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資深介面設計師 @Lexionlu Design Firm_瑞芙國際設計有限公司 網際網路相關業2023 ~ 2024
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UIUX Designer
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4-6 tahun
國立臺北教育大學(National Taipei University of Education)
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玩具與遊戲設計研究所
Past
Sr. Frontend Engineer @旭捷資訊有限公司2022 ~ 2023
・
前端工程師、資深前端工程師
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蔡卓霖 JavaScript | React 曾經是一名5年經驗的遊戲設計師， 現在是一名擁有4.5年開發經驗的資深前端工程師。 [email protected] 工作經驗 旭捷資訊 - Sr. Frontend Engineer | 2022/03 ~ 2023/10 ・ 1 yr 8 mos 個人金融服務產品 - 前端開發 ・使用 React, Redux Toolkit, TypeScript, Ant Design 和 Vite， 從0到1開發產品
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大仁科技大學
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應用英文製程工程師 @台灣美光記憶體股份有限公司2021 ~ Sekarang
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半導體製程工程師,半導體製程整合工程師,半導體研發工程師
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國立雲林科技大學
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化學工程與材料工程Past
Project Management @杰悉科技2021 ~ 2024
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專案經理、產品經理、系統分析師
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國立高雄大學
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工業管理
工程師 @鴻海集團_鴻騰精密科技股份有限公司2018 ~ Sekarang
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後端工程師/軟體工程師
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顏嘉妤 Coco Yen AI模型應用/網頁開發 工作經歷 工程師 • (鴻海集團)鴻騰精密科技股份有限公司 2018/07 - 仍在職 ‧ AI模型訓練與部署 - 使用開源影像辨識類深度學習模型，用於產線瑕疵檢測用 - 製作模型推論模組，提供產線軟體做部署串接使用 ‧ 介面製
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國立台北科技大學 NTUT
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電資學士班-電子工程系
製程工程師 @台達化學工業股份有限公司2022 ~ Sekarang
・
研發工程師、整合工程師、製程工程師
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蘇柏儫 製程工程師 台灣高雄市 六年經驗的製程工程師，主要負責與內外部的團隊合作，擅長專案規劃與執行、製程控制與改進 、指導技術人員 、 製程危害分析 手 機：Email： zzs1280 @gmail.comPresent Process Engineer 製程工程師 台達 化 製成課ABS工場 主要負責 維持產線運作及解
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國立中正大學(National Chung Cheng University)
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化學工程
講師 @雲林縣文光國小、口湖國小、內湖國小2023 ~ Sekarang
・
藝文教育、教育講師、教育顧問
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吳佳穎 Candy Wu 1997 年生於臺南 教室裡的全能老師，教室外的跨域獵人[email protected] 簡介 2020 年以公費生的身份受分發至雲林海線，經歷四年偏鄉教育現場的磨練，看見各種家庭樣貌映射在學生身上，使課室的組成更加多元，上課不再只是 知識的傳授
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4-6 tahun
國立清華大學 National Tsing Hua University
・
藝術與設計研究所
Past
多媒體設計師-主任 @全球人壽保險股份有限公司_總公司2021 ~ 2023
・
多媒體動畫設計、平面設計、剪輯後製
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李玟璇 Coco Lee 完整的影像及設計經驗， 從前端、後端、UI、UX、影音設計皆能勝任。 曾職鼎泰豐、全球人壽的多媒體設計師，曾獲得全部門22人唯一續效A+的殊榮。擁有敏銳的視覺感知能力，文案設計、腳本撰寫、拍攝、後製、一條龍的完整所有工作。 從多媒體
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Build School 軟體開發培訓課程
・
C#、HTML/CSS、SQL、JavaScript、.NET MVC、Azure
Past
行銷&電商營運 @傑西寶寶_鴻奕休閒文化股份有限公司2023 ~ 2024
・
Marketing
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孫晉嘉 我是 Fabio！ 充滿南部熱情的男子，用滿滿的能量及對行銷的熱愛，努力深耕「行銷力」 渴望能夠有一個舞台充分發會自己的長才 與我聯繫 點擊我就能更了解我 專長 廣告投放 ：於Google、Meta累計投放金額超過千萬。 品牌行銷 ： 操作SEO、論壇、新聞媒體及KOL
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國立屏東大學
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行銷與流通管理
Past
品牌視覺設計 @緹威國際有限公司2022 ~ 2024
・
平面設計、視覺設計師
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Huang shun hua 平面視覺設計師 Taiwan 打造品牌形象並推廣，強化商品優點，增加轉換率。 設計能夠幫助我們快速理解一項產品的優缺點，更能快速抓準消費客群需要的的問題。 有行銷、廣告美學方面的經驗。 Shun hua 作品集 Design Tools Illustrator Photoshop lightroom indesign 工作經歷 2022//2 品牌
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天主教輔仁大學 FU JEN CATHOLIC UNIVERSITY
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大眾傳播系Paket Perekrutan Paling Mudah dan Efektif, Pilihan Ratusan Perusahaan
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UI designer -UX
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Dalam tiga bulan
Software Engineer
Jash Data Sciences
・
2020 ~ Sekarang
Latar Belakang Profesional
Status sekarang
Tahap pencarian kerja
Profesi
Data Engineer
Bidang Pekerjaan
Software
Pengalaman Kerja
2-4 tahun
Management
Keterampilan
Bahasa
English
・
Profesional
Marathi
・
Bahasa ibu atau Bilingual
Hindi
・
Bahasa ibu atau Bilingual
Preferensi Pencarian Pekerjaan
Jabatan
Machine Learning Engineer
Tipe Pekerjaan
Full-time
Lokasi
Bekerja jarak jauh
Tertarik bekerja jarak jauh
Freelance
Pendidikan
Institusi Pendidikan
Pune Institute of Computer Technology, Pune
Jurusan
Electronics and Telecommunication
Shishir Joshi
Full Stack Data Scientist and self taught Machine Learning enthusiast with experience of CV, NLP, Deep Learning and backend development.
Pune IN
[email protected]
Ph. : +91 8554067867 | +91 9405181761
Skills
Machine Learning
Transformers (BERT, DistillBERT),
LSTMs, CNNs, SVMs, KNN, Decision Trees,
ULMFiT,
Transfer Learning, Deep Learning, Computer Vision,
APIs / Libraries
OpenCV, Tensorflow, Keras, PyTorch, Fast.ai, Huggingface,
Scikit-Learn, Pandas,
PySpark.
Development
Python, shell scripting, Java, SQL/PL-SQL, GIT, Django, Flask
AWS [ EC2, S3, SQS, RDS, Sagemaker ]
Work Experience
Data Scientist,
GlobalFoundries | Mar 2021 ~ Present
: Predictive Maintenance Model of Semiconductor Etching Tools
- Created multiple POCs for predictive maintenance of semiconductor mfg. tools based on Remaining Useful Life using existing tool sensors and yield data for the Singapore Facility.
- XGBoost Regressor and Autoregressive LSTM models were developed for the same in AWS Sagemaker.
- Deployed models on premises in shadow mode for validation on live data.
: Probability of Failure analysis/ model on Etching Tools
- Built Probability of Failure model using test wafer particle counts to predict tool's internal state.
- Two part model is made of Wafer Particle (defect) Count Regressor (Linear Reg., RandomForest Reg., XGBoost Regressor were used for POCs) and Thresholded CDF of Negative Binomial distribution.
- Used Maximum Likelihood Estimation to model tool state based on multiple internal sensors, semiconductor recipe information and control limit thresholds.
- MLE model used with Threshold CDF framework shows promising results in terms of extending tool uptime and predicting possible failures based on trends in defect measurement.
Data Scientist,
Jash Data Sciences | Feb 2020 ~ Feb 2021
: Document Similarity Semantic Search
- Researched, created and served document level semantic similarity search engine for an AI based hiring tech startup.
- Compared the performance of LSTM Seq2Seq based Autoencoder for language modeling task with DistilBERT model on custom evaluation metric based on semantic similarity.
- Created complete backend API to serve the fine tuned DistilBERT model via Flask and Highly optimized document embedding.
- Implemented Approximate Nearest Neighbor search using graph based HNSW clustering for near real time retrieval.
: Insurance Email Classification and document NER
- Created word embedding and keyword extraction based email classification model (Test Set F1 score: 0.85).
- My model out performed ULMFiT model fine tuned for email classification on same dataset (ULMFiT Test Set F1 score: 0.72).
- Worked on implementing DistilBERT based NER pipeline using Huggingface Transformers.
: Inhovate - Analytics platform focused on the hospitality industry
- Automated complete ETL logic in Bash/Python.
- Worked on Backend API in Django and wrote custom query builders to dynamically compose and execute complex queries beyond the scope of Django's ORM.
- Created linux processes and cron jobs for ETL, web server with load balancing using HAProxy and Gunicorn.
Software Engineer,
Larsen and Toubro Infotech, | Sep 2018 ~ Dec 2019
- Development and extension of BRAINS core banking platform.
- Implemented source code management functionality in-house, saving $25k yearly in licensing costs to outsourced system
- Worked on pilot project for creation of defaulter classification using Gradient Boosted Decision Tree classifier in Scikit-Learn.
Projects (Computer Vision, NLP, etc.)
Click here for colab notebooks
Qualia
- https://github/qualia
- Online Real Time Semantic Search using Transformers and HNSW nearest neighbour search.
- Can be
fine tuned on specific datasets with custom tokenisation requirements.
- Uses Sentence Transformers
as embedding model and HNSWlib as approximate nearest neighbour search index based on cosine
similarity.
- The aim is to make it "Online" - to be able to add new documents in parallel with querying.
StackExchange Question tags extraction
- https://colab/stx
- Multilabel classification for tag extraction from Stackexchange questions.
- Used transfer learning to fine
tune ULMFiT Language model on dataset (83% language modeling accuracy),
- Used beautifulsoup4
(bs4) and regex to clean text.
- Created Multi-Label classifier using ULMFiT as embedding layer.
- Achieved >93% accuracy on tag prediction.
Machine Learning Based Automatic Fruit Grading and Classification
-
Machine Learning Based Automatic Fruit Grading and Classification:Funded by the University of Pune.
- Trained Inception V3 CNN model via transfer learning for detecting grade of fruits based on visual quality, skin texture, and pre-defined standards.
- Used OpenCV for image preprocessing (cropping, segmentation and feature extraction for comparison of classification on SVM) Achieved ~90% accuracy on test set.
MobileNet v2.0 Transfer Learning on the Caltech101 Dataset with TF2.0:
- Trained my custom CNN on the 101 categories of the Caltech101 dataset.
- Prepared a tf.Data input pipeline, and compared performance with transfer-trained MobileNetv2.0 model.
- My Model achieved ~98% accuracy while MobileNet achieved ~80%
Education
Savitribai Phule Pune University, Pune | 2015 ~ 2018
Bachelor of Engineering (B.E.) | Electronics and Telecommunication,
Graduated First Class with Distinction
from
Pune Institute of Computer Technology (PICT), Pune
Shishir Joshi
Full Stack Data Scientist and self taught Machine Learning enthusiast with experience of CV, NLP, Deep Learning and backend development.
Pune IN
[email protected]
Ph. : +91 8554067867 | +91 9405181761
Skills
Machine Learning
Transformers (BERT, DistillBERT),
LSTMs, CNNs, SVMs, KNN, Decision Trees,
ULMFiT,
Transfer Learning, Deep Learning, Computer Vision,
APIs / Libraries
OpenCV, Tensorflow, Keras, PyTorch, Fast.ai, Huggingface,
Scikit-Learn, Pandas,
PySpark.
Development
Python, shell scripting, Java, SQL/PL-SQL, GIT, Django, Flask
AWS [ EC2, S3, SQS, RDS, Sagemaker ]
Work Experience
Data Scientist,
GlobalFoundries | Mar 2021 ~ Present
: Predictive Maintenance Model of Semiconductor Etching Tools
- Created multiple POCs for predictive maintenance of semiconductor mfg. tools based on Remaining Useful Life using existing tool sensors and yield data for the Singapore Facility.
- XGBoost Regressor and Autoregressive LSTM models were developed for the same in AWS Sagemaker.
- Deployed models on premises in shadow mode for validation on live data.
: Probability of Failure analysis/ model on Etching Tools
- Built Probability of Failure model using test wafer particle counts to predict tool's internal state.
- Two part model is made of Wafer Particle (defect) Count Regressor (Linear Reg., RandomForest Reg., XGBoost Regressor were used for POCs) and Thresholded CDF of Negative Binomial distribution.
- Used Maximum Likelihood Estimation to model tool state based on multiple internal sensors, semiconductor recipe information and control limit thresholds.
- MLE model used with Threshold CDF framework shows promising results in terms of extending tool uptime and predicting possible failures based on trends in defect measurement.
Data Scientist,
Jash Data Sciences | Feb 2020 ~ Feb 2021
: Document Similarity Semantic Search
- Researched, created and served document level semantic similarity search engine for an AI based hiring tech startup.
- Compared the performance of LSTM Seq2Seq based Autoencoder for language modeling task with DistilBERT model on custom evaluation metric based on semantic similarity.
- Created complete backend API to serve the fine tuned DistilBERT model via Flask and Highly optimized document embedding.
- Implemented Approximate Nearest Neighbor search using graph based HNSW clustering for near real time retrieval.
: Insurance Email Classification and document NER
- Created word embedding and keyword extraction based email classification model (Test Set F1 score: 0.85).
- My model out performed ULMFiT model fine tuned for email classification on same dataset (ULMFiT Test Set F1 score: 0.72).
- Worked on implementing DistilBERT based NER pipeline using Huggingface Transformers.
: Inhovate - Analytics platform focused on the hospitality industry
- Automated complete ETL logic in Bash/Python.
- Worked on Backend API in Django and wrote custom query builders to dynamically compose and execute complex queries beyond the scope of Django's ORM.
- Created linux processes and cron jobs for ETL, web server with load balancing using HAProxy and Gunicorn.
Software Engineer,
Larsen and Toubro Infotech, | Sep 2018 ~ Dec 2019
- Development and extension of BRAINS core banking platform.
- Implemented source code management functionality in-house, saving $25k yearly in licensing costs to outsourced system
- Worked on pilot project for creation of defaulter classification using Gradient Boosted Decision Tree classifier in Scikit-Learn.
Projects (Computer Vision, NLP, etc.)
Click here for colab notebooks
Qualia
- https://github/qualia
- Online Real Time Semantic Search using Transformers and HNSW nearest neighbour search.
- Can be
fine tuned on specific datasets with custom tokenisation requirements.
- Uses Sentence Transformers
as embedding model and HNSWlib as approximate nearest neighbour search index based on cosine
similarity.
- The aim is to make it "Online" - to be able to add new documents in parallel with querying.
StackExchange Question tags extraction
- https://colab/stx
- Multilabel classification for tag extraction from Stackexchange questions.
- Used transfer learning to fine
tune ULMFiT Language model on dataset (83% language modeling accuracy),
- Used beautifulsoup4
(bs4) and regex to clean text.
- Created Multi-Label classifier using ULMFiT as embedding layer.
- Achieved >93% accuracy on tag prediction.
Machine Learning Based Automatic Fruit Grading and Classification
-
Machine Learning Based Automatic Fruit Grading and Classification:Funded by the University of Pune.
- Trained Inception V3 CNN model via transfer learning for detecting grade of fruits based on visual quality, skin texture, and pre-defined standards.
- Used OpenCV for image preprocessing (cropping, segmentation and feature extraction for comparison of classification on SVM) Achieved ~90% accuracy on test set.
MobileNet v2.0 Transfer Learning on the Caltech101 Dataset with TF2.0:
- Trained my custom CNN on the 101 categories of the Caltech101 dataset.
- Prepared a tf.Data input pipeline, and compared performance with transfer-trained MobileNetv2.0 model.
- My Model achieved ~98% accuracy while MobileNet achieved ~80%
Education
Savitribai Phule Pune University, Pune | 2015 ~ 2018
Bachelor of Engineering (B.E.) | Electronics and Telecommunication,
Graduated First Class with Distinction
from
Pune Institute of Computer Technology (PICT), Pune