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Charles GrettonView profile

Lecturer

• Artificial Intelligence
• Automated Planning
• Automated Reasoning
• +5 more

Charles Gretton is a Lecturer at the Australian National University (ANU), specifically within the ANU College of Systems & Society. He is an Artificial Intelligence (AI) expert with over 20 years of experience in both academia and industry, including co-founding HIVERY, a CSIRO Data61 spinout. His research focuses on Automated Planning, Automated Reasoning, and Machine Learning, with applications in computer security, astronomical instrumentation, retail optimization, and robotics control. Affiliations: ANU, HIVERY (co-founder), CSIRO Data61 Industry Experience: Successfully applied AI in fast-moving consumer goods logistics, continental-scale retail strategies, and robotics. His teaching roles include delivering core courses in Theoretical Computer Science and AI at ANU since 2013, alongside leading the ANU TechLauncher program (2018–2024), a capstone initiative fostering industry collaboration with early-career professionals. His research spans cutting-edge topics like physics-informed neural networks for plasma physics, AI in financial markets, and adaptive optics systems for astronomy. Charles has supervised notable students including Patrick Liston (financial agent-based simulations), Xiaodi Zhang (probabilistic planning), and Jeffrey Smith (astronomical instrumentation AI). Key collaborations involve instrumentation scientists like Damien Gratadour and Jesse Cranney. His work bridges theoretical AI with real-world applications, emphasizing human-centric technology. Notable projects include the Dagster parallel search tool, verified AI planning algorithms, and AI-driven solutions for policing and retail. His contributions to planning theory include formally verified algorithms and compositional methods for problem-solving.

Michael WarnerView profile

Lecturer

• Astronautics
• Applied Physics
• Engineering Education
• +3 more

Michael Warner is a Lecturer of Physics at Colorado Mesa University, teaching freshman physics laboratory courses focused on engineering disciplines. His research expertise lies in astronautics and trajectory optimization, with a focus on computational methods and finite element analysis. He holds a PhD in Aerospace Engineering from Georgia Tech, an MS in Astronautics from George Washington University, and a BAE in Aerospace Engineering from Georgia Tech. His scholarly work includes publications on launch vehicle trajectory optimization, finite element methods, and computational error estimation. Notable contributions address capstone engineering design processes and launch site wind simulations. Warner emphasizes hands-on learning in his courses, fostering student collaboration through experimental setup, data analysis, and report writing. Outside academia, Warner is an avid explorer with extensive travel experience, including visits to all 50 U.S. states, 40 countries, and over 400 national park sites. His recreational interests include hiking, cycling, running, and various ball sports.

• Robot Vision
• Machine Learning
• Autonomous Systems
• +9 more

Prof. Dr. Sebastian Houben is a Professor of Robot Vision and Machine Learning at Bonn-Rhein-Sieg University of Applied Sciences (H-BRS). He holds roles as International Affairs Representative and Head of the Master's Examination Board for Autonomous Systems. His research focuses on AI-driven solutions for autonomous systems, environmental monitoring, and energy efficiency. Key projects include: INNERVATE : Enhancing driver maneuver evaluation via AI for automotive safety. DKZ.2R : Promoting data literacy and high-performance computing across disciplines. DigitalTwin-4-Multiphysics Lab : Urban and industrial digital twins for efficiency optimization. GARRULUS : AI-driven reforestation using drones and precision seeding. He leads the Institute for AI and Autonomous Systems (A2S) and collaborates with industry partners like GKN Driveline. His research spans robotics, computer vision, and sustainable energy systems. Prof. Houben is a member of the Promotionskolleg Nordrhein-Westfalen (Doctoral College North Rhine-Westphalia). His work emphasizes explainable AI (XAI), safety-critical systems, and interdisciplinary applications.

John BowersView profile

Associate Professor

• Computational Geometry
• Origami
• Algorithm Design
• +4 more

John Bowers is an Associate Professor of Computer Science at James Madison University (JMU), part of the College of Integrated Science & Engineering. He holds a Ph.D. in Computer Science from the University of Massachusetts Amherst (2015), an M.S. from the same institution (2012), and a B.S. in Computer Science and Mathematics from Florida State University (2008). His research focuses on computational geometry, origami algorithms, and discrete mathematics, with applications to 3D printing and structural design. He is co-director of the Ars Geometrica Lab, which explores geometric visualization and undergraduate research in computational geometry. Teaching responsibilities include courses in algorithms (CS 452), discrete mathematics (CS 327), and programming fundamentals. He advises first-year students and organizes the CS Research Seminar. Bowers emphasizes the liberal arts mission, advocating for interdisciplinary education to foster societal impact through knowledge preservation and innovation. His research lab collaborates with institutions like Oak Ridge National Labs on projects such as optimized 3D printing infill structures and geometric software tools like Koebe.py . Personal interests include cycling, rock climbing, violin, and Thomistic philosophy.

Ana-Maria CroicuView profile

Professor

• Optimal control
• Optimization
• Uncertainty quantification
• +9 more

Dr. Ana-Maria Croicu is a Professor of Mathematics and Graduate Faculty at Kennesaw State University (KSU), with a Courtesy Professor role at Florida State University's Department of Mathematics. She holds dual Ph.D.s in Applied Computational Mathematics and Statistics (Florida State University, 2005) and Mathematics (Babes-Bolyai University, Cluj-Napoca, 2001), along with degrees in Mathematics and Electrical Engineering from the Technical University of Cluj-Napoca. Her research focuses on optimal control, optimization, uncertainty quantification, and bioscience modeling, with notable contributions to aerodynamics and disease dynamics. She has secured over $1.2 million in NSF funding and led the NSF S-STEM Program supporting disadvantaged STEM students. Dr. Croicu has been recognized with prestigious awards, including the Cobb County Teacher of the Year and multiple teaching honors at KSU. Her academic roles include directing undergraduate research, organizing international conferences, and serving as a Visiting Scientist at NASA Ames Research Center. Education includes advanced training in Romania and the U.S., spanning theoretical mathematics, applied and computational mathematics, statistics, and electrical engineering. She has mentored numerous students across PhD, MS, and undergraduate levels, contributing to research in areas like HIV infection modeling and anthrax disease control. Her work bridges computational methods with real-world applications, emphasizing interdisciplinary collaboration. Key research areas include mathematical modeling of infectious diseases (e.g., Ebola, HIV, anthrax) and robust optimization in engineering contexts. She has presented at major international conferences and co-organized workshops on optimal control and numerical simulations. Her teaching excellence spans online and traditional formats, reflecting her commitment to innovative education.

Bethany EdmundsView profile

Teaching Professor

• Artificial Intelligence
• Reinforcement Learning
• Ethical AI
• +3 more

Bethany Edmunds is a Teaching Professor and Assistant Dean of Computer Programs at Northeastern University’s Vancouver and Seattle campuses. She holds a Ph.D. in Computer Science from Rutgers University, focusing on Reinforcement Learning. Her research emphasizes ethical AI, inclusive technology design, and educational innovation. She co-founded Happy Prime, Inc. in 2024 as CEO to build assistive communication software. Education: Ph.D. in Computer Science, Rutgers University (Reinforcement Learning) Research Interests: Dr. Edmunds explores AI ethics, algorithmic fairness, and leveraging technology for social equity. She advocates for diverse and inclusive tech ecosystems through her work in educational programs and industry collaborations. Her technical contributions span reinforcement learning theory and robotics applications. Awards & Recognition: 2023 Khoury College Award for Broadening Participation in Computing 2019 YWCA Women of Distinction (Education) 2018 BC’s Most Influential Women Leadership & Advocacy: She leads initiatives like Northeastern’s “CS for Everyone” program and frequently speaks on AI ethics at events such as Crossover:AI and BC Tech Summit. Her work promotes women’s leadership in tech and equitable AI adoption. Public Engagement: Bethany has appeared on Nicola Wealth Podcast , Marketplace Tech , and CBC’s Conversations That Matter , discussing AI’s societal impact and educational strategies.

• Structured Linear Algebra
• Society and Computing
• Coding Theory
• +5 more

Rudra Atri is the Katherine Johnson Chair in Artificial Intelligence and a Professor in the Department of Computer Science and Engineering at the University at Buffalo, part of the School of Engineering and Applied Sciences. His research focuses on structured linear algebra, society and computing, coding theory, and database algorithms. He holds a PhD from the University of Washington (2007), an MS from the same institution (2005), and a B.Tech from the Indian Institute of Technology (2000). Dr. Atri has received numerous accolades, including the SUNY Chancellor's Award for Excellence in Teaching (2022), the UB Teaching Innovation Award (2021), and the NSF CAREER Award (2009). His work spans theoretical computer science, machine learning, and practical applications in databases and bioinformatics. He is affiliated with the National Center for Women & Information Technology and the Center of Excellence for Document Analysis and Recognition (CEDAR), contributing to socially responsible computing initiatives. His research explores efficient algorithms for linear algebra, probabilistic databases, and neural network architectures, with publications on topics ranging from sparse recovery to quantum computing simulations. Atri’s teaching and mentorship are widely recognized, emphasizing both technical rigor and ethical considerations in computing.

Jukka KiukasView profile

Lecturer

• Quantum Mechanics
• Quantum Information Theory
• Mathematical Physics
• +4 more

Jukka Kiukas is a Lecturer in the Department of Mathematics at Aberystwyth University. His research focuses on quantum mechanics, quantum information theory, and mathematical physics, particularly exploring topics such as quantum measurement incompatibility, open quantum systems, and foundational aspects of quantum theory. He has contributed extensively to understanding quantum contextuality, decoherence dynamics, and operator theory applications in quantum systems. Kiukas has published over 45 peer-reviewed articles, with recent work addressing topics like no-broadcasting principles and Lindbladian open systems. His research interests span quantum measurement theory, uncertainty relations, and the interplay between decoherence and quantum resource theories. Notably, he has investigated subspace constraints for joint measurability and sharp uncertainty relations for quantum systems. Kiukas has been involved in collaborative projects, including the QSym initiative funded by the Welsh Government (2017–2020), which explored quantum observables and symmetry in quantum systems. Kiukas actively participates in international conferences and workshops, presenting on topics such as quasi-free observables in phase space and time-of-arrival statistics. His work bridges theoretical foundations with applied quantum technologies, contributing to both mathematical rigor and practical quantum information processing challenges.

Sinisa TodorovicView profile

Professor

• Computer Vision
• Machine Learning
• Artificial Intelligence
• +6 more

Sinisa Todorovic is a Professor in the School of Electrical Engineering and Computer Science at Oregon State University (OSU). He holds a Ph.D. (2005) and M.S. (2002) from the University of Florida, and a B.S./M.S. from the University of Belgrade (1994). Before joining OSU, he was a postdoc at the Beckman Institute, University of Illinois, and a software engineer at Siemens (1998–2001). His research focuses on computer vision, machine learning, and AI, particularly semantic/instance segmentation, action segmentation in videos, few-shot learning, weakly-supervised learning, and cross-domain adaptation. He leads projects like fruit orchard segmentation datasets and transformer-based cross-domain semantic segmentation. Key contributions include the Volleyball dataset for group activity analysis, Hough Forest Random Fields for object segmentation, and boundary flow estimation. His work bridges theory and applications, including robotics, medical imaging, and sports video analysis. Todorovic advises over 20 graduate students and collaborates on grants involving AI ethics, explainable systems, and agricultural automation. He is affiliated with OSU's Data Science and Engineering and AI/Robotics research groups.

Weng-Keen WongView profile

Professor

• Machine Learning
• Anomaly Detection
• Computational Sustainability
• +4 more

Weng-Keen Wong is a Full Professor in the School of Electrical Engineering and Computer Science at Oregon State University (OSU), part of the College of Engineering. He serves as the OSU Site Director for the NSF-funded Pervasive Personalized Intelligence Center. His academic journey includes a PhD and MS from Carnegie Mellon University (2004/2001) and a BS from the University of British Columbia (1997). Research focuses on machine learning, with specialties in anomaly detection, explainable AI, computational sustainability, and spatio-temporal data analysis. Notable projects include disease outbreak surveillance algorithms, bird species distribution modeling, and RF device authentication systems. He collaborates across disciplines in public health, ornithology, and environmental sciences. Teaching includes courses like Artificial Intelligence (CS 331), Probabilistic Graphical Models (CS 536), and Ethics in Computer Science (CS 391). He advises a team of 9 current students (4 PhD, 1 MS, 4 undergrad) and has mentored over 25 alumni in roles spanning academia and industry. Labs and initiatives include the Wong Lab focusing on anomaly detection and sustainability projects. He has led NSF grants and industry collaborations, emphasizing practical applications of machine learning in real-world challenges.

Mayrai GindyView profile

Professor

• Structural Health Monitoring
• Sensors and Sensor Networks
• Non-Destructive Testing
• +5 more

Mayrai Gindy serves as Associate Dean for Academic and Faculty Affairs and Professor in the Department of Civil and Environmental Engineering at the University of Rhode Island. Her academic leadership position alongside her professorship demonstrates her significant contributions to both administration and teaching within the College of Engineering. Dr. Gindy received her educational foundation at Rutgers University, earning her B.S. in 2000, M.S. in 2002, and Ph.D. in 2004. Her academic journey at this institution provided the groundwork for her specialized expertise in structural engineering and related fields. Her research interests span multiple critical areas in civil engineering, with particular focus on Structural Health Monitoring, Sensors and Sensor Networks, Non-Destructive Testing methodologies, and Structural Safety and Reliability assessment. She has developed expertise in Reinforced and Prestressed Concrete systems, Live Load Spectra Modeling, Bridge Dynamics and Vibration analysis, Extreme Value Theory applications, Risk Analysis, Applied Probabilistic Methods, and Design Code Calibration. Her work increasingly integrates computer vision and machine learning techniques for structural assessment. Analysis of Dr. Gindy's publication history reveals a consistent research trajectory focused on bridge engineering and structural monitoring. Her recent work demonstrates a clear shift toward integrating computer vision and machine learning techniques with traditional structural engineering methods, particularly for rebar detection and bridge deck assessment using Ground Penetrating Radar technology. Earlier publications show strong foundations in probabilistic methods for bridge load analysis and vibration monitoring. Dr. Gindy has secured recent collaborative funding, serving as Co-PI on two grants from the University of Maine in 2022 for "Vision-Based Detection of Bridge Damage Captured by Unmanned Aerial Vehicles." Her research program demonstrates successful translation of theoretical concepts into practical applications for infrastructure assessment. As Associate Dean for Academic and Faculty Affairs, Dr. Gindy plays a significant role in faculty development and academic policy at URI. Her publications in engineering education, including work on discovery-based learning and comparisons of lecture- versus problem-based learning styles, reflect her commitment to innovative teaching practices within the civil engineering curriculum.

Chris WinsteadView profile

Associate Professor

• Error Correction Algorithms
• Probabilistic Logic
• Neuromorphic Circuits
• +3 more

Chris Winstead is an Associate Professor in the Department of Electrical and Computer Engineering at Utah State University. His research focuses on error correction algorithms, probabilistic logic systems, and neuromorphic hardware design. He has mentored over 15 graduate students and received significant recognition including an NSF Career Award. Dr. Winstead's research spans hardware security, stochastic computing, and communication systems. His work integrates theoretical frameworks with practical implementations for low-power and fault-tolerant systems. Recent publications demonstrate applications in autonomous vehicle security, biological circuit modeling, and probabilistic computing architectures. Honors include the 2014 Fulbright Research Scholarship and 2010 NSF Career Award. He leads research on noise-enhanced computing methods and maintains collaborations in stochastic algorithm development. Current projects investigate adversarial resilience in cyber-physical systems and hardware acceleration for probabilistic decoders.

• Mathematics
• Computer Science
• Stochastic Models
• +3 more

Victor Pestien is an Associate Professor in the Department of Mathematics at the College of Arts and Sciences, University of Miami. His research focuses on stochastic models, queueing theory, and discrete-time network analysis. Role: Associate Professor, Mathematics University: University of Miami Victor Pestien's work examines stochastic networks , Markov processes , and queueing systems with applications in computer networks and decision models. His studies analyze throughput limits, occupancy distributions, and reward functions under varying service rate dependencies. Key publication trends include discrete-time cyclic networks (2002-2008), Markov-achievable payoffs (1993, 1998), and noisy-channel transmission (1994). Collaborations with researchers like S. Ramakrishnan and Hans Daduna highlight his focus on network stability and throughput optimization. Victor Pestien's email is pestien@miami.edu .

Mingzhe ChenView profile

Assistant Professor

• Machine Learning
• Wireless Networks
• Federated Learning
• +7 more

Mingzhe Chen is an Assistant Professor and John S. and James L. Knight Foundation Chair in Data Science and AI at the University of Miami's Department of Electrical and Computer Engineering. He holds affiliations with the Frost Institute for Data Science and Computing. His research focuses on machine learning for wireless networks, federated learning, semantic communications, and UAV applications. He earned his Ph.D. from Beijing University of Posts and Telecommunications (2019), with postdoctoral work at Princeton University and Virginia Tech. Education: Ph.D., Beijing University of Posts and Telecommunications (2019) Postdoctoral Research, Princeton University (2019–2021) Visiting Researcher, Virginia Tech (2016–2019) Research Interests: Machine learning for wireless systems, federated learning fundamentals, semantic communication, UAV networks, and VR over wireless. Recent work emphasizes 6G advancements, digital twins, and secure AI-driven networks. Awards: IEEE Marconi Prize Paper Award (2023) IEEE Katherine Johnson Young Author Award (2023) NSF grants for projects on 6G, digital twins, and secure AI (2022–2024) Advising & Grants: Supervises PhD students including Dongyu Wei and Hanzhi Yu. Secured NSF funding for initiatives like "3D HARMONY" (CNS Core) and "Untethered Extended Reality" (NewSpectrum). Active in organizing IEEE conferences (e.g., WFIOT 2025, MILCOM 2025). Labs & Teams: Leads research groups focusing on AI-driven wireless networks and collaborates with Ericsson Research. His lab explores digital twin synchronization, federated learning, and semantic feature multiple access (SFMA) systems.

• Structural Engineering
• Materials Science
• Mechanical Engineering
• +5 more

Dr. Luan Trinh is an Assistant Lecturer in the Department of the Built Environment at the Faculty of Engineering and the Built Environment. His research focuses on structural mechanics, composite materials, and advanced computational methods. Key areas include buckling analysis of cylindrical shells, vibration behavior of porous microbeams, and the application of modified couple stress theory in size-dependent structural analysis. He employs numerical techniques such as state-space approaches and inverse differential quadrature for modeling complex engineering systems. His work contributes to sustainable development goals through advancements in material science and structural optimization. Research interests include structural stability, composite materials, and finite element analysis. Recent studies emphasize the dynamic and static responses of functionally graded beams, porous microstructures, and elastically supported systems. Collaborations span international institutions, particularly in composite materials and mechanical behavior modeling. Publications highlight innovations in beam theories, buckling modes, and probabilistic analysis, often addressing practical engineering challenges such as compression buckling and boundary condition effects. While no specific grants or advisees are listed, his contributions are reflected in over 560 citations and an h-index of 11, underscoring impactful research in structural and materials engineering.