Martin J. Strauss

Martin J. Strauss

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Martin J. Strauss, PhD, is Professor of Mathematics, College of Literature, Science, and the Arts and Professor of Electrical Engineering and Computer Science, College of Engineering, in the University of Michigan, Ann Arbor.

Prof. Strauss’ interests include randomized approximation algorithms for massive data sets, including, specifically, sublinear-time algorithms for sparse recovery in the Fourier and other domains.  Other interests include data privacy, including privacy of energy usage data.

Ambuj Tewari

Ambuj Tewari

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My research group is engaged in fundamental research in the following areas: Statistical learning theory: We are developing theory and algorithms for predictions problems (e.g., learning to rank and multilabel learning) with complex label spaces and where the available human supervision is often weak. Sequential prediction in a game theoretic framework: We are trying to understand the power and limitations of sequential predictions algorithms when no probabilistic assumptions are placed on the data generating mechanism. High dimensional and network data analysis: We are developing scalable algorithms with provable performance guarantees for learning from high dimensional and network data. Optimization algorithms: We are creating incremental, distributed and parallel algorithms for machine learning problems arising in today’s data rich world. Reinforcement learning: We are synthesizing concepts and techniques from artificial intelligence, control theory and operations research for pushing the frontier in sequential decision making with a focus on delivering personalized health interventions via mobile devices. My research group is pursuing and continues to actively search for challenging machine learning problems that arise across disciplines including behavioral sciences, computational biology, computational chemistry, learning sciences, and network science.

Research to deliver personalized interventions in real-time via people's mobile devices

Research to deliver personalized interventions in real-time via people’s mobile devices

Ji Zhu

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My research interests are in the areas of machine learning, statistical network analysis, analysis of high-dimensional data, and their applications in health sciences, biology, finance and marketing.

H. V. Jagadish

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Research summary: My area of research in the past has been data management, which has since evolved into data science. I’ve worked in many aspects of data science over the course of my career, particularly the integration of data across multiple sources and the usability of database systems by non-technical users. Currently, the bulk of my research focuses on equity issues in data science and AI and, more broadly, issues of ethics and fairness in these fields that are becoming ever more pervasive. I have over 200 major papers, an H-index of 96, and 38 patents. I am also a fellow of the ACM, “The First Society in Computing,” (since 2003), and of the AAAS (since 2018).  I’ve served on the board of the Computing Research Association (2009-2018). I’ve been an Associate Editor for the ACM Transactions on Database Systems (1992-1995), Program Chair of the ACM SIGMOD annual conference (1996), Program Chair of the ISMB conference (2005), a trustee of the VLDB (Very Large DataBase) foundation (2004-2009), Founding Editor-in-Chief of the Proceedings of the VLDB Endowment (2008-2014), and Program Chair of the VLDB Conference (2014). Since 2016, I’ve been Editor of the Morgan & Claypool Synthesis Lecture Series on Data Management. I have received the ACM SIGMOD Contributions Award in 2013 and the David E Liddle Research Excellence Award (at the University of Michigan) in 2008, among other awards.

Interesting projects: I direct the NSF Institute Framework for Integrative Data Equity Systems (FIDES). Equity matters as we increasingly use data and AI methods in so many aspects of our lives. These powerful tools can magnify existing inequities or inadvertently introduce inequities because of system design choices. Our work in this topic is to develop methods that can systematically identify inequities in the data and potentially also address them. 

I developed the first MOOC on Data Science Ethics more than 5 years ago and published it on EdX. Individual case study videos are independently available for download with a creative commons license to encourage teachers everywhere to incorporate these into their classes, picking and choosing only what they want. This MOOC has been so successful that it is now also carried on Coursera and FutureLearn.

My current position and the journey to it: As MIDAS Director, I am responsible for the overall direction and mission of MIDAS. It’s my role to make sure that MIDAS is working productively and strategically towards its stated mission as well as delivering value to the University of Michigan and our research community. I am also the Bernard A Galler Collegiate Professor of Electrical Engineering and Computer Science at the University of Michigan in Ann Arbor, which I joined in 1999.  Prior to that, I served as Head of the Database Research Department at AT&T Labs in Florham Park, NJ.

Why I’m passionate about my work: Data science has so much potential to do good things in so many aspects of life and society. I’m passionate about helping with that transformation and helping Michigan lead in that transformation while at the same time being cognizant of the potential risk and pitfalls. I want to help us get as much of the benefit of data science and AI as we can without suffering the harm they could bring if mismanaged.

Fun fact: MIDAS’ offices are located on the 6th floor of Weiser Hall.  I make it a point never to use the elevator: I always take the stairs.


Accomplishments and Awards

Qiaozhu Mei

Qiaozhu Mei

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I have a broad interest in real world problems related to text information management. My research focuses on information retrieval and text mining, with applications in web, social media, scientific literature, bioinformatics, and health informatics. I also have a strong interest in machine learning, data mining, natural language processing, and social network analysis. To know more about my research, please see my personal site.

Rada Mihalcea

Rada Mihalcea

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The Language and Information Technologies (LIT) lab, directed by Rada Mihalcea, conducts research in natural language processing, information retrieval, and applied machine learning. The group specifically focuses on projects concerned with text semantics (word/text similarity, large semantic networks), behavior analysis (multilingual opinion analysis, multimodal models for deception detection, emotion recognition, alertness detection, stress/anxiety detection, analysis of counseling speech), big data for cross-cultural analysis (geotagging, understanding cross-cultural differences and worldview), educational applications (pedagogical search engines, automatic short answer grading, conversational technologies for student advising).

Several of the projects in the LIT lab are interdisciplinary, acknowledging the fact that language can be used to deepen our understanding in many different fields, such as psychology, sociology, history, and others.  Some of the ongoing projects in the lab are collaborations with psychologists and sociologists, and target a rich modeling of human behavior through language analysis, seeking answers to questions such as “what are the core values of a culture?” and “are there differences in how different groups of people perceive the surrounding world?” The lab is also actively working on multimodal projects to track and understand human behavior, where language analysis is complemented with other channels such as facial expressions, gestures, and physiological signals.

Of interest, Prof. Mihalcea was quoted in a story about sexism and today’s virtual assistants such as Amazon’s Alexa, Apple’s Siri, and Microsoft’s CortanaRefinery29.

The LIT lab conducts research that brings together techniques for natural language understanding, multimodal processing, and social media analysis.

The LIT lab conducts research that brings together techniques for natural language understanding, multimodal processing, and social media analysis.

Barzan Mozafari

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Building data-intensive systems that are more scalable, more robust, and more predictable. He draws from advanced statistical models to deliver practical database solutions to real-world problems. In particular, he adapts concepts and tools from applied statistics, optimization theory, and machine learning.

Long Nguyen

Long Nguyen

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I am broadly interested in statistical inference, which is informally defined as the process of turning data into prediction and understanding. I like to work with richly structured data, such as those extracted from texts, images and other spatiotemporal signals. In recent years I have gravitated toward a field in statistics known as Bayesian nonparametrics, which provides a fertile and powerful mathematical framework for the development of many computational and statistical modeling ideas. My motivation for all this came originally from an early interest in machine learning, which continues to be a major source of research interest. A primary focus of my group’s research in machine learning to develop more effective inference algorithms using stochastic, variational and geometric viewpoints.

Atul Prakash

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My research interests include security, privacy, and adversarial machine learning.
More information about some of the current projects can be found at https://iotsecurity.engin.umich.edu.

9.9.2020 MIDAS Faculty Research Pitch Video.

An adversarial testing pipeline to fool machine learning classifiers. A STOP sign is being modified using stickers so that a state-of-the-art classifier is fooled into thinking it is a 45 SPEED LIMIT sign. For more details, visit https://iotsecurity.engin.umich.edu (Robust Physical Perturbations tab)

Eytan Adar

Eytan Adar

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My work is at the intersection of data mining and human-computer interaction.  My research is focused on both modeling and the construction of new systems that leverage those models.  Current projects include mining large-scale social media systems (Twitter, Reddit), networks (e.g., academic citation graphs), text/networks (meme propagation, information extraction, news articles, political networks), and cross-cutting methods (link prediction, community detection).  Work on systems building includes new visualization systems (automated visualization, interactive machine learning, etc.), ranking systems (social feeds), and data-driven end-user support (end-user and developers).