جست‌وجوی استاد و پژوهشگر

Collin McMillanمشاهده پروفایل

دانشیار

• Software Engineering
• Natural Language Processing
• Program Comprehension
• +۳ مورد دیگر

Collin McMillan, Ph.D., is an Associate Professor of Computer Science at the University of Notre Dame's College of Engineering. He earned his Ph.D. from William & Mary under Denys Poshyvanyk. His research focuses on software engineering and natural language processing, particularly in automated documentation generation, program comprehension, and human-AI collaboration in code analysis. Education: Ph.D. in Computer Science (William & Mary), advised by Denys Poshyvanyk. His work integrates eye-tracking studies, neural networks, and graph-based models to improve code summarization and developer tooling. Research interests include source code summarization, developer behavior analysis, privacy behavior detection in Android apps, and tools for program comprehension. His lab, the Automatic Program Comprehension Lab (APCL), develops datasets and tools like CodeGRITS, available via Huggingface and GitHub. Publications span 2007–2025, emphasizing developer-centric AI models, code summarization techniques, and empirical studies on programmer attention. Key themes include context-aware models, eye-tracking integration, and LLM applications in software engineering. Awards: ASEE Teaching Award (2020), NSF CAREER Award (2015), Distinguished Paper Award (2021). Grants: NSF funding for AI/SE integration, totaling over $2.5M since 2015. Service: Associate Editor for TSE and EMSE, program committee roles at ICSE, FSE, and ASE. Current students include Maria Dhakal (2023), Robert Wallace (2023), and Chia-Yi Su (2022). Graduates have moved to faculty positions (e.g., Clemson University) and industry roles at Google, Qualtrics, and Ascend Health.

• Biomedical Data Analysis
• Signal Processing
• Machine Learning
• +۸ مورد دیگر

Trygve Christian Eftestøl is a Professor of Information Technology at the Department of Electrical Engineering and Computer Science, University of Stavanger. His academic background includes a PhD in signal processing from NTNU and an M.Sc. in Electrical and Computer Engineering from HiS, Stavanger. He is a senior member of IEEE and serves on the board of the Cognitive Lab at UiS since 2025. Educations: PhD in Signal Processing (NTNU/HiS, 2000) M.Sc. in Electrical and Computer Engineering (HiS, Stavanger) Research Interests: His work focuses on biomedical data analysis, including resuscitation, cardiac science, waveform analysis (ECG, thorax impedance), and MRI for myocardial injury. He is involved in multidisciplinary projects such as digital pathology, newborn resuscitation, sports medicine, neurogenerative diseases, and prostate cancer imaging. He co-founded the Biomedical Data Analysis Laboratory (BMDLab) and serves as its deputy leader since 2020. Articles Trends: Recent publications emphasize machine learning applications in healthcare (e.g., EEG-based neurodegenerative disorder classification, MRI segmentation for myocardial injury), predictive models for cardiac arrest outcomes, and AI-driven solutions in oncology and pathology. His work bridges signal/image processing with clinical needs, addressing challenges in resuscitation, cardiology, and diagnostic accuracy. Awards/Grants: No specific awards listed, but his leadership roles and research contributions highlight sustained academic and clinical impact. Advising & Labs: Supervises/co-supervises PhD projects in areas like human activity recognition and prostate cancer detection. Active in BMDLab, collaborating nationally and internationally on biomedical data analysis.

Peyman Moghadamمشاهده پروفایل

استاد مدعو

• Embodied AI
• Robotics
• Computer Vision
• +۵ مورد دیگر

Peyman Moghadam is a Principal Research Scientist at CSIRO Data61 and an Adjunct Professor at Queensland University of Technology (QUT). He leads the Embodied AI Research Cluster at CSIRO, focusing on robotics and machine learning intersections. His roles include former Group Leader of Robotic Perception and Acting Leader of the Spatiotemporal AI portfolio within CSIRO's MLAI Future Science Platform. Education: PhD in Robotics from Nanyang Technological University (2012). Professional experiences include Visiting Professorships at ETH Zurich (2022) and University of Bonn (2019), alongside leadership in multidisciplinary projects. Research interests span self-supervised learning, embodied AI, 3D perception, and agricultural robotics. Awards include CSIRO's Julius Career Award, Collaboration Medal, and national/state iAwards for innovation in robotics. He has held adjunct roles at QUT and the University of Queensland. Current roles emphasize AI-driven solutions for scientific challenges, such as Great Barrier Reef conservation and autonomous systems in agriculture. Key projects include the DARPA Subterranean Challenge (2nd place), Hovermap LiDAR technology, and collaborations with industry partners like Emesent and Georgia Tech. His work bridges foundational research with real-world applications in mining, agriculture, and environmental monitoring.

Abelardo Carlos Martínez Lorenzoمشاهده پروفایل

پژوهشگر ارشد

• Natural Language Processing
• Cross-lingual Semantics
• Information Extraction
• +۳ مورد دیگر

Abelardo Carlos Martínez Lorenzo is a postdoctoral researcher at Sapienza University of Rome working under Professor Roberto Navigli in the Natural Language Processing group. His research focuses on cross-lingual semantics and information extraction, with specialization in semantic parsing and language models. His educational background includes: Software Engineering degree from Universidad de Málaga, Spain Erasmus Mundus Joint Master Degree in Big Data Management and Analytics from Université Libre de Bruxelles (Belgium), Universitat Politècnica de Catalunya (Spain), and Technische Universität Berlin (Germany) Martínez Lorenzo's research centers on overcoming language barriers through advanced semantic representation frameworks. His work develops efficient multilingual parsing systems that address data scarcity in low-resource languages, leveraging transformer architectures and novel linearization techniques. Key contributions include the BabelNet Meaning Representation (BMR) formalism and optimization methods for Abstract Meaning Representation (AMR) parsing, enabling robust cross-lingual semantic analysis without language-specific constraints. His publication record (2022-2024) reveals a consistent trajectory in enhancing AMR parser efficiency and multilingual capabilities. Notable innovations include CLAP's 80% reduction in computational time, cross-lingual alignment through transformer cross-attention, and ensemble methods that maintain structural integrity while improving SMATCH scores. These works collectively advance accessible, high-performance semantic analysis across linguistic boundaries. His scientific recognition includes: Marie Skłodowska-Curie Fellowship as Early Stage Researcher in the Knograph project (Horizon 2020) Funded through the prestigious Marie Curie grant, Martínez Lorenzo conducts research within the Knograph project framework while mentoring junior researchers in the Sapienza NLP group. His work demonstrates significant grant impact through open-source releases (CLAP, LeakDistill) that democratize access to semantic analysis tools. He actively contributes to the Sapienza Natural Language Processing group led by Roberto Navigli, which specializes in knowledge-based multilingual NLP systems. The group maintains strong collaborations through European research initiatives including Horizon 2020 projects, with emphasis on creating interlingual semantic resources and scalable parsing frameworks.

Monica S. Lamمشاهده پروفایل

استاد

• Virtual Assistants
• Natural Language Processing
• Machine Learning
• +۶ مورد دیگر

Monica S. Lam is the Kleiner Perkins, Mayfield, Sequoia Capital Professor in the Computer Science Department at Stanford University's School of Engineering, where she has been a faculty member since 1988. She serves as Faculty Director of the Open Virtual Assistant Lab (OVAL) and holds a courtesy appointment in the Electrical Engineering Department. Lam received her B.Sc. from the University of British Columbia in 1980 and her Ph.D. in Computer Science from Carnegie Mellon University in 1987. Her research spans multiple domains with a focus on creating trustworthy virtual assistants based on Large Language Models. She is particularly renowned for her work on the Almond virtual assistant, which emphasizes privacy preservation and open-source development. Her research interests include virtual assistants, natural language programming, machine learning, distributed mobile computing, and compiler technologies. Lam's recent publications reveal a strong focus on addressing key challenges in virtual assistant technology, particularly around hallucination reduction in LLMs, multilingual dialogue systems, semantic parsing, and multimodal interactions. Her work consistently emphasizes privacy preservation, open-source development, and user control over data. Member of the National Academy of Engineering ACM Fellow NSF Young Investigator Award (1992) ACM Most Influential Programming Language Design and Implementation Paper Award (2001) Wikimedia Foundation's Research of the Year Award (2024) for eliminating LLM hallucinations Popular Science's Best of What's New Award in Security (2019) for Almond virtual assistant Lam has advised numerous PhD students who have gone on to prominent positions at institutions like MIT, Harvard, Google, and Amazon. Her research has been supported by significant funding including NSF grants and industry partnerships. She leads the Open Virtual Assistant Lab (OVAL), which focuses on developing open, privacy-preserving virtual assistant technologies that give users control over their data while maintaining functionality.

• Veterinary Medicine
• Toxicology
• Bioinformatics
• +۴ مورد دیگر

Jiazhang Cai is a Professor in the Department of Precision One Health at the College of Veterinary Medicine, Ohio University. His research focuses on interdisciplinary areas including veterinary toxicology, bioinformatics, microbiome analysis, and machine learning applications in veterinary medicine. Key research interests include: Canine toxicology and emergency care protocols Biomarker discovery for neurodegenerative and environmental health disorders Development of spatial transcriptomics and ensemble algorithms in genomics Machine learning applications for bioinformatics and animal behavior analysis Recent studies explore topics like hypernatremia management in dogs, neuromuscular blockade effects in veterinary critical care, and multi-center clinical trials. His work bridges computational methods with clinical veterinary practice. Cai's publications span 2023–2025, demonstrating a focus on precision health approaches across animal and environmental systems. His research has implications for advancing diagnostic tools, therapeutic strategies, and understanding host-microbiome interactions.

• Quantum Computing
• Quantum Machine Learning
• Quantum Algorithms
• +۴ مورد دیگر

Koustubh Phalak is a researcher focused on quantum computing and machine learning applications. His work emphasizes optimizing quantum circuits, developing quantum machine learning architectures, and exploring educational frameworks for quantum computing. Collaborating closely with Swaroop Ghosh and other experts, his research spans fields such as quantum error correction, drug molecule prediction using neural networks, and hardware selection for quantum inferencing systems. Key contributions include advancements in parametric quantum circuit optimization and QRAM designs. Despite no formal institutional affiliation listed here, his publications reflect active engagement with academic and industrial challenges in quantum technologies.

Victor Zhongمشاهده پروفایل

استادیار

• Machine Learning
• Natural Language Processing
• Reinforcement Learning
• +۳ مورد دیگر

Victor Zhong is an Assistant Professor in the Department of Computer Science at the University of Waterloo. He holds a PhD from the University of Washington (2023), an MS from Stanford University (2016), and a BASc in Computer Engineering from the University of Toronto (2014). His research focuses on machine learning, natural language processing, reinforcement learning, and artificial intelligence, with particular emphasis on dialogue systems, text-to-SQL translation, and multimodal agents. Zhong’s work addresses challenges in automated workflow generation, reward function design, and policy improvement using language feedback. His research interests include developing robust question answering systems (e.g., RoMQA benchmark), simplifying bioinformatics analysis through conversational interfaces, and evaluating language models in real-world enterprise contexts (e.g., Spider 2.0). He has contributed to foundational NLP tasks like dialogue state tracking, neural network-based query translation, and grounding language in dynamic environments. Recent publications explore open-ended tasks in real computer environments (OSWorld benchmark) and the limitations of parametric/non-parametric memories in language models. Zhong’s work bridges theoretical advancements with practical applications, aiming to automate data science workflows and enhance AI agents’ real-world adaptability. Though no specific grants or awards are listed, his prolific publication record reflects sustained innovation in machine learning and NLP domains.

Matthias Schonlauمشاهده پروفایل

استاد

• Survey Methodology
• Natural Language Processing
• Data Visualization
• +۳ مورد دیگر

Matthias Schonlau is a Professor in the Department of Statistics at the University of Waterloo. He previously worked as a statistician at the RAND Corporation (1999-2011), where he led the RAND Statistical Consulting Service. He holds a PhD from the University of Waterloo (1997) and a Master's from Queen's University (1993). His research focuses on survey methodology, natural language processing for open-ended questions, data visualization, and statistical software development. Key contributions include the Hammock Plot for mixed data visualization and automated classification algorithms for open-ended survey responses. His work spans algorithmic innovation (e.g., occupation coding, multi-label classification) and statistical software tools (e.g., HAMMOCK and RFOREST modules for Stata). Recent projects address semi-automated classification, one-shot learning, and text dataset distillation. He has held sabbaticals at the University of Auckland (2015-2016) and DIW Berlin (2009-2010), collaborating with the Max Planck Institute. Major awards include the Humboldt Research Prize (2022) and ASA Fellowship. His publications emphasize bridging statistical methods with practical applications, including books like Applied Statistical Learning (2023) and peer-reviewed articles in computational statistics and machine learning.

Houari Sahraouiمشاهده پروفایل

استاد

• Software Engineering
• Automated Software Engineering
• Model-Driven Engineering
• +۵ مورد دیگر

Houari Sahraoui is a Full Professor and Vice Dean of Planning and Infrastructure at the Faculty of Arts and Sciences, University of Montreal, where he also previously served as Department Director from 2013 to 2017. He leads the GEODES research group (Groupe de recherche sur les systèmes ouverts et distribués et l'expérimentation dans les logiciels) and has been actively supervising graduate students and conducting research in software engineering since at least 2000. Dr. Sahraoui's research focuses on automated software engineering , with particular emphasis on model transformations learning from examples using evolutionary approaches. His work spans reverse engineering (comprehension) and reengineering (refactoring, migration to component-based software), utilizing static and dynamic analysis techniques. He also investigates visualization of large sets of multidimensional data for software comprehension and maintenance. His research integrates artificial intelligence approaches to enhance various phases of the software development lifecycle. His recent publications demonstrate a strong trend toward integrating large language models and artificial intelligence with traditional software engineering practices. The research spans code review automation, model-driven engineering, microservices identification, and digital twins for applications like vertical farming. Many publications focus on improving automation through techniques like parameter-efficient fine-tuning, knowledge distillation, and social diversity metrics. Dr. Sahraoui was named a Fellow of Automated Software Engineering in 2023, an honor recognizing his significant and sustained contributions to the ASE community, both scientifically and professionally. This prestigious title is awarded by the IEEE/ACM International Conference Steering Committee. Dr. Sahraoui has supervised over 50 Master's and PhD students throughout his career, with recent supervision focusing on AI-assisted software engineering, code review automation, and model-driven approaches. His research has been supported by multiple grants from the Natural Sciences and Engineering Research Council of Canada (NSERC), MITACS, and industry partners, with projects spanning from 2000 to projected completion in 2031. He leads the GEODES research group (Groupe de recherche sur les systèmes ouverts et distribués et l'expérimentation dans les logiciels) which focuses on open and distributed systems and software experimentation. His current projects include 'Improving automation and assistance for software engineering tasks with generative AI' (2025-2031) and work on digital twins for vertical farming.

Dongjin Songمشاهده پروفایل

استادیار

• Continual Learning
• Graph Machine Learning
• Time Series Analysis
• +۲ مورد دیگر

Dr. Dongjin Song is an Assistant Professor in the School of Computing at the University of Connecticut, specializing in continual learning, graph representation, and time series analysis. He holds a PhD from UC San Diego (2016) and previously worked at NEC Labs America. His NSF CAREER award (2024) supports research on evolving graph learning for applications in healthcare, energy, and transportation. Recognized with the Frontiers of Science Award (2024) and UConn AAUP Excellence Award (2025), he develops algorithms addressing catastrophic forgetting and generalization in dynamic systems. Research interests include: Robust representation learning for time-series and graph data Meta-knowledge distillation for heterogeneous systems Privacy-preserving federated learning LLM-enhanced multimodal forecasting He actively contributes as Area Chair at NeurIPS and Associate Editor for Neural Networks . Educational initiatives integrate research into AI/ML courses, and outreach targets K-12 STEM engagement. Current projects explore power outage prediction, clinical time-series analysis, and cross-platform mental health monitoring.

Feng Liuمشاهده پروفایل

استادیار

• Computer Vision
• Machine Learning
• Artificial Intelligence
• +۱۱ مورد دیگر

Feng Liu is an Assistant Professor in the Department of Computer Science at Drexel University's College of Computing & Informatics (CCI). He previously served as a Postdoctoral Researcher at Michigan State University's Department of Computer Science and Engineering. His research spans computer vision, machine learning, and biometric recognition, with a focus on 3D scene understanding, generative AI, and human-AI interaction. Research Interests: 3D Computer Vision: 3D object/scene understanding, 3D generation, VR/AR, 3D vision+language understanding 3D Human Digitization: Modeling, reconstruction, rendering, biomechanics Generative AI: Explainability, generalization, controllability in generative models, DeepFake detection Biometric Recognition: Face and gait recognition, person re-identification AI + X: Applications in Education and Healthcare Publication Trends: Dr. Liu's recent publications demonstrate a strong focus on advancing biometric recognition systems, particularly in open-set scenarios and video-based person re-identification. His work integrates 3D vision, diffusion models, and large-scale benchmarking, with increasing emphasis on real-world challenges such as aerial-ground integration, long-range recognition, and controllable synthetic data generation for training robust models. Scientific Recognition: Area Chair for BMVC 2025, ACM MM 2025, FG 2025, IJCNN 2025 Area Chair for FG 2024 Advising and Engagement: Dr. Liu is actively recruiting PhD students and interns, indicating an expanding research group. He regularly presents at major conferences including CVPR, NeurIPS, and WACV, and participates in workshops such as ELFA, demonstrating active engagement with the research community. His collaborations span multiple institutions, including Michigan State University, Queensland University of Technology, and others. Labs and Teams: While specific lab names are not mentioned in the provided texts, Dr. Liu leads a research group focused on computer vision and AI, with projects involving 3D reconstruction, biometrics, and generative models. His work on AG-VPReID and HAMoBE suggests leadership in developing large-scale benchmarks and hierarchical recognition systems.

• Data Management
• Location-Based Services
• Trajectory Data
• +۷ مورد دیگر

Christian S. Jensen is a Professor at the Department of Computer Science, Aalborg University, affiliated with The Technical Faculty of IT and Design. His primary research focuses on data management, spatiotemporal systems, and AI-driven solutions for mobility and cyber-physical systems. He leads projects like DiCyPS (Data-Intensive Cyber-Physical Systems) and MALOT (Managing Mobility Data Quality for Location of Things). He has published over 700 papers, with recent work emphasizing time series forecasting, trajectory analysis, and edge computing. Notable contributions include frameworks like Memory Guided Transformers and TEAM for traffic prediction. His work has been recognized with awards including the IEEE TCDE Impact Award (2019) and the Order of Dannebrog (2016). Jensen actively collaborates internationally, holding roles in organizations like the Max Planck Institute and the Villum Foundation. His research bridges theory and practice, addressing real-world challenges in smart cities, energy systems, and autonomous driving.

Jacob Lahneمشاهده پروفایل

دانشیار

• Sensory Science
• Fermented Foods
• Data Analysis
• +۳ مورد دیگر

Jacob Lahne is an Associate Professor in the Department of Food Science and Technology at Virginia Tech. His research focuses on sensory evaluation of foods, particularly fermented and distilled beverages like cider, whiskey, and kombucha, with an emphasis on developing novel data analysis methods for multivariate sensory datasets. Ph.D., Animal, Nutrition, and Food Sciences, University of Vermont (2014) M.S., Food Science and Human Nutrition, University of Illinois-Urbana-Champaign (2010) B.A., Mathematics, Oberlin College (2006) Dr. Lahne's work bridges sensory science with computational tools, including R programming and Natural Language Processing . He has led workshops on data visualization with ggplot2 and created open-access educational materials like Teaching Data Analytics for Food and Ag Science (2022) and Text-Mining Workflow (Eurosense 2020). His current projects include analyzing Virginia hard cider sensory profiles and integrating food science with food studies scholarship.

• Virtual Reality
• Augmented Reality
• Computer Vision
• +۶ مورد دیگر

Prof. Dr. Didier Stricker is a distinguished Professor of Computer Science at Rhineland-Palatinate University of Technology Kaiserslautern-Landau (RPTU) and serves as Scientific Director and Head of the Augmented Reality Research Department at the German Research Center for Artificial Intelligence (DFKI) in Kaiserslautern. He leads the Augmented Vision Group, which comprises approximately 30 researchers working across various domains of computer vision and augmented reality. His work bridges academic research with industrial applications through collaborations with major companies including Sony, Google, and John Deere. His educational background includes electrical engineering studies at the Polytechnic Institute of Grenoble and the Technical University of Karlsruhe. He earned his doctorate from the Technical University of Darmstadt in 2002 with a dissertation on "Computer Vision-Based Calibration and Tracking Methods for Augmented Reality Applications." Prof. Stricker's research spans virtual and augmented reality, computer vision, human-computer interaction, cognitive interfaces, and on-body sensor networks. His work focuses on developing practical applications that enhance human capabilities through advanced visual computing technologies. He has pioneered approaches in video and sensor analytics, particularly in creating cognitive interfaces that respond intelligently to user needs and environmental contexts. His recent publications reveal a strong emphasis on 3D scene understanding, real-time processing for augmented reality applications, and the integration of large language models with spatial reasoning capabilities. There's a clear trend toward more sophisticated multimodal approaches that combine vision, language, and spatial understanding to create more natural and intuitive human-computer interactions. Among his notable achievements: Innovation Prize of the German Society of Computer Science (2006) Organized the first IEEE & ACM International Symposium on Mixed and Augmented Reality (ISMAR) in 2002 Member of the ISMAR steering committee from 2000-2007 Multiple best paper and demonstration awards at major conferences Several registered patents in tracking and augmented reality technologies Prof. Stricker has supervised numerous PhD and Master's students through his leadership of the Augmented Vision Group. His research is supported by significant funding from both European and national research organizations, as well as through industrial partnerships. He serves as an expert reviewer for various research funding bodies and contributes to the academic community through editorial roles for journals and conferences in VR/AR and computer vision. The Augmented Vision Group under his direction maintains strong connections with industry partners and participates in numerous collaborative research projects including LUMINOUS, SHARESPACE, I-Nergy, BIONIC, and VIDETE. These projects span applications in language-augmented XR systems, social experiences in hybrid spaces, AI for energy systems, personalized body sensor networks, and 4D scene analysis.