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Shuang AoView profile

Research Fellow

• Machine Learning
• Reinforcement Learning
• Curriculum Learning
• +4 more

Mr. Shuang Ao is a Postdoctoral Research Fellow at the School of Computer Science and Engineering, University of New South Wales (UNSW Sydney). He earned his PhD from the University of Technology Sydney in January 2024. His research focuses on machine learning, reinforcement learning, and curriculum learning, with applications in robotic control and antibody drug discovery. Research Interests: Machine Learning Reinforcement Learning Curriculum Learning Graph Algorithms Optimization Techniques Recent Publication Trends: Shuang's work spans large language models for location-based recommendations, spatio-temporal forecasting, reinforcement learning frameworks, and graph algorithm optimizations. His articles address both theoretical advancements and practical applications in scalable systems and data analysis. Contact: Email: shuang.ao@unsw.edu.au

Florije IsmailiView profile

Professor

• Machine Learning
• Semantic Web
• Data Mining
• +6 more

Florije Ismaili is a Full Professor at the Faculty of Contemporary Sciences and Technologies at South East European University (SEEU) in Tetovo, Macedonia. With a PhD in Computer Science from Technical University of Sofia and a Master's in Informatics from New Bulgarian University, her research focuses on Machine Learning , Semantic Web , Internet of Things (IoT) , and Data Mining . She has led research initiatives and taught courses including Web Programming and Distributed Systems. Doctor of Computer Science (2011), Technical University of Sofia, Bulgaria Master of Science (2005), New Bulgarian University, Sofia Bachelor of Mathematics (2003), EGE University, Izmir, Turkey Her publications demonstrate expertise in applying Machine Learning to domains like education, healthcare, and smart farming. She specializes in Semantic Web techniques for IoT data integration and causal reasoning in complex datasets. Recent work includes predictive modeling of student success and ethical considerations in AI applications. Professor Ismaili's research spans IoT semantic annotation, Cloud Computing architectures for healthcare, and causal rule mining. She has contributed to journals such as IEEE Transactions and Springer publications, with a focus on contextual data integration and ethical data analysis. Her career at SEEU includes prior roles as Assistant Professor, Head of Research Office (CST Faculty), and Young Assistant. She has collaborated on projects addressing database migration, hate speech detection, and e-Government maturity assessment.

Bo ZengView profile

Associate Professor

• Optimization
• Robust Optimization
• Stochastic Programming
• +7 more

Bo Zeng is an Associate Professor in the Swanson School of Engineering at the University of Pittsburgh. His research focuses on developing and utilizing optimization and analytics tools to address challenges in real systems, particularly in discrete optimization models with uncertainties, game theory models, and advanced data analysis and computing methods. His work is extensively applied in engineering, healthcare, and management systems. Dr. Zeng earned his PhD from Purdue University in 2007 and his BS from Xian Jiaotong University in 1998. His research interests span optimization, robust optimization, stochastic programming, power systems, demand response, renewable energy integration, high performance computing, game theory, mixed integer programming, and multilevel optimization. Analysis of Dr. Zeng's recent publications reveals a strong focus on power systems and energy applications, with significant contributions to microgrid planning, demand response modeling, renewable energy integration, and robust optimization techniques. His work demonstrates a consistent pattern of applying advanced mathematical optimization methods to solve complex problems in energy systems, with increasing attention to uncertainty modeling and risk management in power grid operations. Dr. Zeng has collaborated extensively with researchers across multiple institutions, particularly in China, reflecting the global nature of energy research and the international collaboration needed to address complex energy challenges. His publications span high-impact journals in power systems, optimization, and interdisciplinary energy research.

• Artificial Intelligence
• Network Administration
• Multi-agent Systems
• +6 more

Horia Popa is a Lecturer at the Faculty of Computer Science , West University of Timișoara. He has taught courses such as Artificial Intelligence , Network Administration , and Functional and Logic Programming since the 2022-2023 academic year, with additional historical courses dating back to 2011-2012. His teaching emphasizes hands-on lab work, software tools (Jess, CLIPS, WEKA), and project-based learning. Education: Not explicitly mentioned in the text. Research: Focuses on multi-agent systems, distributed constraints, asynchronous search algorithms, and system administration. Research Interests: Horia Popa specializes in Artificial Intelligence and Multi-agent Systems , particularly in asynchronous search techniques and constraint satisfaction problems. His work explores scale-free networks, nogood processors, and distributed execution environments. He also investigates Network Administration (DHCP, firewall configuration, kernel recompilation) and Knowledge Discovery through agent-based modeling. Article Trends: His publications (2001-2015) span Computer Science , Artificial Intelligence , and Multi-agent Systems . Key subfields include Asynchronous Algorithms , Constraint Networks , Protein Folding Simulation , and Kernel-Level System Management . He frequently uses NetLogo for large-scale simulations and integrates Samba/ldap for networked environments. Teaching and Projects: Students in his courses work on projects involving Jess , Prolog , and JADE . Assignments include implementing search algorithms (A*, Hill Climbing, RBFS), configuring NIS and Samba servers, and analyzing system monitoring tools like sar and top . He emphasizes practical implementation and cross-language diversity (e.g., Racket, Prolog).

Recep Firat CekinelView profile

Researcher

• Natural Language Processing
• Multimodal Fact-Checking
• Explainable AI
• +7 more

Recep Firat Cekinel is a Turkish NLP researcher who recently obtained his Ph.D. in Computer Engineering from Middle East Technical University (METU). He spent 13 months as a visiting predoctoral researcher at the University of Tübingen and is currently a researcher on the EU-funded EXA4MIND project, where he develops NLP pipelines that convert natural language into database queries using large language models. His research focuses on responsible, scalable AI systems and bridges foundational NLP work with real-world applications. Education: Ph.D. in Computer Engineering, Middle East Technical University (METU), Türkiye Visiting Predoctoral Researcher, University of Tübingen, Germany (13 months) Research Interests: Dr. Cekinel’s work spans natural language processing , multimodal fact-checking , explainable AI , and large language models . He is particularly interested in building responsible and scalable AI systems that integrate foundational research with practical deployments, such as natural-language interfaces for high-performance computing environments. Recent Publication Trends: His 2025 publications reveal a concentrated effort on multilingual and multimodal fact-checking , satire-style debiasing , and NL-to-database-query generation . Earlier work explores graph-based event extraction , Turkish irony detection , and cultural-heritage text mining , demonstrating a trajectory from low-resource Turkish NLP toward globally applicable, responsible-AI systems. Contact & Code: Email: rfcekinel@ceng.metu.edu.tr Office: METU Computer Eng. Dept. A-206, 06800 Ankara, Turkey Phone: +90-(312)-210-5593 GitHub: firatcekinel Google Scholar: profile available

• Machine Learning
• Natural Language Processing
• Network Analysis
• +3 more

Dr. Derek Greene is an Assistant Professor at the School of Computer Science, University College Dublin, and a Funded Investigator at the Insight Centre for Data Analytics and the VistaMilk Research Centre. His research spans machine learning, natural language processing, and network analysis, with a focus on interdisciplinary applications in cultural analytics, smart agriculture, and political science. Dr. Greene has published over 60 research papers at international conferences and journals. His work includes developing methods for natural language processing , network analysis , and deep learning applied to diverse domains such as literary text mining, dairy industry monitoring, and political communication analysis. He leads projects integrating machine learning into cultural analytics, enhancing agricultural practices, and modeling policy agendas. The articles in his Google Scholar profile highlight a trend toward explainable AI , synthetic data generation , and network-based modeling . Key sub-fields include counterfactual explanations , transformer-based frameworks , temporal analysis of historical texts , and interdisciplinary knowledge transfer . His work bridges machine learning with applications in cultural studies , agriculture , and political science . Dr. Greene collaborates with institutions like the Insight Centre for Data Analytics and the VistaMilk Research Centre , integrating academic research with industry and policy needs. His funded investigator roles reflect ongoing support for applied research in data analytics and agricultural technology.

Suchana DattaView profile

Research Fellow

• Information Retrieval
• Natural Language Processing
• Digital Humanities
• +3 more

Suchana Datta is a Research Fellow at the Insight Centre for Data Analytics . Her work bridges Information Retrieval and Artificial Intelligence , with a focus on query performance prediction (QPP) and causality-driven retrieval . She has contributed extensively to improving neural ranking models through relevance feedback and reproducibility studies. Research Highlights Developing novel frameworks for supervised and unsupervised QPP using deep learning and hybrid feature integration Advancing causality modeling in information retrieval systems Exploring temporal trends in 19th-century literature through computational analysis Pioneering work in cloud forensics with dynamic forensic frameworks Publication Trends Her recent work (2022-2025) emphasizes neural QPP models , reproducibility in IR experiments, and causality detection in query events. Earlier contributions (2016-2020) focus on cloud forensic frameworks like DCF and causal analysis in retrieval systems.

Fabrizio SilvestriView profile

Professor

• Artificial Intelligence
• Machine Learning
• Web Search
• +7 more

Fabrizio Silvestri is a Full Professor at Sapienza University of Rome's Department of Computer, Automatic and Management Engineering (DIAG), where he coordinates the Ph.D. program in Data Science. He leads the RSTLess research group focusing on Robust, Safe, and Transparent Deep Learning. Research interests: Artificial Intelligence, Machine Learning, Web Search, Natural Language Processing, Information Retrieval, Graph Neural Networks Research Trends from recent publications reveal: Advancements in sequential recommendation systems using topological and sheaf-based neural networks Focus on sustainable AI through eco-aware graph neural networks Counterfactual explanations for graph models and machine unlearning Security applications in dense retrieval and data poisoning defense Time series analysis for 5G network monitoring Integration of attention mechanisms and positional encoding in Transformers Scientific Achievements : ECIR 2018 Test of Time Award 3 Best Paper Awards (ECIR 2007, IEEE WI 2004, WSDM 2011 Runner-Up) Yahoo! Patent Milestone Award Recipient of Yahoo! Labs Excellence Program (LEAP) and Faculty Research Engagement Program (FREP) Finalist for ERCIM Cor Baayen Award (2005) Academic Leadership : Holds 9 industrial patents from Yahoo! and Facebook AI. Directed Facebook AI research groups combating malicious content. Ph.D. in Computer Science from University of Pisa with thesis on High-Performance Issues in Web Search Engines . Supervises thesis projects through the RSTLess group website .

• Data Science
• Big Data
• Natural Language Query Processing
• +4 more

Prof. Dr. Kurt Stockinger is a Professor of Computer Science at ZHAW School of Engineering and holds a doctorate at the University of Zurich . He serves as Head of the MAS Data Science program and co-leads the ZHAW Datalab . His research focuses on Intelligent Information Systems , bridging information systems, natural language processing, and machine learning. Affiliated with the University of Zurich, he contributes to Quantum Machine Learning and Open Data Exploration initiatives. Stockinger's educational background includes a PhD in Computer Science (University of Vienna & CERN), a Master in Business Informatics (University of Vienna), and a CAS in Didactics & Methodology (ZHAW). He has taught courses in Quantum Computing , Big Data for Natural Sciences , and Data Science programs at ZHAW and University of Zurich. His research spans Data Science , Big Data , Natural Language Query Processing , Knowledge Graphs , and Quantum Machine Learning . Recent publications focus on quantum autoencoders , hybrid quantum neural networks , and prompt engineering for knowledge graph question answering. He has developed frameworks like ScienceBenchmark for real-world NL-to-SQL evaluation and NQuest for natural language query exploration. Scientific awards include the Best Paper Award at 7th Swiss Conference on Data Science (2020) He leads major projects such as DataGEMS (Data Discovery Platform, Horizon Europe) Digital Health Zurich (Clinical Innovation Lab) INODE4StatBot.swiss (NL-to-SQL Translation) GraphQueryML (Graph Database Optimization) ScienceBenchmark (NL-to-SQL Evaluation) Stockinger's work intersects with computer vision , biomedical data , and industrial applications , demonstrated through collaborations with institutions like Lawrence Berkeley National Laboratory, CERN, and University of Washington. He has contributed to establishing QuantumBasel and ZHAW Datalab as research hubs.

• Temporal Databases
• Time Series Analysis
• Database Systems
• +4 more

Anton Dignös is a professor at the Free University of Bozen-Bolzano , specializing in temporal databases , time series analysis , and database systems . His research focuses on efficient query processing for interval data, temporal joins, and schema design, with significant contributions to in-memory and time series databases. Key research areas include: Temporal Data Management : Advanced techniques for interval and duration queries. Time Series Analytics : Machine learning integration and pattern detection. Schema Optimization : Automated design and tuning of database schemas. Visual Analytics : Tools for period data comparison and correlation analysis. His work spans collaborations with researchers like Johann Gamper and Michael H. Böhlen , addressing challenges in healthcare systems, industrial applications, and financial data analytics. Notable contributions include algorithms for temporal anti-joins , range-duration queries , and machine learning-based anomaly detection .

Umakishore RamachandranView profile

Professor

• Distributed Systems
• Edge Computing
• Sensor Networks
• +5 more

Umakishore Ramachandran is a Professor in the School of Computer Science at Georgia Institute of Technology's College of Computing, where he directs the Embedded Pervasive Lab. He received his Ph.D. from the University of Wisconsin-Madison in 1986 and has led transformative initiatives including the Online MS in Computer Science (OMSCS) program. His research spans distributed systems, edge computing, and real-time sensor networks, with applications in smart surveillance and connected vehicles. His research interests include architectural design of parallel/distributed systems, large-scale situation awareness using camera networks, cloud-edge continuum optimization, and latency-sensitive applications for geo-distributed infrastructures. Recent work focuses on elevating edge computing to parity with cloud resources. His publications show strong emphasis on edge computing innovations (MicroEdge, FogStore), real-time video analytics (EVA, ClairvoyantEdge), and adaptive mobile systems (Foresight). Trends include multi-tier architectures, quality-of-experience optimization, and scalable processing for IoT workloads. Major Awards: IEEE Fellow (2014) NSF Presidential Young Investigator (1990) ACM/IFIP Middleware Best Paper (2022) 3x College of Computing Dean's Awards He has advised 40+ PhD students, with recent graduates at Google, Microsoft, and academia. Current NSF/CPS grants support his work on geo-distributed latency-sensitive applications. He co-leads the STAR Center and Samsung-funded embedded software programs. His Embedded Pervasive Lab develops systems like Stampede (stream processing) and DFuse (sensor fusion), with testbeds in the Aware Home and transportation networks. Teams collaborate with Intel, Microsoft, and Bosch on edge-AI deployments.

• Computational Biology
• Bioinformatics
• Privacy-Preserving Data Mining
• +5 more

Kana Shimizu is a Professor at Waseda University’s Faculty of Science and Engineering, School of Fundamental Science and Engineering, and a leading authority on privacy-preserving algorithms for genome and biomedical data. Since obtaining her Dr.Eng. from Waseda, she has held positions at AIST and a visiting investigator role at Memorial Sloan-Kettering Cancer Center, advancing to full Professor at Waseda in 2018. Education: Dr.Eng., Waseda University M.Eng., Waseda University (Dept. of Information Science) B.Eng., Waseda University (School of Science and Engineering) Research Interests: Prof. Shimizu integrates cryptography with life-science data analytics, developing homomorphic encryption and secret-sharing protocols that enable secure genome search, private chemical database queries, and privacy-preserving machine learning. She also contributes to computational biology through algorithms for next-generation sequencing, structural bioinformatics, and prediction of intrinsically disordered proteins. Publication Trends: Her recent works (2022-2024) emphasize practical privacy technologies—secure range queries, function secret sharing, and gradient-clipping for synthetic genomes—while earlier high-impact papers established fast similarity search (SlideSort), binding-site databases (PoSSuM), and encrypted genomic search using the Burrows-Wheeler transform, collectively bridging algorithmic innovation and real-world biomedical privacy needs. Scientific Awards: KDDI Foundation Achievement Award 2022 MEXT Commendation for Science and Technology (Research) 2018 Multiple best-paper/demo awards at CSS, IIBMP, and domestic bioinformatics meetings Grants & Advising: She currently leads JSPS KAKENHI (S) on “Compressed Secure Computation for Large-Scale Data” and an AMED project on precise cancer-genome graphs. She serves as advisor/evaluator for JST PRESTO, AMED, NBDC, and the Tokyo High Court, and has supervised numerous bachelor theses at Waseda. Labs & Teams: Prof. Shimizu heads a laboratory within the School of Fundamental Science and Engineering at Waseda, collaborating with national centers (AIST, RIKEN) and international partners (MSKCC, Finland Tekes/AF), focusing on encrypted bioinformatics platforms and high-performance genomic algorithms.

Ivan LorencinView profile

Assistant Professor

• Artificial Intelligence
• Robotics
• High-Performance Computing
• +3 more

Ivan Lorencin is an Assistant Professor at the Faculty of Informatics in Pula , University of Pula. He holds a PhD in Electrical Engineering and Computer Science (2022) from the University of Rijeka, where he also graduated in 2018. His academic roles include Vice-Dean for Business Cooperation and Science, and he teaches courses such as Artificial Intelligence, Network Systems, and Mechatronics. PhD: 2022, University of Rijeka MSc: 2018, University of Rijeka Dr. Lorencin’s research focuses on Artificial Intelligence , Robotics , and High-Performance Computing . His work spans biomedical applications (e.g., cancer diagnostics), maritime domain (e.g., vessel recognition), and energy systems (e.g., power plant optimization). He has published over 90 papers, with 31 in Q1/Q2 journals. His recent publications emphasize machine learning in healthcare (bladder/cervical cancer diagnosis), symbolic regression for energy systems, and computer vision applications (YOLO algorithms, CLIP). He collaborates on international projects like DATACROSS , EDIH Adria , and Erasmus+ WICT . He serves as an editor for the journal Mathematics and contributes to scientific consortia including the Science Center for Excellence in Data Science . His teaching portfolio includes courses in artificial intelligence, data science, and robotics, with a focus on practical exercises and thesis supervision.

Sebastian BrandstäterView profile

Lecturer

• Scientific Machine Learning
• Inverse analysis & Bayesian Inference
• Uncertainty Quantification
• +5 more

Dr. Sebastian Brandstäter serves as Lecturer at the Institute for Mathematics and Computer-Based Simulation, Bundeswehr University Munich since 2022. His academic trajectory includes research associate positions at Hamburg University of Technology (2021) and Technical University of Munich (2016-2021). His research focuses on: Scientific Machine Learning for biomechanical systems Uncertainty Quantification and Bayesian Inference Global Sensitivity Analysis of complex models Multi-Physics & Multi-Scale Modeling of biological tissues Open-source scientific software development Dr. Brandstäter's work centers on gastrointestinal biomechanics, particularly computational modeling of gastric electromechanics and motility. He has pioneered applications of Gaussian-process metamodelling for sensitivity analysis in vascular and gastric systems, and develops open-source frameworks (QUEENS, 4C) that enable efficient multi-query analysis of large-scale models. He actively supervises student theses on patient-specific modeling and computational biomechanics, teaches advanced numerical methods courses, and contributes to the scientific community through conference organization, peer review, and international collaborations. His recent work demonstrates increasing emphasis on data-driven surrogate modeling and solver-independent computational frameworks for biomedical applications.

Adam PererView profile

Assistant Professor

• Data Visualization
• Human-Computer Interaction
• Machine Learning
• +3 more

Adam Perer is an Assistant Professor at Carnegie Mellon University within the Human-Computer Interaction Institute . His research focuses on data visualization , machine learning , and human-AI collaboration systems, particularly in healthcare contexts. Co-directs the Data Interaction Group Area Papers Chair at IEEE VIS PhD in Computer Science from University of Maryland Former Research Scientist at IBM Research His work integrates visual analytics with machine learning to create interactive systems for clinical data analysis , AI interpretability , and exploratory data science . Recent publications demonstrate applications in epidemiology , ICU medicine , and child welfare decision-making . Key research themes: Designing collaborative AI interfaces Visualizing embedding spaces for ML Interactive clinical decision support tools Explainable AI systems evaluation Temporal data exploration techniques Awards include Best Paper Honorable Mentions at CHI and EuroVis, plus Most Reproducible Paper recognition. He advises multiple PhD candidates and teaches courses in Data Visualization and Interactive Data Science .