Find Academic Supervisors

• 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)

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.

Kasun WijesooriyaView profile

Research Fellow

• Wind Engineering
• Blast and Impact Effects
• Fluid-Structure Interaction
• +3 more

Dr. Kasun Wijesooriya is a Research Fellow at the School of Engineering and Technology, UNSW Canberra, specializing in extreme loading events (wind, blast) and numerical finite element modelling. His research spans wind engineering, fluid-structure interaction, blast effects, sustainable construction, and AI applications in engineering. Education: PhD in Structural Engineering, University of Sydney MEng in Structural Engineering, University of Sydney MEng in Civil Engineering, University of Bristol Research Interests: Dr. Wijesooriya's work integrates computational fluid dynamics (CFD) with machine learning to predict structural responses to wind and blast loads. Recent projects focus on AI-driven blast load prediction, sustainable materials (e.g., graphene-enhanced concrete), and energy-absorbing fractal structures via additive manufacturing. Publications (Trends): His 15 most recent articles (2023–2025) emphasize machine learning applications in blast/wind engineering, experimental mechanics of novel materials, and sustainable construction techniques. Dominant themes include explainable AI for structural safety, graphene-based composites, and fractal geometry in energy absorption. Grants: Fatigue Testing of Armour Grade Materials (2025, $140,000) High-Speed Camera for Resilience & Energy Infrastructure (2025, $235,000) Scanning Pressure Measurement System (2024, $142,000) Teaching: Courses include ZEIT4600/4601 (Civil Design Practice), ZEIT2603 (Civil Engineering Materials), and ZEIT3600 (Structural Analysis) from 2021–2024.

Frederick KlauschenView profile

Professor

• Precision Medicine
• Bioinformatics
• Artificial Intelligence in Pathology
• +7 more

Frederick Klauschen is a Professor and Director at the Institute of Pathology, Ludwig Maximilian University of Munich , focusing on integrating multi-omics data (genomics, epigenetics, transcriptomics, proteomics) with explainable artificial intelligence (AI) to decode tumor complexity. His lab collaborates with institutions like TU Berlin and Max Delbrück Center for Molecular Medicine (MDC) Berlin . Research Highlights Digital Pathology & AI: Automated cell nucleus detection, explainable AI for histological heatmaps Proteomics: Mass spectrometry for signaling pathway analysis DNA Methylation: Epigenetic tumor classification via array and bisulfite methods Single-Cell Analysis: Genomic/transcriptomic profiling of individual cells Multiplex Immunofluorescence: Spatial tumor cell interaction mapping Article Trends : His recent work emphasizes AI-driven multimodal data integration (clinical, imaging, molecular) to predict cancer outcomes, with a focus on explainable foundation models and single-cell resolution of tumor heterogeneity. Key collaborations include Prof. Klaus-Robert Müller (TU Berlin) and Dr. Philipp Mertins (MDC Berlin). Students & Research Associates Yauheniya Zhdanovich (Bioinformatics PhD) Edgar Chimal (Research Associate) Emma Payá Cipalla (Research Associate) Maximilian Leitheiser (Research Associate) Christoph Geisenberger (MD, M.Sc.) Philipp Keyl (MD, PhD) Oliver Buchstab (MD, M.Sc.)

Xipeng ShenView profile

Professor

• Programming Systems
• Machine Learning
• High-Performance Computing
• +12 more

Xipeng Shen is a Professor in the Department of Computer Science at North Carolina State University and Director of the High-Performance Intelligent Computing (HiPIC) lab. He joined NC State in 2014 as part of the Chancellor's Faculty Excellence Program after serving as the Adina Allen Term Distinguished Associate Professor at the College of William and Mary. Shen's research spans programming systems and machine learning, with a focus on enabling extreme-scale, data-intensive, and intelligent computing through innovations in compilers, runtime systems, and ML algorithms. His work has significantly influenced the development of heterogeneous computing and modern AI systems. His research areas include High-Performance Machine Learning & Real-Time AI, Heterogeneous Massively Parallel Computing, and Foundations of Programming Systems & Languages. His recent publications demonstrate strong trends in optimizing AI deployment across various platforms including mobile devices, serverless environments, and edge computing systems, with particular emphasis on efficiency, power consumption, and real-time performance. His research increasingly integrates compiler techniques with machine learning approaches to solve complex optimization problems. ACM Distinguished Member (2018) University Faculty Scholar (2017-18) ACM Distinguished Speaker (2016-2019) IEEE Senior Member (2016) IBM CAS Faculty Research Fellow (2010-2016) Google Faculty Research Award (2015) DOE Early CAREER Award (2011) NSF CAREER Award (2010) Shen has successfully advised 15 PhD students who have gone on to positions at top universities and companies including UC Santa Cruz, UMass Amherst, Google, Meta, and Huawei. His research has been funded by numerous grants from NSF, DOE, and NIH totaling over $4 million. He co-founded CoCoPIE Inc. to commercialize his research on efficient AI deployment and serves as a consultant to major technology companies including Intel, Microsoft, Huawei, Cisco, and Meta. He leads the PICTure research group and teaches courses including Compiler Construction (CSC412/512), Code Optimization for Scalar and Parallel Programs (CSC766), and Real-time AI and Machine Learning Systems (CSC591/791).

• Cybersecurity
• Artificial Intelligence
• Biometrics
• +9 more

Dr. Richard Schöffauer is a researcher affiliated with the Cybersecurity and AI group at the Department of Mathematics and Computer Science, Freie Universität Berlin. He completed his PhD in January 2024 under Prof. Dr.-Ing. Gerhard Wunder, focusing on Model-Predictive Control in Communication Networks . Research Interests: Cybersecurity, AI Explainability, Privacy, Federated Learning, Blockchain, Quantum Cryptography, and Wireless Networks. Projects: Involved in initiatives like AIgenCY (Generative AI in Cybersecurity), UltraSec (UWB-based IoT Security), 6G Research , and Physical Layer Security with ML . Education: PhD in Computer Science, Freie Universität Berlin (2024); supervised by Prof. Gerhard Wunder and externally evaluated by Prof. James Gross (KTH Stockholm) and Prof. Marian Margraf (FUB). Scientific Honors: Graduated Summa Cum Laude for his PhD thesis.

Chirag ShahView profile

Professor

• Information Retrieval
• Generative AI
• Machine Learning
• +5 more

Professor Chirag Shah at the University of Washington Information School (iSchool) is a leading researcher in intelligent information systems and generative AI. With adjunct appointments in Computer Science & Engineering and Human-Centered Design & Engineering, he bridges technical and ethical dimensions of AI. His work focuses on transparency, fairness, and bias mitigation in information access systems. PhD, Information Science - University of North Carolina (2010) MS, Computer Science - University of Massachusetts (2006) MTech, Computer Science - Indian Institute of Technology (2002) BE, Computer Engineering - Dharamsinh Desai Institute (2000) Shah's research spans task-oriented search, conversational systems, and fairness-aware AI. His work addresses critical questions about technology's societal impact, advocating for responsible AI development through his Center for Responsibility in AI Systems & Experiences (RAISE). Recent publications highlight multimodal interaction models, fairness frameworks, and cognitive trust metrics. These works examine generative AI's reasoning capabilities, bias detection in image search, and collaborative information seeking dynamics. Major Awards: 2025 SIGIR Academy Inductee 2024 ASIS&T Research in Information Science Award 2024 IEEE Pioneering Leadership in Trustworthy AI 2023 Outstanding Contributions to Information Behavior Research 2021 Karen Spärck Jones Award As founding director of the InfoSeeking Lab, Shah mentors students working at the intersection of AI and human values. His collaborations span Microsoft, Amazon, and Spotify, translating research into practical AI solutions.