Schahram Dustdar

"Shall we give up writing scientific papers to AI? ACS Energy Letters Editorial by Filippo De Angelis* https://lnkd.in/g_PxGTxE ......One may be tempted to extend the use of AI tools to the write up of the entire paper, but most of the time (with the probable exception of uncritical reviewing of a given field) AI tools would likely generate stylistically good but scientifically trivial text. We now have to face reality and use our own creativity to discuss the main paper outcome and present them in a catchy way. As a matter of fact, one can probably recognize a scientific breakthrough even if its description is not stylistically outstanding. Most notably, we cannot expect AI tools to make connections beyond what they already know.

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Our latest paper, "Channel Gain Modeling Through an Intelligent Reflecting Surface," published in IEEE Networking Letters 2025, presents a generalized #channel #gain model applicable to SISO, MISO, SIMO, and MIMO configurations, along with optimal phase-shift strategies to maximize the signal strength. This channel gain modeling enables real-world optimization of IRS, allowing telecom companies to boost signal strength, extend coverage, and reduce power consumption! #IntelligentReflectingSurface #IRS #WirelessCommunication #6G #MIMO #SISO #ChannelModeling #SignalProcessing #AIinTelecom #SustainableTech IEEE Communications Society School of Electrical, Computer and Energy Engineering — ASU ECEE

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Fabian Theis and his team tested self-supervised learning as a promising approach for analyzing and interpreting huge amounts of data, such as millions of individual cells. #biomedicine #MachineLearning #data TUM School of Life Sciences Munich Data Science Institute (MDSI) Munich Center for Machine Learning Helmholtz Munich 📷iStock/Amiak go.tum.de/217457

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🚀 New Preprint Alert! 📄 We are thrilled to announce the release of our latest preprint: “Port-Hamiltonian Neural Networks: From Theory to Simulation of Interconnected Stochastic Systems” by Luca Di Persio, Matthias Ehrhardt, Youness Outaleb, and Sofia Rizzotto 🎓 🔗 Read the preprint here: https://lnkd.in/dWceDxNz 🔍 What’s inside? This work introduces Port-Hamiltonian Neural Networks (pHNNs) 🧠⚡, a novel framework combining: 🏗️ Port-Hamiltonian Systems (PHS) for energy-consistent modeling 🤖 Neural Networks for data-driven learning 🌪️ Stochastic dynamics to handle uncertainty and noise Our approach bridges the gap between deterministic and stochastic modeling, ensuring stability through passivity-preserving structures and enabling accurate simulations of interconnected stochastic systems. 🧪 Applications & Highlights ✅ Advanced modeling of nonlinear and stochastic systems ✅ Simulation results on damped mass-spring systems, chaotic Duffing oscillators, and robotic control ✅ Robust learning of complex dynamics, even under high noise ✅ A solid foundation for data-driven control of multi-agent and robotic systems 🤝 Collaboration & Context This work is part of an exciting collaboration between the University of Verona 🇮🇹 and the University of Wuppertal 🇩🇪, carried out within Project C05 of the newly established DFG Collaborative Research Centre (CRC 1701) on Port-Hamiltonian Systems 🎯, see https://lnkd.in/dK5K7CtV 📌 Citation Di Persio, L., Ehrhardt, M., Outaleb, Y., & Rizzotto, S. Port-Hamiltonian Neural Networks: From Theory to Simulation of Interconnected Stochastic Systems. IMACM Preprint 25/15, September 2025. 🌟 We are excited to share this milestone and look forward to your feedback! #Preprint #Research #NeuralNetworks #PortHamiltonian #StochasticSystems #MachineLearning #ControlTheory #DFG #CRC1701 #Wuppertal #Verona

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📢 The June issue of ACM Transactions on Evolutionary Learning and Optimization is out with 2 regular research papers and Part II of the Special Issue on Explainable #AI in Evolutionary Computation: https://lnkd.in/dZwRWsHm The two regular papers are: ⭐Multi-Objectivising Acquisition Functions in Bayesian Optimisation. Chao Jiang, Miqing Li. https://lnkd.in/d7QZ7uuq Bayesian optimisation (BO) is an efficient approach for solving expensive optimisation problems, where acquisition functions play a major role in achieving the tradeoff between exploitation and exploration. The exploitation–exploration tradeoff is ... ⭐First Steps Toward a Runtime Analysis When Starting With a Good Solution. Denis Antipov, Maxim Buzdalov, Benjamin Doerr. https://lnkd.in/dKzY5NcX The mathematical runtime analysis of evolutionary algorithms traditionally regards the time an algorithm needs to find a solution of a certain quality when initialized with a random population. In practical applications it may be possible to guess ... The SI papers are: ⭐Introduction to the Special Issue on Explainable AI in Evolutionary Computation—Part 2. Jaume Bacardit, Sandy Brownlee, Stefano Cagnoni, Giovanni Iacca, John McCall, David Walker. https://lnkd.in/dC34Gtmw ⭐Explainable Optimisation through Online and Offline Hyper-heuristics. Ed Keedwell, William B. Yates, Ahmed Kheiri. https://lnkd.in/d4gNkBN2 Research in the explainability of optimisation techniques has largely focused on metaheuristics and their movement of solutions around the search landscape. Hyper-heuristics create a different challenge for explainability as they make use of many more ... ⭐EVOTER: Evolution of Transparent Explainable Rule-sets. Hormoz Shahrzad, Babak Hodjat, Risto Miikkulainen. https://lnkd.in/dmKrfN7R Most AI systems are black boxes generating reasonable outputs for given inputs. Some domains, however, have explainability and trustworthiness requirements that cannot be directly met by these approaches. Various methods have therefore been developed to ... ⭐Towards Explainable Metaheuristics: Feature Mining of Search Trajectories through Principal Component Projection. Martin Fyvie, John McCall, Lee A. Christie. https://lnkd.in/d7X9JWpF While population-based metaheuristics have proven useful for refining and improving explainable AI systems, they are seldom the focus of explanatory approaches themselves. This stems from their inherently stochastic, population-driven searches, which ... ⭐Explainable Benchmarking for Iterative Optimization Heuristics. Niki van Stein, Diederick Vermetten, Anna Kononova, Thomas Bäck. https://lnkd.in/distr2Fk Benchmarking heuristic algorithms is vital to understand under which conditions and on what kind of problems certain algorithms perform well. In most current research into heuristic optimization algorithms, only a very limited number of scenarios, ... #telo #optimization #ai #explainable

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New preprint out today with many great co-authors from across Europe: ”Quantum computing and artificial intelligence: status and perspectives”. This white paper discusses and explores the various points of intersection between quantum computing and AI. Check it out at https://lnkd.in/d6kna9jx. My own contribution was mainly on how AI can help quantum computers.

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Happy to announce that our work led by Kai Rawal on Evaluating Model Explanations without Ground Truth will be presented at ACM FAccT next week in Greece 🇬🇷 We propose a ground truth Agnostic explanation Evaluation framework (AXE) for evaluating and comparing model explanations that satisfies three core and intuitive principles (that are often overlooked in prior works 🤯 ). 🔴 1. Local Contextualization: Explanations should depend on the datapoint being explained. 🔴 2. Model Relativism: Explanations should depend on the model being explained 🔴 3. On-manifold Evaluation: Explanations on-manifold should not depend on changes in off-manifold model behaviour. Unlike many existing approaches AXE also allows practitioners to reliably detect explanation fairwashing. Full details of the framework, experiments and code can be found in the preprint - https://lnkd.in/d4MAuvai and repository https://lnkd.in/dQM_zY_b This is joint work with Fu Zihao and Chris Russell.

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Excellent guest talk on #QuantumComputing and #QuantumMachineLearning by Peter P. Orth (Professor for Theoretical Physics) in my lecture on „Data Science“ at Universität des Saarlandes. Peter gave an overview of quantum computing technologies and the latest scientific developments. It became clear that quantum computing is a thriving field that requires a high level of physical, mathematical and technical skills. Combining all the parts into a machine requires highly developed expertise. It can deliver exceptional results for a range of problems that are currently considered too complex and require exponential resources. He also discussed the impact and prospects of quantum computing on industry and markets. In the second part, he touched on machine learning with quantum and gave some recent examples, such as the recent publication by Huang et al. (2022) in Science on the advantages of quantum computers compared to conventional computers https://lnkd.in/eWy7822G . It is impressive to see how quantum machine learning can learn better models with fewer samples. For the students from computer science and business informatics, this talk opens a field beyond their usual comfort zone. Peter did an amazing job in bridging computer science and quantum computing - a bridge that we shall strengthen in the near future by the Center for Quantum Technologies (QuTe) https://lnkd.in/eC6a3WUz

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Citations Without Borders! When submitting a manuscript to a top-tier journal, one reasonably expects to receive constructive and informed feedback from referees and editors. However, I was surprised to receive feedback on our recent submission that appears demonstrably AI-generated.   The review included recommendations to cite 16+ papers from fields entirely unrelated to our work—such as cochlear implants, cardiac surgery, psychology, and philosophy. More concerning, a single individual appears as co-author across this entire set of irrelevant works (see below for reference), raising the possibility that the review was not only AI-generated but also biased and misleading.   This experience has been both troubling and disappointing, and I believe it raises important concerns about the integrity of the review process.   Quote from review “Add and update the future research inn this article. Clearly articulate potential areas for future research, building on findings from this study and existing literature. Please add the below literatures and show how your techniques can be useful for their topics: Medicine Science: 10.1002/btm2.10752; 10.1016/j.heliyon.2024.e32127; 10.1109/SAMI58000.2023.10044491; 10.3389/fphar.2024.1506437; 10.1109/SACI58269.2023.10158582; https://lnkd.in/gMuvjpPB; https://lnkd.in/gZnYe59B; Environmental Science: 10.1109/SAMI60510.2024.10432830; 10.1109/ICCC62278.2024.10582928; 10.1007/s11269-025-04120-x Engineering Science: 10.1109/ICCC62278.2024.10583113; 10.1109/CINTI-MACRo57952.2022.10029414; https://lnkd.in/giVCagC8; 10.1109/ICCC62278.2024.10582962; 10.1109/SACI60582.2024.10619733; 10.3934/geosci.2022031; https://lnkd.in/gMaxcx9g; https://lnkd.in/gukgmXFA

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📘 New Book Release from Springer in Fundamental Theories of Physics: The Fundamentals of Thermodynamics By David A. Lavis and Roman Frigg 🔗 https://lnkd.in/db4uJXqA It is a rigorous and accessible account of thermodynamics for graduate students and researchers in physics, mathematics, and the philosophy of physics. The book offers a systematic treatment based on the algebraic framework of Lieb and Yngvason, extending it to cover both positive and negative temperatures, as well as systems where entropy may increase or decrease in adiabatic processes. It further develops generalized versions of the Kelvin-Planck and Clausius formulations of the Second Law. A dedicated chapter presents the geometric approach, linking thermodynamics to contact geometry, following the work of Boyling and Arnold. The final chapters expand the discussion to critical phenomena, phase transitions, and non-equilibrium thermodynamics.

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In a new Open Access article published in the proceedings of ACM SIGCSE 2025 and to be presented next week we analyze generative AI (GenAI) policies across a range of computing course syllabi. We found that references to GenAI use were discussed largely as a part of the academic integrity policy for the course and most syllabi prohibited or restricted GenAI use, often warning students about the broader implications of using GenAI, e.g. lack of veracity, privacy risks, and hindering learning. There was also a tendency to conceptualize GenAI as an assistant, often in an anthropomorphic manner. With Areej Ali, Aayushi Hingle Collier, Umama Dewan and Nora McDonald, PhD. https://lnkd.in/eyHzADUM

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🚀 Call for Papers: Special Issue on Semantic Communications with Intellicise (Intelligent and Concise) Networking📡 We are thrilled to announce our special issue of the IEEE Transactions on Network Science and Engineering (TNSE), focusing on Semantic Communications with Intellicise Networking! 🎯 As AI-driven networks and next-generation communication systems evolve, efficient and intelligent data transmission becomes crucial. This special issue will explore cutting-edge research in semantic communications, leveraging AI, ML, and network optimization to enhance communication efficiency and accuracy to transmit the meaning and semantics of information. 🔍 Topics of Interest Include: ✅ Generative AI-driven semantic networking ✅ Semantic knowledge representation in intellicise communications ✅ Context-aware and token-based generative semantic communications ✅ Resource-efficient semantic compression/inference for intellicise networking ✅ Applications in IoT, 6G, edge computing, and beyond 📅 Submission Deadline: 1 December 2025 We invite colleagues to contribute groundbreaking research to this special issue to shape the future of intelligent communications. 📖✨ For full details, visit: https://lnkd.in/e3EKr_px #IEEE #TNSE #SemanticCommunications #TokenCommunications #AI #IntelligentNetworks #MachineLearning #6G #IoT #EdgeComputing #CallForPapers

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