Professor Abdel Lisser is affiliated with CentraleSupélec, where he conducts research in Gif-sur-Yvette, France. His work spans multiple disciplines including stochastic optimization, game theory, and machine learning. Research Interests: Stochastic Optimization, Chance Constrained Optimization, Distributionally Robust Optimization, Stochastic Game Theory, Physics-Informed Neural Networks. His recent publications focus on integrating stochastic programming with deep learning frameworks to address complex optimization problems under uncertainty. Key areas include Markov Decision Processes, joint chance constraints, and applications in autonomous vehicle control and network design. In 2025, he published on single-controller stochastic games, convex approximations for Markov processes, and physics-informed neural networks for nonlinear equations. 2024 contributions include distributionally robust Markov decision processes, neurodynamic optimization, and CNN-based equilibrium prediction in games. Email: abdel.lisser@l2s.centralesupelec.fr Institution: L2S, CentraleSupélec Location: 3 rue Joliot Curie, 91190 Gif-sur-Yvette, France
Sameer Singh is a Professor of Computer Science at the University of California, Irvine's Donald Bren School of Information and Computer Sciences. He also holds affiliations with Linguistics and EECS departments. His research primarily focuses on the robustness and interpretability of machine learning algorithms, along with models that reason with text and structure for natural language processing. Dr. Singh received his PhD from the University of Massachusetts, Amherst in 2014, an MS in Computer Science from Vanderbilt University in 2007, and a BEng in Electrical Engineering from the University of Delhi in 2004. His research interests span machine learning robustness, natural language processing, model interpretability, and knowledge representation. Singh investigates how to make AI systems more reliable and understandable, particularly focusing on testing methodologies for NLP models and developing techniques to improve model behavior. His work bridges theoretical understanding with practical applications in AI safety and reliability. Analysis of Singh's recent publications reveals a strong focus on language model interpretability, bias detection, and model robustness. His work explores how language models process information, where they fail, and how to make them more reliable. A significant portion of his recent research examines the limitations of multimodal models, language model alignment techniques, and addressing social biases in AI systems. Dr. Singh has received numerous prestigious awards including the Kavli Fellowship from the National Academy of Sciences, the NSF CAREER award, UCI Distinguished Early Career Faculty award, and the Hellman Faculty Fellowship. His papers have won multiple awards including at KDD 2016, ACL 2018, EMNLP 2019, AKBC 2020, and ACL 2020. His research group has secured substantial funding from major organizations including the Allen Institute for AI, Amazon, NSF, DARPA, Adobe Research, Hasso Plattner Institute, NEC, Base 11, and FICO. Singh previously served as an Allen Fellow at the Allen Institute for AI (2021-2023) and is currently a co-founder and CTO of Spiffy AI in Seattle. He completed postdoctoral research at the University of Washington after earning his PhD. Dr. Singh maintains an active presence in the AI community through his work on projects like AutoPrompt and Checklist, which have become influential tools for testing and interpreting NLP models. His research continues to shape how the field approaches model evaluation and interpretability.
Daolang Huang is a Doctoral Researcher and Student in the Department of Computer Science at the School of Science, affiliated with Professor Samuel Kaski's group. He holds a Bachelor's degree in Engineering and Technology from Jinan University (2020). His research focuses on advanced machine learning techniques, including Bayesian inference, robust statistical modeling, and simulation-based methods. Key areas include experimental design optimization, neural processes, and equivariance in deep learning. Recent work emphasizes decision-aware algorithms and cost-effective simulation frameworks. Huang has collaborated internationally, with publications in top venues like NeurIPS. Despite no listed awards, his work demonstrates significant contributions to probabilistic modeling and optimization. Education : Bachelor's degree in Engineering and Technology, Jinan University (2020) Research Interests : Bayesian methods and amortized inference Robust statistics under model misspecification Continuous control and neural process architectures Optimization algorithms with decision-theoretic foundations Recent Research Trends : His articles (2020–2025) emphasize Bayesian experimental design, preference-based optimization, and equivariant neural networks. Themes include balancing statistical rigor with computational efficiency, particularly in high-dimensional decision-making contexts. Labs/Teams : Active member of Samuel Kaski’s research group, focusing on interdisciplinary applications of machine learning.
Per Lynggaard is a Professor of Electronics at the Technical University of Denmark (DTU) , leading the B.Eng. program in Electronics. Previously, he held an Associate Professor role at Aalborg University, combining academic excellence with a robust industrial career in technical-scientific research and development. Education: M.Sc. in Electrical Engineering and Information Technology (EE and IT) Ph.D. in Electronics from Aalborg University Research Interests: Focus on Integrated Circuit Design, Wireless Sensor Networks (WSN), Machine Learning, IoT, and Smart City Technologies . His work emphasizes energy-efficient systems, cybersecurity in IoT, AI-driven interference mitigation, and sustainable energy harvesting solutions. He has contributed to UN Sustainable Development Goals through projects addressing smart infrastructure and environmental monitoring. Projects & Collaborations: Leads and participates in EU-funded initiatives such as InnoTech (2023–2025) for green transition solutions and TransportTech (2023–2026) for Industry 4.0 logistics. Active in cybersecurity research via projects like Jamming Against Critical Wireless Communication , aiming to protect critical infrastructure. Awards: Recognized with multiple honors and rewards during his industrial career, though specific names are not listed. His work has been cited widely, with notable impact in IoT security and energy-efficient systems. Advising & Grants: Supervises Turnip T.N. in a PhD project on 6G security protocols. Engaged in securing funding for projects like F2D2: The Community for Dynamic Data (2021–2030), focusing on dynamic data systems and cybersecurity. Labs & Teams: Collaborates in interdisciplinary teams such as the InnoTech TaskForce and F2D2 Community , advancing IoT and AI integration. His research bridges academia and industry, with outputs spanning smart cities, healthcare IoT, and sustainable energy systems.
Chris Valentin Nielsen is an Associate Professor in the Department of Civil and Mechanical Engineering at the Technical University of Denmark (DTU). His research focuses on metal forming, joining processes, and tribology, with expertise in formability, tool development, and numerical modeling. His work contributes to UN Sustainable Development Goals related to sustainable manufacturing. He supervises PhD students in projects such as sustainable busbars for electric vehicles and adjustable tool design for high-volume production. His research interests include metal forming (e.g., deep drawing, ironing), joining technologies (resistance welding, laser welding), and advanced manufacturing methods like additive manufacturing. He employs finite element modeling and experimental analysis to bridge fundamental and applied research. Collaborations span global institutions, addressing challenges in material behavior, process optimization, and tool durability. Recent publications explore topics such as dieless Nakajima testing for additive materials, punch design improvements, and asperity deformation mechanics. His work emphasizes sustainability, robust production systems, and eco-friendly lubrication solutions. Projects involve interdisciplinary teams, integrating numerical simulations with industrial applications to enhance manufacturing efficiency and material performance.
Dr. Nir Rotenberg is an Associate Professor in the Department of Physics, Engineering Physics, and Astronomy at Queen's University. He is affiliated with the Faculty of Arts and Science, the Condensed Matter Physics & Optics group, and the Centre for Nanophotonics. He holds a PhD from the University of Toronto and focuses on quantum photonics and nanophotonics, particularly exploring nonlinear phenomena in solid-state quantum systems. His lab develops novel quantum devices and circuits using nanophotonic platforms, emphasizing single quantum emitter interactions with few photons. Research Interests: Quantum photonics, nanophotonics, quantum optics, nonlinear optics, quantum devices, and solid-state quantum emitters. His work bridges fundamental physics with applications in quantum information processing and photonic technologies. Recent publications highlight advancements in quantum dot positioning, waveguide-QED systems, and reconfigurable photonic circuits. He collaborates internationally and is a member of the Quantum Nanophotonics Lab (QNL), based in Stirling 308H. Dr. Rotenberg's research has implications for quantum computing, secure communication, and advanced optoelectronic systems.
Christopher Paul Anderson is an Assistant Professor affiliated with the University of Illinois at Urbana-Champaign, holding joint appointments in the Departments of Materials Science and Engineering, Physics, and Electrical and Computer Engineering. He is also associated with the Micro and Nanotechnology Lab and the Materials Research Lab. His research focuses on quantum systems, semiconductor materials, and spintronics, particularly exploring defect qubits in materials like silicon carbide and diamond. Anderson leads a team advancing quantum coherence times and applications in quantum computing/sensing. Research Interests : Quantum Coherence and Control Defect-Based Qubits (e.g., Spin Qubits in Diamond, Silicon Carbide) Electro-Optic and Piezoelectric Materials for Quantum Technologies Photoluminescence and Magnetic Materials Key Achievements : Recipient of the NSF CAREER Award (2025) Author of over 36 peer-reviewed publications, including work on tin-vacancy qubits, microwave spin control, and quantum critical materials. Labs/Teams : Anderson’s work is conducted in the Micro and Nanotechnology Lab and Materials Research Lab, focusing on experimental and theoretical advancements in quantum materials.
Norbert Linke is an Adjunct Assistant Professor in the Department of Physics at the University of Maryland , affiliated with the Joint Quantum Institute . His research focuses on experimental quantum computing with trapped atomic ions, emphasizing quantum information processing, quantum algorithms enhanced by machine learning, quantum simulation, and quantum networking using entangled photons. He holds a Dipl. Phys. from the University of Ulm (2007) and a D.Phil. in Atomic & Laser Physics from the University of Oxford (2013), where he worked under David Lucas. Before joining UMD, he conducted postdoctoral research at Oxford. Education: Bachelor's (Dipl. Phys.): University of Ulm, Germany (2007) Doctorate (D.Phil.): University of Oxford, U.K. (2013) Research Interests: Quantum algorithms with machine learning integration Quantum simulation of complex systems (e.g., quantum chromodynamics) Quantum networking via Sr+ ion-photon entanglement Development of robust quantum hardware (e.g., 3D monolithic traps) His recent work explores quantum advantage in finance, blind calibration of quantum computers, and optical control of qubits. The lab emphasizes scalable architectures and hybrid analog-digital simulations. While no scientific awards are explicitly listed, his contributions to trapped-ion systems and quantum networking are widely recognized in the field. Norbert advises researchers at the Joint Quantum Institute and collaborates on grants related to quantum computing hardware and algorithms. His team’s lab focuses on advancing ion-trap technology and explores future applications in quantum communication and computation.
Dana S. Nau is a Professor in the Department of Computer Science and a member of the Institute for Systems Research at the University of Maryland. He is renowned for his contributions to automated planning and game theory, including landmark algorithms like SHOP and foundational studies on game-tree pathology and strategic planning in computer bridge. With over 500 refereed publications and an H-index of 61, his work bridges theoretical computer science and practical applications in multiagent systems and evolutionary game theory. His research interests include hierarchical task network (HTN) planning, Bayesian network inference techniques, and the evolution of social norms through evolutionary game theory. Recent work focuses on spatial evolutionary games, surrogate Bayesian models, and strategic communication in multiagent environments. Awards: AAAI Fellow (202?), ACM Fellow (202?) Key Collaborations: Co-authored papers with leaders like Malik Ghallab (LAAS-CNRS), Satyandra K. Gupta (USC), and Vincent Hsiao (Bayesian networks research). Grants/Advising: Supervised students including Sunandita Patra (17+ joint papers) and Ruoxi Li, contributing to HTN planning and reinforcement learning advancements. His labs and research teams actively explore AI planning systems, probabilistic reasoning, and the intersection of game theory with social science phenomena like gossip evolution.
Prof. Dr. Francesca Biagini is a full Professor at the Department of Mathematics, University of Munich (LMU Munich) , leading the Stochastics and Financial Mathematics working group. She serves as Vice President for International Affairs and Diversity at LMU Munich since October 1, 2019, and as President of the Bachelier Finance Society (2022–2023). She is also a Correspondent of the Deutsche Aktuarvereinigung (DAV) and a member of the Executive Board of the Munich Risk and Insurance Center (MRIC) since 2017. Her research focuses on stochastic processes in financial markets , particularly asset price bubbles , default risk modeling , and robust hedging under model uncertainty. Recent work includes deep learning applications to bubble detection and non-linear affine processes for market dynamics. She actively contributes to academic leadership through teaching and publications, including 15+ recent articles on topics like liquidity-induced bubbles, machine learning calibration, and systemic risk transfer equilibrium. Her workgroup collaborates on quantLab initiatives and DAV certificate programs .
David A. Goldberg is an Associate Professor and Director of Undergraduate Studies in the Operations Research and Information Engineering (ORIE) department at Cornell University's College of Engineering. His research bridges theoretical probability with practical applications in operations management, inventory systems, and queueing networks. Education: Ph.D. in Operations Research, MIT, 2011 B.S. in Computer Science, minors in Applied Math and Industrial Engineering / Operations Research, Columbia University SEAS, 2006 Professor Goldberg's research focuses on advancing theoretical understanding of stochastic systems while developing practical insights for operations management. His work spans applied probability, stochastic processes, queueing theory, inventory models, distributionally robust optimization, and combinatorial optimization. He has made significant contributions to understanding the behavior of complex systems under uncertainty, particularly in many-server queues and inventory management under demand variability. His publications reveal strong trends in asymptotic analysis of stochastic systems, particularly in the Halfin-Whitt regime for queueing systems and in inventory models with large lead times. His work consistently bridges theoretical probability with practical operations management applications, with a strong emphasis on developing models that account for real-world uncertainties while maintaining mathematical tractability. His recent work shows increasing focus on distributionally robust approaches that require minimal assumptions about underlying distributions. Scientific Awards: INFORMS Applied Probability Society Best Publication Award (2019) INFORMS Nicholson student paper competition first place (2019) INFORMS Nicholson student paper competition first place (2015) INFORMS Junior Faculty Interest Group paper competition second place (2015) Professor Goldberg has successfully advised multiple Ph.D. students who have gone on to prestigious academic and industry positions. His research is supported by significant NSF funding, including a CAREER award and a grant for stochastic comparison approaches to parallel server queues. He serves on editorial boards for leading journals including Operations Research and Stochastic Models, and has held leadership positions in the INFORMS Applied Probability Society including Vice-chair (2020-2022) and Council member (2015-2017).
Ramón Luis Rizo Aldeguer is a University Professor in the Department of Computer Science and Artificial Intelligence at the Higher Polytechnic School of the University of Alicante. He has held this position since 1996 and continues to be actively involved in teaching and research as recently as 2025. He previously served in various leadership roles including Director of the Department of Computer Science and Artificial Intelligence (1997-2004) and Deputy Director of the Institutional Projects Area at the University of Alicante (2012-2020). His educational background includes a PhD in Computer Science from the Polytechnic University of Valencia (1992) and a degree in Mathematics from the University of Valencia (1977). He has been a member of the Spanish Association for Artificial Intelligence since 1990 and has held leadership positions within the organization. Rizo Aldeguer's research focuses on artificial intelligence with particular emphasis on swarm robotics, UAV deployment, and deep reinforcement learning. His work bridges theoretical foundations with practical applications in robotics and autonomous systems. He has made significant contributions to educational methodologies, particularly in integrating computational tools into engineering education. His publication record shows a consistent trajectory in swarm intelligence and robotics, with recent publications (2018-2023) demonstrating increasing sophistication in applying deep reinforcement learning to complex multi-agent systems. His research spans both theoretical advancements and practical implementations in robotics and autonomous systems. Fifteen five-year research periods (trienios) Six teaching merit periods Five six-year research periods (sexenios) President of Organizing Committee of VI Conference of Spanish Association for Artificial Intelligence (1995) President of Scientific Committee of CAEPIA (1999) Rizo Aldeguer has supervised 14 doctoral theses, with many receiving the highest honors (SOBRESALIENTE CUM-LAUDE). He has participated as a researcher in over 30 competitive public research projects, serving as principal investigator in 12 of them. His educational projects include innovative teaching methods and the development of computational tools for engineering education. He has been instrumental in the design and implementation of computer science programs at both the University of Alicante and the Polytechnic University of Valencia. He is a founding member of the University Institute for Computer Research and directed the Industrial Computing and Artificial Intelligence research group from 1992 to 2004. His current research continues to focus on swarm robotics and intelligent systems, with active participation in the Valencian Graduate School and Research Network of Artificial Intelligence since 2021.
Christa Cuchiero is a Professor at the Department of Statistics and Operations Research, Faculty of Business, Economics and Statistics, University of Vienna. Her research spans Mathematical Finance, Stochastic Processes, and Machine Learning applications in finance. She has over 48 publications, including recent work on signature-based models for SPX/VIX options, polynomial McKean-Vlasov SDEs, and infinite-dimensional Wishart processes. Her projects include 'Dynamic Uncertainty Modeling in Finance' and a long-term study on 'Universelle Strukturen in Finanzmathematik' (2020–2028). Research Interests: Signature methods for financial modeling Affine and polynomial processes Machine learning in finance Measure-valued stochastic differential equations Volterra equations and rough path theory Portfolio optimization and risk management Scientific Awards: Bruti-Liberati Visiting Fellowship (2018) Fellow at the Center for Advanced Study (CAS), Norwegian Academy of Science and Letters (2024) ETH Medal for Ph.D. thesis (2012) Recent Publications (2023–2025) focus on signature methods, stochastic portfolio theory, energy markets, and robust calibration techniques. Her work integrates advanced mathematical theory with practical financial applications, emphasizing nonlinear SPDEs, polynomial models, and machine learning frameworks.
Vasileios P. Kemerlis is an Associate Professor of Computer Science at Brown University, where he conducts research in systems and software security. He serves as the director of the Secure Systems Lab (SSL) and is a recipient of the prestigious NSF CAREER Award. His work focuses on practical security solutions for real-world systems. Dr. Kemerlis specializes in software, hardware, and systems security with particular emphasis on OS kernel protection, software hardening, fuzz testing, and information flow tracking. His research bridges theoretical security concepts with practical implementations that can be deployed in commodity systems. His work often leverages hardware features to build more robust security mechanisms while maintaining system performance. His recent publications demonstrate a consistent focus on memory safety, control flow integrity, and practical security mechanisms that can be deployed in real-world systems. His work spans multiple security domains including kernel security, network security, and application security, with a strong emphasis on developing solutions that balance security guarantees with practical performance considerations. Scientific Awards: NSF CAREER Award Dr. Kemerlis teaches advanced security courses at Brown University including CSCI 1650: Software Security and Exploitation (offered Fall 2016-2024) and CSCI 2951U: Topics in Software Security (offered Spring 2016-2018, 2020, 2021, 2024). As director of the Secure Systems Lab, he leads research efforts that have resulted in numerous publications at top security venues including IEEE Symposium on Security and Privacy, ACM CCS, and USENIX Security. The Secure Systems Lab under his direction focuses on developing practical security solutions that can be deployed in real-world systems, with recent work exploring hardware-assisted security mechanisms, binary hardening techniques, and novel approaches to memory safety.
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.
