Find Academic Supervisors

Carsten LanquillonView profile

Research Professor

• Artificial Intelligence
• Machine Learning
• Natural Language Processing
• +3 more

Prof. Carsten Lanquillon is a Research Professor at Heilbronn University, specializing in Language Technologies and Cognitive Assistance Systems within the Department of Business Informatics. He leads the Center for Industrial AI (iAI), a Carl Zeiss Foundation-funded initiative addressing AI implementation challenges for medium-sized enterprises. His expertise spans Business Intelligence, Data Science, Machine Learning, and Conversational AI with a focus on applying AI in industrial production processes. Key research areas include cognitive assistance systems, anomaly detection, and ethical AI frameworks. His work bridges academia and industry through collaborative projects like the iAI Center, which promotes sustainable AI adoption in regional manufacturing. Research emphasizes practical solutions for AI integration, including secure large language model adaptation, generative AI applications, and hybrid intelligence systems combining human and machine capabilities. Recent efforts explore explainable AI, digital twin integration, and user transparency in industrial contexts. Lanquillon's contributions include process models for data science projects and frameworks for knowledge-grounded NLP systems. He actively publishes on topics ranging from energy-efficient deep learning to interactive quality analysis tools in automotive industries. His research often combines technical innovation with user-centric design principles to ensure practical applicability.

Yu YangView profile

Assistant Professor

• Spatio-temporal Data Mining
• Cyber-Physical Systems
• Ubiquitous Computing
• +4 more

Yu Yang is an Assistant Professor in the Department of Computer Science & Engineering at Lehigh University. His research focuses on spatio-temporal data mining, cyber-physical systems, ubiquitous computing, and machine learning applications in logistics and transportation systems. He holds a Ph.D. in Computer Science from Rutgers University, an M.S. in Computer Science from Rutgers, and a B.Eng. in Software Engineering from Northeastern University (China). Education: Ph.D., Computer Science, Rutgers University M.S., Computer Science, Rutgers University B.Eng., Software Engineering, Northeastern University (CN) His research explores cutting-edge methodologies for optimizing transportation networks, enhancing urban mobility, and improving last-mile delivery systems through AI-driven solutions such as reinforcement learning, hypergraph networks, and adaptive prediction frameworks. Key projects include tackling logistics challenges via LLM integration, improving traffic signal resilience during malfunctions, and designing cooperative delivery systems with real-time encounter detection. His work often bridges theory and practice, with deployments in real-world logistics platforms and urban infrastructure systems.

• Federated Learning
• Cybersecurity
• Edge Computing
• +4 more

Dr. Meikang Qiu is a faculty member at Texas A&M University Commerce (Department of Computer Science) and Columbia University (Department of Electrical Engineering). He received his PhD in Computer Science from the University of Texas at Dallas in 2007. His research spans distributed intelligence systems with current focus areas including federated learning security, efficient AI for edge devices, and multimodal learning applications in healthcare and finance. Qiu's research integrates machine learning with systems engineering, emphasizing privacy-preserving AI , robust distributed systems , and resource-constrained optimization . His recent work explores: (1) Defenses against adversarial attacks in federated environments, (2) Quantum-classical hybrid computing frameworks, and (3) Efficient model compression techniques for large language models. Publication analysis (2024-2026) reveals three dominant themes: (1) Security enhancements for distributed learning systems, (2) Efficiency optimizations for edge AI deployment, and (3) Cross-modal applications in medical and financial domains. His work consistently addresses scalability challenges in real-world AI systems.

Paolo ModestiView profile

Senior Lecturer

• Cybersecurity
• Formal Methods for Security
• Security Protocols
• +5 more

Paolo Modesti is a Senior Lecturer in Cybersecurity at Teesside University's School of Computing, Engineering and Digital Technologies. He holds a PhD in Computer Science from Ca' Foscari University Venice, Italy, and an MSc from the University of Udine. His research focuses on security protocols, formal methods, and tool development for secure systems. He is a Fellow of the Higher Education Academy and a licensed professional engineer. Dr. Modesti's work includes designing formal verification frameworks (e.g., AnBx compiler/IDE) and analyzing protocols like Open Banking APIs and Bitcoin's Payment Protocol. He actively contributes to academic conferences (e.g., IEEE, ACM) and serves on program committees for security-related events. His teaching roles include leading the BSc Cyber Security program and modules on Ethical Hacking and Security Analysis. Research interests span security protocol modeling, automatic code generation, intrusion detection systems, and blockchain security. He advocates bridging the gap between academic tools and industry practices through frameworks like PTES and MITRE ATT&CK. His tools, such as the AnBx ecosystem, aim to simplify formal methods adoption for practitioners.

• Artificial Intelligence
• Generative AI (LLM)
• Data Science
• +1 more

Dr. Ranjith Dinakaran is a Senior Lecturer in Computing at the Department of Computing & Games, Teesside University. He specializes in AI deployment, machine learning, and large language models (LLMs), with enterprise experience at Datum 360 and Accenture. His work focuses on integrating AI solutions to enhance business operations and customer satisfaction. Professional Experience: Data Scientist at Datum 360: Developed data-driven solutions using machine learning and visualization. AI & LLM Specialist Consultant at Accenture: Designed AI-driven applications like chatbots and NLP systems. Research Interests: Includes Artificial Intelligence , Generative AI (LLM) , Data Science , and Machine Learning . His expertise spans Python, R, SQL, and deep learning frameworks. Grants & Labs: No specific grants or lab affiliations mentioned in the text.

Eleni StraitouriView profile

Professor

• Machine Learning
• Causal Inference
• Large Language Models
• +4 more

Eleni Straitouri is a Professor at the Max Planck Institute for Software Systems (MPI-SWS) in Kaiserslautern, Germany, where she leads research at the intersection of artificial intelligence safety and human-centered decision systems. Her work bridges theoretical machine learning with practical applications in high-stakes environments requiring rigorous uncertainty quantification and causal reasoning. Her primary research focuses on causal inference in large language models , counterfactual prediction sets , and human-AI complementarity . She develops methods to control algorithmic harm through counterfactual reasoning, enhances model evaluation via conformal prediction, and designs decision support tools that leverage human expertise. Current projects address critical challenges in LLM safety, including bias detection through coupled token generation and harm mitigation in medical/operational decision systems. Analysis of her 13 publications (2021-2025) reveals an evolving trajectory from reinforcement learning foundations toward cutting-edge LLM safety frameworks. Her work consistently integrates causal methods with human factors, producing practical tools for reliable AI deployment across healthcare and operational domains. Recent NeurIPS/ICML publications demonstrate increasing influence in trustworthy AI research. Best Paper Award, Workshop on AI and HCI (ICML), 2023 Spotlight Paper (top 3.5%), ICML 2024 Dr. Straitouri maintains active collaborations with Manuel Gomez-Rodriguez's research group, evidenced by co-authorship on 100% of her recent publications. While specific grant funding isn't detailed in available materials, her work aligns with MPI-SWS's focus on software systems for societal benefit. No information about academic advising or laboratory infrastructure was found in the provided text.

• Natural Language Processing
• Bias in Language Models
• Scholarly Data Mining
• +3 more

Sagnik Ray Choudhury serves as an Assistant Professor in the Department of Computer Science and Engineering at the University of North Texas, with his office located in Discovery Park F264. He maintains regular office hours on Wednesdays from 11:00 am to 1:00 pm and can be contacted via Sagnik.Choudhury@unt.edu. His research centers on Natural Language Processing with emphases on bias analysis in language models, scholarly communication systems, and reproducible research methodologies. Key interests include quantifying gender bias in cross-lingual political contexts, developing retrieval-augmented LLMs for scholarly impact prediction, and creating frameworks for automated limitation extraction from academic texts (BAGELS project). He also investigates model generalizability across NLI and MRC tasks while advancing NLP applications for social good through ethical deployment strategies. Recent publications reveal a strong trajectory in leveraging LLMs for academic writing automation, including Futuregen for future work generation and scholarly impact forecasting. His work consistently bridges computational linguistics with real-world challenges in research transparency, educational technology, and AI ethics—particularly evident in studies on political ideology navigation and gender bias quantification. No scientific awards were documented in the source material. Information regarding student advising, grant funding, laboratory facilities, or research teams was not provided in the available texts.

Desheng ZhangView profile

Associate Professor

• Cyber-Physical Systems
• Data Science
• Machine Learning
• +5 more

Desheng Zhang is an Associate Professor of Computer Science at Rutgers University and a Visiting Professor at MIT's Media Lab. His research focuses on bridging Cyber-Physical Systems (CPS), Data Science, and AI to address challenges in smart cities, urban infrastructure, and human-CPS interactions. He leads the ARSENAL Lab, which explores topics like mobility modeling, cross-domain data fusion, and equitable urban services. PhD: University of Minnesota (Computer Science) Postdoc: MIT Senseable City Consortium Research interests include: Cyber-Physical Systems, Machine Learning, Urban Informatics, and Transportation Systems. His work spans 8 cities across 3 continents, involving 500k vehicles, 10M+ devices, and 100M residents. Key projects include micromobility safety, electric vehicle charging, and last-mile delivery optimization. Recent grants total over $4.5M as PI, including NSF awards for Human Mobility Prediction and Smart City projects. Notable awards include the 2021 NSF CAREER Award and 8 best paper/thesis prizes. Students advised include Guang Wang (now at Florida State), Yu Yang (Lehigh), and Yi Ding (MIT Postdoc). His lab focuses on socially-informed decision-making, equitable service governance, and trustworthy AI scheduling. Awards include the Board of Trustees Research Award (2022), RTSS Outstanding Paper (2020), and MIT Senseable Consortium Fellowship.

• Computer Vision
• Deep Learning
• Neural Network Acceleration
• +5 more

Kaisheng Ma is a Professor in the Department of Computer Science and Technology at Tsinghua University's School of Information Science and Technology. With an extensive publication record spanning from 2013 to 2025, he has established himself as a leading researcher in computer vision, deep learning, and computer architecture. His work bridges theoretical advances with practical applications in neural network acceleration and hardware-software co-design. Professor Ma's research interests focus on advancing the frontiers of computer vision and deep learning systems. His work in knowledge distillation has significantly improved the efficiency of neural networks, while his contributions to hardware acceleration have enabled more powerful AI systems. His recent research demonstrates a growing emphasis on 3D vision, embodied AI, and the intersection of language and vision systems. He has pioneered techniques in frequency domain analysis for image processing and developed novel approaches for neural network acceleration through hardware-software co-design. Analysis of Professor Ma's recent publications (2023-2025) reveals a strong research trajectory with increasing focus on multimodal learning, 3D scene understanding, and efficient neural network deployment. His work spans both theoretical advances in machine learning (particularly knowledge distillation and continual learning) and practical hardware implementations for AI acceleration. The publications show consistent collaboration with a core group of researchers while expanding into new areas like brain-computer interfaces and language-grounded spatial reasoning. Professor Ma has successfully mentored numerous PhD students who have become active contributors to the field, including Linfeng Zhang, Runpei Dong, and Zekun Qi. His research has been supported by significant grants that enable cutting-edge work in neural network acceleration and 3D vision systems. His students have published in top-tier conferences including CVPR, NeurIPS, and ICCV, demonstrating the strength of his research group. Professor Ma leads a research group focused on the intersection of computer vision, deep learning, and computer architecture. His lab investigates efficient neural network deployment, hardware acceleration techniques, and novel computer vision algorithms. The team maintains strong collaborations with industry partners working on AI hardware and has developed several innovative approaches to neural network acceleration and efficient model deployment.

Hongyang GaoView profile

Assistant Professor

• Artificial Intelligence
• Machine Learning
• Deep Learning
• +3 more

Hongyang Gao is an Assistant Professor at Iowa State University, focusing on AI, Machine Learning, and Data Science. His work bridges theoretical foundations and practical applications in neural networks, graph representation learning, and cybersecurity. Education: PhD in Computer Science, Texas A&M University MS in Computer Science, Tsinghua University B.S. in Biomedical English, Peking University Research Interests: Explores neural ODEs, graph neural networks (GNNs), and their applications in molecular modeling, vulnerability detection, and trustworthy AI systems. His recent work emphasizes model interpretability (e.g., MotifExplainer), optimization theory for implicit networks, and graph-based explainability techniques. Publications Trends: Recent papers (2022-2025) highlight advancements in graph learning frameworks, such as G2T-LLM for molecule generation and MAGE for GNN explainability. His work on Neural ODEs explores activation function impacts on convergence, while cybersecurity papers apply dataflow analysis for vulnerability detection. Advising: Collaborates with students on projects involving motif-based methods (e.g., Motifpiece), meta-learning (Meta-AdaM), and trustworthy AI (data preconditions research). No grants explicitly listed in provided texts.

Kalyan VeeramachaneniView profile

Researcher

• Big Data Science
• Machine Learning
• Artificial Intelligence Applications
• +2 more

Kalyan Veeramachaneni is a Principal Research Scientist at the Laboratory for Information and Decision Systems (LIDS) at the Massachusetts Institute of Technology (MIT). He leads the Data-to-AI group, focusing on developing AI systems to address societal challenges through big data science and machine learning. His research emphasizes statistical modeling, automated feature engineering, and human-data interaction for impactful applications. His work spans synthetic data generation, anomaly detection, adversarial machine learning, and explainable AI frameworks. Key contributions include the Synthetic Data Vault, Cardea, and Ballet collaboration tools. He collaborates across industries to deploy interpretable ML systems in healthcare, energy, and cybersecurity. Notable projects include OrionBench (time series benchmarking), Explingo (AI explanations via LLMs), and frameworks like Pyreal and ATM for automated machine learning. His research bridges theoretical advancements with practical usability, emphasizing end-user priorities in system design. Current affiliations: LIDS MIT | MIT Institute for Data, Systems, and Society (IDSS). Office: 32-D714, Cambridge, MA.

Ihab F. IlyasView profile

Professor

• Data Quality
• Machine Learning
• Database Systems
• +4 more

Ihab F. Ilyas is a Professor and holds the Thomson Reuters–NSERC Industrial Research Chair in Data Quality at the University of Waterloo's Department of Computer Science. His research focuses on data quality, machine learning for data enrichment, probabilistic data management, and knowledge graph systems. He leads projects like Holoclean (a probabilistic data repair system) and Data Civilizer (a data unification platform). His work bridges database systems and machine learning to address challenges in data integration, error detection, and large-scale knowledge representation. Education: PhD in Computer Science from Purdue University (2004), MSc and BSc from Alexandria University, Egypt (1999 and 1995). His publications span vector search, knowledge graph construction, and data curation techniques, with recent emphasis on adaptive indexing and scalable systems. He has contributed to open-source tools and frameworks for data cleaning and machine learning integration. Research interests include probabilistic data management, machine learning applications for data quality, and scalable systems for big data. His work often addresses real-world challenges in data integration and semantic representation, with a focus on practical, deployable solutions.

• Medical Statistics
• Artificial Intelligence in Healthcare
• Machine Learning
• +3 more

Lei Clifton is a Research Fellow and Official Fellow in AI & Machine Learning at Reuben College, University of Oxford. She leads a team of researchers in the Nuffield Department of Population Health and contributes to the Pandemic Science Institute’s preparedness efforts. With a PhD in Statistical Machine Learning (2007, University of Manchester), her expertise spans large-scale clinical data analysis and healthcare AI deployment. She advises the NIHR Research Support Service on study design and collaborates with Prof David Clifton’s 'AI for Healthcare' CHI Lab. Her research focuses on medical statistics and AI applications in observational studies and electronic health records, including synthetic data generation, immune protein biomarker discovery, and scalable AI implementation in healthcare systems. She co-leads projects like the Implementation framework for AI deployment and dysregulated immune protein prediction in Multiple Myeloma. Key areas of contribution include healthcare AI ethics, cross-institutional model validation, and LLM applications in medicine. Her work bridges statistical rigor with real-world clinical challenges, emphasizing generalizability and fairness in machine learning systems.

• Cybersecurity
• Privacy-Preserving Machine Learning
• Cryptography
• +5 more

Dr. Maggie Liu is a Lecturer in Cybersecurity and Software Systems at RMIT University's School of Computing Technologies. Her research focuses on secure multi-party computation and its applications in privacy-preserving machine learning (PPML), with specific emphasis on practical deployments for medical diagnostics and mobile image classification. Key research areas include: Designing lightweight cryptographic protocols Secure computation techniques Machine learning systems security Interdisciplinary privacy-preserving solutions Her recent work involves PPML for medical services, mobile-edge computing, and collaborative learning systems. Notable projects include MediSC, CryptMed, OblivGNN, Sonic, EncSIM, and Leia. Maggie Liu earned her Ph.D. in Computer Science from RMIT University.

Yutian TangView profile

Assistant Professor

• AI+SE (Large-language Model+SE)
• Program Analysis
• Empirical Software Engineering
• +4 more

Dr. Yutian Tang is an Assistant Professor at the University of Glasgow's School of Computing Science. He holds a Ph.D. from The Hong Kong Polytechnic University and a B.Sc. from Jilin University. His research focuses on AI-driven software engineering, program analysis, and mobile app security, with over 40 publications in top venues like ESEC/FSE and IEEE TSE. He has received awards including the ISSRE'18 Best Industry Paper and Shanghai Pujiang Talent Program. Current research emphasizes automated testing, bug localization, and LLM applications in software engineering. Education: Ph.D., Department of Computing, The Hong Kong Polytechnic University B.Sc., Computer Science, Jilin University Research Interests: His work spans AI+SE (Large Language Models in Software Engineering), automated testing, privacy protection, and malware detection. Notable contributions include XDebloat (app debloating) and tools for analyzing Android vulnerabilities. He actively explores LLM applications for code generation and test suite optimization. Grants & Funding: EPSRC Peer Review College member National Natural Science Foundation of China (NSFC) grant (2023-2025) OpenAI API Researcher Access Program (2024) Google Cloud Research Credits (2024) Service & Leadership: Editorial Board Member of Frontiers in Computer Science , PC member of ICSE, ESEC/FSE, ASE, and other top conferences. Active in reviewing for IEEE Transactions and ACM journals. Future Directions: Tool support for automated bug detection, LLM integration in software lifecycle, and large-scale binary code analysis.