Slides and links to videos of some of my public talks.
Enjoy, and spread the knowledge :)
Mathematician, Algorithmatician, Speaker, Social contributor. My greatest passion is to apply my technical skills for driving social growth.
In my spare time, I enjoy a variety of extra-curricular activities, which include public speaking, organizing professional events, and promoting women in STEM.
Founder of Algorithms Israel and co-organizer of PyData Tel Aviv MeetUps.
You can also find me on LinkedIn, Twitter and Medium.
- "Everyone can Data!" - A Practical Data Science Intro
- TeMS - 17 Lines of Code That Got Me My Dream Job
- PyGraphicsGui
- Personal Branding? Professional Karma!
- Sequence Alignment for Ride Sharing
- Technical Communication
- Understanding Depth and Disparity
- Offline Solution to Online Problems
- Help your colleagues help themselves - a Sphinx tutorial
- How to Find a Great Job
Ever wondered what this "data science" talk is all about? Well, wonder no more!
In this intro talk, we will overview some very basic concepts in data science, and learn how we can all implement them in super cool projects of our own.
Let's visit helpful online resources, throw project ideas in the air, come up with a framework for team collaboration, and pave the path for inspiration and perspiration :)
I gave this talk at the preparation event to HackExtend (video - Hebrew), a super cool hackathon that welcomes everyone!
I also gave this talk in the wonderful Oredev conference (Video - English).
Solving a textual math problem is a complicated task, requiring both mathematical understanding and language proficiency.
Well, what if we could train a neural network to do both?
And what if we could use existing tools to do it in just a few lines of code?
This talk is about a a neural network I constructed, that reads a textual math problem and outputs the underlying equation.
I gave a short version of this talk at a PyDataTLV MeetUp, (video - my talk starts at 19:40).
I also gave a longer version of this talk at Wix Tech Talks Meetup, (video - Hebrew), where I start with an introduction to neural networks, and end with a "meta" description of this project, how it came about, and how working on it helped my advance my career.
You can also read about the technical details of the project in this blog post.
Research Independence. For some of us, it's a necessity since we work for a small startup, for example. For some, it's the mere joy of quickly closing a feedback loop. And for some, like me, it's both.
As a computer vision algorithms engineer, I am frequently tackled with bottlenecks in my pipeline that can be solved by removing some friction for the human-in-the-loop. This can range from annotation tasks to verification and interactive visualizations.
The goal of this talk is to present a CookieCutter for Pythonic GUIs, that can shorten the cycle of tacking such bottlenecks: PyGraphicsGui On top of discussing the motivation for this project, and how it can be useful to you, we will cover the basics of computer graphics - so that you too could easily develop cool tools for your humans in your loops.
This talk will be aired at Reversim Summit 2020, the largest Israeli Conference for developers, by developers.
What comes to your mind when you hear the concept "Personal Branding"?
Most likely, there is at least a tiny piece of you that feels uncomfortable. Either this feels phony or opportunist, or it reminds you of someone who does it badly.
I get it. I fell the same way.
Yet I love public speaking, technical blogging, and participating in all kinds and levels of professional communities - so how does it add up?
Professional Karma is my answer.
When we contribute our knowledge to our professional pool, the act of giving is inherently intertwined with getting something in return.
That something could be anywhere between receiving higher appreciation from colleagues and peers, to finding assistance in a complicated task, and even landing a dream job.
Indeed, our Personal Brand is a by-product of our active participation in professional communities.
If you want to learn how to jumpstart the feedback loop of Professional Karma, join me for this talk.
We will explore the many benefits of extending our professional selves into the public arena, how can we do it best, and what can we do today to launch this process.
I gave this talk at the opening event of the first Technical Women Speakers Academy by Women On Stage
As researchers and developers, we constantly face new problems, and challenge existing solutions to old problems. On beautiful circumstances, solved problems from one domain shed light on new problems from totally different domains.
This happened to me while I was researching a Ride Sharing problem, and got inspiration from a DNA Sequence Alignment algorithm.
The Ride Sharing problem we will discuss is a constrained optimization problem of finding efficient ways of grouping multiple riders on a single taxi. We will also overview the biological context that was the inspiration, and take a close look at the hybrid algorithm that provided a significantly more efficient solution.
I gave a math-geek version of this talk at the Algorithms Israel MeetUp (video - Hebrew), and a developers version at Reversim Summit 2018 (video).
We all have ideas we want to share with the world, be it our colleagues in a brainstorming session, the readers of our technical blog, and sometimes even someone we just met at a conference lunch table.
The skills we use in these occasions, happen to be all similar skills, and improving our technical communications in one setting can help us improve in all other settings as well. Furthermore, when we communicate our technical ideas, be it our problems or our solutions, we reflect on our work and become more professional in our everyday job.
Great! I want in! But how?
Here's the tl;dr - you need to ask yourself 4 questions:
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Why am I here? (make it personal, and share your personal motive with the audience)
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Why are YOU here? (find out the technical background of your audience, and take the setting into account)
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Why are WE here? (define your focus/goal, and have clear takeaways you expect the audience to learn/understand)
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What structure best serves the answers to the questions above?
When you have these questions answered, plan ahead, be goal oriented, and keep everything in a scope that fits the occasion. Having said that, I think this is enough for a Github README, so I'm gonna stop here.
P.S.
I gave this talk at Baot's Tech Blogging Workshop and also recorded this talk as part of the Streaming with Baot initiative (Video - Hebrew).
I also wrote a blog post (English, Hebrew) summarizing the essentials of composing a winning tech talk/blog.
In this session we will learn how elementary Geometry is being used in modern Computer vision algorithms!
Calculating the distance between a set of cameras and an object in front of them is an important task for many applications, such as (semi-)autonomous driving, robotic house cleaning, and, you know, also as a seeing human who just wants to avoid bumping into things...
This task is called calculating depth in stereo vision, and the goal of this talk is to explain how this is done.
I recorded this talk as part of the Baot TechBites initiative (Video-English).
We live in an online world. As consumers we expect our demands to be answered on the spot, and as creators we design our products to answer consumers' immediate requests. In Via, for example, the minute you ask for a ride, you will be assigned a van - a live imitation of a greedy algorithm.
But what if the algorithm had the input that in a minute from now I will also ask for a ride with similar origin and destination? then it will be able to optimize the assignment - it will keep you waiting for just a minute, so that we can ride together, even more cheaply and economically than ever!
This talk revolves a project I lead in Via, which addresses this issue, where the goal is to imagine we knew all requests in advance, and make the best assignment to vans based on this "omniscience".
I gave a technical version of this talk at a PyDataTLV MeetUp PyDataTLV MeetUp, (video), and also a shorter high-level version at the Tel Aviv Women in Data Science Conference 2018, (video).
Have you ever found yourself developing a tool only to find it already exists somewhere else in the code? Have you ever wondered what does AlexsCoolFunction do? Did you ever put a funny comment in your code and was upset that noone will read it?
Then maybe it’s time to have a better documentation system!
In this tutorial, we will sail through the onboarding stage of Sphinx - an automated documentation package, and learn how we can all collaborate better.
This tutorial is backed by a repository that holds an example project - you can browse the output documentation and download the example template so you can easily start documenting your next project!
I gave this talk at PyCon Israel 2018, (video) and also wrote a short blog post.
Job hunting can be tough. Most of the time it is tough.
My method to make it a little a bit easier, is by scraping the internet through and through, constructing a huge data base of relevant companies, and making personal connection to assure my CV go in the right place.
By working in this systematic and high-scale method, I am able to increase my chances of finding a great job, and also make the process less painful as I am maintaining a constant stream of opportunities.
I wrote a blog post describing this method, and also gave a talk about it at Baot's job search program.
