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Thu 03/14/19, 01:30pm, Whitehead 304
Uncertainty Quantification and Nonparametric Inference for Complex Data and Simulations
Ann Lee, CMU

Thu 03/14/19, 10:30am, Hackerman B17
How well do neural NLP systems generalize?
Tal Linzen, JHU

Wed 03/13/19, 10:00am, Clark 110
Adapting Maximum Likelihood Theory to Modern Applications
Feng Ruan, Stanford University

Tue 03/12/19, 10:00am, Clark 110
Large-scale Optimization for Machine Learning
Aryan Mokhtari, MIT

Mon 03/11/19, 09:00am, Clark 110
Nonconvex Recovery of Low-Complexity Models in Data Science
Qing Qu, NYU

Tue 03/05/19, 09:30am, Clark 110
Classification-aware dimensionality reduction and genetic marker selection
Soledad Villar, NYU

Fri 03/01/19, 09:30am, Clark 110
Why Do Neural Networks Learn?
Behnam Neyshabur,, New York University

Thu 02/28/19, 01:30pm, Whitehead 304
Big Data is Low Rank
Madeline Udell, Cornell University

Thu 02/28/19, 11:00am, Clark 110
Deep Learning for Medical Imaging: Mapping Sensor Data to Decisions
Morteza Mardani, Stanford University

Wed 02/27/19, 12:00pm, Malone 228
Active Regression via Linear-Sample Sparsification
Xue Chen, Northwestern University

Fri 02/22/19, 10:00am, Clark 110
Rethinking the Role of Optimization in Learning
Suriya Gunasekar, Toyota Technological Institute at Chicago

Thu 02/21/19, 10:30am, Hackerman Hall B17
Building and Evaluating Conversational Agents
Joao Sedoc, University of Pennsylvania

Mon 02/18/19, 12:15pm, School of Public Health, Room W2008
A Machine Learning Approach to Causal Inference in the Presence of Missing Data
Xiaochun Li, Indiana University

Fri 02/15/19, 11:30am, Clark Hall 110
Machine Learning for Medical Decision Support
Pengtao Xie, Pentuum, Inc.

Tue 02/12/19, 01:00pm, Clark 110
Bayesian Estimation of Sparse Spiked Covariance Matrices in High Dimensions
Yanxun Xu, Johns Hopkins University

Mon 02/11/19, 11:30am, Clark 110
Neural data science: From recordings to theoretical models
Adam Charles, Princeton University

Fri 11/30/18, 12:00pm, Hackerman B17
Improving Customer Support at Uber with Conversational AI
Yi-Chia Wang, Uber AI

Tue 11/27/18, 10:45am, Hackerman B17
Medical image analysis using deep learning
Oge Marques, Florida Atlantic University

Mon 11/19/18, 12:00pm, Hackerman B17
“Differential Fairness for Machine Learning and Artificial Intelligence Systems:
James Foulds, UMBC

Mon 11/12/18, 12:00pm, Hackerman B17
A Regression Approach to Spectral Mapping for Speech Enhancement
Chin-Hui Lee, Georgia Tech

Thu 11/01/18, 01:30pm, Whitehead 304
An Introduction to Randomized Algorithms for Matrix Computations
Ilse Ipsen, North Carolina State University

Fri 10/26/18, 12:00pm, Hackerman B17
Automated Scalable Bayesian Inference via Data Summarization
Tamara Broderick, MIT

Thu 10/25/18, 10:30am, Hackerman B17
Algorithmic Methods for Massively Parallel Data Science
Ben Moseley, CMU

Wed 10/17/18, 12:00pm, Malone 228
Adversarial Bandits with Knapsacks
Karthik Abinav Sankararaman, University of Maryland

Thu 10/04/18, 10:45am, Hackerman B17
Deep Random Forests: Algorithms and Applications
Wei Shen, Shanghai University

Mon 10/01/18, 10:30am, Mergenthaler Hall 526
Bayesian Population Projections with Migration Uncertainty
Adrian Raftery, University of Washington

Thu 09/20/18, 12:15pm, TBA
Targeted Learning Ensembles for Optimal Individualized Treatment Rules with Time-to-Event Outcomes
Ivan Diaz, Cornell Medical School

Thu 09/13/18, 03:00pm, Malone 228
Differentially Private Robust Low-Rank Approximation
Jalaj Upadhyay, JHU

Wed 09/12/18, 12:00pm, Hackerman B17
Broadening the Linear Algebra Toolkit for Engineering Applications
Edinah Gnang, JHU

Tue 09/11/18, 10:45am, Hackerman B17
Acquisition – Analysis – Augmentation: Towards Task-Aware Computer Assistance in the OR
Mathias Unberath, JHU

Thu 07/19/18, 10:00am, Hackerman B17
Direct Methods for 3D Reconstruction and Visual SLAM with Applications to Autonomous Systems
Daniel Cremers, Technical University of Munich

Fri 07/06/18, 09:00am, Hackerman B17
The Mathematics of Deep Learning
René Vidal, Johns Hopkins University

Fri 06/29/18, 10:30am, Hackerman B17
Ethics of AI in Healthcare and Beyond
Debra Mathews, JHU

Fri 05/04/18, 12:00pm, Hackerman B17
Multi-Factor Context-Aware Language Modeling
Mari Ostendorf, University of Washington

Fri 05/04/18, 10:00am, Malone G33/35
Physics-Informed Learning Machines
George Karniadakis, Brown University

Thu 05/03/18, 12:15pm, Biostats Library
A Gentle Introduction to Casual Structure Learning
Dan Malinsky, JHU

Thu 04/26/18, 03:00pm, Wilmer Bldg, Rm 107
Mining Personal, Dense, Dynamic Data Clouds to Optimize Health and Drive Discovery
Nathan Price, Institute for Systems Biology (Seattle)

Thu 04/26/18, 01:30pm, Whitehead 304
Statistical Network Modeling via Exchangeable Interaction Processes
Walter Dempsey, Harvard University

Thu 04/26/18, 10:45am, Shaffer 3
The Deep Learning Revolution in Building Intelligent Computer Systems
Jeff Dean, Google

Wed 04/25/18, 03:00pm, Shaffer 304
A Picture of the Energy Landscape of Deep Neural Networks
Pratik Chaudhari, UCLA

Tue 04/24/18, 03:00pm, Hodson 210
Shallow and Deep Representations for Unconstrained Object and Action Detection and Localization
Rama Chellapa, University of Maryland

Thu 04/19/18, 10:45am, Hackerman Hall B17
New Frontiers in Imitation Learning
Yisong Yue, Caltech

Mon 04/16/18, 12:15pm, School of Public Health, Room W2008
Causal Inference via Convex Optimization
Stefan Wager, Stanford University

Mon 04/16/18, 12:00pm, Hackerman Hall B17
Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)
Been Kim, Google

Thu 04/05/18, 03:00pm, Hodson 203
Deep Networks for Open Set Visual Recognition
Vishal Patel, Rutgers University

Thu 04/05/18, 01:30pm, Whitehead 304
Adaptive Robust Control Under Model Uncertainty
Igo Cialenco, Illinois Institute of Technology

Thu 04/05/18, 12:15pm, Clark Hall 316
A game theoretic approach to numerical approximation and algorithm design
Houman Owhadi, Caltech

Wed 04/04/18, 12:00pm, Schaffer Hall 101
Machine Learning in Health Care
Katherine Heller, Duke University

Fri 03/30/18, 01:30pm, Shaffer 100
Random Walks on Secondary Structure and the Folding of RNA
Stuart Geman, Brown University

Thu 03/29/18, 01:30pm, Shaffer 100
Real and Artificial Neural Networks
Stuart Geman, Brown University

Wed 03/28/18, 03:00pm, Shaffer 304
Learning Along the Edge of Deep Networks
Rama Chellappa, University of Maryland

Wed 03/21/18, 10:30am, Malone 107
How good is your classifier? Revisiting the role of metrics in machine learning
Sanmi Koyejo, University of Illinois

Wed 03/21/18, 10:00am, Krieger 111
Dancing with TURKs or Taiji (Tai Chi) with a Master?
Yanxi Liu, Penn State University

Thu 03/15/18, 01:30pm, Whitehead 304
Bayesian Monotone Regression: Rates, Coverage and Tests
Subhashis Ghoshal, NC SU

Thu 03/15/18, 10:45am, Hackerman B17
Analyzing Human Behavior to Make Computing More Useful
Brian Smith, Columbia University

Thu 03/15/18, 10:00am, Clark Hall 110
From Shallow and Local to Deep and Convolutional Sparse Modeling
Jeremias Sulam, Technion ( Israel Institute of Technology)

Wed 03/14/18, 12:00pm, Hackerman B17
Towards Ambient Intelligence in AI-Assisted Hospitals
Serena Yeung, Stanford University

Mon 03/12/18, 10:00am, Clark Hall 110
Nonconvex Recovery of Low-Complexity Models
Qing Qu, Columbia University

Thu 03/08/18, 10:00am, Clark 110
Advancing Big Neuroimaging Data Analysis for Precision Diagnostics
Aristeidis Soitras, University of Pennsylvania

Tue 03/06/18, 10:45am, Hackerman B17
Testing and Repairing Machine Learning Systems in Adversarial Environment
Yinzhi Cao, Lehigh University

Mon 03/05/18, 12:15pm, BSPH, Room W2008
A Mixed Modeling Framework for Analyzing Multitask Whole-Brain Network Data
Sean Simpson, Wake Forest School of Medicine

Fri 03/02/18, 12:00pm, Hackerman Hall B17
Connecting Vision and Language End-to-End
Kate Saenko, Boston University

Thu 03/01/18, 12:00am, Whitehead 304
PetuumMed: Algorithm and System for EHR-based Medical Decision-Making
Eric Xing, CMU

Sat 02/17/18, 12:15pm, 316 Clark Hall
From Biological Neural Networks to Artificial Neural Networks
Srini Turaga, HHMI Janelia Research Campus

Fri 02/16/18, 11:00am, Clark Hall 316
Restricted Isometry Property of Gaussian Random Projection for Low-Dimensional Subspaces
Yuantao Gu, Tsinghua University

Mon 02/05/18, 12:15pm, BSPH, Room W2008
Prior Adaptive Semi-supervised Learning with Application to Electronic Health Records Phenotyping
Yichi Zhang, Harvard School of Public Health

Wed 01/31/18, 03:00pm, Gilman 219
Principled Non-Convex Optimization for Deep Learning and Phase Retrieval
Tom Goldstein, University of Maryland College Park

Mon 12/04/17, 12:15pm, BSPH, Room W2008
Exponential Family Functional Data Analysis via a Low-Rank Model
Gen Li, Columbia University

Thu 11/16/17, 10:30am, Hackerman Hall B17
Feature Selection and Fusion for 3D Object Category Recognition
Haider Ali, JHU

Tue 11/14/17, 12:00pm, Hackerman B17
End-to-End Deep Learning for Broad Coverage Semantics: SRL, Conference and Beyond
Luke Zettlemoyer, University of Washington

Thu 10/19/17, 12:00pm, Hackerman B17
Online Vehicle Routing: The Edge of Optimization at the Largest Scale
Dimitris Bertsimas, MIT

Tue 10/03/17, 12:00pm, Hackerman B17
Sequence to Sequence Learning: Fast Training and Inference with Fated Convolutions
Michael Auli, Facebook AI Research

Fri 09/29/17, 12:00pm, Hackerman B17
Deep Reinforcement Learning of Sequential Decision Making Tasks with Natural Language Interaction
Satinder Singh, University of Michigan

Fri 09/22/17, 12:00pm, Hackerman B17
Structure-Sensitive Dependency Learning in Recurrent Neural Networks
Tal Linzen, JHU

Thu 09/21/17, 01:30pm, Room 461 Bloomberg Building
Tutorial on Deep Learning with Apache MXNet Gluon
Alex Smola, Amazon Machine Learning and CMU Machine Learning Department

Thu 09/21/17, 10:45am, Hackerman B17
Sequence Modeling: From Spectral Methods and Bayesian Nonparametrics to Deep Learning
Alex Smola, Amazon

Mon 09/18/17, 12:15pm, School of Public Health, Room W2008
Link-Tracing Studies of Hidden Networks
Forrest W. Crawford, Yale University

Wed 09/13/17, 03:00pm, Hodson Hall 203
No Equations, No Variables, No Parameters, No Space, No Time: Data and the Computational Modeling of Complex/Multiscale Systems
Yannis Kevrekidis, ChemBE, JHU

Fri 09/08/17, 12:00pm, Hackerman B17
An Overview of Deep Learning Frameworks and an Introduction to PyTorch
Soumith Chintala, Facebook

Tue 05/09/17, 01:30pm, Clark Hall
Understanding Deep Neural Networks with Rectified Linear Units
Amitabh Basu, JHU

Tue 05/02/17, 01:30pm, Clark Hall 314
Neural Spike Train Analysis Using The Statistical Paradigm
Robert E. Kass, CMU

Fri 04/28/17, 01:30pm, Clark 314
How Distributed ADMM is Affected by Network Topology
Guilherme Franca, Center for Imaging Science, JHU

Thu 04/27/17, 12:15pm, Genome Cafe (SPH E3609)
Enriched Training Sample Selection for Machine Learning in Cancer Screening Studies
Peng Huang, Oncology Biostatistics, JHMI

Tue 04/25/17, 10:45am, Hackerman B17
Compositional Models for Information Extraction
Mark Dredze, JHU

Mon 04/24/17, 12:15pm, SPH Room W2008
Classified Mixed Model Prediction
J. Sunil Rao, University of Miami

Mon 04/24/17, 10:45am, Shaffer 101
Geometry, Optimization and Generalization in Multilayer Networks
Nathan (Nati) Srebro, TTIC and the University of Chicago

Thu 04/20/17, 01:30pm, Whitehead 304
From Solving PDEs to Machine Learning PDEs: An Odyssey in Computational Mathematics
George Kardiadakis, Brown University

Mon 04/17/17, 12:00pm, School of Public Health, Room W2008
C-Learning: A New Classification Framework to Estimate Optimal Dynamic Treatment Regimes
Min Zhang, University of Michigan

Fri 04/14/17, 12:00pm, Hackerman B17
Bayesian Optimization and Other Potentially Bad Ideas for Hyperparameter Optimization
Kevin Jamieson, UC Berkeley

Thu 04/13/17, 01:30pm, Whitehead 304
Reciprocal Graphical Models for Integrative Gene Regulatory Network Analysis
Peter Muller, University of Texas at Austin

Wed 04/12/17, 03:00pm, Whitehead Hall 304
A Sub-Linear Deterministic FFT for Sparse High Dimensional Signals
Andrew Christlieb, Michigan State University

Mon 04/10/17, 01:30pm, Clark 314
Stochastic Approximation for Representation Learning
Raman Arora, JHU

Fri 04/07/17, 12:00pm, Hackerman B17
Sparse Non-Negative Matrix Language Modeling
Ciprian Chelba, Google Research

Wed 04/05/17, 03:00pm, Whitehead 304
On Phase Transitions for Spiked Random Matrix and Tensor Models
Afonso Bandeira, Courant Institute – NYU

Fri 03/31/17, 12:00pm, Hackerman B17
Neural Approaches to Machine Reading Comprehension and Dialogue
Jianfeng Gao, Microsoft Research

Wed 03/29/17, 10:00am, HLTCOE North Conference Room – Stieff Building
The Limits of Unsupervised Syntax and the Importance of Grounding in Language Acquisition
Yonatan Bisk, University of Southern California

Tue 03/28/17, 01:30pm, Clark Hall 314
A Well-Tempered Landscape for Non-Convex Robust Subspace Recovery
Gilad Lerman, University of Minnesota

Tue 03/28/17, 12:00pm, Hackerman B17
Thinking on Your Feet: Reinforcement Learning for Incremental Language Tasks
Jordan Boyd-Graber, University of Colorado

Mon 03/27/17, 01:30pm, Hodson Hall Board Room
Mathematical Mysteries of Deep Neural Networks
Stephane Mallat, Ecole Normale Superieure

Mon 03/27/17, 12:15pm, Room W2008
Nonparametric Spatial-Temporal Modelling of the Association Between Ambient Air Pollution and Adverse Pregnancy Outcomes
Montserrat Fuentes, Virginia Commonwealth University

Mon 03/27/17, 09:30am, HLTCOE North Conference Rm – Stieff Building
Probabilistic Models for Large, Noisy and Dynamic Data
Jay Pujara, University of California at Santa Cruz

Thu 03/16/17, 01:30pm, Whitehead 304
Nuke the Clouds: Using Nuclear Norm Optimization to Remove Clouds from Satellite Images
Peder Olsen, IBM

Wed 03/08/17, 12:00pm, Malone 228
Time-Space Hardness of Learning Sparse Parities
Avishay Tal, Princeton

Fri 03/03/17, 05:00pm, Clark Hall 314
Theoretical Guarantees for Convolutional Sparse Coding, and a Look into Convolutional Neural Networks
Jeremias Sulam, Israel Institute of Technology

Fri 03/03/17, 09:00am, Clark Hall 314
Up-Scaling Dictionary Learning
Jeremias Sulam, Israel Institute of Technology

Thu 03/02/17, 01:30pm, Whitehead 304
Hierarchical Bayesian Modeling of Cosmic Populations: Why and How
Tom Loredo, Cornell University

Fri 02/24/17, 01:30pm, 314 Clark Hall
Object Categorization in Real World
Xilin Chen, Chinese Academy of Sciences

Thu 02/23/17, 04:00pm, Mason Hall Auditorium
Geometric Methods for the Approximation of High-Dimensional Dynamical Systems
Mauro Maggioni, JHU

Tue 02/21/17, 01:30pm, Krieger 143
Objective Functionals of Machine Learning
Dejan Slepcev, CMU

Mon 02/20/17, 03:00pm, Krieger 413
Data-Driven Mathematical Analysis and Scientific Computing for Oscillatory Data
Haizhao Yang, Duke University

Fri 02/17/17, 12:00pm, Krieger 413
Data-Driven Stochastic Model Reduction
Fei Lu, University of California Berkeley

Tue 02/14/17, 12:00pm, Hackerman B17
Algorithmic Bias in Artificial Intelligence: The Seen and Unseen Factors Influencing Machine Perception of Images and Language
Margaret Mitchell, Google Research

Thu 02/09/17, 04:00pm, Mudd Hall, Room 100
Mapping Behavior to Neural Anatomy Using Machine Vision and Thermogenetics
Kristin M. Branson, HHMI, Janelia Research Campus

Tue 02/07/17, 01:30pm, 314 Clark Hall
Low-dimensional Manifold Models for Image Registration and Bayesian Statistical Shape Analysis
Miaomiao Zhang, MIT CSAIL

Mon 02/06/17, 12:15pm, Room W2008
Bayesian Model Calibration and Prediction Applied to (Stochastic) Epidemic Simulations
David Higdon, Biocoplexity Institute of Virginia Tech

Tue 01/31/17, 01:30pm, 314 Clark Hall
Zero-Shot Learning for Object Recognition in the Wild
Fei Sha, University of Southern California

Thu 01/19/17, 11:00am, Malone 107
Edward: A library for probabilistic modeling, inference, and criticism
Dustin Tran, Columbia University

Thu 12/01/16, 02:00pm, Clark 314
Advances in Algebraic Subspace Clustering and Dual Principal Component Pursuit
Manolis Tsakiris, JHU

Thu 12/01/16, 01:30pm, Whitehead 304
Spectral Clustering for Dynamic Stochastic Block Model
Sharmodeep Bhattacharyya, Oregon State University

Wed 11/30/16, 03:00pm, Krieger 309
Measure transport approaches for Bayesian computation
Youssef Marzouk, MIT

Fri 11/18/16, 12:00pm, School of Pubic Health W3008
Doubly Robust Survival Trees and Forests
Jon Steingrimsson, JHU Biostatistics

Fri 11/18/16, 12:00pm, Hackerman B17
Flexible Models for Microclustering with Application to Entity Resolution
Rebecca Steorts, Duke University

Mon 11/14/16, 01:30pm, Charles Commons Conference Center
Diffusion-based Interactions in Noisy Single Cell Data
Smita Krishnaswamy, Yale School of Medicine

Thu 11/03/16, 01:30pm, Whitehead 304
An Introduction to Distance Preserving Projections of Smooth Manifolds
Mark Iwen, Michigan State University

Wed 11/02/16, 12:00am, Krieger 309
Variational Problems on Graphs and their Continuum Limits
Dejan Slepcev, Carnegie Mellon University

Tue 11/01/16, 01:30pm, Clark Hall 314
3D Object Geometry from Single Image
Xiaowei Zhou, University of Pennsylvania

Tue 11/01/16, 11:00am, Clark Hall 110
Change Point Estimation of Brain Shape Data in Relation with Alzheimer;s Disease
Laurent Younes, Johns Hopkins University

Thu 10/27/16, 01:30pm, Whitehead 304
Adaptive Contrast Weighted Learning and Tree-based Reinforcement Learning for Multi-Stage Multi-Treatment Decision-Making
Lu Wang, University of Michigan

Wed 10/26/16, 01:30pm, Clark Hall 314
My Aventures with Bayes: In Search of Optimal Solutions in Machine Learning, Computer Vision and Beyond
Aleix M. Martinez, Ohio State University

Wed 10/26/16, 12:00pm, Malone 228
Privacy, Information and Generalization
Adam Smith, Penn State

Fri 10/21/16, 01:30pm, Clark Hall 314
Segmentation and Tracking in Bioimage Analysis, and the Discrete Optimization Problems they Engender
Fred Hamprecht, University of Heidelberg

Mon 10/10/16, 12:15pm, School of Public Health, Room W2008
Improving the Robustness of Doubly Robust Estimators in Missing Data Analysis
Peisong Han, University of Waterloo

Fri 10/07/16, 12:00pm, Hackerman B17
Procedural Language and Knowledge
Yejin Choi, University of Washington

Thu 10/06/16, 01:30pm, Whitehead 304
Stochastic Search Methods for Simulation Optimization
Enlu Zhou, Georgia Tech University

Mon 10/03/16, 12:15pm, BSPH Room W2008
Real-Time Prediction of Infectious Disease Outbreaks
Nicholas Reich, University of Massachusetts, Amherst

Wed 09/28/16, 03:00pm, Krieger 309
Global Optimality in Matrix and Tensor Factorization, Deep Learning, and Beyond
Rene Vidal, Johns Hopkins University

Tue 09/27/16, 12:00pm, Bloomberg School of Public Health E3609 (Genome Cafe)
Bayesian Nonparametric Models for Causal Inference with Missing-At-Random Covariates
Jason Roy, University of Pennsylvania

Thu 09/22/16, 01:30pm, Whitehead 304
High-Dimensional Analysis of Stochastic Algorithms for Convex and Nonconvex Optimization: Limiting Dynamics and Phase Transitions
Yue Lu, Harvard University

Tue 09/20/16, 02:00pm, Gilman 132
From Molecular Dynamics to Large Scale Inference
Ben Leimkuhler, University of Edinburgh

Thu 09/15/16, 01:30pm, Maryland Hall 110
Stochastic Newton Methods for Machine Learning
Jorge Nocedal, Northwestern University

Mon 09/12/16, 12:15pm, School of Public Health, Room W2008
Online Estimation of Optimal Treatment Allocations for Control of an Emerging Infectious Disease
Eric Laber, North Carolina State University Department of Statistics

Tue 09/06/16, 11:00am, Clark Hall 110
Mining Big Data for Molecular Marker Detection
Su-In Lee, University of Washington

Wed 05/11/16, 01:30pm, Clark Hall 314
Big Data in Behavioral Medicine
James M. Rehg, Georgia Institute of Technology

Tue 05/10/16, 04:30pm, Clark 314
How to break the unrealistic symmetries of data analysis on high-dimensional manifolds:Tactics and prototypes from morphometrics
Fred Bookstein, University of Washington

Tue 05/10/16, 01:30pm, Clark 314
Internal Representations in Deep Networks for Object Detection
Alan Yuille, JHU

Thu 04/28/16, 01:30pm, Mergenthaler 111
Unveiling the mysteries in spatial gene expression
Bin Yu, UC Berkeley

Wed 04/27/16, 01:30pm, Gilman 50
Movie Reconstruction from Brain Signals: “Mind Reading”
Bin Yu, UC Berkeley

Wed 04/27/16, 12:00pm, Malone 107
Multiresolution Matrix Factorization
Risi Kondor, University of Chicago

Mon 04/25/16, 12:10pm, Room W3008, School of Public Health
Sparse CCA: Statistical and Computational Limits
Zongming Ma, Wharton School, University of Pennsylvania

Mon 04/18/16, 12:00am, Room W3008, Bloomberg School of Public Health
Noise-Addition Methods and the False Selection Rate (FSR) Approach
Dennis Boos (Joint Work with Len Stefanski), Dept. of Statistics, North Carolina State University

Fri 04/15/16, 12:00pm, Hackerman B17
Sampling to Efficiently Train Bilingual Neural Network Language Models
Colin Cherry, National Research Council of Canada

Thu 04/14/16, 01:30pm, Whitehead 304
Scalable Bayesian Models of Interacting Time Series
Emily Fox, University of Washington

Fri 04/08/16, 12:00pm, Hackerman Hall B17
Deep Learning and Linguistic Structure
Alexander “Sasha” Rush, Harvard University

Thu 04/07/16, 01:30pm, Whitehead 304
Distributed proximal gradient methods for cooperative multi-agent consensus optimization
Serhat Aybat, Penn State University

Mon 04/04/16, 12:15pm, School of Public Health, Room W3008
Inference of Low Dimensional Parameters with High-Dimensional Data
Cun-Hui Zhang, Rutgers University

Thu 03/31/16, 01:30pm, Whitehead 304
Co-clustering of Nonsmooth Graphons
David Choi, CMU

Tue 03/29/16, 10:45am, Hackerman Hall B17
Graphical Models for Missing Data: Recoverability, Testability and Recent Surprises!
Karthika Mohan, UCLA

Wed 03/23/16, 12:00pm, Hackerman Hall B17
The Computational, Statistical and Practical Aspects of Machine Learning
Yaoliang Yu, Carnegie Mellon University

Wed 03/23/16, 09:00am, Hackerman Hall 320
Using Motion to Understand Objects in the Real World
David Held, U.C. Berkeley

Thu 03/10/16, 01:30pm, Whitehead 304
A Bayesian Nonparametric Model for Comparative Effectiveness Research
Gary Rosner, JHU (School of Medicine)

Fri 03/04/16, 12:00pm, Hackerman B17
Towards Intelligent Audio Analysis and Understanding
John Hershey, MERL

Thu 03/03/16, 01:30pm, Whitehead 304
Mediation: From Intuition to Data Analysis
Ilya Shpitser, Johns Hopkins University

Wed 03/02/16, 04:00pm, Gilman Hall, Room 50
Teaching Machines to See
Rene Vidal, Johns Hopkins University

Tue 02/23/16, 01:30pm, Whitehead 304
Structure-Enhancing Algorithms for Statistical Learning Problems
Paul Grigas, MIT

Tue 02/23/16, 12:00pm, Hackerman B17
Future (?) of Machine Translation
KyungHyun Cho, New York University

Thu 02/18/16, 01:30pm, Whitehead 304
Robust and Efficient Collocation Methods for Parameterized Models
Akil Narayan, University of Utah

Thu 02/18/16, 12:15pm, Room @5030, School of Public Health
Novel Statistical Frameworks for Analysis of Structured Sequential Data
Abhra Sarkar, Duke University

Tue 02/16/16, 01:30pm, Clark 314
Universality Laws for Randomized Dimension Reduction
Joel A. Tropp, California Institute of Technology

Tue 02/16/16, 12:00pm, Hackerman B17
Multimodal Question Answering for Language and Vision
Richard Socher, MetaMind

Fri 02/12/16, 12:15pm, Room W3008 Bloomberg School of Public Health
Nearest Neighbor Gaussian Process Models for Massive Spatial and Spatio-temporal Data
Abhirup Datta, Division of Biostatistics, University of Minnesota

Tue 02/09/16, 12:00pm, Clark 314
Where to Buy It: Matching Street Clothing Photos in Online Shops and Visual Madlibs: Fill in the Blank Image Generation and Question Answering
Alex Berg, University of North Carolina at Chapel Hill

Tue 02/02/16, 12:00pm, Hackerman B17
Binary and Multiclass Calibration in Speaker and Language Recognition
Niko Brummer, AGNITIO

Mon 02/01/16, 12:15pm, Room W3008, School of Public Health
Robust Causal Inference with Continuous Exposures
Edward Kennedy, University of Pennsylvania, Perelman School of Medicine

Fri 01/29/16, 12:15pm, Room W3008, School of Public Health
Precision Medicine, Learning Health Systems, and Improving Surveillance of Low Risk Prostate Cancer
Yates Coley, Johns Hopkins Bloomberg School of Public Health

Thu 01/28/16, 01:30pm, Whitehead 304
Feature Allocations, Probability Functions and Paintboxes
Tamara Broderick, MIT

Tue 01/26/16, 12:00pm, Hackerman B17
Interactive Training of Relation Embeddings Using High-Level Supervision
Sameer Singh, University of Washington

Fri 01/15/16, 12:15pm, Room W4030, School of Public Health
Statistical Methods for Inference in High Dimensional “Omics” Data
Ni Zhao, Fred Hutchinson Cancer Research Center

Mon 01/11/16, 12:15pm, Room W2008, School of Public Health
High-Dimensional Matrix Linear Regression Model
Dehan Kong, University of North Carolina, Chapel Hill

Tue 01/05/16, 12:15pm, Room W4030, School of Public Health
Robust Bayesian Inference via Coarsening
Jeffrey Miller, Duke University

Thu 12/17/15, 01:15pm, Malone Hall 328
A Unified Framework for Large-Scale Block-Structured Optimization
Mingyi Hong, Iowa State University

Wed 12/09/15, 12:15pm, Bloomberg School of Public Health, Room W5030
Fast Bayesian Factor Analysis via Automatic Rotations to Sparsity
Veronika Rockova, The Wharton School, University of Pennsylvania

Tue 12/08/15, 10:00am, Charles Commons, Barber Conference Room
(Data-driven) Strategies to Predict Intra- and Inter- Cellular Signaling Dynamics and Function
Neda Bagheri, Northwestern University

Tue 12/01/15, 01:30pm, Clark 314
A Segmentation Algorithm for Efficient Neural Reconstruction from Electron Microscopy Data
Toufiq Parag, Janelia Farm Research Campus

Tue 11/24/15, 10:45am, Hackerman B17
Data Centers, Energy and Online Optimization
Adam Wierman, California Institute of Technology

Fri 11/20/15, 12:00pm, Hackerman B17
Linear Methods for Linguistics
Dean Foster,

Tue 11/17/15, 01:30pm, Clark 314
Robust and Scalable Approach to Bayesian Inference
Stanislav Minsker, University of Southern California

Thu 11/12/15, 01:30pm, Whitehead 304
Scaling and Generalizing Variational Inference
David Blei, Columbia University

Tue 11/10/15, 12:00pm, Hackerman B17
Better Science Through Better Bayesian Computation
Ryan Adams, Harvard University

Tue 11/03/15, 12:00pm, Hackerman B17
Towards Fast Autonomous Learners
Emma Brunskill, Carnegie Mellon University

Wed 10/28/15, 10:00am, Clark 314
Global Optimality in Representation Learning
Ben Haeffele, JHU

Tue 10/27/15, 12:00pm, Hackerman B17
Beating the Perils of Non-Convexity: Guaranteed Training of Neural Networks Using Tensor Methods
Anima Anandkumar, UC Irvine

Tue 10/20/15, 01:30pm, Clark 314
Signal Recovery from Scattering Convolutional
Joan Bruna, UC Berkeley

Tue 10/20/15, 01:30pm, Clark 314
Signal Recovery from Scattering Convolutional Networks
Joan Bruna, Dept of Statistics at UC Berkeley

Tue 10/13/15, 12:00pm, Hackerman B17
Deep Learning in NLP and Beyond
Tomas Mikolov, Facebook

Thu 10/08/15, 01:30pm, Whitehead 304
Change Point
George Michailidis, University of Florida

Thu 10/08/15, 01:30pm, Whitehead 304
Change Point Inference for Time-Varying Erdo-Renyi Graphs
George Michailidis, University of Florida

Tue 10/06/15, 12:00pm, Hackerman B17
Neuromorphic Language Understanding
Guido Zarrella, MITRE Corporation

Tue 09/29/15, 12:00pm, Hackerman B17
Learning and Mining in Large-Scale Time Series Data
Yan Liu, University of Southern California

Tue 09/29/15, 10:00am, Barber Conference Room at Charles Commons
Deep Learning for Regulatory Genomics
Anshul Kundaje, Stanford University

Thu 09/24/15, 01:30pm, Whitehead 304
Challenges in Graph-Based Machine Learning and Robustifying Data Graphs with Scalable Local Spectral Methods
Michael Mahoney, UC Berkeley

Fri 09/11/15, 12:00pm, Hackerman B17
Machine Reading for Cancer Panomics
Hoifung Poon, Microsoft Research

Tue 09/08/15, 11:00am, Sherwood Room, Levering Hall
Individualized Prognosis of Diseas Trajectories: Application to Scleroderma
Suchi Saria, Johns Hopkins University

Fri 08/28/15, 12:00pm, Hackerman B17
I-Vector Representation Based on GMM and DNN for Audio Classification
Najim Dehak, MIT, CSAIL

Tue 04/21/15, 03:00pm, Arellano Theater
Sensitivity Analysis of Neuronal Behaviors
Rodolphe Sepulchre, Cambridge University, England

Fri 04/17/15, 12:00pm, Room W3030, School of Public Health
Missing Data as a (Restricted) Causal Inference Problem
Ilya Shpitser, University of Southhampton

Thu 04/16/15, 10:45am, Hackerman Hall B17
Generalized Independence Constraints: Models and Inference
Ilya Shpitser, University of Southampton

Wed 04/15/15, 10:30am, COE Stieff Building, North Conference Room
Incorporating Compositional and Relational Semantics into Word Representation Learning
Kevin Duh, Nara Institute of Science and Technology

Wed 04/01/15, 12:00pm, Hackerman Hall B17
Active Information Acquisition with Mobile Robots and Configurable Sensing Systems
George J. Pappas, University of Pennsylvania

Wed 04/01/15, 12:00pm, Malone 228
Bandits with Resource Constraints
Alex Slivkins, Microsoft Research

Mon 03/30/15, 12:15pm, Room W3008, School of Public Health
Effective Connectivity and Dynamic Directional Model for ECoG Data
Dr. Tingting Zhang, Department of Statistics, University of Virginia

Fri 03/27/15, 12:00pm, Hackerman B17
Recursive Estimation of Multivariate Markov Processes
Yariv Ephraim, George Mason University

Tue 03/24/15, 10:45am, Hackerman Hall B17
Generalizability in Causal Inference
Elias Bareinboim, University of California, Los Angeles

Mon 03/23/15, 12:15pm, Room W3008, School of Public Health
Bayes and Big Data: The Consensus Monte Carlo Algorithm
Steven L. Scott, Alexander Blocker, Fernando Bonassi

Fri 03/20/15, 12:00pm, Hackerman Hall B17
Ensembles for the Discovery of Compact Structures in Data
Madalina Fiterau, Carnegie Mellon University

Wed 03/18/15, 12:30pm, Clark 314
Toward Large-Scale Human Behavior Analysis
Minh Hoai Nguyen, Stony Brook University

Tue 03/10/15, 01:30pm, Clark 314
Hamiltonian Monte Carlo in Computational Anatomy
Christof Seiler, Stanford University – Department of Statistics

Fri 02/13/15, 12:15pm, Room W3008, School of Public Health
Robust Covariance Functional Inference
Fang Han, JHU, Department of Biostatistics

Tue 02/10/15, 01:30pm, Clark 314
Operator Splitting and Optimization
Wotao Yin, UCLA, Department of Mathematics

Tue 02/10/15, 10:45am, Hackerman B17
Towards Scalable Analysis of Images and Videos
Eric Xing, Carnegie Mellon University

Wed 01/28/15, 12:15pm, Room W3008, School of Public Health
Instrumental Variables and Mendelian Randomization with Invalid Instruments
Hyunseung Kang, Statistics Department, University of Pennsylvania

Tue 01/27/15, 01:30pm, Whitehead 304
Tuning Parameters in High-Dimensional Statistics
Johannes Lederer, Cornell University

Fri 01/23/15, 12:15pm, Room W3030, School of Public Health
Quantifying Ozone-Related Mortality Under Climate Chance: Methods to Incorporate Uncertainty in Future Ozone Exposures
Stacey Alexeeff, National Center for Atmospheric Research

Mon 01/05/15, 12:15pm, Room W3030, School of Public Health
Optimal Inference After Model Selection
William Fithian, Stanford University

Fri 12/19/14, 12:00am, Room W3008, School of Public Health
Methods for Quantifying Conflict Casualties in Syria
Rebecca C. Steorts, Carnegie Mellon University

Thu 12/18/14, 09:00pm, 1st Floor Hackerman Hall
Data Mining Poster Presentations

Thu 12/11/14, 10:30am, Malone 228
Mediation Analysis: Theory and Methods
Ilya Shpitser, University of Southampton

Wed 12/03/14, 12:00pm, Hackerman Hall B27
Model-Based Tracking Using 2D and 3D Visual Information
Henrik I. Christensen, Georgia Tech

Tue 11/25/14, 10:30am, Hodson 213
Multiscale Geometric Methods for Statistical Learning and Data in High-Dimensions
Mauro Maggioni, Duke University

Thu 11/20/14, 01:30pm, Maryland 110
The Revival of Coordinate Descent Methods
Stephen Wright, University of Wisconsin-Madison

Tue 11/18/14, 12:00pm, Hackerman Hall B17
The Unreasonable Effectiveness of Deep Learning
Yann LeCun, Facebook

Tue 11/11/14, 01:30pm, Clark 314
Bringing Structure to Network Analysis
Blair D. Sullivan, North Carolina State University

Mon 11/10/14, 12:15pm, Room W3008, School of Public Health
Bayesian Latent Factor Models Recover Gene Networks and Expression QTLs
Barbara Engelhardt, Princeton University

Tue 10/28/14, 01:30pm, Clark 314
Feature Selection with Annealing for Big Data Learning
Adrian Barbu, Department of Statistics, Florida State University

Thu 10/23/14, 01:30pm, Maryland 110
Generative Models for Image Analysis
Lo-Bin Chang, Johns Hopkins University

Tue 10/21/14, 10:30am, Hackerman B17
Complexity and Compositionality
Alan Yuille, University of California, Los Angeles

Mon 10/20/14, 12:15pm, Room W308, School of Public Health
Recent Advances in Deep Learning: Learning Structured, Robust and Multimodal Models
Ruslan Salakhutdinov, University of Toronto

Tue 10/14/14, 01:30pm, Clark 314
Convex Biclustering
Eric Chi, Digital Signal Processing Group, Rice University

Mon 10/13/14, 12:15pm, Room W3008, School of Public Health
Time-Varying Networks Estimation and Dynamic Model Selection
Annie Qu, University of Illinois at Urbana-Champaign

Thu 10/09/14, 01:30pm, Maryland 110
Coordinate Descent Methods for Modern Optimization Problems
Rachael Tappenden, Johns Hopkins University

Tue 10/07/14, 12:00pm, Hackerman B17
Single-Channel Mixed Speech Recognition Using Deep Neural Networks
Dong Yu, Microsoft Research

Tue 10/07/14, 10:30am, Hackerman Hall B17
From Sensation to Conception: Theoretical Perspectives on Multisensory Perception and Cross-Modal Transfer
Robert Jacobs, University of Rochester

Mon 10/06/14, 12:15pm, Room W3008, School of Public Health
Analytical Approaches for Characterizing the Diffusion of New Medical Technologies
Sharon-Lise T. Normand, Harvard Medical School & Harvard School of Public Health

Thu 10/02/14, 01:30pm, Maryland 110
The Game of 20 Questions with (1) Noisy Answers and (2) Multiple Targets: A Delight of Information Theory, Probability, Control, and Computer Vision
Bruno Jedynak, Johns Hopkins University

Thu 10/02/14, 10:30am, Hackerman B17
What are Boundedly Rational Mechanisms for Language and Active Perception?
Richard Lewis, University of Michigan

Tue 09/30/14, 01:30pm, Clark 314
Deeply-Supervised Nets
Zhuowen Tu, University of California, San Diego

Tue 09/30/14, 10:30am, Hackerman Hall B17
Synergies in Word Learning
Mark Johnson, Macquarie University

Thu 09/25/14, 01:30pm, Maryland 110
Predictive Monitoring and Analytics for Physiologic Signals
Douglas Lake, University of Virginia

Wed 09/24/14, 12:00pm, Malone 328
Correctness Protection via Differential Privacy
Aaron Roth, University of Pennsylvania

Tue 09/23/14, 10:30am, Hackerman B17
Probabilistic Models of Human Language Comprehension, Production and Acquisition
Roger Levy, University of California, San Diego

Wed 09/17/14, 12:00pm, Malone 228
New Algorithms for Learning Incoherent and Overcomplete Dictionary
Rong Ge, Microsoft Research New England

Wed 09/03/14, 12:00pm, Sherwood Theater, Levering Hall
Machine Learning from Electronic Health Records
C. David Page, University of Wisconsin-Madison

Mon 08/04/14, 12:00pm, Arellano Theater
Discovery and Optimization of Dynamic Treatment Regimes through Reinforcement Learning
Joelle Pineau, McGill University

Thu 04/24/14, 01:30pm, Shaffer 101
Random Time Changes in Quantitative Finance
Rafael Mendoza-Arriaga, UT Austin, McCombs School of Business

Mon 04/21/14, 01:30pm, 314 Clark Hall
Graph Classification via Signal Subgroups: Applications in Statistical Connectomics
Joshua Vogelstein, Duke University

Mon 04/21/14, 12:15pm, SPH, Room W4030
PCA, SVD, and PVD: Regularization, Applications, Asymptotics
Dr. Haipeng Shen, University of North Carolina at Chapel Hill

Mon 04/14/14, 12:00pm, SPH, Room W4030
Scans of Poisson Random Fields and Detection of Genome Variation
Nancy Zhang, University of Pennsylvania

Wed 04/09/14, 10:00am, Clark Hall 314
Learning Hierarchical and Compositional Models and Fast Inference Algorithms for Object Direction and Tracking
Matt Tianfu Wu, University of California

Tue 04/08/14, 12:15pm, 314 Clark Hall
Hierarchical Bayesian Methods for Multiple Outcomes in Network Meta-Analysis
Hwanhee Hong, University of Minnesota

Tue 04/08/14, 12:00pm, Hackerman B17
Scalable Topic Models and Applications to Machine Translation
Ke Zhai, University of Maryland

Mon 04/07/14, 10:00am, 314 Clark Hall
Representation, Inference and Optimization Over Emerging Visual Data: From Simple to Richer Semantics
Ruonan Li, Harvard University

Tue 04/01/14, 01:00pm, 314 Clark Hall
Object Recognition and (Dynamic) Scene Analysis
Dr. Larry Davis, Institute for Advanced Computer Studies, University of Maryland, College Park

Mon 03/24/14, 12:15pm, SPH, Room W4030
Functions, Covariances and Learning Foreign Languages
Dr. John Aston, University of Cambridge, UK

Tue 03/11/14, 12:00pm, Hackerman B17
Deep Learning of Generative Models
Yoshua Bengio, University of Montreal

Thu 03/06/14, 01:30pm, Shaffer 101
Network Histograms and Universality of Blockmodel Approximation
Sofia Olhede, University College London

Wed 03/05/14, 02:00pm, Clark Hall 314
Sparse Modeling for High-Dimensional Multi-Manifold Data Analysis
Ehsan Elhamifar, University of California, Berkeley

Tue 03/04/14, 01:30pm, Clark Hall 314
A Convex-Programming Framework for Super-Resolution
Carlos Fernandez-Granda, Stanford University

Fri 02/28/14, 10:00am, Clark Hall 314
The fshape Theoretical and Numerical Framework for the Analysis of Poplulations of Textured Manifolds
Nicolas Charon, University of Copenhagen

Thu 02/27/14, 10:30am, Hackerman B17
Predicting Viral Infection from High-Dimensional Biomarker Trajectories
Minhua Chen, University of Chicago

Mon 02/17/14, 12:15pm, Room W4030, School of Public Health
Estimation Over Multiple Undirected Graphs
Yunzhang Zhu, University of Minnesota

Tue 02/11/14, 01:00pm, Clark 314
Visualizing and Understanding Convolutional Networks
Rob Fergus, New York University

Mon 02/03/14, 12:15pm, SPH, 615 N. Wolfe St, Room W4030
Bayesian Compression in High Dimensional Regression
Rajarshi Guhaniyogi, Duke University

Thu 01/30/14, 03:15pm, Gilman 400
Clearing the Jungle of Stochastic Optimization
Warren Powell, Princeton University

Thu 01/30/14, 01:30pm, Shaffer 101
Energy and Uncertainty: From the laboratory to policy, unifying the fields of stochastic optimization
Warren Powell, Princeton University

Thu 11/21/13, 01:30pm, Whitehead 304
Functional Data Analysis of Large Neuroimaging Data
Hongtu Zhu, University of North Carolina

Tue 11/19/13, 12:00pm, Hackerman B17
Pursuit of Low-dimensional Structures in High-dimensional Data
Yi Ma, Microsoft

Fri 11/15/13, 11:00am, Weinberg Auditorium (410 North Broadway)
A Bayes Rule for Subgroup Reporting — Bayesian Adaptive Enrichment Designs
Peter Muller, University of Texas at Austin

Thu 11/14/13, 01:30pm, Whitehead 304
Parameter Estimation for Systems with Binary Subsystems
James Spall, Johns Hopkins APL

Thu 11/14/13, 10:45am, Hackerman B17
Casual Inference from Uncertain Time Series Data
Samantha Kleinberg, Stevens Institute of Technology

Tue 11/12/13, 12:00pm, Hackerman B17
Bayesian Models for Social Interactions
Katherine Heller, Duke University

Mon 11/11/13, 12:15pm, Room W4030 School of Public Health
Bayesian Learning from Big Data
David Dunson, Duke University

Tue 11/05/13, 12:00pm, Hackerman B17
Submodularity and Big Data
Jeff Bilmes, University of Washington

Tue 10/22/13, 01:00pm, Clark Hall Room 314
Domain Adaptive Dictionaries for Object Recognition
Dr. Vishal Patel, U of MD Institute for Advanced Computer Studies

Tue 10/22/13, 12:00pm, Hackerman B17
Recent Progress in Acoustic Speaker and Language Recognition

Thu 10/17/13, 12:00am, Clark Hall – 3rd Floor Conference Room
Detailed Mapping of Perceptual, Linguistic and Cognitive Information Across the Human Brain
Jack Gallant, UC Berkeley

Mon 10/07/13, 12:00pm, Room W4030, School of Public Health
Characterizing Genomic Variation from Both Known and Unknown Sources
John Storey, The Lewis-Sigler Institute for Intregrative Genomics & Princeton University

Tue 10/01/13, 12:00pm, Hackerman B17
Modeling “Bootstrapping” in Language Acquisition: A Probabilistic Approach
Sharon Goldwater, University of Edinburgh

Tue 09/24/13, 10:45am, Hackerman B17
Efficiently Learning to Behave Efficiently
Michael Littman, Brown University

Mon 09/09/13, 12:15pm, Room W4030, School of Public Health
Algebraic, Sparse and Low-Rank Subspace Clustering
Rene Vidal, BME/Center for Imaging Science

Tue 08/27/13, 12:00am, Clark 314
Interactive Object Detection
Angela Yao, ETH Zurich

Wed 05/08/13, 12:00pm, Hackerman B-17
Probabilistic Modeling for Large-scale Data Exploration
Chong Wang, CMU

Mon 05/06/13, 01:30pm, Clark 110
Bayesian inference with efficient neural population codes
Alan A. Stocker, University of Pennsylvania

Thu 05/02/13, 12:00pm, Maryland 109
Image Denoising via Non-convex Optimization
Yuantao Gu, Tsinghua University, MIT

Wed 05/01/13, 04:00pm, Room W2030 School of Public Health
Regularized Matrix Decomposition and its Applications
Jianhua Huang, Texas A&M University

Tue 04/30/13, 01:00pm, Clark 314
Towards Open-Universe Image Parsing with Broad Coverage
Svetlana Lazebnik, University of Illinois, Urbana

Fri 04/19/13, 12:00pm, Hackerman B17
The Latest in DNN Research at IBM: DNN-based features, Low-Rank Matrices for Hybrid DNNs, and Convolutional Neural Networks
Tara Sainath, IBM Research

Wed 04/17/13, 01:00pm, Clark 314
Component Analysis for Human Sensing
Fernando De la Torre, CMU

Tue 04/16/13, 10:45am, Hackerman B-17
Who is similar to my patient: Large-scale Patient Similarity Learning for Healthcare Analytics
Jimeng Sun, IBM TJ Watson Research Center

Thu 04/11/13, 01:00pm, Whitehead 304
Detecting Time-dependent Subpopulations in Network Data
Lucy Robinson, Drexel University

Wed 04/10/13, 04:00pm, Room W2030 School of Public Health
Little Data: How Traditional Statistical Ideas Remain Relevant in a Big-Data World
Andrew Gelman, Columbia University

Tue 04/09/13, 12:00pm, Hackerman B17
Learning with Marginalized Corrupted Features
Kilian Weinberger, University of Washington in St. Louis

Fri 04/05/13, 12:00pm, Hackerman B17
Learning from Speech Production for Improved Recognition
Karen Livescu, TTI Chicago

Tue 04/02/13, 12:00pm, Clark 110
Sparse Inverse Covariance Matrix Estimation Using Quadratic Approximation
Inderjit Dhillon, University of Texas at Austin

Fri 03/29/13, 11:00am, Shaffer 3
Clustering Algorithms for Streaming and Online Settings
Claire Monteleoni, George Washington University

Tue 03/12/13, 10:45am, Hackerman B17
When Machines Learn About Humans
Moritz Hardt, IBM Almaden

Thu 03/07/13, 10:45am, Hackerman B17
Unraveling the genetics of disease using informed probabilistic models
Alexis Battle, Stanford University

Mon 03/04/13, 10:00am, COE (Stieff Building), North Conference room
Extracting Knowledge from Informal Text
Alan Ritter, University of Washington

Wed 02/27/13, 12:00pm, Clark 110
Characterizing Abnormal Brain Networks
Archana Venkataraman, Massachusetts Institute of Technology

Tue 02/26/13, 10:45am, Hackerman B17
Perturbation, Optimization and Statistics for Effective Machine Learning
Tamir Hazan, TTI Chicago

Mon 02/25/13, 10:00am, COE (Stieff Building), North Conference room
Building scholarly methodologies with large-scale topic analysis
David Mimno, Princeton

Fri 02/22/13, 12:00pm, Hackerman B17
Big Data Goes Mobile
Kenneth Church, IBM

Thu 02/21/13, 03:00pm, Barton 117
Pushing the Limits of Sparse Recovery: The Power of Correlation Awareness
Piya Pal, California Institute of Technology

Thu 02/21/13, 01:30pm, Levering Hall (the Great Hall)
Improving Christofides’ Algorithm for the s-t Path Traveling Salesman Problem
David Shmoys, Cornell University

Tue 02/19/13, 01:00pm, Clark 314
Efficient Algorithms for Semantic Scene Parsing
Raquel Urtasun, Toyota Technological Institute at Chicago

Tue 02/19/13, 10:45am, Hackerman B17
Fast learning algorithms for discovering the hidden structure in data
Daniel Hsu, Microsoft Research

Mon 02/18/13, 10:00am, HLTCOE (Stieff Building), North Conference room
Symbolic constraints and statistical methods: Use together for best results
Constantine Lignos, University of Pennsylvania

Tue 02/12/13, 10:45am, Hackerman B17
Learning with Humans in the Loop
Yisong Yue, Carnegie Mellon University

Tue 02/05/13, 10:45am, Hackerman B17
Stochastic approximation algorithms for large-scale unsupervised learning
Raman Arora, TTI Chicago

Fri 12/14/12, 01:00pm, Department of Political Science, Yale University
Model Assisted Causal Inference: Theory and Applications
Peter M. Aronow, Department of Political Science, Yale University

Wed 12/12/12, 04:00pm, Room W2030 School of Public Health
Data Analysis: Best Practices and Future Directions
Hadley Wickham, Rice University, Statistics Department

Thu 12/06/12, 01:30pm, Hodson Hall 210
Attribution of Extreme Climatic Events
Richard L. Smith, University of North Carolina, Chapel Hill

Wed 11/28/12, 04:00pm, Room W2030 School of Public Health
Images as Predictors in Regression Models with Scalar Outcomes
R. Todd Ogden, Columbia University

Tue 11/27/12, 12:00pm, Hackerman B17
Bridging the Gap: From Sounds to Words
Micha Elsner, Ohio State University

Thu 11/15/12, 01:30pm, Wolf St building Rm W4013
Through the Lens of Search Logs: Studies of the Online Pursuit of Healthcare Information
Eric Horvitz, Microsoft Research

Thu 11/15/12, 01:30pm, Whitehead 304
Preconditioning for Consistency in Sparse Inference
Karl Rohe, University of Wisconsin

Thu 11/15/12, 10:45am, Hackerman B17
From Data to Decisions: On Learning, Prediction, and Action in the Open World
Eric Horvitz, Microsoft Research

Tue 11/13/12, 12:00pm, Hackerman B17
From Bases to Exemplars, and From Separation to Understanding
Paris Smaragdis, University of Illinois at Urbana-Champaign

Wed 11/07/12, 04:00pm, Room W2030 School of Public Health
More Robust Doubly Robust Estimation
Anastasios (Butch) Tsiatis, North Carolina State University

Thu 10/25/12, 01:30pm, Whitehead 304
Spatial Biclustering and Nonlinear Modeling of a Complex Data Set
Alan Izenman, Temple University

Tue 10/23/12, 12:00pm, Hackerman B17
New Waves of Innovation in Large-Scale Speech Technology Ignited by Deep Learning
Li Deng, Microsoft Research

Thu 10/18/12, 01:30pm, Whitehead 304
Statistical Inference on Errorfully Observed Graphs
Carey Priebe, JHU

Thu 10/18/12, 10:45am, Hackerman B17
Tera-Scale Deep Learning
Quoc Le, Stanford University

Wed 10/10/12, 03:30pm, Room W2030 School of Public Health
GaPMM: Modeling Mixtures of Trajectory Classes in the Presence of Informative Missingness
Dr. Rebecca Nugent, Carnegie-Mellon University

Thu 10/04/12, 01:30pm, Whitehead 304
The Alternating Direction Method of Multipliers
Wotao Yin, Rice University

Tue 10/02/12, 10:45pm, Hackerman B17
Novel Probabilistic Priors for Unsupervised Discovery and Prediction from Time Series Data
Suchi Saria, JHU

Tue 10/02/12, 12:00pm, Hackerman B17
Making Computers Good Listeners
Joseph Keshet, TTI Chicago

Thu 09/27/12, 01:30pm, Whitehead 304
Statistical Modeling of Social Network Dynamics in Relational Event Histories with Multiple Recipients
Josh Lospinoso, RedOwl Analytics

Thu 09/20/12, 10:45am, Hackerman B17
Towards Improving Sampling and Understanding of Biomolecular Information in Molecular Dynamics Calculations
Tom Woolf, JHU School of Medicine

Mon 09/17/12, 04:30pm, Clark Hall 314
Dynamic Models for Human Activity Analysis
Rizwan Chaudhry, JHU

Mon 09/17/12, 12:00pm, Clark Hall 314
Distributed Optimization on Manifolds for Consensus Algorithms and Camera Network Localization
Roberto Tron, JHU

Mon 09/17/12, 09:00am, Clark Hall 314
Sparse Modeling for High‐Dimensional Multi‐Manifold Data Analysis
Ehsan Elhamifar, JHU

Tue 04/24/12, 01:00pm, Clark 314
Latent Conditional Random Fields for Joint Object Categorization and Segmentation
Rene Vidal, Johns Hopkins University BME

Wed 04/11/12, 04:00pm, School of Public Health, Room W2030
Sparsity in Multiple Kernel Learning
Dr. Ming Yuan, Georgia Tech

Tue 04/10/12, 01:00pm, Clark 314
Predicting Visual Memorability
Aude Oliva, MIT

Tue 04/03/12, 10:45am, Hackerman B17
Machine Learning for Complex Social Processes
Hanna Wallach, University of Massachusetts Amherst

Mon 04/02/12, 09:00am, Room #111 Krieger Hall
Learning and Inference as Computational Strategies for Dealing with Uncertainty
Vikranth Rao-Bejjanki, University of Rochester

Fri 03/30/12, 12:00pm, Hackerman B17
Machine Learning in the Loop
John Langford, Yahoo! Research

Thu 03/29/12, 01:30pm, Room #111 Krieger Hall
Optimal Inference with Limited Cognitive Resources
Dr. Edward Vul, University of California, San Diego

Wed 03/28/12, 04:00pm, Room W2030 of the Bloomberg School of Public Health Building
False Discovery Rate Under Arbitrary Dependence
Jianqing Fan, Princeton University

Tue 03/27/12, 12:00pm, Hackerman B17
Linguistic Structure Prediction with AD3
Noah Smith, Carnegie Mellon University

Mon 03/12/12, 04:00pm, MBI Library
Computational Symmetry
Yanxi Liu, Penn State University

Wed 03/07/12, 10:00am, COE (Stieff Building), North Conference room
Variational Bayesian Methods for Unsupervised Latent Factor Models of Text and Audio
Matthew D. Hoffman, Columbia University

Tue 03/06/12, 12:00pm, Hackerman B17
Fast, Accurate and Robust Multilingual Syntactic Analysis
Slav Petrov, Google

Tue 03/06/12, 10:45am, Hackerman B17
Scalable Bayesian Learning for Complex Data
Yuan (Alan) Qi, Purdue University

Fri 03/02/12, 12:00pm, Hackerman B17
Efficient Search and Learning for Language Understanding and Translation
Liang Huang, Information Sciences Institute/ University of Southern California

Tue 02/28/12, 01:00pm, Clark 314
Machine Learning Approaches to Reconstructing Neural Wiring Diagrams
Viren Jain, Howard Hughes Medical Institute

Tue 02/28/12, 10:45am, Hackerman B17
Probabilistic Programming: Beyond Graphical Models
David Wingate, MIT

Mon 02/27/12, 10:00am, Stieff Building, North Conference room
Statistical Modeling and Learning for Machine Translation
Kevin Gimpel, Carnegie Mellon University

Fri 02/24/12, 11:00am, Shaffer 3
Algorithms and Lower Bounds for Sparse Recovery
Eric Price, MIT

Tue 02/21/12, 12:00pm, Hackerman B17
Bayesian Nonparametric Methods for Complex Dynamical Phenomena
Emily Fox, University of Pennsylvania

Tue 02/21/12, 10:45am, Hackerman B17
Machine Learning in the Bandit Setting: Algorithms, Evaluation, and Case Studies
Lihong Li, Yahoo! Research

Mon 02/20/12, 10:00am, Stieff Building, North Conference room
Learning to Efficiently Rank
Lidan Wang, University of Maryland

Fri 02/17/12, 12:00pm, Hackerman B17
Learning to Read the Web
Tom Mitchell, Carnegie Mellon University

Thu 02/16/12, 03:45pm, Krieger Hall, room #134A
Neural Representations of Word Meanings
Tom Mitchell, Carnegie Mellon University

Thu 02/16/12, 03:00pm, Gilman Hall 132
To Adapt or Not To Adapt: The Power and Limits of Adaptive Sensing
Mark A. Davenport, Stanford University

Mon 02/13/12, 10:00am, Stieff Building, North Conference room
Interactive Machine Learning: Combining Learning Strategies with Humans in the Loop
Burr Settles, Carnegie Mellon University

Fri 02/03/12, 04:00pm, School of Public Health W2030
Missing Heritability: New Statistical and Algorithmic Approaches
Or Zuk, Broad Institute (MIT/Harvard)

Wed 02/01/12, 04:00pm, Room W2030 School of Public Health
Inference with Implicit Likelihoods for Infectious Disease Models
Roman Jandarov, Penn State University

Tue 01/31/12, 12:00pm, Hackerman B17
Scalable Topic Models
David Blei, Princeton University

Tue 01/31/12, 10:45am, Hackerman B17
Algorithms for Learning Latent Variable Models
Daniel Hsu, Microsoft Research New England

Wed 12/07/11, 04:00pm, Room W2030 School of Public Health
Bayesian Models for Mining Public Health Information from Twitter
Mark Dredze, Johns Hopkins University

Thu 12/01/11, 01:30pm, Whitehead 304
A Non-Parametric Bayesian Approach to Inflectional Morphology
Jason Eisner, Johns Hopkins University

Thu 11/17/11, 03:45pm, Krieger Hall, Room #111
A Unifying Account of Inductive Reasoning
Dr. Charles Kemp, CMU

Thu 11/17/11, 01:30pm, Gilman Hall, Room 50
A Computationally Tractable Theory of Performance Analysis in Stochastic Systems
Dimitris Bertsimas, MIT

Wed 11/16/11, 04:00pm, Room W2030 School of Public Health
Learning Discrete Graphical Model Structure
Pardeep Ravikumar, University of Texas, Austin

Tue 11/15/11, 04:30pm, Hackerman B17
Object Detection Grammars
David McAllester, Toyota Technological Institute at Chicago

Tue 11/15/11, 01:00pm, Bloomberg 475
Automated Source Classification for the Synoptic Survey Era
Joey Richards, Berkeley

Fri 11/11/11, 12:00pm, Hackerman B17
Learning Semantic Parsers for More Languages and with Less Supervision
Luke Zettlemoyer, University of Washington

Thu 11/10/11, 01:30pm, Whitehead 304
Detecting Change in Multivariate Data Streams Using Minimum Subgraphs
Robert Koyak, Naval Postgraduate School

Wed 11/02/11, 12:00pm, Hackerman B17
Robots with Language: Solving Visual Scene Understanding Tasks
Cornelia Fermüller, University of Maryland

Tue 11/01/11, 10:45am, Hackerman B17
Perception, Action and the Information Knot that Ties Them
Stefano Soatto, UCLA

Mon 10/31/11, 01:30pm, Clark 110
Optimizing the Quantity/Quality Trade-off in Connectome Inference
Carey Priebe, Johns Hopkins University

Tue 10/25/11, 04:30pm, Hackerman B17
Sparse Models of Lexical Variation
Jacob Eisenstein, Carnegie Mellon University

Tue 10/25/11, 01:00pm, Clark 314
Matrix Splitting Methods for Bound-constrained Quadratic Programming and Linear Complementarity Problems
Daniel Robinson, Johns Hopkins University

Thu 10/20/11, 03:00pm, Gilman 132
A Metric between Probability Distributions of Different Sizes
Mathukumalli Vidyasagar, University of Texas-Dallas

Tue 10/18/11, 01:00pm, Clark 314
Capturing Human Insight for Large-Scale Visual Learning
Kristen Grauman, University of Texas at Austin

Fri 10/14/11, 12:00pm, Hackerman B17
Probabilistic Hashing for Similarity Searching and Machine Learning on Large Datasets in High Dimensions
Ping Li, Cornell University

Tue 10/11/11, 10:30am, Levering, Great Hall
Open Science: The Promise and the Challenge
Michael Nielsen

Wed 09/28/11, 04:00pm, Room W2030 School of Public Health
Personalized Medicine and Statistical Learning
Michael Kosorok, UNC Chapel Hill

Tue 09/20/11, 04:30pm, Hackerman B17
When Topic Models Go Bad: Diagnosing and Improving Models for Exploring Large Corpora
Jordan Boyd-Graber, University of Maryland

Fri 09/16/11, 12:00pm, Hackerman B17
Short URLs, Big Data: Machine Learning at Bitly
Hilary Mason,

Wed 09/07/11, 12:00pm, Hackerman B17
Micro and Nano Robotics and Applications in Health Care
Brad Nelson, ETH Zurich

Tue 09/06/11, 04:30pm, Hackerman B17
Learning to Describe Images
Julia Hockenmaier, University of Illinois, Urbana-Champaign

Tue 09/06/11, 10:45am, Hackerman B17
Twenty Questions with Noisy Answers for Object Detection and Tracking
Raphael Sznitman, Johns Hopkins University

Fri 09/02/11, 12:00pm, Hackerman B17
Applications of Weighted Finite State Transducers in a Speech Recognition Toolkit
Daniel Povey, Microsoft

Wed 08/10/11, 10:30am, Hackerman B17
Hierarchical Modeling and Prior Information: An Example From Toxicology
Andrew Gelman, Columbia University

Wed 07/27/11, 10:30am, Hackerman B17
Large Scale Supervised Embedding for Text and Images
Jason Weston, Google

Wed 07/20/11, 10:30am, Hackerman B17
Distribution Fields for Low Level Vision
Erik Learned-Miller, University of Massachusetts, Amherst

Tue 04/26/11, 04:30pm, Hackerman B17
Building Watson: An Overview of DeepQA for the Jeopardy! Challenge
David Ferrucci, IBM

Tue 04/26/11, 01:00pm, Clark 314
Compressive Sensing for Computer Vision
Rama Chellappa, University of Maryland

Tue 04/19/11, 01:00pm, Clark 314
New Methods for Fast, Large, & Accurate Sequence Analytics
Vladimir Pavlovic, Rutgers University

Fri 04/15/11, 12:00pm, Mergenthaler 111
Training a Computer to See People
Deva Ramanan, University of California, Irvine

Wed 04/13/11, 01:00pm, Clark 314
Extracting Wiring Diagrams From Brain: The First 10 Teravoxels
Davi Bock, Janelia Farm Research Campus

Tue 04/05/11, 10:45am, Hackerman B17
Deep Semantics from Shallow Supervision
Percy Liang, University of California, Berkeley

Thu 03/17/11, 10:45am, Hackerman B17
Discovery and Prediction from Clinical Temporal Data
Suchi Saria, Stanford University

Tue 03/15/11, 01:00pm, Clark 314
Landmark-Dependent Hierarchical Beta Process for Robust Sparse Factor Analysis
Lawrence Carin, Duke University

Tue 03/15/11, 10:45am, Hackerman B17
Non-Commutative Harmonic Analysis in Machine Learning
Risi Kondor, Caltech

Tue 03/08/11, 10:45am, Hackerman B17
Scaling Up Probabilistic Inference
Vibhav Gogate, University of Washington

Tue 03/01/11, 01:00pm, Clark 314
Rank/Sparsity Minimization and Latent Variable Graphical Model Selection
Pablo Parrilo, MIT

Tue 03/01/11, 10:45am, Hackerman B17
Learning Hierarchical Generative Models
Ruslan Salakhutdinov, MIT

Thu 02/24/11, 10:45am, Hackerman B17
Get Another Label? Improving Data Quality and Machine Learning using Multiple, Noisy Labelers
Panos Ipeirotis, New York University

Tue 02/15/11, 10:45am, Hackerman B17
Understanding the World with Infinite Models and Finite Computation
Ryan Adams, University of Toronto and Canadian Institute for Advanced Research

Mon 02/14/11, 10:00am, COE (Stieff Building), North Conference room
Markov Logic in Machine Reading
Hoifung Poon, University of Washington

Tue 02/08/11, 04:30pm, Hackerman B17
A Scalable Distributed Syntactic, Semantic and Lexical Language Model
Shaojun Wang, Wright State University

Tue 12/14/10, 01:00pm, Clark 314
Efficient Additive Kernels via Explicit Feature Maps
Andrea Vedaldi, University of Oxford

Tue 11/23/10, 10:45am, Hackerman B17
Machine Learning and Multiagent Reasoning: From Robot Soccer to Autonomous Traffic
Peter Stone, University of Texas at Austin

Fri 11/05/10, 09:00am, Clark 110
A Non-Parametric Model for the Discovery of Inflectional Paradigms from Plain Text using Graphical Models over Strings
Markus Dreyer, Johns Hopkins University

Thu 11/04/10, 10:45am, Hackerman B17
Estimating Ultra-large Phylogenies and Alignments
Tandy Warnow, University of Texas at Austin

Thu 09/23/10, 01:30pm, Whitehead 304
Manifold Matching: Joint Optimization of Fidelity and Commensurability
Carey E. Priebe, Johns Hopkins University

Tue 09/07/10, 04:30pm, Hackerman B17
Lifted Message Passing
Kristian Kersting, University of Bonn

Wed 06/23/10, 10:30am, Hackerman B17
Human Activity Recognition Using Simple Direct Sensors
Henry Kautz, University of Rochester

Wed 05/12/10, 04:00pm, Clark 110
Genes, Networks and Disease
Joel Bader, Johns Hopkins University

Thu 04/29/10, 04:30pm, COE (Stieff Building), North Conference room
Natural Language Processing in Multiple Domains: Linking the Unknown to the Known
John Blitzer, UC Berkeley

Thu 04/29/10, 01:30pm, Whitehead 304
Information-Theoretic Validation of L1 Penalized Likelihood
Andrew Barron, Yale University

Tue 04/27/10, 04:30pm, Hackerman B17
Deep Learning with Multiplicative Interactions
Geoffrey Hinton, University of Toronto and Canadian Institute for Advanced Research

Wed 04/21/10, 03:45pm, Krieger 134A
How to Grow a Mind: Statistics, Structure and Abstraction
Josh Tenenbaum, MIT

Tue 04/20/10, 10:45am, Hackerman B17
Building Confidence in Online Learning
Mark Dredze, Johns Hopkins University

Mon 04/19/10, 11:00am, COE (Stieff Building), North Conference room
A Step Towards Fully Unsupervised, Life-Long, Incremental Learning
Frank Wood, Columbia University

Tue 03/30/10, 10:45am, Hackerman B17
People-Aware Computing: Towards Societal Scale Sensing using Mobile Phones
Tanzeem Choudhury, Dartmouth University

Thu 03/25/10, 10:45am, Hackerman B17
Approximate Inference in Graphical Models using LP Relaxations
David Sontag, MIT

Wed 03/24/10, 12:00pm, Hackerman B17
Apprenticeship Learning for Robotic Control with Application to Quadruped Locomotion and Autonomous Helicopter Flight
Pieter Abbeel, University of California, Berkeley

Tue 03/23/10, 10:45am, Hackerman B17
Understanding the Genetic Basis of Complex Diseases via Genome-Phenome Association
Seyoung Kim, Carnegie Mellon University

Thu 03/11/10, 10:45am, Hackerman B17
Hierarchical Bayesian Methods for Reinforcement Learning
David Wingate, MIT

Mon 03/08/10, 11:00am, COE (Stieff Building), North Conference room
Doing More with Less…Labeled Data: New Directions in Semi-Supervised Learning
Andrew Goldberg, University of Wisconsin-Madison

Thu 03/04/10, 04:00pm, Whitehead 304
Longitudinal Functional Principal Component Analysis
Ciprian M. Crainiceanu, Johns Hopkins University SPH

Tue 03/02/10, 01:00pm, Clark 314
Image Modeling and Enhancement with Structured Sparse Model Selection
Guoshen Yu, University of Minnesota

Mon 03/01/10, 11:00am, COE (Stieff Hall), North Conference room
New Learning Frameworks for Information Retrieval
Yisong Yue, Cornell University

Wed 02/24/10, 12:00pm, Hackerman B17
Learning a Hierarchical Compositional Shape Vocabulary for Multi-class Object Representation
Ales Leonardis, University of Ljubljana

Tue 02/23/10, 10:45am, Hackerman B17
Nonparametric Learning in High Dimensions
Han Liu, Carnegie Mellon University

Thu 02/04/10, 04:00pm, Whitehead 304
Ranking and Selection of Many Alternatives using Correlated Knowledge Gradients
Peter Frazier, Cornell University

Thu 12/03/09, 10:45am, Hackerman B17
Tele-Immersion, The Cyber-Infrastructure for Studying Body Language
Ruzena Bajcsy, University of California, Berkeley

Tue 12/01/09, 01:00pm, Clark 314
Solving Image Matching Problems Using Interior Point Methods
Camillo J. Taylor, University of Pennsylvania

Tue 11/24/09, 01:00pm, Clark Hall 110
Deformable Models and Tensor Algebraic Methods for Imaging Science
Demetri Terzopoulos, University of California, Los Angeles

Tue 11/17/09, 04:30pm, Hackerman B17
Graph Identification
Lise Getoor, University of Maryland

Tue 11/17/09, 01:00pm, Clark 314
Clustering, Gaussian Mixture Models, and Sparse Eigenfunction Bases for Semi-Supervised Learning
Mikhail Belkin, Ohio State University

Tue 11/03/09, 01:00pm, Clark 314
Learning from Data Using Matchings and Graphs
Tony Jebara, Columbia University