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PROFESSORS IN INTERPRETABLE MACHINE LEARNING

Showing page 1 of 1 — 11 professors available publicly

Cynthia D. Rudin

Interpretable Machine Learning Healthcare Predictive Modeling Criminal Justice Risk Assessment

Cynthia D. Rudin is the Gilbert, Louis, and Edward Lehrman Distinguished Professor of Computer Science at Duke University, with joint appointments in Electrical & Computer Engineering, Mathematics, Statistical Science, and Biostatistics & Bioinformatics. She directs the Interpret...

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Jordan Boyd-Graber

human-centric artificial intelligence interpretable machine learning natural language deception detection

I am a full professor in the University of Maryland Computer Science Department (tenure home), Institute of Advanced Computer Studies, INFO, and Language Science Center.rnrnMy research focuses on making machine learning more useful, more interpretable, and able to learn and inter...

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Furong Huang

trustworthy machine learning sequential decision-making high-dimensional statistics

Furong Huang is an Associate Professor in the Department of Computer Science at the University of Maryland. Specializing in trustworthy machine learning, AI for sequential decision-making, and high-dimensional statistics, Dr. Huang focuses on applying theoretical principles to so...

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Mustafa Bilgic

Active Learning Interactive Machine Learning Interpretable Machine Learning

Dr. Mustafa Bilgic is a Professor of Computer Science and Chair of the Department of Computer Science at the Illinois Institute of Technology in Chicago. He also serves as Director of the Machine Learning Laboratory and leads the Master’s in Artificial Intelligence program. Dr....

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Bryan Plummer

Visual Recognition Scene Understanding Interpretable Machine Learning

Bryan is an Assistant Professor in the Department of Computer Science at Boston University, where he also previously worked as a Postdoctoral Associate and Research Assistant Professor, and is a member of the IVC Group. He obtained his PhD in the computer vision group at the Univ...

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Benjamin Schäfer

Interpretable Machine Learning Data-Driven Energy System Design Complex Energy Systems Analysis

I am a Physicist who turned into a Data Scientist to contribute to our society by supporting the energy transition via data-driven approaches. In my research, I investigate complex energy systems, using interpretable machine learning to understand, predict and design future e...

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Dean Hougen

Artificial Neural Networks Deep Learning Interpretable Machine Learning

Dr. Dean F. Hougen is a professor in the School of Computer Science at the University of Oklahoma. Dr. Hougen has a PhD in Computer Science and Engineering from the University of Minnesota, with a graduate minor in Cognitive Science, and a BS in Computer Science from Iowa State U...

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Joshua Glaser

Computational Neuroscience Neural Control of Movement Interpretable Machine Learning

Dr. Glaser specializes in computational neuroscience research, focusing on neural control of movement and interpretable machine learning.

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Li Liao

interpretable machine learning domain-specific biological knowledge integration statistical methods for bioinformatics

My research interests are in bioinformatics. I am particularly interested in developing statistical and machine learning methods that are effective, expressive and interpretable, via incorporating domain specific knowledge of biological systems.

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Effat Farhana

interpretable machine learning rule-mining classification algorithms personalized learning systems

I am an Assistant Professor of Computer Science and Software Engineering at Auburn University. I completed my Ph.D. under Dr. Collin F. Lynch at North Carolina State University and my B.S. from Bangladesh University of Engineering and Technology. Before joining Auburn, I was a po...

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Dr. Qudrat E Alahy Ratul

Interpretable Machine Learning Natural Language Processing (NLP) Artificial Intelligence

Qudrat E Alahy Ratul research interests include Machine Learning, Artificial Intelligence, Interpretable Machine Learning, and Natural Language Processing.

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