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Christin SanderView profile

Researcher

• Middle Eastern History
• Political History
• Postcolonial Studies
• +3 more

Christin Sander is a Scientific Associate at the Institute for Islamic Studies, Free University of Berlin, since April 2022. Her research focuses on the intersection of Middle Eastern history, political movements, and postcolonial historiography, particularly examining neglected narratives of the Syrian Left during 1976-1982. She holds an MA and BA in History from the University of Hamburg, with a minor in Philosophy. MA in History, University of Hamburg (2019-2021) BA in History (major), Philosophy (minor), University of Hamburg (2015-2019) Research Interests Sander’s work challenges dominant narratives in Syrian historiography by exploring oppositional unrests in the 1970s and 1980s. She investigates the Communist Labor League’s (CLL/CLP) role in shaping postcolonial Syria’s intellectual history, situating it within the broader context of the defeated Arab New Left. Her analysis of primary sources and oral histories aims to reconstruct a non-sectarian history of resistance. Article Trends Her publications emphasize computational methods in molecular dynamics, machine learning applications for coarse-graining, and data-driven modeling techniques. Despite this seeming divergence, her core focus remains on historical analysis within Islamic and Middle Eastern studies. Contact: christin.sander@fu-berlin.de | Book consultation hours

• Machine Learning
• Computer Vision
• Data Science
• +4 more

Jacques Wainer is a Professor at the University of Campinas (Brazil), with expertise spanning Machine Learning, Medical Informatics, and Workflow Systems. His research focuses on algorithm optimization (e.g., SVM hyperparameters, imbalanced data strategies), medical applications (diabetic retinopathy detection), and educational technology (Python vs C in programming education). He has published extensively in top journals like Expert Systems with Applications and Journal of Machine Learning Research, and collaborates with institutions globally. His work bridges theoretical advancements and practical implementations, emphasizing reproducibility and real-world impact. Research interests include: Machine Learning methodologies, medical image analysis, workflow systems, educational computing, and bibliometric studies. Notable contributions include Bayesian model comparisons, nested cross-validation critiques, and low-memory face verification systems for mobile devices. His interdisciplinary approach addresses challenges in healthcare, education, and computational efficiency.

Christian HänigView profile

Professor

• Machine Learning
• Deep Learning
• Natural Language Processing
• +7 more

Prof. Dr. Christian Hänig is a Professor at the Department of Computer Science and Languages and a Temporary Lecturer at the Department of Electrical Engineering, Mechanical Engineering and Industrial Engineering at Anhalt University of Applied Sciences. He advises the Data Science (Full-Time Program) Master of Science degree and teaches courses such as Artificial Intelligence, Data Mining, and Deep Learning. His research focuses on Machine Learning, Deep Learning, Natural Language Processing (NLP), Computer Vision, Medical Imaging, Multimodal Document Processing, and Data Science. Recent work emphasizes applications in financial domains (e.g., German financial language models and corpus development) and agricultural sector benchmarking. Earlier research includes VR education analytics, unsupervised NLP techniques, and knowledge extraction from unstructured data. Key publications (2024) include developing benchmarks for Ukrainian language models, evaluating agricultural LLMs, and creating financial domain corpora. His contributions span over 20 years, addressing challenges in domain-specific NLP, clinical text mining, and industrial quality analysis. Prof. Hänig’s academic service includes roles as a degree program advisor and committee member. Office hours are Thursdays 4:30–6:00 PM (by appointment) at the Ratke Building, Room 23-114, Köthen campus.

• Machine Learning
• Deep Learning
• Neural Networks
• +7 more

Sung-Bae Cho is a prolific researcher in computer science with a publication record spanning over three decades from 1990 to 2025, demonstrating sustained academic productivity and research leadership. Their work primarily focuses on machine learning, neural networks, and their applications across diverse domains including cybersecurity, healthcare, and intelligent systems. Cho's research interests center around advanced machine learning techniques including deep learning architectures, Bayesian networks, and context-aware systems. Their work shows a consistent evolution from traditional neural networks to modern deep learning approaches, with recent publications emphasizing graph neural networks, federated learning, and anomaly detection systems. The research demonstrates strong methodological rigor with applications in practical domains such as traffic prediction, medical diagnosis, and security systems. The publication pattern reveals significant research productivity with multiple high-impact papers annually in reputable venues including IEEE Access, Neurocomputing, and Expert Systems with Applications. Recent work shows particular emphasis on addressing contemporary challenges in machine learning including continual learning, few-shot learning, and privacy-preserving approaches in federated settings. The research demonstrates both theoretical contributions and practical implementations across various application domains. Cho has cultivated an extensive collaborative network with numerous co-authors including Kyung-Joong Kim, Satchidananda Dehuri, Jin-Hyuk Hong, and Seok-Jun Bu, suggesting leadership in research groups and projects. The collaborative pattern indicates supervision of junior researchers and students, though specific advisee relationships aren't explicitly documented in the publication metadata. The researcher maintains active contributions to laboratory and team-based research, with recent publications indicating involvement in projects related to healthcare AI, cybersecurity systems, and intelligent transportation. Current research directions appear focused on addressing limitations in deep learning including catastrophic forgetting, data scarcity, and privacy concerns through novel architectural and training approaches.

• Data Mining
• Machine Learning
• Computational Sustainability
• +5 more

Stefan Kramer is a Full Professor at the Institute of Computer Science, Johannes Gutenberg University Mainz. He holds leadership roles including Spokesperson of the DeCoDeML network and involvement in initiatives like the Plattform Lernende Systeme. His research focuses on data mining, machine learning, computational sustainability, and bioinformatics, with over 200 peer-reviewed publications. Kramer has supervised numerous theses and secured significant funding from EU, DFG, BMBF, and industry. Awards include Best Paper recognitions at KDD, ICDM, and ICBK conferences. He actively contributes to academic governance and interdisciplinary collaborations, leading the Institute of Computer Science from 2016–2018. His work emphasizes privacy-preserving techniques, interpretable models, and applications in social good. Education: PhD in Technical Sciences (Dr. techn.) from TU Wien (1999) Positions at Austrian Research Institute for Artificial Intelligence, Siemens AG, and TU München (2003–2011). Research Interests: Kramer’s work spans structured data mining (sequences, graphs), privacy-aware algorithms, computational sustainability, and bioinformatics applications. Notable projects include CheS-Mapper for chemical data visualization and enviPath for biodegradation pathway prediction. Grants & Collaborations: Over 30 projects funded by EU, DFG, BMBF, and industry. Key collaborations include OpenTox Euro, the Predictive Toxicology Challenge, and interdisciplinary networks like the Rhein-Main Universities’ DeCoDeML initiative. Labs/Teams: Leads the Data Mining Group at Mainz, contributing to tools like CheS-Mapper and enviPath. Active in organizing conferences (ECML/PKDD 2021) and editorial roles for Machine Learning journals.

Jiatao GuView profile

Assistant Professor

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +7 more

Jiatao Gu is an Assistant Professor in the Department of Computer and Information Science (CIS) at the University of Pennsylvania, with a planned start in Fall 2025. He also works as a part-time Staff Research Scientist at Apple (MLR) and previously served as a full-time Research Scientist at Meta AI (FAIR Labs). His academic journey includes a Ph.D. in Electrical and Electronic Engineering (2018) from the University of Hong Kong under Prof. Victor O.K. Li, and a B.Eng. in Electronic Engineering (2014) from Tsinghua University.

Alexander I. JordanView profile

Researcher

• Computational Statistics
• Probabilistic Forecasting
• Machine Learning
• +3 more

Dr. Alexander I. Jordan is a Staff Scientist in Computational Statistics at the Technical University of Denmark (DTU), affiliated with the Department of Informatics and Mathematical Modeling. His research focuses on probabilistic modeling, forecast evaluation, and statistical methodology development. Research interests span probabilistic forecasting, classifier validation, quantile analysis, and computational methods in statistics. He has pioneered tools like Pandora for genotype data stability and scoringRules for probabilistic model evaluation, with applications across meteorology, astrophysics, and finance. His publication record from 2012-2025 reveals expertise in Probabilistic Forecast Evaluation Bayesian Modeling Quantile-Based Risk Analysis Stellar Evolution Prediction AI Calibration Techniques Phylogenetic Probability Calculations

Mehrdad MohannazadehView profile

Researcher

• Hydrological Modelling
• Machine Learning
• Applied Mathematics
• +3 more

Mehrdad Mohannazadeh is a postdoctoral researcher at the Department of Computational Hydrosystems (CHS) within the Helmholtz Centre for Environmental Research - UFZ in Leipzig, Germany. His work focuses on hydrological modelling , machine learning , and applied mathematics , particularly for analyzing and improving flood prediction models. Education : Ph.D. in Computer Sciences (2019-2023), Bielefeld University M.Sc. in Applied Mathematics (2016-2018), University of Applied Sciences Mittweida B.E. in Electrical Engineering (Electronics) (2007-2012), Shahid Beheshti University His research involves modifying hydrological models for better forecasting, working with projects like MOSES , HI-CAM II , 4DHydro , and SCENIC . His publications emphasize interpretable neural networks , probabilistic classification , and climate change impacts on water systems. Recent work includes high-resolution drought monitoring in Germany and seasonal hydrological forecasting for Andean-Amazonian basins. He contributes to UFZ Young Scientist Award winning initiatives and European forest model evaluations.

• Linguistic Theories
• Philosophical Semantics
• Multimodal Communication
• +7 more

Dr. Andy Lücking is a postdoctoral researcher at Goethe University Frankfurt, specializing in multimodal communication and cognitive semantics. He serves as Principal Investigator for the GeMDiS project within the ViCom SPP initiative, and previously held research fellowships at Université Paris Cité's Laboratoire de Linguistique Formelle and Frankfurt's Text Technology Lab. Current research focuses on neurocognitive semantics Developed iconic gesture theory with TTR/RTT frameworks Created multimodal corpora (FraGA, DoTT, TGVCorp) Active in computational educational linguistics Contributed to annotation tools (TextAnnotator, DependencyAnnotator) His work combines theoretical modeling with experimental methods, spanning over a decade of contributions to dialogue semantics, gesture-speech integration, and semantic role labeling. Recent projects explore VR-based multimodal research (Va.Si.Li-Lab) and diachronic dependency parsing. He has co-authored 20+ publications including proceedings in SemDial, LREC, and Springer HCI volumes. Collaboration network includes: Jonathan Ginzburg (dialogue theory), Alexander Mehler (computational linguistics), Alexander Henlein (VR research), and Max Planck Institute researchers. Key methodologies involve corpus creation, machine learning, and cognitive modeling of referential phenomena.

• Data Mining
• Machine Learning
• Time-Series Analysis
• +6 more

Michail Vlachos is a researcher affiliated with the University of Lausanne, Switzerland, with a focus on data mining, machine learning, and interpretable AI. His work spans algorithm design, time-series analysis, and data privacy, often intersecting with applications in recommender systems and neural network transparency. Research Interests: Data mining, machine learning, XAI (Explainable AI), time-series analysis, clustering algorithms, privacy-preserving data publishing, and recommender systems. Publication Trends: Recent work emphasizes interpretable deep learning architectures (2024), detection of deceptive AI explanations (2023), and neural recommender systems for educational platforms (2024). Earlier contributions include compressive data mining (2015), clustering preservation techniques (2017), and trajectory analysis (2009). Collaborations: Regularly works with Johannes Schneider, Ahmad Ajalloeian, and teams from institutions like IBM Research, ETH Zurich, and University of Lausanne.

Andrei CarageaView profile

Researcher

• Gabor Analysis
• Exponential Bases
• Neural Networks
• +4 more

Andrei Caragea is a Researcher at the Mathematical Institute for Machine Learning and Data Science (MIDS) within the Faculty of Mathematics and Geography at Katholische Universität Eichstätt-Ingolstadt . His work bridges Gabor analysis , exponential bases , and neural network approximation theory , with side interests in number theory and classical geometry . Education : PhD in Mathematics (2023), Katholische Universität Eichstätt-Ingolstadt Key Collaborators : Prof. Götz Pfander, Felix Voigtlaender, Friedrich Philipp, Dae Gwan Lee His research explores structural limitations in time-frequency representations (Balian-Low theorems for subspaces) and dimension-independent approximation capabilities of complex-valued neural networks. Recent work demonstrates how modReLU-activated networks can overcome the curse of dimensionality for Barron-class classifiers. Publication Trends (2019-2023): 50% focus on Gabor systems and time-frequency invariance 40% on neural network expressivity with complex-valued architectures 10% on exponential basis constructions in signal processing Research Grants : DFG Project (2019-2025): Sampling theory and bases from exponential functions DFG Project (2015-2022): Covariance matrix estimation under sparsity

• Biomedical Engineering
• Machine Learning
• Medical Imaging
• +3 more

Nazila Esmaeili is a Researcher at the Chair of Computer Science Applications in Medicine, Technical University of Munich (TUM). Her work bridges biomedical engineering, machine learning, and surgical robotics, focusing on vibroacoustic sensing and medical imaging technologies. Research Areas: Biomedical Engineering, Machine Learning, Vibroacoustic Sensing, Surgical Robotics Email: nazila.esmaeili@tum.de Her research explores vibroacoustic sensing for tissue characterization, leveraging deep learning and signal processing to enhance diagnostic accuracy in laryngeal and vocal fold lesions. She also develops surgical audio guidance systems and automated tools for laparoscopic and needle interventions. Recent publications highlight applications of deep clustering methods , proximal vibroacoustic sensing , and neural networks in differentiating cartilage degeneration, identifying peritoneum puncture events, and classifying vascular patterns in endoscopic imaging. Her work spans minimally invasive surgery , medical imaging , and robot-assisted procedures . For collaboration or inquiries, contact nazila.esmaeili@tum.de.

• Neuromorphic Information Processing
• Artificial Neural Networks
• Machine Learning
• +6 more

Prof. Dr. Martin Bogdan is a faculty member at the University of Leipzig since 2008, currently holding the Professorship for Neuromorphic Information Processing in the Faculty of Mathematics and Computer Science . His academic career spans roles as a research assistant, assistant professor, and department head at institutions including the University of Tübingen and University of Leipzig. Education : Studied technical computer science at Fachhochschule Offenburg (1987–1993) and industrial informatics at Université Grenoble I (1991–1993); earned PhD in 1998 from University of Tübingen. Research Interests focus on: Neuromorphic Information Processing Spiking Neural Networks Brain-Computer Interfaces (BCI) Embedded Systems for Bio-Analogous Processing Real-Time Signal Processing in Medicine Machine Learning Applications in Neurology Mainframe Computing Techniques Article Trends show expertise in: spiking neural networks for real-time applications; BCI systems for locked-in syndrome patients; hyperspectral imaging for agricultural analysis; FPGA-based evolving hardware; and machine learning applications in medical diagnostics. His work bridges neuroscience, computer science, and biomedical engineering. Academic Roles include leadership of the NeuroTeam (2000–2015), editorial positions, and extensive teaching experience in technical computer science and neuromorphic systems. Labs & Teams : Leads the Neuromorphic Information Processing division; collaborates with researchers including Dr. Sophie Adama, Dr. Jörn Hoffmann, and engineers like Max Braungardt.

Amin Milani FardView profile

Associate Professor

• Software Engineering
• Cybersecurity
• Software Testing
• +5 more

Amin Milani Fard is an Associate Professor of Computer Science at New York Institute of Technology - Vancouver Campus, and a visiting faculty member in Management Information Systems at Simon Fraser University's Beedie School of Business in Vancouver, Canada. Previously, he served as an Assistant Professor at NYIT Vancouver from 2018 to 2023. Dr. Milani Fard received his Ph.D. in Computer Software Engineering from the University of British Columbia (2012-2017), his M.Sc. in Computer Science from Simon Fraser University (2009-2010), and his B.Sc. in Computer Software Engineering from Ferdowsi University of Mashhad. His academic journey began with notable achievements including the 1st Rank Khwarizmi Award (awarded by Iran's President Mohammad Khatami) and an Exceptional Talents Admission Award. His research spans multiple disciplines, primarily focusing on Security, Privacy, and Assurance of Software and Information , with significant contributions to software testing, blockchain security, financial market prediction, and machine learning applications. His work on JavaScript security code smells, Ethereum smart contract vulnerabilities, and financial market prediction using multimodal data has been widely recognized. His publications demonstrate a consistent pattern of high-quality research with numerous papers in top conferences including ASE, ICST, and IEEE journals. Dr. Milani Fard's research portfolio shows a clear evolution from foundational work in software testing and JavaScript analysis toward more complex applications in financial technology and AI-driven security solutions. His most recent work focuses on integrating LLM technologies with security applications and advancing financial prediction models using sophisticated time series analysis. Scientific Awards and Recognition: Most Influential Paper Award at IEEE SCAM 2023 Presidential Excellence Award Finalist for Teaching at NYIT (2023) Best Paper Award at ECIR 2019 Multiple research grants including NSERC Alexander Graham Bell Canada Graduate Scholarship IEEE Best Paper Award Nominee at ICST 2017 As an active researcher, Dr. Milani Fard serves on program committees for major conferences including ASE and has contributed significantly to the software engineering community through his publications and research leadership. His work bridges theoretical computer science with practical applications in cybersecurity and financial technology, demonstrating both academic rigor and real-world impact.

Jiahao XieView profile

Researcher

• Computer Vision
• Machine Learning
• Test-Time Adaptation
• +3 more

Dr. Jiahao Xie is a researcher at the Max Planck Institute for Informatics, affiliated with the Computer Vision and Machine Learning department. Located at the Saarland Informatics Campus in Saarbrücken, Germany, he conducts cutting-edge research in visual computing and machine learning with a focus on robustness and adaptation of computer vision models. His research interests center around computer vision and machine learning, with specific expertise in test-time adaptation techniques and evaluating model robustness under distribution shifts. Xie's work addresses critical challenges in making computer vision systems more adaptable to real-world scenarios where input data may differ from training conditions. His research employs advanced techniques including diffusion models, neural network adaptation, and benchmarking methodologies to tackle these problems. Recent publications demonstrate his focus on developing methods for visual in-context learning during test time and creating comprehensive benchmarks for evaluating classifier robustness. His research has been accepted to top-tier computer vision conferences including CVPR and ICCV in 2025, indicating significant contributions to the field. Dr. Xie collaborates with prominent researchers in the field including Bernt Schiele, Federico Tombari, and Christian Theobalt, suggesting strong integration within the computer vision research community at MPI and beyond.