Machine Learning @ JHU

Pursuing excellence in core machine learning research in the context of high impact applications.

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TRANSFORMATIVE

INTERDISCIPLINARY

COLLABORATIONS

We build on JHU’s interdisciplinary culture to develop a new generation of machine learning methods.

JHU Machine Learning Group

As the first research university in the United States, Johns Hopkins University has long cultivated a fundamentally interdisciplinary culture, fostering collaborations between the different divisions of the university. Our machine learning community reflects that vision by developing new methods in core machine learning topics that can transform a wide range of application domains. We seek to push forward the state of the art in terms of theory, algorithms, models and applications with an eye towards solving the most important challenges of our day.

Machine learning is critical to the future of science, engineering and medicine. These domains
rely more and more on distilling knowledge and insights from digital data, building human-machine collaborations with intelligence machines and relying on machine intelligence to make key decisions. As machine learning researchers, our goal is to understand underlying scientific phenomena and mechanisms, and develop new approaches based on these insights.

Our work spans numerous core areas of machine learning, and we work on applications within robotics, speech and language, bioinformatics, vision, health and medicine, neuroscience, network science, and many others.

People

All
Computational Biology
Graphical Models
Health
Human-compatible AI
Natural Language
Network Science
Neuroscience
Optimization
Robotics
Speech
Trustworthy AI
Vision

Raman Arora

SpeechOptimization

Department of Computer Science

Speech Optimization

Alexis Battle

Computational Biology

Department of Biomedical Engineering

Computational Biology

Rama Chellappa

Vision

Department of Electrical and Computer Engineering Department of Biomedical Engineering

Vision

Mateo Diaz

Optimization

Department of Applied Mathematics and Statistics

Optimization

Mark Dredze

HealthNatural LanguageSpeech

Department of Computer Science

Health Natural Language Speech

Jason Eisner

Natural Language

Department of Computer Science

Natural Language

Benjamin Grimmer

Optimization

Department of Applied Mathematics and Statistics

Optimization

Daniel Khashabi

Natural Language

Department of Computer Science

Natural Language

Holden Lee

Optimization

Applied Mathematics and Statistics

Optimization

Anqi (Angie) Liu

Trustworthy AIHuman-compatible AI

Department of Computer Science

Trustworthy AI Human-compatible AI

Nicolas Loizou

Optimization

Department of Applied Mathematics and Statistics

Optimization

Mauro Maggioni

HealthVision

Department of Applied Mathematics and Statistics

Health Vision

Eric Nalisnick

HealthTrustworthy AI

Department of Computer Science

Health Trustworthy AI

Michael Oberst

HealthComputational Biology

Department of Computer Science

Health Computational Biology

Luana Ruiz

Graphical ModelsOptimization

Department of Applied Mathematics and Statistics

Graphical Models Optimization

Suchi Saria

HealthRobotics

Department of Computer Science

Health Robotics

Ilya Shpitser

HealthComputational Biology

Department of Computer Science

Health Computational Biology

Jeremias Sulam

HealthVision

Department of Biomedical Engineering

Health Vision

Soledad Villar

Optimization

Department of Applied Mathematics and Statistics

Optimization

Joshua Vogelstein

VisionNetwork ScienceNeuroscience

Department of Biomedical Engineering

Vision Network Science Neuroscience

Alan Yuille

Vision

Department of Computer Science

Vision

Courses

Introductory

BME 491/691: Learning Theory I

BioStats 644: Statistical Machine Learning: Methods, Theory, and Applications

BioStats 646-649: Essentials of Probability and Statistical Inference I-IV

BioStats 776: Statistical Computing

CS 475/675: Machine Learning

CogSci 371/671: Bayesian Inference

CogSci 371/671: Formal Methods in Cognitive Science: Inference

CogSci 372/672: Formal Methods in Cognitive Science: Neural Networks

ECE 412/612: Machine Learning for Signal Processing

ECE 447: Introduction to Information Theory and Coding

CS 464/664: Artificial Intelligence

Advanced

AMS 743: Graphical Models

AMS 835: Topics in Statistical Pattern Recognition

BioStats 751-755: Advanced Methods in Biostatistics I-IV

BioStats 763: Bayesian Methods

CS 476/676: Machine Learning: Data to Models

CS 477/677: Causal Inference

CS 479/679: Representation Learning

CS 775: Statistical Machine Learning

CS 792: Unsupervised Learning: From Big Data to Low-Dimensional Representations

CS 875: Selected Topics in Machine Learning

Math 795: Seminar in Data Analysis

CS 482/682: Deep Learning

CS 779: Machine Learning: Advanced Topics

CS 787: Advanced Machine Learning: Machine Learning for Trustworthy AI

CS 437: Federated Learning & Analytics

EN.601.474: Machine Learning: Learning Theory

Mathematical, Statistical, and Computational Background

AMS 420/620: Introduction to Probability

AMS 430/630: Introduction to Statistics

AMS 433/633: Monte Carlo Methods

AMS 669 : Large Scale Optimization for Data Science

AMS 692: Matrix Analysis and Linear Algebra

AMS 723: Markov Chains

AMS 730: Statistical Theory

AMS 732: Bayesian Statistics

AMS 737: Distribution-Free Statistics and Resampling Methods

AMS 761: Nonlinear Optimization I

AMS 762: Nonlinear Optimization II

AMS 763: Stochastic Search and Optimization

AMS 831: Advanced Topics in Bayesian Statistics

AMS: Iterative Algorithms for Machine Learning

BME 616: Introduction to Linear Dynamical Systems

BioStats 771: Advanced Statistical Theory I

BioStats 772: Advanced Statistical Theory II

CS 320/620: Parallel Programming

CS 325/425: Declarative Methods

CS 464/664: Randomized Algorithms

ECE 651: Random Signal Analysis

Research

As one of the top ranked universities in the world, with world leaders in numerous research areas, we host some of the best and most interesting scientific data, and collaboration is one of JHU’s strong suits. As a result, our diverse machine learning research program spans many topics and domains.

Our cross-departmental community of machine learning faculty works on both general-purpose ML and more domain-specific ML methods.

Speech and Natural Language

Computational Biology

Vision and Robotics

Theoretical Machine Learning

Applied Machine Learning

Computational Medicine

Academic Programs

Program Name	Level of Study
Applied and Computational Mathematics	Master’s Degree Post-Master’s Certificate
Applied Mathematics and Statistics	Bachelor’s Degree Full-Time Master’s Degree Doctoral
Artificial Intelligence	Master’s Degree Graduate Certificate
Biomedical Engineering	Bachelor’s Degree Full-Time Master’s Degree Doctoral
Computer Science	Bachelor’s Degree Full-Time Master’s Degree Doctoral
Computer Science	Master’s Degree Post-Master’s Certificate
Data Science	Master’s Degree Post-Master’s Certificate
Electrical and Computer Engineering	Full-Time Master’s Degree Doctoral
Engineering	Doctoral
Master of Science in Engineering in Robotics	Master’s Degree

Affiliated Centers

Center for Language and Speech Processing

The Johns Hopkins Center for Language and Speech Processing (CLSP) is an interdisciplinary research and educational center focused on the science and technology of language and speech.

Mathematical Institute for Data Science

The Johns Hopkins Mathematical Institute for Data Science is establishing the principles behind the analysis and interpretation of massive amounts of complex data.

Laboratory for Computational Sensing and Robotics

The Laboratory for Computational Sensing and Robotics (LCSR) is one of the most technologically advanced robotics research centers worldwide, and is an international leader in the areas of medical robotics, autonomous systems, and bio-inspiration.

Institute for Assured Autonomy

Autonomous systems have become increasingly integrated into all aspects of every person’s daily life. In response, the Johns Hopkins Institute for Assured Autonomy (IAA) focuses on ensuring that those systems are safe, secure, and reliable, and that they do what they are designed to do.

Institute for Computational Medicine

The Institute for Computational Medicine (ICM) uses powerful computational tools to transform the practice of medicine.

Institue for Data Intensive Engineering and Science

IDIES is a team of researchers and engineers actively helping research groups find the best solutions for their big data needs and fill any skill gaps with our broad variety of technical expertise and experience.

Diversity, Inclusion, and Ethics

Johns Hopkins University is deeply committed to the dignity and equality of all persons, regardless of who you are or where you come from. The university has formulated an ambitious Roadmap on Diversity and Inclusion to articulate a shared understanding of the university’s focus and priorities (https://diversity.jhu.edu). Similarly, the Whiting School of Engineering values a diverse and inclusive community (https://engineering.jhu.edu/about/diversity-inclusion/).

The machine learning community’s research mission depends on a diverse and inclusive community. Research requires creativity, innovation, and varying perspectives. Ensuring that these qualities are present in our researchers requires us to have research programs that are accessible to and inclusive of researchers from wide-range and diverse set of backgrounds. We are committed to cultivating a culture that is supportive and welcoming.

Furthermore, awareness of the need for diversity and inclusion is especially critical for our research area since Machine Learning has a long track record of underrepresentation of women and minorities.

Join Us.

Current JHU Community Members

Faculty, students, researchers, etc. are welcome to join our mailing list. Visit https://lists.johnshopkins.edu/ and search for the list name (ml-discuss or ml-announce) to subscribe.

Faculty Jobs

Many of JHU’s relevant departments hold searches for new faculty every year. We suggest you check the website of related departments for more information.

Graduate Programs

We offer many graduate programs related to machine learning. Please review the list of Academic Programs above.

“Fall at Johns Hopkins” by timrawle is licensed under CC BY-NC-ND 2.0