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Michèle FinckView profile

Professor

• Law and Artificial Intelligence
• EU Data Law
• GDPR
• +5 more

Michèle Finck is Professor of Law and Artificial Intelligence at the University of Tübingen, Germany, and co-director of the CSZ Institute for Artificial Intelligence and Law. She is a member of the Cluster of Excellence “Machine Learning: New Perspectives for Science” and represents Tübingen at the CIVIS Hub “Digital and Technological Transformation”. Research Focus: Regulation of artificial intelligence EU data law and GDPR Data intermediaries and the Data Governance Act Explainability of AI systems Access to data for environmental purposes Automation of public administration Her recent publications (2021-2024) chart a trajectory from foundational questions of data protection and AI governance to concrete regulatory challenges posed by the EU AI Act and the Data Governance Act, reflecting a commitment to interdisciplinary and policy-oriented legal scholarship. Service & Expert Advice: Member, Council of Europe ad hoc Committee on Artificial Intelligence Advisor to the European Commission and European Parliament on digitalisation issues Professor Finck has previously held posts at the University of Oxford, the London School of Economics, the Max Planck Institute for Innovation and Competition, and has been a Visiting Professor at LUISS University and a Visiting Fellow at University College London.

Alinane KilembeView profile

Researcher

• Artificial Intelligence
• Power Systems Stability and Dynamics
• Machine Learning
• +5 more

Alinane Kilembe is a researcher in Electronic and Electrical Engineering, affiliated with the University of Strathclyde. His work focuses on leveraging AI/ML to address reliability and security challenges in modern power systems with variable energy sources. He has made significant contributions to research areas such as power systems stability, data analytics, and physics-informed neural networks. Doctorate in Electronic & Electrical Engineering (2020) via Commonwealth Scholarship Master's in Electronic & Electrical Engineering (2019) with Distinction Bachelor's in Renewable Energy Technologies (2016) with First Class honors His research interests include Artificial Intelligence , Power Systems Stability and Dynamics , Grid Integration , and Modelling and Simulations , with recent work emphasizing explainable AI for frequency stability and physics-informed neural networks for power system dynamics. Scientific awards include: 1st Prize for Best Paper at the 59th UPEC conference (2024) Best MSc Project at the University of Strathclyde (2019) Commonwealth and Chevening Scholarships YALI fellowship and multiple MERA, Mzuzu University, and AfDB awards He co-founded Build-Eng Africa, an initiative focused on sustainable cities, and serves as an Administrator for Negawatt Energy & Industrial Engineers. His work aligns with UN Sustainable Development Goals, particularly in renewable energy and sustainability.

• Natural Language Processing
• Machine Learning
• Uncertainty Estimation
• +4 more

Wilker Aziz is an assistant professor at the Institute for Logic, Language and Computation (ILLC) within the Faculty of Science at the University of Amsterdam, where he leads the Probabilistic Language Learning research group. His academic journey includes a PhD from the University of Wolverhampton (2014), followed by research positions at the University of Sheffield and ILLC before joining the UvA faculty in January 2019. His research focuses on the intersection of machine learning, natural language processing, and probabilistic modeling. Aziz investigates how to design models and algorithms that learn to represent, understand, and generate language data, with particular emphasis on uncertainty estimation in language models, probabilistic inference techniques, and developing novel decoding algorithms for text generation. His work addresses fundamental problems in language modeling, machine translation, syntactic parsing, textual entailment, text classification, and question answering. Aziz's recent publications (2022-2024) demonstrate a strong focus on uncertainty in natural language generation, with multiple papers accepted at top-tier conferences including EACL and EMNLP. His research shows how language models can better align with human uncertainty patterns and how to properly evaluate model confidence when humans themselves disagree on annotations. Best paper award at Coling 2020 for 'Is MAP Decoding All You Need? The Inadequacy of the Mode in Neural Machine Translation' Outstanding Area Chair award at ICLR 2022 Active program committee member for major ML/NLP conferences including NeurIPS, ICML, ACL, EMNLP, and ICLR Aziz actively supervises PhD and MSc students, with a current cohort of five ongoing PhD candidates and numerous recent graduates. His Probabilistic Language Learning group develops theoretical frameworks and practical applications for uncertainty-aware natural language processing. He has contributed to open-source projects including PET (Post-Editing Tool), grasp (Randomised Semiring Parsing), and mosesdecoder, demonstrating his commitment to advancing the field through shared research tools.

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

Phil Blunsom is a Professor of Computer Science at the University of Oxford and a Senior Research Fellow at St Hugh's College. His research focuses on the intersection of machine learning and computational linguistics, particularly using deep learning for natural language analysis, understanding, and generation. He has led the Natural Language research group at DeepMind London from 2014 to 2021. University of Oxford St Hugh's College DeepMind London (2014-2021) Blunsom's research explores algorithms for grounding natural language in AI systems, emphasizing compositional semantics, syntax, and multilingual distributed representations. His work spans neural machine translation, semantic parsing, and explainable AI, with a focus on adversarial learning and verification of explanatory methods. Selected publications highlight trends in NLP, robotics, and wireless signal analysis. Key themes include neural inertial tracking, multilingual models, and coreference resolution. Awards include the BEST PAPER at EWSN'13 and the best application paper at ICML 2014. Scientific contributions include: 2020 : Advancing adversarial generation of NLP explanations 2019 : MotionTransformer for domain transfer in robotics 2014 : Multilingual compositional distributional semantics 2013 : NLOS signal mitigation techniques Blunsom has advised numerous students in NLP and ML, including Satwik Bhattamishra, Jan Botha, and Yishu Miao. His research has received recognition for technical innovation in grammar induction, translation models, and lexicon modeling.

• Machine Learning
• Support Vector Machines
• Evolution Strategies
• +4 more

Christian Igel is a Full Professor at the Department of Computer Science (DIKU), University of Copenhagen. He serves as Director of the SCIENCE AI Centre and co-lead of the Pioneer Centre for Artificial Intelligence. Igel holds a Doctoral degree from Bielefeld University (2002) and a Habilitation from Ruhr-University Bochum (2010). Previously, he was a Juniorprofessor at Ruhr-University Bochum (2003-2010). His research focuses on fundamental and applied machine learning, including: kernel methods, evolutionary optimization, deep neural networks, PAC-Bayesian analysis, and applications supporting sustainable development goals. He maintains extensive collaborations across disciplines, particularly in environmental monitoring and medical informatics. Igel's recent publications demonstrate strong interdisciplinary focus with applications in: Environmental science (tree cover mapping, carbon stock quantification) Healthcare (sleep staging, mental health epidemiology) Core ML theory (monotonic networks, PAC-Bayesian bounds) His work consistently appears in high-impact venues including Nature, ICML, and NeurIPS. Awards and Honors: ELLIS Fellow (European Lab for Learning and Intelligent Systems) Editorial Roles: Editor, German Journal on Artificial Intelligence (KI) Associate Editor, Evolutionary Computation Journal (ECJ) Associate Editor, Artificial Intelligence Journal (AIJ)

Scott ReedView profile

Professor

• Pharmacogenomics
• Artificial Intelligence
• Nanoscience
• +4 more

Scott Reed is a Professor in the Department of Chemistry within the College of Liberal Arts and Sciences at the University of Colorado Denver. His office is located in Room 4131 of the Science Building, with mailing address at Department of Chemistry, Campus Box 194, P.O. Box 173364, Denver, CO 80217-3364. Dr. Reed earned his Ph.D. and M.S. from the University of Oregon in 2001 and 1996 respectively, and completed his undergraduate studies with a B.A. from Bard College, NY in 1994. His primary research interests span pharmacogenomics, artificial intelligence, nanoscience, chemical synthesis, and green chemistry. His work particularly focuses on developing computational tools to identify genetic variants that explain adverse drug reactions (ADRs). His research program has two main goals: creating an online tool that identifies specific ADRs and their causative drugs, and developing another tool that generates candidate genetic variants explaining specific ADRs. These tools facilitate rapid hypothesis testing about pharmacogenomics causes of harmful drug side effects. Dr. Reed's publication record shows a strong interdisciplinary approach with recent work (2022-2024) emphasizing AI/ML applications to chemical and pharmacological problems. His research spans from fundamental chemical synthesis and nanoscience to computational biology and AI-driven drug safety analysis. He is actively involved in educational initiatives including a course on "Vibe Coding," a Neural Network Tutorial available on YouTube, and the development of a CNN-in-Excel project (Tetris_CNN) that demonstrates convolutional neural networks within spreadsheet applications. He has also created a Chemistry Knowledgeable GPT to assist with molecular informatics. Dr. Reed maintains an active research laboratory with projects including the Advancing AI-Assisted Molecular Synthesis Challenge, focusing on applying artificial intelligence to chemical synthesis problems.

Husnain RafiqView profile

Lecturer

• Cyber Security
• Machine Learning
• Malware Detection
• +4 more

Dr. Husnain Rafiq is a Lecturer in Cyber Security at Edge Hill University, where he conducts research on machine learning applications in cybersecurity. He earned his PhD in Cyber Security from Northumbria University in December 2022, focusing on ML-based mobile malware defense. With over seven years of teaching experience, he currently supervises graduate research projects in mobile security, adversarial ML, and malware detection. Research Focus His research spans: Machine Learning for Security: Developing adversarial-aware malware detectors and evasion-resistant models Mobile Systems Protection: Specializing in Android malware detection using image processing and deep learning Cyber Defense Innovations: Creating GAN-based botnet detectors and DGA detection systems Trustworthy AI: Exploring explainable AI for fraud detection and content moderation Publication Trends Recent works demonstrate strong focus on adversarial machine learning (77% of publications) with applications in malware detection (57%), network security (35%), and trust-aware AI systems (21%). Emerging themes include explainable fraud detection, sustainable risk frameworks, and low-data regime solutions.

Simone CastagnoView profile

Researcher

• Machine Learning
• Artificial Intelligence
• Medical Informatics
• +3 more

Simone Castagno is a Researcher at the Department of Applied Mathematics and Theoretical Physics (DAMTP), School of Physical Sciences, University of Cambridge. Their work bridges machine learning and medical applications, particularly in orthopedics and rheumatology. Research Interests: Simone focuses on interpretable machine learning for clinical prediction tools, with applications in soft tissue knee injury screening, osteoarthritis progression forecasting, and trauma surgery outcomes. Their research integrates biomedical engineering, clinical data analysis, and AI ethics in healthcare. Publication Trends: Recent articles highlight collaborations between DAMTP and medical institutions. Key themes include AI-driven predictive modeling for joint pathologies, systematic reviews of ML in osteoarthritis, and interdisciplinary studies combining computational methods with orthopedic/rheumatological clinical research. Labs & Teams: Affiliated with the Machine Learning and Artificial Intelligence Research Group at DAMTP, which specializes in data-driven solutions for biomedical challenges.

Sebastian VollmerView profile

Professor

• Machine Learning
• Data Science
• Bayesian Inference
• +8 more

Sebastian Vollmer is a Professor for Application of Machine Learning at the University Kaiserslautern-Landau (RPTU) since 2021 and a Senior Researcher at the German Research Center for Artificial Intelligence (DFKI). He has previously held tenured positions as an Associate Professor at the University of Warwick (2016–2021), a Departmental Lecturer at the University of Oxford (2014–2016), and a Postdoctoral Researcher at Oxford (2013–2014). His research bridges machine learning , Bayesian inference , and computational statistics with applications in health informatics , social good , and physics-informed anomaly detection . He has led initiatives at the Alan Turing Institute, including the Data Science for Social Good program and Health & Medical Sciences Programme . Vollmer's recent work focuses on biologically informed neural networks , narrative priming in public goods games , and knowledge retrieval from large language models . His projects include PIAD (personalized AI-based lifestyle interventions), ML-Bau-Doc+ (ML for building documentation), and TrustifAI (trustworthy AI for health). Scientific Awards : 2014 Faculty Prize, University of Warwick 2009-13 German Academy Foundation Scholarship 2009 3rd Prize, International Mathematics Competition He supervises PhD and MSc students in data science, leads research departments at DFKI, and collaborates with startups like Cera Care and Fly Notes to address public sector challenges.

Simon GeerkensView profile

Researcher

• Artificial Intelligence
• Railway Systems
• Explainable AI
• +3 more

Simon Geerkens serves as a Researcher and PhD candidate at Hochschule Düsseldorf's Faculty of Electrical Engineering & Information Technology under Prof. Dr. Braun. Currently embedded in the EU-funded safe.trAIn. consortium, he works on developing safety architectures for driverless regional trains through AI integration. His research focuses on explainability and trustworthiness of AI systems , data quality assurance , and practical AI applications in safety-critical railway environments. With expertise spanning artificial intelligence, deep learning, and data security, his work bridges theoretical machine learning with industrial railway automation requirements. Analysis of his recent publications reveals a strong emphasis on data quality tools (QI², ECS) and safety assurance pipelines for ML systems in rail transport. His research consistently addresses the challenge of implementing verifiable AI in safety-critical infrastructure, particularly through dataset validation methods and complexity assessment frameworks. Professional activities include active participation in the safe.trAIn. project consortium comprising railway industry leaders, technology suppliers, and standardization organizations working toward GoA4 automation standards.

Nathan B. GawView profile

Assistant Professor

• Machine Learning
• Multimodal Data Fusion
• Healthcare Telemonitoring
• +7 more

Dr. Nathan B. Gaw is an Assistant Professor of Data Science at the Air Force Institute of Technology (AFIT), where he develops advanced statistical machine learning algorithms for multimodal data fusion in defense, healthcare, and remote sensing applications. He holds a Ph.D. in Industrial Engineering from Arizona State University (2019) and completed postdoctoral research at both the ASU-Mayo Clinic Center for Innovative Imaging (2019-2020) and Georgia Institute of Technology's School of Industrial and Systems Engineering (2020-2021). Research Interests: His work focuses on three core areas: Feature extraction/selection using tensor decomposition and functional data analysis Spatio-temporal neural networks with attention-based ConvLSTMs Image registration/fusion through convolutional neural networks Key application domains include aviation human factors, weather remote sensing, and cybersecurity. Scientific Awards: Dr. Gaw has received 14 major scientific awards including: AFWERX Datathon Challenge (5 awards) INFORMS Principal Cup (2nd place) Harold Wolff-John Graham Award (American Academy of Neurology) Tau Beta Pi Fellowship Dean's Fellowship, ASU Professional Engagements: He serves as chair of the INFORMS Data Mining Society and council member of IISE Data Analytics Division, while maintaining IEEE membership. His 16 peer-reviewed publications (12 Q1, 4 Q2) include a U.S. patent and book chapter contributions.

Sun-Yuan KungView profile

Professor

• Machine Learning
• Deep Learning
• Neural Networks
• +7 more

Sun-Yuan Kung is a Professor of Electrical and Computer Engineering at Princeton University specializing in high-performance learning networks and explainable AI. His research develops beyond back-propagation paradigms to create optimal neural architectures through structural learning and discriminant information metrics. His academic credentials include: Ph.D. in Electrical Engineering, Stanford University (1977) M.S. in Electrical Engineering, University of Rochester (1974) B.S. in Electrical Engineering, National Taiwan University (1971) Professor Kung pioneered Explainable Neural Networks (XNN) incorporating Internal Neuron's Learnability (INL) using internal teacher labels and discriminant information (DI) for node/layer ranking. This enables deep compression while enhancing model robustness and adaptability for real-time applications in image processing, privacy protection, and security-sensitive environments. His work directly addresses DARPA's Explainable AI (XAI) initiatives through end-user-adaptive labeling mechanisms. Recent publications (2023-2025) demonstrate sustained innovation in efficient deep learning architectures, with dominant themes in remote sensing applications (object detection, image captioning, change detection), quantum-inspired algorithms, and multimodal representation learning. Key trends include neural architecture search, model compression techniques, and reinforcement learning integration for specialized domains. His distinguished honors include: Life Fellow of IEEE (2016) IEEE Third Millennium Medal (2000) IEEE Signal Processing Society Best Paper Award (1996) IEEE Signal Processing Society Technical Achievement Award (1992) IEEE Fellow (1988) Professor Kung mentors graduate researchers including Katherine Shu-Min Li, Xiaxin Shen, and Zhuoqing Song, with funding supporting projects in deep compression, privacy-preserving ML, and XAI frameworks. His group develops foundational techniques for discriminant information-based network pruning and structural gradient methods. He leads a research team advancing the XNN framework for DARPA's Explainable AI initiatives, focusing on real-time decision support through internal neuron explainability and channel exploration for model optimization.

• Nonlinear System Identification
• Machine Learning
• Computational Neuroscience
• +7 more

Hua-Liang Wei is a Senior Lecturer at the University of Sheffield 's School of Electrical and Electronic Engineering. He leads two innovative research labs: the Dynamical Modelling, Data Mining and Decision Making (3DM) and the Digital Medicine & Computational Neuroscience (DMCN) Research Groups. Specializes in system identification for nonlinear dynamics Develops interpretable AI for healthcare applications Active in space weather and environmental forecasting His methodological expertise spans NARMAX modeling, wavelet neural networks, and multiresolution analysis. Collaborations include Sheffield Teaching Hospitals NHS Trust, multiple University of Sheffield departments (Chemistry, Oncology, Psychology), and international institutions like Beihang University. Scientific Awards include STFC and NERC grants for radiation belt modeling and environmental systems research, EU Horizon 2020 funding, EPSRC Platform grants, Royal Society support, and medical charity partnerships. Recent publications focus on hybrid wavelet-LSTM for wind power forecasting EEG analysis in epilepsy and Alzheimer's domain adaptation for fault diagnosis interpretable models for medical data covering applications from renewable energy to clinical diagnostics.

Mohammed BenaissaView profile

Professor

• Hardware Cryptography
• Error-Control Coding
• Reconfigurable Hardware Design
• +4 more

Mohammed Benaissa is a Professor of Information Engineering at the University of Sheffield within the School of Electrical and Electronic Engineering . As Director of Learning and Teaching (Student Experience) , he leads initiatives for academic quality and student engagement. His research bridges hardware cryptography , error-control coding , and healthcare technology , particularly in data-driven diabetes management via the WithCare+/Glucollector platform deployed in NHS trusts. Education : PhD, Dip.Ing in Electrical and Electronic Engineering Technical Leadership : Patent holder for WithCare+ (GB 2467079) His research interests span cryptosystems , residue number systems , and reconfigurable hardware , with a focus on scalable security architectures and low-power medical devices . Recent publications highlight lattice-based cryptography and AI-driven glucose prediction using NIR/MIR spectroscopy and machine learning . Scientific accolades include a best paper award for RFID security and an innovation prize for diabetes technology. He has supervised over 25 PhD students, including graduates like Abdelaal A , Al-Mbaideen A , and Khan Z U A , and serves on research funding panels and conference Technical Program Committees.

• Software Engineering
• Value-Based Software Engineering
• Software Quality Assurance
• +7 more

LiGuo Huang is an accomplished researcher and academic in the field of software engineering with a publication record spanning over two decades from 2003 to 2025. With 95 publications documented in the dblp database, Huang has established a significant presence in both traditional software engineering domains and emerging areas where machine learning intersects with software development practices. Huang's research has evolved from foundational work in value-based software engineering to cutting-edge applications of artificial intelligence in software analysis and maintenance. Huang's research interests encompass a broad spectrum of software engineering topics, with particular emphasis on value-based software engineering, software quality assurance, defect classification, and software process modeling. More recently, Huang has focused on applying machine learning and deep learning techniques to software engineering problems, including code summarization, vulnerability detection, and software maintenance. This evolution reflects the broader shift in the field toward data-driven approaches for software development and analysis. The publication trends reveal a consistent research trajectory with increasing publication rates in recent years, particularly in the application of machine learning to software engineering problems. Huang's work shows a strategic progression from theoretical foundations in software quality to practical applications of AI in software development. The research spans empirical studies, systematic literature reviews, and novel technical approaches to longstanding software engineering challenges, demonstrating both theoretical depth and practical relevance. Huang has collaborated extensively with researchers across multiple institutions, forming particularly strong partnerships with Jidong Ge, Bin Luo, Chuanyi Li, and Barry W. Boehm. The collaboration with Boehm in early career publications suggests mentorship that evolved into peer collaboration, while more recent work shows Huang mentoring newer researchers who now serve as primary authors on joint publications. Huang's research has practical implications for software development practices, particularly in improving software quality, enhancing developer productivity through AI-assisted tools, and providing empirical evidence for software engineering decision-making. The work bridges theoretical computer science with practical software engineering concerns, making significant contributions to both academic research and industry practice.