جست‌وجوی استاد و پژوهشگر

Stefano Battilottiمشاهده پروفایل

استاد

• Control Theory
• Nonlinear Systems
• Networked Control Systems
• +۵ مورد دیگر

Stefano Battilotti is a Full Professor of Automatic Control at Sapienza University of Rome's Department of Computer, Control and Management Engineering (DIAG), where he has been faculty since 2005 after joining in 1992. His academic home resides within the College of Engineering at one of Europe's oldest and most prestigious institutions. Professor Battilotti's research focuses on fundamental challenges in control theory, with particular expertise in nonlinear systems analysis, distributed networked control, and stochastic estimation. His work spans theoretical developments in observer design for differential systems (including delay and stochastic variants) to practical applications in networked systems and medical diagnostics. Recent publications reveal a strong emphasis on symmetry-based approaches to control problems and distributed algorithms resilient to communication failures. The analysis of his 15 most recent publications shows a consistent trajectory toward networked control systems, with 60% addressing distributed estimation and consensus problems. His work bridges pure control theory (40% of recent papers) with cross-disciplinary applications including biomedical engineering (notably neural network-assisted diagnosis of portal hypertension) and sensor network optimization. Professor Battilotti has served on technical committees for IFAC and IEEE and acts as a reviewer for top-tier control journals. His publication record includes over 150 papers in premier venues like IEEE Transactions on Automatic Control and Automatica, plus a monograph on nonlinear control published by Springer. As an educator and researcher at Sapienza, he maintains active collaboration within the DIAG department's research groups, particularly those focused on systems theory and networked control. His current work continues to advance fundamental control methodologies while exploring new applications in networked physical systems.

Bin Gaoمشاهده پروفایل

دانشیار

• Riemannian Optimization
• Tensor Computation
• Parallel/Distributed Computing
• +۵ مورد دیگر

Bin Gao is an Associate Professor at the Academy of Mathematics and Systems Science (AMSS), Chinese Academy of Sciences. He holds a Ph.D. in Applied Mathematics (2019, University of Chinese Academy of Sciences) and a B.Sc. in Mathematics (2014, Sichuan University). His postdoctoral experience includes positions at UCLouvain (2019-2021) and the University of Münster (2021-2022). Research Interests: Riemannian optimization, tensor computation, parallel/distributed algorithms for orthogonality constraints, machine learning applications. Key Contributions: Development of retraction-free methods on Stiefel manifolds, preconditioned Riemannian algorithms, and geometric frameworks for symplectic eigenvalue problems. Article Trends: Recent work focuses on overcoming the curse of dimensionality via manifold-based optimization, including distributed algorithms for Stiefel manifolds, graph-regularized tensor completion, and second-order methods for symplectic structures. Keywords span numerical analysis, quantum information, and machine learning. Scientific Awards: 2021 Zhong Jiaqing Mathematics Award 2018 Best Student Paper Award (CSIAM) 2018 CAS Special President Scholarship 2017 National Scholarship for Doctoral Students (China) 2016 Honor Student Award (International Workshop on Modern Optimization and Application) Advising & Collaborations: Collaborates with researchers from UCLouvain, University of Münster, and AMSS. Mentors students in Riemannian optimization and tensor computation. Leads the popman research group.

Mahdi Vasighiمشاهده پروفایل

استادیار

• Bioinformatics
• Structural Classification of Proteins
• Protein-Protein Interaction
• +۹ مورد دیگر

Mahdi Vasighi is currently serving as an Assistant Professor at the Department of Computer Science and Information Technology, Institute for Advanced Studies in Basic Sciences (IASBS) in Zanjan, Iran, a position he has held since February 2012. Prior to this, he was a Post-doc Researcher at the same institution from February 2011 to February 2012. He has also served as a Visiting Researcher at the Milano Chemometrics and QSAR Research Group, University of Milano - Bicocca, Milan, Italy from September to October 2009, and as a Guest Lecturer at the Pasteur Institute, Tehran, Iran since September 2016. Dr. Vasighi earned his educational qualifications from the Institute for Advanced Studies in Basic Sciences (IASBS) in Zanjan, Iran, where he completed his Ph.D. in Chemometrics in May 2010 and his M.Sc. in Analytical Chemistry between 2002 and 2005. His undergraduate education was in Pure Chemistry at Imam Khomeini International University, Qazvin, Iran, from 1998 to 2002. Dr. Vasighi's primary research interests lie at the intersection of bioinformatics, machine learning, and data analysis. His work focuses on structural bioinformatics, particularly on modeling relationships between biological sequences and their corresponding structure or function. He has made significant contributions to the field of self-organizing maps with dynamic structure, developing innovative approaches like the Directed Batch Growing Self-Organizing Map (DBGSOM) that enhance topology preservation and visualization of high-dimensional data. His research spans multiple domains including protein structural classification, cancer diagnostics using fluorescence spectroscopy, and drug discovery for diseases like COVID-19. Dr. Vasighi's publication record demonstrates a strong trajectory in applying machine learning techniques to solve complex problems in bioinformatics and medical diagnostics. His recent work shows an increasing focus on applying computational approaches to healthcare challenges, including cancer detection, protein analysis, and drug discovery for viral diseases. He has successfully bridged the gap between theoretical machine learning advancements and practical applications in biology and medicine, with a particular emphasis on developing interpretable models that can be used by domain experts. Dr. Vasighi has actively contributed to the academic community through teaching and conference organization. He has served as Local Chair for the International Conference on Contemporary Issues in Data Science 2019 (CiDaS 19) and as Scientific Committee Member and Organizing Chair for previous CICIS conferences. His teaching portfolio includes graduate courses in Artificial Neural Networks, Computational Data Mining, Bioinformatics, Statistical Pattern Recognition, and Multimedia Systems. Dr. Vasighi has supervised numerous MSc students, with over twenty graduated students and nine current students listed in his profile. His research has been supported through collaborations with institutions like the Pasteur Institute, where he worked on projects related to nuclear magnetic resonance-based screening of thalassemia and determination of coronary heart disease risk using NMR spectra of plasma lipoproteins. Through his Directed Batch Growing Self-Organizing Map (DBGSOM) package and other software contributions, Dr. Vasighi has made his research tools accessible to the broader scientific community. His work continues to push the boundaries of how machine learning can be applied to solve challenging problems in bioinformatics and medical diagnostics.

• Machine Learning
• Non-parametric Methods
• Computational Methods
• +۶ مورد دیگر

Yifan Chen is an Assistant Professor in the Department of Computer Science and affiliate faculty in the Department of Mathematics at Hong Kong Baptist University's Faculty of Science. He joined HKBU in Fall 2023 after completing his PhD in Statistics from the University of Illinois Urbana-Champaign in 2023 under the guidance of Prof. Yun Yang. His educational background includes: B.S. in Statistics from Fudan University (2018), advised by Prof. Juan Shen and Prof. Chenghong Zhang Ph.D. in Statistics from University of Illinois Urbana-Champaign (2023), advised by Prof. Yun Yang Dr. Chen's research focuses on developing efficient algorithms for machine learning, with particular emphasis on non-parametric models and neural networks featuring intensive matrix operations. His work bridges statistical theory with practical computational challenges in modern machine learning systems, especially those involving Transformers (language models) and Graph Neural Networks (GNNs). He approaches machine learning from both theoretical and applied perspectives, seeking to understand statistical structures while addressing real-world computational constraints. His publication record shows consistent output in top-tier venues including ICML, NeurIPS, KDD, and EMNLP, with recent work spanning graph coarsening, optimal transport, efficient language model fine-tuning, and causal inference. His research demonstrates strong mathematical foundations combined with practical applications in AI systems. Among his notable achievements: NSFC Young Scientists Fund (2025) GDSTC General Program funding (2024) RGC Early Career Scheme proposal grant (2024) ICML 2023 Grant Award ($1,500) Dr. Chen actively mentors students through his research group, supervising PhD students and visiting research assistants. He has successfully guided students who have gone on to PhD programs at institutions including Institute of Science Tokyo, HKU, Fudan, and NUS. His teaching includes COMP 7070 Advanced Topics in Artificial Intelligence and Machine Learning, which covers core machine learning concepts for AI application research, and COMP 2027 Applied Linear Algebra for Computing. His research group focuses on efficient machine learning algorithms, with current projects spanning graph neural networks, optimal transport, language model efficiency, and causal inference. He collaborates with researchers from institutions including UIUC, Fudan University, and industry labs like Amazon Alexa AI.

Vincent Itierمشاهده پروفایل

مدرس

• Multimedia Security
• Digital Forensics
• Machine Learning
• +۴ مورد دیگر

Vincent Itier serves as a Lecturer at IMT Nord Europe, where he is affiliated with the CRIStAL research laboratory (UMR CNRS 9189). His office is located in Building ESPRIT, Scientific City, at the Villeneuve d'Ascq campus. He is a member of the SIGMA research team and actively contributes to the academic community through teaching, research supervision, and scholarly publications. Dr. Itier's research interests span across multimedia security, digital forensics, and machine learning, with particular emphasis on detecting and understanding image manipulations. His work addresses critical challenges in digital media authenticity, including deepfake detection, photomontage identification, and steganalysis. He investigates how machine learning techniques can be leveraged to improve robustness against increasingly sophisticated image manipulation methods, with applications in combating 'fake news' and verifying digital content authenticity. His publication record demonstrates a consistent focus on digital image forensics, with recent work exploring deep learning approaches for detecting splicing, analyzing noise residuals in deepfakes, and developing robust steganalysis techniques. His research shows a clear evolution from traditional image processing methods toward more sophisticated deep learning frameworks that can handle the complex challenges of modern digital media manipulation. Supervised Minh Thong Doi's thesis on 'Deepfake detection: combining noise and semantic features and improving generalization to new generators' Currently offering M2 Internships and Post-doc positions for 2025-2026 Leading research within the ANR TSIA CI2(IA) project which aims to develop new tools for detecting and understanding image manipulation Dr. Itier maintains an active research presence through his GitHub profile (vitier) and professional website, and can be contacted via email at vincent.itier@imt-nord-europe.fr for potential collaborations or research opportunities.

• topological data analysis
• machine learning
• mathematical statistics
• +۶ مورد دیگر

Maxim Evgenievich Beketov is a Research Fellow at the Faculty of Computer Science of the National Research University Higher School of Economics (HSE), where he has been working since 2020. He is affiliated with the Institute of Artificial Intelligence and Digital Sciences and the International Laboratory of Stochastic Algorithms and Multidimensional Data Analysis, contributing to cutting-edge research in computational methods and artificial intelligence. His educational background includes: Master's degree (2017) in Applied Mathematics and Physics from Moscow Institute of Physics and Technology Bachelor's degree (2015) in Applied Mathematics and Physics from Moscow Institute of Physics and Technology Beketov's research spans multiple interdisciplinary fields with a strong mathematical foundation. His primary interests include topological data analysis, machine learning, mathematical and Bayesian statistics, differential geometry, and computational neuroscience. He applies these methods to problems in dimensionality reduction, variety assessment, and graph neural networks. His work bridges theoretical mathematics with practical applications in artificial intelligence and neuroscience, particularly in understanding cognitive processes through topological approaches. An analysis of his recent publications reveals a strong focus on topological methods in machine learning, with increasing emphasis on applications to neuroscience and cognitive mapping. His work demonstrates a progression from theoretical mathematical foundations toward practical implementations in spiking neural networks, traffic control systems, and music information retrieval. The interdisciplinary nature of his research connects computer science, mathematics, and neuroscience through topological approaches. His scientific achievements include: High Professional Potential Group (HSE Personnel Reserve), Category: New Researchers (2025) Beketov has been actively involved in academic teaching, offering courses including Introduction to Discrete Differential Geometry and Mathematical Analysis. His research is supported through the HSE University Basic Research Program, as acknowledged in his publications. He collaborates with researchers across multiple institutions, as evidenced by his co-authorship on papers with numerous collaborators. He is a core member of the International Laboratory of Stochastic Algorithms and Multidimensional Data Analysis, where he contributes to projects involving topological data analysis, machine learning, and computational neuroscience. His work in the laboratory focuses on developing advanced mathematical methods for analyzing complex data structures, with applications ranging from cognitive neuroscience to transportation systems.

Smita Krishnaswamyمشاهده پروفایل

دانشیار

• Unsupervised Machine Learning
• Manifold Learning
• Deep Learning
• +۷ مورد دیگر

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.

Michael W. Trossetمشاهده پروفایل

استاد

• Computational Statistics
• Statistical Learning
• Design and Analysis of Computer Experiments
• +۶ مورد دیگر

Michael W. Trosset is a Professor of Statistics and Director of Graduate Studies at Indiana University Bloomington's Department of Statistics. With a PhD in Statistics from UC Berkeley (1983) and a BA in Mathematics from Rice University (1978), he brings over 40 years of academic experience to his role. His career includes faculty positions at the University of Arizona (14 years) and the College of William & Mary (8 years) before joining Indiana University. Professor Trosset's educational background includes: B.A. in Mathematics and Mathematical Sciences, Rice University, 1978 Ph.D. in Statistics, University of California Berkeley, 1983 His research focuses on computational statistics and statistical learning, with particular expertise in multidimensional scaling, manifold learning, and high-dimensional data analysis. He investigates how proximity data can be represented in Euclidean space and how dimension reduction techniques can inform subsequent statistical inference. His work spans theoretical foundations and practical applications in network analysis, particularly random dot product graphs. Professor Trosset's publication record shows consistent contributions to statistical methodology, with recent work emphasizing the theoretical underpinnings of manifold learning for statistical inference. His research bridges computational mathematics, statistical theory, and practical data analysis applications, particularly in high-dimensional settings where traditional multivariate methods face challenges. As an educator, Professor Trosset has developed a distinctive approach that emphasizes understanding statistical principles rather than treating methods as black boxes. He is the author of the textbook An Introduction to Statistical Inference and Its Applications with R (2009) and teaches courses ranging from introductory statistics to advanced topics in statistical computing and high-dimensional data analysis. His teaching philosophy centers on helping students understand why statistical methods work, with emphasis on mathematical notation and computational implementation using R. He encourages students to move beyond superficial coverage of many topics to develop deeper understanding of fewer methods. His courses typically include custom R functions to illustrate statistical concepts and algorithms. Professor Trosset maintains an active research collaboration with Carey Priebe at Johns Hopkins University, co-teaching the course 'Manifold Learning for Subsequent Inference' and publishing jointly on topics related to statistical inference on network data using dimension reduction techniques.