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Hachem KadriView profile

Professor

• Machine Learning
• Kernel Methods
• Functional Data Analysis
• +5 more

Hachem Kadri is a Professor of Artificial Intelligence in the Department of Computer Science at Aix-Marseille University and Head of the Data Science Department at LIS Lab since 2023. He was previously an Assistant Professor at the same institution from 2012 to 2019 and conducted postdoctoral research at INRIA Lille and LAGIS. He holds a PhD in Electrical Engineering from the National Engineering School of Tunis (2008) and completed his HDR (Habilitation) at Aix-Marseille University in 2019 on operator-valued kernel methods. His research spans machine learning with core expertise in kernel methods, functional data analysis, statistical learning theory, deep learning, and quantum machine learning, bridging theoretical foundations with applications in signal processing and quantum computing. Recent publications (2022-2024) demonstrate a strategic shift toward quantum machine learning and C*-algebraic frameworks, with significant contributions in implicit regularization for deep tensor factorization, quantum perceptron algorithms, and operator-theoretic kernel extensions. Key works appear in ICML, NeurIPS, and AISTATS, highlighting interdisciplinary innovation at the intersection of algebra, functional analysis, and ML. Professor Kadri leads the ANR Starting Grant QuantML (2019-2024) as Principal Investigator and co-led the Franco-Tunisian OPERA project (2019-2023). He was nominated to the National Universities Council (CNU) for 2024-2027 and elected Head of LIS Data Science Department in 2023, reflecting his leadership in French academia. As a core member of the QARMA machine learning group at LIS Lab, he co-organized the inaugural Quantum Machine Learning workshop at ECML-PKDD 2022 and serves as Vice-president of SSFAM (Société Savante Francophone d’Apprentissage Machine), actively shaping the European ML research landscape.

• Financial Econometrics
• Asset Pricing
• Monetary Policy
• +2 more

Asad Dossani is an Assistant Professor of Finance at Colorado State University's Department of Finance and Real Estate. His research focuses on financial econometrics, asset pricing, monetary policy impacts, and risk dynamics in currencies and commodities markets.

Satu-Pia ReinikainenView profile

Professor

• Multivariate Statistical Analysis
• Process Control
• Spectroscopy
• +3 more

Satu-Pia Reinikainen is a tenured Professor in Computational Engineering at the Lappeenranta-Lahti University of Technology (LUT) School of Engineering Sciences . With expertise in chemometrics and multivariate analysis, her work bridges statistical modeling, spectroscopy, and environmental monitoring. Research Focus Development of advanced kernel-based methods for process control Application of hyperspectral imaging in material and environmental analysis Microplastic pollution dynamics in aquatic systems Integration of spectroscopic techniques for real-time monitoring Conservation of geological and heritage materials through data-driven approaches Her research combines chemometric algorithms, environmental data analysis, and industrial process monitoring to solve complex analytical challenges.

Gurunath GurralaView profile

Associate Professor

• Power Systems Analysis and Control
• High Performance Computing Applications
• Nonlinear and Intelligent Control
• +4 more

Gurunath Gurrala serves as an Associate Professor in the Department of Electrical Engineering at the Indian Institute of Science (IISc), Bangalore. His research focuses on power systems dynamics, high-performance computing applications, and renewable energy integration. He maintains active collaborations with international institutions including Oak Ridge National Lab and Texas A&M University. His research interests center on Power Systems Analysis and Control , with specialization in High Performance Computing Applications, Nonlinear and Intelligent Control, Weak Grid Integration of Renewables, Microgrid Protection, and Smart Grid Stability. His work bridges theoretical control systems with practical power grid challenges, particularly for renewable-rich grids. His recent publications demonstrate a strong interdisciplinary trend, spanning power systems (35%), control theory (25%), renewable integration (20%), and emerging applications in biomedical engineering and environmental systems (20%). Key recurring themes include grid stability under high renewable penetration, advanced protection schemes for microgrids, and computational methods for power system analysis. IEEE Power and Energy Society (PES) Outstanding Engineer Award 2018 Young Engineer Award 2015, Indian National Academy Engineers Best Conference Paper, IEEE PES General Meeting 2015 Best Ph.D Thesis Award (Prof.D.J.Badkas Medal) 2010 Elevated to Senior Member IEEE (2016) Professor Gurrala has secured competitive research funding including the Young Scientist Grant from DST (2015) and International Travel Support from SERB (2017). He actively mentors students through PhD and Master's programs while teaching advanced courses including Power System Dynamics and Control (E4 231), Computer Control of Power Systems (E4 233), and Selected Topics in Integrated Power Systems (E4 237). His research group collaborates with power utilities and international research labs on grid modernization challenges.

Eray YücelView profile

Lecturer

• Applied Econometrics
• Monetary Economics
• Macroeconomics
• +2 more

Eray Yücel is a Lecturer in the Department of Economics at Bilkent University since 2017. He holds a PhD in Economics (2005) and MA in Economics (2000) from Bilkent University, with a B.Sc in Industrial Engineering (1998). His academic career includes roles as Associate Professor at Kadir Has University (2014-2016), part-time lecturing at multiple institutions, and 10 years as an Economist at the Central Bank of the Republic of Turkey (2003-2013) where he served as Managing Editor of the Central Bank Review (2005-2013).

Andrea VinciView profile

Researcher

• Quantum Computing
• Edge-Cloud Computing
• Internet of Things (IoT)
• +5 more

Andrea Vinci is an accomplished researcher with 66 publications and 1,261 citations, specializing in the intersection of quantum computing, edge-cloud architectures, and Internet of Things (IoT) systems. His work demonstrates significant contributions to solving complex computational problems through innovative approaches that bridge theoretical quantum algorithms with practical distributed computing applications. His research interests span quantum computing applications for resource management, multi-density clustering techniques for urban analytics, and platform-independent IoT application development. Vinci has pioneered work in variational quantum algorithms for cloud/edge resource allocation, quantum kernels for IoT data classification, and distributed AI for cognitive building systems. His research demonstrates a consistent focus on addressing NP-hard problems through quantum-classical hybrid approaches. Analysis of Vinci's publication trends reveals a strategic research trajectory moving from foundational work in smart city analytics and crime prediction toward cutting-edge quantum computing applications for IoT and edge-cloud systems. His recent publications (2023-2025) show increasing focus on quantum machine learning techniques specifically tailored for IoT data processing, with significant attention to practical implementation challenges. Vinci maintains an extensive collaborative network, frequently publishing with researchers including Fabrizio Marozzo, C. Mastroianni, J. Settino, and Antonio Guerrieri across multiple high-impact venues including IEEE Transactions, ACM conferences, and specialized journals in quantum computing and distributed systems. His technical contributions include the development of the COGITO platform for cognitive buildings, novel approaches to multi-density crime prediction, and significant advancements in quantum kernel methods for IoT data analysis. Vinci's tutorial publications indicate his role in educating the broader research community about emerging quantum computing applications for distributed systems.

Justin BradleyView profile

Associate Professor

• Artificial Intelligence and Intelligent Agents
• Cyber-Physical Systems
• Embedded and Real-Time Systems
• +3 more

Justin Bradley is an Associate Professor in the Computer Science Department at North Carolina State University. Previously, he held the position of Richard L. and Carol S. McNeel Associate Professor of Computing in the School of Computing at the University of Nebraska-Lincoln. His educational background includes: Ph.D. in Aerospace Engineering from the University of Michigan (2014) M.S. in Aerospace Engineering from the University of Michigan (2012) M.S. in Electrical Engineering from Brigham Young University (2007) B.S. in Computer Engineering from Brigham Young University (2005) Dr. Bradley's research focuses on cyber-physical systems with particular emphasis on control, autonomy, software, and computing for aerospace vehicles. A key theme in his work involves developing algorithms that can dynamically adjust their resource utilization in response to uncertainty while maintaining performance requirements. His research spans several critical areas including Artificial Intelligence and Intelligent Agents, Cyber-Physical Systems, and Embedded and Real-Time Systems, with applications primarily in unmanned aircraft systems (UAS) operating in challenging environments like wetlands and fire-prone areas. His scientific achievements have been recognized with prestigious awards including: 2021 NSF CAREER award AIAA Associate Fellow designation Dr. Bradley has been actively involved in research funding and mentoring. His NSF CAREER award (NSF-2047971) supports work on "co-regulation" algorithms for unmanned aircraft systems. He has co-directed research labs including the MAGICC lab at BYU, the A2Sys lab at the University of Michigan, and the Nebraska Intelligent MoBile Unmanned System (NIMBUS) lab since 2015. His research group focuses on developing performance-adjustable, resource-aware algorithms for autonomous systems, with applications in environmental monitoring, wetland studies, and fire management. His educational efforts target K-12 through adult engagement to expand the cyber-physical systems educational pipeline. His laboratory work centers around the development of the Co-regulated Hybrid Systems (CHS) framework, Co-regulated Real-Time Kernel (CRTK), and co-regulated Markov Decision Process (MDP) for resource-aware autopilots. These technologies enable unmanned systems to dynamically reallocate computing resources while maintaining mission objectives, particularly valuable in complex environments like rainforests where systems must transition between surveillance, sampling, and sensor emplacement tasks.

Gernot AkemannView profile

Professor

• Random Matrix Theory
• Quantum Chromodynamics (QCD)
• Statistical Mechanics
• +3 more

Gernot Akemann is a Professor at the Faculty of Physics, University of Bielefeld, with a focus on Random Matrix Theory and its applications in high energy physics and statistical mechanics . His work spans topics such as QCD Dirac spectra, effective field theory, orthogonal polynomials, asymptotic analysis, and universality. He has held leadership roles in projects like SFB 1283 and IGK 2235, emphasizing singular and random systems. 2025 : Subproject manager in SFB 1283 2024 : Leverhulme Trust Visiting Professorship at University of Bristol 2019 : Visiting Professor at KTH Stockholm His research includes random matrix theory for applications in quantum chromodynamics, statistical mechanics, and mathematical physics. Recent articles explore complex eigenvalue statistics, Ginibre ensembles, and their connections to Coulomb gases and territorial behavior in ecology. He has contributed to understanding universality in spectral statistics and non-Hermitian systems. Notable scientific awards include the Leverhulme Trust Visiting Professorship , Knut and Alice Wallenberg Foundation support, and DFG Research Grants . His work has been funded through projects like SFB 1283 and RTG 2235.

Christoph EggerView profile

Assistant Professor

• Cryptography
• Computational Complexity
• Statistical Privacy
• +7 more

Christoph Egger is an Assistant Professor at Chalmers University of Technology in the Department of Computer Science and Engineering, where he works with the Security & Privacy Lab and the Crypto Team. Prior to this position, he was a Marie-Curie Fellow at Institut de Recherche en Informatique Fondamentale (IRIF) from fall 2022 to 2024, researching connections between cryptography and complexity theory. His educational background includes: PhD: "On Abstraction and Modularization in Protocol Analysis" Master's: "An implementation of global caching for the alternation-free coalgebraic μ-calculus" Bachelor's: "Analysing and attacking the I2P Network Database" Dr. Egger's research focuses on cryptography and its connections to computational complexity, statistical privacy, and formal methods. His work spans multiple areas including cryptographic foundations (random oracles, key agreement protocols), privacy-enhancing technologies (ring signatures, information flow techniques), and practical applications in genomic data security. He develops both theoretical frameworks and practical tools like CryptoZoo for cryptographic proof visualization, bridging the gap between theoretical cryptography and real-world security challenges. His recent publications demonstrate a strong focus on cryptographic foundations and privacy technologies, with significant contributions to ring signatures, key agreement protocols, and genomic data security. His work bridges theoretical cryptography with practical applications in blockchain, anonymous communications, and healthcare data management, showing consistent productivity across multiple high-impact venues in security and privacy. Dr. Egger has served on program committees for prestigious conferences including IEEE Computer Security Foundations Symposium (CSF), Proceedings on Privacy Enhancing Technologies (PETS), and Conference on Applied Cryptography and Network Security (ACNS). He currently advises PhD students Lucia Lavagnino at Chalmers and Kirthivaasan Puniamurthy at Aalto University. Previously, he advised Master's students including Julian Brost and Kirthivaasan Puniamurthy. His Marie-Curie Fellowship was cofunded by EU H2020 Marie Sklodowska-Curie Action and FSMP Comunity Service. Dr. Egger is an active member of the Security & Privacy Lab and Crypto Team at Chalmers. He is also a founding member of the FAUST CTF team and has been a Debian Developer for over a decade, contributing to various Free Software projects including the Linux kernel and Git version control system, demonstrating his commitment to both academic research and practical software security.

Hamed GhavamniaView profile

Assistant Professor

• System/Software Security
• Attack Surface Reduction
• Memory Safe Languages
• +2 more

Hamed Ghavamnia is a Security Researcher at Bloomberg, where he focuses on software and systems security. Previously, he served as an Assistant Professor at the School of Computing, University of Connecticut (2023–2024). He holds a PhD in Computer Science from Stony Brook University (under Michalis Polychronakis), an M.S. in Computer Engineering from Sharif University of Technology (Network Security focus), and a B.E. in Software Engineering from University of Isfahan. His research interests center on system/software security, particularly attack surface reduction through program analysis and memory-safe languages. Key projects include Confine (system call policy generation for containers), Temporal Specialization (system call filtering based on execution phases), and LeakLess (data protection in serverless platforms). He has contributed to seminal works on configuration-driven security (C2C), memory protection (xMP), and automated policy inference. His work bridges static/dynamic analysis, program specialization, and security hardening. Notable outcomes include reducing attack surfaces via system call filtering and debloating techniques, with applications in cloud and containerized environments.

Emily JensenView profile

Assistant Professor

• Control Systems
• Power Systems
• Robotics
• +3 more

Emily Jensen is an Assistant Professor in the Department of Electrical, Computer & Energy Engineering at the University of Colorado, Boulder. She specializes in the analysis and control of spatially-distributed systems with applications in power grids, satellite constellations, and bio-inspired robotics. Her work focuses on developing theoretical frameworks to optimize system performance under constraints such as communication limitations and nonlinear dynamics. Education: B.S. in Engineering Mathematics & Statistics, UC Berkeley, 2015 M.S./Ph.D. in Electrical & Computer Engineering, UC Santa Barbara, 2020 Professional Appointments: Postdoctoral Researcher at UC Berkeley (2022-2023) Postdoctoral Researcher at Northeastern University (2021) Her research interests center on optimal distributed control, system-level synthesis, and the interplay between system structure and controller design. Key projects include multi-timescale power network control, nonlinear wave dynamics in engineering systems, and distributed parameter systems with limited communication. Dr. Jensen has delivered invited talks at high-profile venues such as the International Symposium on Mathematical Theory of Networks & Systems (2024) and the System Level Synthesis Workshop (2022). She currently advises a team of graduate and undergraduate researchers, including Addie McCurdy, Andy Gusty, and Matt Baughman (co-advised with Prof. Hodge). Awards: UC Regents’ Graduate Fellowship (2016) Zonta Amelia Earhart Fellowship (2019) IFAC Conference Young Author Award Honorable Mention (2022) She teaches ECEN 3300 (Linear Systems) and ECEN 5738 (Nonlinear Control Systems) at CU Boulder. Her work has been published in IEEE journals and conferences, emphasizing theoretical contributions with practical engineering relevance.

Li HanView profile

Professor

• Data Science
• Computational Biology
• Protein Dynamics
• +2 more

Li Han is Professor of Computer Science at Clark University's Becker School of Design and Technology, where she also directs the Data Science program. She holds a Ph.D. from Texas A&M University and M.S./B.S. degrees from Xi'an Jiaotong University. Her research spans computational protein studies, data science, and robotics, with current focus on protein dynamics and allosteric mechanisms. Her publications demonstrate consistent focus on computational approaches to biological systems, particularly protein dynamics, folding mechanisms, and conformation analysis. Recent work (2022) examines allosteric pathways in ubiquitin ligases using advanced simulation techniques. Methodological contributions include dimensionality reduction for protein conformation spaces and novel algorithms for molecular simulation. She teaches diverse courses including Introduction to Data Science, Algorithms, and Robotics, and advises student computing organizations. Her research has received funding from NSF and NIH.

Sylvain ArlotView profile

Professor

• Statistical Learning
• Non-Parametric Statistics
• Data-Driven Tuning of Learning Algorithms
• +5 more

Sylvain Arlot is a Professor at the Mathematics Department of Université Paris-Saclay, affiliated with the Probability and Statistics team at Laboratoire de Mathématiques d'Orsay. He leads the Celeste INRIA Saclay project-team and is a junior member of the Institut Universitaire de France (IUF) since 2020. His research focuses on statistical learning theory, non-parametric methods, model selection, and change-point detection. Arlot has contributed to foundational work on cross-validation, penalization techniques, and random forests. He co-organizes the Séminaire Palaisien and serves as an associate editor for the Annales de l'Institut Henri Poincaré B. Education: PhD in Mathematics from Université Paris-Sud (2007), HDR (Habilitation) from Université Paris Diderot (2014). Research Interests: Core areas include statistical learning theory, resampling methods (e.g., cross-validation and bootstrap), and applications in high-dimensional data analysis. His work bridges theoretical guarantees with practical algorithm design, emphasizing data-driven model selection and robust estimation techniques. Grants & Projects: Leads the PEPR IA Project Causali-t-AI (2023–2028) and was a member of the ANR Fast-Big project (2018–2023). He coordinates the math-AI program under Labex Mathématique Hadamard. Awards: Junior IUF membership (2020–2025). Labs/Teams: Heads the Celeste team at INRIA Saclay, collaborating on statistical machine learning and data science challenges.

Hugues AuvrayView profile

Associate Professor

• Kähler Geometry
• Complex Geometry
• Differential Geometry
• +3 more

Hugues Auvray is an Associate Professor at Université Paris-Sud, affiliated with the Laboratoire de Mathématiques d'Orsay. His primary research focuses on Kähler geometry, particularly the complex Monge-Ampère equation and canonical metrics on non-compact manifolds. He completed his PhD under the supervision of Olivier Biquard, exploring topics like gravitational instantons and Poincaré-type metrics. Auvray has held positions including a postdoc at the Max Planck Institute for Mathematics and a visiting scholar at the CRM in Montréal. His teaching responsibilities include advanced courses in complex analysis and geometry, as well as supervision of graduate students. Notable contributions include work on Bergman kernels on punctured Riemann surfaces and extremal Kähler metrics on toric varieties. Collaborators include leading figures such as Xiaonan Ma, George Marinescu, and Vestislav Apostolov. Auvray actively participates in research projects like ANR GRACK and EMARKS, and has organized conferences such as the CIRM workshop on Constant Scalar Curvature Metrics. His research bridges geometric analysis with complex algebraic geometry, addressing both foundational and applied aspects of metric geometry.

• Compilers
• Parallel Computing
• Automatic Differentiation
• +3 more

Billy Moses is an Assistant Professor in the Department of Computer Science at the University of Illinois Urbana-Champaign (UIUC), with affiliate roles in Electrical and Computer Engineering (courtesy) and the Coordinated Science Library. He holds a PhD and dual S.B. degrees in Electrical Engineering and Computer Science from MIT (2023, 2017), as well as an S.B. in Physics from MIT (2017). His research focuses on compilers, parallel computing, and compiler-driven optimization techniques for high-performance systems. Moses has pioneered work on the Tapir framework for fork-join parallelism, the MLIR compiler infrastructure, and Enzyme for automatic differentiation. Moses' research spans compiler design, GPU acceleration, and AI-driven compiler optimization. Notable contributions include the Polygeist compiler for C-to-MLIR transformation, the Autophase reinforcement learning system for HLS phase ordering, and the Enzyme framework for GPU kernel differentiation. His work emphasizes practical compiler solutions for parallelism, performance portability, and end-to-end code generation in domains like deep learning and scientific computing. Awards: 2024 SIGHPC Doctoral Dissertation Award Courses Taught: CS 598 APE (Advanced Performance Engineering) His recent projects include compiler-based approaches to GPU-to-CPU transpilation, performance portability in heterogeneous systems, and AI-driven compiler decision-making. Moses collaborates with industry and academic partners on advancing compiler technologies for exascale computing and machine learning acceleration.