We can hypothesize that Bayesian optimization using a Gaussian Process surrogate model can dynamically predict and mitigate mmWave frequency-selection spoofing attacks by detecting anomalies in the joint distribution of sensed rainfall data and per-band propagation loss.

Adversarial Debate Score

50% survival rate under critique

Supporting Research Papers

• Bayesian Optimization with Gaussian Processes to Accelerate Stationary Point Searches

  Accelerating the explorations of stationary points on potential energy surfaces building local surrogates spans decades of effort. Done correctly, surrogates reduce required evaluations by an order of...

• Bayesian Feature Extraction using Gaussian and Diffused-gamma Priors for High Dimensional Spatio-Temporal Data

  High-dimensional data with sparse structure and spatio-temporal dependence arise in many scientific domains. We develop a Bayesian feature-extraction framework for spatio-temporal settings that employ...

• Sequential Inference for Gaussian Processes: A Signal Processing Perspective

  The proliferation of capable and efficient machine learning (ML) models marks one of the strongest methodological shifts in signal processing (SP) in its nearly 100-year history. ML models support the...

• Compressing radio interferometric visibility data into a probabilistic model using sparse Gaussian processes

  Next-generation radio interferometers will produce massive data volumes, making it impractical to store original visibility measurements and later combine observations in uv spatial frequency space. V...

• Data-Efficient Generative Modeling of Non-Gaussian Global Climate Fields via Scalable Composite Transformations

  Quantifying uncertainty in future climate projections is hindered by the prohibitive computational cost of running physical climate models, which severely limits the availability of training data. We ...

Formal Verification

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