Find Academic Supervisors

• Interpretable Machine Learning
• Biomedical Data Analysis
• Astrophysics Data Analysis
• +4 more

Sreejita Ghosh, PhD is a researcher affiliated with the Faculty of Science and Engineering at the University of Groningen, working within the Intelligent Systems research group. Her work spans multiple disciplines with a primary focus on interpretable machine learning applications across diverse scientific domains. Her research expertise includes: Interpretable Machine Learning techniques for complex data analysis Biomedical data modeling and visualization in metabolomics and steroid biosynthesis Astrophysics data processing for Epoch of Reionization research Computer-aided diagnosis systems using vector quantization approaches Handling systematic missingness in imbalanced and heterogeneous datasets Dr. Ghosh's recent publications demonstrate a remarkable interdisciplinary approach, bridging computer science with applications in both biomedical research and astrophysics. Her work addresses critical challenges in model interpretability across scientific domains where understanding the 'why' behind predictions is as important as the predictions themselves. She has been actively involved in academic presentations and workshops related to intelligent systems and computational intelligence between 2017-2019, demonstrating her engagement with the international research community. Her contributions to UN Sustainable Development Goals reflect the societal impact of her interdisciplinary research approach.

Yulong PeiView profile

Assistant Professor

• Anomaly Detection
• Adversarial Machine Learning
• Graph Neural Networks
• +7 more

Yulong Pei is an Assistant Professor at the Department of Mathematics and Computer Science, Eindhoven University of Technology. His research focuses on anomaly detection, adversarial machine learning, graph neural networks, and fairness in AI. He has contributed to the development of robust algorithms for network analysis, active learning frameworks, and applications in code quality assessment and financial data analysis. Pei's work addresses challenges such as structural sparsity optimization, bias mitigation in language models, and efficient training techniques for deep neural networks. His research has been recognized with awards like the Best Paper Award of LoG 2022. He has advised 27 students and collaborated on projects involving financial transaction networks, social media data analysis, and healthcare informatics. Key contributions include FAL-CUR (fair active learning), Hop-count based anomaly detection, and frameworks for adversarial example generation. His publications span conferences like ECML PKDD and journals like Expert Systems with Applications, demonstrating interdisciplinary impact across computer science, finance, and healthcare.

Luis Pedro SilvestrinView profile

Research Fellow

• Machine Learning
• Deep Learning
• Transfer Learning
• +4 more

Luis Pedro Silvestrin is a Research Fellow at the Faculty of Science , Vrije Universiteit Amsterdam , specializing in Computer Science . His work bridges theoretical and applied machine learning research. Research Interests : Transfer learning for cross-dataset adaptation Robustness in entropy-based optimization Imbalanced time-series forecasting Predictive maintenance systems Computational modeling for real-world problems Publication Trends : Recent works focus on transfer learning methodologies (41% overlap), deep learning architectures (100% coverage), and applied machine learning for industrial and temporal data challenges. All four publications (2019–2023) emphasize algorithmic innovation for practical datasets, including dimensionality adjustments and entropy-based robustness analysis.

Ye LuView profile

Associate Professor

• Structural Health Monitoring
• Smart Materials and Structures
• Advanced Signal Processing
• +5 more

Ye Lu is an Associate Professor in Structural Engineering at Monash University's Department of Civil and Environmental Engineering, leading the Civil Master Program. He holds a PhD in Mechanical Engineering from the University of Sydney (2007) and has been awarded an Australian Research Council (ARC) Discovery Project and ARC Australian Postdoctoral Fellowship (2008). His research focuses on structural health monitoring, smart materials, and artificial intelligence applications in civil engineering infrastructure. Dr. Lu leads projects such as the ARC Research Hub for Smart Next Generation Transport Pavements and the Non-contact Integrity Assessment of High-rise Building Facades. Education: PhD in Mechanical Engineering, University of Sydney, Australia (2007) Research Interests: Structural health monitoring Smart materials and structures Advanced signal processing and AI Infrastructure durability Projects: SPARC: ARC Research Hub for Smart Transport Pavements (2019–2025) Non-contact Façade Panel Integrity Assessment (2020–2025) Smart Sensing for Construction Monitoring (2019–2024) Awards: ARC Australian Postdoctoral Fellowship (2008) ARC Discovery Project Grant (2008) Teaching & Advising: PhD supervision in structural engineering and smart materials Courses: CIV3294, CIV5135 (Advanced Structural Design), CIV5887 (Infrastructure Monitoring) Labs/Teams: Leader of the Civil Master Program, collaborator in interdisciplinary projects across Monash and industry partners like Transurban and EON Reality.

• Statistics
• Applied Mathematics
• Data Science
• +5 more

Rohan Dalpatadu is a Professor in the Department of Mathematical Sciences at the University of Nevada, Las Vegas (UNLV). His research expertise spans statistical methodology, applied mathematics, and interdisciplinary applications in healthcare, environmental science, and gaming. He holds a terminal academic rank and has contributed to both theoretical and applied research areas. Research Interests include: Statistical modeling and machine learning applications in biomedicine Numerical methods for optimal control and environmental systems Probability distributions and their applications in gambling and actuarial science Data analysis techniques for imbalanced datasets and customer satisfaction studies Recent work focuses on cancer classification using gene expression data, contaminant transport modeling, and innovative statistical approaches for medical diagnostics. His publications demonstrate a strong interdisciplinary orientation across mathematics, engineering, and health sciences. Advising and Grants: No specific advising or grant information is explicitly listed in the provided text. His research has been applied to diverse sectors including airport operations, agricultural economics, and ergonomic design. Labs/Teams: No dedicated lab or collaborative team affiliations are mentioned in the current data.

Pietro LesciView profile

Researcher

• Machine Learning
• Natural Language Processing
• Active Learning
• +1 more

Pietro Lesci is a PhD student in the Department of Computer Science and Technology at the University of Cambridge . His research focuses on Machine Learning , Natural Language Processing (NLP) , and Active Learning . Pietro's work addresses challenges such as tokenization bias, model stability, and efficient active learning for large datasets. He leads projects like ByteSpan (subword tokenization) and AnchorAL (active learning in imbalanced data). Key research areas include causal estimation of memorization profiles, dialogue state tracking via Diable, and small language model convergence. His contributions span theoretical frameworks and applied techniques for improving NLP and machine learning systems. Outside research, Pietro collaborates with the Cambridge Computational Linguistics group and contributes to open-source tools for language model analysis.

Jiangpeng HeView profile

Research Fellow

• Continual Learning
• Long-Tail Learning
• AI for Healthcare
• +2 more

Jiangpeng He is a Research Fellow in Mechanical Engineering at MIT, working on rehabilitation robotics with Dr. Hermano Igo Krebs. Previously an Adjunct Professor at Purdue University, he holds a PhD in Electrical and Computer Engineering from Purdue and a bachelor's from the University of Electronic Science and Technology of China. He will join Indiana University Bloomington as Assistant Professor in Fall 2025. His research focuses on developing AI systems for healthcare applications, particularly continual learning approaches for visual recognition tasks. Current projects include food recognition for dietary monitoring and rehabilitation technology development. His publications demonstrate consistent focus on machine learning applications in healthcare, with recent advances in continual learning architectures and their implementation in medical domains. Work spans theoretical algorithms to clinical applications in motor assessment and rehabilitation. Awards include: Top-5 Postdoctoral Mentoring Award (Purdue University) Outstanding Reviewer for CVPR 2024

Xin TongView profile

Associate Professor

• Asymmetric Statistical Learning
• Neyman-Pearson Classification
• High-dimensional Statistics
• +5 more

Xin Tong is an Associate Professor in the Department of Data Sciences and Operations at the Marshall School of Business, University of Southern California. His research focuses on asymmetric statistical learning, Neyman-Pearson classification, high-dimensional statistics, and network analysis. He holds a PhD in Operations Research from Princeton University (2012) and a B.S. in Mathematics from the University of Toronto (2007). Key contributions include developing Neyman-Pearson classification frameworks for imbalanced datasets and advancing spectral clustering techniques. His work has been supported by NSF grants and NIH funding. Tong serves as an Associate Editor for the Journal of the American Statistical Association and Journal of Business and Economic Statistics. Recent research highlights include studies on label-noise-adjusted classification algorithms, citation network analysis, and fairness-adjusted machine learning. He teaches courses on modern statistical learning and GenAI/ML strategies for business transformation. Notable awards include the Zellner Thesis Award (2013) and multiple NSF grants. His lab's work bridges statistical theory with applications in genomics, social networks, and healthcare risk assessment.

Yiqiao ZhongView profile

Assistant Professor

• Generative AI
• Large Language Models
• Deep Learning Theory
• +5 more

Yiqiao Zhong is an Assistant Professor of Statistics at the University of Wisconsin–Madison, affiliated with the School of Computer, Data & Information Sciences. He holds a PhD from Princeton University (2019) and a B.S. in Mathematics from Peking University (2014). His research focuses on the theoretical foundations of generative AI, Large Language Models (LLMs), and deep learning, with emphasis on model generalization, interpretability, and safety. Recent work includes analyzing OOD generalization in LLMs through compositional latent structures and revealing geometric properties of Transformer models. Key research contributions include studies on out-of-distribution generalization, hidden neural network geometry, and reliability of visualization techniques like t-SNE/UMAP. He has received recognition for his work on phase synchronization, including the SIAM best student paper prize (2018). Zhong teaches advanced courses such as STAT 709 (Mathematical Statistics) and STAT 453 (Introduction to Deep Learning), and actively collaborates on projects related to statistical theory and AI ethics. His lab explores intersections between theory and practice in generative models, with ongoing work on model editing, toxicity reduction in LLMs, and robust statistical methods for high-dimensional data. Current efforts aim to bridge gaps between empirical AI advancements and rigorous scientific understanding through interdisciplinary approaches.

Lijing LinView profile

Lecturer

• Numerical Analysis
• Scientific Computing
• Computational Statistics
• +4 more

Lijing Lin is a Lecturer in Data Science Technology at the Division of Informatics, Imaging & Data Sciences (L5). She holds a Doctor of Philosophy in Numerical Analysis from The University of Manchester (2011). Her research expertise spans Numerical Analysis, Scientific Computing, Machine Learning, and Clinical Prediction Research, with affiliations to the Institute for Data Science and AI and the Christabel Pankhurst Institute Biotechnology. Her research focuses on computational methods for integrative analysis of multiomics data, causal inference in healthcare, and immune response modeling. Notable contributions include work on matrix factorization techniques (e.g., TriTan method), CFTR modulator therapy effects, and asthma pathogenesis via rhinovirus infection studies. Recent publications highlight advancements in data integration for single-cell multiomics, intervention-based predictive models using the PREDICT-CVD cohort, and proteomic analyses in cystic fibrosis. Her work contributes to UN Sustainable Development Goals related to health and innovation. Lin collaborates globally on topics like viral immunity and clinical decision-making frameworks. She has supervised research on matrix roots and fractional powers, and her methods are applied in epidemiology, oncology, and systems immunology.

• Bankruptcy Prediction
• Machine Learning
• Data Mining
• +3 more

Peter Gnip is an Assistant Professor at the Technical University of Košice. He teaches courses such as Formal Languages (FJ) and conducts laboratory exercises in computer science-related subjects. His research focuses on bankruptcy prediction and imbalanced data classification, with a strong emphasis on machine learning methodologies. He has published extensively on ensemble learning, imbalanced datasets, and financial risk analysis. Notable projects include studies on TabNet vs. XGBoost for imbalanced data and genetic algorithm-optimized autoencoders for bankruptcy prediction. No scientific awards are explicitly listed, but his work contributes significantly to financial and computational research fields. He advises no listed students and has undefined grant activities. His teaching and research are centered at TUKE, with office PK6 Room 011-1.

• Handwriting Analysis
• Speech Processing
• Bankruptcy Prediction
• +2 more

Peter Drotár is an Associate Professor at the Technical University of Kosice's Department of Computers and Informatics. His research intersects AI, healthcare, and financial analytics. Core research areas include: 1) Medical AI for Parkinson's detection using voice/handwriting analysis (38% of publications); 2) Financial prediction models for bankruptcy risk (22%); and 3) Medical image synthesis and segmentation (28%). Significant contributions address data imbalance challenges in financial datasets. Recent works showcase innovations in generative models for medical imaging and domain-robust abdominal CT segmentation. Collaborative projects include diabetic foot ulcer image generation and Parkinson's detection across diverse datasets.

• Mobile Application Development
• Business Informatics
• Data Mining
• +6 more

Prof. Mathias Walther is a Professor of Business Informatics at Wildau University of Applied Sciences, specializing in mobile application development for eBusiness. He holds a doctorate from Martin Luther University Halle-Wittenberg, focusing on paralinguistic features in call center analysis. Previously, he worked as a research assistant, IT analyst, data scientist, and founder of an online travel portal. He lectures at two universities and is actively involved in research on mobile applications, speech therapy technology, and big data. Education: Studies in Business Informatics at Martin Luther University Halle-Wittenberg and University of Wollongong, UAE. Doctorate from Martin Luther University with a thesis on 'Automatic recognition of paralinguistic features for assessing call quality in call centers.' Research Interests: Cross-platform mobile technologies, decision support systems, text mining, speech data pattern recognition, simulation, and big data. Current projects include AphaDigital, a digital speech therapy solution for aphasia patients. Teaching: Courses include Mobile Applications, Business Intelligence, Data Mining, IT Strategic Management, and Database Systems across undergraduate and graduate levels. Awards: No scientific awards explicitly mentioned. Advising & Grants: Advises students on thesis topics like AI in manufacturing, digital therapeutics, and business analytics. Active in developing digital solutions for healthcare and enterprise processes. Labs/Teams: Leads the AphaDigital project, focusing on speech therapy through avatar-based digital platforms.

Mihail BogojeskiView profile

Researcher

• Geometric Deep Learning
• Quantum Chemistry
• Computational Biology
• +3 more

Dr. Mihail Bogojeski is a postdoctoral researcher at the Technical University of Berlin , affiliated with the Machine Learning Group and the Berlin Institute for the Foundations of Learning and Data (BIFOLD) . His research focuses on advancing machine learning techniques for applications in the physical sciences and medical sciences , particularly in quantum chemistry , computational biology , brain-computer interfaces , and sequential data analysis . B.Sc. in Software and Information Engineering (2014), Vienna University of Technology M.Sc. in Computer Science (2017), TU Berlin Ph.D. in Computer Science (2023), TU Berlin His research integrates geometric deep learning with quantum chemistry to develop models for predicting electronic structure of molecular systems and electron densities . Recent work includes equivariant neural networks for molecular wavefunction prediction, generative time-series models for industrial aging processes, and explainable AI frameworks for catalyst discovery. Publications highlight his expertise in machine learning , quantum chemical accuracy , and multimodal data augmentation . His 15 most recent articles focus on machine learning applications in chemistry , neuroscience , and industrial process modeling , with subfields such as generative models , equivariant networks , density functional theory , and neural signal processing .

Wenlu ZhangView profile

Assistant Professor

• Computer Science
• Data Mining
• Machine Learning
• +2 more

Dr. Wenlu Zhang is an Assistant Professor in the Department of Computer Engineering & Computer Science at California State University, Long Beach (CSULB). She earned her Ph.D. in Computer Science from Old Dominion University in 2016 and held a postdoctoral position at Washington State University's School of Electrical Engineering and Computer Science before joining CSULB in Fall 2017. Her academic service includes program committee roles for KDD and CIKM conferences. Her research spans machine learning, data mining, and biomedical informatics applications. Key focus areas include shark behavior classification, healthcare analytics, online review analysis, and deep learning for image segmentation in biomedical and geospatial domains. She has contributed to additive manufacturing quality control and pandemic-era online education studies. Recent work trends reveal expertise in neural networks for ecological monitoring, text analytics for business intelligence, and medical imaging AI. Her publications emphasize practical implementations of deep learning frameworks in diverse fields like marine biology, healthcare, and financial markets. Advising: Associate Advisor for Applied Data Science and Computer Science undergraduates (A-G) Contact: ECS-550, wenlu.zhang@csulb.edu