Bayesian optimization (BO) has recently emerged as a powerful and flexible tool for hyper-parameter tuning and more generally for the efficient global optimization of expensive black box functions. Systems implementing BO has successfully solved difficult problems in automatic design choices and machine learning hyper-parameters tuning. Many recent advances in the methodologies and theories underlying Bayesian optimization have extended the framework to new applications and provided greater insights into the behavior of these algorithms. In this paper, we summarize our recent research in Bayesian optimization, highlight our contribution and present future research directions.

Scaling Bayesian optimization to high dimensions is challenging task as the global optimization of high-dimensional acquisition function can be expensive and often infeasible. Existing methods depend either on limited “active” variables or the additive form of the objective function. We propose a new method for high-dimensional Bayesian optimization, that uses a drop-out strategy to optimize only a subset of variables at each iteration. We derive theoretical bounds for the regret and show how it can inform the derivation of our algorithm. We demonstrate the efficacy of our algorithms for optimization on two benchmark functions and two real-world applications - training cascade classifiers and optimizing alloy composition.

Quantum devices with a large number of gate electrodes allow for precise control of device parameters. This capability is hard to fully exploit due to the complex dependence of these parameters on applied gate voltages. We experimentally demonstrate an algorithm capable of fine-tuning several device parameters at once. The algorithm acquires a measurement and assigns it a score using a variational auto-encoder. Gate voltage settings are set to optimize this score in real-time in an unsupervised fashion. We report fine-tuning times of a double quantum dot device within approximately 40 min.

Accumulating evidence from human-based research has highlighted that the prevalent one-size-fits-all approach for neural and behavioral interventions is inefficient. This approach can benefit one individual, but be ineffective or even detrimental for another. Studying the efficacy of the large range of different parameters for different individuals is costly, time-consuming and requires a large sample size that makes such research impractical and hinders effective interventions. Here an active machine learning technique is presented across participants—personalized Bayesian optimization (pBO)—that searches available parameter combinations to optimize an intervention as a function of an individual’s ability. This novel technique was utilized to identify transcranial alternating current stimulation (tACS) frequency and current strength combinations most likely to improve arithmetic performance, based on a subject’s baseline arithmetic abilities. The pBO was performed across all subjects tested, building a model of subject performance, capable of recommending parameters for future subjects based on their baseline arithmetic ability. pBO successfully searches, learns, and recommends parameters for an effective neurointervention as supported by behavioral, simulation, and neural data. The application of pBO in human-based research opens up new avenues for personalized and more effective interventions, as well as discoveries of protocols for treatment and translation to other clinical and non-clinical domains.

In recent years, nanoparticles have been highly investigated in the laboratory. However, only a few laboratory discoveries have been translated into clinical practice. These findings in the laboratory are limited by trial-and-error methods to determine the optimum formulation for successful drug delivery. A new paradigm is required to ease the translation of lab discoveries to clinical practice. Due to their previous success in antiviral activity, it is vital to accelerate the discovery of novel drugs to treat and manage viruses. Machine learning is a subfield of artificial intelligence and consists of computer algorithms which are improved through experience. It can generate predictions from data inputs via an algorithm which includes a method built from inputs and outputs. Combining nanotherapeutics and well-established machine-learning algorithms can simplify antiviral-drug development systems by automating the analysis. Other relationships in bio-pharmaceutical networks would eventually aid in reaching a complex goal very easily. From previous laboratory experiments, data can be extracted and input into machine learning algorithms to generate predictions. In this study, poly (lactic-co-glycolic acid) (PLGA) nanoparticles were investigated in antiviral drug delivery. Data was extracted from research articles on nanoparticle size, polydispersity index, drug loading capacity and encapsulation efficiency. The Gaussian Process, a form of machine learning algorithm, could be applied to this data to generate graphs with predictions of the datasets. The Gaussian Process is a probabilistic machine learning model which defines a prior over function. The mean and variance of the data can be calculated via matrix multiplications, leading to the formation of prediction graphs—the graphs generated in this study which could be used for the discovery of novel antiviral drugs. The drug load and encapsulation efficiency of a nanoparticle with a specific size can be predicted using these graphs. This could eliminate the trial-and-error discovery method and save laboratory time and ease efficiency.