Lin Ma

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My research interests lie in the intersection of database management systems (DBMSs) and machine learning (ML), especially using ML/AI techniques to automate database administration/tuning to remove human impediments.

Srijita Das

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My research is about building sample-efficient machine learning models. My long term goal is to develop collaborative systems that can actively seek advice from humans and make faster decisions, resulting in reliable and practical systems. I specifically focus on design of sequential decision-making models to make them learn faster. We leverage advice from humans in various forms (implicit and explicit) to encourage favorable decisions and avoid decisions having catastrophic consequences. We also focus on minimizing the cost of seeking advice by building suitable machine learning models from historical advice data and reusing them when required. Our research also develops ways to solve complex tasks in Reinforcement Learning by leveraging various kinds of knowledge transfer mechanisms, curriculum learning, teacher-student framework etc. Advances in these directions would make decision-making models sample-efficient and better suited for solving real-world problems. Along the supervised machine learning spectrum, we also focus on problems related to learning with less data, traditionally known as Active Learning, semi-supervised learning, and learning from multiple experts.

Mosharaf Chowdhury

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I am a computer scientist and an associate professor at CSE Michigan, where I lead the SymbioticLab (https://symbioticlab.org/). My research improves application performance and system efficiency of AI/ML and Big Data workloads with a recent focus on optimizing energy consumption and data privacy. I lead the ML Energy initiative (https://ml.energy/), a consortium of researchers focusing on understanding, controlling, and reducing AI/ML energy consumption. Over the course of my career, I have worked on a variety of networked and distributed systems. Recent major projects include Infiniswap, the first scalable memory disaggregation solution; Salus, the first software-only GPU sharing system for deep learning; FedScale, a scalable federated learning and analytics platform; and Zeus, the first GPU energy optimizer for AI. In the past, I invented the coflow abstraction for efficient distributed communication, and I am one of the original creators of Apache Spark. Thanks to my excellent collaborators, I have received many individual awards, fellowships, and paper awards from top venues like NSDI, OSDI, ATC, and MICRO.

Mohamed Abouelenien

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Mohamed Abouelenien’s areas of interest broadly cover data science topics, including applied machine learning, computer vision, and natural language processing. He established the Affective Computing and Multimodal Systems Lab (ACMS) which focuses on modeling human behavior and developing multimodal approaches for different applications. He has worked on a number of projects in these areas, including multimodal deception detection, multimodal sensing of drivers’ alertness levels and thermal discomfort, distraction detection, circadian rhythm modeling, emotion and stress analysis, automated scoring of students’ progression, sentiment analysis, ensemble learning, and image processing, among others. His research is funded by Ford Motor Company (Ford), Educational Testing Service (ETS), Toyota Research institute (TRI), and Procter & Gamble (P&G). Abouelenien has published in several top venues in IEEE, ACM, Springer, and SPIE. He also served as a reviewer for IEEE transactions and Elsevier journals and served as a program committee member for multiple international conferences.

Grant Schoenebeck

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My current research combines machine learning tools and economic approaches (e.g game theory, mechanism design, and information design) to develop and analyze systems for eliciting and aggregating information from of diverse group of agents with varying information, interests, and abilities.
This work applies to scenarios where a collective decision-making process is required, such as peer grading, peer review, crowd-sourcing, content moderation, misinformation detection, surveys, and employment hiring/evaluation.
More broadly, I am interested in multi-agent systems, a subfield of AI; data economics; and algorithmic game theory.

Cyrus Omar

Cyrus Omar

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I lead the Future of Programming Lab (FP Lab), where we design modern user interfaces for modern programming languages. Much of how we program today is rooted in tools designed 40+ years ago, e.g. how we enter code (using simple text editing, which leads to profligate parse errors), how we validate code (using tests or impoverished type systems), how we explore code (in a slow, batched, textual manner), how we communicate change (by throwing away the edits we performed and forcing diff algorithms to guess what we did), and so on. My lab develops new programming language and editor mechanisms, starting from theoretical foundations in mathematics and building up to human interfaces.

Integrating live GUIs into programs with holes

Integrating live GUIs into programs with holes

Andrew Wu

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My research focuses on the interface of technology, finance and operations management. I develop and apply new approaches in natural language processing (NLP) and text analytics to study emerging and classic OM problems including (1) new marketplaces in both Fintech and Edtech, (2) supply chain risks, and (3) societal impact of OM/financial decisions.

Cheng Li

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My research focuses on developing advanced numerical models and computational tools to enhance our understanding and prediction capabilities for both terrestrial and extraterrestrial climate systems. By leveraging the power of data science, I aim to unravel the complexities of atmospheric dynamics and climate processes on Earth, as well as on other planets such as Mars, Venus, and Jupiter.

My approach involves the integration of large-scale datasets, including satellite observations and ground-based measurements, with statistical methods and sophisticated machine learning algorithms including vision-based large models. This enables me to extract meaningful insights and improve the accuracy of climate models, which are crucial for weather forecasting, climate change projections, and planetary exploration.

Mark Draelos

Mark Draelos

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My work focuses on image-guided medical robots with an emphasis on clinical translation. My interests include medical robotics, biomedical imaging, data visualization, medical device development, and real-time algorithms.

A major ongoing project is the development of robotic system for automated eye examination. This system relies on machine learning models for tracking and eventually for interpretation of collected data. Other projects concern the live creation of virtual reality scenes from volumetric imaging modalities like optical coherence tomography and efficient acquisition strategies for such purposes.