VMware Intern - VMware Research Group - Resource Efficient Statistical Machine Learning - Opportunity for Working Remotely in Boston, Massachusetts
Come be part of VMware’s Research Group (VRG)! With a dedicated research group, VMware continues its long tradition of research focus and academic reach. We continue VMware's history of ground-breaking, technological innovation and a culture of technical leadership and market disruption. We aim to build a community of deep and substantial relationships between academic research and the rest of the company and to maintain deep interaction and collaboration by recruiting premier researchers who are keen on driving impact.
Job Role and Responsibilities
We seek students who are passionate about machine learning, systems and solving challenging real-world problems. In this position, you’ll conduct research on improving the system and machine learning performance of well-known machine learning models, such as federated and distributed ML, and neural networks.
Machine Learning systems require guarantees to adhere to necessary resource efficiency constraints. For instance, federated learning requires low bandwidth; anomaly detection over edge devices requires fast classification and low memory footprint; cloud systems require fast training to reduce costs.
In this project, we are looking into efficiency from compute and network perspectives.
The increase in available information and complexity of ML models results in excessive compute resource consumption.
It is therefore of major importance to re-examine and better understand the trade-offs of an ML system from compute-efficiency perspective.
Distributed and federated learning systems are key enablers of global, scalable and fair learning procedures.
However, these systems require significant network resources that threaten the feasibility of the solutions.
Therefore, acquiring a better understanding of the inherent ML performance and network efficiency tradeoffs of different distributed and federated learning systems is of major importance.
Such understanding can help to reason about better architectures and algorithms for distributed and federated learning.
More information on the body of work this project belongs to can be found here:
Pursing a PhD degree in Computer Science or other related concentration.
You have extensive knowledge of machine learning and systems.
Ability to work independently.
Ability to generate new ideas and innovate in an academic research setting.
Strong teamwork and communication skills.
Peer-reviewed research publication(s) at top conferences.
Experience in Python is a plus (scikit-learn, TensorFlow, Pytorch).
Prior experience working with Tensorflow Federated (TFF)
Willingness to contribute to open-source projects is a plus.
Previous industry experience is a plus.
Category : University and Recent Grads
Full Time/ Part Time: Full Time
Posted Date: 2021-04-05
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