Georgia Institute of Technology
Circuit mix
Where Georgia Institute of Technology shows up, by circuit. Each event counts once: 2 if it sponsored, 1 if it only had speakers. Hover a segment for the conference.
- Research50%3 events
- Modern data stack & practitioner17%1 event
- Postgres & open-source databases17%1 event
- Cloud providers17%1 event
Presence by conference and year
Each cell is one conference in one year. Fill shows the company sponsored, shaded by its best tier that year; a dot means it had speakers there. Hover for details; click to open the event.
- Title
- Platinum
- Gold
- Silver
- Bronze
- Other / unlabeled tier
- Had speakers
Activity by year
Three measures on one year axis; each panel has its own scale. 2027, 2028 is still mostly ahead, so its bars are faded and incomplete.
Conferences present at
Distinct conferences each year, split by whether the company sponsored or only had speakers there.
- Sponsored
- Speakers only
Distinct speakers
People from this company on a program that year, each counted once.
Where they've grown or pulled back
Georgia Institute of Technology has never sponsored a tracked conference, so this follows where its speakers appear instead.
DownHad speakers at 1 conference in 2026, down from 2 in 2022.
Counted per conference. 2026 is the latest year whose conferences have mostly happened. A conference counts as no longer seen only when later events published speaker lists.
New conferences spoken at 2
First speaker in 2025 or later.
- IEEE ICDE (International Conference on Data Engineering)first spoke 2026, 2 speakers
- Postgres Extensions Dayfirst spoke 2025, 1 speaker
No longer seen 4
No speakers since 2024 or earlier, though the conference published speaker lists after that.
- ACML (Asian Conference on Machine Learning)last seen 2019; the conference ran again with speaker lists in 2020–25
- AWS re:Inventlast seen 2022; the conference ran again with speaker lists in 2023–25
- Budapest Data+AI Forumlast seen 2022; the conference ran again with speaker lists in 2023–26
- NeurIPS (Conference on Neural Information Processing Systems)last seen 2020; the conference ran again with speaker lists in 2021–25
Lookalikes
Companies with the most similar conference footprint: they sponsor and speak at the same conferences. Bars are cosine similarity, from 0 (nothing shared) to 1 (identical).
- DRW0.77
- Yandex Cloud0.69
- Veeam0.65
- Fred Hutchinson Cancer Center0.63
- Johnson & Johnson0.63
- Zalando0.62
- Open AI0.62
- Tencent0.62
11 Cluster: NeurIPS · ACM SIGKDD circuit →
Often sponsors alongside
Companies that sponsored the same events, by number of shared events (out of 0 this company sponsored).
No co-sponsors: this company has no recorded sponsorships, or sponsored alone.
Neighborhood: companies with nearby conference footprints
Georgia Institute of Technology (ringed) and the 30 companies closest to it on the company map. Close together means they sponsor and speak at many of the same conferences. Distances are only meaningful locally; the axes have no meaning. Dot size: conferences present at. Color: cluster.
Sponsorships 0
No sponsorships recorded for Georgia Institute of Technology.
Speakers 7
| Speaker | Event | Role | Talk | Links |
|---|---|---|---|---|
| Harshavardhan Kamarthi | ICDE 2026 (42nd IEEE International Conference on Data Engineering) | industry paper | Hierarchical Industrial Demand Forecasting with Temporal and Uncertainty Explanations | — |
| Zhiyuan Zhao | ICDE 2026 (42nd IEEE International Conference on Data Engineering) | industry paper | TAT: Temporal-Aligned Transformer for Multi-Horizon Peak Demand Forecasting | — |
| Sweta Vooda | Postgres Extensions Day Montréal 2025 | speaker | Postgres as a Control Plane: Challenges in Offloading Compute via Extensions | — |
| Charles Isbell | AWS re:Invent 2022 | breakout | Building more inclusive, responsible AI | Slides · Video |
| Jay Wang | Budapest ML Forum 2022 | speaker | StickyLand: Breaking the Linear Presentation of Computational Notebooks | — |
| Charles Isbell | NeurIPS 2020 | keynote | You Can’t Escape Hyperparameters and Latent Variables: Machine Learning as a Software Engineering Enterprise | Video |
| Le Song | ACML 2019 | keynote | Graph Neural Networks for Representation Learning and Symbolic Reasoning | — |