Aggregate Intellect

DISCUSSION GROUPS

Stay on top of topics in research, engineering and product development

1: Meet and collaborate with like-minded people
2: Document your knowledge and share it with the world
3: Become unstoppable

Access Experts

Get guidance from discussion group leads and senior industry practitioners, on various topics of your discussion group.

Plug into the Network

Learn and build collaboratively with peers. Connect with like-minded individuals in our 10,000+ community of data scientists and ML engineers.

Become Unstoppable

Attract more opportunities by strengthening and diversifying your portfolio by documenting and sharing your knowledge with the world.

Discussion Group Leads

TOPICS

All groups connect through Slack on a weekly schedule for discussion sessions. There are 5-week milestones each week culminating with a “mega meetup” where all groups meet to discuss their most interesting topics and network!

Note: all sessions will be held over Meet/ Zoom. Calendar invites with links will be sent after you sign up.

ML Research

Group Lead: Ozan Ozyegen
Co-lead: Praveen Sundaresan Ramesh
Why join this group?
Ozan: I have research experience and multiple publications related to Time Series. In my undergraduate thesis, I predicted stocks (really :D) using deep learning models (of course it didn't work well). In my master's thesis, I used both traditional models (ARIMA) and ML based models for predicting spectrum occupancy in Land Mobile Radio bands to investigate potential 5G applications. Since then, I have been doing a lot of research related to time series and I will continue to do so. My PhD thesis is about Explainable AI for Time Series.
Praveen: My undergrad supervisor is a time series guru and has inspired me to pursue this area. Ever since, I have been researching and keeping myself current in this field having experience in both statistical and deep-learning time series methods. I recently worked on the problem of forecasting electricity consumption in Toronto incorporating several factors including the effects of COVID and daylight savings. I am excited to contribute and learn from beginners and experts alike.
We have been meeting weekly for the past month and have around 15 members from both industry and academia. There are lots of great discussions and opportunities for collaborations.
Meeting Time: Fridays 6pm ET
Potential Guest Speaker: TBD
Next Milestone: 
  1. Basics of time series
  2. Popular time series models
  3. State-of-the-art time series methods
What do existing members say? n/a
Techniques Covered: Both statistical and deep learning based time series forecasting approaches are covered.

Group Lead: Willie Costello
Co-lead: Somaieh Nikpoor
Why join this group?
Willie is a data scientist with a PhD in Philosophy, and has taught several classes and workshops on AI and machine learning ethics.
Somaieh is a research advisor for AI/ML working for the federal government, where she leads experiments to explore machine learning applications for service delivery.
Together, Willie and Somaieh have been running Aggregate Intellect's AI Ethics stream since last year, where they have hosted a number of discussions with leading AI ethics researchers.
Through this study group, AI & ML practitioners of all levels and backgrounds will get up to speed on the ethical and social issues surrounding machine learning today. We will begin by discussing a general text, before turning our attention to various specific topics in machine learning ethics, to be chosen based on group interest.
Meeting Time: Mondays 8pm ET
Potential Guest Speaker: We will be reaching out to the authors of the texts and articles we will be reading!
Next Milestone: 

“Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence” by Kate Crawford (published April 2021)

What do existing members say? n/a
Techniques Covered: Power, measurement & classification, epistemology & objectivity, work & labour

Group Lead: Suhas Pai
Co-lead: Zach Nguyen
Why join this group?
I am an Applied NLP Researcher and the CTO at a Toronto-based startup, Bedrock AI, where I researched, developed, and deployed unique solutions to hard problems in FinTech, involving the state-of-the-art in language modeling and NLP interpretability. My research areas include enabling large language models to process long-form text, controlled text generation and textual 'style transfer', and privacy-preserving NLP. I write a weekly newsletter on NLP research at AISC, (where I am also the NLP lead), with a readership base of ~3000, and also (these days, irregularly) write long-form NLP blog posts. I have been successfully leading a paper reading group at AISC for several months now and am also a mentor at SharpestMinds, where I guide mentees on NLP research projects.
Dozens of NLP papers are uploaded on arXiv every single day. While a lot of them unfortunately doesn't pass the test of time, every once in a while we are blessed with brilliant ideas that change the direction of our field. In this group, we will rigorously dissect recent papers that show a lot of promise. We love language, and while we acknowledge the power of deep learning and its dominance in NLP today, we won't ignore linguistic principles and insights wherever they are relevant.
 
As part of this group, you will form groups of 3 to give deep-dive presentations on recent papers or implement these papers on a rotating basis. The foundational principles of this group are: curiosity, intellectual rigor, inclusiveness, and low-ego.
Meeting Time: Sundays at 11am ET
Potential Guest Speaker: Researchers with interesting ideas from all across the world
Next Milestone: 
  1. Take part in presenting at least one paper.
  2. Take part in implementing at least one paper.
  3. Take part in creating a 'recipe' with original content.
What do existing members say? n/a
Techniques Covered: Transformer variants, Adversarial methods, Meta-learning, NLP Interpretability

Group Lead: Abdul Rahman Sattar
Co-lead: Manjeet Kaur
Why join this group?
Abdul is the Lead Architect of Cybersecurity Analytics at TELUS. He is looking into applications of AI and ML to cybersecurity analytics at scale for enterprise and IoT security using Machine Learning, Deep Learning and Reinforcement Learning. Reinforcement Learning is something he has been exploring to streamline various processes in the TELUS SOC and also researching applications of Reinforcement Learning for intrusion and threat detection use cases.
Manjeet: I am a Ph.D. student at Concordia University. My Ph.D. research is focused on the area of Deep Learning and Reinforcement Learning. I am very passionate about the development of more transparent and interpretable models to address the performance-transparency trade-off in AI models.
The objectives of this group is:
- Cover breadth in reinforcement learning using various online resources
- Have paper discussions on applied reinforcement learning. Applications related to gaming, robotics, finance, cybersecurity etc. will be explored
- Attempt handons lab exercises and projects in reinforcement learning
Meeting Time: Sundays 12pm ET
Potential Guest Speaker: TBD
Next Milestone: 
  1. Cover foundations of reinforcement learning theory
  2. Attempt lab and coding exercises to apply reinforcement learning theory
  3. Paper discussions on applications of reinforcement learning
What do existing members say? n/a
Techniques Covered: TBD

Group Lead: Nabila Abraham
Co-lead: Karim Khayrat
Why join this group?
Nabila: I’m an R&D data scientist focusing on health problems with a keen interest in graph theory. I am also currently one of the GNN stream owners at AISC where I lead paper discussions with top authors in the field.
Graphs are a ubiquitous data structure which can be used to model text, point clouds, social networks, physics simulations and more. Recently, graph neural network (GNN) papers are on the rise and our goal with this discussion group is to cover newer trends in the GNN space. We will aim to cover GNN papers that are on the application side and have a noticeable benefit over traditional neural network approaches. Members of this group will include folks with a background in graph theory and machine learning as well as amateurs looking to jump into this exciting field!
Meeting Time: Sundays 10am ET
Potential Guest Speaker: TBD
Next Milestone: 
We aim to have papers presented by teams of members every week
What do existing members say? n/a
Techniques Covered: scalable graph nets, graph embeddings

ML Applications

Group Lead: Om Patri
Co-lead: Candice Cloutier

Why join this group?

Om holds a Computer Science PhD and 5 years of industry experience in applied AI/ML. In his current role as a Tech Lead at Amazon Web Services Canada, he delivers cloud data analytics & AI/ML infrastructure projects for Canadian AWS customers. He is passionate about emerging technologies, and how they are modernizing large-scale, traditional industries such as supply chain, which is ripe for innovation.

Candice is a Sr Product Owner who has been focused on eCommerce, but is now transitioning into the Supply Chain space.  She has a background in design, and a keen interest in the social and environmental implications of AI/ML

Users in this group will obtain deep knowledge of advancements in AI/ML and emerging technologies as they relate to supply chain optimization. With the rise in amounts of data collected from traditional supply chains, the availability of modern data platforms, and new technologies such as blockchain to track-and-trace items throughout a chain, there are large opportunities for modernization, business innovation, and new digital experiences.

Meeting Time: Wednesdays at 6pm ET
Potential Guest Speaker: TBD
Next Milestone: 
  1. A review of state of the art of AI/ML use cases in the supply chain lifecycle
  2. How blockchain and ledger solutions fit into supply chain use cases
  3. Connections between Industry 4.0 and the modern, agile, supply chain
Techniques Covered: AI, Machine Learning, Blockchain, IoT

Group Lead: Serena McDonnell
Co-lead: Sina Dibaji

Why join this group?

Serena is a senior data scientist at Delphia. As part of the asset management team, she is responsible for developing machine learning models which are incorporated into the team’s systematic quantitative investment strategy.

Sina is a data scientist at Periculum.io, where he focuses on credit assessment using alternative and financial data.

Through this study group, users will develop their knowledge in using machine learning in financial markets. They can expect to study a range of papers from the quantitative finance field, applications to financial markets using machine learning, time series prediction, and portfolio construction methods.

Meeting Time: Wednesdays at 6pm ET
Potential Guest Speaker: TBD
Next Milestone: 
  1. Learn the difference between fundamental and quantitative investing
  2. Learn about the range of applications of machine learning in financial markets (hint: it’s not just stock price prediction!)
  3. Learn about why machine learning is hard to use in finance
Techniques Covered: TBD 

Group Lead: Karthik Bhaskar
Co-lead: Mary Fallah
Why join this group?
Karthik Bhaskar - I am a Data Scientist/ML Engineer at CIBC. Previously, I worked as a ML Researcher at WangLab affiliated with Vector Institute and University Health Network. I completed my M.Sc in Machine Learning at University of Toronto. My research focused on at the intersection of Machine Learning, Natural Language Processing and Healthcare. Especially using Weak Supervision and Self Supervised Learning for HealthCare data. Our Team BeatCovid at Vector Institute designed and built a DL model to predict the COVID-19 cases across worldwide in XPrize’s Pandemic Response Challenge and secured 16th place out of 250 teams participated in this challenge worldwide.
 
Mary Fallah - I am a Machine Engineer at Ezra (www.ezra.com). Ezra is dedicated to providing full-body MRI scans for cancer screening by using the most advanced medical imaging technology. I completed my M.A.Sc. in electrical engineering at the University of Toronto and conducted my master’s thesis study at Hollland Bloorview Research Institute. My research focused on Brain Computer Interfaces (BCI), where I developed a virtual speller that can be controlled with the brain allowing individuals with speech difficulty to communicate.
 
We both are experts in Machine Learning and HealthCare and worked on several cutting edge research projects and we have good industrial knowledge of how to apply ML in Healthcare space.
 
Machine Learning in HealthCare discussion group promotes the idea of sharing via focused presentations and free-flowing discussions — one paper at a time. In our group we will discuss the basics of Machine Learning in Healthcare to the latest state of the art papers in medical imaging, single cell RNA and drug discovery, etc.
Meeting Time: Saturdays 1pm ET
Potential Guest Speaker: TBD
Next Milestone: 
  1. Natural Language Processing in HealthCare (including Graph Neural Networks)
  2. Computer Vision in HealthCare
  3. Reinforcement Learning in HealthCare
What do existing members say? n/a
Techniques Covered: research papers on computer vision and transformers, brain-computer interfaces, genomics, privacy and autism

Group Leads: Jiri Stodulka & Somaieh Nikpoor
Why join this group?
Somaieh is a research advisor for AI/ML with extensive experience in developing machine learning models. She holds a PhD in economics with research experience in dynamic stochastic General Equilibrium (DSGE) models.
Jiri: I have a masters in Applied Economics and successfully transitioned into ML product development. I've hosted many live sessions on this topic with speakers from places like SalesForce, and perhaps my biggest achievement was a contribution upon which the author made changes in his paper and cited me. 
 
This group will bring ML practitioners and economists together and build a bridge between the two fields. While economist are constrained with transparent methods for inference or prediction, the real environment (economies) is very dynamic. On the other hand, research in ML/AI provides methods for modelling and predicting evolution of dynamic systems. We will aim to find and curate resources to achieve two objectives: 1.) Deal with dynamics in environment 2.) Build the bridge between explainability and ML tools utilized in the task where economists would be limited to methods used in statistics OR casual inference. This group should cultivate innovative mindset through engaging discussions and the power of collective learning. 
Meeting Time: Wednesdays 7pm ET
Potential Guest Speaker: C-level economists from prestigious tech companies and researchers
Next Milestone: 
  1. Build base collection of resources to address dynamics in economies/environments
  2. Build the bridge (by discussion and content creation) between ML techniques economist may utilize and explainability
  3. Develop persuasive strategy that promotes utilization of ML in tasks that have been traditionally approached "conservatively"
What do existing members say? n/a
Techniques Covered: 
- Explore and get familiar with Reinforcement Learning for Economists
- Markov Chains choice
- Transparent AI

Group Leads: Sajeda Mokbel & Arash Feizpour
Why join this group?
Sajeda Mokbel has a background in Physics with a few years of software development experience. Combining her two passions makes Machine Learning in Physics the ideal field of study for her. In her research work, she is currently exploring the use of neural networks for fluid simulation acceleration.
Arash Feizpour is currently doing his PhD in Biophotonics at Vrije Universiteit Brussel. In his research work, he will be using machine learning for classification/manipulation of spectroscopic data.
Both leads are eager to study the latest trends in ML, and find the best ways to incorporate Machine Learning into their Physics projects.
 
Joining this group will allow you to bridge the gap between Physics and Machine Learning. Whether you are experienced in the field, or just starting out, this group is meant to help you excel your Physics research with ML. You will be surrounded by leads who will support your progress, and like-minded members who you can learn from.
Meeting Time: Sundays at 9am ET
Potential Guest Speaker: TBD
Next Milestone: 
  1. Literature review & discussion - Finding interesting papers in the broad topic of machine learning in physics
  2. Focusing on a few areas of interest and getting deeper
  3. Mini projects - Taking the ideas learned and implementing them
What do existing members say? n/a
Techniques Covered: 
TBD

Group Leads: Andre Erler & Yan Nusinovich
Why join this group?

Andre is a climate scientist working for a startup in the water and agriculture space. His background is in "traditional" climate modelling and climate change impact, but he is following the literature on AI/ML in climate science closely and has contributed amongst other initiatives to the Annual Climate Informatics workshop.

Yan is a data scientist working for an alternative energy startup. He has many years of experience in environmental engineering.
Climate change and sustainable development are perhaps the defining challenges of our generation, and surely machine learning can be of help to overcome these challenges. However, use of ML/AI techniques in environmental sciences (broadly speaking) is only in its infancy.
The purpose of this group is to survey the progress of AI/ML adoption and use in the broad field of sustainability, climate change, and environmental/Earth science in general.
We welcome both, experts in ML who want to learn about applications in these fields, as well as practitioners from these fields who want to learn about ML and how it can be used in their fields. However, for the latter group some quantitative skill and knowledge of basic ML methods would be desirable, or the learning curve could be steep (we would still be happy to help, of course).
Meeting Time: TBD
Potential Guest Speaker: TBD
Next Milestone: 
Since this group is fairly new, the first milestone would be to survey the field and define some common trends and challenges that can be observed across the broad range of topics.
A secondary goal would be to define the goals of the group itself and the way it operates, since it is expected that participants will have a wide range of backgrounds and interests.
If there is sufficient interest, participants may team up and attempt to replicate specific implementations and improve upon them, or implement them as a product or service.
What do existing members say? n/a
Techniques Covered: 
Since the field if very broad, many techniques can be covered, ranging from simple regression techniques to more advanced deep learning (e.g. autoencoders).

Group Leads: Kevin McPherson & Willy Rempel
Why join this group?
Kevin is trained as a biophysicist and works as a data scientist and machine learning engineer for Foresight Mental Health. He has worked as a data engineer for Quest Analytics, a software company that helps with doctor and practice network auditing. Prior to his work in software, he was a research scientist at the National Institutes of Health (NIH) in the National Heart Lung and Blood Institute (NHLBI) in Washington, DC. He is interested in bioengineering and the intersection of computing and genomics.
 
Willy is a Research Associate at Aggregate Intellect and contributes to many of AISC's workshops in deep learning. He has degrees in computer science, mathematics and has a specialization in artificial intelligence. He is interested in protein folding, omics modeling, nanotechnology for direct intervention, and tissue engineering.
Members of this group will gain insight into how AI and machine learning are being used to find answers to tough biological questions. The group will be for beginners to advanced students of biology or computer science, as we will seek to learn how basic data science techniques are helping to understand biological phenomena as well as get at the cutting edge of deep learning in biomedicine.

Meeting Time: Sundays at 12pm ET

Potential Guest Speaker: TBD
Next Milestone: 
  1. Build out a recipe of resources for beginner members to onboard into the group.
     
  2. Understand the current state of deep learning in research and in industry and explore the partnerships between the two worlds.
     
  3. Build out a recipe of resources to understand how deep learning is used in biological research.
What do existing members say? n/a
Techniques Covered: 
Bioinformatics, deep learning, predictive cell communication, biophysical computing methods

Group Lead: Abdul Rahman Sattar
Co-lead: Rouzbeh Afrasiabi
Why join this group?
Rouzbeh is a Data Scientist at MS Society of Canada who is a TinyML, Edge Computing and IOT enthusiast.
Abdul is the Lead Architect of Cybersecurity Analytics at TELUS. He is looking into applications of AI and ML to cybersecurity analytics at scale for enterprise and IoT security using Machine Learning, Deep Learning and Reinforcement Learning. We will:
- Learn about the basics of using TinyML on Microcontrollers, Single-board computer, ...
- Learn about the most recent advances in the TinyML area including the theory and application.
- Invite companies active in the TinyML space to present their products (hardware and software).
- Invite authors and have paper discussions.
- Explore hands-on full cycle TinyML projects.
Meeting Time: Sundays 13:00 ET
Potential Guest Speaker: We will invite professionals from startups, government agencies, and engineering firms, as well as researchers whose work we would like to learn about. Please bring your suggestions as well.
Next Milestone: 
  1. Review literature on TinyML
  2. Invite practitioners from companies that are active in this area
  3. Invite researchers active in this area
What do existing members say? n/a
Techniques Covered: 
tbd

ML Ops

Group Lead: Denys Linkov
Co-lead: Ali Darbehani
Why join this group?
Both leads have experience building scalable data and ml platforms in cloud environments. Breadth in architecture, application development, infrastructure and security data science. 
Congrats you just trained a revolutionary model discovery, now what? As you bask in fame, some shmuck has to figure out your spaghetti code, nonsense commence and variable names, and get your model into the hands of users.
 
If you want to learn how to get those models into prod, you can join our group and enjoy fun meetings, while learning a thing or two! We pick a weekly topic and then brainstorm while sharing some stories along the way. We also share our MLOps research recipes along the way, so join us for the ride.
Meeting Time: Thursdays 5pm ET
Potential Guest Speaker: AWS, GCP, Azure engineers, RBC ML Managers
Next Milestone: 
  1. Present a story of getting from a local ML model to deploying it in production! Be creative and share some technical insight along the way.
  2. Create a cartoon about improving your ML model with cool diagrams baked in.
  3. Create a mind map of all the concepts you've learned along the way (there will be many boxes!)
What do existing members say? "Great group to learn many domains at the same time"
Techniques Covered: MLOps, ML Infra, Data engineering, CI/CD, Logging and Monitering

ML Product Development

Group Lead: Ashley Beattie
Co-lead: TBD
Why join this group?
I have been building AI products for business ventures, consulting engagements and personal projects since 2015. I have also been consulting teams in Financial Services on product management/delivery for the past 3 years.
Together we're going to discuss the drivers of an ultimate AI Product development "stack" and its associated AI product development flow.
Here are some topical starting points:
1. The AI & Product "stance", its perspectives, ethics, values and principles
2. The AI & Product team organization and how to best organize your team to deliver value
3. The goals of an AI Product development system and how they vary across types of AI problems
4. Creating Valuable AI Products: the techniques, tools and approaches to understand, design, develop and test an AI opportunity
5. Rapidly testing AI Product hypothesis: how to slice your opportunity to define an MVP that matters
6. Delivering Valuable AI Products: how to advertise, market, launch and sell your product
7. Operationalizing your AI Product: how to scale, support and maintain your AI system
Meeting Time: TBD
Potential Guest Speaker: TBD
Next Milestone: 
  1. What is an AI Product Development stack? What are others using? How to think about yours? (topics 1, 2, 3)
  2. Defining, refining and exercising your AI Product Development stack (topics 4, 5)
  3. Delivering and operationalizing valuable AI Products (topics 6 & 7)
What do existing members say? n/a
Techniques Covered: Lean Canvasing, AI Experiment/Pilot canvasing, AI Story mapping & Slicing, Value stream mapping, AI Value Model identification, Product delivery system design, a light touch of ML Ops

Other Emerging Tech

Group Lead: Kris Kaczmarek
Co-lead: Josiah Sinclair
Why join this group?
Kris: I am currently the head of product at a Quantum Tech startup in London, UK. I defended a PhD from University of Oxford with several high profile publications and patents.
This group is a chance for people to learn the basics, study the applications and discuss the future of the rapidly growing field of quantum computing. As part of this group, we've been looking at the current state of the art in quantum hardware and software, as well as the economics and policies of quantum tech around the world. Members of the group include both quantum computing pros as well as amateurs looking to jump into this exciting new field.
Meeting Time: Saturdays 5pm GMT
Potential Guest Speaker: TBD
Next Milestone: 
  1. Understand the basic concepts of quantum programming
  2. Compare the available software platforms
  3. Benchmark leading quantum computing hardware approaches.
What do existing members say? n/a
Techniques Covered: n/a

Group Leads:Abdul Rahman Sattar & Apurva Kumar
Why join this group?
Abdul is a Lead Cybersecurity Analytics Architect and Apurva is a Staff Security Engineer. At work they are building intelligent solutions for malware analysis and threat hunting using various automation techniques including AI and ML.
Users in this group will gain a deeper knowledge of cybersecurity, with a focus on malware hunting, penetration testing techniques and behavioural analysis of malware. Applications of machine learning and deep learning to threat hunting and malware detection will be discussed.
Meeting Time: Wednesdays 6pm ET
Potential Guest Speaker: TBD
Next Milestone: 
  1. Understanding the cybersecurity field and what problems it faces.
  2. Techniques for malware reversing, malware analysis and malware classification
  3. Application of machine learning to malware analysis and threat hunting, anomaly detection in large datasets, vulnerability management and detection, fuzzing
What do existing members say? n/a
Techniques Covered: TBD

 
Group Leads: Stuart Culpepper & Noah Workman
 
Why join this group?

The ai.science Blockchain discussion group will focus on current business applications for blockchain technology, fostering an information packed hour of diverse business owners and leaders, deep dives into use cases and connection building between participants.

This group is for technical engineers who want to know how blockchain can be applied to business problems and non-technical entrepreneurs building (or considering building) businesses using blockchain technology. 

Stuart - I’ve been following blockchain technology since 2009 and developing AI enabled software for health, tech and fintech companies for over fifteen years.

Noah - I’ve been exploring blockchain and startups since 2015 at Techstars and StartupWeekend, co-founder of Art + Blockchain, content strategy and Consensys

Meeting Time: Wednesdays 6pm ET
Potential Guest Speaker: TBD
Next Milestone: TBD

Potential Guest Speakers: TBD

 

What do existing members say? n/a

Techniques Covered: Understanding of Blockchain ledger mechanics, Proof of Work v Proof of Stake consensus validation, Centralized v Decentralized validation, L2 mechanisms for scaling like Ethereum Sharding, Bitcoin Lightning Network, Smart Contract Basics, Fundamental concepts of NFTs and relationship to copyright, decentralized ID systems for individual sovereignty, the list goes on.

Participation

Open to everyone!

We are very welcoming and inclusive! As long as you bring lots of enthusiasm, a thirst for learning, and contribute to the community, we’re all good!

FAQ

While having a basic knowledge of the topic of your group can be very helpful, it is not strictly required as long as you're actively learning and improving. At some point during the 5 weeks you and the group lead will agree on a topic you will present, so if you're starting from a more entry level, you have to work a bit harder to get ready for it. 

We generally don't prefer that. We would like anybody who joins the groups does so with the intention of participating. Every 5 weeks you can join the "mega meetups" and be an observer there, but for weekly touchpoints the expectation is that you participate as much as you can. 

Well, you're at the right place; we got you covered!!! You can join our community slack workspace, just hang out there and use all our free content until you're ready. Post any questions you have in #community-questions (or DM Amir Feizpour).

Log in/sign up to see the discussion group schedules and join their corresponding slack channels

Become a Group Lead

Set yourself apart as a leading expert and gain personal brand exposure

Organize and formalize your own knowledge of the topic

Build a community of like-minded people centered around your take on the topic

Group Lead FAQ

Our target audience are folks in any stage in their career. Mostly technical leaders & developers, though we keep discussion groups (DGs) open to all. Average years of experience will depend on the topic and the cohort. We recommend you get a sense of who is in your group when you start.

Their desired outcomes are typically having a structured way to stay abreast of the topic, as well as connecting with like-minded people. You can think of it as a micro-community.

  • Each batch runs for 5 weeks and at the end of it you can decide if you want to continue or not
  • Time commitment each week is around 1 hour in session and about 1 hour prep and answering questions async on slack
  • Generally speaking we recommned having a co-lead so that you can cover for each other in case of unforeseen circumestances, but also to have someone to brainstorm and plan together
  • Every 5 weeks we have a "mega meetup" where all discussion groups meet and exchange thoughts about what they have done and their future plans

In short it is intellectual and logistical leadership of the group (and eventually the micro-community you are building). Your role is to set the tone about what kind of topics should be covered, and how. The majority of the research and documentation should be done by your group members but you're there to ensure they research and document the right things and generally explore the right directions. 

No and No!

Our discussion groups attract people who are, generally speaking, aware of the topics and proactive in learning and improving themselves in them. Most of the activities in the groups happen on a rotation where members form small teams, learn particular topics, and present to others. Your role as the lead is not so much as preparing material and teaching but more about facilitating the discussions and ensuring they happen with high quality. 

You can check out the Discussion Groups Schedule page and join any of the groups you want. Feel free to reach out to the group leads and ask for their input. DG leads are a bunch of awesome and welcoming people!

Present in a Discussion Group
(Researchers, Founders, etc)

Get exposure for your work (session can be recorded and published on our YouTube channel: 14,000+ subscribers)

Find potential collaborators, hires, friends, … whatever you’re looking for

Directly interact with our global community of ML researchers and practitioners, and get inspired for your future work

Presenting FAQ

Generally speaking anybody can request to speak in a discussion group. However, we review your request and ensure that what you present adds value to our community members, and can also add value to you. If it's not the right fit we try to introduce you to other communities. 

Generally speaking if we have a discussion group on the topic, we are most likely good to go. See Topics section above.  If we don't have a discussion group on the topic of your interest, we can still post on our slack channel and if there's enough interest we can set it up. Long story short, tell us what you're thinking and we'll do our best to make it work. 

Nope! This is a free community activity. 

You will provide some high level info when you request to do one of these sessions. We use that info to qualify that we can deliver value to you, and if we think we can, we would invite you to join one of our upcoming sessions. You are expected to spend about 20 mins presenting, and leave around 30-40 minutes for discussions. 

  • 10 mins: arrivals and intros
  • 20 mins: your presentation 
  • 30 mins: discussions