Massimo Melucci serves as Associate Professor of Computer Science at the University of Padua's Department of Information Engineering, maintaining an active research and teaching profile with office location in Building DEI/G, Room 312, Padova, Italy. His instructional responsibilities include undergraduate Information Retrieval and Databases courses, alongside graduate-level Computing Methods for Data Science instruction, complemented by freely available lecture notes on information retrieval fundamentals. His research program centers on two pioneering domains: Quantum-inspired Information Retrieval , where he develops mathematical frameworks applying quantum probability to model contextual relevance and interference effects in search systems, and Algorithmic Fairness , investigating trade-offs between ranking effectiveness and equitable outcomes. These threads converge in his exploration of fairness-effectiveness optimization through eigensystems and variation modeling, while his interdisciplinary work extends into diving medicine neurophysiology , analyzing EEG-based brain connectivity disruptions in professional divers under hyperbaric conditions. Recent publication analysis (2020-2025) reveals three dominant trajectories: quantum-inspired neural architectures for multimodal sentiment analysis, rigorous fairness-effectiveness quantification in ranking algorithms, and biomedical applications of signal processing in extreme environments. He actively fosters academic collaboration through initiatives like the BIRDS workshop series and CIRCLE conference, which bridge information science, retrieval, and data science communities. His work demonstrates consistent innovation in applying non-classical probability models to information access challenges while maintaining strong connections to real-world applications in both search technology and human safety.
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نمایش ۴۶-۶۰ از ۱۲۹ استاد راهنما
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Federico Holikمشاهده پروفایل
پژوهشگر ارشد
Federico Holik serves as a Research Fellow at Argentina's National Scientific and Technical Research Council (CONICET) with primary affiliation to Vrije Universiteit Brussel (VUB) in Belgium. His institutional presence is anchored through VUB's CRIS profile and publications portal, reflecting active engagement in quantum research despite the absence of specified departmental or school affiliations within the university structure. His research program critically examines the logical, algebraic, and geometrical frameworks underpinning quantum mechanics, with concentrated efforts in quantum information theory and foundational probability interpretations. Key investigations include quantum resource management for NISQ-era devices, ontological indistinguishability of quantum entities, and the development of quantum mereology to address part-whole relationships in quantum systems. His interdisciplinary reach extends to quantum-inspired AI through quasi-set theory and epidemiological applications via information quantifiers in pandemic data analysis. Analysis of his 2023-2025 publications reveals two dominant trajectories: (1) practical quantum computing challenges centered on resource optimization, error mitigation, and software engineering frameworks for noisy hardware, and (2) deep foundational inquiries into quantum ontology, probability structures, and mereological paradoxes. This dual focus bridges theoretical rigor with emerging quantum technologies while maintaining strong connections to philosophical questions about quantum identity and agency.
- Digital Signal Processing (DSP)
- Field-Programmable Gate Arrays (FPGA)
- Machine Learning Hardware Acceleration
- +۱۳ مورد دیگر
Gian Carlo Cardarilli is a researcher specializing in digital hardware design and machine learning acceleration. His work focuses on FPGA implementations, Residue Number System (RNS) architectures, and reconfigurable computing for applications in wireless communication, edge AI, and fault-tolerant systems. Key collaborations with institutions like IEEE and ACM through publications. Active in translating theoretical algorithms into practical hardware solutions for real-time systems. Research interests include: Optimizing deep learning models for heterogeneous platforms. Developing radiation-hardened memory systems. Creating energy-efficient signal processing architectures. Advancing reconfigurable functional units for embedded processors. His article analyses span fields like Quantum Cellular Automata , Variable Fractional Delay Filters , and RNS-Based Position Estimation , reflecting a trend toward adaptive, low-power, and domain-specific hardware.
Jesse Thaler is a Professor in the MIT Physics Department and the Center for Theoretical Physics. He joined MIT in 2010 after a Miller Institute fellowship at UC Berkeley (2006-2009). His research focuses on collider phenomenology, quantum computing applications in particle physics, and machine learning methods for high-energy data analysis. He holds a Ph.D. from Harvard University (2006) and a Sc.B. from Brown University (2002). Key honors include the DOE Early Career Award (2011), Presidential Early Career Award (2012), Sloan Fellowship (2013), and MIT's Edgerton Award (2016). His work bridges theoretical physics with cutting-edge computational techniques, emphasizing jet substructure analysis, energy correlator studies, and symmetry discovery via AI. Thaler leads the MIT Center for Theoretical Physics' efforts in collider physics and co-founded the Institute for Artificial Intelligence and Fundamental Interactions (IAIFI). His research group collaborates on LHC Olympics challenges and explores quantum algorithms for jet clustering. Recent work includes developing Lorentz-equivariant neural networks and anomaly detection frameworks for CMS open data.
Prof David Windridge is a Professor in the Department of Computer Science at Middlesex University, leading the University’s Data Science activities. His research focuses on Machine Learning, Quantum Computing, Computer Vision, and their applications in healthcare and medical imaging. He has contributed to advancements in explainable AI, quantum algorithms, and multimodal data fusion. His work spans theoretical foundations and practical applications, including medical diagnostics, gesture recognition, and smart home systems. Education details are not explicitly provided in the text, but his expertise suggests a strong academic background in computer science and AI. His research interests reflect a blend of computational methods and interdisciplinary challenges, such as quantum-enhanced machine learning and AI-driven healthcare solutions. Prof Windridge’s recent publications emphasize visual attribution in medical imaging, quantum computing optimizations, and AI ethics in pediatric care. His work on adversarial learning and ensemble techniques highlights contributions to robust AI systems. Collaborations and grants are inferred from his leadership roles and publications but remain unspecified in detail. He is affiliated with Middlesex University’s Department of Computer Science and has engaged in knowledge transfer and research collaborations, though specific lab affiliations or teams are not explicitly mentioned.
R. (Ro) Jeffersonمشاهده پروفایل
استادیار
- Physics
- Machine Learning
- Quantum Field Theory
- +۷ مورد دیگر
Dr. R. (Ro) Jefferson is an Assistant Professor at Utrecht University with a dual 50/50 appointment between the Institute for Theoretical Physics (ITP) and the Department of Information and Computing Sciences (ICS). Their research focuses on interdisciplinary applications of physics and information theory to deep learning and neuro-inspired AI, including mathematical connections between neural networks and renormalization group flows. Additionally, they study holography, black holes, quantum gravity, and quantum information theory's role in fundamental physics. They actively contribute to EDI efforts through committee memberships and public advocacy for marginalized groups in academia. Education background: PhD (Theoretical Physics, University of Amsterdam, 2017), MSc (Physics, McGill University, 2013). Former roles include Nordita Fellow (2020–2022) and postdoctoral researcher at the Max Planck Institute for Gravitational Physics (2017–2020). Courses taught include Advanced Quantum Field Theory and Research Methods for Computer Science. Research interests include: quantum field theory applications in AI, modular theory's implications for black hole interiors, and statistical field theory methods for deep network analysis. Their work bridges theoretical physics with machine learning through frameworks like the NN/QFT correspondence. Recent publications explore traversable wormholes, entanglement entropy, and critical phenomena in neural networks. Outreach activities include speaking at I, Scientist 2019, Berlin Museum für Naturkunde, and LGBTQIA+ science showcases. Advocacy focuses on combating pseudoscience affecting trans healthcare access and climate misinformation.
Patrick Gelßمشاهده پروفایل
پژوهشگر
Patrick Gelß is a Postdoctoral Researcher at Zuse Institute Berlin, working in the AI in Society, Science, and Technology department. He leads the iol.QUANT research group focused on quantum-inspired and tensor-based methods, with affiliations to Freie Universität Berlin and collaborative projects like MATH+ Cluster of Excellence. Diploma in Mathematics/Physics (2013, Freie Universität Berlin) PhD in Mathematics (2017, summa cum laude, Freie Universität Berlin) Postdoctoral roles at Zuse Institute Berlin (2020-present) and CRC 1114 (2017-2022) His research bridges tensor decompositions, quantum computing, and dynamical systems, with notable contributions to: Graph Isomorphism: Continuous optimization approaches via doubly stochastic matrices and Frank-Wolfe algorithms Quantum Simulation: Tensor network representations for quantum circuits and the open-source WaveTrain package KvN Mechanics: Mathematical analysis of existence/uniqueness solutions for bounded domains Fredholm Networks: Novel training paradigms for neural networks using integral equations Scientific achievements include: Developing Scikit-TT for tensor-train computations Organizing major workshops (QML@SC2024, TMQS 2024) Leading the Thematic Einstein Semester 2024 on Mathematics for Quantum Technologies His work spans chemical kinetics, quantum dynamics, and quantum machine learning, with applications to CO oxidation models, exciton-phonon systems, and mutational hierarchies in medicine.
Christian Kokeمشاهده پروفایل
پژوهشگر
Christian Koke is a PhD student and AI Researcher at the Technical University of Munich (TUM), affiliated with the Computer Vision Group under Informatics 9 (Department of Informatics). He is jointly supervised by Prof. Daniel Cremers (TUM) and Prof. Michael Bronstein (Oxford University), with additional collaborations with Bastian Rieck . His academic background includes M.Sc. degrees in Mathematics and Theoretical Physics from Ludwig Maximilian University Munich (LMU) and TU Munich, where he studied under Prof. Gitta Kutyniok on graph neural networks. Education: PhD in Computer Science (ongoing), TUM M.S. in Mathematics, Ludwig Maximilian University Munich M.S. in Theoretical Physics, LMU & TUM B.S. in Physics, Heidelberg University His research focuses on the mathematical foundations of machine learning , particularly in graph neural networks, directed graphs, and spectral methods. He explores stability guarantees, multi-scale consistency, and geometric deep learning frameworks, with applications in quantum technologies and theoretical physics. His work bridges abstract mathematical concepts with practical AI advancements. Christian has presented at major conferences, including ICLR 2024 (oral presentation), NeurIPS 2023 Workshops , and Learning on Graphs events in Paris and Madrid. His publications include theoretical and applied contributions to graph learning and physics-inspired AI. Awards: Outstanding Extended Abstract, ICML 2024 Workshop on Geometry-grounded Representation Learning He is an ELLIS PhD Student and collaborates with labs at TUM, Oxford, and Ecole Polytechnique. His work has been featured in the Computer Vision Group at TUM, with a focus on foundational research rather than applied projects.
Sebastian Bugge Loeschckeمشاهده پروفایل
پژوهشگر ارشد
Sebastian Bugge Loeschcke is a PhD Fellow at the Machine Learning Section of the Department of Computer Science (DIKU), University of Copenhagen . His research spans theoretical and applied machine learning with focus on quantum machine learning, language modeling, and sustainability. Current affiliation: Machine Learning Section, DIKU Key research areas: Quantum-classical hybrid models, neural language processing, geospatial analysis Collaborative initiatives: SCIENCE AI Centre, TreeSense Centre Loeschcke's recent work includes Coarse-To-Fine Tensor Trains for compact representations and LoQT: Low-Rank Adapters for Quantized Pretraining , reflecting his focus on efficient neural architectures and quantum-inspired methods. His publications address cross-disciplinary challenges in climate modeling, healthcare, and quantum computing. Scientific contributions include: 2024: Tensor train compression methods for visual representations 2024: Low-rank adapter techniques for quantized models 2025: Quantum computing applications in molecular binding energy calculation 2025: Ethical frameworks for sustainable AI development 2025: Quantum dot array simulation tools (QDarts) Loeschcke contributes to interdisciplinary projects involving: TreeSense (remote sensing of global tree resources) Quantum computing optimization with Danish research consortia
Simon Bartelsمشاهده پروفایل
پژوهشگر
Simon Bartels is a researcher affiliated with the Department of Computer Science at the University of Copenhagen , contributing to the Machine Learning section. His work spans interdisciplinary applications of artificial intelligence, including quantum computing, healthcare diagnostics, and environmental modeling. Research activities at the department cover both theoretical and applied machine learning, with participation in the SCIENCE AI Centre . Key domains include medical data analysis , remote sensing , sustainability , and biological data modeling . Recent publications highlight contributions to quantum-inspired neural networks , geospatial biodiversity analysis , and energy-aware AI systems . Collaborations include rare disease research (e.g., MOSAIC framework ) and quantum computing optimizations.
Houk Jangمشاهده پروفایل
پژوهشگر
Houk Jang is a Staff Scientist in the AI Accelerated Nanoscience division at Brookhaven National Laboratory's Center for Functional Nanomaterials. His expertise spans 2D materials, silicon nanomembranes, micro/nanofabrication, and analog neural networks. He leads the QPress initiative, developing autonomous tools for 2D material-stack production and characterization, aiming to automate and accelerate material research for CFN users. Education: B.S. (2010) in Materials Science and Ph.D. (2014) in Nano Engineering, both from Sungkyunkwan University, Korea. Postdoctoral research at Yonsei University and Harvard University. His work focuses on integrating AI with nanomaterials to advance optoelectronics, flexible electronics, and quantum systems. Research highlights include creating graphene-based flexible electronics, developing optical machine vision processors, and pioneering methods for 2D material layer detection. His 2022 Nature Electronics publication demonstrated in-sensor optoelectronic computing using silicon, while 2020 Advanced Materials work introduced atomically thin optoelectronic processors. Publications span over 40 peer-reviewed articles, emphasizing photonics, quantum effects in 2D materials, and neuromorphic devices. His lab collaborates on projects involving terahertz spectroscopy, nanoscale imaging, and bioinspired optical systems. Currently directs nanofabrication efforts and advises on scalable material synthesis techniques. Active in developing high-performance infrared photodetectors and tunable electronic devices leveraging graphene and topological materials.
Gianvito Urgese is an Associate Professor at the Interuniversity Department of Territorial Sciences, Planning and Policies (DIST) at Politecnico di Torino, where he is also a member of the EDA research group and the SmartData@PoliTO Big Data and Data Science Laboratory. His academic and research activities are deeply integrated into the Department of Control and Computer Engineering (DAUIN), reflecting his interdisciplinary focus on computer engineering and data science. His research spans artificial intelligence, bioinformatics, neuromorphic computing, edge computing, embedded systems, and Industry 4.0. He investigates optimized task-specific algorithms, designs heterogeneous software-hardware architectures for bioinformatics acceleration, and develops computational paradigms for neuromorphic platforms. His work also extends to digital lifecycle management in Industry 4.0, aligning with Sustainable Development Goals 9, 11, and 12. His recent publications reveal a strong trend in neuromorphic computing and quantum-inspired optimization, with contributions to benchmarking frameworks (NeuroBench), neuron-based encoding tools (WiN-GUI), and quantum annealing methods. These works are published in high-impact journals such as Nature Communications , IEEE Transactions on Emerging Topics in Computing , and Science Translational Medicine , indicating a multidisciplinary and high-impact research profile. Urgese is actively involved in supervising PhD students and teaching graduate-level courses such as Neuromorphic Computing and Engineering, Applied AI and Machine Learning, and System-on-Chip Architecture. He serves on doctoral colleges and course committees, demonstrating leadership in academic governance. Scientific and Research Leadership: Principal Investigator (Scientific Manager) in multiple commercial research projects on data analytics, fog computing, and firmware design (2019–2025). Supervision of PhD research on neuromorphic systems, bioinformatics algorithms, and AIoT solutions. Active contributor to European-funded initiatives in neuromorphic and Industry 4.0 domains. He collaborates with multidisciplinary teams, including researchers at the Candiolo Cancer Institute and participants in the Telluride Neuromorphic Cognition Engineering workshop, highlighting the collaborative and applied nature of his work.
Frank Kirchner is a Professor at the Faculty of Mathematics and Computer Science of the University of Bremen and Executive Director of the Robotics Innovation Center at DFKI Bremen since 2008. He holds a chair for Robotics and leads a team of over 100 employees. Kirchner is also a Research Fellow at Tsinghua University and a member of the Berlin-Brandenburg Academy of Sciences and Humanities. Education: Diploma in Informatics and Neurobiology (1994), University of Bonn Doctorate (Dr. rer. nat) in Computer Science (1999), University of Bonn Research Interests: Kirchner specializes in biologically inspired behavior and motion sequences for highly redundant, multifunctional robot systems. His work spans soft robotics, space robotics, and human-centered robotic systems, with recent projects like AI-REEFSHIELD (AI for marine restoration monitoring) and FieldCoBots (collaborative field robotics for strawberry harvesting). He integrates generative AI (e.g., ActGPT) and quantum control methods into robotics, pushing the boundaries of autonomous systems. Scientific Contributions: With over 350 publications and leadership in initiatives like the German Softrobotics Priority Program and the Brazilian Institute of Robotics, Kirchner bridges robotics, AI, and interdisciplinary applications. His 15 most recent papers highlight trends in robotic design, quantum control algorithms, and planetary exploration. Awards & Memberships: Honorary Doctorate from SENAI CIMATEC, Brazil (2017) Research Fellow, Tsinghua University (2018) Member, Berlin-Brandenburg Academy of Sciences and Humanities (2015–) Committee member, Leopoldina (2021–) Academic Leadership: Kirchner has founded institutes like MarTech (maritime technologies) and Ground Truth Robotics, and co-founded journals like the International Journal of Advanced Robotic Systems. He oversees projects funded by the EU, DFG, and national agencies, with a focus on security-relevant research and hostile-to-life environments.
Paolo Arcaini is a Project Associate Professor at the National Institute of Informatics in Japan, where he has been employed since April 2019. Previously, he served as a Project Assistant Professor at the same institution from March 2018 to March 2019. Prior to that, he was an Assistant Professor at the Department of Distributed and Dependable Systems of Charles University from March 2015 to February 2018. His academic journey includes postdoctoral fellowships at the University of Bergamo and CNR-IDPA, followed by completing his PhD in Computer Science at the University of Milan. PhD in Computer Science at the University of Milan (January 2010 - December 2012) Postdoctoral fellow at University of Bergamo (October 2013 - February 2015) Postdoctoral fellow at CNR-IDPA (January 2013 - September 2013) Professor Arcaini's research focuses on the intersection of quantum computing and software engineering, particularly in quantum software testing and verification. He has pioneered approaches to applying search-based techniques to quantum software development, including mutation testing and error mitigation for quantum programs. His work also extends to autonomous systems testing, where he develops methods for scenario generation, safety assessment, and falsification of autonomous driving systems. His research in formal methods for cyber-physical systems combines signal temporal logic with search-based techniques to verify complex system behaviors. A significant portion of his work addresses the challenges of testing AI-enabled systems, particularly deep neural networks in safety-critical contexts. Analysis of Professor Arcaini's recent publications reveals a strong trend toward quantum software engineering, with numerous papers on testing methodologies for quantum programs, error mitigation techniques, and quantum-inspired optimization for software testing. His work demonstrates a consistent focus on applying search-based techniques to challenging domains, with increasing attention to safety-critical applications of AI and quantum computing. The interdisciplinary nature of his research connects formal methods, machine learning, and software engineering to address verification challenges in emerging technologies. Best paper award on testing AI system at IEEE International Conference on Artificial Intelligence Testing (AITest 2020) Distinguished paper award at Symposium on Search-Based Software Engineering (SSBSE 2021) Best paper award at Symposium on Search-Based Software Engineering (SSBSE 2020) Professor Arcaini is highly active in the software engineering research community, serving on program committees for numerous top conferences including ASE, ICSE, ISSTA, and ESEC/FSE. His work on quantum software engineering has positioned him as a leader in this emerging field, with multiple tool demonstrations and journal-first papers that bridge theoretical advances with practical applications. His research on autonomous systems testing has resulted in several tools for scenario generation and safety assessment that are being adopted by the automotive industry. While specific grant information isn't detailed in the provided materials, his extensive publication record across multiple high-impact venues suggests substantial research funding support. Professor Arcaini leads the MMM research group (as indicated by his personal website at http://group-mmm.org/~arcaini/), which focuses on model-based methods for software engineering. His team appears to specialize in applying formal methods and search-based techniques to emerging domains like quantum computing and autonomous systems. The group maintains active collaborations with researchers worldwide, as evidenced by the international co-authorship of his publications across European and Asian institutions.
Sara Achourمشاهده پروفایل
استادیار
- Programming Languages
- Compilers
- Runtime Systems
- +۲ مورد دیگر
Sara Achour is an Assistant Professor jointly appointed to the Computer Science and Electrical Engineering departments at Stanford University. She earned her PhD in Computer Science from MIT in 2021. Her research develops programming languages, compilers, and runtime systems for emerging analog computing platforms. Dr. Achour leads research in hardware-aware optimization frameworks and novel analog compute paradigms, with applications spanning quantum computing, IoT devices, and neuromorphic systems. Her research focuses on: Bridging software abstractions with unconventional hardware capabilities Energy-efficient computing paradigms for edge devices Cross-layer optimization of analog and hybrid computing systems Publication analysis reveals consistent themes in analog computing architectures, hardware-aware optimizations, and emerging computing platforms. Recent work explores quantum compilation techniques, hyperdimensional computing optimizations, and hardware security metrics. Dr. Achour advises multiple graduate students including: 16 doctoral candidates across computer architecture and quantum computing 10 master's students in software-hardware co-design She maintains active research collaborations through the Stanford SystemX Alliance.