Lucy Park

• ESTJ in 2007, INFP in 2018.
• Friends call me a dreamer or romantist to a fault - but I enjoy living my life as such.
• I am a productivity geek, and spend much of my free time automating things.
• I easily get addicted to things, so I don't start things easily.

• Riding my bike, swimming, scuba diving, playing the piano and making my own fresh coffee with a siphon.
• Adventures, smiling, learning, and pursuing community action.
• Exploration, as opposed to exploitation. I particularly enjoy exploring the realm of cuisines. I have two particular dishes I do not enjoy -- 홍어, 추어탕 -- but who knows? I may even come to like these in the future.
• Openness, sharing, and cooperation (ex: Everyware)

• 近者悅遠者來.
• Life is about the journey, not about the destination.
• Languages themselves are not goals but tools of communication.
• Happiness brings smiles, and smiles also bring happiness.
• That there is no wrong and no right, is right.
• The individual is not something complete, perfect or finished, but something moving, changing, and above all initiating instead of final.
• You can make money without doing evil.
• Todo lo que es tiene más razones para ser que para no ser.
• Treat those around you as reasonable people, and when they have different opinions, try to discover what problem they are solving.
• Explicit is better than implicit.
• Pessimists sound smart. Optimists achieve things.

# 134 chars (Twitter)
data hacker; loves geeks, smiling, learning, community action, 장난꾸러기s, people that see the bright side of things; 사람을 좋아하는 사람을 좋아하는 사람

# 370 chars (lucypark.kr)
My life goal is to lower the barriers of knowledge through data. I am currently working for NAVER Papago – and lowering the barriers of linguistic knowledge by building machine translation models. Current areas of interest include context-aware machine translation, and translation evaluation. You can often find me on the Web as Eunjeong, echojuliett, echojuli&t, e9t.

# 472 chars (NDI talk 2014)
Lucy Park is a data miner, and an advocate of open data, open source and open government. She directs Team POPONG, a South Korea based non-partisan voluntary group which merges and analyzes various government resources, to make the legislative process more accessible and easier to understand. She currently devotes her best efforts in improving Politics in Korea, an award winning National Assembly monitoring service, with the belief that technology can make a change.

# 492 chars (IEEE ToM 2019)
Eunjeong Park recieved the M.S. and Ph.D degrees in Data Mining from Seoul National University in 2011 and 2016, respectively, where she has persued various studies on text mining in the fields of manufacturing, politics, multimedia, and marketing. After her studies, she joined NAVER, the South Korea bsaed search company in 2016. and is currently working on natural language processing. Her reserach interests include multilingual text mining, representation learning and domain adaptation.

# 493 chars (INFORMS 2014)
Eunjeong Park is a data miner and Ph.D. candidate specializing in data research at Seoul National University. She has recieved a B.S. and M.S. from the same university for Industrial Engineering and Data Mining, but is interested myriad domains. Her recent projects range from user log analysis and segmentation, sensor data summarization, to legislative prediction and multimedia content recommendation. Ms. Park describes her lifetime goal is to make the world a better place via technology.

# 648 chars (NLPOSS 2020)
Lucy is a machine learning engineer at NAVER. She has participated in some open source projects, particularly KoNLPy which is a tool for Korean NLP, and is also interested in open data. She received her Ph.D. in Data Mining from Seoul National University in 2016, where she has pursued various studies on text mining in the fields of manufacturing, political science, and multimedia. After her studies, she joined NAVER, a South Korea based search-engine company, and is currently working on machine translation for Papago. Her research interests include machine translation, multilingual text mining, and evaluation of machine learning algorithms.

# 1794 chars (MIT IU35 2021)
The Korean language is used by more than 80M people and is increasingly gaining popularity due to K-dramas, K-pop, variety shows, and games. However, despite its popularity, it is a minor language in natural language processing research, while most research is carried out in English. During her graduate studies at Seoul National University, Lucy learned that many open source packages for the Korean language were often difficult to access, if not less documented. So in 2014, she developed and released KoNLPy (https://konlpy.org), an open-source Python package for Korean natural language processing, focused on easy access, usability and scalability. The package picked up many users in Korea and is still widely used for production and educational purposes. She continued her affair for the Korean language while working on Papago, NAVER's machine translation application. She built an honorific-sensitive machine translation model in 2017, which gained popularity from many people learning the Korean language. In 2020 Lucy co-founded Upstage, where she keeps developing AI models to make AI beneficial to many people. Upstage is an AI solution company working on computer vision, recommender systems, and natural language processing. One of the efforts they recently made for the Korean language processing is to lead the initiative for KLUE, a Korean language understanding evaluation benchmark. This is a big deal since the released dataset is one of the first and most extensive Korean datasets free from copyright license issues. Lucy participated in creating data for the machine reading comprehension task. Upstage has earned 8.8 billion won in revenue in just eight months since its founding by providing AI solutions. They will be releasing reusable AI packs in the near future.

# 159 chars (한국경제 2021)
전 파파고의 모델팀을 리드하며 각종 기능 개발에 앞장섰다. 제조, 정치, 멀티미디어 등 다양한 도메인의 텍스트 모델링 경험이 있으며, 한국어 관련 연구, 프로덕션, 및 교육 등에 널리 쓰이고 있는 한국어처리 오픈소스 라이브러리인 konlpy 및 한국어 데이터셋인 nsmc를 릴리즈했다.

# 160 chars (PyConKR 2014, PyConKR 2015)
박은정은 서울대학교에서 데이터마이닝을 전공하고 있으며, 마케팅, 반도체, 영화 등의 영역에서 데이터 분석 프로젝트를 진행했다. 기술을 이용해 지식의 장벽을 낮추는 것에 관심이 있어, 여가 시간에는 팀포퐁에서 "대한민국 정치의 모든 것"을 만들며 입법 정보의 확산에 노력을 기울이고 있다.

# 292 chars (PyConKR 2019)
박은정은 데이터를 통해 지식의 장벽을 낮추고자하는 기계학습 엔지니어다. 파이썬을 이용해서 팀포퐁, KoNLPy 등의 프로젝트를 진행했고, 현재 업무에서도 파이썬을 활발하게 이용하고 있다. 서울대학교에서 제조, 정치학, 멀티미디어 등 다양한 도메인의 텍스트를 다룬 후 2016년, 데이터마이닝으로 박사학위를 받았다. 졸업 직후 네이버에 입사해서, 현재는 기계번역 모델을 개발하며 언어 장벽을 낮추기 위해 노력하고 있다. 기계번역 뿐 아니라 다국어 텍스트 마이닝, 기계학습 알고리즘의 평가 방법론에 대해 관심 가지고 있다.

# 506 chars (SNU invited lecture 2021)
박은정은 데이터를 통해 지식의 장벽을 낮추고자하는 기계학습 엔지니어다. 서울대학교에서 제조, 정치, 멀티미디어 등 다양한 도메인의 텍스트를 다루다가 konlpy 라는 한국어처리 오픈소스 라이브러리 및 nsmc 라는 한국어 데이터셋을 릴리즈했다. 현재 이 라이브러리와 데이터셋은 한국어 관련 연구, 프로덕션, 및 교육 등에 널리 쓰이고 있다. 박은정은 2016년 데이터마이닝으로 박사학위를 받은 후 네이버에 입사해서 기계번역 모델을 개발하며 언어 장벽을 낮추기 위해 노력했다. 파파고의 모델팀을 리드했으며, 번역기의 평가 방식과 관련된 연구, 높임말 번역기와 같은 파파고의 각종 기능 개발에 앞장섰다. 현재는 업스테이지를 공동창업하여 CSO(chief scientific officer)로서 사내 연구 활동을 총괄하고 있으며, NLP(natural language processing) 연구에 집중하고 있다. 최근에는 국내 최초이자 최대 규모의 자연어 이해 평가 데이터셋인 KLUE를 릴리즈하는데 일조했다.