The University of Massachusetts Amherst
University of Massachusetts Amherst

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Event Calendar


Scott Linderman of Harvard University

Ari Kobren of UMass CS

Jean-Baptiste Tristan from Oracle Labs

Karthik Raman from Cornell University

Bishan Yang from Cornell University

Marek Petrik from IBM Research

Francesca Rossi from University of Padova and Harvard University

Kai-Wei Chang from University of Illinois at Urbana Champaign

Jennifer Listgarten from Microsoft Research

Rob Platt from Northeastern University

Hack UMass

Jason Weston - Memory Networks

Full Contact Entrepreneurship and the Liberal Arts

Computational Aspects of Complex Pattern Formation

Graph Construction for Manifold Discovery

Disciplinary Thinking, Computational Doing

Steve Willis

Chinese Restaurant Processes

Deep Learning Poster Session

Mass Big Data Western Mass Tech Treks

Data Science Career Mixer

CICS Career Fair

Xialoan Wang

Karthik Narasimhan

Laura Haas

Zhaoliang Lun

Zornitsa Kozareva

Jason Yosinski

David Duvenaud

Statistical Modeling in Global Health

Scott Linderman

Jean-Baptiste Tristan

Microsoft AzureTM Cloud Computing Workshop

Samuel Madden

DataCamp: Teaching Data Science at Scale

Variable Selection is Hard

Implicit methods for principled estimation with large data sets

Data Science at MassMutual

3 D's of Anomaly Mining in Complex Graphs: Definition, Detection, and Description

DS Research Symposium

Protecting Computer Systems by Eliminating Vulnerabilities

Amazon Echo and Alexa

Inference and Learning with Deep Structured Distributions

Five College DataFest - informational meeting

Learning in Strategic Environments: Theory and Data

Scalable Gaussian Processes for Scientific Discovery

Poster Session

Customized Stroke Rehabilitation & Software Development Tool-building

Eye Tracking: Methods & Applications

Rachel Cummings, Adaptive Learning With Robust Generalization Guarantees

Manzil Zaheer

Brad Hayes

Christina Lee

Matthew Gombolay

Matthew Taylor

Five College DataFest

Innovation Challenge Finale

Data Science Projects at MassMutual

UMass Linguistics

Professor Xiangnan Kong

Grad Student talks

Beating The News: Predicting Significant Societal Events From Open Source Data

Texts Come from People - How Demographic Factors Influence NLP Models

2017 Data Science Research Symposium

GWIS & GRiD Presents: Free R/Python Workshops

GWIS & GRiD: R/Python - Data Extraction/Pre-processing

GWIS & GRiD: R/Python - Models & Inference

GWIS & GRiD: R/Python - Viz & Optimization

Antoine Bordes - Reasoning With Memory Networks Successes And Challenges

Thien Nyugen - Neural Information Extraction with Memory

Yoshua Bengio

Tommi Jaakkola

Data Science Concentration Information Session

George Grinstein

Sheila Werth, Kevin Winner and Garrett Bernstein

Deep Learning Whiteboard Talks

Frank Linton

Demographic Dialectal Variation in Social Media and Structured Prediction Models for RNN based Sequence Labeling in Clinical Text

Charalampos Mavroforakis

Whiteboard Talks

Miriam Madsen

CS585 NLP Poster Session

Career Mixer 2016

Career Mixer Practice

Chao Chen - Topological Analysis of Modern Data

Entrepreneur-in-Residence office hours

Steve Willis Office hours

Chris Kedzie, Real-Time Web Scale Event Summarization Using Sequential Decision Making

Tianan Xue - Visual Dynamics Probabilistic Future Frame Synthesis Via Cross Convolutional Networks

Andreas ten Pas - Grasp Pose Detection In Dense Clutter

Jayant Krishnamurthy - Semantic Parsing To Probabilistic Programs For Situated Question Answering

John Lalor - Building Evaluation Scales For NLP Using Item Response Theory

Siva Reddy - Freebase Semantic Parsing With And Without QA Pairs

Hugo Larochelle - Fighting our Big Data Addiction with Representation Learning

Jiajun Wu - Computational Perception of Physical Object Properties

Jamie Morgenstern

Byron Boots

Evan Shelhamer - A Fuller Understanding Of Fully Convolutional Networks

Samantha Kleinberg - Causal Inference and Explanation to Improve Human Health

Pegram Rooshenas - Learning Tractable Graphical Models

Steve Willis - Entrepreneurial advice

Mohit Iyyer, University of Maryland

MassMutual Data Science Development Program Info Session

Philip Thomas - Safe Machine Learning

Stephan Mandt - Advances In Scalable Probabilistic Modeling Theory Applications And Challenges

Anastasia's Kyrillidis - Rethinking algorithms in Data Science: Scaling up optimization using non-convexity, provably

Nanyun (Violet) Peng - Representation Learning with Joint Models for Information Extraction

Hao Tang - Sequence Prediction With Neural Segmental Models

Naomi Fitter - Exploring Human-Inspired Haptic Interaction Skills For Robots

Andrew Trapp - Using Density To Identify Fixations In Gaze Data Optimization-Based Formulations And Algorithms

Carsten Eickhoff - Clinical Text Understanding and Decision Support

Georges Grinstein - Opportunities for Collaborative Research in Visual Analytics (with me)

Rachel Cummings - The Implications of Privacy-Aware Choice

Amit Sharma - Causal data mining: Estimating causal effects at scale

Hack2O - Weekend Hackathon with GRiD

Yingyu Liang - Theory for New Machine Learning Problems and Applications

Nan Jiang - New Results in Statistical Reinforcement Learning

Five College DataFest

"Practical Challenges and Applications of Structured Predictions”

Ming Yin - “Peeking into the On-Demand Economy”

Mohair Iyyer - “Using Deep Learning to Understand and Answer Questions about Creative Language”

David Moorman - Making Sense of Neuron Ensembles: Advances and Issues in Neural Coding

Mikael Henaff - Tracking The World State With Recurrent Entity Networks

Alp Kucukelbir - Towards Automated Machine Learning

Ilya Razenshteyn - New Algorithms for High-Dimensional Data

Travis Mandel - "Better Education Through Improved Reinforcement Learning"

Arthur Spirling - "Text Preprocessing For Unsupervised Learning: Why It Matters, When It Misleads, And What To Do About It"

Robert Kozma - Computational Aspects of Brain Dynamics: Experiments, Models, and New AI Approaches

Sainbayar Sukhbaatar - Intrinsic Motivation And Automatic Curricula Via Asymmetric Self-Play

Marco Serafini - Democratizing Graph Analytics

She Started It

A Morning with Cathy O'Neil, Mount Holyoke College, April 8th

Hari Balasubramanian - Models Based on Longitudinal Healthcare Event Data

Electrophysiological Measures of Attention and Speech Processing

Yexiang Xue - Combining Reasoning and Learning for Data Science and Decision Making: Integrating Concepts from AI, Sustainability, and Scientific Discovery

Faisal Nawab - Efficient Coordination for Global-Scale Data Management

Tom Williams - Genuine Helpers: Enabling Natural Language Capabilities for Interactive Robots

Scott W. Linderman - Bayesian Learning and Inference in Recurrent Switching Linear Dynamical Systems

Mark Dredge - Compositional Models for Information Extraction

MS Industry Mentorship Student Presentations

Qiang Liu - A Stein Variational Framework for Deep Probabilistic Modeling

Career Mixer 2017

Center for Data Science introduction

Pat Flaherty - A Nonparametric Bayesian Model for Single-cell Variant Calling

Nika Haghtalab - Oracle-efficient Online Learning And Applications To Auction Design

Francesco Orabona - Coin Betting For Backprop Without Learning Rates And More

Varun Jampani - Bilateral Neural Networks for Image,Video and 3D Vision

Kyunghyun Cho - Deep Learning, Where are you going?

Claudia Pérez D'Arpino

Tsendsuren Munkhdalai - Language Understanding And Reasoning With Memory Augmented Neural Networks

Jeff Flanigan - Parsing And Generation For The Abstract Meaning Representation

Alessandro Epasto (Google NYC) - Mining Graphs at Scale: Ego-Networks, Clusters and Privacy

Shilpa Nadimpalli Kobren - Data-driven approaches for discovering perturbed interaction interfaces in cancer

Josh Speagle (Harvard) - Big Data "Inference": Combining Hierarchical Bayes and Machine Learning to Improve Photometric Redshifts

Career Mixer Poster Session

Robert Moss (MIT Lincoln Laboratory) - A Decision Theoretic Approach to Future Aircraft Collision Avoidance

Yogarshi Vyas - Detecting Asymmetric Semantic Relations in Context : A Case-Study on Hypernymy Detection

Ben Baumer - How often does the best team win? A unified approach to understanding randomness in North American Sport

Song Gao - Travel Decision Making in an Uncertain, Dynamic, Information-Rich Urban Network

Brittany Johnson - Producing Productive Programmers: A Tool (Mis)communication Theory and Adaptive Approach for Supporting Developer Tool Use

Whose Analysis? Whose Expertise? Partnering for Better Data Analytics for Small Cities

Transforming EA using Artificial Intelligence

Beomjoon Kim - Learning to Guide Task and Motion Planning by Predicting Constraints

Mennatallah El-Assady - Visual Analysis of Verbatim Text Transcripts

What Makes a Good Argument? Understanding and Predicting High Quality Arguments Using NLP Methods

Jen Gong, Tristan Naumann - Predicting Clinical Outcomes Across Changing Electronic Health Record Systems

Steven Wu - A Smoothed Analysis of the Greedy Algorithm for the Linear Contextual Bandit Problem

Screening of AlphaGo

Claudia Pérez D’Arpino - Learning How to Plan for Multi-Step Manipulation in Collaborative Robotics

Rajesh Ranganath - Black Box Variational Inference: Scalable, Generic Bayesian Computation and its Applications

Richard Scheines - Causal Discovery with Measurement Error

Rishab Nithyandand - Online Political Discourse in the Trump Era

Robert Kozma - Dynamical Aspects of AI and Neural Networks

Women in Data Science

MassMutual Data Science Development Program Info Session

Michael I. Jordan - On Computational Thinking, Inferential Thinking and Data Science

Ventures @ CICS Panel & Networking Event

Graham Neubig - What Can Neural Networks Teach us about Language?

Bharath Hariharan - Visual recognition beyond large labeled training sets

Adji Dieng - Deep Sequence Models: Context Representation, Regularization, and Application to Language.

Yulia Tsvetkov


Michael Hughes - Discovering Disease Subtypes that Improve Treatment Predictions: Interpretable Machine Learning for Personalized Medicine

Allison Chaney - The Social Side of Recommendation Systems: How Groups Shape Our Decisions

Data Science Research Symposium 2018

Mark Bun - Finding Structure in the Landscape of Differential Privacy

Christopher Musco - Building an Algorithmic Toolkit for Data Science

Liangliang Cao - "Understanding Personal Photos and Videos"

Judy Hoffman - “Adaptive Adversarial Learning for a Diverse Visual World”

Cameron Musco - The Power of Simple Algorithms: From Data Science to Biological Systems

Yu Su - Bridging the Gap between Human and Data with AI

Microsoft's Kendall Square HT Women’s forum -Workshop: Preparing your 2018 Grace Hopper Speaker/Panel submission

Daniel Lokshtanov - "Coping with NP-hardness"

Nika Haghtalab - "Machine Learning by the People, for the People"

Soroush Vosoughi - Tribal Networks and Diffusion of News on Social Media

Thomas Steinke - Protecting Privacy and Guaranteeing Generalization with Algorithmic Stability

Stephen Bach - Programming Statistical Machine Learning Models with High-Level Knowledge

Joseph Tassarotti - Hashing and Sketching for Latent Dirichlet-Categorical Models

New England Statistics Symposium (NESS)

Huan Sun - "The Quest for Knowledge: Question Answering Beyond Knowledge Bases and Texts"

Francesco Orabona - Parameter-free Machine Learning through Coin Betting

Allison Chaney - The Social Side of Recommendation Systems: How Groups Shape Our Decisions

Soroush Vosoughi - Tribal Networks and Diffusion of News on Social Media

Kirill Levchenko - Spam, Drugs, and Diesel: An Evidence-Based Approach to Computer Security

Yelena Mejova - Capturing Digital Signals for Health Research

Danqi Chen - Knowledge from Language via Deep Understanding

Madalina Fiterau - Hybrid Machine Learning Methods for the Interpretation and Integration of Heterogeneous Multimodal Data

Christian Kroer - Solving Large-Scale Sequential Games

New and Old Concentration Inequalities - Philip Thomas

Ryan Enos - The Space Between Us: Social Geography and Politics

Five College Data Fest

Abraham Wyner - Explaining the Success of AdaBoost, Random Forests and Deep Neural Nets as Interpolating Classifiers

Exponential Family Embeddings - Maja Rudolph

Andrew Lan - Machine Learning Methods for Personalized Learning

Mohammad Hajiesmaili - Handling Uncertainty in Networked Systems: An Online Algorithm Design Approach

Chao Zhang - Knowledge Cube Construction from Massive Social Sensing Data

Insights from Deep Representations - Maithra Raghu

Bridging Probabilistic Inference And Motion Planning With Markov Chain Monte Carlo - Daqing Yi

Robot Perception For Manipulation - Peter Yu

Huacheng Yu - Better understanding of efficient dynamic data structures

Przemyslaw Grabowicz - Understanding and Augmenting Human Behavior in Social Computing Systems

UMass CICS/CDS Industry Mentorship Program Poster Presentations

Berthiaume Idea Jam 1

Berthiaume Idea Jam 2

Innovation Challenge: The Minute Pitch

Innovation Challenge: The Seed Pitch

Career Mixer 2018

Bill Howe -Systems and Algorithms for Responsible Data Science

Liping Liu - Embedding: Choose Right Relations to Embed

Maggie Makar - Spread of Contagions in the Presence of Latent Spreaders

Anna Rogers - What's in your embedding, and how it predicts task performance.

Jun-Yan Zhu - Learning to Generate Images

Noémie Elhadad

Ellie Pavlick - Why should we care about linguistics?

Subhro Roy - Towards Natural Human Robot Communication


Natesh Ganesh - Thermodynamic Intelligence, A Heretical Theory

Fredrik Johansson

Alexander Mathis - DeepLabCut: markerless pose estimation of user-defined body parts with deep learning

Hamed Zamani (UMass Amherst) - Neural Information Retrieval with Weak Supervision

Moumita Dasgupta (Smith College) - Exploring Data At the Intersection of Healthcare and Transportation

Sravana Reddy (Spotify) - Canceled

Jay Taneja (UMass Amherst) - Assigning a Grade: Accurate Measurement of Road Quality Using Satellite Imagery

Andy Reagan (MassMutual) - TBA

Alexandra Olteanu - Social Data Biases Methodological Pitfalls And Social Good Applications

Sarah Adel Bargal - Grounding Deep Models of Visual Data

Alane Suhr - Modeling and Learning Agents that Understand Language in Context

Irene Chen - Why is my classifier discriminatory?

Patrick Verga - Neural Knowledge Representation And Reasoning

Minsuk Kahng - Informatics Seminar: Human-Centered AI through Scalable Visual Data Analytics

Bill Howe - Informatics Seminar - Raw Data Considered Harmful: Systems and Algorithms for Synthetic Training Set Management

Jimeng Sun -Informatics Seminar: Doctor AI - Computational Phenotyping from Electronic Health Records

Berthiaume - Innovation Challenge: The Semifinal

Alex Fleiss - CANCELLED

Raw Data Considered Harmful: Systems and Algorithms for Synthetic Training Set Management

Farhad Pourkamali Anaraki - Scalable and Robust Sparse Subspace Clustering

Data Science for Common Good - Info Session

Data Science for Common Good - Info Session II

Data Science for Common Good - Info Session III

Alex Gittens - Intelligent Randomized Algorithms for the Low CP-Rank Tensor Approximation Problem

Xiaolong Wang - Learning and Reasoning with Visual Correspondence in Time

Deqing Sun - Adapting CNNs to the Dynamic 3D World

Alfred Z. Spector - Data Science: Opportunities and Challenges

Yair Zick - Fair, Transparent and Collaborative Algorithms in Data-Driven Environments

Ishan Misra - Scaling Self-supervised Visual Representation Learning

Swabha Swayampdipta - Learning Challenges in Natural Language Processing

Alexander Rush - Controllable Text Generation with Deep Latent-Variable Models

Mrinmaya Sachan - Towards Literate Artificial Intelligence

Shlomo Zilberstein - AI Will Change Everything, But Not So Fast

Christoph Riedl - Quantifying Reputation and Success in Art

Dylan Foster - Logistic Regression: The Importance of Being Improper

Stephen Roller- Parl AI And Open-Domain Dialogue Research

Veronika Thost

Laure Thompson

Data Science for the Common Good

Brian McFee (NYU) - Discovering multi-level structure in music

Robyn Speer (Luminoso) - Knowledge Graphs in the Era of Neural Nets

Data Science for the Common Good Celebration and 2020 Launch

Ehimwenma Nosakhare - Probabilistic Latent Variable Modeling for Predicting Future Well-Being and Assessing Behavioral Influences on Stress

Yoon Kim (Harvard NLP)

Byron Wallace (Northeastern, NLP)

Shrimai Prabhumoye (CMU) (NLP)

Grant Van Horn (Cornell) (Vision)

Zi Wang (MIT, robotics)

Shubhendu Trivedi (MIT) - Clebsch-Gordan Networks

Priya Donti (CMU) - Tackling Climate Change with Machine Learning

Sebastian Macaluso (NYU) - Looking into Jets with Machine Learning

Przemyslaw A. Grabowicz (UMass Amherst) - Discrimination as Data Perturbation

David Smith (Northeastern) - Textual Criticism as Language Modeling

Zack Weinberg (UMass Amherst) - Data Science versus Internet Censorship

Mark Maybury, CTO of Stanley Black & Decker

Bhuwan Dhingra (Carnegie Mellon): Text as a Virtual Knowledge Base

Qian Yang (Carnegie Mellon): Leveraging AI as a Material for User Experience Design

Octavia Eugen Ganea (CSAIL-MIT): Hyperbolic Geometry in Machine Learning

Weiwei Pan: What Are Useful Uncertainties in Deep Learning and How Do We Get Them?

Amanda Stent (Bloomberg): "NLP for Natural Documents"

Yonatan Belinkov (Harvard and MIT): Causal Mediation Analysis for Interpreting Neural NLP: The Case of Gender Bias

2020 Data Science Virtual Career Mixer

Tech Jobs & Internships Fair

GRAD + CDS Partner Mixer

John Wieting (Carnegie Mellon): Natural Language Processing

David Harwath (University of Texas at Austin): Multimodal Perception

Yunzhu Li (MIT): Computer Vision/Robotics

Fatemeh Mireshghallah (UCSD): Privacy for Machine Learning

Marcus Gualtieri (Northeastern University): Deep Reinforcement Learning

Kalesha Bullard (Facebook Artificial Intelligence Research): Multi-Agent Reinforcement Learning

Ankur Parikh (Google): Natural Language Processing

Kelsey Allen (MIT): Robotics

Maria De-Arteaga (UT Austin): Human-Centered Machine Learning

Tamara Broderick (An Automatic Finite-Sample Robustness Metric: Can Dropping a Little Data Change Conclusions?

Vered Shwartz (Commonsense Knowledge and Reasoning in Natural Language)

Nima Hamidi

Marinka Zitnik

Gedas Bertasius

Luciana Benotti

Adam Dziedzic