Session 1: Linear Algebra Session 2: Calculus and Session 3: Distributions Session 4: Probability Session 5: Inference Session 6: Bayesian Statistics read more about MSS Math/Stat Bootcamp »
Please join us on Friday, July 29 at 2:00 p.m. in the Gross Hall Energy Hub (first floor) and 3rd floor iiD atrium for the Plus Programs 2022 Poster Session!Plus Programs has continued to grow, and… read more about PLUS PROGRAMS POSTER SESSION »
The Statistical Science Department invites all to attend the defense of this dissertation. read more about New Tools for Bayesian Clustering and Factor Analysis »
The Statistical Science Department encourages all to attend this dissertation defense. read more about Tree-Based Methods for Learning Probability Distribution »
The Statistical Science Department encourages all to attend the defense of this dissertation. read more about Bayesian Modeling for Annual Abundance in Ecological Communities Incorporating Zero-Inflation »
Gelman and Rubin's (1992) convergence diagnostic is one of the most popular methods for terminating a Markov chain Monte Carlo (MCMC) sampler. Since the seminal paper, researchers have developed… read more about Revisiting Gelman-Rubin with Global Centering »
This talk will introduce two new tools for summarizing a probability distribution more effectively than independent sampling or standard Markov chain Monte Carlo thinning:1. Given an initial n… read more about Kernel Thinning and Stein Thinning »
A consistent theme of the work done in my lab group is to take modern computational approaches and develop theory that enable their interpretations to be related back to classical genomic principles… read more about Variable Selection and Prioritization in Bayesian Machine Learning Methods »
It has been estimated that nearly 70% of individuals will experience signs and symptoms of impostor syndrome at least once in their life. Typically affecting high achievers, imposter syndrome (IS)… read more about I’m a Fraud: Kicking Imposter Syndrome to the Curb »
In recent years, samples of time-varying object data such as time-varying networks that are not in a vector space have been increasingly collected. These data can be viewed as elements of a general… read more about Functional Models for Time Varying Random Objects »
Kinaxis has been on the Leader board of Gartner for Supply Chain Technology and Software with breakthroughs such as concurrent planning. Today, we will present some innovative machine learning driven… read more about Machine Learning Applications in Supply Chain Problems »
In many high-dimensional statistical problems, it is necessary to simultaneously discover signals and localize them as precisely as possible. For instance, genetic fine-mapping aims to discover… read more about Controlled Discovery and Localization of Signals via Bayesian Linear Programming »
The Statistical Science Department invites all to attend the defense of this virtual dissertation. read more about Euler Integration with Applications to Statistical Shape Analysis and Imaging »
We will go over the challenges of building Hortifrut's Data Analytics team, with special emphasis on structuring models, data quality problems and user engagement and communication. Two successful… read more about Analytics at Hortifrut: Challenges and applications in the Blueberry industry »
Deep learning excels with large-scale unstructured data - common across many modern application domains - while probabilistic modeling offers the ability to encode prior knowledge and quantify… read more about Bridging the Gap Between Deep Learning and Probabilistic Modeling »
Sorted L1 regularization has been incorporated into many methods for solving high-dimensional statistical estimation problems, including the SLOPE estimator in linear regression. In this talk, we… read more about Characterizing the Type 1-Type 2 Error Trade-off for SLOPE »
Dr. Gunes will go over a machine learning approach that her team took to forecast daily COVID total confirmed cases and fatalities worldwide for a Kaggle competition. This will cover using… read more about Machine Learning Approach to Kaggle COVID 19 Global Forecasting Challenge »
In recent years, reinforcement learning algorithms have achieved strong empirical success on a wide variety of real-world problems. However, these algorithms usually require a huge number of samples… read more about Towards a Foundation for Reinforcement Learning »
The Statistical Science Department encourages all to attend the defense of this dissertation. read more about Community Structure in Social Networks: Detection, Heterogeneity and Experimentation »
Modern machine learning (ML) methods commonly postulate strong assumptions such as: (1) access to data that adequately captures the application environment, (2) the goal is to optimize the objective… read more about How to Handle Biased Data and Multiple Agents in Machine Learning? »
This week's department seminar will feature talks from Statistical Science undergraduate students involved in research. Each of the four student speakers will have 5-7 minutes to present their… read more about StatSci Undergraduate Research Presentations »
Advanced digital technologies rely on collecting and processing various types of sensitive data from their users. These data practices could expose users to a wide array of security and privacy risks… read more about Empowering People to Have Secure and Private Interactions with Digital Technologies »
In this workshop, with the right mindset and voice and self-awareness that we have developed from the fall workshop, we will be ready to delve into the actual strategies that are acceptable and part… read more about Communicating Effectively in a Professional Setting »
Machine learning (ML) is widely used today, ranging from applications in medicine to those in autonomous driving. Across all these applications, various forms of sensitive information is shared with… read more about Information Leakage in ML Deployments: How, When, and Why? »
The Statistical Science Department encourages all to attend the defense of this dissertation. read more about Advances in Choquet Theories »