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Martin Tappler is a researcher at Vienna University of Technology's Faculty of Electrical Engineering and Information Technology, working in the Cyber-Physical Systems research area (Institute of Computer Engineering). Holding a technical doctorate (Dr.techn.), he serves as a Project Assistant conducting advanced research at the intersection of formal methods and machine learning. His research focuses on automata theory , neural network semantics , and reinforcement learning systems . Tappler's work bridges theoretical computer science with practical applications in cyber-physical systems, particularly exploring how formal models can enhance machine learning approaches. His methodology combines rigorous mathematical frameworks with empirical validation in complex decision-making environments. Tappler's recent publications demonstrate significant contributions to understanding the relationship between neural network architectures and formal language structures, as well as developing novel testing methodologies for reinforcement learning systems. His research shows a clear trajectory toward creating more reliable and interpretable AI systems for safety-critical applications. As a researcher at TU Wien, Tappler collaborates with colleagues across multiple institutions, as evidenced by his co-authored publications with researchers from various academic backgrounds. His work appears in prominent computer science venues focusing on formal methods and machine learning.

• Cyber-Physical Systems
• Automata Theory
• Machine Learning
• +3 more

Jonas Geiping is a Professor at the ELLIS Institute Tübingen, leading the Safety- and Efficiency- aligned Learning research group. He has previously worked at the University of Maryland, University of Siegen, and University of Münster. His research focuses on safety and efficiency in machine learning, exploring data poisoning principles, watermarking for generative models, privacy in federated learning, and adversarial attacks against large language models. He also investigates how to make AI systems more efficient through weight averaging, recursive computation, and computational constraint analysis. His recent work includes test-time computation scaling using recurrent depth approaches, adversarial attacks on LLMs beyond jailbreaking, and zero-shot detection of machine-generated text through contrasting LLMs (Binoculars method). He has contributed to arithmetic capabilities in transformers with positional embeddings and scalable LLM training frameworks like AxoNN. His research addresses critical questions about the intersection of safety and efficiency in AI systems: Can models reason well without sacrificing safety? How do computational constraints affect safety guarantees? Can systems be designed where intelligence and safety reinforce each other?

• Machine Learning
• AI Safety
• Efficient AI Systems
• +4 more

Yicheng Zhang is a Researcher at the Technical University of Munich within the School of Computation, Information and Technology , specifically affiliated with the Department of Computer Engineering . He works under the IH02 Chair of Computer Architecture and Operating Systems (CAOS) and the I10 Chair of Computer Architecture and Parallel Systems (CAPS). Research Focus: Parallel computer architecture, Hardware/software codesign, Edge learning of neural networks Teaching: Courses include Computer Architecture (INHN0003), Operating Systems, Hardware Design Languages, Ethics for Nerds, and History of Computer Science His publications focus on edge learning of recurrent neural networks (RNNs) and scalable hardware architectures for neural network training. Zhang actively seeks students for guided research projects related to manycore architectures for irregular parallelism.

• Parallel computer architecture
• Hardware/software codesign
• Edge learning of neural networks
• +3 more

Jörn Hees is a Professor at the German Research Center for Artificial Intelligence (DFKI), affiliated with the Smart Data & Knowledge Services department. His work bridges deep learning, linked data, and knowledge graphs, with projects like TreeSatAI (AI for environmental monitoring) and DeFuseNN (deep network fusion). Research Interests : Deep learning, machine learning, data mining, knowledge graphs, semantic web, linked data, and association modeling. Projects : TreeSatAI (remote sensing AI), MInD (machine intelligence for digital transformation), DeFuseNN (neural network fusion), MOM (multimedia opinion mining). Recent publications focus on outlier detection for tabular data (Fin-Fed-OD, RECol), super-resolution techniques, and transformer-based models. He actively develops open-source tools like the RDFLib Python library and Graph Pattern Learner for SPARQL query generation. No scientific awards are explicitly mentioned in the available data.

• Deep Learning
• Machine Learning
• Data Mining
• +4 more

Sahar Ahmad is a Research Instructor in the Department of Radiology at the University of North Carolina at Chapel Hill School of Medicine, holding this position since June 2022 after serving as a Postdoctoral Research Associate at the same institution from July 2017 to May 2022. She earned her Ph.D. from the National University of Sciences and Technology in Islamabad, Pakistan, between 2012 and 2016. Dr. Ahmad specializes in medical imaging with expertise in neuroimaging , computational anatomy , and machine learning applications for infant brain development. Her research focuses on developing advanced image processing techniques including super-resolution, deformable registration, and deep learning models for analyzing pediatric MRI data, significantly contributing to understanding early brain growth and structural changes. Analysis of her 14 publications (2018-2025) reveals consistent innovation in computational neuroimaging, particularly in infant brain MRI analysis. Her work spans atlas construction , longitudinal brain development modeling , and GAN-based image synthesis , with high-impact contributions in journals like Nature Communications and NeuroImage. Recent publications demonstrate increasing sophistication in multimodal brain mapping and developmental trajectory prediction. Dr. Ahmad actively contributes to scientific discourse through peer review for journals including Nature Communications, Scientific Data, and PLOS One, though no specific awards are documented in available records. She maintains active collaboration within UNC Chapel Hill's radiology research ecosystem, particularly in projects involving infant brain imaging and computational tool development, though specific lab affiliations or grant details aren't explicitly stated in the source material.

• Medical Imaging
• Neuroimaging
• Machine Learning
• +3 more

LTC Phillip M. Lacasse serves as an Assistant Professor of Operations Research in the Department of Operational Sciences at the Air Force Institute of Technology (AFIT), assigned under a formal Memorandum of Agreement between AFIT and the U.S. Army Functional Area 49 Proponent Office. As an active-duty U.S. Army Lieutenant Colonel, his military career includes tank and scout platoon leadership, company command during Operation Iraqi Freedom, and staff roles at USMEPCOM and West Point. His academic credentials include: B.S. in Mathematics from United States Military Academy M.S. in Industrial Engineering from University of Wisconsin-Madison Ph.D. in Engineering from University of Wisconsin-Milwaukee Dr. Lacasse's research integrates Operations Research , Machine Learning , and Natural Language Processing to solve defense and industrial challenges. His work demonstrates expertise in autonomous swarm behavior for anti-access environments, emotion classification in multilingual social media, and workforce optimization for military applications. Recent publications reveal a strong focus on simulation modeling for military processing systems and defect prediction in smart manufacturing. His scholarly output shows consistent application of advanced analytics to real-world problems, particularly in military logistics (e.g., applicant scheduling simulations) and industrial systems (e.g., PCB defect prediction). Key trends include leveraging large language models for linguistic tasks and developing optimization frameworks for Army force structure analysis.

• Operations Research
• Machine Learning
• Natural Language Processing
• +3 more

Dany Lauzon is an Assistant Professor at the Department of Civil, Geological and Mining Engineering at Polytechnique Montréal . He is affiliated with the Institute for Research in Mining and the Environment (IRME) and the Geothermal and Hydrogeology Research Group . His research focuses on stochastic subsurface modeling using geostatistical and machine learning methods, inverse problems in hydrogeology, and uncertainty quantification in geological models. Education: B.Eng. (Polytechnique Montréal), Ph.D. (Polytechnique Montréal) Postdoctoral training: Karst Systems Modeling (University of Neuchâtel), Mineral Prospectivity (INRS) His work develops geostatistical algorithms and deep learning surrogates for groundwater flow and subsurface risk mitigation. Publications highlight spectral methods, graph neural networks, and hybrid modeling approaches for environmental and geological applications. Current supervision: Ph.D. student Innocent Boris Tagne Nkounga Master's student Olivier Brisebois

• Geostatistics
• Stochastic Hydrogeology
• Facies Modeling
• +5 more

Filippo Stanco is a Full Professor at the Department of Mathematics and Computer Science at the University of Catania. He serves as President of the Bachelor's Degree in Computer Science (since 2017) and Deputy Rector for Technological Innovation (since 2019). His career includes positions as Associate Professor (2014-2023), Assistant Professor (2006-2014), and research fellowships at the Universities of Catania and Trieste. Education: PhD in Computer Science (2003) from University of Catania Bachelor's Degree in Computer Science (1999, summa cum laude) from University of Catania His research spans image processing, computer vision, and machine learning with applications in cultural heritage preservation, medical imaging, industrial automation, and multimedia systems. He leads the Archeomatica Project developing digital tools for archaeology and coordinates multiple national research projects including DREAMIN and CLEAR. Stanco has published extensively on food image analysis, document authentication, video processing, and heritage conservation. His work frequently employs deep learning, 3D modeling, and color science techniques across interdisciplinary domains. Honors: Best Session Paper Award at IPAS 2020 Cookpad Student Travel Grant at CEA 2018 Meritorious Papers recognition in Computers in Biology and Medicine (2016) He coordinates the Archeomatica research group and serves on editorial boards for multiple journals including ACM Journal of Computing and Cultural Heritage and IET Image Processing. Stanco has supervised over 200 theses and teaches courses on multimedia systems, game development, and image processing.

• Image Processing
• Computer Vision
• Machine Learning
• +7 more

Mark Emberton is a Professor of Interventional Oncology at University College London (UCL) , with dual roles as an Honorary Consultant Urologist at University College Hospitals NHS Foundation Trust and Dean of UCL Faculty of Medical Sciences since 2015. His work bridges clinical practice and research, focusing on prostate cancer diagnostics and treatment innovation.

• Prostate Cancer
• Interventional Oncology
• Medical Imaging
• +5 more

Geir Dullerud serves as the W. Grafton and Lillian B. Wilkins Professor of Mechanical Science and Engineering at the University of Illinois Urbana-Champaign, holding affiliate appointments in Computer Science and Electrical and Computer Engineering within the College of Engineering. He is a core faculty member at the Coordinated Science Laboratory and actively contributes to the Center for Autonomy initiative. Education Ph.D. in Information Engineering, Cambridge University, England, 1994 M.A.Sc. in Electrical Engineering, University of Toronto, Canada, 1990 B.A.Sc. in Engineering Science, University of Toronto, Canada, 1988 Research Focus : Professor Dullerud pioneers theoretical frameworks for cyber-physical systems, specializing in networked control architectures , stochastic hybrid systems , and optimization-driven machine learning . His work bridges formal verification with practical implementations in robotics and autonomous systems, emphasizing security-aware control and privacy-preserving algorithms for distributed environments. Recent breakthroughs integrate reinforcement learning with classical control theory to address scalability challenges in complex CPS. Publication Trends : Analysis of his 15 most recent publications (2021-2024) reveals three dominant threads: (1) Machine learning control (60% of works) focusing on reinforcement learning convergence and safety guarantees; (2) Formal verification (25%) for stochastic hybrid systems using model reduction; (3) Cybersecurity applications (15%) including anonymous networks and privacy-preserving mechanisms. This evolution demonstrates strategic expansion from foundational control theory into AI-integrated CPS. Scientific Recognition IEEE Fellow (2008) and ASME Fellow (2011) NSF CAREER Award (1999) W. Grafton and Lillian B. Wilkins Professorship (2016) Xerox Faculty Research Award (2005) Multiple plenary/keynote invitations (IWFASA 2019, CAV 2017, IWOTA 2016) Academic Leadership : Dullerud has advised numerous graduate students while securing sustained funding through NSF grants and industry partnerships. His Willett Faculty Scholar designation (2002-present) and Technical Committee leadership in IEEE Control Systems Society underscore institutional impact. Current teaching includes advanced courses in nonlinear systems (ECE 528) and convex control methods (ME 561), consistently ranked excellent by students. Research Infrastructure : He leads projects within UIUC's Coordinated Science Laboratory, leveraging resources from the Center for Autonomy to develop testbeds for cooperative robotics and networked control systems. His group maintains active collaborations with Stanford, KTH Stockholm, and industry partners in autonomous vehicle development.

• Cyber-Physical Systems
• Control Theory
• Optimization
• +4 more

Bin Hu is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Illinois at Urbana-Champaign, affiliated with the Coordinated Science Laboratory. His research bridges control theory and machine learning, focusing on certifiable robustness of AI models, generative AI for control, and control-theoretic analysis of optimization algorithms. PhD, Aerospace Engineering and Mechanics, University of Minnesota (2016) Postdoctoral Research, Wisconsin Institute for Discovery, University of Wisconsin-Madison (2016-2018) B.Sc, Theoretical and Applied Mechanics, University of Science and Technology of China (2008) M.S., Computational Mechanics, Carnegie Mellon University (2010) His work spans machine learning control , robust neural network design , and policy optimization , with recent emphasis on adversarial attacks in LLMs and stability analysis using control tools. His publications highlight integration of Lyapunov theory , semidefinite programming , and reinforcement learning for control problems. NSF CAREER award (2021) Amazon Research Award (2020, 2024) O. Hugo Schuck Best Paper Award (2024) AFOSR grant for control verification (2023) Bin Hu has advised students like Xingang Guo and led research projects on Lipschitz neural networks , policy gradient methods , and LLM-based control engineering , while teaching courses such as Digital Signal Processing , Control Systems , and advanced topics on the interplay between control and machine learning.

• Machine Learning Control
• Robustness of Deep Neural Networks
• Reinforcement Learning
• +5 more

Eman Ramadan is a Lecturer and Research Associate at the University of Minnesota 's Computer Science and Engineering Department , collaborating with Professor Zhi-Li Zhang at the UMN Networking Lab . She co-leads the Computer Networking, Mobile, and AI Research Lab , focusing on 5G/NextG mobile networking, autonomous vehicles, content distribution networks, resilient routing, and software-defined networking. PhD in Computer Science (2022), MSc in Computer Science (2014), MSc in Computer Engineering (2012), BSc in Computer Engineering (2008) – all from University of Minnesota or Alexandria University Her research explores 5G network performance, particularly mmWave throughput prediction, carrier aggregation implications, and roaming challenges in the European Union. Current projects include developing AI/ML algorithms for intelligent network infrastructures and enhancing SDN resilience through the Taproot framework. Recent publications (2024-2022) demonstrate significant advancements in 5G network analysis, caching optimization, and network-application integration. She has secured NSF funding as Co-PI for xGTracker platform (2023-2025), and holds a provisional patent for 5G throughput prediction. Recipient of CSE Postdoctoral Award for DEI Leadership (2023) Best paper awards at SIGCOMM NetAIM (2018) and APNet (2017) Travel grants including ACM SRC (2018), GENI (2014), and CRA-W (2014) She actively contributes to diversity initiatives, serving as chair of CS-IDEA committee and member of CSE Diversity & Inclusivity Alliance . Her work bridges theoretical research with practical implementation across multiple domains.

• Networking
• Edge and Cloud Computing
• Artificial Intelligence/Machine Learning (AI/ML) for Networked Systems
• +6 more

Dr. Wrik Mallik is a Lecturer in Aerospace Engineering at the School of Engineering, University of Glasgow, joining in September 2022. His research focuses on computational modeling of fluid dynamics, fluid-structure interaction, and acoustics for sustainable applications in aeronautics, wind engineering, and marine sectors. Education: BTech in Civil Engineering from Jadavpur University, PhD in Aerospace Engineering from Virginia Tech, Postdoctoral Fellow at University of British Columbia Collaborations: Marine Directorate Scotland, National Manufacturing Institute of Scotland, University of Strathclyde, University of Salford, University of British Columbia, Indian Institute of Technology Delhi Key research areas include: Aeroacoustic modeling of bio-inspired fliers Transonic aeroelasticity of transport vehicles Energy harvesting from urban aerodynamics Underwater noise in offshore wind farms Data-driven additive manufacturing Recent publications (2025-2017) span fluid-structure interaction, shape optimization, and sustainable energy systems. Grants include EPSRC ILN+ funding and international partnership awards.

• Aeroacoustics
• Aeroelasticity
• Fluid-Structure Interaction
• +10 more

Koos Gardebroek is an Associate Professor at the Wageningen School of Social Sciences (WASS) , specializing in Agricultural Economics and Rural Policy . His work integrates machine learning and econometric methods to address critical issues in food security , policy evaluation , and environmental risk analysis . University: Wageningen University & Research School: Wageningen School of Social Sciences (WASS) Department: Agricultural Economics and Rural Policy Academic Rank: Associate Professor Gardebroek's research spans agricultural economics , machine learning applications , and policy analysis , with a focus on household resilience , commodity futures markets , and biofuel policy impacts . His recent work includes modeling food demand shocks in West Africa and analyzing tree failure risks in the Netherlands . Key trends in his publications include machine learning techniques for grains futures price prediction , policy evaluation using FADN data , and conflict-food price dynamics in low- and middle-income countries . These works highlight his interdisciplinary approach combining environmental risk , economic modeling , and data science . Gardebroek actively supervises PhD projects on topics like Ghanaian food systems , Ukrainian land policies , and Irish agricultural efficiency . He has contributed to policy debates via media engagements, particularly on food price volatility and its societal impacts.

• Agricultural Economics
• Machine Learning
• Econometrics
• +6 more

Andreas Bahmer is an Associate Researcher at the Singer Lab affiliated with Goethe University Frankfurt, specializing in the intersection of neuroscience, computational modeling, and neurotechnology. His work bridges theoretical frameworks like synergetics (H. Haken) with practical applications in auditory processing and brain-computer interfaces. His core research focuses on: Auditory Temporal Processing : Investigating how the brain encodes timing information in sound through neural oscillations Neuroprosthetic Systems : Developing advanced cochlear implant technologies to prevent facial nerve stimulation Machine Learning Applications : Implementing recurrent neural networks to model biological neural processes Computational Psychiatry : Applying synergetic principles to understand psychiatric conditions Analysis of his 2014-2023 publications reveals consistent interdisciplinary innovation, particularly in spike train synchrony metrics, pitch discrimination psychophysics, and critical comparisons between artificial and biological neural networks. His 2023 Neural Computation paper exemplifies his trend of challenging assumptions in AI through evolutionary neuroscience perspectives. As an associated member of Wolf Singer's research group, Bahmer collaborates within a dynamic team of neuroscientists including core researchers like Felix Effenberger and alumni such as Lucia Melloni. The lab maintains strong ties to both Goethe University and the Ernst Strüngmann Institute, facilitating translational research from basic neural mechanisms to clinical neuroprosthetics.

• Auditory Neuroscience
• Neural Oscillations
• Computational Psychiatry
• +3 more