In this lecture we present an overview of dynamic linear models for analysis and forecasting of univariate time series. We will discuss principles for model building and methods for Bayesian filtering, smoothing and forecasting. We will illustrate the use of these models in several case studies arising in different applied areas including environmental sciences and neuroscience.

Differential Privacy (DP) allows for rich statistical and machine learning analysis, and is now becoming a gold standard for private data analysis. Despite the noticeable success of this theory, existing tools from DP are severely limited to regular datasets, e.g., datasets need to be or are assumed to be clean and normalized before performing DP algorithms.

Continuing on with Lecture 1, this short course introduces various cutting-edge methods that handle survival outcome data with ultrahigh dimensional predictors, that is, when the dimension of predictors is much higher than the sample size. We will also discuss several new methods for quantifying the uncertainty of estimates in a high dimensional survival setting, a very active area in machine learning.

This talk focus on advanced modeling and optimization techniques to solve complex problems in the scheduling and planning of processes, energy systems, and water systems. Modeling and optimization techniques integrated with other disciplines have a paramount role in supporting the energy transition planning and the water-energy nexus. Specifically, optimizing the integration of renewable energy sources and new energy carriers in the energy mix, increasing the efficiency of existing and novel systems (enterprise-wide, industry, buildings, supply chains), and supporting decarbonization in industry. First, we describe canonical optimization formulations and show their flexibility and capabilities to address complex optimization problems. Examples of applications include process synthesis, production scheduling, short-term hydro scheduling, unit commitment, and even the transfer of human resources within large organizations. Then we focus on applying stochastic optimization approaches to address a virtual power plant's self-scheduling and market involvement, and finally on applying advanced optimization strategies for trajectory planning of underwater vehicles in uncertain and transient flow fields.

In social dilemmas, a class of multi-agent games, agents' rationality-based strategic interactions to learn a payoff-maximizing strategy would result in diminishing returns. Such games include the prisoner's dilemma and public goods game where individually rational decision making reults in all decision-making agents receiving smallest rewards. In this presentation, I will explain a new decision-making model that elicits cooperative behavior in social dilemmas. The model enables the social interaction (reciprocity) between agents in their decision making, which allows cooperative behavior to emerge. We discuss how methods for feedback system design and analysis can be applied to explain the emergence of cooperative behavior and how we can tune such behavior.

Though Gaussian graphical  models have been widely used in many scientific fields, relatively limited progress has been made to link graph structures to external covariates. We propose a Gaussian graphical regression model, which regresses both the mean and the precision matrix of a Gaussian graphical model on covariates. In the context of co-expression quantitative trait locus (QTL) studies, our method can determine how genetic variants and clinical conditions modulate the subject-level network structures, and recover both the population-level and subject-level gene networks.

Mixing is a key process in a wide variety of technological applications. Pharmaceuticals, consumer products, and oil & gas derivatives, among many other products, rely on an energy-efficient, robust and effective mixing process. We will formulate the mixing process as a PDE-constrained optimization problem using special norms that concentrate on the mixedness of a passive scalar transported by nonlinear governing equations.

In this thesis, energy-efficient Micro-/Nano-electromechanical (M/NEM) relays are introduced, their nonleaking property and abrupt switch ON/OFF characteristics are studied, designs and applications in the implementation of ultra-low-power integrated circuits and systems are explored. The proposed designs compose of core building blocks for any functional microprocessor, for instance, fundamental logic gates; arithmetic adder circuits; sequential latch, and flip-flop circuits; input/output (I/O) interface data converters, including an analog-to-digital converter (ADC) and a digital-to-analog converter (DAC); system-level power management DC-DC converters and energy management power-gating scheme. Another contribution of this thesis is the study of device non-ideality and variations on the functionality of circuits. We have thoroughly investigated energy-efficient approximate computing with non-ideal transistors and relays for the next generation of ultra-low-power VLSI systems.

In the era of biomedical big data, survival outcome data with high-throughput predictors are routinely collected. These high dimensional data defy classical survival regression models, which are either infeasible to fit or likely to incur low predictability because of overfitting. This short course will introduce the basic concepts of survival analysis and various cutting-edge methods that handle survival outcome data with high dimensional predictors. I will cover statistical principles and concepts behind the methods, and will also discuss their applications to the real medical examples.

The power system is facing unprecedented changes in operation and control as more and diverse sources and loads are being connected to this complex cyber-physical energy system.

The advancement of silicon semiconductor technology since the 1940s has been changing the world by redefining human living style and addressing global challenge of energy shortage. Since the 1960s, GaAs, InP research started to bring human beings into modern life with mobile communications and high-speed networks. When it goes to the 1980s, wide band gap semiconductors including GaN and SiC which have high breakdown field and high mobility, were demonstrated into high-power electronics as well as solid-state lighting.

A personal presentation describing current “data analysis / statistical” challenges in state-of-the art biomedical projects. First, I will provide an overview of the transition between a PhD in Mathematics to a postdoc in Computational Biology: How did it happen? What were the challenges? Secondly, I will briefly present several current case-studies where statistics and machine learning are core to understand novel biological data related to multi-omic data analysis, spatial profiling, gene therapy and more.

Randomized experiments are the gold standard for causal inference. In experiments, usually, one variable is manipulated and its effect is measured on an outcome. However, practitioners may also be interested in the effect of simultaneous interventions on multiple covariates on a fixed target variable.

Sakhaa Al-Saedi: We conduct a systematic genetic analysis of risk variants related to increasing the severity of COVID-19. It leads to a better understanding of its genetic basis and identifies the host genes to be targeted to tackle the COVID-19 pandemic and reduce its death toll. Azza Althagafi: We developed DeepSVP, a computational method to prioritize structural variants involved in genetic diseases by combining genomic and gene functions information. DeepSVP significantly improves the success rate of finding causative variants in several benchmarks and can identify novel pathogenic structural variants in consanguineous families.

Geospatial health data are essential to inform public health and policy. These data can be used to quantify disease burden, understand geographic and temporal patterns, identify risk factors and measure inequalities. In this talk, I will give an overview of statistical methods and computational tools for geospatial data analysis and health surveillance.

The aim of this course is to introduce some numerical methods to solve mean field games and related problems.

The aim of this course is to introduce some numerical methods to solve mean field games and related problems.

Recognizing and localizing human activities in long-form untrimmed video is one of the most important applications of computer vision. This remains a difficult problem given the many challenges inherent to untrimmed video ranging from its large-scale nature to the need for rich video representation and context modeling. In an effort to better understand this problem, ActivityNet was proposed in 2015 to provide a large-scale benchmark to train and evaluate activity localization methods. Since then, it has become a standard in the community, and its annual workshop at CVPR has become a popular venue for research groups to compete and present their newest approaches. Despite the advances in the past several years, the performance of activity localization methods in video remains limited, especially in comparison with human performance. In this colloquium, I will give a compact view of the progress made in the task of temporal activity localization, identify some key remaining challenges, and present some future directions.

Wearable, fast-response, and efficient displays are suitable to enjoy the AR and VR world.

The aim of this course is to introduce some numerical methods to solve mean field games and related problems.

The aim of this course is to introduce some numerical methods to solve mean field games and related problems.

Two or multiple phases in fluid mixture commonly occur in petroleum industry, where oil, gas and water are often produced and transported together.  Petroleum reservoir engineers spent great efforts in drainage problems arising from the development and production of oil and gas reservoirs so as to obtain a high economic recovery, by developing, conducting, and interpolating the simulation of subsurface flows of reservoir fluids, including water, hydrocarbon, CO2, H2S for example in porous geological formation.

The aim of this course is to introduce some numerical methods to solve mean field games and related problems. Mean Field Games (MFGs) systems were introduced independently by [4] and [5] in order to model dynamic games with a large number of indistinguishable small players. In the model proposed in [5], the asymptotic equilibrium is described by means of a system of two Partial Diferential Equations (PDEs).

The aim of this course is to introduce some numerical methods to solve mean field games and related problems. Mean Field Games (MFGs) systems were introduced independently by [4] and [5] in order to model dynamic games with a large number of indistinguishable small players.

Finite element approximation of Stokes equations with non-smooth data