Flight trajectory prediction(FTP) with high precision is the core technology for the autonomous flight of quadrotor unmanned aerial vehicles (UAVs) in environments with limited navigation signals. In response to the problem that most existing methods focus on the features of a single domain and ignore the cross-domain feature correlation, making it challenging to maintain high accuracy in FTP, a model based on Time-Frequency-Spatial Network named TiFSN is proposed. Firstly, based on wavelet transform (WT), the velocity signal is extended to time-frequency joint features. Furthermore, a fusion mechanism is established to construct a multi-domain feature set. Finally, an extended channels-based temporal convolutional network (EC-TCN) is designed, which achieves high-precision FTP. Experiments were conducted on real flight datasets, and the results show that the model significantly improved the evaluation metrics compared to baseline methods.

In this paper, we investigate how polling systems with intelligent policies perform compared to the optimal service policy. To this end, we introduce two new policies: an intelligent server idling policy incorporating information about shortterm future arrivals, and an informed switching policy that uses queue lengths to switch to the next non-empty queue. For benchmarking purposes, the optimal service schedule is obtained by formulating the problem as a Mixed Integer Program, with full arrival information. Our results show that with these intelligent policies, polling systems significantly improve in performance, thereby narrowing the gap between polling policies and optimal schedules.

There has been a growing interest in strategies to optimize the cost-performance tradeoff of LLMs, in particular LLM selection, where the aim is to understand which language models perform better than others for a given task. However, there arises some applications where simply finding the LLM which performs the best on the task is not sufficient as the task may be too specialized and difficult for an LLM to handle alone. In such cases considering a pipeline of LLMs seems to be more suitable to decrease the difficulty of the task by breaking it down into smaller subtasks where an LLM's output is fed as the input to another LLM. The task of suggesting diagnosis for patients based on provided medical reports of patients which are lengthy and include medical language is such an application. We believe that for such a task, the LLMs can benefit from being given a summarized version of the medical report (summary obtained through another LLM) which highlights the key points and necessary information which can guide diagnosis decisions.

Maintaining low delay is an aim of virtually every computer system, but any system with limited resources inevitably runs into queueing delays when load is high. Queueing delays occur at every level of abstraction, e.g. packet flows wait at network switches, queries wait in databases, and large-scale simulations wait for supercomputer time. One of the main tools we have to combat delay is load balancing, i.e. dispatching jobs (e.g. packet flows, queries, simulations) across servers (e.g. network switches, databases, supercomputers) to reduce queueing. The main metrics one evaluates load balances with are tail delays, or P[T > x] for large x, where T is the response time. This formalizes the goal of having few jobs experience large delays, which is a realistic goal for many computer systems practitioners.

To meet the growing demand for wireless services with diverse throughput, latency, and reliability requirements, network providers employ virtualization to create multiple virtual networks over a shared physical infrastructure. Network slicing (NS) builds on this by partitioning the infrastructure into logically isolated, end-to-end slices, each tailored for specific use cases. Leveraging software-defined networking (SDN) and network function virtualization (NFV), these slices can dynamically allocate computing, storage, and networking resources across the radio access, transport, and core networks. As systems evolve toward multi-domain, multi-technology architectures integrating terrestrial, aerial, and satellite components, NS must intelligently allocate radio, computing, and storage resources to diverse applications- including virtual, augmented, and mixed reality, autonomous vehicles, smart cities, Industry 4.0, digital twins, telemedicine, immersive education, and intelligent transportation- across heterogeneous infrastructures, while addressing device mobility and dynamic channel conditions. This work evaluates a novel NS framework that dynamically manages resources across fog, edge, and cloud layers within terrestrial domains and adapts allocation between terrestrial and nonterrestrial domains to improve reliability and reduce outages. The framework incorporates resilience and covert communication mechanisms for enhanced security in heteroge-neous IoT scenarios. Simulations show improved reliability, lower latency, and strong security, enabling effective support for diverse use cases in integrated multi-domain networks.

Federated Learning (FL) enables collaborative model training across distributed clients while preserving data privacy. However, practical FL systems face challenges due to partial client participation and device heterogeneity, which introduce significant variance in training updates and hinder model convergence. Existing methods naïvely reuse stale updates from inactive clients to stabilize training without accounting for varying levels of staleness or optimizing the balance between fresh and stale updates to minimize training variance. To address these issues, we propose FedVarM, a novel method that optimally leverages stale updates by adjusting client aggregation coefficients to minimize training variance. Additionally, we introduce FedVarM-Approx, an efficient approximation without extra computation on the client-side to ensure its efficient deployment. Experiments demonstrate FedVarM achieves 17.4% higher accuracy compared to methods that naïvely reuse stable updates without optimal balancing.

Large Language Models (LLMs) offer unprecedented language understanding and generation capabilities. However, these advancements come at a cost. LLMs rely heavily on high-performance computing, not only for training but also for inference. This dependence translates into substantial energy consumption, with inference potentially exceeding the already significant environmental impact of training, which can generate thousands of tons of CO2eq. Therefore, quantifying the energy consumption of LLM inference is crucial. This research focuses on measuring this energy use across a wide range of transformer models, from smaller architectures to cutting-edge models like DeepSeek V3/R1. We utilize state-of-the-art inference frameworks and high-end GPUs to ensure accurate assessments.

His research is centered on sequential decision-making under uncertainty, aiming both to deepen theoretical understanding of decision-making with realistic feedback and to enhance practical performance in advanced application domains-especially multi-agent systems and quantum networks. His work was recognized as a Best Paper Finalist at SIGMETRICS 2025.

We frame community detection as a hedonic game where nodes face "frustrated choices" between maximizing internal links and minimizing internal non-links. The resolution parameter balances these objectives, and the framework ensures any sequence of selfish moves converges to a stable equilibrium, viewing community structure as the outcome of a strategic game.

My research focuses on designing resource-efficient, intelligent, and reliable systems that span the cloud-to-edge continuum and built environments. My primary methodology is to leverage the flexibility of computing workloads to design efficient and dynamic systems that adapt to the real world's dynamics. By integrating insights from systems, machine learning, theory, and optimization, my work aims to reimagine and establish foundational design principles to build efficient, agile, and reliable systems. Utilizing this methodology enabled me to make fundamental contributions to system design and resource management algorithms, optimizing the carbon emissions of AI training and inference workloads, as well as AI servicing systems for resource-constrained edge environments.