Title: Data Structures for Computational Geometry
Title: Algorithms for learning and testing high-dimensional statistical and causal relations
New local differentially private protocols for frequency and mean estimation
Speaker: Jelani Nelson
Time and Location: Monday April 3 1-2pm in 3043 Coover
Resource management significantly impacts the performance and availability of cloud infrastructure. In the first part of my talk, I will introduce my work on managing memory, cache, and power resources to boost the performance and availability of cloud infrastructure. On memory resources, we propose UH-MEM, a utility-based data placement technique for hybrid memory.
The Midwest Big Data Summer School will be held May 16th - 19th, 2022 at Iowa State University, on campus. This is an in-person event. More details will be coming soon!
Abstract:
Joseph Sifakis, Verimag Laboratory
Abstract:
TADS Lunch-n-Learn
Abstract:
Abstract:
Abstract:
Abstract:
Abstract:
Abstract:
Abstract:
Abstract:
Abstract:
Abstract:
Abstract:
Artificial intelligence (AI) technologies have become increasingly pervasive, offering both promises and perils. In response, researchers and
organizations have been working to publish principles for the responsible use of AI. To bridge from these principles to responsible AI practice, in this talk, I will introduce
Abstract:
Abstract:
Bio:
Abstract:
Abstract:
Abstract:
Biography:
Daniela Witten is a coauthor of the popular text Introduction to Statistical Learning by James, Witten, Hastie, and Tibshirani. Since earning her PhD at Stanford in 2010, Professor Witten has had many impressive accomplishments. At the 2018 Women in Data Science Conference, Professor Witten gave a lecture watched by 100,000 via livestream.
Abstract, via Professor Witten:
About
Instance-Optimal Compressed Sensing via Conditional Resamplin
Presenter: Agus Sudjianto
Title: Managing Machine Learning Risk: Interpretability and Robustness
Presenter: Dr. Cynthia Rudin
Title: The Extremes of Interpretability in Machine Learning: Sparse Decision Trees, Scoring Systems and Interpretable Neural Networks
Presenter: Dr. Aurore Delaigle
Title: Estimating a Covariance Function from Fragments of Functional Data
Presenter: Dr. Ali Anwar
Title: Analyze and rebuild: Redesigning distributed computing systems for the next killer app
Boa is a domain-specific language and infrastructure that eases mining software repositories. Boa's infrastructure leverages distributed computing techniques to execute queries against hundreds of thousands of software projects very efficiently. Boa has existed for almost 7 years with a large user base of more than 1,200 users in all over the world.
Presenter: Laura Miller
Title: Integrate structural analysis, isoform diversity, and interferon-inductive propensity of ACE2 to predict SARS-CoV-2 susceptibility in vertebrates
Presenter: Dan Gianola
Title: David vs. Goliath: the COVID-19 epidemic in Uruguay in a regional and world context
Presenter: Dr. Xiaoming Huo
Title: A Homotopic Method to Solve the Lasso Problems with an Improved Upper Bound of Convergence Rate
Presenter: Dr. Tianbao Yang
Title: Deep AUC Maximization and Applications in Medical Image Classification
Presenter: Dr. Hongfeng Yu
Title: Exploring and Evaluating Edge Bundling for Large Graph Visualization
Presenter: Walter Moss
Title: Finding and Folding Functional RNA in SARS-CoV-2
Bio: Walter Moss is an Assistant Professor in the Department of Biochemistry, Biophysics, and Molecular Biology at Iowa State University.
Presenter: Dr. Tamal Dey
Title: Computational Topology and Data Analysis: A New Way of Looking at Data
Presenter: Yumou Qiu
https://www.stat.iastate.edu/people/yumou-qiu
Title: Comparing containment measures by epidemiological effects of COVID-19
Presenter: Ness Shroff
http://newslab.ece.ohio-state.edu/home/
Title: Delay Optimality in Network Clouds via Load Balancing
Presenter: Lily Wang
https://faculty.sites.iastate.edu/lilywang/
Title: Nowcasting and Forecasting COVID-19 in the United States
Presenter: Dr. Stephen Wright
Title: Second-order Methods for Nonconvex Optimization with Complexity Guarantees
Presenter: Dan Jacobson
https://www.ornl.gov/staff-profile/daniel-jacobson
Title: A mechanistic model and therapeutic interventions for COVID-19 involving a RAS-mediated bradykinin storm
Presenter: Dr. In-Ho Cho
Title: Parallel Fractional Hot Deck Imputation for Large/Big Missing Data Curing for Improving Machine Learning and Statistical Inference
Presenter: Helen Zhang
https://www.math.arizona.edu/~hzhang/
Title: Sparse and Smooth Function Estimation in Reproducing Kernel Hilbert Spaces
Presenter: Claus Kadelka
https://faculty.sites.iastate.edu/ckadelka/
Title: A model-based evaluation of the efficacy of COVID-19 social distancing, testing and hospital triage policies
Presenter: Dr. Lev Reyzin
Title: Differential Privacy, Adaptive Data Analysis, and Free Speedups via Sampling
Presenter: Dr. Vipin Kumar
Title: Physics Guided Machine Learning: A New Framework for Accelerating Scientific Discovery
Presenter: Yan Wang
Title: Wasserstein subsampling: Theory and Empirical Performance
Presenter: Xiaoyun Fu
Title: Quantifying attitude of individuals in social networks
Presenter: Jiaming Qiu
Title: Nonparametric estimates for repeated densities with heterogeneous sample sizes
Presenter: Chancellor Johnstone
Title: Distribution Free Prediction Intervals
Presenter: Sumon Biswas
Title: Do the Machine Learning Models on a Crowd Sourced Platform Exhibit Bias? An Empirical Study on Model Fairness
Presenter: Dr. Lily Wang
Title: Spatiotemporal Dynamics, Nowcasting and Forecasting COVID-19 in the United States
Presenter: Myungjin Kim
Title: Spatiotemporal Dynamics, Nowcasting and Forecasting COVID-19 in the United States
Presenter: Dr. Praneeth Narayanamurthy
Title: Provable and Efficient Algorithms for Robust Subspace Learning and Tracking
Presenter: Geoffrey Thompson
Title: A k-means based image compression method
Presenter: Gianfranco Ciardo
http://web.cs.iastate.edu/~ciardo/
He will be presenting exciting recent work so please join us!
Presenter: Dr. Fan Dai
Title: A Matrix-Free Likelihood Method for Exploratory Factor Analysis of High-Dimensional Gaussian Data
Presenter: Myra Cohen
https://www.cs.iastate.edu/people/myra-cohen
A discussion about what makes a good research paper.
Presenter: Mikaela Cashman
Title: The Software Engineer’s Role in Predictive Biology
Abstract: In the age of big data, predictive biology has scaled dramatically creating a community of biologists and bioinformaticians with a need for strong computational power. This talk will introduce a large open-source system aimed to meet these goals known as the Department of Energy’s Systems Biology Knowledgebase (KBase, http://kbase.us). KBase includes over 200 applications in the areas of genome assembly and annotation, sequence alignment, metabolic modeling, expression and more. There is also support for external tool integration through an SDK. KBase is designed to be open and collaborative where users can utilize public datasets, import custom data, create workflows built upon Jupyter notebooks, and share these workflows with others.
Presenter: Peter Dixon
Title: Distribution Testing and Complexity
Presenter: Dr. Jia (Kevin) Liu
Title: Can We Achieve Fresh Information with Selfish Users in Mobile Crowd-Sensing?
Presenter: Christopher Quinn
https://www.cs.iastate.edu/people/christopher-quinn
Title: Sparse Approximations for Time-Series Networks
Presenter: Christopher Quinn
https://www.cs.iastate.edu/people/christopher-quinn
Title: Sparse Approximations for Time-Series Networks
Presenter: Dr. Jane Cleland-Huang
Title: Human-Drone Partnerships in Emergency Response
Presenter: Dr. Yuliya Lierlier
Title: Answer Set Programming and Automatic Optimization Methods in its Realm
Presenter: Eduardo Blanco
Title: Towards Deeper Natural Language Understanding
Presenter: Lynna Chu
Title: Graph-based Change-point Detection for Non-Euclidean and Multivariate Data
Abstract:
Presenter: Lee Przybylski and Nate Harding
Title: Two Challenge Problems from the Algorithms for Threat Detection Program
Abstract:
Presenter: Dr. Eric Hansen
Title: A New Approach to Integrating Graphical Models in Decision-Theoretic Planning
Presenter: Dr. Qi Li
Title: The Role of Machine Learning in Materials and Chemical Discovery
Presenter: Dr. Matthew G. Panthani
Presenter: Dr. Carolyn Lawrence-Dill
Title: Computing on Natural Language Descriptions of Phenotypes: Making the language of genetics computable
Abstract:
Presenter: Dr. Max Morris
Title: Decomposing Functional Model Inputs for Variance-Based Sensitivity Analysis
Presenter: Dr. Sigurdur Olafsson
Presenter: Durga Paudyal
Title: Coupling Materials Physics with Data Science to Predict New Materials and Properties
Abstract:
Presenter: Dr. Guiping Hu
Title: Maize Yield and Nitrate Loss Prediction with Optimal Machine Learning Model Ensembles
Presenter: Gavin Nop
Title: Derivative-free Optimization
Presenter: Dr. Sarah Ryan
Title: The DataFEWSion Traineeship for Innovations at the Nexus of Food Production, Renewable Energy and Water Quality.
Presenter: Dr. Jing Dong
Topic: Electric Autonomous Taxis Dispatching
Presenter: Dr. Ranjan Maitra
Topic: Three-dimensional Radial Visualization of High-dimensional Continuous or Discrete Datasets
Presenter: Dr. Wallapak Tavanapong
Topic: Dealing with class imbalance and a limited labeled dataset with active deep learning
Presenter: Dr. Pavan Aduri
Topic: Simultaneous Time and Memory Efficient Algorithm for Reachability in Graphs
Presenter: Dr. Dan Nettleton
Topic: Random Forest Prediction Intervals
Presenter: Dr. Namrata Vaswani
Topic: (Dynamic) Robust PCA and Phaseless PCA
Presenter: Dr. Steve Holland
Presenter: Dr. Kevin Liu
Topic: Compressed Distributed Gradient Descent: Communication-Efficient Consensus over Networks.
Presenter: Dr. Eric Weber
Topic: Distributed Data Analysis using the Kaczmarz Algorithm
Presenter: Dr. Steve Holland
Topic: Big Data for nuclear power plans and creating discoverable data repositories for nondestructive evaluation
Presenter: Dr. Ranjan Maitra
Topic: Kernel-estimated Nonparametric Overlap-Based Syncytial Clustering
Presenter: Dr. Hal Schenck
Topic: Topological data analysis
Presenter: Dr. Chinmay Hedge
Topic: Theoretical aspects of neural network learning
Presenter: Bill Gallus
Presenter: Md Johirul Islam
Topic: MODE: Automated Neural Network Model Debugging via State Differential Analysis and Input Selection
Presenter: Dr. Eric Weber
Topic: Harmonic Analysis of Neural Networks
Presenter: Md Johirul Islam
Topic: What Do Developers Ask About ML Libraries? A Large-scale Study Using Stack Overflow
Presenter: Dr. Wei Le
Topic: Seeking for machine learning approaches to enable software testing in parallel
Presenter: Dr. Jennifer Newman
Topic: What’s in a Picture: Steganography, Digital Image Forensics, and more
Presenter: Dr. Myra Cohn
Topic: Evolutionary algorithms and hyper heuristic search
Presenter: Dr. Jin Tian
Topic: Causal inference
Presenter: Dr. Jonathan Smith
Topic: Concept identification and logic for neural network layers
Presenter: Hamid Bagheri
Expressing and Verifying Probabilistic Assertions, PLDI'14 paper
For information visit the Midwest Big Data Summer School website.
![[Ryan Martin]](https://www4.stat.ncsu.edu/~rmartin/Images/mypic.jpg)
