We design, develop, and evaluate AI systems that run the real world, and shape the researchers and founders who will define its future. Based at ETH Zurich.
Earlier this year, we launched the first student batch of the ๐๐ง๐ ๐๐ด๐ฒ๐ป๐๐ถ๐ฐ ๐ฆ๐๐๐๐ฒ๐บ๐ ๐๐ฎ๐ฏ. Today, applications for Batch 2 are officially open! ๐
๐ฆ๐๐๐ฑ๐ฒ๐ป๐๐ ๐ถ๐ป ๐ผ๐๐ฟ ๐ณ๐ถ๐ฟ๐๐ ๐๐ฎ๐๐ฐ๐ต ๐ต๐ฎ๐๐ฒ ๐ฎ๐น๐ฟ๐ฒ๐ฎ๐ฑ๐:
โ Published papers at ICLR and ICML
โ Advised executives at SMI-listed companies
โ Raised seven-figure funding for their startups
โ Secured full-time offers from frontier AI labs
Today, our work spans the cutting edge of AI, from Agentic Systems and Multimodal AI to TSLMs and RLMs โ applied across sectors like healthcare, energy, financial services, manufacturing, and robotics.
๐ก๐ผ๐, ๐ถ๐โ๐ ๐๐ถ๐บ๐ฒ ๐ณ๐ผ๐ฟ ๐๐ต๐ฒ ๐๐ฒ๐ฐ๐ผ๐ป๐ฑ ๐๐ฎ๐๐ฐ๐ต.
The goal? Help you turn your semester project, bachelorโs thesis, or masterโs thesis into frontier AI research, an AI startup, or a transformative industry collaboration. With you in the driver's seat.
๐๐ฝ๐ฝ๐น๐ถ๐ฐ๐ฎ๐๐ถ๐ผ๐ป๐ ๐ฎ๐ฟ๐ฒ ๐ผ๐ฝ๐ฒ๐ป ๐๐ผ ๐๐ง๐ ๐๐๐๐ฑ๐ฒ๐ป๐๐ ๐ฎ๐ป๐ฑ ๐ด๐๐ฒ๐๐ ๐๐๐๐ฑ๐ฒ๐ป๐๐ ๐ณ๐ฟ๐ผ๐บ ๐ผ๐๐ต๐ฒ๐ฟ ๐๐ป๐ถ๐๐ฒ๐ฟ๐๐ถ๐๐ถ๐ฒ๐.
Who's next? ๐
๐ Link to apply in the comments.
You can find more information about the lab, available student tracks, and the application process here:
๐ย Lab Website: https://t.co/S2grmYTMT4
๐ย Student Tracks: https://t.co/YheWnd6ECa
๐ย Application Form: https://t.co/sYdVheDI3i
Earlier this year, we launched the first student batch of the ๐๐ง๐ ๐๐ด๐ฒ๐ป๐๐ถ๐ฐ ๐ฆ๐๐๐๐ฒ๐บ๐ ๐๐ฎ๐ฏ. Today, applications for Batch 2 are officially open! ๐
๐ฆ๐๐๐ฑ๐ฒ๐ป๐๐ ๐ถ๐ป ๐ผ๐๐ฟ ๐ณ๐ถ๐ฟ๐๐ ๐๐ฎ๐๐ฐ๐ต ๐ต๐ฎ๐๐ฒ ๐ฎ๐น๐ฟ๐ฒ๐ฎ๐ฑ๐:
โ Published papers at ICLR and ICML
โ Advised executives at SMI-listed companies
โ Raised seven-figure funding for their startups
โ Secured full-time offers from frontier AI labs
Today, our work spans the cutting edge of AI, from Agentic Systems and Multimodal AI to TSLMs and RLMs โ applied across sectors like healthcare, energy, financial services, manufacturing, and robotics.
๐ก๐ผ๐, ๐ถ๐โ๐ ๐๐ถ๐บ๐ฒ ๐ณ๐ผ๐ฟ ๐๐ต๐ฒ ๐๐ฒ๐ฐ๐ผ๐ป๐ฑ ๐๐ฎ๐๐ฐ๐ต.
The goal? Help you turn your semester project, bachelorโs thesis, or masterโs thesis into frontier AI research, an AI startup, or a transformative industry collaboration. With you in the driver's seat.
๐๐ฝ๐ฝ๐น๐ถ๐ฐ๐ฎ๐๐ถ๐ผ๐ป๐ ๐ฎ๐ฟ๐ฒ ๐ผ๐ฝ๐ฒ๐ป ๐๐ผ ๐๐ง๐ ๐๐๐๐ฑ๐ฒ๐ป๐๐ ๐ฎ๐ป๐ฑ ๐ด๐๐ฒ๐๐ ๐๐๐๐ฑ๐ฒ๐ป๐๐ ๐ณ๐ฟ๐ผ๐บ ๐ผ๐๐ต๐ฒ๐ฟ ๐๐ป๐ถ๐๐ฒ๐ฟ๐๐ถ๐๐ถ๐ฒ๐.
Who's next? ๐
๐ Link to apply in the comments.
๐ Paper 6/6
Preserving Plasticity in Continual Learning via Dynamical Isometry
Rosseau A, Mรผller R, Nowe A.
Accepted at ICML 2026 Main Track
ICML: https://t.co/7g9RKRDeZ5
@deepqlearning
Excited to share that members of our lab co-authored 6 papers accepted at #ICML2026, including three Main Track and three Workshop papers ๐ฅ๐
๐ Accepted papers:
โช๏ธ OpenTSLM: Time-Series Language Models for Reasoning over Multivariate Medical Text- and Time-Series Data [Main Track]
โช๏ธ Auditing Emotion-Vector-Steered Political Bias in Open-Weight LLMs [AI4GOOD Workshop]
โช๏ธ Reinforcement Learning of Karma Bidding Strategies [NExT-Game Workshop]
โช๏ธ Cinematic Source Separation with Dialogue-Driven Sidechain Ducking [Workshop on Machine Learning for Audio]
โช๏ธ Reinforcement Learning for Tool-Calling Agents in Fast Healthcare Interoperability Resources (FHIR) [Main Track]
โช๏ธ Preserving Plasticity in Continual Learning via Dynamical Isometry [Main Track]
@robertjakob@PatrickLanger20@gaborhollbeck@f14wn@DerRiehl@atoof_sh@deepqlearning@kev_osull@cs06thegreat@nzuma0@maxrosenblattl
Huge congratulations to everyone involved. We are looking forward to presenting these works, reconnecting with colleagues, and meeting new friends in Seoul ๐ฐ๐ท
๐ Full links in the comments.
๐ Paper 5/6โ
Reinforcement Learning for Tool-Calling Agents in Fast Healthcare Interoperability Resources (FHIR)
Knorr M*, Mรผller R*, Bremer JP, Schweingruber N.
Accepted at ICML 2026 Main Track
arXiv: https://t.co/Oa4t6oqHtL
ICML: https://t.co/e3IY8pA3UT
@deepqlearning
๐ Paper 4/6
Cinematic Source Separation with Dialogue-Driven Sidechain Ducking
Shakir A, Groetschla F, Lanzendoerfer L, Wattenhofer R.
Accepted at ICML 2026 Workshop on Machine Learning for Audio
Workshop: https://t.co/xiVVEH6d7D
@atoof_sh
๐ Paper 2/6
Auditing Emotion-Vector-Steered Political Bias in Open-Weight LLMs
Hollbeck G, Peters B, von Recum A, Riehl K, McGail A, Windeck J, OโSullivan K, Jakob R.
Accepted at ICML 2026 Workshop: AI4GOOD
OpenReview: https://t.co/iIfuVswGEJ
@gaborhollbeck@f14wn@DerRiehl@kev_osull@robertjakob
๐ Paper 1/6
OpenTSLM: Time-Series Language Models for Reasoning over Multivariate Medical Text- and Time-Series Data
Langer P, Kaar T, Rosenblattl M, Xu MA, Chow W, Maritsch M, Jakob R, Wang N, Liu J, Verma A, Han B, Kim DS, Chubb H, Ceresnak S, Zahedivash A, Sandhu ATS, Rodriguez F, McDuff D, Fleisch E, Aalami O, Barata F, Schmiedmayer P.
Accepted at ICML 2026 Main Track
arXiv: https://t.co/BPr5T3NcUL
Github: https://t.co/sw81tmyFcD (+1200 โญ๏ธ)
ICML: https://t.co/xFjz6xMie4
Website: https://t.co/kt881bDtlT
X: @OpenTSLM@PatrickLanger20@robertjakob@cs06thegreat@nzuma0@maxrosenblattl@maxxu05@PSchmiedmayer
Why do models that score well on standard benchmarks still make bad strategic decisions?
In Cattle Trade, accepted at @MALGAI_ICLR2026 , LLMs play a 4-player 50+ turn game of auctions, hidden offers, bluffing, and limited cash.
Some LLMs lose to simple Python agents.
๐งต
Our members Nicolas Zumarraga & Robert Jakob are in Brazil ๐ง๐ท for ICLR 2026 presenting our poster on TS-Haystack: A Multi-Scale Retrieval Benchmark for Time Series Language Models
Come by the poster session today and say hi!
Paper: https://t.co/RIUHpATAFx
@iclr_conf@nzuma0@robertjakob
recap of our zurich visit with @theresidency:
> spent 3 days in zurich, it feels like the place to be in europe
> technical talent, high substance, no noise
> 3 places you must visit:
1. @ethroboticsclub - ETH robotics club. a hangar with 50 students on a saturday, working on robotics & physical ML projects
2. @ETH_agent_lab - an ETH chair that let's european students work on their projects, while giving them their master's thesis + credits -> perfect for students who want to build but NOT drop out
3. @thejfloor - the community that formed at the ETH's student project house, now a coworking space for startups in zurich
funniest roadtrip I could have imagined with @ArvindAGI22 , @_sethmorton and @chrisbrolin123
the conversations we've been having can be boiled down to one question:
will transformers get to AGI faster than other, more exotic models that run on new alternative hardware (e.g. neuromorphic or thermo compute).
until next time!
update:
> went to zurich
> crashed into @thejfloor
> went to eth student bar
> asked random ppl if they know tbpn
> 5th group we asked said yes
> turns out theyโre all building at โthe labโ in zurich
> 10/10 culture fit with the residency
> 10/10 serendipity
> immediately decided to stay longer in zurich
Rajiv is now building Studyflash โก๐
Theyโre building a โCursor for Learningโ that helps students turn notes, slides, and docs into effective study resources.
Bootstrapped, ~10 full-time builders, and already at multi-million $ ARR.
Check them out: https://t.co/rE6rRjMTBq
Congrats to our lab member Rajiv Manichand (@frederik_rajiv) on his AAAI 2026 paper, โDigital Scale: Open-Source On-Device BMI Estimation from Smartphone Camera Images Trained on a Large-Scale Real-World Datasetโ ๐
The paper presents a privacy-preserving way to estimate BMI from smartphone camera images, with the full pipeline running entirely on-device.
Thread below ๐งต
#AAAI2026 #DigitalHealth #MachineLearning
@frederik_rajiv Digital Scale shows that BMI estimation can be made more private, portable, and usable in real-world settings.
๐ Full paper: https://t.co/oPPZloWTM1
๐ป Code: https://t.co/oPPZloWTM1
Congrats to our lab member Rajiv Manichand (@frederik_rajiv) on his AAAI 2026 paper, โDigital Scale: Open-Source On-Device BMI Estimation from Smartphone Camera Images Trained on a Large-Scale Real-World Datasetโ ๐
The paper presents a privacy-preserving way to estimate BMI from smartphone camera images, with the full pipeline running entirely on-device.
Thread below ๐งต
#AAAI2026 #DigitalHealth #MachineLearning
@frederik_rajiv A key contribution is deployment: the full pipeline runs on Android using CLAID, including image filtering and local BMI inference.
The code is released open-source, making the system easier to reproduce and extend.