CENTER FOR DATA SCIENCE LAUNCH Scott Linderman of Harvard University 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 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 Chinese Restaurant Processes Deep Learning Poster Session Mass Big Data Western Mass Tech Treks Data Science Career Mixer Statistical Modeling in Global Health Microsoft AzureTM Cloud Computing Workshop 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 Protecting Computer Systems by Eliminating Vulnerabilities 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 Customized Stroke Rehabilitation & Software Development Tool-building Eye Tracking: Methods & Applications Rachel Cummings, Adaptive Learning With Robust Generalization Guarantees Innovation Challenge Finale Data Science Projects at MassMutual 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 Data Science Concentration Information Session Sheila Werth, Kevin Winner and Garrett Bernstein Deep Learning Whiteboard Talks Demographic Dialectal Variation in Social Media and Structured Prediction Models for RNN based Sequence Labeling in Clinical Text 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 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 "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 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 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? 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 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 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. 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 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 Innovation Challenge: The Minute Pitch Innovation Challenge: The Seed Pitch 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 Ellie Pavlick - Why should we care about linguistics? Subhro Roy - Towards Natural Human Robot Communication Natesh Ganesh - Thermodynamic Intelligence, A Heretical Theory 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 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 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 Byron Wallace (Northeastern, NLP) Shrimai Prabhumoye (CMU) (NLP) Grant Van Horn (Cornell) (Vision) 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 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)
