Emine Ayaz is a Professor at Istanbul Technical University's Department of Electrical Engineering. Her research spans fault detection in electric motors, signal processing, and nuclear power plant monitoring, with recent work integrating deep learning (e.g., dual RNN architectures) and medical applications (e.g., parasitology, plant-based wound healing). Key Collaborations : International partnerships in motor diagnostics and nuclear engineering. Projects : Led grants on high-voltage training and predictive maintenance for TEİAŞ and industrial processes. Research Trends : Recent publications emphasize neural networks for motor fault classification, coherence analysis for insulation diagnostics, and interdisciplinary work in plant biotechnology and parasitology. Labs & Teams : Involved in projects analyzing vibration signals, wavelet transforms, and sensor fusion for industrial and nuclear systems.
Ivon Arroyo is a Professor in the Department of Teacher Education & Curriculum Studies (TECS) at the University of Massachusetts Amherst. Her research focuses on integrating novel technologies into math and computational thinking education, emphasizing affective and metacognitive states. She develops intelligent tutoring systems, such as COVES, which personalize learning in real-time and utilize facial expression recognition to enhance engagement. Her work on WearableLearning explores embodied, physically active multiplayer games for K-12 classrooms, leveraging mobile devices and wearable technologies to create immersive learning experiences. Dr. Arroyo holds an Ed.D. (2003) and M.S. (2000) from UMass Amherst and a B.S. from Universidad Blas Pascal in Argentina (1995). She has been recognized with multiple awards, including Best Paper Awards at the 2009 International Conference on Artificial Intelligence in Education and the 2010 Educational Data Mining Conference, a Fulbright Fellowship (1996), and a 1994 undergraduate prize for computer vision research. Her research interests span interdisciplinary areas such as Learning Sciences , Computer Science , Data Science , and Psychology . She prioritizes culturally responsive pedagogical agents and cross-cultural studies in educational technology, particularly in Argentina, India, and the U.S. Her projects often address challenges in developing countries, including localization of tutoring systems to Spanish. Advising and grants are central to her work, with grants like the NSF CAREER Award (2020) supporting embodied math classrooms. She collaborates on teacher dashboard frameworks and explores ethical AI integration in education. Her labs focus on creating tools that merge computational innovation with theoretical learning science principles, emphasizing real-world applications like the WearableLearning Cloud Platform.
Lei Lei is an Associate Professor at the University of Guelph, specializing in Computer Engineering. Her research focuses on Machine Learning/Deep Reinforcement Learning, Internet of Things (IoT)/Internet of Vehicles (IoV), Mobile Edge Computing, and Smart Grid Optimization. She explores cutting-edge applications in energy-efficient systems, autonomous vehicles, and intelligent transportation networks. Her work integrates advanced AI techniques with real-world challenges in communication and control systems. Key research areas include optimizing electric vehicle charging schedules using hierarchical deep reinforcement learning and enhancing vehicular networks through 6G communication protocols. She has pioneered methods for joint communication-control systems, securing federated learning models, and developing robust resource allocation strategies in IoT and edge computing environments. Lei Lei’s publications emphasize interdisciplinary solutions, bridging computer science, electrical engineering, and transportation systems. Her recent work addresses challenges in smart grid security, multitimescale control systems, and the application of AI tools like ChatGPT in connected vehicles. She is affiliated with the AI Affiliated Faculty at the University of Guelph, reflecting her contributions to artificial intelligence research.
Xiaowen Dong is an Associate Professor in the Department of Engineering Science at the University of Oxford, affiliated with the Machine Learning Research Group and the Oxford-Man Institute. He is also a Tutorial Fellow at Lady Margaret Hall. Prior to Oxford, he was a postdoctoral researcher at MIT Media Lab and earned his PhD from EPFL. His research focuses on signal processing and machine learning for analyzing network data, with applications in social, urban, and financial systems. Education: PhD from École Polytechnique Fédérale de Lausanne (EPFL), Switzerland. Research Interests: Graph signal processing, geometric deep learning, network topology inference, computational social science, and urban computing. He has received awards including the Turing Fellowship and outstanding paper recognitions. His work spans theoretical advancements and practical applications in network analysis, with collaborations extending to institutions like MIT, EPFL, and the Alan Turing Institute. Notable achievements include contributions to understanding urban segregation, pandemic impacts on mobility, and financial network dynamics. He advises multiple doctoral and master's students across disciplines and actively organizes workshops and conferences in graph-based learning and network science.
Gang (Gary) Tan is a Professor at the Pennsylvania State University's College of Engineering, specializing in computer security, formal methods, and programming languages. He co-directs the Institute for Networking and Security Research (INSR) and leads the Security of Software (SOS) Group, focusing on compiler, programming language, and formal method techniques to enhance computer security. Education: B.E. in Computer Science from Tsinghua University Ph.D. in Computer Science from Princeton University His research integrates formal verification with practical security applications, particularly emphasizing: Compiler-based security enforcement Side-channel mitigation in speculative execution Fairness analysis in machine learning systems Formal grammar approaches for software reliability Key article trends show: Security-focused formal methods (15% of publications) ML fairness verification (20% of recent work) Compiler-based security solutions (30% of output) Side-channel defense mechanisms (25% of research) Parser design and formal grammar synthesis (10% of contributions) Scientific achievements include: NSF CAREER Award Google Research Awards (2x) PLDI 2024 Best Paper James F. Will Career Development Professorship Outstanding Research Award at Penn State Ruth and Joel Spira Excellence in Teaching Award Dr. Tan actively contributes to academic communities through: DARPA ISAT study group membership Program committee roles (CGO 2024, ECOOP 2018, etc) Leadership in security research initiatives
Francesca Grisoni serves as an Assistant Professor in the Department of Biomedical Engineering at Eindhoven University of Technology (TU/e), where she currently leads the Molecular Machine Learning team. She additionally holds appointments as an ICMS Core member and Associate Professor at EAISI (Eindhoven Artificial Intelligence Systems Institute), reflecting her cross-disciplinary role at the intersection of computational science and biomedical applications. Academic Background : Grisoni completed her Environmental Sciences degree and earned a Ph.D. in 2016 from the University of Milano-Bicocca, where her dissertation focused on interpretable machine learning for molecular property prediction. During doctoral studies, she conducted research at ETH Zurich's Department of Chemistry and Applied Biosciences and the U.S. EPA's National Center for Computational Toxicology. Ph.D., University of Milano-Bicocca, 2016 (Dissertation: Interpretable machine learning for molecular property prediction) Environmental Sciences, University of Milano-Bicocca Her research integrates artificial intelligence, chemistry, and biology to develop computational methods for drug discovery, emphasizing wet-lab experimental validation alongside algorithmic innovation. Key focus areas include overcoming activity cliffs in molecular machine learning, generative modeling for scaffold hopping, and AI-augmented decision-making in therapeutic development, with the ultimate goal of achieving 'better decisions faster' in drug discovery pipelines. Analysis of her recent 2025 publications reveals a concentrated trend toward chemical language models and generative deep learning frameworks, specifically addressing low-data drug discovery challenges through active learning and neural network architectures. These works bridge computer science with pharmacology, targeting bioactivity prediction, molecular representation, and enzyme design while maintaining strong ties to experimental validation. Scientific Awards : Lush Young Researcher Prize Early Career Award 2022 from the Dutch Royal Netherlands Academy of Arts and Sciences (KNAW) ERC Starting Grant (2022) Grants and Supervision : Dr. Grisoni secured the prestigious ERC Starting Grant in 2022 to advance her molecular machine learning research. Institutional records indicate she has supervised 7 students (as shown in TU/e's 'Supervised Work (7)' repository section), though specific names aren't provided in the source material. Her group maintains active industry collaborations, including past engagement with Bracco Pharmaceuticals. Laboratory and Team : The Molecular Machine Learning team operates under the ICMS and EAISI frameworks, merging computational AI development with experimental wet-lab validation. This collaborative unit focuses on fragment-based molecular design, chirality representation (evidenced by fragSMILES work), and high-throughput nanoparticle identification using machine learning, as highlighted in recent press coverage and datasets.
Xiangyu Zhu is a faculty member at the University of Chinese Academy of Sciences (UCAS), School of Artificial Intelligence, and affiliated with the State Key Laboratory of Multimodal Artificial Intelligence Systems, Chinese Academy of Sciences, Beijing, China. His research focuses on Computer Science , Artificial Intelligence , and 3D Face Reconstruction . His work spans Face Recognition , Image Processing , and Computer Vision , with recent advancements in Masked Face Recognition , 3D Avatar Reconstruction , and Face Anti-Spoofing . He has contributed to Neural Network Architectures for High-Fidelity 3D Face Modeling and Image Fusion . Xiangyu Zhu has co-authored numerous high-impact publications in journals like IEEE Transactions on Image Processing and conferences such as CVPR and ICCV , including recent works on Diffusion Models , Mamba Networks , and Weakly Aligned Feature Fusion . His research emphasizes Deep Learning and Optimization Techniques for Computer Vision applications.
Changjian Li is an Assistant Professor in the School of Informatics at the University of Edinburgh. He leads the GraphViX Group (Graphics, Vision and X) and is a member of the Institute of Perception, Action and Behaviour (IPAB). His research spans computer graphics, computer vision, and human-computer interaction with a focus on 3D generation and analysis. Education: Bachelor's Degree from Shandong University (2014) Ph.D. from the University of Hong Kong (2019) under Prof. Wenping Wang Postdoc at University College London (UCL) with Prof. Niloy Mitra Starting Researcher position at Inria with Dr. Adrien Bousseau Research Interests: Changjian's research focuses on sketch-based 3D modeling, CAD modeling, point cloud processing, and medical imaging applications. He develops systems that bridge intuitive sketching with precise CAD workflows, enhances 3D animation pipelines, and applies neural methods to sparse medical data reconstruction. Scientific Recognition: Best Paper Honorable Mention Award (MICCAI 2021) CADTalk selected as Highlight (CVPR 2024 top 10%) ACM SIGGRAPH Asia 2018 cover image selection ACM SIGGRAPH Asia 2015 technical paper highlight CVPR 2019 poster highlighted in 'Computer Vision News' Advising & Collaborations: He mentors postdocs and PhD students including Duolikun Danier, Haocheng Yuan, Ankan Bhunia, and Lei Zhong. Former advisees include Salvatore Esposito (now at Edinburgh), Guangshun Wei (Shandong University), and Mingjun Yang (University of Melbourne). Collaborates with Oisin Mac Aodha, Hakan Bilen, and Niloy Mitra. Professional Service: Currently serves as Associate Editor for IEEE TVCG and participates in program committees for SIGGRAPH Asia, SIGGRAPH, EuroGraphics, and Geometry Design and Computing (GDC) conferences.
Roummel F. Marcia is a Professor and current Chair of the Department of Applied Mathematics at the University of California, Merced, within the School of Natural Sciences. He received his Ph.D. from UC San Diego under Professor Philip Gill and previously held postdoctoral positions at the San Diego Supercomputer Center and University of Wisconsin-Madison, as well as a research scientist position in electrical engineering at Duke University. His research spans multiple areas in optimization and its applications, with a focus on signal processing, data science, machine learning, linear algebra, and mathematical biology. Dr. Marcia's work has significant interdisciplinary impact, particularly in biomedical imaging, computational biology, and quantum computing applications. His research methodology often combines theoretical optimization approaches with practical applications in data-intensive fields. Dr. Marcia's recent publications demonstrate a strong trend toward integrating optimization theory with deep learning architectures, particularly in applications requiring sparse data handling, biomedical imaging, and quantum computing. His work shows increasing focus on developing novel optimization algorithms specifically designed for machine learning contexts, including quasi-Newton methods adapted for deep learning and specialized techniques for handling non-convex optimization problems. School of Natural Sciences Faculty Award for 'Developing or Improving Academic Programs and Tracks' (2021-22) Leadership roles in SIAM Activity Group on Applied Mathematics Education Recognition as a Math Alliance Mentor for supporting underrepresented students Dr. Marcia has successfully mentored numerous doctoral students to completion, with graduates moving to positions at Meta, Johns Hopkins University Applied Physics Laboratory, Lawrence Livermore National Laboratory, and other prestigious institutions. His research has been consistently funded by major agencies including NSF (with grants IIS 1741490, DMS 1840265, DMS 2229495, CCF 2343610), DARPA, and ARPA-E. As the current graduate chair of the Applied Math Graduate Program, he plays a key role in shaping the next generation of mathematical scientists. His work with the SMaRT (Scientific Mathematics Research and Training) team demonstrates his commitment to collaborative, interdisciplinary research.
Markus Vincze is an Associate Professor at the Institute of Automation and Control Engineering (ACIN) at Vienna University of Technology (TU Wien). He founded the Vision for Robotics (V4R) group in 1996 to advance robotic perception, particularly in real-world environments and homes. His work focuses on cognitive computer vision techniques for robotics. Education: Diplom in Mechanical Engineering (1988) and PhD (1993) from TU Wien; M.Sc. (1990) from Rensselaer Polytechnic Institute. V4R coordinates EU projects like ActIPret, robots@home, HOBBIT, and national initiatives like vision@home. Markus has edited a book on Robust Vision with Gregory Hager and authored 62 peer-reviewed journal articles and over 400 reviewed publications. His recent research explores zero-shot 6D pose estimation, sim-to-real transfer, and transparent object detection. Markus has served as program chair for ICRA 2013 and organized HRI 2017 in Vienna. He has advised numerous students and secured grants from the Austrian Academy of Sciences for work at HelpMate Robotics and Yale's Vision Laboratory. The V4R group leads innovations in robotic vision, including frameworks for synthetic data generation (Unrealgensyn), depth completion (CAGT), and educational robotics applications for sustainability. Their work spans household robotics (RH3), agricultural robotics (EdgeSoil), and human-robot collaboration.
Anqi Liu is an Assistant Professor in the Department of Computer Science at the Whiting School of Engineering, Johns Hopkins University. She maintains significant affiliations with the Johns Hopkins Mathematical Institute for Data Science (MINDS) and the Johns Hopkins Institute for Assured Autonomy (IAA), while also collaborating extensively with the Center for Language and Speech Processing (CLSP) and the Laboratory for Computational Sensing and Robotics (LCSR). Her research focuses on developing principled machine learning algorithms for building reliable, trustworthy, and human-compatible AI systems in real-world applications. Key research areas include: Distributionally robust learning under covariate shift Uncertainty quantification for AI safety and fairness Safe exploration in control systems Fair machine learning under distribution shift Active learning under label shift Dr. Liu's work addresses critical challenges in high-stakes AI applications where reliability, safety, and societal impact are paramount. Her methods ensure AI systems remain robust to changing data environments, provide accurate uncertainty estimates, and incorporate human preferences in interactions. Analysis of her recent publications reveals a strong trajectory in trustworthy AI research with significant contributions to distribution shift handling, uncertainty quantification techniques, and safe decision-making frameworks. Her work bridges theoretical foundations with practical applications across healthcare, robotics, and social media analysis. Amazon Research Award Dr. Liu actively mentors eight PhD students and teaches specialized courses on Machine Learning for Trustworthy AI and standard Machine Learning at Johns Hopkins University, preparing the next generation of researchers to address critical challenges in AI safety and reliability.
Jean Provost is a Full Professor in the Department of Engineering Physics at Polytechnique Montréal , with affiliations to the Montreal Heart Institute , IVADO , and the Institute of Biomedical Engineering . His research focuses on ultrasound imaging , cardiac and cerebral vascular imaging , and superresolution image reconstruction using machine learning and optimization . Based on 96 publications, his work emphasizes ultrasound localization microscopy , neural network applications , and microvascular hemodynamics . Education : Ph.D. (Columbia University), MPhil (Columbia University), M.Sc.A. (École Polytechnique Montréal), Engineering Degree (École Centrale Paris), License (Université Paris XI), B.Eng. (École Polytechnique Montréal) Research trends from 15 recent articles include: 3D and dynamic ultrasound localization microscopy for microvascular mapping Deep learning for image reconstruction and neural network pruning Machine learning-driven aberration correction and superresolution imaging Acoustoelectric and cavitation-based imaging techniques Applications in cardiac diagnostics and dementia detection Supervision includes 2 Ph.D. and 8 Master's theses completed at Polytechnique Montréal (2023), covering topics like optical ultrasound detection , microbubble modulation , and spatiotemporal sampling .
Golnoosh Farnadi is an Associate Professor at the Department of Computer Science and Operational Research at the University of Montreal and an Assistant Professor at the School of Computer Science at McGill University. She holds a Canada-CIFAR Chair in Artificial Intelligence and serves as a Senior Academic Member at Mila - Quebec Institute for Artificial Intelligence. Her interdisciplinary work bridges computer science, operations research, and ethical AI considerations. Her educational background includes a Ph.D. in Computer Science from KU Leuven and Ghent University (2017), followed by postdoctoral positions at the University of Montreal/MILA (2018-2020) and the University of California, Santa Cruz (2017-2018). Her research focuses on algorithmic fairness, responsible AI, deep learning, and probabilistic models, with applications spanning healthcare, recommender systems, and public policy. Farnadi's recent publications demonstrate a strong emphasis on addressing fairness in machine learning systems, with particular attention to cultural diversity in recommender systems, fairness in healthcare optimization (particularly kidney exchange programs), and mitigating hallucinations in large language models. Her work consistently combines theoretical rigor with practical applications, often employing novel mathematical frameworks to tackle complex ethical challenges in AI. Among her notable recognitions are the Google Scholar Award (2021), Facebook Research Award (2021), Google Award for Inclusion Research (2023), and being named one of the 100 Brilliant Women in AI Ethics (2023). She was also recognized as a Rising Star in AI Ethics in 2021. Farnadi supervises numerous graduate students through her EQUAL Lab (EQuity & EQuality Using AI and Learning algorithms), which focuses on developing AI systems that promote fairness and equity. Her teaching includes courses on Responsible AI, Machine Learning, and Trustworthy Machine Learning at both McGill University and HEC Montreal.
AnHai Doan is the Vilas Distinguished Achievement Professor and Gurindar S. Sohi Professor in the Department of Computer Science at the University of Wisconsin-Madison. His research focuses on data integration, entity matching, and data science, with particular emphasis on building end-to-end systems that leverage machine learning, scalable data management, and human-data interaction. He leads the Magellan project, which develops open-source tools for entity matching as part of the Python data ecosystem. Dr. Doan's research interests include: Data cleaning and integration: Building end-to-end data integration systems as parts of the Python ecosystem of open-source data tools Data science: Developing an agenda that integrates research, system building, education, and outreach, with focus on data quality Crowdsourcing: Pioneering work on using crowdsourcing for data management and integration Knowledge bases: Building community-centric knowledge bases His recent work shows a strong trend toward developing practical systems for data integration that combine machine learning with traditional database techniques. The Magellan project represents a comprehensive effort to build an end-to-end entity matching system, with numerous publications spanning entity matching algorithms, debugging tools, and cloud-based matching services. His research increasingly focuses on the intersection of data science and data management, particularly on data quality issues. Selected scientific awards: Gurindar S. Sohi Professorship (2020) Vilas Distinguished Achievement Professorship (2018) SIGMOD Research Highlight Award (2017) Vilas Associate, UW-Madison (2016) Alfred P. Sloan Research Fellowship (2007) NSF CAREER Award (2004) ACM Doctoral Dissertation Award (2003) Dr. Doan has been actively involved in service to the data management community, including serving on the SIGMOD Advisory Board, as associate editor for VLDB, and co-chairing the industrial program for VLDB. He has also played a key role in strategic initiatives at UW-Madison, including helping to establish the School of Computer, Data, and Information Sciences. He has mentored numerous students and researchers through his work on the Magellan project and related research efforts. Additionally, he co-founded GreenBay Technologies to commercialize Magellan, which was later acquired by Informatica. He leads the Database Group at UW-Madison and has been instrumental in developing data science educational programs at both undergraduate and graduate levels. His work bridges research, education, and practical applications in the rapidly evolving field of data management and data science.
Cecilia R. Aragon is a Professor in the Department of Human Centered Design & Engineering at the University of Washington, where she also serves as an Adjunct Professor in Computer Science & Engineering, Electrical and Computer Engineering, and the Information School. She is additionally a Senior Data Science Fellow at the eScience Institute. Aragon directs the Human-Centered Data Science Lab and has made significant contributions at the intersection of human-computer interaction and data science. Her research interests focus on human-centered data science, human-centered artificial intelligence, human-centered machine learning, human-computer interaction (HCI), computer-supported cooperative work (CSCW), visual analytics, aviation and astronautics sociotechnical systems, and emotion in informal text communication. Aragon's work bridges technical and social aspects of data science, particularly examining how humans interact with and gain insight from large datasets through both quantitative and qualitative methods. Aragon's recent publications demonstrate a strong focus on understanding online communities, sentiment analysis, distributed mentoring systems, and the ethical implications of AI. Her work spans multiple disciplines including social computing, data visualization, and astrophysics data analysis, showing her interdisciplinary approach to human-centered data science. Presidential Early Career Award for Scientists and Engineers (PECASE) 2008 Fulbright Fellowship 2017-18 HCDE Faculty Innovator in Research Award, University of Washington, 2015 Distinguished Alumni Award, Computer Science, University of California, Berkeley, 2013 Top 25 Women of the Year, Hispanic Business Magazine, 2009 Aragon has secured over $28 million in research funding from organizations including the National Science Foundation, National Institute of Standards and Technology, Department of Energy, Gordon and Betty Moore Foundation, Alfred P. Sloan Foundation, Washington Research Foundation, and industry partners like Microsoft and Intel. Her educational background includes a Ph.D. in Computer Science from UC Berkeley (2004), an M.S. in Computer Science from UC Berkeley, and a B.S. with Honors in Mathematics from Caltech. She leads the Human-Centered Data Science Lab and is affiliated with the eScience Institute, the Nearby Supernova Factory, and various research groups focused on data-intensive scientific collaborations. Her work on collaborative visual analytics systems like Sunfall has had significant impact in both academic and applied settings.
