JASON M. EISNER
Curriculum Vitae (PDF)


Email: jason@cs.jhu.edu
Home page: http://cs.jhu.edu/~jason
Phone: (410) 516-8438
Fax: (410) 516-6134
Snailmail: Department of Computer Science, NEB 224
Johns Hopkins University
3400 N. Charles St.
Baltimore, MD 21218-2691
U.S.A.

Education and Honors

2001Univ. of Pennsylvania Ph.D. in Computer Science.
Thesis: Smoothing a probabilistic lexicon via syntactic transformations.   Advisor: Mitch Marcus.
Graduate Teaching Award.
1993 Univ. of Cambridge B.A. / M.A. in Mathematics (first-class honours).
(Note: Second undergraduate degree.)
1990 Harvard Univ. A.B. in Psychology, Cognitive Science track (summa cum laude; junior-year election to Phi Beta Kappa).

Professional Experience

7/2000-Department of Computer Science, Johns Hopkins University
Assistant professor of computer science.
Joint appointment in Cognitive Science (1/2003-).
1/2000-6/2001Department of Computer Science, University of Rochester, NY
Assistant professor of computer science.
Secondary appointment in Linguistics.
1994-iReactor Inc., Philadelphia, PA (consultant)
Solved research problems, wrote patents, proposed commercial ventures and strategies, met with potential clients, and supervised programmers, on a variety of Internet-oriented research projects.
1989-1992AT&T Bell Labs, AI Research Department, Murray Hill, NJ (summers)
Designed and built an early probabilistic left-to-right parser. Final version used a Lexical-Functional unification grammar and incorporated semantic and speech-recognition probabilities. Publications describe earlier version.
1988Microsoft Corporation, Seattle, WA (summer)
Designed a flexible, high-performance multi-line editor widget for OS/2 Presentation Manager, and led the team that implemented it in C.
1987-1988IBM Research Center, Yorktown Heights, NY (consultant)
Designed interactive system to help navigate company budget performance data.

Professional Activities

Fellowships and Awards

2005 Robert B. Pond, Sr. Excellence in Teaching Award
(JHU Engineering)
2002, 2005 Nominated for best paper award(EMNLP, ACL)
1993-1996 NSF Graduate Research Fellowship (U. Pennsylvania)
1993-1996 NSF Graduate Research Fellowship (U. Pennsylvania)
1991-1993 Herchel Smith Harvard Scholarship (Cambridge U.)
1990-1991 Fulbright Scholarship (U. Cape Town)
1986-1990 Harvard National Scholarship(Harvard U.)

Grants

2006-2007 JHU WSE-APL Partnership Fund: Learning with Less Systems(PI, $68K)
2005-2010 NSF PIRE: Investigation of Meaning Representations in Language Understanding for Machine Translation Systems(co-PI, $2.5M)
2004-2009 NSF CAREER: Finite-State Machine Learning on Strings and Sequences(PI, $500K)
2003-2007 NSF ITR: Weighted Dynamic Programming for Statistical Natural Language Processing(PI, $425K)
2001-2006 ONR: Improving Statistical Translation Models Via Text Analyzers Trained From Parallel Corpora(co-PI, $4.3M)
2001-2006 NSF ITR/IM+PE+SY: Summer Workshops on Human Language Technology(co-PI, $2.35M)

Publications and Presentations

Invited talks

Journal articles

Book chapters

Book reviews

Ph.D. thesis

Refereed conference and workshop proceedings

Refereed presentations

Invited papers

  • Karakos, Damianos, Sanjeev Khudanpur, Jason Eisner, and Carey E. Priebe (2005). Unsupervised classification via decision trees: An information-theoretic perspective. Proceedings of the 2005 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1081-1084, Philadelphia, March. Invited talk.

    Technical reports

    Outreach (general audience)

    Patents

    Teaching

    7/2000-Department of Computer Science, Johns Hopkins University
    Assistant Professor and Graduate Program Chair.
    Robert B. Pond, Sr. Excellence in Teaching Award (Whiting School of Engineering, 2005).
    Natural Language Processing (2001, 2002, 2003, 2004, 2006)
    A mixed graduate-undergraduate class that teaches a synthesis of statistical models, formal grammars, and linguistic theory, with associated algorithms. It is reputed to be one of the most challenging classes in the Computer Science department, requiring both rigor and intellectual flexibility. Faculty at several other universities have asked to use the extensive online course materials. Enrollment: about 30.
    Declarative Methods (2005, 2006)
    A new course for juniors, seniors, and graduate students. It surveys computational problems that tend to pop up frequently in different guises (e.g., constraint satisfaction); the specification languages used to describe instances of these problems; general toolkits for solving these instances; and the algorithms run by these toolkits. Enrollment: about 35.
    Seminar in Natural Language Processing (ongoing)
    A weekly reading and discussion group, exploring important current research in natural language processing and potentially relevant material from related fields. Topics are chosen by the group; each lasts 3-4 weeks. Attendance: up to 10 (not all enrolled).
    Totally Random (2004, 2005)
    A 4-class discussion unit about random numbers and the uses of randomness in computer science. Part of the department's new freshman experience course. Enrollment: 8-10.
    Data Structures (2003, 2004)
    A sophomore-level class, the third and last in the programming sequence for majors. Covers basic data structures and algorithms, basic analysis of algorithms, and object-oriented programming style. Online "warmups" and highly interactive classes stimulated the students to come up with designs and variations. The challenging weekly assignments often used real-world data. Faculty at a dozen other universities have asked to use the course materials. Enrollment: about 50.
    Finite-State Methods in Natural Language Processing (2000-2001)
    A graduate class on semiring-weighted finite-state transducers. Covers theory and practice, including the theory of formal power series, the extended regular expression calculus, and a range of applications to natural language. Rigorous assignments. Attendance: about 20 (mostly not enrolled).
    Statistical Language Learning (2002)
    A graduate class about past and present research that has attempted, with mixed success, to induce the structure of language from raw data such as text. Lectures are intermixed with reading and discussion of the primary literature. Attendance: about 10 (mostly not enrolled).
    2002-Lecturer, NAACL Summer School in Human Language Technology
    1/2000-6/2001Department of Computer Science, University of Rochester
    Assistant Professor.
    Statistical Learning of Natural Language (2000)
    (see Statistical Language Learning, above)
    Graduate Problem Seminar (2000)
    Boot camp for new Ph.D. students. Students learn research skills by teaming up to tackle a series of open-ended engineering problems that touch on research in the department. (I made them build systems for face orientation detection, distributed calendar management, and information retrieval.) Several written and oral presentations are required and receive extensive feedback. The class also includes career advice, familiarization with departmental resources, presentations by other faculty, and a final research project. Enrollment: 10.
    1994-95Department of Computer Science, University of Pennsylvania
    TA in Introduction to Programming. Graduate Teaching Award.

    Advising

    Ph.D. students

    B.S/M.S.E. research students

    Ph.D. thesis committees


    Last Modification $Date: 2006/10/19 18:59:31 $ (GMT)