جست‌وجوی استاد و پژوهشگر

Jiangang Maمشاهده پروفایل

مدرس ارشد

• Data Science
• Algorithms Design
• Artificial Intelligence
• +۳ مورد دیگر

Dr. Jiangang (Mike) Ma is a Senior Lecturer in Information Technology at Federation University Australia's Institute of Innovation, Science and Sustainability (IISS). Previously, he held academic positions at James Cook University and Victoria University, focusing on data science and IoT research. He earned his PhD in Computer Science from Victoria University and a Master's in Information Technology from Queensland University of Technology. His research interests include data science, algorithms design, artificial intelligence, image/video processing, IoT, and health informatics. He has published over 40 papers in top-tier conferences/journals such as IEEE ICDM and IEEE ICDE. Current research projects involve anomaly detection in healthcare, real-time IoT activity identification, and big data similarity search. Dr. Ma's work spans cloud service selection frameworks, medical data stream analysis, and deep learning applications. He supervises PhD students and collaborates on grants related to healthcare IoT and data mining. His contributions include frameworks like WS-Finder and Cloud-FuSeR, addressing challenges in service discovery and SLA assurance. Labs/Teams: Active participant in the IISS's interdisciplinary research groups, contributing to projects in health informatics and IoT applications.

Isabel Valeraمشاهده پروفایل

استاد

• Machine Learning
• Fairness in AI
• Causal Inference
• +۷ مورد دیگر

Isabel Valera is a full Professor in the Department of Computer Science at Saarland University, Saarbrücken, Germany. She also holds an adjunct faculty position at the Max Planck Institute for Software Systems (MPI-SWS) and is a fellow of the European Laboratory for Learning and Intelligent Systems (ELLIS), contributing to the Robust Machine Learning Program and the Saarbrücken AI & Machine Learning (Sam) Unit. Research Interests: Her work lies at the intersection of machine learning, fairness, and causality. She focuses on developing methods for algorithmic recourse , fair decision-making , causal modeling , and robust learning . She is particularly interested in designing models that provide actionable explanations and ensure equitable outcomes in AI systems. Her research leverages probabilistic modeling, variational inference, and deep generative architectures. Publication Trends: Her recent publications (2021–2023) show a strong emphasis on algorithmic recourse , fairness under uncertainty , causal representation learning , and multimodal and graph-based generative models . She frequently publishes in top-tier venues such as NeurIPS, ICML, ICLR, AAAI, and FAccT, often in collaboration with leading researchers like Bernhard Schölkopf and Zoubin Ghahramani. Scientific Awards and Fellowships: Humboldt Post-Doctoral Fellowship Minerva Fast Track Fellowship (Max Planck Society) ELLIS Fellow Advising and Grants: She has mentored several early-career researchers who are now active contributors in machine learning, including Adrián Javaloy and Amir-Hossein Karimi. While specific grant details are not listed, her leadership roles and fellowships indicate sustained funding support from major research organizations. She has served as a co-editor for major conferences such as AISTATS and ECML PKDD, demonstrating her active role in the academic community. Labs and Research Groups: She leads or has led research groups at both Saarland University and the Max Planck Institutes (MPI for Intelligent Systems and MPI for Software Systems), focusing on foundational and applied aspects of machine learning with societal impact.

Predrag Radivojacمشاهده پروفایل

استاد

• Computational Biology
• Machine Learning
• Biomedical Informatics
• +۶ مورد دیگر

Predrag Radivojac is a Professor and former Associate Dean of Research at the Khoury College of Computer Sciences, Northeastern University. His academic roles include tenure-track faculty positions and courtesy appointments at the Department of Chemistry and Chemical Biology and the Roux Institute. He leads research in computational biology, machine learning, and biomedical informatics, focusing on protein function prediction, genome interpretation, and semi-supervised learning. Education details are limited in the text, but his career trajectory reflects advanced academic training in computer science and computational biology. His research interests include protein structure/function relationships, machine learning from biased data, and precision medicine applications. He collaborates widely, contributing to consortia like the IGVF and CAGI. Radivojac has authored over 200 publications, with recent focus on variant interpretation (e.g., MutPred2), fairness in ML, and genomic analysis tools. Awards include 2025 ISCB Fellowship and 2007 NSF CAREER Award. He advises numerous PhD students and oversees grants in AI, genomics, and pregnancy outcomes. His lab develops methods for variant prioritization, functional annotation, and proteomics analysis. Key Projects: CAGI challenges, ClinGen collaborations, Hoosier Moms Cohort study Grants: NIH-funded studies on pregnancy genetics, NSF CAREER Award, and institutional research initiatives He serves as ISCB President and editorial board member for Bioinformatics and Human Genetics. His lab is affiliated with Northeastern’s Experiential AI Institute and the Barnett Institute.

• Computer Vision
• Computer Graphics
• Artificial Intelligence
• +۴ مورد دیگر

Alexei Efros is a Professor in the Electrical Engineering and Computer Sciences Department at UC Berkeley's College of Engineering, where he holds the Howard Friesen Professorship. He is a leading researcher in computer vision and computer graphics, with a focus on data-driven techniques and self-supervised learning. His work bridges the intersection of vision and graphics, leveraging large quantities of unlabeled visual data to tackle complex problems. Dr. Efros received his PhD from UC Berkeley in 2003 and joined the Berkeley faculty in 2013 after spending a decade at Carnegie Mellon University. He has also held affiliations with École Normale Supérieure/INRIA and the University of Oxford. His research group is part of the Berkeley Artificial Intelligence Research Lab (BAIR). His research interests span computer vision, computer graphics, artificial intelligence, and machine learning, with particular emphasis on data-driven approaches, self-supervised learning, computational photography, and visual data mining. Efros has pioneered numerous techniques in image synthesis, scene understanding, and visual representation learning that have shaped modern computer vision research. An analysis of his recent publications reveals a strong focus on generative models, particularly diffusion models and their applications; interpretability of vision-language models like CLIP; 3D scene understanding and reconstruction; and the application of self-supervised techniques to video and spatial reasoning problems. His work continues to push boundaries in how machines understand and generate visual content. Among his numerous accolades are the ACM Prize in Computing (2016), multiple Helmholtz Test-of-Time Prizes, the SIGGRAPH Significant New Researcher Award, Sloan and Guggenheim Fellowships, and teaching awards including the Jim and Donna Gray Award for Excellence in Undergraduate Teaching (2023). Professor Efros has mentored an impressive cohort of students who have gone on to prominent positions in academia and industry, including faculty positions at CMU, TTIC, Stanford, and research scientist roles at OpenAI, Google DeepMind, and Anthropic. His teaching includes core computer vision courses at both undergraduate and graduate levels.

Xiaoqian Wangمشاهده پروفایل

استادیار

• Trustworthy Machine Learning
• Explainable AI
• Algorithmic Fairness
• +۳ مورد دیگر

Xiaoqian Wang is an Assistant Professor in the Elmore Family School of Electrical and Computer Engineering at Purdue University, with a courtesy appointment in Biomedical Engineering. She leads research in trustworthy machine learning, focusing on explainability, fairness, robustness, and healthcare applications. Her work has earned her the 2022 NSF CAREER Award and IEEE Senior Membership. Education: PhD in Electrical Engineering from the University of Pittsburgh (2019), advised by Prof. Heng Huang; Bachelor's from Zhejiang University (2013). Research interests include developing interpretable AI systems, mitigating algorithmic bias, and applying machine learning to healthcare challenges such as Alzheimer's disease progression modeling. Recent efforts explore spurious correlation mitigation in multi-task and vision-language models. Awards and recognitions: Recipient of the NSF CAREER Award (2022), IEEE Senior Member status (2023+). Teaching: Courses include ECE 264 (Advanced C Programming), ECE 570 (Artificial Intelligence), and ECE 695 (Machine Learning in Bioinformatics/Healthcare). Lab affiliations: Regenstrief Center for Healthcare Engineering at Purdue University.

Michael Cummingsمشاهده پروفایل

عضو هیئت علمی

• Bioinformatics
• Computational Biology
• Molecular Evolution
• +۴ مورد دیگر

Michael Cummings is an Affiliate Professor of Biology at the University of Maryland, College Park, with an appointment in the University of Maryland Institute for Advanced Computer Studies (UMIACS). He serves as Director of the Center for Bioinformatics and Computational Biology (CBCB) and the Master of Science Program in Bioinformatics and Computational Biology. His research focuses on bioinformatics, computational biology, molecular evolution, and machine learning applied to genetic and health challenges. He holds a Ph.D. in Organismic and Evolutionary Biology from Harvard University (1992). His work includes developing the BEAGLE library for phylogenetic analysis, improving computational efficiency in evolutionary studies. Recent projects involve genetic ancestry inference, Parkinson’s disease diagnostics via wearable sensors, and malaria vaccine antigen prediction. He has received grants from NSF, NVIDIA, and the University of Maryland Grand Challenges Program. Cummings teaches courses like Bioinformatics and Genomics (CBMG688Y) and advises students such as Alexis Boleda in the Biological Sciences Graduate Program. Key achievements include the NVIDIA Global Impact Award (2017) for BEAGLE, MPower Professor recognition (2025), and leadership in interdisciplinary research collaborations with medical and computational institutions.

Hyebin Songمشاهده پروفایل

استادیار

• High-dimensional Statistics
• Statistical Learning
• Statistical Computing
• +۴ مورد دیگر

Hyebin Song is an Assistant Professor of Statistics at the Pennsylvania State University since 2020. She holds a PhD in Statistics from the University of Wisconsin-Madison (2020) and a BA in Applied Statistics from Yonsei University (2012). Previously, she worked as a Statistician at the Bank of Korea. Her research focuses on developing statistical methodologies for high-dimensional and complex datasets, with applications in neuroscience, systems biology, and computational protein modeling. Key areas include semi-parametric inference, statistical learning, and computational biology. She has developed influential software tools like PUlasso for high-dimensional variable selection and contributed to advancements in Markov chain autocovariance estimation. Her work has been recognized with the ASA SLDS Student Paper Competition Award (2018). Education: PhD in Statistics (UW-Madison, 2020), BA in Applied Statistics (Yonsei University, 2012). Current affiliations include the Department of Statistics at Penn State and collaborations with computational biology labs. She actively advises PhD students in statistical methodology and applications. Research interests span high-dimensional statistics, shape-constrained inference, and statistical computing, with applications to protein structure analysis and functional genomics. Notable software includes the 'momentLS' package for Markov chain estimation and 'pudms' for deep mutational scanning analysis.

• Artificial Intelligence
• Machine Learning
• Information Retrieval
• +۳ مورد دیگر

Dr. Sarah Zelikovitz is an Associate Professor in the Department of Computer Science at the College of Staten Island (CUNY) and holds a faculty position at the Graduate Center of CUNY. She joined these institutions in 2002, focusing on research that integrates artificial intelligence, machine learning, and information retrieval to enhance text classification using background knowledge. Her work addresses challenges arising from the exponential growth of digital data. Dr. Zelikovitz earned a BS and MA from Brooklyn College and a PhD in Computer Science from Rutgers University (New Brunswick). Her research explores methodologies for incorporating unlabeled data and background knowledge into learning algorithms, improving document classification tasks such as categorizing technical articles, web pages, and advertisements. She actively involves graduate and undergraduate students in research, co-authoring papers and securing funding for collaborative projects. Dr. Zelikovitz has published extensively in top-tier Artificial Intelligence and Information Retrieval conferences/journals. She serves on program committees for AI conferences and reviews for journals in her field. Office: Building 1N Room 212, Phone: 718.982.2849. Visit her website at http://www.cs.csi.cuny.edu/~zelikovi/publications.htm for publications and advising details.

Animashree Anandkumarمشاهده پروفایل

استاد

• Machine Learning
• Non-Convex Optimization
• High-Dimensional Statistics
• +۶ مورد دیگر

Animashree (Anima) Anandkumar is the Bren Professor of Computing and Mathematical Sciences at the California Institute of Technology (Caltech). She holds a B.Tech from the Indian Institute of Technology Madras (2004) and a Ph.D. from Cornell University (2009). Prior to her current role, she was a Visiting Associate at Caltech (2017) and served as a Senior Director of AI Research at NVIDIA and Principal Scientist at Amazon Web Services. Her research focuses on large-scale machine learning, non-convex optimization, and high-dimensional statistics, with pioneering work on tensor decomposition methods for probabilistic models and neural operators for scientific simulations. Research Interests: Anima's work bridges AI and science, emphasizing scalable and interpretable algorithms. Key areas include quantum optimization, climate modeling via Fourier Neural Operators, medical AI for surgical skill assessment, and physics-informed neural networks for PDEs. Her lab, Anima AI+Science, drives interdisciplinary projects in climate, aerospace, engineering, and medicine. Awards & Recognition: She has received the IEEE Fellowship, Alfred P. Sloan Fellowship, NSF Career Award, and Blavatnik National Awards Finalist distinction. Her contributions to AI+Science include 45000x faster weather forecasting and AI-aided catheter design. Advising & Teams: Advises over 20 Ph.D. students and postdocs, many of whom hold faculty positions or lead roles in industry. Her lab collaborates with institutions globally on projects like the DeepSpeed4Science initiative for large-scale scientific discovery. Labs & Projects: The Anima AI+Science Lab advances AI methods for climate prediction (FourCastNet), aerospace control (FALCON), and molecular design (OrbNet). Ongoing efforts include quantum computing solvers, turbulence-resistant flight systems, and surgical skill analytics.

Steven Abneyمشاهده پروفایل

دانشیار

• Computational Linguistics
• Parsing
• Semi-Supervised Learning
• +۱۰ مورد دیگر

Steven Abney is an Associate Professor of Linguistics at the University of Michigan, affiliated with the Computer Science and Engineering department and the Michigan Institute for Data Science. He holds a Ph.D. from MIT (1987) and has held positions at Bellcore, Tübingen University, and AT&T Labs before joining U-M in 2002. His research focuses on computational linguistics, including language technology, digital documentation of endangered languages (e.g., Ojibwe), semantics, and parsing. He has developed software tools like CLD for language digitization and Selkie for corpus editing. His work bridges linguistics, computer science, and data science, emphasizing computational models for language analysis. Abney teaches courses such as Mathematics of Language, Computational Linguistics, and Machine Learning for NLP. His expertise spans semisupervised learning, information extraction, and bidirectional grammar systems. Current projects include inductive general grammar and musicolinguistics, applying parsing techniques to musical analysis. His affiliations include cross-disciplinary collaborations, and he has contributed to workshops on language documentation and revitalization. While no specific awards are listed, his extensive publications reflect significant contributions to computational linguistics theory and applications.

• Machine Learning
• Geological Data Analytics
• Remote Sensing
• +۴ مورد دیگر

Dr. Ehsan Farahbakhsh is a Postdoctoral Research Fellow at the EarthByte Group within the School of Geosciences, University of Sydney. His work bridges geoscience and advanced data analytics, focusing on machine learning/deep learning applications in mineral exploration and remote sensing data processing. He contributes to sustainable exploration practices and environmental monitoring through multidisciplinary research. Education: PhD in Mining Engineering - Mineral Exploration Research Interests: Machine learning for geological data analysis, development of multidimensional prospectivity models, advanced processing of remote sensing data, and leveraging AI to address challenges in mineral exploration and environmental change monitoring. Labs/Teams: Part of the EarthByte Group, a globally recognized research team specializing in computational geosciences. Grants/Advising: Involved in industry-academia collaborative projects but no explicitly listed grants or advisees.

• Statistical Learning
• Machine Learning
• Image Analysis
• +۴ مورد دیگر

Rui Zhu is a Senior Lecturer in Statistics at Bayes Business School, City, University of London. She holds a PhD in Statistics from University College London (UCL) and has held academic positions including Lecturer roles at City University (2018–present) and the University of Kent (2017–2018), alongside an Honorary Lecturer role at UCL since 2018. Her research focuses on statistical learning and interdisciplinary applications in high-dimensional data analysis, image quality assessment, hyperspectral imaging, and machine learning techniques for fine-grained classification. Education: PhD in Statistics, University College London (UK) MSc in Statistics, University College London (UK) BEng, Xiamen University (China) Her work bridges statistical theory with practical applications, including developing novel algorithms for few-shot learning, metric learning, and mortality forecasting using multi-task neural networks. Recent research also explores wearable technologies like cognition-aware smart eyewear systems for studying human behavior. Rui’s publications span domains like image super-resolution, domain-specific language models, and mortality modeling, reflecting her expertise in statistical methodologies and their real-world implementations. She collaborates across disciplines, evidenced by projects in actuarial science, computer vision, and health analytics.

• Machine Learning
• Computer Vision
• Distance Metric Learning
• +۴ مورد دیگر

Xiaochen Yang is an Assistant Professor in the Department of Artificial Intelligence at the School of Computer Science and Control Engineering, University of Chinese Academy of Sciences. With a PhD from University College London completed in 2020, Dr. Yang has established a prolific research career spanning machine learning, computer vision, and computational mathematics. Their work bridges theoretical foundations with practical applications across multiple domains. Dr. Yang's research interests focus on advancing machine learning methodologies, particularly in distance metric learning, few-shot learning, and multimodal systems. Their work spans both theoretical aspects of numerical methods for stochastic differential equations and practical applications in computer vision, hyperspectral imaging, and security of large language models. This interdisciplinary approach has led to significant contributions in multiple fields. Analysis of Dr. Yang's recent publications reveals a strong trajectory in developing novel machine learning architectures, with particular emphasis on few-shot learning techniques, multimodal fusion approaches, and applications of diffusion models. Their work demonstrates increasing sophistication in handling complex data modalities while maintaining mathematical rigor, particularly evident in their publications from 2023-2025. Dr. Yang has established productive collaborations across institutions, as evidenced by their extensive co-author network spanning Chinese academic institutions and international partners. Their research has been published in top-tier venues including IEEE Transactions, Pattern Recognition, NeurIPS, and CVPR, reflecting the high impact of their contributions to the field.

Li Xiaoliمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Data Mining
• +۷ مورد دیگر

Professor Li Xiaoli is the Head of the Information Systems Technology and Design (ISTD) Pillar at Singapore University of Technology and Design (SUTD), effective 15 August 2025. An internationally recognised AI researcher with over 30 years in academia and industry, he was previously Department Head of Machine Intellection at A*STAR I²R and Technical Director of the S$35.8 million national AIMfg centre. He holds adjunct professorial appointments at NUS and NTU and has advised Singapore government agencies including MTI, MOE, MOH and SNDGO. Education: PhD, Institute of Computing Technology, Chinese Academy of Sciences, 2001 Research Interests: Prof Li’s work straddles machine learning, data mining, graph learning and time-series analytics, with high-impact applications in smart manufacturing , semiconductors and the digital economy . He pioneered sensor feature learning with deep neural networks and co-coined the term positive-unlabelled (PU) learning ; his 2015 IJCAI paper alone exceeds 1,600 citations. Additional contributions span social & biological network mining, NLP & text analytics, and AI sustainability. Recent Publication Landscape (2022-2025): His latest 15 papers reveal a dual focus on time-series intelligence (domain adaptation, self-supervised representation, sensor alignment, remaining useful-life prediction) and efficient AI deployment (green AI, hardware-aware deep learning, game-theoretic NAS). Interdisciplinary threads link biomedical informatics (sleep stage, gene networks) and advanced NLP (preference optimisation, video grounding), underscoring a strategy of algorithmic innovation coupled with real-world validation . Honours & Awards: IEEE Fellow (2024) Fellow, Asia-Pacific Artificial Intelligence Association (2023) Clarivate Highly Cited Researcher Listed among World’s Top 2% Scientists (Stanford University) Three IEEE/Conference Best Paper Awards for sensor-data and network-mining research Grants, Labs & Industry Collaboration: Prof Li has secured and led more than 10 major collaborative grants with industry partners such as DBS, Singtel and KPMG, including the S$35.8 million AIMfg programme. He directs joint research labs, translating AI advances into aerospace, telecom, insurance and aviation solutions. His group maintains cutting-edge GPU clusters and sensor test-beds for manufacturing analytics, and he currently supervises a large multi-disciplinary team of research staff and graduate students at SUTD.

• Formal Methods
• Automata Theory
• Temporal Logic
• +۵ مورد دیگر

Daniel Neider is a Professor at TU Dortmund University, Germany. He holds former affiliations with Carl von Ossietzky University Oldenburg and the Max Planck Institute for Software Systems (Kaiserslautern). His research focuses on formal methods, automata theory, temporal logic, and their applications in verification, AI, and machine learning. Neider earned his PhD in 2014 from RWTH Aachen University and a second PhD in 2022 from Kaiserslautern University. His work bridges theoretical foundations with practical tools, such as developing algorithms for automata learning, robust temporal logics (rLTL), and neuro-symbolic verification frameworks for deep neural networks. Key research areas include: (1) learning-based formal methods for system verification, (2) robust temporal logics for handling noisy environments, (3) reinforcement learning with temporal logic constraints, and (4) automated specification mining from data. He has contributed to tools like libALF (Automata Learning Framework) and Sorcar for property-driven invariant synthesis. His recent work explores: (a) scalable LTL learning from noisy data using MaxSAT, (b) neuro-symbolic verification techniques, and (c) applying large language models to automate reinforcement learning with reward machines. He has published over 135 papers in top venues like AAAI, TACAS, and FMCAD, focusing on formal methods intersections with AI and systems verification. Current projects involve: (i) robustness-aware synthesis for reactive systems, (ii) temporal logic inference from positive examples, and (iii) developing interpretable verification tools for complex operational domains. His lab collaborates on applying formal methods to real-world systems like smart contracts and neural networks.