I’m Rachel, and I’m a cofounder of fast.ai and the assistant instructor for this course. I’m interested in the use of deep learning for social good, for medicine, and in under-funded areas. I’m also VERY interested in education (how deep learning is taught) and diversity within the field. I have a math PhD, and have worked in the past as a quant, Uber data scientist, backend engineer, and instructor (teaching calculus and full-stack web development). I’m originally from Texas, but have lived in Pennsylvania and North Carolina, before I moved to the Bay Area 5 years ago.
This course is a novel approach to teaching deep learning, and I’m interested to see what parts people find hardest, as well as what applications everyone is interested in.
Outside of data science, my hobbies are hiking, yoga, writing about diversity, and playing with my daughter. I’m looking forward to meeting you all!
If you’re interested, introduce yourself by posting to this thread with:
Thanks for setting up these forums Rachel! I know how powerful good forums can be - I started an email provider in 1999 called FastMail (still going strong at www.fastmail.com) and we had our own forum on www.emaildiscussions.com . IIRC, I had over 25,000 posts there! I hope that we can create a community here that can lead to a lot of interesting and friendly conversations.
I’m Jeremy, Rachel’s co-founder and the main instructor for this course. I’m originally from Melbourne (pronounced “mel-bun”, not “mel-born”!), Australia, and have lived in SF for around 5 years. I moved here when we got funding for an earlier startup, Kaggle, and the VCs were keen to have us in the bay area. I’m very glad to have moved, because I’ve found SF an exciting and stimulating area.
As well as Kaggle and FastMail, I’ve also founded a couple of other startups - Enlitic, and Optimal Decisions Group (now part of Lexis-Nexis), and before that I was in management consulting.
I’m passionate about the idea that deep learning can help to solve a lot of society’s most important problems, and believe that that won’t happen well unless we make it much more accessible. I hope that this course and community will help to make that happen.
Hi everybody. I am excited for the class. My wife uses myfastmail.com for her personal address (her day job is adjunct professor at the School of Management). She loves it and tells everyone that it is very big in Canada.
My background is math and computer science with a masters in operations research. I love problem solving and have been a quant, analyst, developer - both web and mobile over the years. My biggest success was Wunderradio, one the top apps in the store for its first year. My cofounder for that and myself are working again try to get up to speed in natural language processing for an auction site. It always is an adventure.
Over the course of a career, I have built up a useful set of tools and my hope for the course is to add deep learning to that, and to figure out when to use (or not), how the topology of a network should be laid out, and in general to see some interesting problems (a grandmaster of kaggle should have quite a few).
Back when I was at CMU in the early 90’s neural nets were in vogue, and then they disappeared from the lexicon. I am curious what the change was.
I am a San Francisco native and outside of sitting in front of a computer screen, I love running, photography (which would be cool to throw networks at), and chasing after my two young boys who seem to run around at GPU speeds to my single core CPU. Their threads are hyper.
I’m Samar and I’m a Research Assistant at the Center for Language Engineering in Lahore, Pakistan, where I work on using machine learning and natural language processing to improve support for low-resource languages like Urdu, Pakistan’s lingua franca.
I have a BS in Computer Science and did my senior thesis on deep learning for computer vision. My interest in machine learning (and subsequently deep learning) was first sparked upon taking Andrew Ng’s Coursera class a couple of years back. I have been hooked onto it since and have made my way through numerous MOOCs, research papers, and books on the subject to learn all I can about it.
I am fascinated by the flexibility of deep learning algorithms that allows their application to data of every modality, from image to speech to text, modeling each so effectively. I hope to form a solid foundation in the field through this course and plan to use this knowledge to develop better natural language processing systems for regional langauges in an effort to bridge the communication gap between different peoples.
In my spare time I enjoy reading books, (occasionally) writing short fiction, sketching, and playing table tennis.
Fun fact: I was homeschooled right up to grade 10 - a time I fondly recall as one of frequent trips to the local library, playing with Lego, and watching the Discovery Channel. It is thus fitting that I’m passionate about learning and education, and hope to pursue a career in academia myself.
I’m Ana, I am an applied scientist here in SV. I did my Ph.d in information extraction and then worked on various text mining problems at Yahoo! Labs and then as an NLP consultant at a couple of startups (e.g Pinterest). After I had my kid I took a couple of years off and before I go back to full-time I’d love to understand more of what deep learning approaches can do for NLP, recommendation systems and/or search applications.
Outside of work, I love the usual: books, movies, museums and more recently being outside with my son ( I finally understand why people love the weather here and would have a hard time moving at this point ).
I am Jennifer, a current USF MSAN (MS in Analytics) student. I’m excited to meet you in the first class on Monday!
About myself: I graduated from Peking University in Beijing, China, majored in Biology and minored in Economics. After that I completed my PhD at Cornell. Inspired by the rising of next generation sequencing and genomics, I joined Memorial Sloan Kettering Cancer Center to study reproduction (aka how to make babies) by molecular and computational biology.
During my research, I “fell in love” with data analysis, and I believe that one of big data’s most important roles will be in digital medicine. That’s why I turned to data science (and came to USF).
I love traveling and trying different cuisines. (To remain in balance, I’m a big fan of yoga.) So don’t forget to drag me to good restaurants! Namaste =)
I’m a product manager at Planet Labs, an SF startup that designs, builds and operates a constellation of small satellites a bit larger than a loaf of bread. We launch 'em by the dozen and will soon be able to image the whole world every day at 3-5m resolution. This lets us watch cities and landscapes evolve is near realtime (at a scale that isn’t creepy).
I’m taking this class because we’re capturing more imagery than people can look at, and we’re using machine learning to do automated image analysis. We’ve had some early successes with CNNs in particular.
I’ve been working with satellite imagery for the better part of a decade now. I co-created GlobalForestWatch.org and one of its principal deforestation-detection algorithms, and co-founded SpaceNinja, a (now-defunct) satellite analytics company. I studied Cold War history as an undergrad, and development economics in grad school in Paris.
I’m from the North Bay, and I love reading, cycling, running, and learning languages.
Finally, I’ve been a Fastmail customer for many years. Thanks @jeremy for all the hard work!
I’m Eric. I’m a student in the MSAN program at USF. I previously worked in the aerospace and oil & gas industries before making the transition to analytics. Previous stints have included Boeing and ConocoPhillips.
This month I am starting a 9-month internship with the Houston Astros MLB team doing baseball analytics.
I have a sweet dog named Stanley, and I love to travel (have been to 65 different countries). Looking forward to this exciting class.
My name is Vincent Rideout. I am also an MSAN student and have just started an internship at Thomson-Reuters. Before coming to USF I played poker for a living for ten years.
I don’t know much about deep learning, but I was completely inspired by Jeremy’s talk at the launch of the Data Institute. I consider myself better at coding than I am at math, so I’m also excited about the format of the course. I’m hoping to develop a basic understanding of deep learning models and how I can implement them in my internship.
In my spare time I like to do indoor rock climbing and play Magic: The Gathering competitively.
My name is Vijay Ivaturi and I am an Assistant Professor in Pharmacometrics at the University of Maryland, Baltimore. I build quantitative data-driven clinical pharmacology and systems pharmacology models that are used for drug development and clinical therapeutics. I would like to leverage the power of AI via machine and deep learning methods to advance research and analytics in my field. I have formally not used Machine Learning methods, but as a trained biostatistician and a R programmer, I understand applied statistics and the concepts that come along with it including regression methods for continuous and discrete data, markov models and other related methods. I am a bit rusty with my calculus and linear algebra but confident that I will be able to pick it up with quick review and help from others. We are in a fabulous era for innovation and I believe this program will help me take the next step towards applying AI to solve grander challenges in healthcare, especially global health!
I love traveling and do so a lot along with reading non-fiction! I play a lot of ping-pong, so if anyone is interested, Id love to.
My name is Brendan Fortuner. I’m a Software Engineer from Seattle currently on sabbatical for side projects and study. After spending 4 years at Amazon, I thought it would be nice to take a short pause and figure out what to do with my life. My life plans change daily, but I’ve started to narrow in on using machine learning to solve problems in biology and healthcare—particularly neuroscience, mental health, and medical diagnostics.
My interest in deep learning stems from my interest in studying the brain, which arose after reading The Future of The Mind by Michio Kaku. I started working with researchers at the Allen Institute for Brain Science to bring their algorithms into the cloud. One highlight was attending a hackathon at the Oak Ridge National Laboratory.
Outside of work I enjoy reading Isaac Asimov novels, doing yoga, attending hackathons, and playing piano, tennis, golf and go (board game).
I hope to complement this course with lots of side projects, so let me know if you’re interested in collaborating!
Hi, my name is Nichol Bradford. I spent 14 years in video games, ultimately operating World of Warcraft and all Blizzard Entertainment’s properties in China. Until I went on a meditation retreat and had an experience that inspired me to focus on harnessing technology to support the mental and emotional wellbeing of humans. So I co-founded a psychology tech lab @ Sofia University (www.transtechlab.org), a conference (www.ttconf.org) and a list to recognize the key contributing companies, projects and researchers (www.transtech200.com).
The potential of machine learning and AI to this end is clear – so I want to understand more about it. Additionally, we’re starting a new project where we are taking a statistically reliable meditation course that causes dramatic improvements in well being and adding bio data - HRV, EEG, and GSR. We have recruited a great team of experts in these signals to help with the data, and I wanted to be able to really participate in the strategic discussions. I was a student at Singularity University in the GSP2015 class and saw 1st hand how Jeremy makes DS and machine learning accessible. So when I found out he’d be teaching a course, I jumped on it.
Hobbies – reading, exercising, meditation and video games.
I’m Pandu, product manager with software engineering background. I have both technology and business background. I have MBA from Kellogg, but, I can also code
I’ve worked on product analytics (mobile, web, cloud) at my company, worked with data engineers and data scientists to build a big data stack from ground up, and have completed a number of MOOCs related to machine learning.
I’m interested in learning about deep learning to better understand what sort of problems can be solved with it. I’m currently working on starting an analytics focused product company and would love to learn more about the possibilities of deep learning.
I’m Ella and I am from Iceland but moved to Stockholm, Sweden this summer with my family. I’m a biologist and a computer scientist. I worked for a short time as a research assistant studying protein-protein interactions of the BRCA2 breast cancer gene before studying computer science. Since then I’ve spent most of my carrier working with data; starting with airline data, then the human genome and now on building self service discovery tool in the BI world.
I’m fascinated with the possibilities of deep learning and how it can be applied both in our every day lives and in research.
I have three boys on the age of 4-10 and I just love spending all the spare time I have with them. I also love exercising, outdoor activities, karate and biking.
Look forward to work with you all!
My name is Andre, and I’m a current MSAN student. My previous background is a BS+MS in bioinformatics and modeling from INSA of Lyon, in France. This is where I started to learn how to combine Math and CS to different areas of interest (in this case, Biology).
Afterwards, I did research in bone mechanics at CUNY in New York, and found out that I was more interested in the Data Analytics part of the job than the pipetting and bio-lab work. So now here I am at USF, only just starting my journey into the world of Data Science and Machine Learning.
I honestly believe that ML and Deep Learning in particular will play a major role in all aspects of our lives in the near future, so I’m incredibly excited to be a part of this.
Hi! I’m currently working on the HBO show “Silicon Valley”, and I’m also an Executive in Residence (EIR) at a VC firm in the Bay Area, while I take a break from the startup grind.
I’m interested in Deep Learning because I think it will permeate most of tech, 10 years from now. I hope this course will take me from the small AI apps I’ve been building, to a deeper understanding of AI frameworks & techniques.
When I’m not coding or working on startups I enjoy road cycling (OLH 30min, but Kings Mountain is my favorite).
My name’s Sam Haaf, and I’m a student in MSAN. I recently got my degree in Material’s Science, but spent the last two years learning to apply machine learning towards forex signal-chasing.
From this course, I’m hoping to get a strong mathematical understanding of each of the differentials involved in these techniques so that I can summon them at will in the development of niche algorithms.
I keep myself busy with a number of personal projects, including (what I call) a recursive website and, in the near future, to make a bot that can play the flash game Motherload using computer vision and something resembling general intelligence.
I’m recent Hackbright Academy alum just finishing an open source developer grant sponsored by Github and Travis CI. I also completed a Googe Summer of Code internship where I contributed to a popular open source bioinfomatics visualization tool. I’m also a refugee from academia with a 4/5 complete dissertation in mid-century American poetry and university teaching experience. I have interests in applying deep learning techniques to literature, education or other narratives like pull requests on Github.
My hobbies are generally finding dive bars, yoga, and social Latin dancing (Salsa, Bachata, ChaChaCha or Kizomba). I rescued road bike that i’m too afraid to ride after spending $$ to fix it up and kit it out.
My name is Layla. I did my PhD at USC in Electrical Engineering and worked for several companies in wireless/wireline communications/radar technologies for a couple years. I’v become very interested in machine learning recently and have worked on several data science projects. I am fascinated by deep learning and its potential applications in different domains especially in healthcare and automatic machine translation.
I am originally from Iran but have been living in US (for the most part in Los Angles) since 1996. Outside of data science, my hobbies are reading non fiction books, yoga and meditation.