جست‌وجوی استاد و پژوهشگر

Ilias Tachmazidisمشاهده پروفایل

مدرس ارشد

• Knowledge Representation and Reasoning
• Semantic Web
• Artificial Intelligence in Medicine
• +۳ مورد دیگر

Ilias Tachmazidis is a Senior Lecturer in the Department of Computer Science at the University of Huddersfield's School of Computing and Engineering. He actively contributes to research within the Centre for Autonomous and Intelligent Systems and the Centre of Artificial Intelligence for Mental Health. His academic journey includes a PhD from the University of Huddersfield (2015) and previous research roles in the PARK group. Education: University of Crete (B.Sc. 2010, M.Sc. 2012) Current academic rank: Senior Lecturer Active research centers: Centre for Autonomous and Intelligent Systems, Centre of Artificial Intelligence for Mental Health His research spans foundational AI concepts and applied implementations, with specific emphasis on: Knowledge Representation and Reasoning Semantic Web Technologies High Performance and Distributed Computing Artificial Intelligence Applications in Medicine and Law Recent publications highlight his work at the intersection of large language models and formal verification, alongside practical implementations for ADHD diagnosis using AI. The research demonstrates consistent engagement with UN Sustainable Development Goals through technology solutions that balance scientific rigor with societal impact. While not explicitly listing scientific awards, his activities show substantial contributions through peer-review work at IEEE conferences (2016-2020) and collaborative projects like MIREL (MIning and REasoning with Legal texts) that produced notable outputs in regulatory compliance systems.

Claudio Luccheseمشاهده پروفایل

استاد

• Machine Learning
• Information Retrieval
• Data Mining
• +۴ مورد دیگر

Claudio Lucchese is a Full Professor in the Department of Environmental Sciences, Computer Science and Statistics at Ca' Foscari University of Venice. His research focuses on Machine Learning in Information Retrieval, specifically Learning to Rank, Adversarial ML, and Explainable AI. He has published over 100 articles and won awards including the 2015 ACM SIGIR Best Paper Award. He coordinates the Hospitality Innovation and e-Tourism degree program and leads the Data Mining and Information Retrieval Lab. Research Interests: Efficiency-Effectiveness Trade-offs in IR Adversarial Machine Learning Explainable AI Large-Scale Data Mining Recent Projects: Data Science for Mobility (Humco S.r.l., 2020) Flexymob (Currant s.r.l., 2022) Lucchese serves on editorial boards for ACM Transactions on Information Systems and Data Mining and Knowledge Discovery. His teaching spans Computer Science and Engineering Physics programs, emphasizing Massive Data Learning and High-Performance Computing.

• Machine Learning
• Remote Sensing
• Artificial Intelligence
• +۹ مورد دیگر

Jordi Muñoz Mari is an Associate Professor in the Department of Electronic Engineering at the School of Engineering, University of Valencia. He is a key member of the Image and Signal Processing (ISP) research group within the Image Processing Laboratory (IPL), a recognized research institute (ERI) at the university. His research focuses on the intersection of artificial intelligence, remote sensing, and environmental science. Key areas include machine learning for Earth observation, biophysical parameter retrieval, crop yield forecasting, climate modeling, and causal inference in ecological systems. He also explores applications of AI in education, particularly generative models for assessment and digital learning tools. The most recent publications highlight a strong trend toward physics-aware machine learning, uncertainty quantification, and large-scale applications using platforms like Google Earth Engine. His work spans climate extremes (medicanes, hurricanes, droughts), agricultural monitoring, biodiversity, and educational technology, demonstrating interdisciplinary innovation. Jordi Muñoz Mari completed his PhD at the University of Valencia in 2004 on telemonitoring systems for cardiac patients. His ongoing research involves advanced signal processing, kernel methods, and AI-driven geoinformation systems.

Franz Christopher Kunzeمشاهده پروفایل

پژوهشگر

• Industrial Automation
• Alarm Management Systems
• Causal Analysis
• +۳ مورد دیگر

Franz Christopher Kunze is a Researcher at the Department of Automation Technology, Faculty of Electrical Engineering and Information Technology, Ruhr-Universität Bochum. His work focuses on developing advanced alarm management systems for industrial processes using causal directed graphs and machine learning techniques. He collaborates with TU Munich on the DFG-funded CausAlITI project, aiming to improve root-cause analysis during industrial alarm floods. Research interests include causal analysis of alarm patterns, adaptive system modeling, and operator-centric visualization. He teaches courses like Process Automation and Programming for ETIT/ITE students, and supervises bachelor's theses in alarm system development. His publications (2023–2025) address alarm flood dynamics, causal graph applications, and automated model generation using LLMs. Current work emphasizes real-time alarm processing and uncertainty management in industrial systems.

• Social networks
• Causal inference modeling
• Behavioural Analytics
• +۳ مورد دیگر

Bhattacharya Prasanta is an Adjunct Assistant Professor at the Department of Analytics and Operations (DAO) within the National University of Singapore (NUS) Business School. He has designed graduate and executive-level courses on AI, business analytics, and network science. Additionally, he serves as Innovation Lead & Senior Research Scientist at the Social and Cognitive Computing Department of A*STAR's Institute of High Performance Computing (IHPC), focusing on interdisciplinary research. His research bridges network science, behavioral analytics, and psychometric modeling with AI applications in emerging markets. Key areas include: Analysis of social networks and causal inference Development of AI-driven tools for qualitative data collection Stance detection using large language models Behavioral drivers of online misinformation Teaching includes the course DBA5104 - Introduction to Network Science & Analytics . His work integrates computational methods with real-world challenges in finance, public health, and education. At IHPC, he contributes to projects like Project Communicate, which empowers children with autism in India through technology. Recent research trends show focus on: Multi-modal data analysis for pandemic forecasting Policy impact modeling in social networks Ethical AI implementation in financial literacy No scientific awards are explicitly listed in the provided materials. His roles combine academic teaching with applied research in Singapore's tech ecosystem, emphasizing practical AI solutions for socio-economic challenges.

Aviral Kumarمشاهده پروفایل

استادیار

• Artificial Intelligence
• Machine Learning
• Reinforcement Learning
• +۳ مورد دیگر

Aviral Kumar is an Assistant Professor in the School of Computer Science at Carnegie Mellon University (CMU), jointly appointed in the Computer Science Department (CSD) and Machine Learning Department (MLD). He earned his PhD from UC Berkeley in 2023, where he received prestigious awards including the CV Ramamoorthy Distinguished Research Award, Apple PhD Fellowship, and Facebook PhD Fellowship. His research focuses on reinforcement learning (RL), particularly offline RL, scaling RL methods, and their intersection with foundation models. He leads the CMU AI & Reinforcement Learning (AIRe) lab, exploring core RL algorithms, robotics applications, and AI foundation models. Education: PhD in Computer Science from UC Berkeley (2023) Research Interests: Reinforcement Learning Offline Reinforcement Learning Robotics Foundation Models His lab emphasizes scalable RL techniques and real-world applications. Current advisees include Bhavya Kumar Agrawalla and Max Sobol Mark. Notable awards include the CV Ramamoorthy Distinguished Research Award for pioneering contributions to CS research. Lab activities and future work focus on advancing RL for autonomous systems and integrating foundation models into decision-making frameworks. The lab recruits PhD students annually from CMU SCS programs (CS, ML, RI). Undergraduates and MS students can apply via a dedicated form for research opportunities.

Huajie Zhangمشاهده پروفایل

استاد

• Machine Learning
• Data Mining
• Graphical and Probabilistic Models
• +۵ مورد دیگر

Huajie Zhang is a Professor at the Faculty of Computer Science , University of New Brunswick, with over 14 years of service. He holds a PhD in Computer Science from the University of Western Ontario and an MSc from Harbin Institute of Technology. Research Interests: Machine Learning Data Mining Graphical and Probabilistic Models Intelligent Systems Transfer Learning Semi-supervised Learning Efficient Learning Algorithms for Large Data Publication Trends: Huajie Zhang's work focuses on probabilistic models like Bayesian networks and Naive Bayes, emphasizing accuracy, efficiency, and adaptability in semi-supervised and transfer learning contexts. His recent papers explore scalable algorithms for big data, hybrid classifiers, and optimization techniques. Scientific Awards: Best Paper Award (Second Place) at FLAIRS Conference 2004 Teaching Activities: CS6735 Machine Learning CS3383 Algorithm Design

• Information Retrieval
• Search Engine Technologies
• Data Compression
• +۴ مورد دیگر

Alistair Moffat is a prominent faculty member at the University of Melbourne's School of Computing and Information Systems, with an extensive publication record spanning over four decades from 1980 to the present. His career demonstrates sustained contributions to information retrieval, data compression, and evaluation metrics within the field of computer science. Moffat's primary research interests encompass: Information Retrieval and Search Engine Technologies Data Compression Algorithms and Indexing Techniques Development and Analysis of Evaluation Metrics Process Mining and Event Log Analysis User Modeling in Information Seeking Behavior Theoretical Foundations of Search Effectiveness His recent work (2023-2025) reveals a continued focus on refining search evaluation methodologies, with particular attention to user-oriented metrics, rank-biased quality measurement, and the relationship between query variations and experimental consistency. Moffat has also expanded into process mining applications, developing entropy-based metrics like Entropia for measuring log representativeness. His publications consistently appear in top-tier venues including SIGIR, ACM Transactions on Information Systems, and Information Processing & Management. Moffat maintains extensive research collaborations, most notably with Justin Zobel (70 joint publications), J. Shane Culpepper (35), and Matthias Petri (32), demonstrating a strong network within the information retrieval research community. His work bridges theoretical computer science with practical applications in search technology, medical information retrieval, and process analysis. As evidenced by his continuous publication output through 2025, Moffat remains an active and influential researcher in his fields of expertise, contributing both to foundational theories and practical implementations in information access systems.

• Geoinformatics
• Artificial Intelligence
• Machine Learning
• +۷ مورد دیگر

Omid Reza Abbasi is a Research Fellow in Geoinformatics at the University of Salzburg , specializing in the integration of Artificial Intelligence, Machine Learning, and Geographic Information Systems (GIS) to address complex spatial challenges. His work spans diverse domains including spatio-temporal modeling, human mobility prediction, and semantic similarity measures in geospatial contexts. Projects : Collaborated on 'RegioWoodTrain,' a project focusing on sustainable regional wood transport through cooperative supply chain management using simulation and AI technologies. Research Interests : Omid's research emphasizes the application of AI and GIS in urban planning, transportation systems, and social media analysis. Key areas include: Large Language Models (LLMs) for geographical representation Semantic similarity assessment in geospatial data Human mobility pattern prediction using social networks Accessibility in WebGIS via audio interfaces Recommender systems for tourism and retail Spatio-temporal analysis of public services and environmental impacts Publications : His recent work explores the use of LLMs in GIS, novel semantic similarity metrics, and spatio-temporal modeling for urban governance. Articles from 2018-2025 highlight trends in AI-driven spatial analysis, collective mobility prediction, and georeferencing of semi-structured data. Contact : Email: omidreza.abbasi@plus.ac.at

Lihua Leiمشاهده پروفایل

استادیار

• High Dimensional Statistical Inference
• Causal Inference
• Econometrics
• +۴ مورد دیگر

Lihua Lei is an Assistant Professor of Economics at Stanford Graduate School of Business (GSB) and holds a courtesy appointment in the Department of Statistics at Stanford University. He earned his Ph.D. in Statistics from UC Berkeley (2019) under advisors Peter Bickel and Michael Jordan, followed by postdoctoral work at Stanford's Statistics Department with Emmanuel Candès. Current affiliations: Stanford GSB, Department of Statistics, and Institute for Economic Policy Research (SIEPR) Previous training: UC Berkeley (Ph.D.), Carnegie Mellon University (undergraduate) His research spans causal inference, conformal inference, and high-dimensional statistics, with a focus on methodology development and theoretical guarantees. Key contributions include adaptive false discovery rate control and conformal prediction frameworks. Recent publications appear in Annals of Statistics , Journal of the Royal Statistical Society , and SIAM Journal on Optimization . Collaborators include prominent researchers like Emmanuel Candès, Michael Jordan, and Peter Bickel. He has received teaching awards as Graduate Student Instructor and develops R packages for methodological implementations including adaptMT and mkn .

• Computer Networks
• Machine Learning
• Reinforcement Learning
• +۶ مورد دیگر

Alessio Sacco is a Fixed-term Assistant Professor at the Department of Control and Computer Engineering (DAUIN) at Politecnico di Torino, Italy. He's affiliated with the NETGROUP - Computer Networks Group and actively contributes to research in computer networks, machine learning, and software-defined networking. Current academic position: Assistant Professor Research group: NETGROUP Key collaborations: Guido Marchetto, Flavio Esposito Research Interests focus on: Computer networks and communications Dynamic programming and reinforcement learning Machine learning applications in network management Software-defined networks Cybersecurity and privacy analysis Intelligent Internet of Things systems Recent Publications demonstrate expertise across: Network security verification (PathSafe) AI-driven agricultural optimization (FertilizeSmart) Cross-layer congestion control (Flecto) Reinforcement learning for computing continuum scheduling Explainable AI for network transparency (ClearNET) Multi-agent exploration systems (MARS) Scientific Contributions include: Commercial research on network security and anomaly detection ERC sector expertise in distributed systems and machine learning Teaching in computer engineering programs since 2019 Advising involves: Supervising Federico Rinaudi (ongoing) Co-supervising Doriana Monaco (ongoing)

• Artificial Intelligence and Law
• Legal Technology
• Computational Law
• +۵ مورد دیگر

Frank Fagan is an Associate Professor at South Texas College of Law Houston and a Research Associate at EDHEC Augmented Law Institute in France. His scholarly work bridges traditional legal scholarship with emerging computational methodologies, positioning him at the forefront of legal technology research. Professor Fagan's research program centers on the transformative impact of artificial intelligence on legal systems. His work spans multiple critical domains: Legal applications of large language models and AI systems Computational law and algorithmic governance frameworks Social media regulation and digital platform accountability Algorithmic decision-making in judicial and regulatory contexts Big data approaches to legal scholarship and practice His publication trajectory reveals an accelerating focus on AI-law intersections, with recent work examining benchmarking methodologies for legal reasoning in LLMs, ownership frameworks for autonomous AI, and the implications of language models for legal practice transformation. Fagan frequently collaborates with prominent scholars including Saul Levmore on projects exploring computational approaches to legal theory and practice. With 34 scholarly papers accumulating over 9,000 downloads and 28 citations, his research demonstrates significant scholarly impact. His work appears in leading venues including the University of Chicago Law Review, Southern California Law Review, and Virginia Journal of Law and Technology, reflecting both interdisciplinary reach and legal academic recognition.

• Software Engineering
• Artificial Intelligence
• Cyber-Physical Systems
• +۳ مورد دیگر

Mason Brown serves as a Visiting Assistant Professor of Computer Science at Hamilton College, based in Taylor Science Center 3005. His research integrates software engineering and artificial intelligence to advance cyber-physical systems across ground, air, and space domains, with emphasis on practical implementations for real-world applications. Educational Background: Ph.D. in Computer Science from Griffith University M. IT. from Griffith University B. I.T. from Charles Sturt University His doctoral dissertation focused on An Ilities Tool for Minisatellite Software Validation , developed in collaboration with Gilmour Space Technologies. Research expertise spans satellite systems validation, multi-robot coordination, and AI-driven cyber-physical solutions. His postdoctoral work at the University of Technology Sydney involved multi-robot target tracking and path planning under Professor Robert Fitch. Current investigations include aircraft visual inspection via machine learning and natural language interfaces for robotic systems, demonstrating strong interdisciplinary connections between space engineering and robotics. Publication analysis reveals a clear trajectory from foundational satellite software validation (2020-2021) toward advanced human-robot collaboration (2023-2024), with consistent focus on verification methodologies and real-world deployment challenges. Key thematic threads include ilities-based validation frameworks, multi-robot field experimentation, and machine learning applications in aerospace. Teaching includes Computer Science for All and Algorithms and Data Structures , with active supervision of student projects in mechatronics and computer science. He particularly encourages new students to engage in research. Professional activities include appointment to the Australian Computer Science faculty in 2025 and collaborations with laboratories at Griffith University (Professor Paulo de Souza) and University of Technology Sydney (Graeme Best).

Song Hanمشاهده پروفایل

دانشیار

• Efficient Deep Learning
• Neural Network Compression
• Hardware-Aware AI
• +۷ مورد دیگر

Song Han is an Associate Professor in the Department of Electrical Engineering and Computer Science (EECS) at the Massachusetts Institute of Technology (MIT). His research focuses on efficient deep learning computing, bridging algorithm and hardware design to enable scalable AI systems. PhD in Electrical Engineering from Stanford University Research Interests Efficient Deep Learning Neural Network Compression Hardware-Aware Transformers Sparse Attention Mechanisms Quantization Techniques Edge and IoT Computing Recent Publication Trends highlight advances in LLM optimization, diffusion model quantization, and quantum-classical co-design. His work emphasizes reducing computational costs while maintaining model fidelity. Scientific Awards Best Paper, ICLR and FPGA Symposium NSF CAREER Award MIT Technology Review 35 Innovators Under 35 Collaborations include the MIT-IBM Watson AI Lab, focusing on AI hardware and system co-design. Many of his techniques are integrated into commercial AI chips.

Yanan Fanمشاهده پروفایل

استاد مدعو

• Bayesian analysis
• Markov chain Monte Carlo methods
• Approximate Bayesian computation
• +۳ مورد دیگر

Professor Yanan Fan is an adjunct full Professor of Statistics at the School of Mathematics and Statistics , University of New South Wales, and a senior Principal research scientist at CSIRO's Data61 in Australia. Their expertise lies in Bayesian models and computational methodology for real-world problem-solving. Current research includes Bayesian semiparametric models, computational methods for large spatial data from medical images, and approximate Bayesian inference. Supervision areas: Bayesian inference, computational statistics, quantile regression, medical image analysis, and bias detection in language. Based in The Red Centre, Room 2055, Sydney NSW 2052, they bridge statistical theory with applications in ecology, climate models, and medical imaging.