جستجوی استاد راهنمای دانشگاهی

Sainyam Galhotraمشاهده پروفایل

استادیار

• Artificial Intelligence
• Causal Inference
• Database Systems
• +۴ مورد دیگر

Sainyam Galhotra is an Assistant Professor in the Department of Computer Science at Cornell University. His research focuses on developing data science tools for trustworthy analytics, integrating causal inference, data management, and machine learning to enhance robustness, explainability, and fairness in algorithmic systems. PhD: University of Massachusetts Amherst (2020), advised by Barna Saha B.Tech: Indian Institute of Technology Delhi (2014), advised by Amitabha Bagchi Postdoctoral Research: University of Chicago (Computing Innovation Fellow) His work spans artificial intelligence, causal inference, and responsible data science, emphasizing ethical algorithm design and reliable data integration. Recent publications highlight advancements in fair clustering, causal feature selection, and entity resolution frameworks. His research trends from 2023–2024 include contributions to spatio-temporal data correlation, community detection in geometric graphs, and distribution-aware dataset search. Key themes are fairness in machine learning, causal modeling, and scalable data management solutions. Computing Innovation Fellowship (2021) DAAD AInet Fellow (2021) ACM SIGMOD Entity Resolution Programming Contest Finalist (2021) Krithi Ramamritham Computer Science Scholarship (2019) BEST Paper Award in SIGSOFT FSE 2017 He actively seeks PhD or Master’s students interested in data science and trustworthy AI. Contact via email: sg@cs.cornell.edu .

• Process Mining
• Healthcare Informatics
• Data Security
• +۳ مورد دیگر

Dr. Jeewanie Jayasinghe Arachchige is a Lecturer in the Department of Computer Science at Vrije Universiteit Amsterdam, Faculty of Science. She teaches undergraduate courses including Bachelor Project Computer Science, Professional Development, and Software Engineering Processes for the academic year 2024–2025. Her research focuses on process mining , healthcare informatics , and data security . She applies process mining to analyze healthcare pathways and subpopulation treatment variations, develops explainable AI frameworks for predictive analytics, and examines data governance in emerging architectures like Data Lakehouses. Her work intersects legal informatics, particularly formalizing Sri Lankan civil court processes using ontology engineering. Recent publications highlight trends in balancing simplicity and complexity in process modeling, Industry 4.0 healthcare applications, and cybersecurity in model-driven web development. She has contributed to over 20 peer-reviewed articles since 2006, spanning topics from service-oriented architectures to value network analysis. Her teaching and research emphasize practical applications of IT in healthcare, legal systems, and enterprise environments. No ancillary activities are currently recorded.

Richard E. Turnerمشاهده پروفایل

استاد

• Probabilistic Machine Learning
• Environmental Prediction
• Spatio-temporal Modelling
• +۴ مورد دیگر

Richard E. Turner is a Professor of Machine Learning at the University of Cambridge's Department of Engineering and Research Lead for AI for Weather Prediction at the Alan Turing Institute. He serves as Cambridge Lead for the EPSRC Probabilistic AI Hub and previously held roles including Visiting Researcher at Microsoft Research, Co-Director of the AI4ER CDT, and Course Director for the Machine Learning and Machine Intelligence MPhil program. Current research focuses on probabilistic machine learning fundamentals, environmental prediction (weather/climate), and spatio-temporal modeling combining deep learning with Bayesian methods Supervised 26 PhD students (13 graduated) and 7 research assistants/associates Secured over £30M in research funding from EPSRC, Microsoft, Toyota, Google, DeepMind, Amazon, and Improbable Featured in BBC Radio 5 Live's The Naked Scientist, BBC World Service's Click, and Wired Magazine His recent publications demonstrate expertise in diffusion models for PDE simulations, Gaussian Processes for environmental applications, and Bayesian methods for spatio-temporal forecasting. Key trends include climate modeling using ML, neural PDE solvers, and scalable probabilistic inference. Awards : Cambridge Students' Union Teaching Award for Lecturing; supervised Qualcomm Innovation Fellowship winner Collaborations : Microsoft Research (AI4Science), Alan Turing Institute, EPSRC Probabilistic AI Hub Turner leads the Turner Group within Cambridge's Machine Learning Group, focusing on uncertainty-aware ML for scientific applications. Current research assistants work on topics like meta-learning, Bayesian inference, and climate science applications.

Mohamed Noureldinمشاهده پروفایل

استادیار

• Structural Engineering
• Artificial Intelligence in Civil Engineering
• Structural Health Monitoring
• +۵ مورد دیگر

Mohamed Noureldin is an Assistant Professor at the Department of Civil Engineering, Aalto University , Finland, with prior academic roles at Sungkyunkwan University, South Korea (2015–2022). His expertise lies in integrating Artificial Intelligence (AI) with Structural Health Monitoring (SHM) , Structural Digital Twin , Predictive Maintenance , and Seismic Retrofitting . Research Focus : AI-powered sustainable structural design, smart retrofitting, predictive maintenance, structural material innovation, and next-generation performance-based seismic/wind design. Industrial Experience : 20+ years in offshore/onshore structural engineering (Hyundai Heavy Industries, Samsung Engineering, Arab-Swiss Engineering Company, Zuhair Fayez Partnership). Teaching : Courses in structural analysis, seismic design, dynamics, and reinforced concrete at Aalto and Sungkyunkwan Universities. Laboratory : Leads the Structural Design AI Lab (SDAI), focusing on AI-driven resilient infrastructure. Contact : mohamed.noureldin@aalto.fi , +358504544861. His publications explore cutting-edge applications of AI, ML, and DL in seismic retrofitting, structural durability, soil stabilization, and hybrid damping systems. Collaborative work emphasizes life-cycle cost assessment and augmented reality for predictive maintenance.

Suchi Sariaمشاهده پروفایل

دانشیار

• Machine Learning
• Computational Health Informatics
• Probabilistic Methods
• +۶ مورد دیگر

Suchi Saria is the John C. Malone Associate Professor at Johns Hopkins University , with appointments in the Whiting School of Engineering (Computer Science), the Bloomberg School of Public Health (Health Policy & Management), and the Whiting School (Applied Math & Statistics). She directs the Machine Learning and Healthcare Lab and co-founded the Bayesian Health startup. Education: PhD in Computer Science from Stanford University (advisor: Daphne Koller), NSF Computing Innovation Fellowship at Harvard (2011), prior research at UMass (Barto, Madhavan), and industry experience at Aster Data Systems (acquired by Teradata). Research Focus: Saria develops statistical machine learning tools to extract insights from heterogeneous clinical data (structured/unstructured EHRs, sensor streams). Her work enables counterfactual reasoning for personalized treatment plans, dataset shift mitigation in healthcare AI, and weak supervision frameworks for mobile health apps. Key applications include sepsis prediction , Parkinson’s symptom tracking , and critical care optimization . Article Trends: Recent publications emphasize AI safety (2024-2025), addressing racial bias , transparency frameworks , and dynamic monitoring for clinical deployments. Her work spans conformal prediction , causal modeling , and policy guidelines for health AI. Scientific Awards: Sloan Research Fellowship (2018) DARPA Young Faculty Award (2016) MIT Technology Review TR35 Innovator (2017) Popular Science Brilliant 10 (2016) IEEE Intelligent Systems AI’s 10 to Watch (2015) NSF Computing Innovation Fellowship (2011) Rambus Fellowship (2004-2010) Best Paper Awards in ML, Informatics, and Medicine venues Advising & Grants: Saria mentors PhD students/postdocs in machine learning and health informatics , including funded projects like the NSF Smart and Connected Health Grant (2014) and Google Research Award (2014). Her lab’s TREWScore system (Science Translational Medicine 2015) is deployed in hospitals, while her Bayesian Health startup commercializes AI solutions for provider experience.

Hamed Zamaniمشاهده پروفایل

دانشیار

• Information Retrieval
• Neural Models
• Conversational Search
• +۳ مورد دیگر

Hamed Zamani is an Associate Professor at the Manning College of Information and Computer Sciences (CICS) at the University of Massachusetts Amherst, where he also serves as Associate Director of the Center for Intelligent Information Retrieval (CIIR). He joined UMass Amherst in 2020 after working as a researcher at Microsoft. His research focuses on designing and evaluating statistical and machine learning models for information access systems, including search engines, recommender systems, and question answering. Education: PhD in Computer Science, University of Massachusetts Amherst MS in Computer Engineering, University of Tehran BS in Computer Engineering, University of Tehran Zamani's current research explores neural information retrieval, conversational search, and retrieval-enhanced machine learning. He develops efficient neural models for core IR tasks and emerging areas like conversational information seeking. His work bridges information retrieval with large language models to enhance capabilities in understanding complex queries and generating relevant responses. His recent publications demonstrate a strong focus on retrieval-augmented generation, personalized information access, and efficient neural ranking models. There's a clear trend toward integrating large language models with information retrieval systems, optimizing multi-agent frameworks, and developing evaluation metrics for generative AI applications in search contexts. Scientific Awards: NSF CAREER Award ACM SIGIR Early Career Excellence in Research & Community Engagement Awards (2023) UMass CICS Outstanding Dissertation Award Paper awards at SIGIR (2022, 2023, 2024), CIKM (2020), ICTIR (2019) Microsoft Research Award (AI and New Future of Work program) Amazon Research Award (Optimization of Retrieval-Enhanced ML Models) Zamani actively advises PhD students and postdoctoral researchers, with his students receiving prestigious awards including NSF Graduate Research Fellowships and SIGIR Best Paper awards. He leads the CIIR Talk Series, hosting IR researchers to share recent findings. His Alexa Prize TaskBot Challenge team was selected for two consecutive years, advancing task-oriented dialogue systems. He directs research at the Center for Intelligent Information Retrieval (CIIR), where he oversees projects in neural retrieval models, conversational AI, and retrieval-augmented generation. The center serves as a hub for developing next-generation information access systems with industry and academic collaborators.

• Artificial Intelligence
• Machine Learning
• Knowledge Representation
• +۷ مورد دیگر

Dr. George Stamou is a Professor at the School of Electrical and Computer Engineering of the National Technical University of Athens (NTUA), serving as Director of the Artificial Intelligence and Learning Systems Laboratory (AILS). His expertise spans knowledge representation, machine learning, neural networks, and semantic technologies. He leads interdisciplinary initiatives such as the postgraduate program 'Data Science and Machine Learning' (2018–2022). Research Interests: Focuses on knowledge graphs, interpretable AI, semantic web applications, and multimodal learning. His work integrates formal logic systems (e.g., description logics) with modern deep learning techniques, addressing challenges in explainability, bias detection, and ethical AI applications. Publications: Over 150 articles in AI journals/conferences with an h-index of 34 (Google Scholar). Notable contributions include datasets like CHORDONOMICON (music analysis), GOSt-MT (gender bias in MT), and methodologies for counterfactual explanations in machine learning. Awards & Committees: Active in W3C and RuleML standardization bodies. Co-organized major AI conferences. Recognized for contributions to semantic interoperability and knowledge-based systems. Labs & Teams: Directs AILS-NTUA lab and collaborates with CISRI (Computer & Information Systems Research Institute). Engages in EU projects like CultureLabs (cultural heritage digitalization) andsmarty4covid (health data analysis).

Shai Ben-Davidمشاهده پروفایل

استاد

• Machine Learning Theory
• Unsupervised Learning and Clustering
• Domain Adaptation and Transfer Learning
• +۳ مورد دیگر

Shai Ben-David is a Professor and University Research Chair at the Department of Computer Science, University of Waterloo. He is affiliated with the Cheriton School of Computer Science and can be reached at shai@uwaterloo.ca . His office is located in DC 2643. Education: Ph.D., Hebrew University, Jerusalem, Israel (1987) M.Sc., Hebrew University Jerusalem, Israel (1979) B.Sc., Hebrew University Jerusalem, Israel (1978) Research interests focus on foundational aspects of machine learning theory, including unsupervised learning (clustering), domain adaptation, fairness, interpretability, and alternative approaches to worst-case computational complexity. He also explores logic applications in computer science theory. His research trends emphasize theoretical challenges in machine learning, particularly clustering, fairness in representations, and the interplay between computational feasibility and learnability. He investigates how unlabeled data and sample compression techniques impact learning robustness and efficiency. No scientific awards are listed. His advising record shows no formal advisees listed here. Grants and funding details are not provided in the text. He has contributed to organizing events like the Dagstuhl Seminar on Foundations of Unsupervised Learning (2017) and co-edited MFCS 2016 proceedings. His work addresses both theoretical questions and practical gaps in ML implementation.

• Trustworthy Machine Learning
• Conformal Prediction
• Explainable Artificial Intelligence
• +۲ مورد دیگر

Henrik Boström is a Professor of Computer Science specializing in Data Science Systems at the Division of Software and Computer Systems, KTH Royal Institute of Technology. His research focuses on trustworthy machine learning , with emphasis on conformal prediction (for confidence-calibrated predictions) and explainable AI . He is the developer of Python packages crepes (conformal classifiers/regressors) and xrf (explainable random forests). His primary research domains include: Developing robust methods for uncertainty quantification in predictive models Creating interpretable machine learning frameworks Optimizing ensemble techniques for high-dimensional data Applying ML to healthcare informatics and industrial diagnostics Analysis of his recent publications reveals strong emphasis on: (1) advancing conformal prediction theory for trustworthy AI, (2) enhancing interpretability of complex models like random forests and GNNs, and (3) developing efficient algorithms for uncertainty-aware learning in domains including healthcare, graph data, and high-dimensional regression. He serves as examiner for multiple degree projects and teaches courses including Programming for Data Science (ID2214) and Research Methodology and Scientific Writing (II2202) . He leads development of open-source tools for conformal prediction and model interpretation.

Arman Cohanمشاهده پروفایل

استادیار

• Machine Learning
• Natural Language Processing (NLP)
• Language Modeling
• +۷ مورد دیگر

Arman Cohan is an Assistant Professor of Computer Science at Yale University, affiliated with the School of Engineering & Applied Science. His research focuses on the intersection of Machine Learning and Natural Language Processing (NLP), particularly in language modeling, representation learning, retrieval systems, and applications in specialized domains such as scientific text processing. He earned his Ph.D. in Computer Science from Georgetown University and has received notable awards, including the Dr. Harold N. Glassman Distinguished Doctoral Dissertation Award (2019) and the EMNLP 2017 Best Long Paper Award. His work emphasizes ethical AI, robustness of LLMs, and interdisciplinary applications in healthcare, science, and education. Cohan's research group, the Yale NLP Lab, develops advanced techniques for multi-document summarization, adversarial fact-checking, and LLM-driven tools for scientific discovery. Recent projects include frameworks like SciBERT, Longformer, and ChemAgent, which enhance domain-specific reasoning and safety in AI systems. His publications address challenges in table reasoning, uncertainty expression, and multimodal reasoning, with applications in medical decision-making and educational problem-solving. He collaborates on initiatives like the Roberts Innovation Fund to advance AI in healthcare and environmental technology.

Bryan A. Plummerمشاهده پروفایل

استادیار

• Artificial Intelligence
• Multimodal Machine Learning
• Efficient Neural Networks
• +۵ مورد دیگر

Bryan A. Plummer is an Assistant Professor in the Department of Computer Science at Boston University, affiliated with the IVC Group and the Artificial Intelligence Research (AIR) initiative at the Rafik B. Hariri Institute. He holds a PhD from the University of Illinois at Urbana-Champaign, specializing in computer vision. His research focuses on multimodal machine learning, efficient neural architectures, explainable AI, and robust ML systems. Plummer's work bridges vision and language, addressing challenges in domain generalization, synthetic data utilization, and model efficiency. Notable contributions include the Flickr30K Entities dataset and advancements in vision-language model robustness against web artifacts. He has advised over 20 students, with several securing roles at top institutions like NVIDIA and Google. His recent awards include the 3M Foundation Fellowship and NSF GRFP honorable mention. Plummer actively serves on conference committees (NeurIPS, CVPR, ICCV) and leads initiatives like the 1st Findings Workshop at ICCV'25.

Olga Finkمشاهده پروفایل

استادیار

• Machine Learning
• Predictive Maintenance
• Industrial IoT
• +۳ مورد دیگر

Olga Fink is a Tenure Track Assistant Professor at the École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the Department of Intelligent Maintenance and Operations Systems (IMOS) within the School of Architecture, Civil and Environmental Engineering (ENAC). She also holds roles in PhD program committees for Civil and Environmental Engineering (EDCE) and Robotics, Control, and Intelligent Systems (EDRS). Her research focuses on machine learning for infrastructure monitoring, predictive maintenance, and physics-informed AI models. She teaches courses on machine learning, data science for infrastructure, and advanced deep learning topics. Fink advises multiple PhD students and is involved in interdisciplinary projects such as ThermoNeRF (multimodal 3D thermal modeling) and physics-informed neural networks for fault diagnostics. Her work bridges AI and engineering with applications in smart infrastructure, energy systems, and industrial IoT. Education: PhD in Engineering (inferred from role) Affiliations: IMOS Lab, ENAC-SGC, EPFL PhD Committees (EDCE, EDRS) Key Research Themes: Explainable AI, Digital Twins, Structural Health Monitoring, Domain Adaptation Her publications (2023–2025) emphasize robust AI for industrial systems, including fault detection in high-voltage equipment, multimodal data fusion, and physics-consistent models. She collaborates on EU and industry-funded projects, focusing on real-world applications like predictive maintenance and energy efficiency.

Linyi Liمشاهده پروفایل

استادیار

• Deep learning
• Trustworthy machine learning
• Computer security
• +۴ مورد دیگر

Linyi Li is an Assistant Professor in the School of Computing Science at Simon Fraser University (SFU), leading the Trustworthy Artificial Intelligence (TAI) Lab. His research focuses on certifiably trustworthy deep learning systems, combining machine learning and computer security. He holds a PhD from the University of Illinois Urbana-Champaign (UIUC) and a B.Eng. from Tsinghua University. Affiliations: Simon Fraser University, TAI Lab Education: PhD in Computer Science, UIUC, 2023 B.Eng (Cum Laude), Tsinghua University, 2018 His research interests include deep learning , trustworthy machine learning , large language models , and software engineering . He emphasizes rigorous certification of robustness, fairness, and numerical reliability in AI systems. Recent work includes the InfiBench benchmark for evaluating code LLMs and advancements in neural network verification. Recent Research Trends: His publications span certified robustness, fairness guarantees, and scalable verification techniques for deep learning models. He also explores scientific evaluation of foundation models and adversarial defense mechanisms. Awards: Rising Stars in Data Science AdvML Rising Star Award Wing Kai Cheng Fellowship Finalist: Qualcomm Innovation Fellowship (2022) Winner: VNN-COMP'23 Competition (Team α, β-CROWN) Advising & Grants: As a PI, he oversees the TAI Lab's research. Though no specific grants are listed, his work is funded through competitive awards and university resources. Labs/Teams: Leads the TAI Lab at SFU, focusing on foundational and applied research in trustworthy AI.

• Visual Analytics
• Information Visualization
• Computational Linguistics
• +۳ مورد دیگر

Mennatallah El-Assady serves as Assistant Professor at ETH Zurich's Department of Computer Science, where she leads the Interactive Visualization and Intelligence Augmentation Lab (IVIA). Her academic trajectory includes research fellowships at ETH's AI Center and doctoral work at University of Konstanz and OntarioTech University, establishing her expertise at the intersection of visualization and artificial intelligence. Her research focuses on advancing responsible data-driven decision-making through human-centered analytics, with particular emphasis on explainable machine learning systems. Dr. El-Assady combines data mining techniques with visual interfaces to create transparent AI workflows, specializing in text data analysis. Her work bridges computational linguistics, digital humanities, and information visualization to develop tools that make complex AI processes interpretable for end users. Recent publications demonstrate growing focus on generative AI's impact on visualization practices and sophisticated frameworks for interactive machine learning. Her research consistently addresses the challenge of maintaining human agency in increasingly automated systems, with applications spanning political debate analysis, musicology, and healthcare data interpretation. The trend shows increasing sophistication in evaluation methodologies for visual analytics systems. Best Paper Award: Honorable Mention for 'Progressive Learning of Topic Modeling Parameters: A Visual Analytics Framework' Dr. El-Assady actively shapes her field through workshop leadership including ArgVis (Argument Visualization), Vis4DH (Visualization for Digital Humanities), and VISxAI (Visualization for AI Explainability). Her teaching includes 'Interactive Machine Learning- Visualization and Explainability' at ETH Zurich, training next-generation researchers in human-centered AI development. She maintains strong industry connections with coverage in Forbes and ETH News regarding human-AI collaboration frameworks. The Interactive Visualization and Intelligence Augmentation Lab (IVIA) develops cutting-edge tools including explAIner for transparent machine learning, LingVis for linguistic analysis, VisArgue for debate structure visualization, and VALIDA for political deliberation analysis. These projects share a common thread of enhancing human understanding through carefully designed visual interfaces that expose AI decision processes.

Frank Rudziczمشاهده پروفایل

دانشیار

• Natural Language Processing
• Machine Learning
• Healthcare
• +۱۲ مورد دیگر

Dr. Frank Rudzicz is an Associate Professor in the Faculty of Computer Science at Dalhousie University. His research lies at the intersection of artificial intelligence, natural language processing, and healthcare, with a focus on developing machine learning systems that improve clinical decision-making, patient outcomes, and accessibility in medicine. He holds a BSc from Concordia University (2004), an MEng from McGill University (2006), and a PhD from the University of Toronto (2011). His research interests include Natural Language Processing, Machine Learning, Healthcare, Speech Technologies, Explainable AI, and Fairness in ML. Dr. Rudzicz's recent publications span a wide range of topics, including Alzheimer's detection through speech analysis, surgical outcome prediction, mental health monitoring, privacy in AI, and the application of large language models in clinical settings. His work consistently emphasizes ethical AI, patient privacy, and real-world clinical integration. He has received several awards, including a Best Paper award at EMNLP 2020, a Best Student Paper award at ICASSP 2021, and the ISCA Best Student Paper award in 2013. His research has been published in top-tier journals such as Nature Scientific Reports , JAMA Network Open , IEEE Access , and Frontiers in Human Neuroscience , as well as leading conferences including NeurIPS, ACL, ICML, and Interspeech. Dr. Rudzicz supervises a dynamic research group working on AI for health, with active projects in voice-based diagnostics, ambient clinical documentation, explainable AI for surgery, and wearable-based monitoring for chronic diseases. He collaborates widely across disciplines, including with clinicians, neuroscientists, ethicists, and public health experts. He is also involved in major initiatives such as the Genetics Navigator study and Bridge2AI-Voice, aiming to build ethically sourced, diverse biomedical datasets. His lab actively explores the societal implications of AI in healthcare, including fairness, trust, and resistance to malicious fine-tuning.