Hallac’s career is emblematic of a rare intersection between cutting-edge technological innovation and high-level financial stewardship. She currently serves as an advisor to VectoIQ, a firm renowned for fostering advancements in smart transportation technologies. VectoIQ is at the forefront of autonomous vehicle integration, wireless electric vehicle charging systems, and sophisticated fleet management solutions, which collectively represent the future contours of urban mobility and sustainability. Her role within VectoIQ underscores her deep engagement with the transformative potential of technology sectors that are increasingly reliant on complex data analytics and systems engineering.

Additionally, Hallac holds a directorial position with the VectoIQ acquisition corporation. This involvement positions her uniquely at the nexus of startup incubation and the commercialization of emergent technologies. Such experience equips her with a nuanced understanding of market dynamics and innovation workflows critical for aligning scientific discovery with practical applications. Her expertise in navigating the financial landscapes that underpin technological growth offers the Salk Institute an amplified capacity to translate laboratory breakthroughs into societal advancements.

Beyond her direct engagements with cutting-edge technologies, Hallac is also a consultant to BlackRock on strategic philanthropic initiatives. BlackRock, as one of the world’s largest asset managers, plays a pivotal role in shaping investment patterns with implications across industries, including biotech and life sciences. Hallac’s consultation role indicates her proficiency in orchestrating large-scale philanthropic efforts that can fuel scientific research ecosystems. This indicates a sophisticated grasp of the interface between capital allocation and long-term research funding stability—an ongoing challenge for institutions committed to breakthrough science.

Earlier in her distinguished career, Hallac cultivated her analytical prowess as an investment banker at Bear, Stearns and Company. Her tenure in the Financial Analytics and Structured Transaction Group entailed intricate financial modeling and deal structuring, skills that sharpened her ability to synthesize complex data into actionable business intelligence. These skills translate seamlessly into the governance needs of research institutions that increasingly rely on data-driven decision-making to prioritize investments in emerging research areas and infrastructure.

The Salk Institute’s President, Dr. Gerald Joyce, MD, PhD, warmly welcomed Hallac’s appointment, emphasizing the crucial timing of her integration into the Board. Dr. Joyce highlighted that the Institute is currently navigating a transformative era marked by rapid scientific progress and the necessity for resilient institutional frameworks. This sentiment reflects a growing awareness that sustaining innovation in fundamental biology demands governance that not only appreciates scientific rigor but also masterfully manages evolving economic and technological landscapes.

Hallac’s academic background further complements her multidisciplinary expertise. She holds a Bachelor of Science in Economics and a Bachelor of Science in Electrical Engineering from the University of Pennsylvania. This dual foundation provides her with a rare analytical duality—melding quantitative technical skills with comprehensive economic theory and applications. Such interdisciplinarity is critically valuable in scientific advisory roles where technology commercialization and economic viability are deeply intertwined.

In addition to her corporate and advisory roles, Hallac contributes to educational initiatives through her membership on the Board of Advisors for Georgia Tech and the School of Engineering at the University of Pennsylvania. These roles indicate her commitment to nurturing future generations of innovators and researchers in engineering and related disciplines. This ongoing engagement with academic institutions aligns seamlessly with the Salk Institute’s mission to foster cutting-edge environments where science and engineering converge to accelerate discovery.

Moreover, Hallac supports cultural and humanistic causes through her involvement with Centro Primo Levi in New York. This organization honors the legacy of Primo Levi, an iconic chemist and writer known for his profound humanistic perspective on science and society. Hallac’s board membership here reflects a nuanced appreciation for the ethical and philosophical dimensions of scientific inquiry, a perspective that enriches leadership frameworks within research institutions.

The Salk Institute, renowned worldwide for its pioneering research in life sciences, stands to gain significantly from Hallac’s appointment, which symbolizes the increasing convergence of scientific advancement with strategic governance informed by technological innovation, economic acumen, and philanthropic insight. Her presence on the Board heralds a new chapter for the Institute—a chapter poised to harness interdisciplinary expertise to propel transformative discoveries in biology, medicine, and related fields.

In an era where foundational scientific breakthroughs increasingly rely on sophisticated data analytics, systems-level engineering, and complex financial sustainment, Hallac’s role ensures that the Salk Institute will be guided with a visionary approach that balances scientific ambition with pragmatic stewardship. Her appointment is a testament to the evolving landscape of research governance where multidisciplinary expertise drives innovation ecosystems that effectively bridge discovery and application.

As Hallac steps into her role, the scientific community and broader public alike may anticipate strategic initiatives geared towards enhancing the Salk Institute’s research infrastructure and funding mechanisms. Her leadership is expected to catalyze fresh partnerships across technology sectors and philanthropic networks, accelerating the translation of fundamental science into solutions that address global health challenges and sustainable technological advancement.

The intersection of life sciences with next-generation technologies, autonomous systems, and strategic financial modeling represents an exciting frontier. With Hallac joining the Board, the Salk Institute is uniquely positioned to navigate this frontier, ensuring that its world-class scientific programs receive the visionary oversight necessary to sustain and expand their impact for decades to come.

Subject of Research: Not specified
Article Title: Not specified
News Publication Date: May 6, 2026
Web References: Salk Institute Board of Trustees
References: None provided
Image Credits: None provided

Keywords
Life Sciences, Research Leadership, Board of Trustees, Sarah Wolf Hallac, Salk Institute, Technology Innovation, Smart Transportation, Autonomous Vehicles, Philanthropy, Financial Analytics, Scientific Governance, Biomedical Research

Fog computing, often described as a decentralized computing infrastructure, operates at the edge of the network, closer to where data is generated. This proximity allows for faster data processing and reduced latency, crucial for real-time applications like autonomous vehicles and smart healthcare solutions. The researchers underline the importance of placing microservices effectively within this architecture to enhance service accessibility and performance while mitigating potential bottlenecks that could arise from cascading service requests.

The study begins by introducing a comprehensive taxonomy that encapsulates various microservices and their functionalities within the fog computing environment. By categorizing these services, IoT developers can better understand the potential synergies and interactions between different services, leading to improved system design and optimization. This taxonomy forms the backbone of their research, providing a structured approach to analyzing how microservices can be positioned to best serve IoT applications.

One of the standout features of this research is its forward-thinking nature. It doesn’t just present a static model; rather, it offers prospective directions for future implementations. The authors emphasize that as IoT technology continues to evolve, so too must our strategies for managing and deploying microservices within fog computing paradigms. They advocate for adaptive strategies that can evolve with changing network conditions and user demands, ensuring that IoT systems remain agile and responsive.

The research outlines several key objectives aimed at enhancing microservice functionality within fog environments. Specifically, the authors target improvements in resource allocation, service discovery, and data management. By utilizing advanced algorithms and intelligent decision-making processes, the proposed microservices can dynamically adjust their operations to meet fluctuating demands, ultimately enhancing user experience and operational efficiency.

A significant advantage of the proposed paradigm is its potential to minimize latency. In the context of IoT applications, where milliseconds can mean the difference between success and failure, reducing latency is paramount. By strategically placing microservices within the fog, closer to where data is generated and utilized, the system can process requests more rapidly. This enhances overall application performance, making the IoT ecosystem not just smarter but also significantly quicker.

Furthermore, the research considers the critical aspect of security within microservices in fog computing. As IoT systems are notoriously vulnerable to cyber threats, integrating robust security measures into the design of microservices is non-negotiable. The authors suggest implementing layered security protocols at various levels of service interaction, ensuring that data integrity and privacy are upheld. This proactive approach to security will be pivotal as IoT systems continue to scale and evolve.

The collaboration between hardware and software is another important dimension addressed in this research. By closely examining the interplay between IoT devices and their corresponding microservices, the authors propose a unified framework that harmonizes operations across the ecosystem. This alignment is essential for facilitating seamless communication and data sharing between devices, which is crucial for the effective functioning of IoT systems.

In their conclusion, the researchers highlight the collaborative nature of their work, calling for further interdisciplinary studies to refine and expand upon their findings. As the landscape of IoT continues to evolve, the need for innovative solutions to tackle its inherent challenges remains apparent. The dialogue around microservices placement in fog computing systems is increasingly relevant, suggesting that future research should build upon this foundational work to explore new pathways for IoT development.

Overall, the contributions of Dalal et al. to the discourse on IoT and cloud computing stand to make a significant impact. Their proposed taxonomy and strategic insights into microservice placement represent a crucial step towards creating a more efficient, secure, and adaptable IoT ecosystem. As industries across the globe embrace the potential of IoT technologies, this research lays the groundwork for strategies that enable smarter and more responsive systems—ultimately paving the way for smarter cities, industries, and everyday interactions.

As organizations strive to leverage IoT in their operations, understanding and implementing effective microservices within fog computing will be pivotal for future success. This research not only sheds light on an area of growing importance but also opens numerous avenues for ongoing exploration and collaboration in the field of smart technologies. The capacity for real-time data processing, improved service resilience, and enhanced security are not merely theoretical advancements; they represent tangible shifts toward a future where IoT solutions can operate seamlessly within the complexities of our interconnected world.

In summary, the path towards realizing the full potential of IoT demands relentless innovation and adaptation. As researchers and practitioners delve deeper into this study, the insights gained will undoubtedly inspire a new generation of smart applications, ultimately transforming the way we interact with technology and its boundaries in everyday life.

Subject of Research: Microservices Placement in Fog Computing for IoT

Article Title: Towards smarter IoT through taxonomy and prospective directions for microservices placement in fog computing paradigms.

Article References:

Dalal, Y.M., Supreeth, S., Rohith, S. et al. Towards smarter IoT through taxonomy and prospective directions for microservices placement in fog computing paradigms.
Discov Artif Intell (2026). https://doi.org/10.1007/s44163-025-00601-5

Image Credits: AI Generated

DOI:

Keywords: IoT, fog computing, microservices, security, latency, resource allocation, service discovery, data management, smart technologies.

Mironchenko’s research addresses the complex field of infinite-dimensional systems, a sophisticated area of mathematics essential for modeling dynamic and adaptive systems that cannot be adequately described by classical finite-dimensional approaches. These systems form the backbone of various high-tech applications such as optimized traffic control, autonomous vehicle coordination, and intelligent power grids. His work in mathematical systems theory aims to establish theoretical frameworks essential for the control and stabilization of these intricate dynamical constructs.

Control systems deployed in engineering are often prone to uncertainties stemming from hidden dynamics, parameter inaccuracies, and external disturbances. Even minor disruptions can dramatically degrade system performance and even render a system unstable. Dr. Mironchenko’s groundbreaking methods focus on the robustness of these control mechanisms, ensuring that they maintain functionality and stability amidst inherent system complexities and environmental perturbations. His approaches are vital in safeguarding the reliability of contemporary automated and regulated infrastructures.

A fundamental challenge in mathematical systems theory is the assumption that the underlying space of a system remains invariant over time. However, this assumption is increasingly untenable in real-world systems where network configurations and system topologies evolve dynamically. For instance, urban traffic control systems must adapt seamlessly as roads and vehicles enter or leave the network; similarly, modern power grids must efficiently incorporate microgrids that can be connected or disconnected without causing instability. Mironchenko is pioneering a new paradigm that models these adaptable systems with shifting spatial and topological properties, an essential step towards resilient, scalable control solutions.

After completing his undergraduate studies in applied mathematics at Odessa University, Ukraine, Mironchenko earned his doctoral degree from the University of Bremen. His dissertation meticulously examined the stability properties of infinite-dimensional control systems, laying the foundation for his research career. Mironchenko then expanded his expertise internationally through a postdoctoral position at the University of Würzburg and a competitive fellowship from the Japan Society for the Promotion of Science at the Kyushu Institute of Technology. Further postdoctoral work at the University of Passau culminated in his habilitation in 2023, a significant academic milestone in German-speaking countries marking advanced scholarly qualifications.

In December 2024, Mironchenko took a faculty position at the University of Bayreuth, where he continues his research under the esteemed Heisenberg Programme funded by the DFG. Throughout his career, he has authored over 70 peer-reviewed journal and conference articles, making substantial contributions to control theory and applied mathematics. His scholarly output demonstrates a blend of theoretical rigor and practical relevance, influencing both academic circles and real-world technological advancements.

Mironchenko also plays an instrumental role in the academic community beyond his publications. He co-founded and co-organizes the “Stability and Control of Infinite-Dimensional Systems” workshop series, which convenes leading experts to foster collaboration and innovation in this niche yet impactful research domain. His standing in the field is further evidenced by his Senior Membership in the Institute of Electrical and Electronics Engineers (IEEE), one of the world’s most respected professional associations for engineers and technology researchers.

Recognition of Mironchenko’s work extends beyond the von Kaven Award. In 2023, he received the IEEE Control Systems Society’s George S. Axelby Outstanding Paper Award, highlighting his contributions to cutting-edge control research. More recently, in 2024, the University of Passau honored him with the Outstanding Habilitation Award for his exceptional post-doctoral thesis. These accolades underlie the high esteem in which his work is held by international peers.

The von Kaven Award itself is a distinguished prize intended to honor mathematicians progressing advanced research within the DFG’s Heisenberg or Emmy Noether Programmes. However, it can also be bestowed on remarkable researchers within the European Union upon recommendation. The award is administered by the DFG’s Mathematics review board and supported financially by a foundation established in 2004 by Herbert von Kaven, a mathematician dedicated to promoting foundational research in mathematics throughout his long life. Von Kaven passed away in 2009 at the age of 101, leaving a legacy of intellectual commitment encapsulated in this award.

At the core of Mironchenko’s contributions lies the philosophical and mathematical challenge of handling infinite dimensionality in real-world control settings. Infinite-dimensional systems arise naturally when the state of a system depends on functions or distributions over continuous spatial or temporal domains, rather than finite-dimensional vectors. Handling stability and control in such settings demands novel mathematical tools, often involving functional analysis, operator theory, and partial differential equations. Mironchenko’s work integrates these disciplines to not only model but also enhance the controllability and robustness of evolving systems.

As modern infrastructure continues to evolve towards interconnected and adaptive frameworks, controlling these infinite-dimensional models becomes essential for operational stability. Traffic systems capable of dynamically incorporating new roads and vehicles without failure; energy grids that adaptively manage fluctuating demand and supply across microgrids; these are testaments to the practical impact of theoretical advancements pioneered by researchers like Mironchenko. His developments enable the design of controllers resilient to network topology changes and environmental uncertainties, representing a transformative shift in systems engineering.

Furthermore, the interplay between theoretical mathematics and applied engineering in Mironchenko’s work exemplifies the growing synergy required to address complex 21st-century challenges. His achievements spotlight the pivotal role of abstract mathematical research in solving tangible global problems such as sustainable energy management and intelligent transportation systems. By bridging pure and applied mathematics, his research advances both foundational knowledge and technological innovation, ensuring a better, more adaptive future built on sound mathematical principles.

In the broader scientific community, these advances contribute to ongoing efforts to understand how complex systems behave under evolving conditions—a question central to disciplines from physics and biology to economics and social sciences. The frameworks Mironchenko develops may have far-reaching implications beyond engineering, influencing how researchers model and optimize complex, distributed systems in environments marked by change and uncertainty.

Overall, Dr. Andrii Mironchenko’s recent recognition with the von Kaven Award is a testament to the critical significance and growing impact of infinite-dimensional systems theory. His rigorous and visionary work extends the boundaries of control theory by accommodating the dynamic nature of modern networks and infrastructures, paving the way for more resilient and intelligent technological ecosystems worldwide.

Subject of Research: Mathematical systems theory, with a focus on infinite-dimensional systems and robust control mechanisms for dynamically changing networks.

Article Title: Dr. Andrii Mironchenko Awarded von Kaven Prize for Pioneering Research in Infinite-Dimensional Systems Control

News Publication Date: October 2024

Keywords: Mathematics, Mathematical logic, Mathematical analysis, Technology, Environmental sciences, Complex analysis

The cornerstone of their research focuses on the semantic structure information in backbone stages of Convolutional Neural Networks (CNNs). The principle behind this innovation lies in the observation that pixels belonging to the same object class tend to correlate strongly, particularly as the CNN deepens through its layers. This correlation often results in certain pixels within feature maps displaying enhanced brightness, indicating a higher resemblance to the marked pixels of interest. By leveraging this intrinsic characteristic, the researchers assert that it is possible to obtain CAMs of superior quality and robustness.

Employing an experimental study design, the team set out to devise a method that extends beyond the traditional use of CAM in weakly-supervised object localization and semantic segmentation. Their proposal, the Semantic Structure Aware Inference (SSA) model, introduces a mechanism that enhances object recognition capabilities and reinforces the overall quality of CAM outputs. The SSA model effectively integrates semantic structure information derived from multiple scales, enabling a more nuanced understanding of relationships among the detected objects.

Central to their findings is the utilization of SSM or the Semantic Structure Modeling module, integrated into various backbone stages of the CNN. This allows for the generation of semantic relevance representations that articulate the intricate relationships between different object classes within the images being processed. The researchers provided compelling evidence supporting their hypothesis, illustrated dramatically by visual examples where the stronger pixel correlations were evident at deeper network levels. These visual insights underpin the significance of semantic structure information, which not only deepens the understanding of object correlations but also enhances the interpretability of model predictions.

A notable advancement within this research is that the SSA model does not incur additional training costs, making its integration into existing frameworks significantly more feasible for developers. Initially, a seed CAM is generated using standard CNN architecture, which then undergoes refinement through the semantic structure modeling module. The dynamic fusion of CAMs produced from various backbone stages culminates in the final, enhanced CAM, representing an innovative stride toward achieving state-of-the-art performance in visual recognition tasks.

Moreover, this research sheds light on the critical role of semantic structures in deep learning, illustrating how by recognizing and incorporating these structures, one can significantly enhance generalization capabilities across various tasks. The methodology concocted by the authors opens up new avenues for future investigations, particularly in expanding the generalization abilities of their proposed model. This involves refining existing methods and augmenting representations to ensure that the model can adapt and perform robustly across diverse applications.

Looking forward, the team envisions further developments aimed at enriching the representation of semantic structures within their assessment frameworks. Enhancing the model’s capacity to generalize and function accurately irrespective of specific training conditions is a priority that they have set to impel the advancement of machine learning in the field of computer vision. This endeavor represents not only a pivotal shift in recognizing pixel-wise correlations but also signifies a substantial leap towards achieving higher accuracy and efficiency in various technological applications.

The implications of this research extend beyond theoretical advancements; they promise practical enhancements in real-world applications where machine learning serves a vital role. Significant improvements in semantic structures could potentially convert into more accurate outcomes in critical fields such as healthcare diagnostics, enhancing the capabilities of automated systems that depend heavily on intricate image analysis. In addition, these advancements could solidify the relevance of deep learning methods in areas like remote sensing and surveillance, where precise object localization denotes a crucial requirement.

In summary, Yanpeng Sun and Zechao Li’s exploration into semantic structure aware inference paves the way for a new era within computer vision. Their innovative approach to improving CAM represents not only a theoretical breakthrough but also establishes a robust foundation for practical applications. The SSA model embodies a significant stride toward unearthing the full potential of machine learning in recognizing complex object structures, assuring a promising future in the domain of artificial intelligence and its manifold applications across various sectors.

Subject of Research:
Article Title: SSA: semantic structure aware inference on CNN networks for weakly pixel-wise dense predictions without cost
News Publication Date: 15-Feb-2025
Web References: Frontiers of Computer Science
References: 10.1007/s11704-024-3571-9
Image Credits: Credit: Yanpeng SUN, Zechao LI

Keywords

Computer Science, Semantic Structure, Convolutional Neural Networks, Class Activation Mapping, Object Recognition, Weakly-Supervised Learning.

The method, called Cached Decentralized Federated Learning (Cached-DFL), moves beyond traditional approaches that rely on real-time interactions between vehicles to disseminate learned information. Conventional vehicle learning systems typically only allow for data exchange during brief encounters, which limits the potential for rapid adaptation to new conditions that the vehicle has not faced directly. Cached-DFL transcends these limitations by allowing vehicles to indirectly exchange valuable knowledge, facilitating a networked learning environment that enhances the overall intelligence of each vehicle within the system.

Imagine a self-driving car that has only navigated the bustling streets of Manhattan—Cached-DFL allows it to glean vital information about road conditions in Brooklyn without ever needing to drive there. This capability represents a paradigm shift in vehicle intelligence; the car can dynamically enhance its operational safety and efficiency based on experiences shared by other vehicles. The implications of this technology extend far beyond mere convenience; it has the potential to significantly improve the safety and reliability of autonomous driving systems.

At the heart of Cached-DFL is a decentralized approach to model training. Vehicles are designed to build their AI models locally, which eliminates the need for a centralized server to coordinate updates. Instead, when vehicles come within a 100-meter range of one another, they utilize high-speed device-to-device communication to exchange trained models rather than transferring raw data. This system is a game-changer for privacy, as it enables vehicles to share insights while keeping sensitive data secure. Moreover, the method allows cars to relay models they have received from previous encounters, spreading knowledge across a vast network of vehicles, regardless of their direct interactions.

The researchers conducted simulations using an accurate model of Manhattan’s road layout, allowing them to test the effectiveness of their approach under realistic conditions. Virtual vehicles were programmed to navigate the city at a speed of approximately 14 meters per second, making probabilistic turns at intersections to mimic real-world driving behavior. What emerged from these simulations was a powerful demonstration of Cached-DFL’s capability to facilitate multi-hop learning across vehicles, a feature that significantly amplifies the potential for knowledge transfer in urban environments.

This relay mechanism is analogous to information dissemination in social networks, whereby devices can share insights gleaned from other encounters, enhancing the overall learning capacity of the fleet. By breaking free from the limitations of one-to-one vehicle interactions, Cached-DFL fosters a robust learning ecosystem in which knowledge can flow freely and efficiently from vehicle to vehicle—even if no direct encounter was ever made. This improvement could not only bolster road safety but could also enhance operational efficiencies in environments marked by complex and ever-changing conditions.

The experiments indicated that several factors impact the efficiency of learning, including vehicle speed, cache size, and the expiration of stored models. Notably, faster speeds and more frequent interactions produced better results, while older models adversely affected accuracy. A strategic approach to caching, designed to prioritize a diverse range of models over merely the most recent, further improved the efficacy of the system. This finding underscores the importance of maintaining a varied cache, allowing vehicles to learn from a broader array of experiences rather than being confined to a narrow dataset.

As the landscape of artificial intelligence continues to evolve, the shift from centralized learning models to edge devices such as autonomous vehicles becomes increasingly important. Cached-DFL exemplifies this shift by providing a model that not only enhances efficiency but also fortifies security. This framework can also find applications in other domains, where multiple smart mobile agents, such as drones or robotic systems, require collective intelligence for optimal performance.

The research underscores a broader trend within the scientific community, where decentralization and privacy are becoming paramount. As vehicles continue to learn from road experiences without extensive data sharing, the potential for safer and more reliable autonomous systems becomes more attainable—a goal that has driven engineers and researchers for years. This means that, as connected vehicles grow smarter, they can better navigate the intricacies of urban environments, respond proactively to road hazards, and really revolutionize the way we view transportation in the modern age.

The technical foundation of Cached-DFL has been thoroughly documented, with the research team providing access to their project’s code in an effort to promote transparency and collaborative improvement. Participating institutions, including NYU Tandon School of Engineering and collaborators from Stony Brook University and New York Institute of Technology, have laid the ground for future advancements in decentralized learning. The transition towards such innovative technologies is supported by several funding agencies, demonstrating a commitment to fostering cutting-edge research that holds promise for real-world applications.

The strength of Cached-DFL lies not just in its potential to improve self-driving technology but also in how it exemplifies the shift towards decentralized systems in artificial intelligence. Moving forward, vehicles and other smart agents will benefit from this collaborative learning framework, paving the way for scenarios where technology not only elevates individual performance but also enhances collective capabilities. This exciting development marks a new chapter in the quest for safer, more intelligent vehicle systems that can adapt seamlessly to the challenges of the modern transportation landscape.

As research and experimentation continue, the implications of Cached-DFL will undoubtedly catalyze advancements across various domains, potentially contributing to the larger goal of developing swarm intelligence in networked systems. This will have far-reaching effects for autonomous vehicles, robotics, drones, and other smart agents, coinciding with an era where artificial intelligence thrives on communication and collective knowledge accumulation.

In sum, the implications of Cached-DFL extend beyond self-driving cars; they contribute a vital chapter in the ongoing narrative of artificial intelligence’s evolution towards decentralized, privacy-focused systems capable of robust learning and adaptation. The journey ahead is bound to be as thrilling as the technology itself, with possibilities that stretch well into the future of advanced computing and autonomous capabilities.

Subject of Research: Cached Decentralized Federated Learning for autonomous vehicles
Article Title: NYU Researchers Pioneer New Method to Enhance Learning in Autonomous Vehicles
News Publication Date: February 27, 2025
Web References: arXiv Paper, GitHub Repository
References: National Science Foundation grants, RINGS program, NYU’s computing resources
Image Credits: NYU Tandon School of Engineering

Keywords: Autonomous Vehicles, Federated Learning, Decentralized Systems, AI Privacy, Collective Intelligence