Department of Mathematics and Statistics, Binghamton University

Data Science Seminar

2026-04-07T14:19:34-04:00

Data Science Seminar

The Data Science Seminar is evolved from the former Statistical Machine Learning Seminar which covered topics in statistical theory that was important for machine learning research as well as development and applications of machine learning techniques in interdisciplinary research. The scope of the Data Science Seminar has been broadened to facilitate dialogue among different communities in the data science circle.

It is listed as course MATH 568.

Location: Whitney 100E (See the directions to the department)
Time: Tuesday 12:15 pm –1:15 pm
Organizers: Minghao Rostami and Minjie Wang

Spring 2026

February 10, 2026
Speaker: Dr. Yizeng Li (Department of Biomedical Engineering at Binghamton University)
Topic: Multiphase Continuum Models for Cell Migration.
Abstract

April 14, 2026
Speaker: Dr. Yang Feng (New York University)
Topic: Transfer and Multi-task Learning: Statistical Insights for Modern Data Challenges.
Abstract

April 21, 2026
Speaker: Dr. Yiqun T. Chen (Johns Hopkins University)
Topic: .
Abstract

April 28, 2026
Speaker: Dr. Doug Turnbull (Ithaca College)
Topic: .
Abstract

May 5, 2026
Speaker: Dr. Jun Yan (University of Connecticut)
Topic: .
Abstract

Fall 2025

September 9, 2025
Speaker: Dr. Nancy Guo (School of Computing at Binghamton University)
Topic: AI-empowered precision medicine.
Abstract

October 21, 2025
Speaker: Dr. Fei Xue (Purdue University)
Topic: Statistical Methods for Mobile Health Data.
Abstract

October 28, 2025
Speaker: Dr. Yufeng Liu (University of Michigan)
Topic: Low-Rank Online Dynamic Assortment with Dual Contextual Information.
Abstract

November 18, 2025
Speaker: Dr. Bingxin Zhao (University of Pennsylvania)
Topic: Resampling-based pseudo-training in genomic predictions.
Abstract

Spring 2025

February 25, 2025
Speaker: Dr. Zhaohan Xi (School of Computing at Binghamton University)
Topic: From Text to Impact: Large Language Models as Responsible Cross-Disciplinary Copilots.
Abstract

March 18, 2025
Speaker: Dr. Kun Chen (University of Connecticut)
Topic: Hybrid and Integrative Learning for Rare Event Modeling with EHR Data.
Abstract

April 8, 2025
Speaker: Dr. Lingzhou Xue (Pennsylvania State University)
Topic: Federated On-Policy Reinforcement Learning.
Abstract

April 15, 2025
Speaker: Dr. Min Xu (Rutgers University - New Brunswick)
Topic: Optimal Convex M-Estimation via Score Matching.
Abstract

Fall 2024

October 15, 2024
Speaker: Dr. Yang Ning (Cornell University)
Topic: Estimation and Inference in Multivariate Response Regression with Hidden Variables.
Abstract

October 22, 2024
Speaker: Dr. JoonHwan Cho (Department of Economics at Binghamton University)
Topic: Testing for exogenous participation in ascending auction with unobserved heterogeneity.
Abstract

November 5, 2024
CANCELLED AND POSTPONED
Speaker: Dr. Yunlong Feng (SUNY Albany)
Topic: Understanding robust loss functions in machine learning.
Abstract

November 12, 2024
Speaker: Ben Jones
Topic: An Integrated Experimental and Modeling Approach to Design Rotating Algae Biofilm Reactors (RABRs) via Optimizing Algae Biofilm Productivity, Nutrient Recovery, and Energy Efficiency.
Abstract

December 3, 2024
Speaker: Dr. Yingxue Zhang (School of Computing at Binghamton University)
Topic: Leveraging Unlabeled Data in Offline Reinforcement Learning.
Abstract

Spring 2024

January 23, 2024
Speaker: Yili Zhang (MathWorks)
Topic: Low-Code Machine Learning in SIMULINK & MATLAB APPS.
Abstract

March 26, 2024
Speaker: Dr. Zeyu Ding (Department of Computer Science at Binghamton University)
Topic: Differential Privacy in Practice: How the US Government Protects Your Sensitive Information in the 2020 Census.
Abstract

Fall 2023

September 26, 2023
Speaker: Dr. Marianthi Markatou (SUNY University at Buffalo)
Topic: Distances and their role in statistical inference.
Abstract

October 3, 2023
Speaker: Dr. HaiYing Wang (University of Connecticut)
Topic: Rare Events Data and Maximum Sampled Conditional Likelihood.
Abstract

October 10, 2023
Speaker: Dr. Yiming Ying (SUNY University at Albany)
Topic: Interplay between Generalization and Optimization via Algorithmic Stability.
Abstract

October 17, 2023
Speaker: Dr. Peter D. Hoff (Duke University)
Topic: Core Shrinkage Covariance Estimation for Matrix-variate Data.
Abstract

October 24, 2023
Speaker: Dr. Luis Carvalho (Boston University)
Topic: Deviance Matrix Factorization.
Abstract

October 31, 2023
Speaker: Dr. Ruiqi Liu (Texas Tech University)
Topic: Estimation and Hypothesis Testing of Derivatives in Smoothing Spline ANOVA Models.
Abstract

November 14, 2023
Speaker: Dr. Jiguo Cao (Simon Fraser University)
Topic: Machine Learning for Functional Data.
Abstract

November 28, 2023
Speaker: Dr. Li Zhang (University of California San Francisco)
Topic: NAIR Software: Unlocking the Immune System's Secrets by Network Analysis and Advanced Machine Learning.
Abstract

December 5, 2023
Speaker: Dr. Xuexia Wang (Florida International University)
Topic: Genetic Association Test and Risk Prediction Modeling for Cardiomyopathy in Cancer Survivors.
Abstract

Spring 2023

February 14, 2023
Speaker: Dr. Yuan Fang (Binghamton University)
Topic: Clustering disease trajectories: statistical method applications and evaluation.
Abstract

February 21, 2023
Speaker: Dr. Xuming He (University of Michigan)
Topic: How Good is Your Best Selected Subgroup.
Abstract

February 28, 2023
Speaker: Dr. James D. Wilson (University of San Francisco)
Topic: The Political Brain: Associations of Tasked-based Functional Connectivity Networks and Political Ideology.
Abstract

March 7, 2023
Speaker: Dr. Sijian Wang (Rutgers, The State University of New Jersey)
Topic: Dynamic Attention-Based Functional Data Analysis.
Abstract

March 14, 2023
Speaker: Dr. Holger Dette (Ruhr-Universitaet Bochum)
Topic: Functional data analysis on Banach spaces.
Abstract

March 21, 2023
Speaker: Dr. Hamsa Bastani (University of Pennsylvania)
Topic: Efficient and targeted COVID-19 border testing via reinforcement learning.
Abstract

March 28, 2023
Speaker: Dr. Jie Peng (UC Davis)
Topic: Statistical methods for diffusion MRI.
Abstract

April 11, 2023
Speaker: Dr. Chris Haines (Internal)
Topic: Independent Spacings Theorem with a Maximum Product Spacings Estimation Application.
Abstract

April 18, 2023
Speaker: Dr. Konstantin G. Arbeev (Duke University)
Topic: How Good is Your Best Selected SubgroupStochastic process models: Bringing biology to statistics to advance research on aging.
Abstract

April 27, 2023
Speaker: Dr. Runze Li (Penn State University)
Topic: Model-Free Conditional Feature Screening with FDR Control.
Abstract

Fall 2022

September 20, 2022
Speaker: Dr. Soumik Banerjee (Internal)
Topic: Likelihood-based Approach for Testing the Homogeneity of Risk Difference in a Multicenter Randomized Clinical Trial.
Abstract

October 4, 2022
Speaker: Dr. Chao Huang (Florida State University)
Topic: Shape-on-Scalar Regression Models: Going Beyond Prealigned Non-Euclidean Responses.
Abstract

October 25, 2022
Speaker: Dr. John Stufken (George Mason University)
Topic: Musings on Subdata Selection.
Abstract

November 1, 2022
Speaker: Dr. Jinchi Lv (University of Southern California)
Topic: High-Dimensional Knockoffs Inference for Time Series Data.
Abstract

November 8, 2022
Speaker: Dr. Rui Song (North Carolina State University)
Topic: On statistical inference for sequential decision making.
Abstract

November 15, 2022
Speaker: Dr. Rachael Hageman Blair (University at Buffalo)
Topic: Harnessing stability estimation for module detection, clustering, and ensemble clustering.
Abstract

December 6, 2022
Speaker: Dr. Alexander Franks (University of California, Santa Barbara)
Topic: Sensitivity to Unobserved Confounding in Studies with Factor-structured Outcomes.
Abstract

Spring 2022

Apr. 5, 2022
Speaker: Dr. Soumik Banerjee (Internal)
Topic: Multistage Minimum Risk Point Estimation (MRPE) with First-Order and Second-Order Asymptotic Properties.
Abstract

Apr. 26, 2022
Speaker: Dr. Krishnakumar Balasubramanian (The University of California, Davis)
Topic: Fractal Gaussian Networks: A sparse random graph model based on Gaussian Multiplicative Chaos.
Abstract

May 3, 2022
Speaker: Dr. Hongtu Zhu (University of North Carolina)
Topic: Challenges in Biobank-scale: Imaging Genetics and Beyond.
Abstract

May 10, 2022
Speaker: Dr. Yao Zheng (University of Connecticut)
Topic: Tensor methods for high-dimensional time series modeling.
Abstract

Fall 2021

Sep. 14, 2021
Speaker: Dr. Matthew Reimherr (Pennsylvania State University)
Topic: KNG - A New Mechanism for Data Privacy.
Abstract

Sep. 21, 2021
Speaker: Dr. Haoda Fu (Eli Lilly and Company)
Topic: Our Recent Development on Cost Constraint Machine Learning Models.
Abstract

Sep. 28, 2021
Speaker: Dr. Eric F. Lock (University of Minnesota)
Topic: Bidimensional Linked Matrix Decomposition for Pan-Omics Pan-Cancer Analysis.
Abstract

Oct. 19, 2021
Speaker: Dr. Damla Senturk (UCLA)
Topic: Multilevel Modeling of Spatially Nested Functional Data: Spatiotemporal Patterns of Hospitalization Rates in the U.S. Dialysis Population.
Abstract

Oct. 26, 2021
Speaker: Dr. Giles Hooker (UC Berkeley)
Topic: There is No Free Variable Importance: Traps in Interpreting Black Box Functions.
Abstract

Nov. 2, 2021
Speaker: Dr. Yuanjia Wang (Columbia University)
Topic: Machine Learning Approaches for Optimizing Treatment Strategies for Mental Disorders.
Abstract

Nov. 9, 2021
Speaker: Dr. Megan Johnson (Internal)
Topic: The Interconnectivity Vector and the Betti Sequence: Finite-Dimensional Vector Representations of Persistent Homology.
Abstract

Nov. 16, 2021
Speaker: Dr. Cen Wu (Kansas State University)
Topic: Robust Bayesian variable selection for gene-environment interactions.
Abstract

Nov. 30, 2021
Speaker: Dr. Antonio Linero (University of Texas at Austin)
Topic: Bayesian Decision Tree Ensembling Strategies for Nonparametric Problems.
Abstract

Dec. 7, 2021
Speaker: Dr. Annie Qu (University of California Irvine)
Topic: Correlation Tensor Decomposition and Its Application in Spatial Imaging Data.
Abstract

Spring 2021

Mar. 02, 2021
Speaker: Brian Franczak (MacEwan University)
Topic: On using mixtures of shifted asymmetric Laplace distributions for model-based classification.
Abstract

Mar. 23, 2021
Speaker: Adam Ciarleglio (The George Washington University)
Topic: Multiple imputation in functional regression with applications to EEG data in a depression study.
Abstract

March 30, 2021
Speaker: Wenshu Dai (Binghamton University)
Topic: Finite Mixtures of Regression Models and Finite Mixtures of Regression Models with Concomitant Variables for Clustering Microbiome Data.
Abstract

April. 13, 2021
Speaker: Nalini Ravishanker (University of Connecticut)
Topic: Biclustering Approaches for High-Frequency Time Series.
Abstract

April. 27, 2021
Speaker: Melody Ghahramani (The University of Winnipeg)
Topic:Time Series Regression for Zero-Inflated and Overdispersed Count Data: A Functional Response Model Approach.
Abstract

May 04, 2021
Speaker: Zhou Wang (Binghamton University)
Topic: Multiclass Anomaly Detector: the CS++Support Vector Machine
Abstract

Fall 2020

Oct. 20, 2020
Speaker: Sumanta Basu (Cornell University)
Topic: Measuring Systemic Risk with Graphical Models of Time Series Data.
Abstract

Oct. 27, 2020
Speaker: Yuan Luo (Northwestern University)
Topic: A Multidimensional Precision Medicine Approach Identifies an Autism Subtype Characterized by Dyslipidemia
Abstract

Nov. 3, 2020
Speaker: Shaofei Zhao (Binghamton University)
Topic: Distribution-free and nonparametric multivariate feature screening via measure transportation for high dimensional response and predictor variables.
Abstract

Spring 2020

Mar. 24, 2020
CANCELLED AND POSTPONED
Speaker: Sumanta Basu (Cornell University)

April. 21, 2020
Speaker: Liang Li, Yunhui Liu and Han Zhang
Topic: Capstone Project: Factors Affecting PhD Student Success.
Abstract

April. 21, 2020
CANCELLED AND POSTPONED
Speaker: Adam Ciarleglio (George Washington University)

May. 05, 2020
CANCELLED AND POSTPONED
Speaker: Yuan Luo (Northwestern University)

—

Fall 2019

See the schedule of the Interdisciplinary Dean's Speaker Series in Data Science.

Oct. 9, 2019 (special day and time)
Interdisciplinary Dean's Speaker Series in Data Science
Speaker: Joseph W Hogan (Brown University)
Topic: Using Electronic Health Records Data for Predictive and Causal Inference About the HIV Care Cascade
Abstract

Oct. 11, 2019 (Special Date and time)
Speaker: Paul McNicholas (McMaster University)
Topic: Clustering Higher-Order Data
Abstract

Oct. 22, 2019
Speaker: Gen Li (Columbia University)
Topic: Integrative multi-view regression: Bridging group-sparse and low-rank models.
Abstract

Oct. 24, 2019 (1:15 pm, stat seminar time)
Speaker: Doug Turnbull (Ithaca College)
Topic: TBA
Abstract

Oct. 29, 2019
Speaker: Wangshu Tu (Binghamton University)
Topic: A family of mixture models for biclustering
Abstract

Nov. 5, 2019
Speaker: Wangshu Tu (Binghamton University)
Topic: Non existence of fixed sample estimator for prescribed proportional closeness
Abstract

Nov. 8, 2019
Interdisciplinary Dean's Speaker Series in Data Science
Speaker: Andrew Gordon Wilson (New York University; Courant Institute of Mathematical Sciences)
Topic: How do we build models that learn and generalize?
Abstract

Nov. 12, 2019
Speaker: Kexuan Li (Binghamton University)
Topic: A Hausman test for the presence of market microstructure noise in high frequency data
Abstract

Nov. 19, 2019
Interdisciplinary Dean's Speaker Series in Data Science
Time: 10am-11:30am
Location: UUW325
Speaker: Arthur Spirling (New York University; Politics and Data Science)
Topic: Word Embeddings: What works, what doesn’t, and how to tell the difference for applied research
Abstract

Nov. 26, 2019
Speaker: Wei Yang (Binghamton University)
Topic: Random Covariance Matrix and the Marchenko-Pastur law
Abstract

—

Spring 2019

March 05, 2019
Speaker: Rebecca Nugent (Carnegie Mellon University)
Topic: Before Teaching Data Science, Let’s First Understand How People Do It
Abstract

March 12, 2019
Speaker: Subhadeep (Deep) Mukhopadhyay (Temple University)
Topic: Graph Data Science
Abstract

March 26, 2019
The Dean's Speaker Series in Statistics and Data Science
Speaker: Regina Y. Liu (Rutgers University)
Topic: Fusion Learning: Efficient Combination of Inferences from Diverse Data Sources
Abstract

April 9, 2019
Speaker: David Hunter (Pennsylvania State University)
Topic: Multivariate Nonparametric Mixture Models
Abstract

April 16, 2019
The Dean's Speaker Series in Statistics and Data Science
Speaker: David Madigan (Columbia University)
Topic: Towards honest inference from real-world healthcare data
Abstract

April 23, 2019
Speaker: Daphney-Stavroula Zois (SUNY Albany)
Topic: Spatiotemporal Quickest Change Detection for Traffic Accident Nowcasting
Abstract

April 30, 2019
Speaker: Lin Yao (Binghamton University)
Topic: Dissertation Defense - JAMES-STEIN-TYPE OPTIMAL WEIGHT CHOICE FOR FREQUENTIST MODEL AVERAGE ESTIMATOR
Special time and location: 3:30 pm at OR 100D
Abstract

—

Fall 2018

October 9, 2018
The Dean's Speaker Series in Statistics and Data Science
Speaker: Sidney Resnick (Cornell University)
Location: Old Champlain Atrium (unusual location)
Topic: Fitting the Linear Preferential Attachment Model for Social Network Growth
Abstract

October 23, 2018
The Dean's Speaker Series in Statistics and Data Science
Speaker: David Ruppert (Cornell University)
Topic: Density Estimation with Noisy Data
Abstract

November 13, 2018
Speaker: Yudong Chen (Cornell University)
Topic: Byzantine-Robust Distributed Learning with Non-converxity
Abstract

—-

Spring 2018

January 30
Speaker: Qiqing Yu (Binghamton University)
Topic: Identifiability Conditions For The Linear Regression Model Under Right Censoring
Abstract

February 20
Speaker: Yinsong Chen (Binghamton University)
Topic:The Conductance and Mixing Time
Abstract

March 13
Speaker: Jiexin Duan (Purdue University)
Topic: Large-Scale Nearest Neighbor Classification with Statistical Guarantee
Abstract

March 20
Speaker: Yuan Fang (Binghamton University)
Topic: Bayesian Approach to Parameter Estimation for the mixtures of Multivariate Normal Inverse Gaussian Distributions
Abstract

April 10
Speaker: Leila Setayeshgar (Providence College)
Topic: Large Deviations for a Class of Stochastic Semilinear Partial Differential Equations
Abstract

April 17
Speaker: Wenbo Wang (Binghamton University)
Topic: A look at distance-weighted discrimination
Abstract

April 24
Speaker: Haomiao Meng (Binghamton University)
Topic: Multicategory Angle-based Large-margin Classification
Abstract

May 1
Speaker: Chen Liang (Binghamton University)
Topic: Goodness of fit tests for clustered spatial point processes
Abstract

—-

Fall 2017

September 19
Speaker: Dan Yang (Rutgers University)
Topic: Autoregressive Model for Matrix Valued Time Series
Abstract

September 26
Speaker: Ji Meng Loh (New Jersey Institute of Technology)
Topic: Single-index model for inhomogeneous spatial point processes
Abstract

November 14
Speaker: Weijie Su (Wharton School of the University of Pennsylvania)
Topic: TBA
Abstract

Previous talks in the Data Science Seminar

2017-06-12T11:53:20-04:00

Previous talks in the Data Science Seminar

Spring 2017

January 17
NO SEMINAR OR GROUP MEETING

January 24, January 31, February 7, February 14, February 21, February 28
Internal Machine Learning Working Group meeting

March 7
WINTER BREAK

March 21
Speaker: Utkarsh Dang (Mathematical Sciences and CLT at Binghamton University)
Topic: Tools for Efficient Statistical Computing in R
Abstract

March 28
Speaker: Yunzhang Zhu (Ohio State University)
Topic: Maximum Likelihood Inference for a Large Precision Matrix
Abstract

April 4
Speaker: Guanhua Yan (CS at Binghamton University)
Topic: Improving Efficiency of Link Clustering on Multi-Core Machines
Abstract

April 11
SPRING BREAK

April 18
Speaker: Changqing Cheng (SSIE at Binghamton University)
Topic: Heterogeneous Recurrence Monitoring of Dynamic Transients in Ultraprecision Machining Processes
Abstract

April 20 (special time: 1:15 – 2:15 pm)
Speaker: Qiusheng Wu (Geography at Binghamton University)
Topic: Environmental Monitoring and Analysis with Geospatial Big Data Powered by Google Earth Engine
Abstract

April 25
Speaker: Lingzhou Xue (Pennsylvania State University)
Topic: Sufficient Forecasting Using Factor Models
Abstract

May 2
No Data Science seminar. There is a capstone seminar at noon and an admission to candidacy exam at 1:30 pm.

May 9
Speaker: Yuying Xie (Michigan State University)
Topic: Joint Estimation of Multiple Dependent Gaussian Graphical Models with Applications to Mouse Genomics
Abstract

Previous talks in the Statistical Machine Learning Seminar

Fall 2016

September 27
Speaker: Wei Qian (Rochester Institute of Technology)
Topic: Sufficient Dimension Reduction in High Dimension
Abstract

October 18
Speaker: Yang Feng (Columbia University)
Topic: Community detection with nodal information
Abstract

October 25
Speaker: Guan Yu (University at Buffalo)
Topic: Supervised Learning Incorporating Graphical Structure among Predictors
Abstract

November 1
Speaker: Yu Chen (ECE at Binghamton University)
Topic: Enabling Smart Urban Surveillance at The Edge
Abstract

November 8
Speaker: Chengbin Deng (Geography at Binghamton University)
Topic: Use of Big Geospatial Data for Better Mapping and Understanding Urban Environment
Abstract

November 15
Speaker: Ziang Zhang (ECE at Binghamton University)
Topic: Asymptotical Frequency Synchronization of Kuramoto Oscillators by Topology Evolution
Abstract

December 06
Speaker: Yang Ning (Cornell University)
Topic: A General Framework for High-Dimensional Inference and Multiple Testing
Abstract

Spring 2016

February 23
Speaker: Xin Qi (Mathematical Sciences)
Topic: RE: Joint estimation of multiple graphical models
Abstract

March 1
Speaker: Wenbo Wang (Mathematical Sciences)
Topic: RE: Multicategory Angle-based Large-margin Classification and Reinforced SVM
Abstract

March 8
Speaker: Lin Yao (Mathematical Sciences)
Topic: RE: Sparse Regression Incorporating Graphical Structure among Predictors
Abstract

March 15
Speaker: Junyi Dong (Mathematical Sciences)
Topic: Diagnostic Plotting Methods for Proportional Hazards Models With Time-dependent Covariates or Time-varying Regression Coefficients
Abstract

March 22
Speaker: Grace Wang (Syracuse University)
Topic: ConceFT: Concentration of Frequency and Time via a multitapered synchrosqueezed transform
Abstract

April 12
Speaker: Ruiqi Liu (Mathematical Sciences)
Topic: TBD
Abstract

April 19
Speaker: Rachael Kline (Mathematical Sciences)
Topic: RE: Joint Estimation of Multiple Precision Matrices with Common Structures
Abstract

April 26
Speaker: Wolfgang Wefelmeyer (Universit?t zu K?ln)
Topic: Density estimators in regression models with errors in covariates
Abstract

May 10 (Ph.D. dissertation defense)
Time: 12-2 pm
Location: WH 100E
Speaker: Qinggang Diao (Mathematical Sciences)
Topic: Cox proportional hazards model with time-dependent covariates
Abstract

Fall 2015

September 29
Speaker: Xu Chu (Mathematical Sciences)
Topic: Lasso, AdaLasso and Sure Independence Screening
Abstract

October 6
Speaker: Wenbo Wang (Mathematical Sciences)
Topic: Selection consistency of lasso and Stability Selection
Abstract

October 13
Speaker: Wenming Deng (Mathematical Sciences)
Topic: The Bolasso, the Dantzig selector and more on Stability Selection
Abstract

October 20
Speaker: Xin Qi (Mathematical Sciences)
Topic: On False Discovery Rate (I)
Abstract

October 27
Speaker: Lin Yao (Mathematical Sciences)
Topic: On False Discovery Rate (II)
Abstract

November 3
Speaker: Xiaojie Du (Binghamton University)
Topic: Inference About the Slope in Linear Regression with Missing Responses: An Empirical Likelihood Approach.
Abstract

November 10
Speaker: Junyi Dong (Mathematical Sciences)
Topic: The Family of Distributions With Proportional Hazards
Abstract

December 1
Speaker: Chen Liang (Mathematical Sciences)
Topic: On Information Criterion
Abstract

December 8
Speaker: Baiyang Qi (Mathematical Sciences)
Special Location: WH G02
Topic: Building a model for scoring 20 or more runs in a baseball game
Abstract

Fall 2014

September 9
Speaker: Ganggang Xu (Mathematical Sciences)
Topic: Asymptotic optimality and efficient computation of the leave-subject-out cross-validation
Abstract

September 11 (talk in the Statistics Seminar)
Time: 1:15-2:15 pm (NOTE SPECIAL DAY AND TIME)
Speaker: Guang Cheng (Purdue University)
Topic: Semi-Nonparametric Inference for Massive Data
Abstract

September 23
Speaker: Wei Sun (Purdue University)
Topic: Causal Inference Framework for Complex Advertising Effect Measurement
Abstract

October 29
Note for special day, time & location.
Time: 12:00 - 1:30
Location: OW-G002
Speaker: Rob Graessle, MathWorks Application Engineer
Topic: Machine Learning with MATLAB
Abstract: Learn about machine learning techniques available in MATLAB and how you can use them to quickly explore your data, evaluate algorithms, compare the results, and apply the best technique to your problem.
Poster
Lunch will be provided. Please register at this link: http://www.mathworks.com/seminars/BU2014
Walk-in is welcome.
The time of the session has been changed due to the change of venue in response to the unexpected number of registrations.

November 18
Speaker: Ming Yang (Computer Science)
Topic: Multi-task learning with Gaussian Matrix Generalized Inverse Gaussian Model
Abstract

THIS TALK IS POSTPONED TO SPRING
Speaker: Yingming Li (Computer Science)
Topic: Scientific Articles Recommendation
Abstract

Machine Learning Working Group

2017-06-12T11:41:20-04:00

Machine Learning Working Group

Spring 2017

Jan. 24, 2017
- Discussant: Wenbo Wang
- Topic: MM Principle in Optimization
- Note taken by: Lin Yao

Feb. 14, 2017
- Discussant: Haomiao Meng
- Topic: Angle-based Multicategory Classification
- Note taken by: Yuan Fang

Feb. 21, 2017
- Discussant: Chen Liang
- Topic: TBD
- Note taken by: TBD

Feb. 28, 2017
- Discussant: Yuan Fang
- Topic: TBD
- Note taken by: TBD

Fall 2016

Sept. 6, 2016
- Discussant: Wenbo Wang
- Topic: Classification with confidence (1)
- Note taken by: Yuan Fang

Sept. 13, 2016
- Discussant: Wenbo Wang
- Topic: Classification with confidence (2)
- Note taken by: Yuan Fang

Sept. 20, 2016
- Discussant: Haomiao Meng
- Topic: Individualized treatment rules
- Note taken by: Lin Yao

Oct. 4, 2016
- Discussant: Yuan Fang
- Topic: Finite Mixtures of Matrix Normal Distributions for Classifying Three-way Data
- Note taken by: Chen Liang

Nov. 29, 2016
- Discussant: Lin Yao
- Topic: Optimal weight choice for frequentist model average estimators.
- Note taken by: Haomiao Meng

April 25, 2017

2017-06-12T11:42:10-04:00

Data Science Seminar
Hosted by Department of Mathematical Sciences

Date: Tuesday, April 25, 2017
Time: 12:00-1:00
Room: WH-100E
Speaker: Lingzhou Xue (Pennsylvania State University)
Title: Sufficient Forecasting Using Factor Models

Abstract

We consider forecasting a single time series using a large number of predictors when a nonlinear forecasting function is present. The linear forecasting is very appealing due to its simplicity. However, it only reveals one dimension of the predictive power in the underlying factors. This paper develops a new forecasting methodology called the sufficient forecasting, which provides several sufficient predictive indices to deliver additional predictive power. The sufficient forecasting correctly estimates projections of the underlying factors even in the presence of an arbitrary and unknown forecasting function. Our work identifies the effective factors that have impacts on the forecast target when the target and the cross-sectional predictors are driven by different sets of common factors. We derive asymptotic properties for the estimate of the central subspace spanned by these projection directions as well as the estimates of the sufficient predictive indices. We also prove that when the assumed linear forecasting function is violated, the simple linear estimate actually falls into this central subspace. Our method and theory allow the number of predictors to be larger than the number of observations. We finally demonstrate that the sufficient forecasting improves upon the linear forecasting in both simulation studies and an empirical study of forecasting macroeconomic variables.

More details about the Data Science seminar can be found at https://www2.math.binghamton.edu/p/seminars/sml

April 18, 2017

2017-06-12T11:42:36-04:00

Data Science Seminar
Hosted by Department of Mathematical Sciences

Date: Tuesday, April 18, 2017
Time: 12:00-1:00
Room: WH-100E
Speaker: Changqing Cheng (SSIE at Binghamton University)
Title: Heterogeneous Recurrence Monitoring of Dynamic Transients in Ultraprecision Machining Processes

Abstract

In-situ monitoring and control of process variations are important for quality assurance in ultraprecision machining (UPM) processes. Recent advance in sensing and communication technology have fueled increasing interests to develop sensor-based monitoring approaches for anomaly detection in the UPM process. However, conventional approaches are limited in their ability to address the complex dynamics hidden in the nonlinear and nonstationary processes. As a result, it is difficult for them to effectively capture the process variations of UPM. We present a new heterogeneous recurrence monitoring approach for in-situ monitoring and predictive control of the UPM process, and it has the capability to detect the shift from stable to unstable cutting in UPM processes with an average run length of 1.

More details about the Data Science seminar can be found at https://www2.math.binghamton.edu/p/seminars/sml

April 20, 2017

2017-06-12T11:42:23-04:00

Data Science Seminar
Hosted by Department of Mathematical Sciences

Date: Thursday, April 20, 2017 (note special day and time)
Time: 1:15–2:15
Room: WH-100E
Speaker: Qiusheng Wu (Geography at Binghamton University)
Title: Environmental Monitoring and Analysis with Geospatial Big Data Powered by Google

Abstract

Earth observation data have been collected by various satellite and airborne sensors through remote sensing technologies since the 1970s. On the one hand, the petabyte archives of remote sensing imagery are crucial for many environmental and societal applications such as surface water mapping, deforestation tracking, drought monitoring, disaster response, disease surveillance and so on. On the other hand, the ever-increasing volume and variety of geospatial big data pose great challenges in data storage, management, analysis, and visualization. Until recently, global-scale environmental analyses with fine-resolution remote sensing imagery have been very limited due to the excessive computational cost associated with these geospatial big data. Google Earth Engine is a new cloud computing platform that combines a multi-petabyte catalog of satellite imagery and geospatial datasets with planetary-scale analysis capabilities, allowing scientists and researchers to access and analyze vast amounts of satellite imagery free of charge. The platform offers parallel computational access to thousands of computers in Google's data centers. This talk will demonstrate the application of Google Earth Engine and high-resolution light detection and ranging (LiDAR) data for various environmental applications, such as wetland mapping and monitoring.

More details about the Data Science seminar can be found at https://www2.math.binghamton.edu/p/seminars/sml

May 9, 2017

2017-06-12T11:41:59-04:00

Data Science Seminar
Hosted by Department of Mathematical Sciences

Date: Tuesday, May 9, 2017
Time: 12:00-1:00
Room: WH-100E
Speaker: Yuying Xie (Michigan State University)
Title: Joint Estimation of Multiple Dependent Gaussian Graphical Models with Applications to Mouse Genomics

Abstract

Gaussian graphical models are widely used to represent conditional dependence among random variables. In this paper, we propose a novel estimator for data arising from a group of Gaussian graphical models that are themselves dependent. A motivating example is that of modeling gene expression collected on multiple tissues from the same individual: here the multivariate outcome is affected by dependencies acting not only at the level of the specific tissues, but also at the level of the whole body; existing methods that assume independence among graphs are not applicable in this case. To estimate multiple dependent graphs, we decompose the problem into two graphical layers: the systemic layer, which affects all outcomes and thereby induces cross-graph dependence, and the category-specific layer, which represents graph-specific variation. We propose a graphical EM technique that estimates both layers jointly, establish estimation consistency and selection sparsistency of the proposed estimator, and confirm by simulation that the EM method is superior to a simple one-step method. We apply our technique to mouse genomics data and obtain biologically plausible results.

More details about the Data Science seminar can be found at https://www2.math.binghamton.edu/p/seminars/sml