Roni Kobrosly
Employers on the program
- 2023–2024Capital One2 talks
- 2022DrFirst1 talk
The year around now
Talks in the past 12 months and announced talks ahead, one row per conference, January to December. Hover a mark for the talk.
Appearances by year
- PyData
- SciPy Conference (Scientific Computing with Python)
Talks
| Date | Event | Conference | Role | Title | Links |
|---|---|---|---|---|---|
| Jul 13, 2026 | SciPy 2026 | SciPy Conference (Scientific Computing with Python) | tutorial | Introduction to Causal Inference (Room HSEC 3-110)This tutorial session is intended to give attendees a gentle introduction to applying causal thinking and inference using python. Causal data analysis is very…Show lessThis tutorial session is intended to give attendees a gentle introduction to applying causal thinking and inference using python. Causal data analysis is very common in many academic domains (e.g. | |
| Nov 6, 2024 | PyData NYC 2024 | PyData | speaker | Holistic root cause analysis of software breakages through structural causal modeling as Capital One The ability to quickly identify and resolve breakages among interconnected microservices is critical for any tech organization running production software. | Video |
| Nov 1, 2023 | PyData NYC 2023 | PyData | speaker | The dangers of storytelling with feature importance as Capital One It's common for machine learning practitioners to train a supervised learning model, generate feature importance metrics, and then attempt to use these values…Show lessIt's common for machine learning practitioners to train a supervised learning model, generate feature importance metrics, and then attempt to use these values to tell a data story that suggests what interventions should be taken to drive the outcome variable a favorable way (e.g. | |
| Jul 10, 2023 | SciPy 2023 | SciPy Conference (Scientific Computing with Python) | tutorial | Introduction to Causal InferenceThis tutorial session is intended to give attendees a gentle introduction to applying causal thinking and causal inference to data using python. Causal data…Show lessThis tutorial session is intended to give attendees a gentle introduction to applying causal thinking and causal inference to data using python. Causal data analysis is very common in many academic domains (e.g. | |
| Nov 9, 2022 | PyData NYC 2022 | PyData | speaker | Introduction to Causal Inference | Video |
| Jul 11, 2022 | SciPy 2022 | SciPy Conference (Scientific Computing with Python) | tutorial | Introduction to Causal Modeling as DrFirst |
Frequent co-speakers
| Speaker | Shared events | Shared talks | Last together |
|---|---|---|---|
| Thomas J. Fan | 5 | — | 2024 |
| Gil Forsyth | 4 | — | 2026 |
| Cliff Kerr | 3 | — | 2026 |
| David Nicholson | 3 | — | 2026 |
| Jacob Tomlinson | 3 | — | 2026 |
| James A. Bednar | 3 | — | 2026 |
| Jarrod Millman | 3 | — | 2026 |
| Jim Pivarski | 3 | — | 2026 |
| Juanita Gomez | 3 | — | 2026 |
| Michael Zargham | 3 | — | 2026 |
| Naty Clementi | 3 | — | 2026 |
| Thomas Caswell | 3 | — | 2026 |
| Tracy Teal | 3 | — | 2026 |
| Dharhas Pothina | 3 | — | 2024 |
| Hugo Bowne-Anderson | 3 | — | 2024 |
Speakers with the same name are merged into one entry, so this page may combine more than one person. Affiliations are shown as each conference listed them.