FAccT 2023: My Random Highlights
tl;dr It has been a while but for those who didn't attend (and those who did) I am sharing some of my of my experience at FAccT 2023. THE...
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SHIRAN DUDY
I am a research scientist at Northeastern University
Hi. Welcome! I Recently joined Northeastern University working on technical (NLP, speech processing) and social-technical questions promoting Responsible AI (RAI) practices. I am part of the RAI team at the Institute for Experiential AI (EAI). My main interests are around participatory design, textual personalization, and accessibility.
PROFESSIONAL BIO
Current Research
M research is driven by the value-based research approach recognizing that developing applied technical systems, should ensure understanding of socio-technical implications, in ways that increase social and ecological justice
PUBLICATIONS
Selected Research Papers
2021
S. Dudy, S. Bedrick, B. Webber
Empirical Methods in Natural Language Processing (EMNLP)
The figure shows the percentage of Types (%) predicted stratified by frequency in known SotA language models
2018
2018
2018
The figure shows the percentage of Types (%) predicted stratified by frequency in a categorical vs. continuous language models
2020
S. Dudy, S. Bedrick
WiNLP Workshop
Association for Computational Linguistics (ACL)
2019
R. Dong, D. Smith, S. Dudy, S. Bedrick
SPLAT Workshop
North American Association for Computational Linguistics (NAACL)
GIT REPOS
From a few projects I've worked on
FST SPECIALIZER
2017
A Python library to create Special Fsts as described in OpenFst source code. This specializer library enables sigma, rho, and phi operations over fsts over using the command line operations of OpenFst. To have a small footprint, this functionality was implemented in Cython and included generating C++ files.
DOCKER IMAGES FOR FST TOOLS
2016
Openfst and Pywrapfst library images that provide an openfst with pywrapfst environment on ubuntu 14.4. The most recent images are the pynini_1.4.1 and the fst_1.6.1 and their installation details as well as content is available.
OBFUSCATION DETECTION WITH DEEP LEARNING
2016
The basic code for obfuscation detection provides a trained classifier to analyze and detect the encoding language used to obfuscate parts of code in order to hide there sensitive information or potnetially harmful behavior.
PROSODY DETECTION
2015
Code for different sequence classifiers as described in the paper “Textual Prediction of Prosodic Prominence in Spontaneous Speech with Sequence Classifiers”. There are viterbi, greedy first and second order and, local search first second and third order.
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