing Bachelor's and Master's degrees at the Indian Institute of Technology, Madras. His honors include ACM and IEEE Fellowships recognizing his contributions to distributed systems and mobile computing. ACM Fellow IEEE Fellow
Alex Warstadt is an Assistant Professor at the University of California San Diego, holding appointments in the Department of Linguistics and the Halıcıoğlu Data Science Institute (HDSI). His research focuses on computational linguistics, applying advances in Large Language Models (LLMs) to understand human language acquisition, processing, and structure. Key contributions include developing the CoLA and BLiMP benchmarks for evaluating grammatical ability in LLMs, and the BabyLM Challenge to promote data-efficient language models. His work bridges theoretical linguistics, experimental methods, and computational modeling, particularly in pragmatics and discourse structure. Education: He earned B.A.s in Linguistics and Music Theory from Brown University and a Ph.D. in Linguistics from New York University (NYU), with a dissertation on 'Artificial Neural Networks as Models of Human Language Acquisition.' Postdoctoral work at ETH Zürich furthered his interdisciplinary research. He leads the LeM🍋N Lab at UC San Diego, which investigates language learning, meaning representation, and natural language processing through interdisciplinary collaboration. Research Interests: Warstadt’s research emphasizes leveraging machine learning to explore developmental linguistics, computational cognitive modeling, and pragmatic phenomena such as relevance and presupposition. His lab’s work aims to create models that align with human learning processes while advancing efficient NLP techniques. Recent projects include studying multimodal input effects and optimizing models for developmental plausibility. Labs/Teams: Director of the Learning, Meaning, and Natural Language (LeM🍋N) Lab, focusing on interdisciplinary research across linguistics, cognitive science, and data science.
Abhinav Shrivastava is an Associate Professor in the Department of Computer Science at University of Maryland, College Park, with a joint appointment in the Institute of Advanced Computer Studies (UMIACS). Previously, he served as an Assistant Professor at the same institution from August 2018 to June 2024, and spent one year as a Visiting Research Scientist at Google Research from September 2017 to August 2018. His educational background includes: PhD in Robotics and Artificial Intelligence from Carnegie Mellon University (2017), advised by Abhinav Gupta, with thesis titled 'Discovering and Leveraging Visual Structure for Large-scale Recognition' MS in Artificial Intelligence from Carnegie Mellon University (2011), supervised by Alyosha Efros and Martial Hebert BTech in Computer Science and Engineering from Jaypee Institute of Information Technology (2010) Professor Shrivastava's research focuses on computer vision and machine learning, with particular expertise in object detection, image recognition, and neural representations. His work bridges theoretical advances with practical applications, exploring how visual systems can discover and leverage structure in large-scale recognition problems. He has made significant contributions to understanding the role of supervision in vision transformers, developing novel approaches for object-state composition recognition, and creating efficient neural representations for videos and 3D scenes. His research often addresses fundamental challenges in visual recognition, including handling novelty in open-world environments and improving the efficiency of visual systems. An analysis of his recent publications reveals a strong emphasis on neural representations, particularly for dynamic content like videos and 3D scenes. His work demonstrates increasing sophistication in handling open-world vision problems, with research spanning object discovery, localization, and representation learning. The publications show a clear progression toward more efficient and scalable models, with recent work focusing on model compression, sparse representations, and addressing the challenges of working with limited annotations. His scientific contributions have been recognized with several prestigious awards: Best Paper Award (Applications) at IEEE Winter Conference on Applications of Computer Vision (2020) Microsoft Research PhD Fellowship (2014-2016) Best Student Paper Award at IEEE Winter Conference on Applications of Computer Vision (2014) Outstanding Reviewer Award at IEEE CVPR (2015) Professor Shrivastava has successfully mentored numerous graduate students, many of whom have become prominent researchers in computer vision. His Amazon Research Awards (2020 and 2023) have supported innovative projects including 'The pursuit of knowledge: discovering and localizing new concepts using dual memory' and 'Audio-conditioned Diffusion Models for Generating Lip-synchronized Videos.' He has served as Area Chair for major conferences including ICCV, CVPR, and AAAI, demonstrating his leadership in the computer vision community. His research has attracted significant funding from both academic and industry sources, supporting his exploration of fundamental questions in visual recognition and representation learning.
Gedas Bertasius is an Assistant Professor in the Department of Computer Science at the University of North Carolina at Chapel Hill. Previously, he served as a postdoctoral researcher at Meta AI (Facebook AI) and earned his PhD in Computer Science from the University of Pennsylvania. His academic journey began with a bachelor’s degree in Computer Science from Dartmouth College. Dr. Bertasius specializes in computer vision and machine learning with specific interests in: Video understanding First-person vision (egocentric vision) Human behavior modeling Multimodal deep learning Transfer learning Computer vision for sports analytics Video+robotics integration His research produces practical frameworks like Video ReCap for hierarchical captioning of long videos, SiLVR for language-based video reasoning, and BASKET for fine-grained skill estimation. He focuses on developing models that can process videos across multiple temporal granularities while maintaining computational efficiency. Key research themes in his work include: Recursive video processing architectures Space-time attention mechanisms Generative video modeling LLM integration with vision systems 3D-aware representation learning Continual learning for video QA He has received notable recognition, including: CVPR 2024 Egocentric Vision (EgoVis) Distinguished Paper Award CVPR 2020 Best Paper Award Nomination First Place at CVPR 2025 Multi-Discipline Lecture Understanding Workshop Dr. Bertasius collaborates with prominent researchers like Mohit Bansal and Lorenzo Torresani . His recent publications demonstrate expertise in advancing video-language models, with applications in semantic alignment, temporal grounding, and cross-modal reasoning. For detailed information about his research, publications, and ongoing projects, please visit his official website .
Hirokatsu Kataoka serves as Chief Senior Researcher at the National Institute of Advanced Industrial Science and Technology (AIST) in Japan, with multiple academic affiliations including Academic Visitor at the Visual Geometry Group (VGG) at University of Oxford, Visiting Associate Professor at Keio University, and Adjunct Associate Professor at Tokyo Denki University. He is Principal Investigator of both cvpaper.challenge and LIMIT.Lab, and serves as Research Advisor for SB Intuitions. Dr. Kataoka earned his Ph.D. in Engineering from Keio University (April 2011 - March 2014), where he received the Fujiwara Prize in 2014 as valedictorian equivalent. His research primarily focuses on innovative pre-training methodologies that eliminate dependency on natural image datasets, with his Formula-Driven Supervised Learning (FDSL) framework being particularly influential in the field. Kataoka's research interests center around representation learning with limited data resources, including zero-shot, unsupervised, and synthetic learning approaches. His work explores how visual/multimodal models can be effectively trained with minimal real-world data, addressing critical ethical concerns related to large-scale datasets. He has pioneered methods using fractal geometry, mathematical formulas, and procedural generation to create effective pre-training frameworks that rival traditional ImageNet-based approaches. His publication record shows a clear trajectory toward solving the challenges of learning with limited resources, with recent work expanding FDSL to audio processing, microfossil analysis, and visible-to-infrared translation. His papers consistently address the core challenge of building robust visual recognition systems without relying on massive annotated datasets, with increasing focus on practical applications across diverse domains. Scientific Awards & Recognition ACCV 2020 Best Paper Honorable Mention Award for 'Pre-training without Natural Images' AIST Best Paper Award (2019, 2022) BMVC 2023 Best Industry Paper Finalist Featured in MIT Technology Review His 3D ResNets paper ranks among the top 0.5% most-cited CVPR papers over a five-year period Dr. Kataoka actively advises numerous researchers across multiple institutions, with his research team comprising Ph.D. and Master's students from various universities. He has served as Area Chair for CVPR 2024 and 2025, will serve as IEEE TPAMI Associate Editor beginning in 2025, and organizes the LIMIT Workshop series at major computer vision conferences. His LIMIT.Lab, established in June 2025, serves as a collaboration hub focused on building multimodal AI models under constrained resources including compute, data, and labels.
Professor Yizhou Sun is affiliated with the University of California Los Angeles (UCLA) and the Henry Samueli School of Engineering and Applied Science . Her academic work focuses on Machine Learning , Artificial Intelligence , and Graph Neural Networks within the Computer Science department. Her research spans High-Level Synthesis , Causal Inference , and Computational Biology , with recent publications addressing neural network compression, language model safety, and dynamical system modeling. The trends in her recent 2025 and 2024 publications emphasize Deep Learning , Graph Theory , and Language Model Optimization , reflecting interdisciplinary applications in Biomedical Data , Hardware Design , and Physical Simulation .
Roles and Affiliations : Dimitris Kolovos is a Professor of Software Engineering at the University of York's Department of Computer Science. He leads the Automated Software Engineering (ASE) research group and is an Eclipse Foundation committer, leading development of the Epsilon open-source platform. His roles include research leadership, teaching, and academic service. Education : PhD in Software Engineering - University of York MSc in Software Engineering with Distinction - University of York First Class Honours Degree in Informatics - Athens University of Economics and Business Research Interests : Kolovos focuses on advancing Model-Driven Engineering (MDE), GenAI integration in software development, low-code platforms, and data analytics. His work emphasizes scalable modeling tools, education technology (e.g., MDENet platform), and industry collaboration with organizations like NASA, BAE Systems, and Siemens. Labs and Projects : He leads the Epsilon project under the Eclipse Modelling initiative, developing tools for model transformation, validation, and code generation. His research group also explores AI-driven model transformations and hybrid graphical-textual editors.
Malvina Nissim is a leading researcher in computational linguistics and NLP at the University of Groningen's Department of Artificial Intelligence, with a focus on multilingual modeling, bias mitigation, and human evaluation frameworks. Key Contributions : Developed CALAMITA (Italian LLM benchmark), IT5 models for Italian language processing, and ReproHum framework for NLP evaluation reproducibility Research Pillars : Multilingual reasoning consistency, perspective-based text analysis, and figurative language modeling Her work spans activation steering techniques, cross-lingual transfer learning, and the creation of specialized language resources like the EurekaRebus dataset and MAGPIE idiom corpus. She pioneered methods for gender bias measurement in BERT and developed the SocioFillmore tool for perspective visualization. Recent publications explore model uncertainty as MCQ difficulty proxy, Italian headline generation benchmarks, and multilingual multi-figurative language detection. She actively participates in teaching initiatives like the "NLP with Bracelets" workshop for Italian high school students. Scientific Awards : ACL Best Paper Award (2025) EMNLP Outstanding Reviewer (2023) EVALITA Leadership Recognition (2024) She advises PhD students in model bias analysis and has contributed to the development of the Dutch Abusive Language Corpus (DALC) and the ReproNLP reproducibility framework. Her collaborations span institutions in Italy, Netherlands, and international NLP communities.
