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Dr. Omar Alhussein serves as an Assistant Professor in the Department of Computer Science at Khalifa University. Previously, he worked as a Senior Research Engineer at Huawei Technologies Canada (2020-2023), where he developed machine-learning solutions for networks and filed six patent applications. His educational qualifications include: Ph.D. in Electrical and Computer Engineering, University of Waterloo (2020) M.A.Sc. in Engineering Science, Simon Fraser University (2015) B.Sc. in Electrical and Computer Engineering, Khalifa University (2013) Research focuses on the convergence of networks and artificial intelligence: Network Automation and Intelligence Network Resource Management and Optimization AI and Networking NFV/SDN Wireless Networks He employs interdisciplinary methods to advance network intelligence through fundamental research principles. Scientific recognition includes: Outstanding Individual Award (Huawei Technologies) Quality Research Award (Huawei Technologies) Dr. Alhussein mentors PhD candidates and holds six patent applications for probabilistic/distributed AI in next-generation networks and AI trustworthiness. He actively recruits PhD students for ongoing research. He maintains active affiliations with Khalifa University's 6G Research Center and Center for Cyber-Physical Systems.

• Network Automation and Intelligence
• Network Resource Management and Optimization
• AI and Networking
• +2 more

Corina Pasareanu is an ACM Fellow and IEEE ASE Fellow serving as a Principal Scientist at Carnegie Mellon University's CyLab Security and Privacy Institute and Technical Professional Leader for Data Science at NASA Ames Research Center through KBR. Her work bridges formal methods, software verification, and artificial intelligence to ensure the safety and security of complex systems, particularly autonomous systems and machine learning applications. Dr. Pasareanu received her academic training at: Ph.D. in Computer Science, Kansas State University (2001) M.S. in Computer Science, University Politehcnica of Bucharest (1995) B.S. in Computer Science, University Politehcnica of Bucharest (1994) Her research focuses on developing formal verification techniques that can provide mathematical guarantees about the behavior of complex software systems. She specializes in applying model checking, symbolic execution, and compositional verification methods to challenges in autonomy, security, and AI safety. Her recent work addresses the verification of systems incorporating machine learning components, particularly neural networks used in safety-critical applications like autonomous vehicles. She investigates how to ensure these systems behave correctly even when their perception components have uncertainties or are subject to adversarial attacks. Analysis of her recent publications shows a strong trend toward verifying AI and machine learning systems, particularly focusing on neural networks in autonomous systems. Her work increasingly addresses the challenges of Large Language Models, examining both their vulnerabilities to attacks and methods to defend against them. She also continues to advance traditional software verification techniques while adapting them to modern programming languages and paradigms. Dr. Pasareanu has received numerous prestigious awards recognizing her contributions to the field: ACM Fellow IEEE ASE Fellow ETAPS Test of Time Award (2021) ASE Most Influential Paper Award (2018) ESEC/FSE Test of Time Award (2018) ISSTA Retrospective Impact Paper Award (2018) ACM Impact Paper Award (2010) ICSE 2010 Most Influential Paper Award (2010) As an advisor, Dr. Pasareanu mentors several PhD students and postdoctoral researchers, often in collaboration with other faculty members at CMU. Her students focus on cutting-edge research at the intersection of formal methods and AI safety. Her research is supported by substantial funding from diverse sources including NSF, DARPA, NASA, AWS, and industry partnerships. She leads multiple projects focused on AI security, formal verification of neural networks, and software analysis techniques. Dr. Pasareanu also plays a significant role in the broader research community, serving as Program/General Chair for major conferences including ICSE 2025, and as an associate editor for IEEE TSE and STTT. Dr. Pasareanu leads research teams working on projects like "Trinity: Neurosymbolic Learning and Reasoning" (DARPA) and "HUGS: Human-Guided Software Testing and Analysis" (NSF). Her work often involves interdisciplinary collaboration between computer scientists, formal methods experts, and domain specialists to address complex safety challenges in autonomous systems.

• Model Checking
• Symbolic Execution
• Compositional Verification
• +7 more

John G. Kerns is a Professor in the Department of Psychology at the University of Missouri, leading the Cognitive and Emotional Control Lab. His research integrates clinical psychology and neuroscience to understand psychotic disorders, with a focus on striatal functioning and aberrant salience. His primary research interests include: Translational clinical neuroscience of psychotic disorders Cognitive and emotional control mechanisms Striatal functioning and feedback-related learning Aberrant salience and positive schizotypy Openness to experience in psychopathology Analysis of Dr. Kerns' recent publications shows a strong emphasis on neuroimaging and behavioral studies of psychosis risk, particularly examining corticostriatal pathways and their relationship to cognitive deficits. His work consistently bridges personality factors with neural mechanisms. No scientific awards are mentioned in the provided text. Dr. Kerns mentors a vibrant team of graduate and undergraduate students. Current graduate students include Megan Liew and Tyler Rogers, and his alumni have secured prestigious positions in academia and clinical practice. He is accepting applications for graduate students starting in Fall 2026. The Cognitive and Emotional Control Lab provides a collaborative environment for research on psychotic disorders, utilizing behavioral, clinical, and neuroimaging methods to advance understanding and treatment.

• Translational Clinical Neuroscience
• Psychotic Disorders
• Cognitive and Emotional Control
• +5 more

Peter W. Glynn is the Thomas Ford Professor in the Department of Management Science and Engineering (MS&E) at Stanford University's School of Engineering, and also holds a courtesy appointment in the Department of Electrical Engineering. Additionally, he serves as a Senior Fellow of the Hong Kong Institute for Advanced Study at City University of Hong Kong. His distinguished career spans over four decades, with significant contributions to the fields of simulation, computational probability, and stochastic modeling. Professor Glynn received his Ph.D. in Operations Research from Stanford University in 1982 and his B.S. with Honors in Mathematics from Carleton University in 1978. His academic journey began at the University of Wisconsin at Madison (1982-1987) before returning to Stanford, where he has held various leadership positions including Deputy Chair of MS&E (1999-2005), Director of Stanford's Institute for Computational and Mathematical Engineering (2006-2010), and Chair of MS&E (2011-2015). His research interests focus on simulation , computational probability , queueing theory , statistical inference for stochastic processes , and stochastic modeling . Professor Glynn's work has developed algorithms widely used across the field of Monte Carlo simulation, with applications in financial risk management, service systems engineering, logistics, and retail operations. His recent publications demonstrate continued innovation in areas such as numerical methods for stochastic systems, rare-event simulation, and analysis of queueing systems under various traffic conditions, showing a strong trajectory of advancing both theoretical foundations and practical applications. Professor Glynn's scholarly contributions have been recognized with numerous prestigious awards, including: Fellow of INFORMS (2007) Fellow of the Institute of Mathematical Statistics (1998) John von Neumann Theory Prize from INFORMS (2010) Member of the US National Academy of Engineering (2012) Lifetime Professional Achievement Award, INFORMS Simulation Society (2021) Philip McCord Morse Lecturer, INFORMS (2020) Throughout his career, Professor Glynn has mentored numerous doctoral students whose research has made significant contributions to operations research and related fields. His editorial service has been extensive, including founding Editor-in-Chief of Stochastic Systems and service on the editorial boards of leading journals in operations research, probability, and statistics. His professional service extends to numerous advisory boards and committees at national and international levels, reflecting his standing as a leader in his field.

• Simulation
• Computational Probability
• Queueing Theory
• +2 more

Shayan Aziznejad is a Senior ML Scientist at Distran, working on the intersection of machine learning and acoustic imaging. He was previously an ML researcher at Daedalean AI (October 2022–December 2024) and a Ph.D. candidate at Ecole Polytechnique Fédérale de Lausanne (EPFL) , where he focused on mathematical optimization and signal processing under Prof. Michael Unser. His academic background includes dual B.Sc. degrees in Electrical Engineering and Pure Mathematics from Sharif University of Technology . Research Focus: Machine learning, neural network certification, wavelet analysis, Hessian-Schatten regularization, and sparse modeling. Scientific Recognition: Swiss National Science Foundation Postdoc Fellowship (2021) Best Student Paper Award at ICASSP (2019) Gold Medalist at Iranian National Mathematics Olympiad (2011) Academic Contributions: Authored 15+ publications in top-tier journals (SIAM, IEEE, etc.) and conferences (ICASSP, EUSIPCO), with a focus on Lipschitz-regularized models, spline-based optimization, and inverse problems. Advising Experience: Supervised 11+ students across master's theses, summer internships, and semester projects, including Eliana Renzo, Joaquim Campos, and Haojun Zhu. Email: shayan.aziznejad@gmail.com

• Machine Learning
• Acoustic Imaging
• Neural Networks
• +3 more

Børge Rokseth is an Associate Professor at the Department of Engineering Cybernetics , Norwegian University of Science and Technology (NTNU). His work focuses on integrating advanced methodologies for safety and risk control in autonomous maritime systems. He has held academic positions since at least 2014, with a consistent record of research collaboration and publication. Research Areas: Maritime risk analysis, autonomous ship systems, safety engineering, dynamic positioning systems, systems-theoretic process analysis (STPA) Key Publications: 15 most recent articles cover topics like trajectory prediction for autonomous vessels, hybrid power systems safety, machine learning in risk assessment, and dynamic positioning system reliability His publications (2014-2025) emphasize safety-critical systems in marine environments. Common themes include: Application of STPA for hazard analysis in autonomous shipping Development of risk-informed control systems Integration of machine learning with engineering risk assessment Comparative studies of different ship autonomy levels As a supervisor, Rokseth has guided master's students including Ane Joramo Stokke and Ludvig Vik Løite. His work has been presented at international conferences such as the European STAMP Workshop, International Conference on Conceptual Modeling, and the International Seminar on Safety and Security of Autonomous Vessels.

• Maritime Risk Analysis
• Autonomous Ship Systems
• Safety Engineering
• +3 more

Kihong Heo is an Associate Professor in the School of Computing and Graduate School of Information Security at KAIST (Korea Advanced Institute of Science and Technology) in South Korea. His academic career includes serving as an Assistant Professor at KAIST from 2017-2019 before being promoted to Associate Professor in 2020, following his postdoctoral research at the University of Pennsylvania. He earned both his Ph.D. and B.S. in Computer Science & Engineering from Seoul National University. Dr. Heo's research focuses on developing program reasoning systems for safe and reliable software, with specific interests in AI-based program analysis systems for detecting deep semantic software bugs, general-purpose program simplification systems for secure and efficient software, and scalable program synthesis systems for automatic software generation and repair. His work bridges the gap between programming languages, program analysis, and machine learning techniques to create next-generation programming systems. Analysis of his recent publications reveals a strong trend toward integrating machine learning techniques with traditional program analysis methods, with significant contributions in compiler validation, software security, fault localization, and program debloating. His research has practical impact, with some of his work incorporated into Facebook's Infer static analyzer. ACM SIGSOFT Distinguished Paper Award, FSE 2025 Amazon Research Award, 2024 The Soo-Young Lee Teaching Innovation Award, KAIST, 2024 Prize for Excellence in Teaching, KAIST, 2024 Best Artifact Award, ICSE 2022 ACM SIGPLAN Distinguished Paper Award, PLDI 2019 ACM SIGSOFT Distinguished Paper Award, ICSE 2019 Dr. Heo actively mentors graduate students, currently advising several Ph.D. candidates including Yeonhee Ryou, Taeeun Kim, and Sujin Jang, as well as master's students. He has served on program committees for major software engineering and programming language conferences including PLDI, ICSE, POPL, and SPLASH, demonstrating his active role in the academic community. His laboratory, the Programming Systems Laboratory at KAIST, focuses on creating innovative programming systems that leverage both semantic-based program analysis and AI techniques.

• Program Analysis
• Programming Language
• Machine Learning
• +3 more

Dr. Alexander Artikis is an Associate Professor of Artificial Intelligence at the University of Piraeus and a Research Associate at the National Centre for Scientific Research (NCSR) "Demokritos". He leads the Complex Event Recognition (CER) group , focusing on symbolic and probabilistic approaches to event recognition and forecasting. University of Piraeus (2025–present) NCSR Demokritos (2017–present) Complex Event Recognition Group (2017–present) His research spans Artificial Intelligence and Distributed Systems , with a focus on: Complex Event Recognition (CER) : Developing logic-based systems for detecting events in real-time data streams Event Calculus : Creating probabilistic and incremental versions for runtime reasoning Multi-Agent Systems : Modeling norm-governed interactions Maritime Informatics : Applying CER to vessel trajectory analysis and fleet management Key publications reveal trends in: Neuro-symbolic forecasting models combining deep learning and logic-based reasoning Symbolic automata with memory for pattern detection Online learning techniques for dynamic event rule generation Tensor-based formalizations for efficient temporal reasoning Handling uncertainty in real-time maritime data streams Optimizing memory usage for scalable stream processing He contributes to open-source tools like RTEC (Run-Time Event Calculus) and holds a European patent on complex event forecasting. His work addresses challenges in: Proactive decision-making systems Knowledge Graph consistency Hybrid human-machine discovery of movement patterns Big Data analytics for time-critical applications

• Artificial Intelligence
• Distributed Systems
• Complex Event Recognition
• +4 more

Sara Magliacane is an Assistant Professor at the University of Amsterdam and a Research Scientist at the MIT-IBM Watson AI Lab . She leads research at the intersection of causality and machine learning , focusing on improving AI robustness, generalization, and safety through causal reasoning. Her work spans causal representation learning , causal discovery , and causality-inspired ML in domains like reinforcement learning and dynamical systems. PhD in Artificial Intelligence (2017), VU Amsterdam MSc in Computer Engineering (2011), Politecnico di Milano/Torino BSc in Computer Engineering (2008), Università degli Studi di Trieste Her research explores causal variable identification from high-dimensional data (e.g., images, sequences) and causal graph discovery for domain adaptation. Methods include CITRIS , FANS-RL , and SNAP , with applications in embodied AI and biomedical data. The group emphasizes theoretical guarantees and scalable algorithms for real-world systems. Recent work trends include temporal causal modeling , intervention-efficient learning , and nonstationary reinforcement learning . Publications cover topics like causal discovery in partially observed settings , causal graph pruning , and sample-efficient concept learning , often combining neurosymbolic approaches with deep learning. Scientific Awards : ELLIS Scholar Sara supervises PhD students across universities (UvA, University of Pisa) and collaborates with institutions like TU Delft , Harvard , and IBM Research . She co-organizes workshops at premier conferences (NeurIPS, ICML, AISTATS) and teaches causality courses at the University of Amsterdam and Harvard Data Science Initiative. Her lab, Amsterdam Machine Learning Lab (AMLab) , investigates causal structure in embodied agents , safe reinforcement learning , and hybrid dynamical system modeling . The group maintains active partnerships with institutions such as MIT-IBM Watson AI Lab , Qualcomm , and Adyen .

• Causality
• Causal Representation Learning
• Causal Discovery
• +4 more

Jie Chen is an Assistant Professor in the Department of Mechanical Engineering at Virginia Tech's College of Engineering. Their research bridges machine learning with engineering analysis and design under uncertainty, focusing on process-structure-property-performance relationships. PhD, Mechanical Engineering (2022) – Arizona State University MS, Civil Engineering (2018) – Beihang University BS, Civil Engineering (2015) – Beihang University Research interests include: physics-informed machine learning, uncertainty quantification, predictive maintenance, materials design, and advanced manufacturing. The SEAD Lab develops methods to integrate engineering analysis into stochastic machine learning algorithms and uses AI for knowledge discovery in uncertain environments. Recent publications emphasize: Digital twin frameworks combining machine learning and Bayesian optimization Graph neural networks for high-entropy alloy and molecular mixture property prediction Physics-guided neural networks for fatigue life analysis of additively manufactured alloys Uncertainty quantification in imbalanced regression tasks and multi-fidelity data fusion Real-time imaging of polymer deformation mechanisms The lab actively mentors students, including PhD candidate Yisheng Lu, and manages projects in predictive maintenance, fatigue modeling, and materials design.

• Advanced Manufacturing
• Design Optimization
• Machine Learning
• +4 more

Shuhao Fu is a Program Postdoctoral Fellow at the Santa Fe Institute (SFI) researching the intersection of machine learning and cognitive science. He completed his Ph.D. in Psychology at UCLA under advisors Hongjing Lu and Ying Nian Wu, following a B.S. in Computer Science and Mathematics from Hong Kong University of Science and Technology. His research examines human-like relational reasoning in AI systems through cognitive modeling and computational approaches. Research focuses on: Bridging human-machine reasoning gaps via analogical mapping Developing explicit relational representations in vision models Structural cognitive modeling for compositional understanding Multimodal reasoning and scene interpretation Relational knowledge representation in biological and artificial systems Publication trends show concentrated work in computational cognitive science (2021-2025), with evolving focus from visual analogy fundamentals to applications in 3D recognition, social interaction modeling, and mental health diagnostics. Recent work demonstrates increased emphasis on transformer architectures, multimodal integration, and human-AI comparative studies. Professional experience includes research internships at Google X and Mineral.ai, with prior affiliation at Johns Hopkins University's CCVL lab under Alan Yuille. Currently serves as reviewer for ICML, ICCV, and Cognitive Science Society conferences.

• Machine Learning
• Cognitive Science
• Relational Reasoning
• +3 more

Jessica Hullman is the Ginni Rometty Professor of Computer Science at Northwestern University's McCormick School of Engineering and a Faculty Fellow at the Institute for Policy Research. Her research develops theoretical frameworks and interfaces for human-AI collaboration, focusing on uncertainty quantification, statistical modeling, and decision-making in domains like scientific research and AI-assisted analysis. Education: PhD in Information (Visualization), University of Michigan (2013) MS in Information Analysis, University of Michigan (2008) BA in Comparative Studies, Ohio State University (2003) Tableau Postdoctoral Fellowship, UC Berkeley (2015) Research Focus: Hullman's work bridges formal models of rational inference (e.g., Bayesian decision theory) with real-world applications. Key areas include: human-AI complementarity in decision-making, visualization of uncertainty, statistical reform, and LLM applications in behavioral science. Her research consistently addresses the alignment of data-driven interfaces with human cognitive capabilities. Publication Trends: Recent work demonstrates a strong emphasis on human-AI collaboration frameworks, decision-theoretic evaluation of visualizations, and methodological rigor in machine learning and social science. Key themes include uncertainty quantification (conformal prediction, privacy tradeoffs), behavioral experiments in AI-assisted tasks, and critical analyses of scientific practices. Awards & Honors: Microsoft Faculty Fellow (2019) Google Faculty Award NSF CAREER, Medium, and Small Awards Multiple best paper/honorable mention awards at top HCI/visualization venues (CHI, VIS) Funding & Labs: Principal Investigator for NSF-funded projects including HCC: Medium on visualization tools. Previously affiliated with University of Washington's Interactive Data Lab and DataLab. Current research includes NSF-supported work on improving data visualization for reasoning about analytical assumptions.

• Artificial Intelligence
• Human-Computer Interaction
• Uncertainty Quantification
• +4 more

Edwin Romeijn holds the Jill Stewart Archer Family Chair and Professor position in the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Institute of Technology. He served as School Chair from 2015-2024, overseeing the nation's top-ranked industrial engineering program. Previously, he held faculty positions at the University of Michigan, University of Florida, and Erasmus University Rotterdam, and served as Program Director at the National Science Foundation. Education: Ph.D. in Operations Research (1992), Erasmus University Rotterdam M.S. in Econometrics (1988), Erasmus University Rotterdam Romeijn's research centers on optimization theory and applications , with dual focus areas in radiation therapy treatment planning and supply chain management . His radiation therapy work develops algorithms for cancer treatment planning and clinic scheduling, while his supply chain research addresses integrated optimization of production, inventory, and transportation under demand flexibility, resource constraints, perishability, and uncertainty. His methodologies bridge theoretical operations research with real-world healthcare and logistics systems. His publication portfolio demonstrates consistent contributions to optimization methods across diverse application domains, with recent work spanning healthcare systems, renewable energy, sports analytics, and unconventional logistics. The research exhibits strong methodological continuity in stochastic programming, network optimization, and decision-making under uncertainty. Scientific Awards: Fellow of IISE and INFORMS (2017) Richard C. Wilson Faculty Scholar (2012-2013) Multiple best paper awards in industrial engineering conferences Pierskalla Best Paper Award (2003) Young Investigator’s Award at ICCR (2004) Romeijn has advised numerous graduate students and secured significant research funding through NSF and other agencies. His leadership extends to program direction at NSF and chairing Georgia Tech's Industrial and Systems Engineering school. He maintains active collaborations with healthcare institutions and manufacturing enterprises, translating theoretical advances into practical solutions for radiation oncology and supply chain resilience.

• Optimization Theory
• Radiation Therapy Treatment Planning
• Supply Chain Management
• +2 more

Professor Oliver Johnson is a faculty member at the School of Mathematics, University of Bristol, UK, where he serves as Head of School and holds the Professor of Information Theory position. His research bridges information theory, probability, and statistics, focusing on entropy convergence, group testing, and fundamental limits in data analysis. Current PhD students: Kieran Morris, Conor Crilly Ex-PhD students: Matt Aldridge, Leonardo Baldassini, Dan Cowley, Vaia Kalokidou, Tom Kealy, Jennifer Chakravarty, Zichen Gui, Chrys Paschou Ex-postdoc: Erwan Hillion His work includes ORCiD profile and collaborations across information theory, cybersecurity, and ecological modeling.

• Information Theory
• Probability
• Statistics
• +3 more

Tracy Becker is an Adjunct Assistant Professor in the Department of Civil Engineering at McMaster University, where she has been since 2014. Her expertise lies in the design, modeling, and experimental testing of high-performance structural systems with a focus on seismic isolation. Education: BS in Structural Engineering, University of California, San Diego MS and PhD in Structural Engineering, Mechanics and Materials, University of California, Berkeley Post-doctoral research at Kyoto University's Disaster Prevention Research Institute Research Interests: Becker specializes in seismic isolation systems, hybrid simulation methods, and structural performance under extreme events. Her work spans bridge engineering, nuclear infrastructure protection, and innovative materials for earthquake resilience. She integrates computational modeling with experimental validation to address challenges in: Nonlinear system behavior in isolated structures Multi-hazard optimization for seismic and wind loads Bridge management using data-driven and fuzzy logic frameworks Advanced gusset plate design for seismic retrofit Adaptive isolation systems for nuclear facilities Probabilistic lifetime demand predictions for infrastructure Teaching: She has instructed courses in Seismic Design (CIVENG 4ED4), Structural Mechanics (CIVENG 2C04), and Earthquake Engineering (CIVENG 730) at McMaster University.

• Earthquake Engineering
• Structural Optimization
• Seismic Isolation
• +3 more