Find Academic Supervisors

Przemysław KazienkoView profile

Professor

• Data Science
• Social Networks
• Complex Networks
• +12 more

Przemysław Kazienko is a Full Professor at the Department of Artificial Intelligence , within the Faculty of Information and Communication Technology at Wrocław University of Science and Technology , Poland. His research spans data science , social and complex networks , machine learning , and human-centered AI , with a focus on emotional analysis via wearables , LLM ethics , and information overload in social systems. Research Interests: Data science applications in social networks, temporal network epistemology, affective computing with wearables, NLP for LLMs, and ethical considerations in AI. Leadership: Founder of ImpactAI (2024), Emognition (2019), and HumaNLP (2020). Previously led Social Network Group and Data Science Group. Education: Pioneered Poland’s first data science master’s program (2018), later converted to an AI master’s program with plans for an AI undergraduate program (2025). Publication Trends: Kazienko’s recent work addresses LLM manipulation , emotion detection via ECG , and ethical AI frameworks . His 2025 article on Pulse Rate Variability integrates adaptive filtering for health monitoring, while 2024 studies explore LLM self-training , wearable-based emotion systems , and responsible AI . Scientific Awards: Best Paper Award at WristSense 2022 Collaboration & Grants: He actively collaborates on projects involving multi-label NLP , temporal network analysis , and wearable health systems . His grants focus on evaluating research funding effectiveness through bibliometrics. Labs & Teams: Leads ImpactAI , Emognition , and HumaNLP , fostering interdisciplinary research on AI’s societal impact, emotion recognition, and human-centric NLP. His teams have developed systems like Emognition for real-life emotion detection and PALS for personalized active learning in NLP.

• Machine Learning
• Maritime Engineering
• Robotics
• +4 more

Shengkai Zhang is an active researcher with 26 publications and 444 citations spanning engineering, computer science, and environmental disciplines. His work demonstrates strong interdisciplinary collaboration through co-authorship with researchers like Kezhong Liu and Mozi Chen across multiple high-impact venues including IEEE conferences, arXiv, and specialized journals. His research interests center on Machine Learning applications in maritime systems , with significant contributions to Large Language Model integration for ship navigation, wireless sensing for bridge officer monitoring, and sensor fusion techniques. Additional expertise spans robotics perception (visual-inertial systems, mmWave radar enhancement), environmental modeling (urban energy systems, climate studies), and biomedical applications of traditional medicine. Recent work shows increasing focus on AI foundation models and their security implications. Zhang's publication trajectory reveals consistent output with accelerating impact since 2023, featuring 15+ papers in 2024 alone. His research clusters around three core themes: Maritime AI Systems (LLM navigation, track association, watchkeeping monitoring) Advanced Sensing Technologies (mmWave radar, Wi-Fi sensing, GNSS fusion) Environmental & Biomedical Applications (urban energy modeling, gut microbiome studies) These areas demonstrate both technical depth in signal processing/computer vision and practical focus on real-world engineering challenges.

Maciej PondelView profile

Professor

• Artificial Intelligence
• Machine Learning
• Business Intelligence
• +7 more

Dr. Maciej Pondel is a Professor and Head of the Department of Business Intelligence in Management at Wrocław University of Economics (Wrocław, Poland). He holds a PhD in Economics and has over 25 years of experience in IT, business strategy, and academic research. His work bridges artificial intelligence (AI), machine learning (ML), and business management, with a focus on digital transformation, data-driven decision-making, and healthcare analytics. Educations: Bachelor's Degree in Computer Science from Wrocław University of Science and Technology PhD in Economics from Wrocław University of Economics Research Interests: Applications of AI/ML in healthcare (e.g., kidney transplantology, heart failure management) Business intelligence systems, data warehouses, and big data analytics Digital twin technology and smart city solutions Marketing analytics, customer segmentation, and churn prediction IT project management and enterprise systems optimization Research Trends: Pondel's recent work emphasizes AI-driven solutions for both medical diagnostics and business strategy. He has explored predictive models in healthcare, financial market analysis via multi-agent systems, and urban sustainability applications like parking optimization. His interdisciplinary approach combines technical expertise with real-world business challenges. Grants & Projects: Developed the Upsaily system for sales data analysis using AI Led numerous commercial and R&D projects since 2000 Contributed to knowledge portals for local governments and bioscience initiatives Labs/Teams: Heads the Department of Business Intelligence in Management, fostering collaboration between academia and industry through applied research.

ZHANG JiahengView profile

Assistant Professor

• Cryptography
• Security
• Artificial Intelligence
• +7 more

ZHANG Jiaheng is an Assistant Professor in the Department of Computer Science at the National University of Singapore (NUS), School of Computing. His work bridges cryptography, artificial intelligence, and system security, with a focus on scalable and privacy-preserving technologies. He teaches CS3235 – Computer Security and leads research in zero-knowledge proofs, LLM safety, and trustworthy AI. Research Interests: His research spans Cryptography , Security , Machine Learning & AI , Privacy , and Algorithms & Theory . He specializes in making zero-knowledge proofs practical at scale and securing large language models against jailbreaking, backdoors, and privacy leaks. His recent projects include zkGPT, BatchZK, and Guardreasoner, highlighting his dual focus on theoretical foundations and real-world applications. The recent publications show a strong trend toward scalable zero-knowledge systems and AI security , particularly in verifying and protecting LLMs. These works integrate cryptographic rigor with modern AI challenges, reflecting a cohesive research vision at the frontier of trustworthy computing. Scientific Contributions: Developed scalable collaborative zk-SNARKs for efficient proof generation. Pioneered techniques for secure LLM inference and jailbreak detection. Advanced GPU-accelerated and distributed zero-knowledge proof systems. Advising & Grants: While specific students and grants are not listed, his active publication record in top-tier venues suggests ongoing research supervision and external funding in cybersecurity and AI. He is likely involved in advising PhD and Master’s students in cryptography and AI security. Labs & Teams: He is part of the NUS School of Computing research ecosystem, potentially affiliated with cybersecurity or AI labs, contributing to Singapore’s leadership in privacy-preserving technologies.

• Natural Language Processing
• Biomedical Informatics
• Health Communication
• +3 more

Yue Guo is an Assistant Professor in the Department of Information Sciences at the University of Illinois. Her research focuses on natural language processing (NLP), biomedical informatics, and healthcare communication. She explores how large language models (LLMs) can improve lay language summarization of complex scientific content, particularly in healthcare contexts. Her work emphasizes personalized jargon detection, factuality evaluation of summaries, and accessibility of health information for diverse audiences. Guo has also conducted extensive studies on radiation oncology outcomes, including xerostomia (dry mouth) recovery in head and neck cancer patients and the impact of radiation dose patterns on patient-reported symptoms. Her interdisciplinary research bridges computational methods with clinical needs, aiming to enhance patient understanding and care through advanced NLP techniques. Her recent articles (2021–2025) highlight trends in LLM-driven biomedical communication, evaluation of health summaries, and data-driven approaches to cancer treatment optimization. Notable projects include APPLS (a summarization metric framework) and personalized jargon identification systems for interdisciplinary collaboration. Guo teaches IS 504: Sociotechnical Information Systems , integrating technical and human factors in information systems design.

Xuan WangView profile

Assistant Professor

• Natural Language Processing
• Data Mining
• AI for Sciences
• +7 more

Xuan Wang is an Assistant Professor in the Department of Computer Science at Virginia Tech, affiliated with the Sanghani Center for Artificial Intelligence and Data Analytics. She holds a Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign (UIUC), with additional M.S. degrees in Statistics and Biochemistry from UIUC, and a B.S. in Biological Science from Tsinghua University. Her research focuses on Natural Language Processing (NLP), Data Mining, AI for Sciences, and AI for Healthcare, emphasizing applications in complex reasoning with LLMs, multi-modal science foundation models, and healthcare informatics. Her work has been recognized through awards including the Nvidia Academic Grant (2025), Cisco Research Award (2025), and NSF NAIRR Pilot Award (2024-2025). She has organized workshops at ACL, VL/HCC, and ICDM, and serves on program committees for top conferences like NeurIPS, EMNLP, and KDD. Xuan's research spans scientific text mining (e.g., knowledge extraction from biomedical literature), multi-agent LLM systems for clinical triage, and foundational models for multi-omics data analysis. Her lab actively collaborates with institutions like Children’s National Hospital and the Fralin Biomedical Research Institute, with funding from NSF, CCI, and industry partners. Education: Ph.D. in Computer Science, UIUC (2022) M.S. in Statistics, UIUC (2017) M.S. in Biochemistry, UIUC (2015) B.S. in Biological Science, Tsinghua University (2013) Grants & Awards: NVIDIA Academic Grant (2025) – Small LLM Agent Systems Cisco Research Award (2025) – Complex Reasoning with LLMs NSF NAIRR Pilot (2024-2025) – Multi-omics Analysis Lab & Teams: Wang Lab focuses on AI-driven biomedical research, including single-cell omics analysis, brain signal interpretation, and LLM-based scientific discovery. Collaborations include the Virginia Tech Presidential Postdoctoral Fellowship program and industry initiatives like the Amazon + VT Center for Efficient ML.

Ziang XiaoView profile

Assistant Professor

• Human-Computer Interaction (HCI)
• Natural Language Processing (NLP)
• Social and Personality Psychology
• +4 more

Ziang Xiao is an Assistant Professor in Computer Science at Johns Hopkins University. He holds a Ph.D. from the University of Illinois Urbana-Champaign (UIUC) and a B.S. in Psychology and Computer Science from UIUC. His research focuses on human-computer interaction (HCI), natural language processing (NLP), and social psychology, aiming to bridge human behavior, decision-making, and technology. Education : Ph.D. in Computer Science, UIUC (Advisors: Prof. Hari Sundaram & Prof. Karrie Karahalios) B.S. in Psychology and Computer Science, UIUC (Advisor: Prof. Dov Cohen) Research Interests : Xiao’s work explores AI for social science, human-centered model evaluation, and information seeking. He designs systems that leverage LLMs to study human behavior at scale, emphasizing ethical and transparent AI integration. His interdisciplinary approach combines HCI, NLP, and psychology to address challenges like bias in multilingual models and trust in AI systems. Teaching : EN.601.790: Advanced HCI: Research Methods (Spring 2025) EMNLP 2024 Tutorial: Human-Centered Evaluation of Language Technologies EN.601.490/690: Introduction to Human-Computer Interaction (Fall 2024) Key Awards : Best Paper Award at CHI 2024 for work on LLM-driven search systems Honorable Mention at ICML 2023 for rethinking model evaluation frameworks Grants & Labs : Xiao leads projects on LLM ethics, adaptive AI systems, and HCI methodologies. His lab collaborates with industry partners (e.g., Microsoft, Google) and academic institutions to advance human-AI interaction paradigms.

• Assistive Technology
• Augmented Reality
• Computer Vision
• +5 more

Zhigang Zhu is the Herbert G. Kayser Professor of Computer Science at The City College of New York (CUNY), affiliated with the Grove School of Engineering. He holds academic roles in the Computer Science PhD Program and M.S. Program in Cognitive Neuroscience at the CUNY Graduate Center. As Director of the City College Visual Computing Laboratory (CCVCL) and Co-Director of the Master’s Program in Data Science and Engineering, he focuses on advancing assistive technologies, computer vision, and human-computer interaction. His research emphasizes accessibility solutions for visually impaired individuals, leveraging AR/VR, machine learning, and multimodal perception. Education: Ph.D. (Computer Science, with honor) from Tsinghua University (1997), M.E. (1991), B.E. (1988) Affiliations: Department of Computer Science, CCNY; CUNY Graduate Center Research interests include assistive technology applications, augmented reality systems, and energy efficiency analysis. Notable projects include the BLV App Arcade for visually impaired navigation and MAC-U-Vision+ for AMD patients. He has received awards such as the President’s Award for Excellence (2013) and the CUNY Salute to Scholars recognition. His work integrates AI, computer vision, and IoT to address urban accessibility challenges, with contributions to sidewalk material analysis, real-time indoor navigation, and emotion recognition systems. Ongoing projects explore multimodal data fusion and energy-efficient building systems.

• Geospatial Digital Twins
• Virtual Geographic Environments
• Remote Sensing
• +7 more

Jun Zhu is a professor at the Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu, China. His research is centered on geospatial digital twins, virtual geographic environments, and intelligent visualization for disaster risk management, with strong interdisciplinary work in AI, remote sensing, and VR-based simulation. His research interests include: Geospatial Digital Twins Virtual Geographic Environments AI for Remote Sensing 3D and VR-based Disaster Visualization Knowledge Graphs in GIS Public Risk Communication The recent articles (2023–2025) demonstrate a strong trend in integrating large language models, knowledge graphs, and deep learning with geospatial data to build intelligent, interactive, and immersive systems for infrastructure monitoring, disaster simulation, and public engagement. His work emphasizes data-knowledge fusion, human-centered visualization, and real-world applicability in urban and environmental contexts. Scientific Awards: No awards listed in the provided text. Advising and Grants: While Jun Zhu has extensive collaboration with researchers such as Weilian Li, Qing Zhu, Yakun Xie, and Jianbo Lai, and appears to lead research projects, there is no explicit mention of student advising, grant funding, or project titles in the provided data. Labs and Teams: Jun Zhu is likely part of a research group focused on digital twins and geospatial AI at Southwest Jiaotong University, collaborating closely with colleagues in geoinformatics and remote sensing, though specific lab names are not mentioned.

Michael WhiteView profile

Professor

• Computational Linguistics
• Natural Language Generation
• Spoken Language Dialogue Systems
• +3 more

Michael White is a Professor in the Department of Linguistics at The Ohio State University, specializing in computational linguistics, natural language generation, and spoken language dialogue systems. He holds a Ph.D. in Computer Science from the University of Pennsylvania and has collaborated with interdisciplinary teams in both academia and industry, including roles at CoGenTex, Inc., and visiting positions at Facebook/Meta and Australian universities. His research emphasizes controllable and ethical AI, with a focus on neural language models and their applications in healthcare and conversational systems. Education: Ph.D. in Computer Science, University of Pennsylvania. Professional Background: Former Research Fellow at the University of Edinburgh School of Informatics, and founder of CoGenTex, Inc. Collaborations include researchers in Computer Science and Linguistics at Ohio State, such as Eric Fosler-Lussier and William Schuler. Research Interests: Neural language generation controllability, ethical AI alignment, discourse processing, and conversational systems. Recent projects include developing virtual patient simulators for medical education and exploring knowledge distillation to mitigate AI-generated harm. Articles Trends: Focused on advancing neural NLG techniques, improving dialogue system safety, and leveraging large language models for domain-specific tasks (e.g., healthcare, KBQA). Recent work addresses transparency, data efficiency, and ethical implications in AI. Scientific Awards: Lumley Interdisciplinary Research Award (2023), PRE Accelerator Award. Grants: Supported by interdisciplinary initiatives such as the virtual patient project collaboration with Eric Fosler-Lussier and Laura Wagner. Advising and Grants: Mentors students in computational linguistics and leads projects funded by Ohio State’s interdisciplinary programs. Current work involves neuro-symbolic approaches for patient preparation assistants and data-efficient conversational AI. Labs/Teams: Collaborates with the Ohio State Computational Linguistics Group and industry partners. Active in organizing workshops on discourse-driven NLG and ethical AI challenges.

Siegfried HandschuhView profile

Professor

• Data Science
• Artificial Intelligence
• Machine Learning
• +4 more

Prof. Dr. Siegfried Handschuh is a Full Professor for Data Science and Natural Language Processing at the Institut für Informatik (ICS-HSG), University of St. Gallen. His research focuses on advanced NLP techniques, financial text analysis, and AI-driven solutions in cybersecurity and education. He leads projects like CS-AWARE-NEXT, enhancing cybersecurity awareness in public institutions. Prof. Handschuh has authored over 100 publications, with recent work emphasizing transformer optimization, generative AI applications, and educational tools for argumentative writing. His team collaborates with companies like Rheasoft and Peracton on AI-powered financial analytics and cybersecurity systems. Education: Doctorate in Computer Science, specialized in knowledge representation and semantic web technologies. Research Interests: Data science, machine learning, financial NLP, cybersecurity, and AI in education. His work bridges academia and industry, addressing real-world challenges in finance, cybersecurity, and educational technology through innovative AI frameworks.

• Computer Science
• Computational Linguistics
• Environmental Science
• +3 more

Weihong Zhong is an active researcher with dual expertise in Computer Science (particularly large language models and controllable text generation) and Environmental Science (focusing on biodegradation of industrial pollutants like phthalate esters and plastic waste). Their work includes methodological innovations in ACL and EMNLP venues, as well as biochemical studies in Applied and Environmental Microbiology and Synthetic and Systems Biotechnology . Key collaborators include Xiaocheng Feng , Lei Huang , and Bing Qin , with whom they co-author multiple papers across NLP and environmental research. Weihong Zhong’s computational research addresses LLM hallucinations (via frameworks like RHIO, Seal, and FRONT), context window extension (through positional encoding optimization), and controllable generation (using latent space probability density estimation). In environmental science, their work focuses on degrading phthalate esters (PAEs) and plastic pollutants using bacterial consortia and enzymatic engineering. Papers like the 2024 GroundBench study and 2023 MMHalSnowball framework highlight their contributions to multimodal hallucination mitigation. Scientific awards or honors are not explicitly documented in the provided materials. Their interdisciplinary approach bridges technical innovation in AI/ML with applied biological solutions for environmental remediation, as evidenced by publications in both top-tier computational conferences and environmental journals .

Yiwei WangView profile

Assistant Professor

• Natural Language Processing
• Large Language Models
• Graph Machine Learning
• +3 more

Yiwei Wang is an Assistant Professor at the Department of Computer Science, University of California, Merced, leading the UC Merced NLP Lab . He holds a Ph.D. from National University of Singapore (2023), M.Phil from Hong Kong University of Science and Technology (2019), and B.S. from Southeast University (2017). His research focuses on natural language processing , large language models , and graph machine learning , with emphasis on trustworthy AI systems.

• Artificial Intelligence
• Natural Language Processing
• Knowledge Management
• +7 more

Jorjeta Jetcheva is an Assistant Professor in the Computer Engineering Department at San José State University, part of the Charles W. Davidson College of Engineering. She brings extensive industry experience from leadership roles at Accenture, Fujitsu Laboratories of America, Itron, and Firetide, where she led innovations in AI, smart grids, and wireless networking. Her research focuses on Artificial Intelligence-based Personal Assistants, Natural Language Processing, and Knowledge Management . Her work bridges human-centered AI and enterprise applications, with emphasis on intelligent agents, knowledge platforms, and ethical AI systems. She explores how AI can enhance user productivity, decision-making, and system security across domains such as healthcare, energy, and customer service. The recent publications highlight a strong trend in Natural Language Processing applied to real-world problems like fake health news detection, agent assist systems, and knowledge graph integration. Her research also spans Smart Grid analytics, cybersecurity, and AI ethics , reflecting a multidisciplinary approach grounded in both theoretical rigor and practical deployment. Scientific Awards: Best Application Paper Award at IEEE Big Data Service 2022 Best Paper Award at SmartGridComm 2014 Top 10 Finalist, Fujitsu Next Generation Product Idea Contest 2016 ('Robo Butler') CSU STEM-NET Faculty Fellow (2023) Advising and Grants: Professor Jetcheva is the Principal Investigator of a $2.5 million NSF S-STEM grant titled 'Empowering Students to Succeed in Engineering and Computer Science'. She actively mentors graduate students, including Master’s student Garima Chaphekar, with whom she co-authored an award-winning paper on fake health news detection. She serves on the SJSU AI Advisory Committee and leads the AI Research Cluster, fostering interdisciplinary collaboration and student success. Labs and Teams: She leads the AI Research Cluster at SJSU, promoting innovation in artificial intelligence across departments. Her work involves close collaboration with students, industry partners, and interdisciplinary teams focused on deploying scalable, ethical AI solutions.

• Natural Language Processing
• Text Mining
• Machine Learning
• +3 more

Yuping Wu is a researcher specializing in Natural Language Processing , Text Mining , and Machine Learning . Her work focuses on enhancing large language models , data augmentation , and attention mechanisms in computational linguistics and clinical domains. She actively contributes to peer-reviewed research outputs in conferences like EACL , ProbSum , and journals such as Entropy . Research areas: Natural Language Processing , Machine Learning , Data Augmentation , Clinical Informatics , Large Language Models , and Text Mining . Key publications: Extractive summarization, clinical named entity recognition, data augmentation with black-box LLMs, attention networks in computer vision, and cost index prediction models. Collaborations: Active with teams like PULSAR and researchers including X. Zeng , G. Nenadic , and V. Schlegel . Her research spans artificial intelligence , computer vision , and healthcare text mining , often leveraging transformer architectures and neural network innovations . She explores applications in clinical data summarization and construction cost forecasting, demonstrating interdisciplinary impact through UN Sustainable Development Goals (SDGs) related to technological advancement and healthcare.