Fireside Chat with Hiromu Hota - Transitioning from Research to Industry

Imaad Mohamed Khan · Beginner ·📄 Research Papers Explained ·5y ago

Key Takeaways

Transitioning from research to industry, PhD to ML Engineer at Snorkel AI, discussing career paths and roles in research and industry

Original Description

In this episode of the Fireside Chat, we have Hiromu Hota, ML Engineer at Snorkel AI. He earlier worked with Hitachi for many years. Before that, he got his PhD from a University in Japan where he studied brain-computer interfaces. In this chat, we talk about how Hiromu started his career with a PhD and then moved on to doing research at a company before moving into a more product related role.
Watch on YouTube ↗ (saves to browser)
Sign in to unlock AI tutor explanation · ⚡30

Playlist

Uploads from Imaad Mohamed Khan · Imaad Mohamed Khan · 21 of 34

← Previous Next →
1 Does AI know Fashion? - Mitali Sodhi - Mantissa Data Science Meetups
Does AI know Fashion? - Mitali Sodhi - Mantissa Data Science Meetups
Imaad Mohamed Khan
 2 Mantissa Data Science Webinar - 1 with Santhosh Shetty
Mantissa Data Science Webinar - 1 with Santhosh Shetty
Imaad Mohamed Khan
 3 Recommender Systems - Imaad Mohamed Khan - Mantissa Data Science Meetups
Recommender Systems - Imaad Mohamed Khan - Mantissa Data Science Meetups
Imaad Mohamed Khan
 4 Data Science is more than just Data Scientist - Different Roles in the field of Data Science
Data Science is more than just Data Scientist - Different Roles in the field of Data Science
Imaad Mohamed Khan
 5 What topics to prepare for Data Science Interviews in 2020?
What topics to prepare for Data Science Interviews in 2020?
Imaad Mohamed Khan
 6 Programming as a human activity
Programming as a human activity
Imaad Mohamed Khan
 7 What are the languages or tools used by Data Scientists in their work?
What are the languages or tools used by Data Scientists in their work?
Imaad Mohamed Khan
 8 Linear Regression From Scratch - Part 1
Linear Regression From Scratch - Part 1
Imaad Mohamed Khan
 9 Linear Regression From Scratch - Part 2
Linear Regression From Scratch - Part 2
Imaad Mohamed Khan
 10 Linear Regression From Scratch - Part 3
Linear Regression From Scratch - Part 3
Imaad Mohamed Khan
 11 Journey into Data Science - Fireside chat with Adarsha and Karthikeyan
Journey into Data Science - Fireside chat with Adarsha and Karthikeyan
Imaad Mohamed Khan
 12 Off the ground - Python in 5 Steps
Off the ground - Python in 5 Steps
Imaad Mohamed Khan
 13 How LinkedIn uses Data Science to build your feed - LinkedIn Feed Algorithm Explained
How LinkedIn uses Data Science to build your feed - LinkedIn Feed Algorithm Explained
Imaad Mohamed Khan
 14 Fireside chat with Eric Weber - Learnings in Data Science
Fireside chat with Eric Weber - Learnings in Data Science
Imaad Mohamed Khan
 15 Part 2 - How LinkedIn uses Data Science to build your feed | LinkedIn Feed Algorithm Explained
Part 2 - How LinkedIn uses Data Science to build your feed | LinkedIn Feed Algorithm Explained
Imaad Mohamed Khan
 16 Using Streamlit's Share Feature to easily deploy (and share) videos using Github
Using Streamlit's Share Feature to easily deploy (and share) videos using Github
Imaad Mohamed Khan
 17 Airbnb Experiences Ranking Algorithm Explained - Part I
Airbnb Experiences Ranking Algorithm Explained - Part I
Imaad Mohamed Khan
 18 Airbnb Experiences Ranking Algorithm Explained - Part II
Airbnb Experiences Ranking Algorithm Explained - Part II
Imaad Mohamed Khan
 19 Airbnb Experiences Ranking Algorithm Explained - Part III
Airbnb Experiences Ranking Algorithm Explained - Part III
Imaad Mohamed Khan
 20 Big Data, Hadoop and Machine Learning Explained using Dams
Big Data, Hadoop and Machine Learning Explained using Dams
Imaad Mohamed Khan
 ▶ Fireside Chat with Hiromu Hota - Transitioning from Research to Industry
Fireside Chat with Hiromu Hota - Transitioning from Research to Industry
Imaad Mohamed Khan
 22 Introduction to Anomaly Detection and One Class Classification
Introduction to Anomaly Detection and One Class Classification
Imaad Mohamed Khan
 23 Reading and manipulating Google Sheets (GSheets) using Python libraries
Reading and manipulating Google Sheets (GSheets) using Python libraries
Imaad Mohamed Khan
 24 Writing to Google Sheets (GSheets) using Python libraries
Writing to Google Sheets (GSheets) using Python libraries
Imaad Mohamed Khan
 25 Fireside Chat with Mirza Rahim Baig - Business Problem Solving and Data Science Career Tips
Fireside Chat with Mirza Rahim Baig - Business Problem Solving and Data Science Career Tips
Imaad Mohamed Khan
 26 Six types of Data Analysis you will do as a Data Scientist
Six types of Data Analysis you will do as a Data Scientist
Imaad Mohamed Khan
 27 Automatic Speech Recognition (ASR) with Facebook AI's wav2vec 2.0 model using Huggingface
Automatic Speech Recognition (ASR) with Facebook AI's wav2vec 2.0 model using Huggingface
Imaad Mohamed Khan
 28 9 Anti-patterns to avoid MLOps mistakes
9 Anti-patterns to avoid MLOps mistakes
Imaad Mohamed Khan
 29 8 pitfalls to avoid while using Machine Learning Interpretation Techniques (SHAP, PDP, LIME, PFI)
8 pitfalls to avoid while using Machine Learning Interpretation Techniques (SHAP, PDP, LIME, PFI)
Imaad Mohamed Khan
 30 Fireside Chat with Shadab Khan - AI in Healthcare and Data Science Career Tips
Fireside Chat with Shadab Khan - AI in Healthcare and Data Science Career Tips
Imaad Mohamed Khan
 31 Features and Feature Engineering in Machine Learning - An Introduction
Features and Feature Engineering in Machine Learning - An Introduction
Imaad Mohamed Khan
 32 Building your own AI text generation tool with aitextgen using GPT-2/GPT-3
Building your own AI text generation tool with aitextgen using GPT-2/GPT-3
Imaad Mohamed Khan
 33 Organising Data Science projects using CRISP-DM
Organising Data Science projects using CRISP-DM
Imaad Mohamed Khan
 34 Introduction to Prompt Engineering
Introduction to Prompt Engineering
Imaad Mohamed Khan

Learn how to transition from a research background to an industry role, and understand the skills required to succeed as an ML Engineer. Hiromu Hota shares his experiences and insights on making this transition.

Key Takeaways
  1. Pursue a PhD in a relevant field
  2. Gain research experience in a company
  3. Transition into a product related role
  4. Develop skills in ML engineering
  5. Stay up-to-date with industry trends
💡 Transitioning from research to industry requires a combination of technical skills and industry knowledge, as well as the ability to adapt to new roles and responsibilities

Related Reads

📰
On July 1, 2026, arXiv will spin out from Cornell University, its home for the past 25 years, to become an independent nonprofit organization. Major funding support from Simons Foundation and Schmidt Sciences. Ditching the red for their website. [N]
arXiv is becoming an independent nonprofit organization after 25 years at Cornell University, backed by major funding, which will impact the future of research and academia
Reddit r/MachineLearning
 📰
CS-NRRM™ Official Publications: Paper 1 and Paper 2 Are Now Available
Learn about the CS-NRRM's official publications on a 12-year longitudinal human observation archive and its significance in research and development
Medium · Data Science
 📰
Found a potential mistake in an ICLR 2026 blogpost [D]
Verify a potential mistake in an ICLR 2026 blog post and learn how to effectively report errors in academic publications
Reddit r/MachineLearning
 📰
Rebuttals Move Peer-Review Scores, but Initial-Review Structure Bounds the Movement
Learn how author rebuttals impact peer-review scores and the factors that influence their effectiveness in ICLR 2024-2025, using LLMs for measurement
ArXiv cs.AI
Up next
Indians Under House Arrest in America? 😱 Immigration Crisis Explained | SumanTV Classroom
SumanTV Classroom
Watch →