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Jayneel ParekhView profile

Research Fellow

• Neural Network Interpretability
• Audio Processing
• Computer Vision
• +3 more

Jayneel Parekh is a Postdoctoral Researcher in the MLIA (Machine Learning and Artificial Intelligence) team at ISIR (Institut des Sciences et Industries du Réel), Faculty of Science, Sorbonne University, working with Prof. Matthieu Cord. His research focuses on understanding and enhancing large multimodal models, with applications across audio, visual, and multimodal domains. Parekh completed his PhD at LTCI, Telecom Paris under Prof. Florence d'Alche and Prof. Pavlo Mozharovskyi, researching neural network interpretability applied to image and audio data. He earned his undergraduate degree in Electrical Engineering from IIT Bombay, where he worked with Prof. Preeti Rao and Prof. Yi-Hsuan Yang on Speech-to-Singing conversion. His research spans neural network interpretability, audio processing, computer vision, and multimodal models, with emphasis on explainable AI. His work demonstrates a consistent trajectory from foundational audio/image interpretability methods to cutting-edge large multimodal model analysis, showing increasing complexity and impact across NeurIPS, ICML, and ICCV publications. L2I paper awarded 2nd prize for STIC Best Scientific Contribution 2023 Top Reviewer at NeurIPS 2023 Parekh actively contributes to the academic community through workshop organization (ICCV on Explainable Computer Vision, ELLIS Unconference on Robustness/Fairness/Explainability) and presentations at institutions including IIT Jodhpur, Deezer Research, and IBM Research. His collaborative network spans MPI Informatics, TU Darmstadt, TU Munich, and Télécom Paris.

Grey BallardView profile

Associate Professor

• Numerical Linear Algebra
• High Performance Computing
• Computational Science
• +6 more

Dr. Grey Ballard is an Associate Professor in the Department of Computer Science at Wake Forest University . He earned a B.S. in Math and Computer Science (2006), M.A. in Math (2008) from Wake Forest, and PhD in Computer Science (2013) from the University of California, Berkeley. He was a Truman Fellow at Sandia National Laboratories before joining Wake Forest. Research Focus: Ballard develops communication-optimal algorithms for high-performance computing , particularly in tensor decompositions , symmetric matrix computations , and nonnegative matrix factorization . His work combines numerical linear algebra with parallel algorithm design to reduce data movement costs in distributed systems. Publications demonstrate expertise in communication lower bounds , randomized tensor rounding , and visualization tools for parallel algorithms. He has contributed software packages such as TuckerMPI , GentenMPI , and PLANC for large-scale data compression and clustering. Scientific Awards: Wake Forest Excellence in Research Award NSF CAREER Award SIAM Linear Algebra Prize Three Conference Best Paper Awards (SPAA, IPDPS, ICDM) C.V. Ramamoorthy Distinguished Research Award (UC Berkeley) ACM Doctoral Dissertation Award – Honorable Mention Teaching: Courses include Introduction to Computer Science , Numerical Linear Algebra , and Parallel Algorithms . He has developed educational tools using the Thread-Safe Graphics Library to visualize parallel dynamic programming and collective communication.

• Cyber Security
• Federated Learning
• Network Security
• +5 more

Jonathan White serves as a Senior Lecturer in Cyber Security within the College of Arts, Technology and Environment at the University of the West of England (UWE). With over 23 years of prior industry experience in telecommunications critical infrastructure systems, he joined UWE in January 2020 after transitioning from roles as software developer, product specialist, and management leader in real-time embedded systems. His educational background includes an M.Sc. in Cyber Security (with Distinction) and B.Sc. in Computing for Real-time Systems, both from UWE, where he is currently pursuing a PhD focused on Federated Learning security tradeoffs. White's research centers on Federated Learning applications for IoT security, container security analysis, and machine learning-driven threat detection in home networks. Analysis of his publication record reveals a strong focus on practical security implementations, particularly in containerized environments (Docker security analysis, cyber ranges) and Federated Learning security frameworks. His work consistently bridges theoretical machine learning concepts with tangible security applications for IoT and edge devices, emphasizing privacy-performance tradeoffs in distributed systems. Scientific Recognition: Fellow of the Higher Education Academy (FHEA) White actively contributes to cyber security education through innovative teaching methods including the 'Cyber Funfair' immersive learning platform and Scalextric-based physical system hacking demonstrations. His industry background in telecommunications critical infrastructure informs his practical approach to security education and research, particularly regarding real-time system vulnerabilities and high-availability network security requirements. His technical expertise spans C and Python programming, network security protocols, and specialized knowledge in securing containerized environments and IoT ecosystems. Current research includes longitudinal analysis of container image vulnerabilities and development of modular cyber range infrastructure for security training.

Richard NaudView profile

Assistant Professor

• Computational Neuroscience
• Neural Coding
• Statistical Methods in Neuroscience
• +5 more

Richard Naud is an Assistant Professor in the Department of Cellular and Molecular Medicine at the Faculty of Medicine, University of Ottawa, with a cross-appointment in Physics at the Faculty of Science. He holds dual research positions at the Center for Neural Dynamics (CND) and Brain and Mind Research Institute (BMRI), focusing on computational approaches to decode neural signaling mechanisms and develop brain-machine interfaces. His educational background includes: PhD in Neuroscience from École Polytechnique Fédérale de Lausanne (EPFL), 2011 MSc in Physics from McGill University, 2006 BSc in Physics from McGill University, 2004 Dr. Naud's research investigates how neurons encode information through spikes, bursts, and silences using mathematical models and statistical analysis of electrophysiology data. His lab develops computational protocols for synaptic dynamics analysis, studies dendritic computation in neurological diseases, and creates neuromorphic algorithms for spiking neural networks. Current work emphasizes serotonin system dynamics, burst coding mechanisms, and neural network simulations for demyelinating conditions. Analysis of his recent publications reveals three dominant research vectors: (1) Burst coding as an independent information channel beyond firing rates, (2) Serotonin-mediated value coding in decision systems, and (3) Neuromorphic implementation of biologically plausible learning rules. His work bridges theoretical neuroscience with clinical applications in stroke recovery and neurological disorders. Dr. Naud leads the Neural Coding Lab, which actively recruits postdoctoral fellows, graduate students, and undergraduates for projects in neural coding theory, computational psychiatry, and neuromorphic engineering. The lab maintains collaborations with experimental neuroscience groups for model validation and develops open-source tools like SRPlasticity for synaptic dynamics analysis.

Grzegorz ChrupałaView profile

Associate Professor

• Computational Linguistics
• Artificial Intelligence
• Cognitive Science
• +3 more

Grzegorz Chrupała is an Associate Professor at the Department of Cognitive Science and Artificial Intelligence , Tilburg University, where he leads research in computational approaches to multimodal communication. Previously, he was a postdoctoral researcher at Saarland University's Spoken Language Systems group and earned his PhD from Dublin City University's School of Computing. His research bridges biological and artificial computation , focusing on enabling machines to learn language from multimodal data (speech, gestures, visual-auditory stimuli) as children do naturally. This involves developing and interpreting deep learning architectures, analyzing emergent representations, and advancing speech technology for under-resourced languages. Key themes include Visually grounded speech modeling Feature attribution and model interpretability Human-inspired learning paradigms BlackboxNLP workshop leadership His recent publications examine speech model reliability , lexical tone encoding , and contextual dependencies in NLP systems. He mentors a team of PhD candidates and alumni working on topics like user-centric interpretability, bioacoustics, and disentangled speech representations. He also serves on the board of the Dutch Open Speech Technology Foundation, chairs Interspeech 2025 tutorials, and contributes as an Action Editor for TACL.

• MRI Physics
• Quantitative MRI
• Oncological Imaging
• +15 more

Jie Deng, Ph.D., is a Professor in the Department of Radiation Oncology at UT Southwestern Medical Center, where she serves as faculty in the Division of Medical Physics & Engineering. She is a certified MRI and MRI for radiation therapy medical physicist by the American Board of Medical Physics and holds a leadership role as a magnetic resonance safety officer. Dr. Deng is actively involved in both clinical and research aspects of medical imaging and radiotherapy, with a strong emphasis on integrating advanced imaging technologies into therapeutic workflows. Dr. Deng earned her academic degrees from prestigious institutions: a Bachelor of Science in Biomedical Engineering from Southeast University in China, a Master’s in Bioengineering from the University of Illinois at Chicago, and a Ph.D. in Biomedical Engineering from Northwestern University. She further enhanced her expertise by obtaining a Master of Science in Law from the Northwestern Pritzker School of Law, reflecting a multidisciplinary approach to her scientific work. Her research interests center on MRI physics , quantitative imaging , oncological imaging , and the application of artificial intelligence in medical imaging. She has pioneered work in MRI-guided radiation therapy, imaging biomarkers for therapeutic response, and AI-driven image reconstruction and artifact reduction. Her recent publications demonstrate a consistent focus on improving imaging accuracy, speed, and clinical utility, particularly in liver, pediatric, and oncological applications. The analysis of her 15 most recent articles reveals a strong trend toward deep learning-based image reconstruction , quantitative MRI biomarkers , and synthetic image generation for radiotherapy planning. Topics such as 4D-MRI, synthetic CT, motion artifact reduction, and AI fusion models dominate her scholarly output, indicating a forward-looking research trajectory centered on intelligent, fast, and precise imaging for personalized cancer therapy. Dr. Deng actively contributes to the scientific community through presentations at major conferences including the International Society for Magnetic Resonance in Medicine (ISMRM) and the American Association of Physics in Medicine (AAPM), where she shares innovations in MRI, adaptive radiotherapy, and AI integration. As an educator, Dr. Deng mentors medical physics residents and graduate students, delivering lectures on MR-only simulation, MR-linear accelerator practices, and medical imaging fundamentals. While no specific grants are mentioned in the text, her extensive publication record in high-impact journals suggests active research funding and collaborative projects. She is affiliated with key professional organizations and serves on UT Southwestern’s MRI Safety Committee, ensuring safe and effective use of MRI in clinical and research settings. Her work bridges the gap between engineering innovation and clinical application, making significant contributions to the field of radiation oncology and medical physics.

• Data Mining
• Bioinformatics
• Machine Learning
• +3 more

Domingo Savio Rodríguez Baena is a Professor at Pablo de Olavide University, affiliated with the Department of Computer Languages and Systems. His research focuses on data mining, bioinformatics, and computational biology, with a particular emphasis on biclustering algorithms, gene co-expression networks, and high-performance computing applications. PhD in Engineering, Data Science, and Bioinformatics (2012) from Pablo de Olavide University His work spans interdisciplinary domains, including recommender systems , livestock behavior analysis , and biological data interpretation . Recent articles highlight his contributions to multi-GPU optimization , ensemble learning , and historical database construction . Key collaborations include the DATAi Intelligent Data Analysis and DASE Data Analytics Science & Engineering research groups. He has developed tools like the CyEnGNet–App for gene network visualization and BIGO for gene enrichment analysis. Contact: dsrodbae@upo.es

• Architecture
• Urban and regional planning
• Design
• +5 more

Professor Alan Penn is a leading academic at University College London's The Bartlett School of Architecture , where he holds the title of Professor in Architectural and Urban Computing. He previously served as Dean of the Bartlett Faculty of the Built Environment from 2009 to 2019 and has been instrumental in establishing Space Syntax Ltd , a UCL knowledge transfer spin-out company. His affiliations include membership in the Space Syntax Laboratory, board membership of UCL Consultants Ltd, and trustee status at Shakespeare North Trust. Education: BSc (1978), Dip Arch (1980), MSc (1983) - all from University College London Alan Penn’s research investigates how spatial design influences social and economic behaviors through innovative space syntax methodologies . Key areas include: Agent-based simulations of human behavior Spatio-temporal representations of built environments Urban spatial network analysis Urban sustainability across multiple dimensions Cognitive markers in architectural design Historical urban growth modeling His recent publications demonstrate a strong focus on computational urbanism, evolutionary city patterns, and behavioral architecture. Research trends show interdisciplinary approaches combining architectural theory with: Machine learning applications Network science analysis Behavioral psychology insights Historical GIS techniques Complex systems modeling Public health considerations Scientific recognition includes: HEFCE Business Fellowship (2001-2005) KTP SE Region Award (2010) Multiple UCL Enterprise awards ‘Spirit of Enterprise’ Award (2008) As Principal Investigator he leads the £5m EPSRC-funded Urban Dynamics Lab , demonstrating sustained research excellence. His work extends to public engagement through: Shakespeare North Trust educational theatre development Media appearances (New Scientist, Slashdot) Public policy contributions

Sirojan TharmakulasingamView profile

Lecturer

• Machine Learning
• Artificial Intelligence
• Data Science
• +6 more

Dr. Sirojan Tharmakulasingam serves as a Lecturer and Research and Development Coordinator at the Signals, Information & Machine Intelligence lab within the Faculty of Engineering at the University of New South Wales (UNSW) Sydney. His work bridges theoretical machine learning with practical applications in edge computing and high-performance systems. His research spans multiple cutting-edge domains including machine learning, artificial intelligence, data science, edge computing, and high-performance computing. Dr. Tharmakulasingam specializes in developing next-generation inference models by integrating machine learning, signal processing, mathematical modeling, and computing across diverse data types including images, video, audio, and quantum molecular data. His work has significant implications for scientific computing, telecommunications, and healthcare applications. Analysis of his publication trends reveals a strong focus on practical AI implementations, with increasing emphasis on edge computing solutions, quantum applications, and energy-efficient models. His recent work demonstrates progression from foundational machine learning techniques toward specialized applications in scientific computing and real-time systems. Dr. Tharmakulasingam holds a Doctor of Philosophy from UNSW Sydney and a Bachelor of Science of Engineering from the University of Moratuwa in Sri Lanka. His academic journey reflects a strong foundation in both theoretical and applied engineering principles. As Research and Development Coordinator for the Signals, Information & Machine Intelligence lab, he oversees critical research infrastructure and collaborations. His work location in Room 447 of the EE&T Building (G17) places him at the heart of UNSW's engineering research ecosystem, with access to the Mark Wainwright Analytical Centre's extensive facilities.

Dejan MarkovicView profile

Professor

• Implantable neuromodulation systems
• Domain-specific compute architectures
• Design methodologies
• +3 more

Dejan Markovic is a Professor of Electrical and Computer Engineering at the University of California, Los Angeles (UCLA), holding the Mukund Padmanabhan Term Chair in Electrical Engineering and serving as Area Director for Circuits and Embedded Systems. His research spans implantable neuromodulation systems, domain-specific compute architectures, and energy-efficient design methodologies for biomedical and embedded applications. Dr. Markovic earned his PhD (2006) and MS (2000) from UC Berkeley and BS (1998) from the University of Belgrade, Serbia. His research focuses on ultra-low-power integrated circuits for neural interfaces, including artifact-free stimulation/sensing platforms and neural recording front-ends, alongside domain-specific architectures for sparse linear algebra in IoT and mobile computing. Recent work demonstrates strong trends in medical neuromodulation and energy-efficient VLSI for biomedical signal processing and wireless communications. His scientific contributions have been recognized with prestigious awards including IEEE Fellow (2021), ISSCC Lewis Award (2014), ISSCC Jack Raper Award (2010), and NSF CAREER Award (2009). Additional honors include UC Berkeley's David J. Sakrison Memorial Prize (2007) and multiple best paper awards. Dr. Markovic co-founded semiconductor IP startup Flex Logix Technologies (2014) and leads the DMGroup research lab at UCLA. He teaches core courses including ECE 115C (Digital Integrated Circuits), ECE M216A (VLSI Design), and BME M260 (Neuroengineering), mentoring students in cutting-edge VLSI design for medical and communication applications. The DMGroup lab specializes in developing implantable neuromodulation systems, domain-specific compute engines, and novel design methodologies, with recent projects featured in UCLA's coverage of brain-computer interface technology and Anari AI's $2M funding for personalized AI hardware.

• Machine Learning
• Network Performance
• IoT Security
• +3 more

Andreas Johnsson is an Adjunct Senior Lecturer at the Department of Information Technology , Uppsala University, Sweden. His research spans Machine Learning , Network Performance , and IoT Security in the context of 5G/6G Networks and Edge Computing . Research interests include federated learning, transfer learning, and network optimization techniques. His recent work (2024-2021) focuses on self-regulated learning models for 6G, multi-objective neural architecture search, IoT intrusion detection generalizability, and delay prediction in heterogeneous networks. He has co-authored over 15 publications in high-impact venues like IEEE Transactions on Machine Learning in Communications and Networking and IEEE NOMS . Andreas actively collaborates with researchers such as Jalil Taghia, Farnaz Moradi, and Hannes Larsson. His contributions extend to change detection algorithms, policy adaptation frameworks, and feature selection methodologies in dynamic network environments. No formal scientific awards or student advisement details are currently documented.

• Wireless Communication
• Machine Learning
• Signal Processing
• +4 more

Prof. Dr. Haris Gačanin is a faculty member at RWTH Aachen University, affiliated with the Institute for Distributed Signal Processing under the College of Electrical Engineering. His research focuses on integrating machine learning with wireless communication systems, particularly in industrial IoT, edge computing, and network optimization. Current academic rank: Professor Contact: harisg@dsp.rwth-aachen.de Research Interests: Wireless systems, machine learning, signal processing, and network optimization. Key contributions include: Adaptive resource allocation in IIoT and vehicular networks AI-driven channel estimation and feedback mechanisms Security-oriented emitter identification via metric learning Federated/transfer learning for edge environments Hardware-efficient deep learning models for mmWave and THz communications Methodological Focus: Combines reinforcement learning, attention mechanisms, and robust neural architectures with practical implementations on FPGA and vehicular systems.

Arianna BisazzaView profile

Associate Professor

• Natural Language Processing
• Multilingualism
• Low-resource Language Modeling
• +7 more

Arianna Bisazza is an Associate Professor in the Computational Linguistics Group at the University of Groningen, where she leads the InClow research group focused on Interpretable, Cognitively inspired, Low-resource language models. Her work bridges computational linguistics, cognitive science, and language acquisition to develop more robust and interpretable language processing algorithms that can adapt to diverse linguistic phenomena worldwide. Dr. Bisazza's research interests span statistical modeling of human languages in multilingual contexts, with particular focus on improving language model performance for "challenging" or low-resource languages. Her work explores how insights from human language acquisition can inform better language modeling techniques, and she investigates methods to make state-of-the-art NLP systems more interpretable and transparent. As a cross-disciplinary researcher, she actively seeks to enhance our understanding of human language processing and evolution through computational modeling tools. Her recent publications reveal a strong emphasis on multilingual evaluation frameworks (like TurBLiMP and MultiBLiMP), interpretability of language models, and connections between human language acquisition and neural network learning. Her work consistently addresses the challenge of making language technology more robust across diverse linguistic structures and typological features. Outstanding Paper Award at the BabyLM Challenge (CoNLL'24 Shared Task) for "BabyLM Challenge: Exploring the Effect of Variation Sets on Language Model Training Efficiency" Dr. Bisazza currently leads a Vidi project funded by the Dutch Research Council (NWO) on improving low-resource language modeling through child language acquisition insights. She is also part of two national consortium projects funded by NWA-ORC initiatives: InDeep (Interpreting deep learning models for language, speech & music) and LESSEN (Low Resource Chat-based Conversational Intelligence). She supervises multiple PhD students, including two China Scholarship Council (CSC)-funded researchers working on simulating human patterns of language learning and change. Her earlier research was supported by a Veni grant (2017-2021) focused on understanding and improving the encoding of linguistic structure in Neural Machine Translation models. As head of the InClow research group, Dr. Bisazza oversees a team investigating interpretable, cognitively inspired approaches to low-resource language modeling. The group's work combines insights from cognitive science and linguistics with cutting-edge NLP techniques to develop language models that better reflect human language processing capabilities, particularly in resource-constrained settings.

Christoph DellagoView profile

Professor

• Computational Physics
• Statistical Mechanics
• Molecular Dynamics Simulations
• +5 more

Christoph Dellago is a full Professor of Computational Physics at the Faculty of Physics of the University of Vienna, where he has been a faculty member since 2003. He currently serves as Director of the Erwin Schrödinger Institute for Mathematics and Physics, Head of the Computational and Soft Matter Physics Group, and Project lead of EuroCC Austria - National Competence Centre for Supercomputing. Previously, he served as Dean of the Faculty of Physics (2009-2012) and Coordinator of the Doctoral College Advanced Functional Materials (DCAFM). Full Professor, Faculty of Physics, University of Vienna (2003-present) Director, Erwin Schrödinger Institute for Mathematics and Physics (2017-present) Head, Computational Physics and Soft Matter Group (2024-present) Coordinator, Doctoral College Advanced Functional Materials (DCAFM) Austrian Representative, Council of CECAM Dellago received his PhD in Physics from the University of Vienna in 1996, followed by postdoctoral research at UC Berkeley as a Schrödinger Fellow of the Austrian Science Foundation. His research focuses on developing computational methods to study rare events in condensed matter systems, particularly transition path sampling methodology for simulating nucleation, chemical reactions, and biomolecular reorganizations. He has pioneered the application of machine learning to molecular structure recognition and potential energy surfaces. Recent work examines self-assembly of nanocrystals, biopolymer folding, aqueous interfaces, phase separation in alloys, thermo-polarization, cavitation, and freezing phenomena. Analysis of Dellago's recent publications (2023-2025) reveals a strong emphasis on machine learning applications in computational physics, particularly neural network potentials for simulating water interfaces, crystal defects, and phase transitions. His work bridges traditional statistical mechanics with modern computational techniques, creating powerful tools for studying complex dynamical processes that occur on timescales far beyond conventional molecular dynamics simulations. The publications demonstrate increasing integration of machine learning with rare event sampling methods, reflecting the cutting-edge direction of computational statistical mechanics. Förderpreis der Stiftung Futura zur Förderung junger Südtiroler im Ausland (1997) The Raymond and Beverly Sackler Prize in the Physical Sciences (2005) UNIVIE Teaching Award of the University of Vienna (2014) Dellago leads an active research group with multiple PhD students and postdocs, focusing on computational statistical mechanics. His group develops trajectory-based sampling methods and machine learning approaches for molecular simulation. He has secured significant funding through EuroCC Austria and various research platforms including the Research Platform Accelerating Photoreaction Discovery and the Research Platform Erwin Schrödinger International Institute for Mathematics and Physics. His research has been supported by numerous grants enabling advanced computational infrastructure for high-performance simulations. The Dellago Group operates within the Computational and Soft Matter Physics division at the University of Vienna, with strong connections to the Research Network Data Science. The group collaborates extensively with international research institutions and maintains close ties with the Erwin Schrödinger Institute, which Dellago directs. Their research environment combines theoretical physics, computational chemistry, and machine learning expertise to tackle fundamental questions in condensed matter physics and soft matter systems.

• Computational Neuroscience
• Neural Networks
• Deep Learning
• +4 more

Prof. Sander M. Bohte holds a part-time appointment as a Professor of Computational Neuroscience at the Swammerdam Institute for Life Sciences (SILS), University of Amsterdam, and is a researcher at the CWI Machine Learning group. His research focuses on computational models of neural information processing, emphasizing spiking neural networks, predictive coding, and reinforcement learning. He bridges computational neuroscience and machine learning, exploring how biological insights can improve neural network designs and vice versa. Key collaborations include work with Cyriel Pennartz (UvA), Pieter Roelfsema (NIN), and Steven Scholte (B&C). His applied research spans scientific machine learning applications in finance and genomics. He actively supervises MSc thesis students, prioritizing those from UvA, with projects ranging from biologically inspired neural architectures to efficient spiking network simulations. Research highlights include developing biologically plausible learning rules for deep networks, predictive coding models for sensory data, and spiking network models for working memory tasks. His work also addresses challenges in temporal dynamics and scalable neural computation, leveraging both theoretical and applied perspectives.