January 30, 2019

James D.Fielder, Jr., PhD Secretary Maryland Higher Education Commission 6 N. Liberty Street, 10th Floor Baltimore, MD 21201

Dear Dr. Fielder:

On behalf of Provost Sunil Kumar, Dean T.E. Schlesinger , and our Whiting School of Engineering, I write to request your review and endorsement of the enclosed proposal. The Whiting School proposes a new Area of Concentration in Human Language Technology within the MSE in Electrical and Computer Engineering and the Master's in Computer Science.

The proposed area of concentration in Human Language Technology will be firmly rooted in computer engineering, and will provide in-depth instruction in human language technology. This area of study is applied machine learning to problems that involve human language in both speech and text. Instruction will include core computer skills in big data, use of Linux computer clusters, programming in the cloud and on GPUs, and use of prevalent toolkits in the subject area. Students will learn data-driven methods that are broadly applicable and will be given specific instruction on how to apply these methods to a large range of natural language tasks, such as speech recognition, information retrieval, information extraction, text analytics, machine translation, and many more. .

The proposed program is consistent with the Johns Hopkins mission and the State of Maryland’s Plan for Postsecondary Education. The proposal is fully endorsed by The Johns Hopkins University.

A business check for the review of this proposal has been sent to the Commission. Should you have any questions or need further information, please do not hesitate to contact Natalie Lopez at (410) 516- 6430 or alo@jhu.edu. Thank you for your continuing support of Johns Hopkins.

Sincerely,

Janet Simon Schreck, PhD Associate Vice Provost for Education

cc: Dr. Sunil Kumar

Enclosures

265 Garland Hall 3400 N. Charles Street Baltimore, MD 21218 410-516-8070 http://web.jhu.edu/administration/provostO�ce of the Provost and Senior Vice President for Academic A�airs

Ms. Natalie Lopez

Addendum

For the new AOC in Human Language Technology within the existing MSE in Computer Science:

Adequacy of Faculty Resources COMAR 13B.02.03.11

• Andreas Andreou, PhD in Electrical Engineering and Computer Science, professor, ECE, Full Time. Sensory Information Processing.

• Raman Arora, PhD in Electrical and Computer Engineering, assistant professor,

CS, Full Time. Machine Learning Machine Learning : Optimization Selected Topic in Machine Learning

• Najim Dehak, PhD in Engineering, assistant professor, ECE, Full Time.

Machine Learning for Signal Processing Speech Technologies Reading Group

• Mark Dredze, PhD in Computer Science, associate professor, CS, Full Time.

Machine Learning

• Kevin Duh, PhD in Electrical & Computer Engineering, assistant research professor, CS, Full Time. None

• Jason Eisner, PhD in Computer Science, professor, CS, Full Time.

Natural Language Processing Selected Topics in Natural Language Processing Machine Learning: Linguistic & Sequence Modeling.

• Mounya Elhilali, PhD in Electrical & Computer Engineering, associate professor,

ECE, Full Time. Audio Signal Processing

• Leibny Paola Garcia, PhD in Information Technology and Mobile Network

Communications, assistant research scientist, CLSP, Full Time. Speech Technologies Reading Group

• Hynek Hermansky, Doctorate of Engineering, professor, ECE, Full Time.

Speech and Auditory Processing by Humans and Machines

• Sanjeev Khundanpur, PhD in Electrical Engineering, associate professor, ECE, Full Time. Information Theory Information Extraction from Speech and Text

• Philipp Koehn, professor, CS, Full Time.

Artificial Intelligence

For the new AOC in Human Language Technology within the existing MSE in Computer Science:

Machine Translation Selected Topic in Machine Translation

• Tom Lippincott, PhD in Computer Science, research scientist, CoE, Part Time.

None • Matt Post, PhD in Computer Science, research scientist, CoE, Part Time.

None

• Daniel Povey, PhD in Computer Science, assistant research professor, CLSP, Full Time. None

• Suchi Saria, PhD in Computer Science, assistant professor, CS, Full Time. Machine Learning : Data to Models

• Jan Trmal, PhD in Computer Engineering, associate research scientist, CLSP, Full

Time. Current Topics in Language and Speech Processing

• Ben Van Durme, PhD in Computer Science and Linguistics assistant professor, CS, Full

Time. Artificial Intelligence Selected Topics in Meaning, Translation and Generation of Text

• Jesus Villalba, PhD in Biomedical Engineering, assistant research scientist, CLSP,

Full Time. Speech Technologies Reading Group

• David Yarowsky, PhD in Computer and Information Science, professor, CS, , Full Time.

Information Retrieval and Web Agents

• Shinji Watanabe, Doctorate of Engineering, Associate research Professor, ECE, Full Time. Information Extraction from Speech and Text

For the new AOC in Human Language Technology within the existing MSE in Computer Science:

COMAR 13B.02.03.10 and COMAR 13B.02.03.02B

Introduction to Human Language Technology, (3 credits, Fall Semester)

This course gives an overview of basic foundations and applications of human language technology, such as: morphological, syntactic, semantic, and pragmatic processing; machine learning; signal processing; speech recognition; speech synthesis; information retrieval; text classification; topic modelling; information extraction; knowledge representation; machine translation; dialog systems; etc. It is taught by several professors who are experts in these areas.

Natural Language Processing (3 credits, Fall Semester)

This course is an in-depth overview of techniques for processing human language. How should linguistic structure and meaning be represented? What algorithms can recover them from text? And crucially, how can we build statistical models to choose among the many legal answers?

The course covers methods for trees (parsing and semantic interpretation), sequences (finite-state transduction such as tagging and morphology), and words (sense and phrase induction), with applications to practical engineering tasks such as information retrieval and extraction, text classification, part-of-speech tagging, speech recognition, and machine translation. There are a number of structured but challenging programming

Information Extraction from Speech and Text (3 credits, Spring Semester)

Introduction to statistical methods of speech recognition (automatic transcription of speech) and understanding. Topics include elementary probability theory, hidden Markov models, and n-gram models using maximum likelihood, Bayesian and discriminative methods, and deep learning techniques for acoustic and language modeling.

Masters thesis or project in the area of human language technology (6 credits, both semesters) In contrast to a traditional master’s degree in ECE, the proposed concentration has a stronger focus on the practical application of advanced human language technology methods. The students are required to spend at least 2 semesters doing research with the CLSP faculty members. To ensure that there is a good match for Masters theses, we will institute the following process Each CLSP faculty will propose at least 3 topics for theses. CLSP research staff may also propose

topics. Each Masters student will provide a ranked list for preference among the proposed topics, or

propose their own topic with consent of a CLSP faculty or research staff member. CLSP faculty members and research staff may indicate preferences for students for their topics. The coordinator of the Masters program will match student and faculty preferences to ensure that

each student will have a thesis project.