.01

About

Short Bio

I obtained a PhD degree in Computational Linguistics at the University of Pennsylvania under the supervision of Ellen Prince and Aravind Joshi. My research has focused on discourse structure, text analysis, pronoun resolution, and readability. My PhD work was published in top publications venues in NLP and led to the development of Antelogue, a pronoun resolution for handling pronoun resolution in dialogues. It uses discourse structure to resolve pronouns, including identification of non-referential instances of ‘it’ and ‘they’. In collaboration with Ben Taskar and his group, we proposed a novel approach for movie-script alignment that solves the correspondence problem between the visual appearance of actors in the movie and pronouns in the script. In collaboration with Rashmi Prasad and Bonnie Webber I led the development of the annotation of discourse relations of Aravind Joshi’s Penn Discourse Treebank project, the first Discourse Treebank that was based on our previous work on Discourse Parsing using basic principles from Lexicalized Tree Adjoining Grammars. During my internship with Karen Kukich at ETS I developed a novel Centering-based approach to detecting incoherent writing in student essays.

I am the founder of Choosito, an educational technology startup with a strong research record in using NLP and Machine Learning to select Open Educational Resources and other documents freely available on the web that match the reading comprehension ability and interests of the learners. With the support of two NSF grants, I have led the development and deployment of a K-12 web search engine that filters websites by reading level and theme in real-time search. With the support of a third NSF grant (in collaboration with the University of Pennsylvania) Chris Callison-Burch and our teams are currently working on the development and deployment of simplification AI for workforce upskilling and reskilling.


.02

Resume

  • Education
  • 2003
    Philadelphia

    Computational Linguistics - PHD

    University of Pennsylvania

    Advisors: Ellen Prince and Aravind Joshi
  • 1991
    UK

    Applied Linguistics - MA

    University of Essex

    Advisor: Keith Johnson
  • 1988
    Greece

    English - BA

    Aristotle University of Thessaloniki

  • Academic and Professional Positions
  • NOW
    2021
    Philadelphia

    Co-Founder & Chair of the Board

    Choosito! inc.

     
     
  • NOW
    2021
    Philadelphia

    Senior Researcher, Dept. of Computer & Information Science

    University of Pennsylvania

     
     
  • 2021
    2014
    Philadelphia

    Co-Founder & CEO

    Choosito! inc.

     
     
  • NOW
    2013
    Philadelphia

    Adjunct Professor, Graduate School of Education

    University of Pennsylvania

     
     
  • 2012
    2006
    Philadelphia

    Research associate/lecturer, Graduate School of Education

    University of Pennsylvania

     
     
  • 2006
    2005
    Greece

    Lecturer (tenure-track), Department of Theoretical and Applied Linguistics

    Aristotle University of Thessaloniki

     
     
  • 2005
    2003
    Philadelphia

    Postdoctoral Fellow, Institute Of Research in Cognitive Science

    University of Pennsylvania

     
     
  • 2000
    1998
    Princeton

    Summer Fellow

    Educational Testing Service

     
     
  • GRANTS
  • 2019

    STTR Phase I: Simplification AI for workforce upskilling

    NSF

  • 2015

    SBIR Phase II: Linguistic analysis of web content for 21st century inquiry learning

    NSF

  • 2013

    SBIR Phase I: Linguistic analysis of web content for 21st century inquiry learning

    NSF

  • HONORS AND AWARDS
  • 2001

    School of Arts and Sciences dissertation fellowship 2001-2002

    University of Pennsylvania

  • 2000

    Summer fellowship 2000

    Educational Testing Service

  • 1999

    Institute of Research in Cognitive Science fellowship 2000-1

    University of Pennsylvania

.03

Research

Research Statement

Intelligence comes from the Latin word intelligere, which means “to understand.” The ability to comprehend is central to intelligence and a prerequisite to reasoning and using acquired knowledge to affect your environment which is what we, humans, have been doing with a lot of success for over 100,000 years. Given the complexity of processing human language, it is not surprising that in the past several decades of research in Natural Language Processing (NLP), we have happily assumed that linguistic input can be understood by anyone speaking the language. In other words, NLP is a field of AI that strives to make machines capable of understanding language like a human, tacitly assuming that the “human” is a constant: any speaker of English can understand any input spoken or written in English. This tacit assumption has served us well both from a theoretical and a practical perspective.

The advent of the internet has slowly but dramatically changed the landscape. Twenty years ago there were hardly any school-aged children consuming information from the internet. There was hardly any need for out of the workforce adults to try to reskill or upskill in order to be able to have a job. To give an example, most of the Wikipedia articles are beyond the level of understanding of elementary school students and most of the scholarly articles are beyond the level of understanding of even college graduates. Most of Coursera or other MOOCs initially designed to provide free education opportunities of high quality to underserved people around the world ended up being attended by people who already had Master’s and PhD degrees. Using NLP to enable access to web content to an unprecedented range of learners opens new and exciting lines of AI research with the potential of enormous societal impact.

Research Projects

Current Research

At the core of my research interests is identifying robust representations of content in unstructured data that can be combined with personalized representations of content consumed by learners.

In the course of the past seven decades, several models of document/content representations have been proposed ranging from early set theoretic models that represent documents as sets of words, to algebraic models that represent documents as vectors and matrices and probabilistic models, e.g., language models, that treat language understanding as a probabilistic problem. It is interesting to evaluate how these models perform when we introduce learner models as a factor. In other words, how can we model and extract meaning from text to allow and evaluate if this extracted meaning can, indeed, be processed and understood by individual learners?

It is well-known, yet poorly modeled, that a learner’s ability to extract information from texts depends not only on their ability to decode written language but, also, their level of interest and familiarity with the content of texts. The more familiar a learner is with a topic, the more topic related content words and concepts the student will know, and the easier it is for the reader to make sense of texts. To address the problem of analyzing linguistic meaning that we extract from unstructured data with respect to the learner’s ability to comprehend it, we need to be able to a) analyze the quality and complexity of unstructured content in real time web search and b) create a model of the learner that represents his or her current and prior knowledge in order to predict his or her ability to comprehend a previously unseen document, if any, on the topic of interest.

This line of research involves addressing the following questions.

  1. Text analysis and readability
    1. How do we collect, represent, and level the semantic/thematic content of unstructured data?
    2. How do we detect new topics as they emerge?
  2. Learner model
    1. How do we capture automatically the reading level of a learner from sparse and unstructured data?
    2. How do we represent and update a learner’s knowledge model?
    3. How do we learn a learner’s model from incomplete and unstructured data?
  3. Text Simplification
    1. How can we simplify content for different reading abilities?
    2. How can we make technical content accessible to the wide variety of learner backgrounds?
    3. How do we determine what concepts are prerequisites to understanding a new concept?
  4. Information retrieval
    1. How do we evaluate the quality and reliability of web content?
    2. How do we determine if prior information consumed by the learner provides the required background to understand novel content?

Text Analysis and Readability

The problem of analyzing the reading level of a document with the intent of evaluating its suitability for the reading ability of the intended reader dates back to ‘20s. In the 50s and 60s, several readability formulas were proposed, which to this day they are the go-to formulas for practical applications. In the 80s, research efforts focused was on creating models that reflect insights from cognition, and in the beginning of 2000s NLP and Machine Learning approaches gained ground, including models with semantic and discourse features. Despite the extensive efforts, the state of the art never reached accuracy beyond 70% for specific domains.

Just like earlier work on readability, recent work using NLP and Machine Learning to evaluate text difficulty is focused on evaluating linguistic content regardless of the learner. I posit that in order to have a breakthrough in this area, it is critical to develop a model of the learner. In my research approach, we address the above problem by a) building global readability and theme models as a first step and b) personalizing the readability prediction based on a dynamic model of learner’s world knowledge. We have shown that this approach is both scientifically innovative and it addresses the practical implementation challenges of building a robust smart search engine for K-12 teachers and students that returns results based on a user query and filters them by reading level and theme in real-time search (choosito.com).

Text Simplification

It is not the first time in the history of scientific discovery that looking into solutions for practical problems can lead us to novel ways of thinking about an old problem. The whopping number of people currently missing from the workforce combined with a declining rate of workforce growth is making efficient workforce training an acute necessity. A major challenge in current workforce development and upskilling is that employees or potential employees come from very diverse backgrounds. Most training courses, tutorials, and other materials available for training assume a knowledge background that most of the employees do not have. Despite initial hopes that MOOCs would enable quality learning for people who need training to get into the workforce, a significant number of people attending MOOCs have already Master’s and Doctorate Degrees. Trying to address this problem with traditional text simplification approaches has revealed some fundamental shortcomings. Splitting sentences into smaller chunks or replacing difficult words with easier synonyms is not sufficient in this context. What synonym can you offer for “gradient descent” that will help the learner know what it is?

This basic insight has led us to recasting text simplification as an information retrieval problem. In collaboration with Chris Callison Burch, we proposed a hybrid solution: a) improve traditional text simplification techniques such as word substitution and sentence rewriting, b) introduce a novel information retrieval approach based on identifying concepts and prerequisite concepts critical for the comprehensibility of advanced documents, and c) retrieve semantically resources that expose these critical concepts at the appropriate reading level. At the core of this research is the identification of terms and concepts that are predicted to be challenging for a specific learner, critically taking into account his or her background knowledge and experience with the target content or skill (or lack thereof).

This new line of research is aspiring to make significant contributions to contribute to our understanding of the strengths and limitations of highly influential NLP frameworks such as BERT, TransformerXL, OpenAI’s GPT-3, XLNet, ERNIE2.0, RoBERTa etc., on novel classification tasks that require capturing much deeper understanding of lexical semantics and discourse structure.

Antelogue

is a pronoun resolution system that uses natural language techniques to process dialogues and identify co-referring relations between pronouns and their antecedents in the dialogue. Antelogue is a computationally efficient system which uses linguistically rich resources to achieve high precision resolution. The current version of Antelogue is specifically designed to process dialogues from the popular TV series 'Lost'. It achieves 93% accuracy for first, second and third person pronouns. Plural pronouns are not handled yet.

The PENN Discourse Treebank

To-date the largest discourse annotated corpus. PDTB 1.0 was released in 2006 and contained annotations of discourse connectives (explicit and implicit) and their arguments. PDTB 2.0, released in January 2008, is enriched with annotations of speaker attribution and the senses of connectives.

Automated Evaluation of Coherence

In student essays implements a centering-based algorithm to identify topic discontinuities in students essays. Statistical analysis of the performance of the algorithm on a corpus of student essays shows that the topic discontinuity model can improve the performance of e-rater, the automated essay scoring system developed at ETS. Related publications link coming.

.04

Publications

Publications List

Corpus-driven semantics of concession: where do expectations come from?

Journal
Robaldo, L. and E. Miltsakaki
In Discourse & Dialogue, Vol. 5, No 1.
Publication year: 2014

Evaluation of text coherence for electronic essay scoring systems.

Journal
Eleni Miltsakaki and Karen Kukich
In Journal of Natural Language Engineering, 10(1).
Publication year: 2004

D-LTAG system - discourse parsing with a lexicalized tree-adjoining grammar.

Journal
Kate Forbes, Eleni Miltsakaki, Rashmi Prasad, Anoop Sarkar, Aravind Joshi, and Bonnie Webber
In Journal of Logic, Language and Information, 12(3).
Publication year: 2003

Towards an aposynthesis of topic continuity and intrasentential anaphora.

Journal
Eleni Miltsakaki.
In Computational Linguistics, 28(3):319–355.
Publication year: 2002

Not all subjects are born equal: a look at complex sentence structure.

Book Chapter
Eleni Miltsakaki
The Processing and Acquisition of Reference. Cambridge, MA: MIT Press.
Publication year: 2011

Centering theory on Greek narratives and newspaper articles (paper written in Greek).

Book Chapter
Nikiforos Karamanis and Eleni Miltsakaki
(With Nikiforos Karamanis).
Publication year: 2006

Anaphora resolution: a centering approach.

Book Chapter
Aravind Joshi, Rashmi Prasad, and Eleni Miltsakaki
Encyclopedia of Language and Linguistics, 2nd Edition.
Publication year: 2005

The predicate-argument structure of connectives: a corpus-based study.

Book Chapter
Cassandre Creswell, Kate Forbes, Eleni Miltsakaki, Rashmi Prasad, Aravind Joshi, and Bonnie Webber
In Anaphora Processing: Linguistic, Cognitive and Computational modelling.
Publication year: 2004

Complexity-weighted loss and diverse reranking for sentence simplification.

Conference
Kriz, R., J. Sedoc, M. Apidianaki, C. Zheng, Gaurav Kumar, E. Miltsakaki, and C. Callison-Burch
In Proceedings of 16th Annual Conference of the North American Chapter of the Association for Computational Linguistics and Human Language Technologies.
Publication year: 2019

Simplification using paraphrases and context-based lexical substitution.

Conference
Kriz, R., E. Miltsakaki, M. Apidianaki, and C. Callison-Burch
In Proceedings of 16th Annual Conference of the North American Chapter of the Association for Computational Linguistics and Human Language Technologies.
Publication year: 2018

Information literacy across the curriculum.

Conference
Eleni Miltsakaki
Colonial Technology Conference.
Publication year: 2015

Do NLP and machine learning improve traditional readability formulas.

Conference
Francois, T. and E. Miltsakaki
In Proceedings of the First Workshop on Predicting and Improving Text Readability for Target Reader Population, NAACL 2012, Montreal.
Publication year: 2012

A study of students poor research skills as demonstrated in a record of search behavior on the internet.

Conference
Eleni Miltsakaki
In Proceedings of Society for Information Technology and Teacher Education 2012, Austin, Texas.
Publication year: 2012

Sources of expectation in concession.

Conference
Robaldo, L., E Miltsakaki , and A. Bianchini
Proceedings of Conference on Semantics and Formal Modelling (JSM '10), Nancy, France.
Publication year: 2010

Corpus-based semantics of concession: where do expectations come from?

Conference
Robaldo, L., E. Miltsakaki and A. Bianchini
Proceedings of the Seventh Conference on Language, Resources and Evaluation (LREC '10).
Publication year: 2010

Referential properties of dropped subjects and pronouns in Greek.

Conference
Eleni Miltsakaki
Proceedings of the International Conference on Greek Linguistics 9.
Publication year: 2009

Movie/script: alignment and parsing of video and text transcription.

Conference
Cour, T., C. Jordan, E. Miltsakaki, and B. Taskar.
Proceedings of the 10th European Conference on Computer Vision, ECCV 2008.
Publication year: 2008

The Penn Discourse Treebank 2.0.

Conference
Eleni Miltsakaki
Proceedings of the the 6th International Conference on Language Resources and Evaluation (LREC 2008).
Publication year: 2008

Sense annotation in the Penn Discourse Treebank.

Conference
Miltsakaki, E., L. Robaldo, A. Lee, and A. Joshi
Proceedings of the 9th International Conference on Intelligent Text Processing and Computational Linguistics.
Publication year: 2008

Read-x: automatic evaluation of reading difficulty of web text.

Conference
Eleni Miltsakaki and Audrey Troutt
In Proceedings of E-Learn 2007, sponsored by the Association for the Advancement of Computing in Education.
Publication year: 2007

A rethink of the mapping between salience and referring expression.

Conference
Eleni Miltsakaki
Presented at the 20th Annual CUNY Conference on Human Sentence Processing.
Publication year: 2007

The role of animacy in the ranking of entities in complex nps: evidence from Greek.

Conference
Eleni Miltsakaki
Presented at the 31st Penn Linguistics Colloquium.
Publication year: 2007

A rethink of the relationship between salience and anaphora resolution.

Conference
Eleni Miltsakaki
In Proceedings of the 6th Discourse Anaphora and Anaphor Resolution Colloquium.
Publication year: 2007

The (non)-tension between structural and semantic focusing: evidence from Greek.

Conference
Eleni Miltsakaki, Martha Lambropoulou, and Athanasios Koutroupis
Poster presentation, in Proceedings of the 12th Annual Conference on Architectures and Mechanisms of Language Processing (AMLaP 2006).
Publication year: 2006

The Penn Discourse Treebank.

Conference
Eleni Miltsakaki, Rashmi Prasad, Aravind Joshi, and Bonnie Webber
In Proceedings of the 4th International Conference on Language Resources and Evaluation (LREC 2004).
Publication year: 2004

Structural vs semantic focusing: distributional evidence from referential forms in adverbial clauses.

Conference
Eleni Miltsakaki
To be presented at 17th Annual CUNY Conference on Human Sentence Processing, University of Maryland, College Park.
Publication year: 2004

A centering analysis of relative clauses in english and Greek.

Conference
Eleni Miltsakaki
To appear in Proceedings of the 28th Penn Linguistics Colloquium.
Publication year: 2004

An empirical investigation of discourse coherence, salience, and reference in Greek relative clauses.

Conference
Eleni Miltsakaki
6th International Conference of Greek Linguistics, Rethymno, Greece.
Publication year: 2003

Reference in relative clauses: the significance of marking low salience and its implications for 'recency' and 'subjecthood'.

Conference
Eleni Miltsakaki
Presented at the 16th Annual CUNY Conference on Human Sentence Processing.
Publication year: 2003

Anaphoric arguments of discourse connectives: semantic properties of antecedents versus non-antecedents.

Conference
Eleni Miltsakaki, Cassandre Creswell, Kate Forbes, Aravind Joshi, and Bonnie Webber
In Proceedings of the Computational Treatment of Anaphora Workshop, EACL 2003.
Publication year: 2003

The syntax-discourse interface: reference in subordinate clauses.

Conference
Eleni Miltsakaki
Presented at the 77th Annual Meeting of the Linguistic Society of America (LSA 2003).
Publication year: 2003

The discourse anaphoric properties of connectives.

Conference
Cassandre Creswell, Katherine Forbes, Eleni Miltsakaki, Rashmi Prasad, Bonnie Webber, and Aravind Joshi
In Proceedings of the 4th Discourse Anaphora and Anaphor Resolution Colloquium (DAARC 2002).
Publication year: 2002

Effects of subordination on referential form and interpretation.

Conference
Eleni Miltsakaki
In Proceedings of the 26th Penn Linguistics Colloquium.
Publication year: 2002

On the interpretation of weak and strong pronominals in Greek.

Conference
Eleni Miltsakaki
In Proceedings of the 5th International Conference on Greek Linguistics.
Publication year: 2001

Centering in Greek.

Conference
Eleni Miltsakaki
In Proceedings of 15th International Symposium on Theoretical and Applied Linguistics.
Publication year: 2001

The role of centering theory's rough-shift in the teaching and evaluation of writing skills.

Conference
Eleni Miltsakaki and Karen Kukich
In Proceedings of ACL 2000.
Publication year: 2000

A study on Greek questions conveying disagreement.

Conference
Eleni Miltsakaki
Presented at PRAGMA '99.
Publication year: 1999

Demo of antelogue: pronoun resolution for dialogues.

System Demo
Eleni Miltsakaki
Demo session of Third IEEE International Conference on Semantic Computing (ICSC '09), Berkeley, U.S.A.
Publication year: 2009

Matching readers' preferences and reading skills with web text.

System Demo
Eleni Miltsakaki
Demo session of 12th Conference of the European Chapter of the Association for Computational Linguistics (EACL '09), Athens, Greece.
Publication year: 2009

Refining the meaning of sense labels in PDTB: "Concession".

Workshop
Robaldo, L., E. Miltsakaki, and J.R. Hobbs
Proceedings of Symposium on Semantics in Text Processing, STEP 2008.
Publication year: 2008

Real time web text classification and analysis of reading difficulty.

Workshop
Miltsakaki, E. and A. Troutt
Proceedings of the 3rd Workshop on Innovative Use of NLP for Building Educational Applications, at the 46th Meeting of the Association for Computational Linguistics and Human Language Technologies.
Publication year: 2008

Animacy effects on discourse prominence in Greek complex nps.

Workshop
Stella Tsaklidou and Eleni Miltsakaki
Poster presentation, in Proceedings of ISCA Tutorial and Workshop on Experimental Linguistics.
Publication year: 2006

Effects of structural prominence on anaphora: the case of relative clauses.

Workshop
Eleni Miltsakaki and Paschalia Patsala
Poster presentaiton, in Proceedings of ISCA Tutorial and Workshop on Experimental Linguistics.
Publication year: 2006

The Penn Discourse Treebank as a resource for natural language generation.

Workshop
Rashmi Prasad, Eleni Miltsakaki, Nikhil Dinesh, Alan Lee, Aravind Joshi, and Bonnie Webber
In Proceedings of the Corpus Linguistics Workshop on Using Corpora for Natural Language Generation.
Publication year: 2006

Experiments on sense annotations and sense disambiguation of discourse connectives.

Workshop
Eleni Miltsakaki, Nikhil Dinesh, Rashmi Prasad, Aravind Joshi, and Bonnie Webber
In Proceedings of the 4th Workshop on Treebanks and Linguistic Theories (TLT2005).
Publication year: 2005

Attribution and the (non-) alignment of syntactic and discourse arguments of connectives.

Workshop
Nikhil Dinesh, Alan Lee, Eleni Miltsakaki, Rashmi Prasad, Aravind Joshi, and Bonnie Webber
ACL 2005 Workshop on Frontiers in Corpus Annotation II: Pie in Sky.
Publication year: 2005

Investigating salience in complex sentences: a look at relative clauses.

Workshop
Eleni Miltsakaki
On the Syntax-Pragmatics Interface: A Workshop in Honor of Ellen Prince.
Publication year: 2005

Annotating discourse connectives and their arguments.

Workshop
Eleni Miltsakaki, Rashmi Prasad, Aravind Joshi, and Bonnie Webber
In Proceedings of the Frontiers in Corpus Annotation 2004 NAACL/HLT Conference Workshop.
Publication year: 2004

Annotation and data mining of the Penn Discourse Treebank.

Workshop
Rashmi Prasad, Eleni Miltsakaki, Aravind Joshi, and Bonnie Webber
In Proceedings of the ACL 2004 Workshop on Discourse Annotation.
Publication year: 2004

Anaphoric arguments of discourse connectives: semantic properties of antecedents versus non-antecedents.

Workshop
Eleni Miltsakaki, Cassandre Creswell, Kate Forbes, Aravind Joshi, and Bonnie Webber
In Proceedings of the Computational Treatment of Anaphora Workshop, EACL 2003.
Publication year: 2003

D-LTAG system - discourse parsing with a lexicalized tree-adjoining grammar.

Workshop
Eleni Miltsakaki
In Proceedings of the Information Structure, Discourse Structure and Discourse Semantics ESSLLI 2001 Workshop.
Publication year: 2001

Automated evaluation of coherence in student essays.

Workshop
Eleni Miltsakaki and Karen Kukich
In Proceedings of the Workshop on Language Resources and Tools in Educational Applications, LREC 2000.
Publication year: 2000

Locating topics in text processing.

Workshop
Eleni Miltsakaki
In Proceedings of CLIN '99.
Publication year: 1999

A short introduction to the Penn Discourse Treebank.

Working Paper
Webber, B., A. Joshi, E. Miltsakaki, R. Prasad, N. Dinesh, A. Lee, and K. Forbes
To appear in Copenhagen Working Papers in LSP.
Publication year: 2005

Empirical studies of centering shifts and cue phrases as embedded segment boundary markers.

Working Paper
Forbes, K. and E. Miltsakaki
In E. Kaiser (ed.), Penn Working Papers in Linguistics 7.2: Current work in linguistics. pp. 39--57.
Publication year: 2000

The Penn Discourse Treebank 2.0. annotation manual.

Technical Report
Prasad., R, E. Miltsakaki, N. Dinesh, A. Lee, A. Joshi, B. Webber, and L. Robaldo
IRCS Technical Report, IRCS-06-01, Institute of Research in Cognitive Science, University of Pennsylvania.
Publication year: 2008

The Penn Discourse Treebank 1.0. annotation manual.

Technical Report
IRCS Technical Report, IRCS-06-01, Institute of Research in Cognitive Science, University of Pennsylvania.
Publication year: 2006

The syntax-discourse interface: effects of the main-subordinate distinction on attention structure.

Phd Thesis
Eleni Miltsakaki
PhD thesis. Department of Linguistics, University of Pennsylvania. Thesis co-advisors: Ellen Prince and Aravind Joshi.
Publication year: 2003

.05

Deployed Systems and Prototypes

Search And Learn

Deployed search engine for K-12, filtering websites by reading level and theme in real time search.

Library

Over 200,000 curated educational websites with reading level and theme labels

Virtual Librarian

Personalized recommendations of websites based on learner’s familiarity with topics

Upskilled

Prototype for concept identification in technical content

Antelogue

Pronoun resolution system for dialogues, including handling of non-referential pronouns: It is deployed on Penn servers. Available on request.
.07

Teaching

  • Current
  • 2016

    Machine Learning and Natural Language Processing for Personalized Learning

    Invited Webinar for AI with the Best.

  • NOW
    2008

    EDU 526: Technology for Educators.

    University Of Pennsylvania

     
     
  • Teaching History
  • 2007

    EDUC 644: Technology Mediated Teaching and Learning.

    University Of Pennsylvania

  • 2007

    EDUC 535: Technology for Educators

    University Of Pennsylvania

  • 2007

    EDUC 000: Special seminar on Technology in Teacher Education.

    University Of Pennsylvania

  • 2006

    EDUC 639: Design of Learning Environments.

    University Of Pennsylvania

  • 2006

    EDUC 644: Technology Mediated Teaching and Learning.

    University Of Pennsylvania

  • 2006

    LING 342 Introduction to Computational Linguistics.

    Aristotle University of Thessaloniki

  • 2006

    LING 121 Introduction to Linguistics II.

    Aristotle University of Thessaloniki

  • 2005

    LING 342 Introduction to Computational Linguistics.

    Aristotle University of Thessaloniki

  • 2005

    LING 412 Special Topics in Computational Linguistics: Processing Anaphoric Expressions.

    Aristotle University of Thessaloniki

  • 2003

    GREK 018 Intermediate Modern Greek II.

    University Of Pennsylvania

  • 2002

    GREK 017 Intermediate Modern Greek I.

    University Of Pennsylvania

  • 1999

    GREK 018 Intermediate Modern Greek II

    University Of Pennsylvania

  • 1998

    LING 550 Syntax I

    University Of Pennsylvania

  • 1998

    LING 106 Verbal Art and Language.

    University Of Pennsylvania

  • 1998

    GREK 017 Intermediate Modern Greek I.

    University Of Pennsylvania

  • 1998

    GREK 015 Elementary Modern Greek I.

    University Of Pennsylvania

.08

Publicity

Publicity List

Machine Learning for Learning Panel

In LearnLaunch Across the Boundaries Conference.
Publication year: 2019
Retrieved 10/4/2020

AI in Education.

In Disruptor Daily.
Publication year: 2019
Retrieved 10/4/2020

Runner-up winner for IBM Watson AI XPRIZE milestone competition.

In NeurIPS, Montreal.
Publication year: 2018
Retrieved 10/4/2020

Carnegie Learning to Deliver Choosito Through Its EMC Platform.

In Businesswire.
Publication year: 2018
Retrieved 1/22/2019

15 Women Who Are Changing the Face of Tech in the Region.

In Philadelphia Business Journal.
Publication year: 2016
Retrieved 10/4/2020

Choosito: A New Way to Search the Web

In TV Segment (4/16/2016).
Publication year: 2016
Retrieved 10/4/2020

Choosito! Supporting Kid Search, Discovery, and Literacy

In School Library Journal.
Publication year: 2016
Retrieved 10/4/2020

Will Librarians Be the Overseers of the Information Age? Interview by Rod Berger

At Edcircuit.
Publication year: 2016
Retrieved 10/4/2020

She’s Changing the Way Our Kids Surf the Web

In Huffington Post (8/22/2015).
Publication year: 2015
Retrieved 10/4/2020