Machine Learning for Networking/Robotics/Healthcare/Manufacturing Applications:

Reinforcement Learning for Network Resource Allocation:

Reinforcement Learning has been increasingly used for efficient resource allocation. However, there are multiple challenges in networking applications that make the reinforcement learning tools not applicable. Some examples include multiple agents where the fairness objective causes non-linearity, scaling to large networks leads to efficient solutions splitting the network, constraints in decision making, etc. In this theme, we propose novel algorithms to take these aspects into account for efficient algorithms for traffic engineering, power control, and caching decisions.

Teleoperated Robotic Surgery via Transfer Learning:

In austere environments, teleoperated surgical robots could save the lives of critically injured patients if they can perform complex surgical maneuvers under limited communication bandwidth. The bandwidth requirement is reduced by transferring atomic surgical actions (referred to as “surgemes") instead of the low-level kinematic information. While such a policy reduces the bandwidth requirement, it requires accurate recognition of the surgemes. In this work, we demonstrate that transfer learning across surgical tasks can boost the performance of surgeme recognition.

Machine Learning for Manufacturing:

Today's manufacturing starts with CAD models. The CAD model carries all the geometrical information of the workpiece (e.g., surface position, size, fluctuations etc.) and influences the entire planning for manufacturing processes. Due to the complexity of an engineering CAD model, the manufacturing industry usually decomposes the CAD model into several geometrical features - called the machining features. The machining features serve certain functions of the final product and also require typical machining processes. This CAD model decomposition and feature-recognition processes together are called machining feature identification, which is the focus of our work, where efficient data representation techniques and machine learning approaches are used.

Machine Learning for Healthcare:

Using efficient learning based techniques are essential for predicting risk in individuals. In many cases, passive face videos can be used to predict the health risks. We have used learning based techniques for early prediction of Sepsis, prediction of lifting load risk, force exertions, and health monitoring.

Applications of Exploiting Data Structure in Matrix/Tensor for Better Sampling, Fingerprinting, and Estimation:

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Exploiting structure in the data across multiple dimensions can lead to efficient techniques for efficient sampling and fingerprinting. The figure alongside shows different network service atributes dependence on time, spatial locations and data service quality measurements thus motivating tensor structure. Further, the map shows the received signal strength in indoor has spatial dependence and the signals from different APs are correlated and thus adaptive sampling can reduce fingerprints needed for localization.

Exploiting Network Structure for Better Scheduling Strategies:


Learning the network characteristics is crucial for coming up with next generation network designs. Getting the measurements of the received power from a tower leads to path loss which helps in design of efficient codes. Fingerprinting roads can help find efficient scheduling/handoff decisions for a service provider. Learning attributes at the network helps learn interaction between different layers to achieve better designs. The distribution of interference can lead to better interference management techniques. This understanding will be useful to achieve new and better system designs. The figure alongside depicts a mobile user trajectory along a road through 3 cellular sectors showing interactions with the cellular network and the measured values of channel quality (Ec/Io) during 3 different drives. This structure has been used to novel scheduling strategies with prediction.

Modeling Application QoE from Passive Network Measurements:


Video and web applications are the most prominent applications in today's world. It is important to understand how radio network characteristics (such as signal strength, handovers, load, etc.) influence users' Quality-of-Experience (QoE). Understanding the relationship between QoE and network characteristics is a pre-requisite for cellular network operators to detect when and where degraded network conditions actually impact QoE. Unfortunately, cellular network operators do not have access to detailed server-side or client-side logs to directly measure QoE metrics, such as abandonment rate, stalls, and video/session length. We devise a machine-learning-based mechanism to infer web QoE metrics from network traces accurately. The image alongside depicts partial web downloads as a function of network attributes.