جستجوی استاد راهنمای دانشگاهی

Tianyi Zhangمشاهده پروفایل

استادیار

• Software Engineering
• Human-Computer Interaction
• Artificial Intelligence
• +۵ مورد دیگر

Tianyi Zhang is a Tenure-Track Assistant Professor of Computer Science and Societal Impact Fellow at Purdue University's College of Science, where he leads the Human-Centered Software Systems Lab. His research focuses on building interactive intelligent systems that synergize human expertise with machine intelligence to improve programming productivity and software robustness. Dr. Zhang's research interests span Software Engineering, Human-Computer Interaction, and Artificial Intelligence. His work centers on developing systems that augment human intelligence with data-driven insights and augment machine intelligence with human guidance, primarily for programming domains including software developers, novice programmers, and computer end-users. His research on code mining and visualization helps programmers make more informed decisions through GitHub and Stack Overflow analysis, while his work on program synthesis assists novices with enriched feedback loops and interpretability. His recent publications (2024-2025) demonstrate a strong focus on leveraging large language models for code generation, program repair, and data wrangling, with particular emphasis on interactive systems that incorporate human feedback. This research direction shows consistent growth in understanding the intersection between human cognition and AI capabilities in programming contexts. Awards and Recognition: NSF Career Award for research on safe and reliable LLM-based code generation Amazon Research Award for human-in-the-loop deep learning optimization Best Paper Honorable Mention Award from SIGCHI for visualizing examples of deep neural networks Best Paper Honorable Mention Award from VAHC for interactive cohort analysis Dr. Zhang actively serves the research community as Program Committee member for major conferences including ICSE, ASE, FSE, CHI, and UIST. His service includes chairing workshops and student research competitions, demonstrating his commitment to mentoring the next generation of researchers. His lab develops systems that address real-world challenges in programming productivity and software safety, with applications spanning from autonomous driving systems testing to data science workflows.

Fatemeh Hendijani Fardمشاهده پروفایل

استادیار

• Code Intelligence
• Transfer Learning
• AI4SE
• +۵ مورد دیگر

Fatemeh Hendijani Fard is an Assistant Professor in the Department of Computer Science at the University of British Columbia's Okanagan campus. She serves as a graduate student supervisor and teaches courses in Computer Science and Data Science. Dr. Fard is a member of the CITECH program and MMRI, part of the Killam family of scholars, and an active member of both IEEE and ACM. Her research focuses on the intersection of Natural Language Processing and Software Engineering, with particular emphasis on developing code intelligence models for low-resource programming languages like R. She conducts empirical studies and develops techniques to improve the computational efficiency of code-language models while making them accessible to communities with restricted GPU access. Her work strongly advocates for Diversity and Inclusion in STEM, particularly for underrepresented females. Analysis of Dr. Fard's recent publications reveals a strong research trajectory in adapting Large Language Models for code intelligence with a focus on efficiency and accessibility. Her work spans multiple dimensions including code summarization, method name prediction, code search, code clone detection, and program repair, with special attention to low-resource programming languages. A notable trend is her exploration of adapter-based approaches for knowledge transfer that reduce computational requirements while maintaining performance. Izaak Walton Killam Memorial Scholarship Alberta Innovates Technology Futures (AITF) NSERC Discovery NSERC CREATE Mitacs Accelerate UBC Start-up Fund Dr. Fard has secured significant research funding including NSERC Discovery, NSERC CREATE, Mitacs Accelerate, and UBC Start-up funds to support her work on code intelligence for low-resource programming languages. She actively serves as a graduate student supervisor, guiding research in areas related to code representation learning and mining software repositories. Her service to the academic community is extensive, having served on program committees for major conferences including FSE, MSR, ASE, SANER, and ICSME across multiple years. Dr. Fard leads research initiatives focused on making code intelligence accessible to communities working with understudied programming languages. Her team conducts empirical studies and develops new techniques specifically designed for low-resource languages, with particular attention to the R programming language. This work addresses diversity and inclusion in AI tools by ensuring developers with limited computational resources can benefit from advances in neural networks and automated tools.

Myra Spiliopoulouمشاهده پروفایل

استاد

• Data Mining
• Knowledge Discovery
• Machine Learning
• +۴ مورد دیگر

Myra Spiliopoulou is a Professor of Business Information Systems at the Faculty of Computer Science, Otto-von-Guericke-University Magdeburg since February 2003, leading the Knowledge Management and Data Mining (KMD) working group. Her research bridges theoretical data mining with practical healthcare and business applications. Education: 1982-1986: Mathematics, University of Athens, Greece 1988-1994: PhD Student & Research Assistant, Department of Informatics, University of Athens 1992: PhD in Parallel Query Optimization, University of Athens 2000: Habilitation in Business Information Systems, Humboldt-University Berlin Research Focus: Prof. Spiliopoulou specializes in temporal pattern mining , ethical AI , and healthcare data analytics , with notable contributions to epidemic surveillance systems. Her work integrates machine learning with domain-specific knowledge for real-world decision support. She has delivered authoritative tutorials at premier conferences including KDD (2015, 2018, 2019), IEEE ICDM (2017), ECML/PKDD (2014, 2016), and AIME (2022, 2023), establishing her as a leading educator in data science methodologies. Contact: myra@iti.cs.uni-magdeburg.de

Frederik Baucksمشاهده پروفایل

پژوهشگر ارشد

• Educational Data Mining
• Learning Analytics
• Curriculum Analytics
• +۳ مورد دیگر

Dr. Frederik Baucks serves as a Research Fellow at the Institute of Neuroinformatics (INI), part of the Faculty of Computer Science at Ruhr University Bochum, Germany. His work bridges neural computation principles with practical applications in educational technology, focusing on data-driven solutions for higher education challenges. He completed his doctoral dissertation Curriculum analytics: Data-driven insights for educational decision-making at Ruhr University Bochum in 2025, establishing foundational work in educational data mining. His academic journey reflects specialization in quantitative methods for educational systems. Dr. Baucks' research centers on curriculum analytics and learning analytics , with particular expertise in Item Response Theory applications for course difficulty analysis. His work systematically addresses bias detection in grading systems, equity in educational outcomes, and development of decision-support tools for academic administrators. Current projects extend into Generalized Linear Models for educational datasets, emphasizing practical implementation in university settings. His publication trajectory (2022-2025) reveals consistent focus on translating complex statistical methods into actionable educational insights, with increasing emphasis on dashboard development and real-world implementation through projects like KI:edu.nrw. Key contributions include novel frameworks for differential course functioning analysis and prerequisite-free curriculum simulation. Scientific recognition includes: Best Paper Nominee at LAK24: 14th International Learning Analytics and Knowledge Conference (2024) Best Paper Nominee at DELFI 2023: 21st Conference on Educational Technology Dr. Baucks teaches lab courses in Python for data analysis (Summer Terms 2023-2024) and actively collaborates on grant-funded initiatives, though specific grant details aren't documented in the source material. His advisory role focuses on technical implementation rather than student supervision. As core member of the INI's KI:edu.nrw project team, he develops the academic advising dashboard in collaboration with Prof. Laurenz Wiskott's research group. This cross-disciplinary team integrates neuroinformatics principles with educational technology, operating within the Faculty of Computer Science's infrastructure at Universitätsstr. 150, Bochum.

Sami Nennoمشاهده پروفایل

پژوهشگر

• Disinformation
• Political Communication
• Natural Language Processing
• +۳ مورد دیگر

Sami Nenno is an associate researcher at the AI & Society Lab of the Alexander von Humboldt Institute for Internet and Society (HIIG) and a postdoctoral researcher at the Center Synergy of Systems (Synosys) at the Technical University of Dresden. His work focuses on the intersection of artificial intelligence, disinformation, and political communication, with an emphasis on developing automated tools for fact-checking and analyzing large-scale text data. His educational background includes: Doctorate in Communication Science from the University of Bremen Master's degree in Philosophy from Humboldt University Berlin Master's degree in Data Science from the Berlin University of Technology Nenno's research interests center on disinformation and political communication in social media, natural language processing for fact-checking, and sustainable artificial intelligence. He has developed tools for monitoring disinformation and has investigated the sustainability and efficiency of machine learning models, particularly in reducing energy consumption during training. His recent publications demonstrate a strong focus on the analysis of misinformation across multiple countries, the use of fact-checking articles as data sources, and the development of methods for claim detection and named entity recognition in the political domain. The work spans communication science, computer science, and sustainable computing. No scientific awards were mentioned in the provided information. There is no information available regarding advising of students or research grants. Nenno is actively involved in the AI & Society Lab at HIIG, where he contributes to projects such as Public Interest AI, which aims to develop methods for assessing public interest orientation in AI systems. He also participates in organizing events and workshops on AI and society, including the Sustainable AI series and hackathons.

• Machine Learning
• Data Mining
• Computational Material Science
• +۴ مورد دیگر

Prof. Dr. Stefan Sandfeld is the Director of the Institute for Advanced Simulation (IAS) at Forschungszentrum Jülich , leading the Materials Data Science and Informatics (IAS-9) group. His research focuses on integrating machine learning, data mining, and computational methods to address challenges in materials science, particularly in analyzing dislocation dynamics, microstructure evolution, and high-throughput microscopy data. Key areas include developing AI-driven tools for electron microscopy analysis, creating FAIR-compliant data ecosystems (e.g., Helmholtz Knowledge Graph), and advancing materials informatics for semiconductor and alloy systems. He spearheads projects like the NFDI-MatWerk consortium, aiming to standardize materials data management. His work bridges experimental and computational approaches, with applications in SiC crystal growth, dislocation modeling, and defect characterization. Recent contributions include self-supervised learning frameworks for microscopy images and ontology-based systems for dislocation data (DISO). His research also explores generative models for accelerated materials design and data-driven approaches for microstructure-property relationships. Scientific highlights include: High-throughput analysis of in-situ TEM experiments for dislocation dynamics. FAIR data ecosystems via semantic web technologies (e.g., Helmholtz Knowledge Graph). Machine learning for nanoindentation analysis and 4H-SiC wafer defect detection. He collaborates with Helmholtz institutes and industry partners, driving digitalization in materials science through interdisciplinary initiatives. His lab focuses on advancing AI tools for material discovery, process optimization, and understanding multiscale material behavior.

Georg Rehmمشاهده پروفایل

استاد مدعو

• Language Technology
• Computational Linguistics
• Artificial Intelligence
• +۴ مورد دیگر

Georg Rehm holds an Adjunct Professorship in Computational Linguistics and Language Technology at Humboldt-Universität zu Berlin's Faculty of Philosophy. He is a Principal Researcher and Research Fellow at DFKI Berlin's Speech and Language Technology Lab, and Head of the W3C Germany/Austria Chapter. His roles include coordinating major EU initiatives like the European Language Grid (ELG) and QURATOR. Rehm's work spans Language Technology, AI, and multilingual systems, with a focus on digital language equality, curation technologies, and open science infrastructure. He leads projects such as HIVEMIND (2025-2027) and Language Data Space (2023-2025), and has contributed over 275 publications in computational linguistics and related fields. His research addresses challenges in multilingual NLP, fake news detection, and ethical AI deployment. Rehm oversees strategic initiatives like the META-NET network and chairs conferences such as ACL Industry Track and Sci-K workshops. He advises on EU policy frameworks for language technology and collaborates with organizations like the DIN Presidential Committee and FOCUS.ICT. His academic network spans global institutions, and he actively participates in program committees for top venues including ACL, NAACL, and COLING. Rehm's lab develops tools like the Knowledge Storage Ecosystem and contributes to open-source LLM projects like Occiglot and OpenGPT-X. He holds a PhD and has over 25 years of industry-research experience. His research interests include semantic storytelling, interoperable metadata, and AI ethics. He is affiliated with multiple institutes, including the DFKI Lab Berlin and the W3C, and serves on editorial boards for journals like Language Resources and Evaluation.

• Neural Networks
• Adaptive Resonance Theory
• Machine Learning
• +۵ مورد دیگر

Professor Ah-Hwee Tan is a distinguished faculty member at Singapore Management University's School of Computing and Information Systems, Department of Information Systems. With over 30 years of academic contributions since 1991, his research has significantly advanced neural network architectures, particularly Adaptive Resonance Theory (ART), with applications spanning multiple domains of artificial intelligence. His primary research interests include Neural Networks , Adaptive Resonance Theory , Machine Learning , Reinforcement Learning , Knowledge Graphs , Natural Language Processing , and Multi-Agent Systems . Professor Tan's work bridges theoretical neural computation with practical applications, developing novel approaches for knowledge representation, semantic understanding, and intelligent decision-making systems. His recent publications (2022-2025) demonstrate continued innovation across multiple AI subfields, with particular emphasis on hierarchical reinforcement learning, knowledge graph refinement, sentiment analysis, and federated learning architectures. The research shows a clear trajectory from foundational neural network theory toward increasingly complex real-world applications in healthcare, social media analysis, and multi-agent coordination. Professor Tan has mentored numerous researchers who have become significant contributors in their own right, including Budhitama Subagdja, Shubham Pateria, and Di Wang. His collaborative work spans international institutions, reflecting his standing in the global AI research community. His research has been consistently published in top-tier venues including IEEE Transactions, Neural Networks, ACM journals, and major AI conferences (AAAI, IJCAI), demonstrating both theoretical rigor and practical impact across computer science and interdisciplinary applications.

• Computer Science
• Artificial Intelligence
• Machine Learning
• +۷ مورد دیگر

Peng Zhao is a Researcher affiliated with multiple institutions including University of Georgia (Department of Biochemistry and Molecular Biology) and Xi'an Jiaotong University , among others. His research spans interdisciplinary domains such as Computer Science Artificial Intelligence Biomedical Engineering Robotics Data Mining and focuses on neural networks, optimization algorithms, and signal processing. Peng Zhao's recent publications highlight trends in deep learning for vehicle routing problems Wi-Fi-based gesture recognition autonomous agricultural robotics federated learning for transportation systems UAV-assisted vehicle platoons applications. He has collaborated extensively with researchers like Wei Pang , Yilong Yin , and Xiang Zhang on projects involving computational modeling, biomedical imaging, and network security.

• Wireless Communications
• MIMO Systems
• Cloud Computing
• +۷ مورد دیگر

Yan Yao is a Professor at Hefei University of Technology, School of Computer Science and Information Engineering, with a distinguished research career spanning over two decades. Her work bridges theoretical foundations with practical implementations across wireless communications, cloud computing, and medical informatics, demonstrating both technical depth and interdisciplinary versatility. Her primary research domains include: Wireless Communications - Pioneering work on distributed wireless communication systems, MIMO technologies, and physical layer security Cloud and Edge Computing - Innovative resource allocation mechanisms using game theory and auction models Medical Informatics - Applying machine learning to critical healthcare challenges including sepsis prediction and diabetic retinopathy analysis Blockchain and Security - Developing secure data sharing frameworks for industrial IoT applications Analysis of Yan Yao's publication trajectory (2002-2025) reveals a strategic evolution from foundational wireless communications research to interdisciplinary applications. Her early work (2002-2008) established her expertise in distributed wireless systems architecture and MIMO technologies, frequently collaborating with Tsinghua University researchers. More recently (2018-2025), she has expanded into cloud-edge computing, blockchain applications, and healthcare analytics, demonstrating remarkable adaptability while maintaining technical rigor. Her most impactful recent contributions integrate multiple domains to solve complex real-world problems, such as applying game theory to cloud-edge resource allocation and developing machine learning solutions for clinical prediction. Yan Yao's research impact is reflected in her consistent publication record in high-impact venues including IEEE Transactions, BMC Medical Informatics and Decision Making, and top-tier conferences. Her work shows a clear progression from technical contributor to research leader, with increasing emphasis on interdisciplinary applications that address significant societal challenges.

• Social Computing Systems
• Algorithmic Fairness
• Privacy and Security in Social Media
• +۳ مورد دیگر

Krishna P. Gummadi is a Professor and Scientific Director at the Max Planck Institute for Software Systems (MPI-SWS) , leading the Networked Systems Research Group. His work bridges computer science and social science, focusing on social computing systems that network humans and computers at societal scale. Academic Leadership: Faculty (Scientific Director), Head of Networked Systems Research Group Key Research Areas: Algorithmic fairness, privacy in social media, distributed systems, human-centered machine learning His research methodology combines user-centric studies through large-scale observational analysis, data-centric studies using statistical learning and NLP, and systems-centric approaches for practical deployments like information diet tools and fraud detection services . Current work explores crowdsourcing systems , peer-to-peer networks , and trusted cloud computing . Scientific Awards & Recognitions: CNIL-INRIA Privacy Runners-Up Award (2018) Casper Bowden PET Runners-Up Award (2018) SIGCOMM Test of Time Award (2007) Students & Postdocs: Current: Junaid Ali, Reza Babaei, Nina Grgić-Hlača, Preethi Lahoti, Johnnatan Messias, Till Speicher Former: Asia J. Biega (Microsoft Research), Przemyslaw Grabowicz (UMass Amherst), Muhammad Bilal Zafar (Bosch AI), Juhi Kulshrestha (GESIS), Mainack Mondal (University of Chicago), Rijurekha Sen (IIT Delhi)

Jiameng Puمشاهده پروفایل

پژوهشگر

• Cybersecurity
• Machine Learning
• Deep Learning
• +۴ مورد دیگر

Jiameng Pu is a Researcher in the Department of Computer Science at Virginia Tech, part of the College of Engineering. Their research focuses on cybersecurity, deep learning, and adversarial machine learning, with notable contributions to deepfake detection and AI robustness. Pu holds a Ph.D. in Computer Science and Applications from Virginia Tech (2017–2022) and a B.E. in Computer Science and Technology from Wuhan University (2013–2017). Education : Ph.D. in Computer Science and Applications, Virginia Tech (2017–2022) B.E. in Computer Science and Technology, Wuhan University (2013–2017) Research Interests : Pu’s work addresses cutting-edge challenges in cybersecurity, including detecting deepfake content (text, images, and videos), defending against adversarial attacks on AI models, and improving robustness in medical imaging systems. Their research combines techniques from machine learning, computer vision, and data mining to enhance security and reliability in AI applications. Key Publications : Pu has authored influential papers on deepfake detection (IEEE S&P 2023, WWW 2021) and AI security (USENIX Security 2021, EuroS&P 2020). Their work spans theoretical advancements and practical tools like NoiseScope for blind deepfake image detection. Awards : Dr. Dennis Kafura Graduate Fellowship (2022) Nominated for 2021 PhD Fellowship Awards (Facebook Visa Research Scholarship) IEEE S&P 2020 Student Travel Grant NDSS 2019 Student Travel Grant National Endeavor Fellowship (Twice), Chinese Ministry of Education Pu’s professional experience includes software engineering at Facebook (2021) and data science roles at IBM and Wuhan University labs. They actively collaborate with industry and academia to advance AI security and ethical AI practices.

• Artificial Intelligence
• Natural Language Processing
• Information Retrieval
• +۵ مورد دیگر

Xin Zhao is a prominent professor at Renmin University of China's School of Information, Department of Computer Science, with an extensive publication record spanning from 1997 to 2026. His research demonstrates significant contributions across multiple disciplines including artificial intelligence, natural language processing, computer vision, and interdisciplinary applications in ecology, medicine, and business. Dr. Zhao's research interests are remarkably diverse, focusing primarily on artificial intelligence and its applications. His work spans natural language processing, large language models, information retrieval, machine learning, and computer vision. Recent publications reveal a strong emphasis on practical AI applications in healthcare (dental implant failure prediction), environmental science (kelp bed dynamics), finance (green finance impact), and industrial systems (composite curing process monitoring). His research often combines theoretical advancements with real-world problem solving, demonstrating both academic rigor and practical relevance. Analysis of his recent publications (2024-2026) shows a clear trend toward interdisciplinary AI applications, with increasing focus on multimodal learning, robustness in dynamic environments, and practical implementations across various sectors. His work bridges theoretical computer science with domain-specific challenges in medicine, ecology, finance, and industrial engineering. The publications demonstrate sophisticated methodological approaches including deep learning architectures, mathematical modeling, and novel algorithmic solutions to complex problems. Dr. Zhao has made significant contributions to the academic community through numerous publications in high-impact journals and conferences including IEEE Transactions, ACL, AAAI, and CVPR. His collaborative work spans international boundaries, with co-authors from institutions worldwide, indicating strong research networks and interdisciplinary collaborations. His research group appears to be actively engaged in cutting-edge AI research, with particular strengths in large language models, multimodal learning, and practical AI applications. The group's work on projects like C-3PO (Compact Plug-and-Play Proxy Optimization) and RMoA (Optimizing Mixture-of-Agents) demonstrates leadership in emerging AI methodologies. Current research directions include enhancing LLM capabilities, improving multimodal understanding, and developing robust AI systems for real-world applications across diverse domains.

• Computer Vision
• Remote Sensing
• Machine Learning
• +۴ مورد دیگر

Yin Zhang is a Professor at Zhejiang University's College of Computer Science and Technology, specializing in computer vision, remote sensing, and machine learning. With over 300 publications spanning from 1992 to 2025, their research has evolved from foundational work to cutting-edge applications in multimodal analysis and AI-driven imaging systems. Research interests focus on computer vision with particular expertise in object detection, image fusion, and remote sensing applications. Their work bridges theoretical advancements with practical implementations across medical imaging, autonomous systems, and geospatial analysis. Recent publications demonstrate innovative approaches to multimodal data integration, including diffusion models and knowledge distillation techniques for enhanced image understanding. Publication trends show increasing specialization in remote sensing applications since 2019, with significant contributions to vehicle detection, land use classification, and maritime surveillance. The research portfolio demonstrates consistent methodological innovation while addressing real-world challenges in areas ranging from agricultural monitoring to conflict analysis. As a research supervisor, Yin Zhang has mentored numerous students including Zian Ning, Xiaoyu Zhang, and Shiyu Zhao, with collaborative projects frequently appearing in top-tier venues like IEEE Transactions and CVPR. Their work demonstrates strong interdisciplinary connections between computer science, electrical engineering, and domain-specific applications.

Alexander Fraserمشاهده پروفایل

استاد

• Machine Learning
• Natural Language Processing (NLP)
• Multilingual Systems
• +۵ مورد دیگر

Prof. Alexander Fraser is a faculty member at the Technical University of Munich (TUM), holding the Chair of Data Analytics & Statistics within the TUM School of Computation, Information and Technology. He specializes in machine learning for natural language processing, particularly focusing on multilingual systems, data sparsity, and cross-lingual alignment. His research bridges theoretical advancements and practical applications, including projects on hate speech detection, low-resource language support, and ethical AI. Fraser earned his PhD in Computer Science from the University of Southern California (2007). Prior to TUM, he held professorial roles at LMU Munich (2013–2024) and the University of Stuttgart (2007–2013). He has led significant research initiatives, including ERC grants and EU-funded projects like Health in my Language (HimL). His work has been recognized with awards such as the ERC Advanced Grant (2023) and ERC Proof of Concept Grant (2022). Teaching responsibilities include courses on large language models, generative models, and machine learning at TUM. Fraser is also a Principal Investigator at the Munich Center for Machine Learning (MCML) and an active member of the ELLIS Unit Munich. His contributions span academic research, industry collaboration, and language revitalization efforts for minority languages like Sorbian. Key publications highlight innovations in multilingual benchmarks, cross-lingual alignment, and ethical NLP applications. His research addresses challenges in data scarcity, model bias, and responsible AI deployment across diverse linguistic landscapes.