Learning The Internals Of Machine Learning Systems And Tips For PhD | Maithra Raghu, Google Brain Jay Shah podcast

1:19:38

Manos is the CEO of Oumi, a platform focused on open sourcing the entire lifecycle of foundation and large models. Prior to that he was at Google leading efforts on developing large language models within Cloud services. He also has experience working at Facebook on AR/VR projects and at Microsoft’s cloud division developing machine learning based services. Manos received his PhD in computer engineering from Princeton University and has extensive hands-on experience building and deploying models at large scale. Time stamps of the conversation00:00:00 Highlights00:01:20 Introduction00:02:08 From Google to Oumi00:08:58 Why big tech models cannot beat ChatGPT00:12:00 Future of open-source AI00:18:00 Performance gap between open-source and closed AI models00:23:58 Parts of the AI stack that must remain open for innovation00:27:45 Risks of open-sourcing AI00:34:38 Current limitations of Large Language Models00:39:15 Deepseek moment 00:44:38 Maintaining AI leadership - USA vs. China00:48:16 Oumi 00:55:38 Open-sourcing a model with AGI tomorrow, or wait for safeguards?00:58:12 Milestones in open-source AI01:02:50 Nurturing a developers community01:06:12 Ongoing research projects01:09:50 Tips for AI enthusiasts 01:13:00 Competition in AI nowadays More about Manos: https://www.linkedin.com/in/koukoumidis/And Oumi: https://github.com/oumi-ai/oumiAbout the Host:Jay is a PhD student at Arizona State University working on improving AI for medical diagnosis and prognosis. Linkedin: https://www.linkedin.com/in/shahjay22/Twitter: https://twitter.com/jaygshah22Homepage: https://jaygshah.github.io/ for any queries.Stay tuned for upcoming webinars!***Disclaimer: The information in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.***…

Jay Shah Podcast

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Differential Privacy, Creativity & future of AI research in the LLM era | Niloofar Mireshghallah 1:29:23

12 weeks之前1:29:23

1:29:23

Niloofar is a Postdoctoral researcher at University of Washington with research interests in building privacy preserving AI systems and studying the societal implications of machine learning models. She received her PhD in Computer Science from UC San Diego in 2023 and has received multiple awards and honors for research contributions. Time stamps of the conversation 00:00:00 Highlights 00:01:35 Introduction 00:02:56 Entry point in AI 00:06:50 Differential privacy in AI systems 00:11:08 Privacy leaks in large language models 00:15:30 Dangers of training AI on public data on internet 00:23:28 How auto-regressive training makes things worse 00:30:46 Impact of Synthetic data for fine-tuning 00:37:38 Most critical stage in AI pipeline to combat data leaks 00:44:20 Contextual Integrity 00:47:10 Are LLMs creative? 00:55:24 Under vs. Overpromises of LLMs 01:01:40 Publish vs. perish culture in AI research recently 01:07:50 Role of academia in LLM research 01:11:35 Choosing academia vs. industry 01:17:34 Mental Health and overarching More about Niloofar: https://homes.cs.washington.edu/~niloofar/ And references to some of the papers discussed: https://arxiv.org/pdf/2310.17884 https://arxiv.org/pdf/2410.17566 https://arxiv.org/abs/2202.05520 About the Host: Jay is a PhD student at Arizona State University working on improving AI for medical diagnosis and prognosis. Linkedin: https://www.linkedin.com/in/shahjay22/ Twitter: https://twitter.com/jaygshah22 Homepage: http://jayshah.me/ for any queries. Stay tuned for upcoming webinars! ***Disclaimer: The information in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.***…

Jay Shah Podcast

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Reasoning in LLMs, role of academia and keeping up with AI research | Dr. Vivek Gupta 1:48:32

18 weeks之前1:48:32

1:48:32

Vivek is an Assistant Professor at Arizona State university. Prior to that, he was at the University of Pennsylvania as a postdoctoral researcher and completed his PhD in CS from the University of Utah. His PhD research focused on inference and reasoning for semi structured data and his current research spans reasoning in large language models (LLMs), multimodal learning, and instilling models with common sense for question answering. He has also received multiple awards and fellowships for his research works over the years. Conversation time stamps: 00:01:40 Introduction 00:02:52 Background in AI research 00:05:00 Finding your niche 00:12:42 Traditional AI models vs. LLMs in semi-structured data 00:18:00 Why is reasoning hard in LLMs? 00:27:10 Will scaling AI models hit a plateau? 00:31:02 Has ChatGPT pushed boundaries of AI research 00:38:28 Role of Academia in AI research in the era of LLMs 00:56:35 Keeping up with research: filtering noise vs. signal 01:09:14 Getting started in AI in 2024? 01:20:25 Maintaining mental health in research (especially AI) 01:34:18 Building good habits 01:37:22 Do you need a PhD to contribute to AI? 01:45:42 Wrap up More about Vivek: https://vgupta123.github.io/ ASU lab website: https://coral-lab-asu.github.io/ And Vivek's blog on research struggles: https://vgupta123.github.io/docs/phd_struggles.pdf About the Host:Jay is a PhD student at Arizona State University working on improving AI for medical diagnosis and prognosis. Linkedin: https://www.linkedin.com/in/shahjay22/ Twitter: https://twitter.com/jaygshah22 Homepage: http://jayshah.me/ for any queries. Stay tuned for upcoming webinars! ***Disclaimer: The information in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.***…

Jay Shah Podcast

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Time series Forecasting using GPT models | Max Mergenthaler Canseco 1:10:21

31 weeks之前1:10:21

1:10:21

Max is the CEO and co-founder of Nixtla, where he is developing highly accurate forecasting models using time series data and deep learning techniques, which developers can use to build their own pipelines. Max is a self-taught programmer and researcher with a lot of prior experience building things from scratch. 00:00:50 Introduction 00:01:26 Entry point in AI 00:04:25 Origins of Nixtla 00:07:30 Idea to product 00:11:21 Behavioral economics & psychology to time series prediction 00:16:00 Landscape of time series prediction 00:26:10 Foundation models in time series 00:29:15 Building TimeGPT 00:31:36 Numbers and GPT models 00:34:35 Generalization to real-world datasets 00:38:10 Math reasoning with LLMs 00:40:48 Neural Hierarchical Interpolation for Time Series Forecasting 00:47:15 TimeGPT applications 00:52:20 Pros and Cons of open-source in AI 00:57:20 Insights from building AI products 01:02:15 Tips to researchers & hype vs Reality of AI More about Max: https://www.linkedin.com/in/mergenthaler/ and Nixtla: https://www.nixtla.io/ Check out TimeGPT: https://github.com/Nixtla/nixtla About the Host: Jay is a PhD student at Arizona State University working on improving AI for medical diagnosis and prognosis. Linkedin: https://www.linkedin.com/in/shahjay22/ Twitter: https://twitter.com/jaygshah22 Homepage: https://www.public.asu.edu/~jgshah1/ for any queries. Stay tuned for upcoming webinars! ***Disclaimer: The information in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.***…

Jay Shah Podcast

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Generative AI and the Art of Product Engineering | Golnaz Abdollahian 35:22

33 weeks之前35:22

35:22

Golnaz Abdollahian is currently the senior director of big idea innovation at Dolby Laboratories. She has a lot of experience developing and shaping technological products around augmented and virtual reality, smart homes, and generative AI. Before joining Dolby, she had experience working at Microsoft, Apple, and Sony. She also holds PhD in electrical engineering from Purdue University. Time stamps of the conversation 00:00 Highlights 01:08 Introduction 01:52 Entry point in AI 03:00 Leading Big Idea Innovation at Dolby 06:55 Generative AI, Entertainment and Dolby 08:45 How do content creators feel about AI? 10:30 From a Researcher to a Product person 14:27 Traditional Tech products versus AI products 17:52 From concept to product 20:35 Lesson in Product design from - Apple, Microsoft, Song & Dolby 25:34 Interpreting trends in AI 29:25 Good versus Bad Product 31:25 Advice to people interested in productization More about Golnaz: https://www.linkedin.com/in/golnaz-abdollahian-93938a5/ About the Host: Jay is a PhD student at Arizona State University working on improving AI for medical diagnosis and prognosis. Linkedin: https://www.linkedin.com/in/shahjay22/ Twitter: https://twitter.com/jaygshah22 Homepage: https://www.public.asu.edu/~jgshah1/ for any queries. Stay tuned for upcoming webinars! ***Disclaimer: The information in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.***…

Jay Shah Podcast

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Future of Software Development with LLMs, Advice on Building Tech startups & more | Pritika Mehta 37:31

36 weeks之前37:31

37:31

Pritika is the co-founder of Butternut AI, a platform that allows the creation of professional websites without hiring web developers. Before butternut, Pritika had entrepreneurship experience building some other products, which later got acquired. Time stamps of the conversation 00:00 Highlights 01:15 Introduction 01:50 Entry point in AI 03:04 Motivation behind Butternut AI 05:00 Can software engineering be automated? 06:36 Large Language Models in Software Development 08:00 AI as a replacement vs assistant 10:32 Automating website development 13:40 Limitations of current LLMs 18:12 Landscape of startups using LLMs 19:50 Going from an idea to a product 27:48 Background in AI for building AI-based startup 30:00 Entrepreneurship 34:32 Startup Culture in USA vs. India More about Butternut AI: https://butternut.ai/ Pritika's Twitter: https://x.com/pritika_mehta And LinkedIn: https://www.linkedin.com/in/pritikam/ About the Host: Jay is a PhD student at Arizona State University working on improving AI for medical diagnosis and prognosis. Linkedin: https://www.linkedin.com/in/shahjay22/ Twitter: https://twitter.com/jaygshah22 Homepage: https://www.public.asu.edu/~jgshah1/ for any queries. Stay tuned for upcoming webinars! ***Disclaimer: The information in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.***…

Jay Shah Podcast

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Instruction Tuning, Prompt Engineering and Self Improving Large Language Models | Dr. Swaroop Mishra 1:31:39

42 weeks之前1:31:39

1:31:39

Swaroop is a research scientist at Google-Deepmind, working on improving Gemini. His research expertise includes instruction tuning and different prompt engineering techniques to improve reasoning and generalization performance in large language models (LLMs) and tackle induced biases in training. Before joining DeepMind, Swaroop graduated from Arizona State University, where his research focused on developing methods that allow models to learn new tasks from instructions. Swaroop has also interned at Microsoft, Allen AI, and Google, and his research on instruction tuning has been influential in the recent developments of LLMs. Time stamps of the conversation: 00:00:50 Introduction 00:01:40 Entry point in AI 00:03:08 Motivation behind Instruction tuning in LLMs 00:08:40 Generalizing to unseen tasks 00:14:05 Prompt engineering vs. Instruction Tuning 00:18:42 Does prompt engineering induce bias? 00:21:25 Future of prompt engineering 00:27:48 Quality checks on Instruction tuning dataset 00:34:27 Future applications of LLMs 00:42:20 Trip planning using LLM 00:47:30 Scaling AI models vs making them efficient 00:52:05 Reasoning abilities of LLMs in mathematics 00:57:16 LLM-based approaches vs. traditional AI 01:00:46 Benefits of doing research internships in industry 01:06:15 Should I work on LLM-related research? 01:09:45 Narrowing down your research interest 01:13:05 Skills needed to be a researcher in industry 01:22:38 On publish or perish culture in AI research More about Swaroop: https://swarooprm.github.io/ And his research works: https://scholar.google.com/citations?user=-7LK2SwAAAAJ&hl=en Twitter: https://x.com/Swarooprm7 About the Host: Jay is a PhD student at Arizona State University working on improving AI for medical diagnosis and prognosis. Linkedin: https://www.linkedin.com/in/shahjay22/ Twitter: https://twitter.com/jaygshah22 Homepage: https://www.public.asu.edu/~jgshah1/ for any queries. Stay tuned for upcoming webinars! ***Disclaimer: The information in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.***…

Jay Shah Podcast

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Role of Large Language Models in AI-driven medical research | Dr. Imon Banerjee 46:49

1 year之前46:49

46:49

Dr. Imon Banerjee is an Associate Professor at Mayo Clinic in Arizona, working at the intersection of AI and healthcare research. Her research focuses on multi-modality fusion, mitigating bias in AI models specifically in the context of medical applications & more broadly building predictive models using different data sources. Before joining the Mayo Clinic, she was at Emory University as an Assistant Professor and at Stanford as a Postdoctoral fellow. Time stamps of the conversation 00:00 Highlights 01:00 Introduction 01:50 Entry point in AI 04:41 Landscape of AI in healthcare so far 06:15 Research to practice 07:50 Challenges of AI Democratization 11:56 Era of Generative AI in Medical Research 15:57 Responsibilities to realize 16:40 Are LLMs a world model? 17:50 Training on medical data 19:55 AI as a tool in clinical workflows 23:36 Scientific discovery in medicine 27:08 Dangers of biased AI models in healthcare applications 28:40 Good vs Bad bias 33:33 Scaling models - the current trend in AI research 35:05 Current focus of research 36:41 Advice on getting started 39:46 Interdisciplinary efforts for efficiency 42:22 Personalities for getting into research More about Dr. Banerjee's lab and research: https://labs.engineering.asu.edu/banerjeelab/person/imon-banerjee/ About the Host: Jay is a PhD student at Arizona State University. Linkedin: https://www.linkedin.com/in/shahjay22/ Twitter: https://twitter.com/jaygshah22 Homepage: https://www.public.asu.edu/~jgshah1/ for any queries. Stay tuned for upcoming webinars! ***Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.***…

Jay Shah Podcast

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Algorithmic Reasoning, Graph Neural Nets, AGI and Tips to researchers | Petar Veličković 1:12:29

1 year之前1:12:29

1:12:29

Dr. Petar Veličković is a Staff Research Scientist at Googe DeepMind and an Affiliated lecturer at the University of Cambridge. He is known for his research contributions in graph representation learning; particularly graph neural networks and graph attention networks. At DeepMind, he has been working on Neural Algorithmic Reasoning which we talk about more in this podcast. Petar’s research has been featured in numerous media articles and has been impactful in many ways including Google Maps’s improved predictions. Time stamps 00:00:00 Highlights 00:01:00 Introduction 00:01:50 Entry point in AI 00:03:44 Idea of Graph Attention Networks 00:06:50 Towards AGI 00:09:58 Attention in Deep learning 00:13:15 Attention vs Convolutions 00:20:20 Neural Algorithmic Reasoning (NAR) 00:25:40 End-to-end learning vs NAR 00:30:40 Improving Google Map predictions 00:34:08 Interpretability 00:41:28 Working at Google DeepMind 00:47:25 Fundamental vs Applied side of research 00:50:58 Industry vs Academia in AI Research 00:54:25 Tips to young researchers 01:05:55 Is a PhD required for AI research? More about Petar: https://petar-v.com/ Graph Attention Networks: https://arxiv.org/abs/1710.10903 Neural Algorithmic Reasoning: https://www.cell.com/patterns/pdf/S2666-3899(21)00099-4.pdf TacticAI paper: https://arxiv.org/abs/2310.10553 And his collection of invited talks: @petarvelickovic6033 About the Host: Jay is a PhD student at Arizona State University. Linkedin: https://www.linkedin.com/in/shahjay22/ Twitter: https://twitter.com/jaygshah22 Homepage: https://www.public.asu.edu/~jgshah1/ for any queries. Stay tuned for upcoming webinars! ***Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.***…

Jay Shah Podcast

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Combining Vision & Language in AI perception and the era of LLMs & LMMs | Dr. Yezhou Yang 1:53:47

2 years之前1:53:47

1:53:47

Dr. Yezhou Yang is an Associate Professor at Arizona State University and director of the Active Perception Group at ASU. He has research interests in Cognitive Robotics and Computer Vision, and understanding human actions from visual input and grounding them by natural language. Prior to joining ASU, he completed his Ph.D. from the University of Maryland and his postdoctoral at the Computer Vision Lab and Perception and Robotics Lab. Timestamps of the conversation 00:01:02 Introduction 00:01:46 Interest in AI 00:17:04 Entry in Robotics & AI Perception 00:20:59 Combining Vision & language to Improve Robot Perception 00:23:30 End-to-end learning vs traditional knowledge graphs 00:28:28 What do LLMs learn? 00:30:30 Nature of AI research 00:36:00 Why vision & language in AI? 00:45:40 Learning vs Reasoning in neural networks 00:53:05 Bringing AI to the general crowd 01:00:10 Transformers in Vision 01:08:54 Democratization of AI 01:13:42 Motivation for research: theory or application? 01:18:50 Surpassing human intelligence 01:25:13 Open challenges in computer vision research 01:30:19 Doing research is a privilege 01:35:00 Rejections, tips to read & write good papers 01:43:37 Tips for AI Enthusiasts 01:47:35 What is a good research problem? 01:50:30 Dos and Don'ts in AI research More about Dr. Yang: https://yezhouyang.engineering.asu.edu/ And his Twitter handle: https://twitter.com/Yezhou_Yang About the Host: Jay is a PhD student at Arizona State University. Linkedin: https://www.linkedin.com/in/shahjay22/ Twitter: https://twitter.com/jaygshah22 Homepage: https://www.public.asu.edu/~jgshah1/ for any queries. Check-out Rora: https://teamrora.com/jayshah Guide to STEM PhD AI Researcher + Research Scientist pay: https://www.teamrora.com/post/ai-researchers-salary-negotiation-report-2023 Stay tuned for upcoming webinars! ***Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.***…

Jay Shah Podcast

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Risks of AI in real-world and towards Building Robust Security measures | Hyrum Anderson 51:33

2 years之前51:33

51:33

Dr Hyrum Anderson is a Distinguished Machine Learning Engineer at Robust Intelligence. Prior to that, he was Principal Architect of Trustworthy Machine Learning at Microsoft where he also founded Microsoft’s AI Red Team; he also led security research at MIT Lincoln Laboratory, Sandia National Laboratories, and Mendiant, and was Chief Scientist at Endgame (later acquired by Elastic). He’s also the co-author of the book “Not a Bug, But with a Sticker” and his research interests include assessing the security and privacy of ML systems and building Robust AI models. Timestamps of the conversation 00:50 Introduction 01:40 Background in AI and ML security 04:45 Attacks on ML systems 08:20 Fractions of ML systems prone to Attacks 10:38 Operational risks with security measures 13:40 Solution from an algorithmic or policy perspective 15:46 AI regulation and policy making 22:40 Co-development of AI and security measures 24:06 Risks of Generative AI and Mitigation 27:45 Influencing an AI model 30:08 Prompt stealing on ChatGPT 33:50 Microsoft AI Red Team 38:46 Managing risks 39:41 Government Regulations 43:04 What to expect from the Book 46:40 Black in AI & Bountiful Children’s Foundation Check out Rora: https://teamrora.com/jayshah Guide to STEM Ph.D. AI Researcher + Research Scientist pay: https://www.teamrora.com/post/ai-researchers-salary-negotiation-report-2023 Rora's negotiation philosophy: https://www.teamrora.com/post/the-biggest-misconception-about-negotiating-salaryhttps://www.teamrora.com/post/job-offer-negotiation-lies Hyrum's Linkedin: https://www.linkedin.com/in/hyrumanderson/ And Research: https://scholar.google.com/citations?user=pP6yo9EAAAAJ&hl=en Book - Not a Bug, But with a Sticker: https://www.amazon.com/Not-Bug-But-Sticker-Learning/dp/1119883989/ About the Host: Jay is a Ph.D. student at Arizona State University. Linkedin: https://www.linkedin.com/in/shahjay22/ Twitter: https://twitter.com/jaygshah22 Homepage: https://www.public.asu.edu/~jgshah1/ for any queries. Stay tuned for upcoming webinars! ***Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.***…

Jay Shah Podcast

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Being aware of Systematic Biases and Over-trust in AI | Meredith Broussard 37:15

2 years之前37:15

37:15

Meredith is an associate professor at New York University and research director at the NYU Alliance for Public Interest Technology. Her research interests include using data analysis for good and ethical AI. She is also the author of the book “More Than a Glitch: Confronting Race, Gender, and Ability Bias in Tech” and we will discuss more about this with her in this podcast. Time stamps of the conversation 00:42 Introduction 01:17 Background 02:17 Meaning of “it is not a glitch” in the book title 04:40 How are biases coded into AI systems? 08:45 AI is not the solution to every problem 09:55 Algorithm Auditing 11:57 Why do organizations don't use algorithmic auditing more often? 15:12 Techno-chauvinism and drawing boundaries 23:18 Bias issues with ChatGPT and Auditing the model 27:55 Using AI for Public Good - AI on context 31:52 Advice to young researchers in AI Meredith's homepage: https://meredithbroussard.com/ And her Book: https://mitpress.mit.edu/9780262047654/more-than-a-glitch/ About the Host: Jay is a Ph.D. student at Arizona State University. Linkedin: https://www.linkedin.com/in/shahjay22/ Twitter: https://twitter.com/jaygshah22 Homepage: https://www.public.asu.edu/~jgshah1/ for any queries. Stay tuned for upcoming webinars! ***Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.***…

Jay Shah Podcast

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P2 Working at DeepMind, Interview Tips & doing a PhD for a career in AI | Dr. David Stutz 1:42:28

2 years之前1:42:28

1:42:28

Part-2 of my podcast with David Stutz. (Part-1: https://youtu.be/J7hzMYUcfto) David is a research scientist at DeepMind working on building robust and safe deep learning models. Prior to joining DeepMind, he was a PhD student at the Max Plank Institute of Informatics. He also maintains a fantastic blog on various topics related to machine learning and graduate life which is insightful to young researchers out there. 00:00:00 Working at DeepMind 00:08:20 Importance of Abstraction and Collaboration in Research 00:13:08 DeepMind internship project 00:19:39 What drives research projects at DeepMind 00:27:45 Research in Industry vs Academia 00:30:45 Interview tips for research roles, at DeepMind or other companies 00:44:38 Finding the right Advisor & Institute for PhD 01:02:12 Do you really need a Ph.D. to do AI/ML research? 01:08:28 Academia vs Industry: Making the choice 01:10:49 Pressure to publish more papers 01:21:35 Artificial General Intelligence (AGI) 01:33:24 Advice to young enthusiasts on getting started David's Homepage: https://davidstutz.de/ And his blog: https://davidstutz.de/category/blog/ Research work: https://scholar.google.com/citations?user=TxEy3cwAAAAJ&hl=en About the Host: Jay is a Ph.D. student at Arizona State University. Linkedin: https://www.linkedin.com/in/shahjay22/ Twitter: https://twitter.com/jaygshah22 Homepage: https://www.public.asu.edu/~jgshah1/ for any queries. Stay tuned for upcoming webinars! ***Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.***…

Jay Shah Podcast

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Negotiating Higher Salary for AI & Tech roles after Job Offer | Jordan Sale 57:43

2 years之前57:43

57:43

Rora helps top AI researchers and professionals negotiate their pay -- often as they transition from academia into industry. Moving into tech is a huge transition for many PhDs and post-docs -- the pay is much more significant and the terms of employment are often quite different. In the past 5 years, the Rora team has helped over 1000 STEM professionals negotiate more than $10M in additional earnings from companies like DeepMind, OpenAI, Google Brain, and Anthropic -- and advocate for better roles, more alignment with their managers, and more flexible work. Referral link: https://teamrora.com/jayshah Guide to STEM Ph.D. AI Researcher + Research Scientist pay: https://www.teamrora.com/post/ai-researchers-salary-negotiation-report-2023 (the majority of the STEM PhDs we support are going into tech roles) Rora's negotiation philosophy: https://www.teamrora.com/post/the-biggest-misconception-about-negotiating-salaryhttps://www.teamrora.com/post/job-offer-negotiation-lieshttps://www.teamrora.com/post/roras-3-keys-to-negotiating-a-new-job-offer00:00 Highlights 00:55 Introduction 01:42 About Rora 05:40 Myths in Job Negotiations 08:58 Fear of losing job offers 12:36 30-60-90 day roadmap for negotiation 15:28 Knowing if you should negotiate 20:46 Negotiating with only one offer 24:40 What to negotiate? 29:00 Knowing if you're low-balled in offers 31:31 When negotiations don't workout 35:00 When & How to Negotiate? 43:00 Negotiating promotions 46:45 Is there always room for Negotiation? 49:42 Quick advice to people who have offers in hand 55:32 Wrong assumptions Learn more about Jordan: https://www.linkedin.com/in/jordansale And Rora: https://teamrora.com/jayshah Also check-out these talks on all available podcast platforms: https://jayshah.buzzsprout.com About the Host: Jay is a Ph.D. student at Arizona State University. Linkedin: https://www.linkedin.com/in/shahjay22/ Twitter: https://twitter.com/jaygshah22 Homepage: https://www.public.asu.edu/~jgshah1/ for any queries. Stay tuned for upcoming webinars! ***Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.***…

Jay Shah Podcast

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P1 Adversarial robustness in Neural Networks, Quantization and working at DeepMind | David Stutz 1:32:28

2 years之前1:32:28

1:32:28

Part-1 of my podcast with David Stutz. (Part-2: https://youtu.be/IumJcB7bE20) David is a research scientist at DeepMind working on building robust and safe deep learning models. Prior to joining DeepMind, he was a Ph.D. student at the Max Plank Institute of Informatics. He also maintains a fantastic blog on various topics related to machine learning and graduate life which is insightful to young researchers out there. Check out Rora: https://teamrora.com/jayshah Guide to STEM Ph.D. AI Researcher + Research Scientist pay: https://www.teamrora.com/post/ai-researchers-salary-negotiation-report-202300:00:00 Highlights and Sponsors 00:01:22 Intro 00:02:14 Interest in AI 00:12:26 Finding research interests 00:22:41 Robustness vs Generalization in deep neural networks 00:28:03 Generalization vs model performance trade-off 00:37:30 On-manifold adversarial examples for better generalization 00:48:20 Vision transformers 00:49:45 Confidence-calibrated adversarial training 00:59:25 Improving hardware architecture for deep neural networks 01:08:45 What's the tradeoff in quantization? 01:19:07 Amazing aspects of working at DeepMind 01:27:38 Learning the skills of Abstraction when collaborating David's Homepage: https://davidstutz.de/ And his blog: https://davidstutz.de/category/blog/ Research work: https://scholar.google.com/citations?user=TxEy3cwAAAAJ&hl=en About the Host: Jay is a Ph.D. student at Arizona State University. Linkedin: https://www.linkedin.com/in/shahjay22/ Twitter: https://twitter.com/jaygshah22 Homepage: https://www.public.asu.edu/~jgshah1/ for any queries. Stay tuned for upcoming webinars! ***Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.***…

Jay Shah Podcast

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Tips & Insights into Program Manager Role | Divy Thakkar, PM at Google Research 1:03:04

3 years之前1:03:04

1:03:04

Divy is a Program Manager and one of the founding members of Google research in India. He is actively involved and leading strategic programs that connect academia and research at Google, programs that focus on AI-for-Social-Good initiatives, and educational programs for schools in India with a particular focus on building computer science foundations. 00:00:12 Introductions 00:01:12 Background prior to joining Google 00:08:40 Programs you are working on as a Program Manager at Google Research and what are your responsibilities 00:14:35 Lifecycle of a program & various phases 00:17:55 Getting involved in research while being a Program Manager 00:25:55 Learning skills for strategic thinking as a PM 00:35:08 How did you get your PM role at Google and what was the interview like? 00:40:35 Resources people can use to prepare for PM interviews 00:41:58 Difference b/w Product vs Program vs Technical Manager 00:46:10 Previous experiences that helped develop skills for Program Manage role 00:53:58 Tips on being more organized with work Divy's Homepage: https://sites.google.com/view/divythakkar Twitter: https://twitter.com/divy93t LinkedIn: https://www.linkedin.com/in/divythakkar/ About the Host: Jay is a Ph.D. student at Arizona State University, doing research on building Interpretable AI models for Medical Diagnosis. Jay Shah: https://www.linkedin.com/in/shahjay22/ You can reach out to https://www.public.asu.edu/~jgshah1/ for any queries. Stay tuned for upcoming webinars! #research #programmanager #googleresearch #india #aiforsocialgood #manager ***Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.*** Checkout these Podcasts on YouTube: https://www.youtube.com/c/JayShahml About the author: https://www.public.asu.edu/~jgshah1/…

Jay Shah Podcast

1
How do you decide which research problems to work on? Manish Gupta, Director of Google Research, India 13:44

3 years之前13:44

13:44

Watch the full conversation with Dr. Manish Gupta here: https://youtu.be/-Tl6-DKxEMU Dr. Manish Gupta is currently the Director of Google Research in India. Prior to that he was the Vice-president and led the Xerox Research Center in India majorly working on data analytics and mobile computing. Before that, he was also at IBM research in India leading the efforts and building a lab focused on high-performance computing and business analytics. He also led the efforts at Goldman Sachs developing technologies relating to cloud, databases, and networking aiding business functions. We also co-founded and was the CEO of an educational technology startup called VideoKen. Dr. Manish Gupta's Homepage https://www.iiitb.ac.in/faculty/manish-gupta https://research.google/people/106704/ About the Host: Jay is a Ph.D. student at Arizona State University, doing research on building Interpretable AI models for Medical Diagnosis. Jay Shah: https://www.linkedin.com/in/shahjay22/ You can reach out to https://www.public.asu.edu/~jgshah1/ for any queries. Stay tuned for upcoming webinars! #research #ai #machinelearning #googleresearch #india #aiforsocialgood ***Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.*** Checkout these Podcasts on YouTube: https://www.youtube.com/c/JayShahml About the author: https://www.public.asu.edu/~jgshah1/…

Jay Shah Podcast

1
Writing a good Research Thesis | Dr. Hanie Sedghi, Google Research 5:52

3 years之前5:52

5:52

Check out the full conversation with Hanie here: https://youtu.be/hFJLuqaSakA Hanie is a senior research scientist at Google Brain working on research problems related to understanding and improving deep learning techniques. She works on designing algorithms with theoretical guarantees such that they work efficiently in real-world applications. Prior to that, she was a research scientist at Allen Institute of AI, and before that she was a Post-Doc fellow at UC Irvine. She graduated from USC with a Ph.D. with minors in Mathematics. Dr. Hanie Sedghi's links Twitter: https://twitter.com/haniesedghi?ref_src=twsrc%5Etfw Homepage: https://haniesedghi.com/ About the Host: Jay is a Ph.D. student at Arizona State University, doing research on building Interpretable AI models for Medical Diagnosis. Jay Shah: https://www.linkedin.com/in/shahjay22/ You can reach out to https://www.public.asu.edu/~jgshah1/ for any queries. Stay tuned for upcoming webinars! #theoryofmachinelearning #deeplearning #ai #machinelearning #fundamentals ***Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.*** Checkout these Podcasts on YouTube: https://www.youtube.com/c/JayShahml About the author: https://www.public.asu.edu/~jgshah1/…

Jay Shah Podcast

1
Building a research lab from scratch & using AI for Social Good, Manish Gupta Google Research India 1:12:26

3 years之前1:12:26

1:12:26

Dr. Manish Gupta is currently the Director of Google Research in India. Prior to that he was the Vice-president and led the Xerox Research Center in India majorly working on data analytics and mobile computing. Before that he also at IBM research in India leading the efforts and building a lab focused on high-performance computing and business analytics. He also worked on the IBM Blue Gene supercomputer project in the early 2000s at IBM TJ Watson research center for which IBM received the National Medal of Technology and Innovation from the President of the US. He also led the efforts at Goldman Sachs developing technologies relating to cloud, databases, and networking aiding business functions. We also co-founded and was the CEO of an educational technology startup called VideoKen. He has received many distinguished awards for his efforts and has coauthored many academic papers in the domains of computer science. Time-Stamps 00:00:00 Introductions 00:01:50 What kind of research projects are you currently spearheading at Google Research and what does your work routine look like? 00:06:30 What was your thought process prior to joining Google Research? 00:13:40 What’s the difference between a Researcher | Senior Researcher | Director of research? 00:23:00 What should robust AI systems look like? 00:33:22 How do you decide which research problems to work on? 00:46:46 What kind of challenges have you encountered while working on AI research problems specific to India? 00:56:15 How do you design and evaluate the impact of these AI projects? 00:59:27 What made you consider shifting back to India after a long streak of a career in the USA? What factors did you consider? 01:03:06 Any skills you would suggest students nurture apart from technical expertise? 01:05:40 There’s always a concern about AI usage and automation. Where do you think the balance lies? Dr. Manish Gupta's Homepage https://www.iiitb.ac.in/faculty/manish-gupta https://research.google/people/106704/ About the Host: Jay is a Ph.D. student at Arizona State University, doing research on building Interpretable AI models for Medical Diagnosis. Jay Shah: https://www.linkedin.com/in/shahjay22/ You can reach out to https://www.public.asu.edu/~jgshah1/ for any queries. Stay tuned for upcoming webinars! #research #ai #machinelearning #googleresearch #india #aiforsocialgood ***Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.*** Checkout these Podcasts on YouTube: https://www.youtube.com/c/JayShahml About the author: https://www.public.asu.edu/~jgshah1/…

Jay Shah Podcast

1
Benefits of understanding Theory of Deep Learning | Dr. Hanie Sedghi, Google Brain 53:26

3 years之前53:26

53:26

Hanie is a senior research scientist at Google Brain working on research problems related to understanding and improving deep learning techniques. She works on designing algorithms with theoretical guarantees such that they work efficiently in real-world applications. Prior to that, she was a research scientist at Allen Institute for AI, and before that she was a Post-Doc fellow at UC-Irvine. She graduated from USC with a Ph.D. with minors in Mathematics. Dr. Hanie Sedghi's links Twitter: https://twitter.com/haniesedghi?ref_src=twsrc%5Etfw Homepage: https://haniesedghi.com/ About the Host: Jay is a Ph.D. student at Arizona State University, doing research on building Interpretable AI models for Medical Diagnosis. Jay Shah: https://www.linkedin.com/in/shahjay22/ You can reach out to https://www.public.asu.edu/~jgshah1/ for any queries. Stay tuned for upcoming webinars! #theoryofmachinelearning #deeplearning #ai #machinelearning #fundamentals ***Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.*** Checkout these Podcasts on YouTube: https://www.youtube.com/c/JayShahml About the author: https://www.public.asu.edu/~jgshah1/…

Jay Shah Podcast

1
CNNs & ViTs (Vision Transfomers) - Comparing the internal structures, Maithra Raghu, Google 5:21

3 years之前5:21

5:21

Do Vision Transformers work in the same way as CNNs? Do the internal representational structures of ViTs and CNNs differ? An in-depth analysis article: https://arxiv.org/pdf/2108.08810.pdf Listen to the full conversation here: https://youtu.be/htnJxcwJqeA Dr. Maithra Raghu is a senior research scientist at Google working on analyzing the internal workings of deep neural networks so that we can deploy them better keeping humans in the loop. She recently graduated from Cornell University with a Ph.D. in CS and previously graduated from Cambridge University with BA and Masters in Mathematics. She has received multiple awards for her research work including the Forbes 30 under 30. Maithra's Homepage: https://maithraraghu.com About the Host: Jay is a Ph.D. student at Arizona State University, doing research on building Interpretable AI models for Medical Diagnosis. Jay Shah: https://www.linkedin.com/in/shahjay22/ You can reach out to https://www.public.asu.edu/~jgshah1/ for any queries. Stay tuned for upcoming webinars! #explainableai #reliableai #robustai #machinelearning ***Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.*** Checkout these Podcasts on YouTube: https://www.youtube.com/c/JayShahml About the author: https://www.public.asu.edu/~jgshah1/…

Jay Shah Podcast

1
How to find a Research Topic that interests you? 15:15

4 years之前15:15

15:15

How to decide and choose a research/thesis to work on that interests you and is also relevant to current research directions. Full episodes with Maithra, Google: https://youtu.be/htnJxcwJqeA Natasha Google: https://youtu.be/8XpCnmvq49s Milind Google: https://youtu.be/eqwF3NpZFb4 Hima Harvard University: https://youtu.be/8Ym4oYTd8Fo Ishan Facebook AI: https://youtu.be/Pb5RQAEtznk About the Host: Jay is a PhD student at Arizona State University, doing research on building Interpretable AI models for Medical Diagnosis. Jay Shah: https://www.linkedin.com/in/shahjay22/ You can reach out to https://www.public.asu.edu/~jgshah1/ for any queries. Stay tuned for upcoming webinars! #machinelearning #ai #phd #research #thesis ***Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.*** Checkout these Podcasts on YouTube: https://www.youtube.com/c/JayShahml About the author: https://www.public.asu.edu/~jgshah1/…

Jay Shah Podcast

1
Learning the internals of Machine Learning systems and tips for PhD | Maithra Raghu, Google Brain 1:15:27

4 years之前1:15:27

1:15:27

Dr. Maithra Raghu is a senior research scientist at Google working on analyzing the internal workings of deep neural networks so that we can deploy them better keeping humans in the loop. She recently graduated from Cornell University with a PhD in CS and previously graduated from Cambridge University with BA and Masters in Mathematics. She has received multiple awards for her research work including the Forbes 30 under 30. Questions that we cover 00:00:00 Introductions 00:01:00 To understand more about your research interests, can you tell us what kind of research questions you are interested in while working at Google Brain? 00:04:45 What interested you about it and how did you get started? 00:15:00 What is one thing that surprises/puzzles you about deep learning effectiveness to date? 00:22:05 What’s the difference between being a researcher in academia/PhD student vs being a researcher at a big organization (Google)? 00:28:35 In what use cases do you think ViTs might be a good choice to perform image analysis over CNN vs where do you think CNNs still have an undoubted advantage? 00:37:15 Why does ViT perform better than ResNet only on larger datasets and not on mid-sized datasets or smaller? 00:43:55 In regards to medical imaging tasks, would it be theoretically wrong to pre-train the model on dataset A and fine-tune it on dataset B? 00:47:35 Do you think ViT or transformer-based models already have/have the potential to cause a paradigm shift in the way we approach imaging tasks? Why? 00:5:25 Medical datasets are often limited in size, what are your views on tackling these problems in the near future 00:55:55 From an internal representation perspective, do you think deep neural networks can have the ability of reasoning? 00:58:20 How did you decide on your own PhD research topic? Advice you would give to graduate researchers trying to find a research problem for their thesis? 01:04:00 Many times researchers/students feel stuck/overwhelmed with a particular project they are working on, how do you suggest based on experience to tackling that? 01:10:35 How do you now/as a graduate student used to keep up with the latest research in ML/DL? Maithra's Homepage: https://maithraraghu.com Blogpost talked about: https://maithraraghu.com/blog/2020/Reflections_on_my_Machine_Learning_PhD_Journey/ Her Twitter: https://twitter.com/maithra_raghu About the Host: Jay is a PhD student at Arizona State University, doing research on building Interpretable AI models for Medical Diagnosis. Jay Shah: https://www.linkedin.com/in/shahjay22/ You can reach out to https://www.public.asu.edu/~jgshah1/ for any queries. Stay tuned for upcoming webinars! #explainableai #reliableai #robustai #machinelearning ***Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.*** Checkout these Podcasts on YouTube: https://www.youtube.com/c/JayShahml About the author: https://www.public.asu.edu/~jgshah1/…

Jay Shah Podcast

1
How can students contribute to Impactful AI projects? | Dr. Milind Tambe, Google Research 4:47

4 years之前4:47

4:47

How can you students and researchers work on projects using AI that creates a real impact and difference in society? Watch the full podcast with Dr. Tambe here: https://youtu.be/eqwF3NpZFb4 Dr. Milind Tambe is a Professor of Computer Science at Harvard University and Director of the Center for Research in Computation and Society. He is also the Director of AI for Social Good at Google Research in India. He has been leading and working on projects that are creating an impact, ranging from wildlife conservation, public health, and safety using AI techniques. Prof. Milind Tambe's Homepage: https://teamcore.seas.harvard.edu/tambe About the Host: Jay is a PhD student at Arizona State University, doing research on building Interpretable AI models for Medical Diagnosis. Jay Shah: https://www.linkedin.com/in/shahjay22/ You can reach out to https://www.public.asu.edu/~jgshah1/ for any queries. Stay tuned for upcoming webinars! #aiforsocialgood #googleai #ai #machinelearning #socialimpact ***Disclaimer: The information contained in this video represents the views and opinions Checkout these Podcasts on YouTube: https://www.youtube.com/c/JayShahml About the author: https://www.public.asu.edu/~jgshah1/…

Jay Shah Podcast

1
Challenges and beauties of building a Tech Startup | Nasrin Mostafazadeh 5:21

4 years之前5:21

5:21

Watch the full conversation with Nasrin here: https://youtu.be/59kRUmhA5yI Nasrin is the co-founder of a deep-tech startup Verneek and has been in the space of AI startups for the past 5 years now. Before that, she was a senior research scientist at Elemental Cognition & BenevolentAI, and prior to which she graduated with a Ph.D. from the University of Rochester and her major research interests are in building intelligent systems that can demonstrate commonsense reasoning & generate causal explanations in order to improve human-AI collaborations. She was featured in Forbes 30u30 for her work in NLU. Nasrin's LinkedIn Profile: https://www.linkedin.com/in/nasrinm/ Her startup Verneek: https://www.linkedin.com/company/verneek/ About the Host: Jay is a Ph.D. student at Arizona State University, doing research on building Interpretable AI models for Medical Diagnosis. Jay Shah: https://www.linkedin.com/in/shahjay22/ You can reach out to https://www.public.asu.edu/~jgshah1/ for any queries. Stay tuned for upcoming podcasts! ***Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.*** Checkout these Podcasts on YouTube: https://www.youtube.com/c/JayShahml About the author: https://www.public.asu.edu/~jgshah1/…

Jay Shah Podcast

1
Intuition for research in Social Reinforcement Learning | Natasha Jacques 6:52

4 years之前6:52

6:52

How can we build intuition for interdisciplinary fields in order to tackle challenges in social reinforcement learning? Natasha Jaques is currently a Research Scientist at Google Brain and a post-doc fellow at UC Berkeley, where her research interests are in designing multi-agent RL algorithms while focusing on social reinforcement learning. She received her Ph.D. from MIT and has also received multiple awards for her research works submitted to venues like ICML and NeurIPS She has interned at DeepMind, Google Brain, and is an OpenAI Scholars mentor. About the Host: Jay is a Ph.D. student at Arizona State University, doing research on building Interpretable AI models for Medical Diagnosis. Jay Shah: https://www.linkedin.com/in/shahjay22/ You can reach out to https://www.public.asu.edu/~jgshah1/ for any queries. Stay tuned for upcoming webinars! ***Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.*** Checkout these Podcasts on YouTube: https://www.youtube.com/c/JayShahml About the author: https://www.public.asu.edu/~jgshah1/…

Jay Shah Podcast

1
Challenges of productionizing Machine Learning Research in Industry | Aarti Bagul 5:35

4 years之前5:35

5:35

Why and where do companies fail at productionizing ML models? Watch the full podcast with Aarti here: https://youtu.be/VWJXiszQpTU Aarti is a machine learning engineer at Snorkel AI. Prior to that, she worked closely with Andrew Ng in various capacities. She graduated with a master’s in CS from Stanford, and bachelor's in CS and Computer Engineering from @New York University, and at @Microsoft Research as a research intern for John Langford, where she contributed to Vowpal Wabbit, an open-source project. About the Host: Jay is a Ph.D. student at Arizona State University, doing research on building Interpretable AI models for Medical Diagnosis. Jay Shah: https://www.linkedin.com/in/shahjay22/ You can reach out to https://www.public.asu.edu/~jgshah1/ for any queries. Stay tuned for upcoming webinars! ***Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.*** Checkout these Podcasts on YouTube: https://www.youtube.com/c/JayShahml About the author: https://www.public.asu.edu/~jgshah1/…

Jay Shah Podcast

1
Doing a PhD & Excelling at Research | Dr. Hima Lakkaraju 18:47

4 years之前18:47

18:47

Deciding whether to do a Ph.D. or not and things to focus on while writing your research thesis. Watch the full podcast here: https://youtu.be/8Ym4oYTd8Fo Dr. Himabindu Lakkaraju is an Assistant Professor at Harvard University and her major research interests are along the lines of explainability, fairness, and robustness in AI systems. Prior to that she graduated with a Ph.D. from @Stanford and has received multiple awards for her research work. Dr. Lakkaraju's homepage: https://himalakkaraju.github.io/ About the Host: Jay is a Ph.D. student at Arizona State University, doing research on building Interpretable AI models for Medical Diagnosis. Jay Shah: https://www.linkedin.com/in/shahjay22/ You can reach out to https://www.public.asu.edu/~jgshah1/ for any queries. Stay tuned for upcoming webinars! #explainableai #reliableai #robustai #machinelearning ***Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.*** Checkout these Podcasts on YouTube: https://www.youtube.com/c/JayShahml About the author: https://www.public.asu.edu/~jgshah1/…

Jay Shah Podcast

1
Founding a Startup, doing a PhD, Interpretability | Dr. Geneviève Patterson 1:33:22

4 years之前1:33:22

1:33:22

Dr. Geneviève Patterson is the head of applied research at VSCO. Prior to that, she was the CTO of a video editing company she co-founded called TRASH - later acquired by VSCO. She holds a Ph.D. in CS from Brown University with a focus on research in video understanding. She was also a postdoctoral researcher at Microsoft Research where she worked on interpreting deep neural networks and much more. Geneviève Homepage: http://genp.github.io/ About the Host: Jay is a Ph.D. student at Arizona State University, doing research on building Interpretable AI models for Medical Diagnosis. Jay Shah: https://www.linkedin.com/in/shahjay22/ You can reach out to https://www.public.asu.edu/~jgshah1/ for any queries. Stay tuned for upcoming webinars! ***Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.*** Checkout these Podcasts on YouTube: https://www.youtube.com/c/JayShahml About the author: https://www.public.asu.edu/~jgshah1/…

Jay Shah Podcast

1
Using AI for Social Good | Dr. Milind Tambe 33:33

4 years之前33:33