Tanmoy Chakraborty

Towards privacy-aware mental health AI models Nature Computational Science - In this Perspective, the authors examine privacy risks in mental health AI, and explore solutions and evaluation frameworks to balance privacy–utility...

📄 Towards privacy-aware mental health AI models
🔗 rdcu.be/eKoDA

Follow the authors for more updates:
👨‍💻 @jitaishik.bsky.social – UKP Lab, TU Darmstadt (NLPsych Group)
👨‍🏫 @tanmoy-chak.bsky.social – IIT Delhi, Humboldt Research Fellow at UKP Lab
👩‍🔬 @igurevych.bsky.social – UKP Lab, TU Darmstadt

13.10.2025 12:12 — 👍 5    🔁 2    💬 1    📌 0

We expose key gaps and chart how richer, more inclusive datasets can unlock the full potential of Mental Health AI.

📃Paper: arxiv.org/pdf/2508.09809

(2/🧵)

01.10.2025 07:17 — 👍 1    🔁 1    💬 1    📌 0

Flowchart illustrating aspects of clinical mental health datasets. Starting from a dataset icon, branches include: Disorders: depression, anxiety, PTSD, bipolar disorder, schizophrenia. Accessibility: public, controlled, private. Tasks: classification, questionnaire scores, response generation, analysis. Synthetic Data: artificial data creation. Modalities: single modality (text, audio, video, brain signals) and multiple modality combinations. Languages/Cultures: diversity of linguistic and cultural contexts.

👉 Interested in working on AI for mental health? This is a 𝗺𝘂𝘀𝘁-𝗿𝗲𝗮𝗱 𝘀𝗶𝗻𝗰𝗲 𝘁𝗵𝗲 𝗱𝗮𝘁𝗮 𝗮𝘃𝗮𝗶𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗶𝘀 𝗮 𝗺𝗮𝗷𝗼𝗿 𝗯𝗼𝘁𝘁𝗹𝗲𝗻𝗲𝗰𝗸!

Our survey maps the mental health dataset landscape, covering a wide range of disorders, tasks, modalities, accessibility, synthetic data, and cultural reach.

(1/🧵)

01.10.2025 07:17 — 👍 1    🔁 1    💬 1    📌 0

New Desk Rejection Practice for EMNLP 2025 For some time there has been substantial concern within the community regarding many aspects of reviewing, from poor quality, to too few reviewers in the pool, to poor quality reviews, to reviewers no...

This year, EMNLP ended up desk rejecting ~100 papers. For more insight into the process, and potential future changes, please see this blog post from the PCs: 2025.emnlp.org/desk-rejecti...

@christos-c.bsky.social @carolynrose.bsky.social @tanmoy-chak.bsky.social @violetpeng.bsky.social

20.08.2025 16:22 — 👍 13    🔁 4    💬 1    📌 1

🚨 𝐖𝐡𝐚𝐭 𝐡𝐚𝐩𝐩𝐞𝐧𝐬 𝐰𝐡𝐞𝐧 𝐭𝐡𝐞 𝐜𝐫𝐨𝐰𝐝 𝐛𝐞𝐜𝐨𝐦𝐞𝐬 𝐭𝐡𝐞 𝐟𝐚𝐜𝐭-𝐜𝐡𝐞𝐜𝐤𝐞𝐫?
new "Community Moderation and the New Epistemology of Fact Checking on Social Media"

with I Augenstein, M Bakker, T. Chakraborty, D. Corney, E
Ferrara, I Gurevych, S Hale, E Hovy, H Ji, I Larraz, F
Menczer, P Nakov, D Sahnan, G Warren, G Zagni

01.06.2025 07:48 — 👍 16    🔁 8    💬 1    📌 0

ARR Reviewer Guidelines A peer review platform for the Association for Computational Linguistics

📢 Submitting to the May 19th ARR deadline?

✅ Make sure your profile is complete, including your DBLP link: aclrollingreview.org/reviewerguid...

📄 Do not forget to review:
(1) Author Guidelines aclrollingreview.org/authors
(2) ACL Publication policy aclweb.org/adminwiki/in...
#NLProc #EMNLP25 #ARR

16.05.2025 17:36 — 👍 7    🔁 3    💬 0    📌 0

New Tracks at EMNLP 2025 and Their Relationship to ARR Tracks As we prepare for EMNLP 2025, many of you may have noticed that we are introducing several new tracks and making adjustments to our area list. You might also notice that there are some differences bet...

See also:
🧵 Blog on track changes: 2025.emnlp.org/track-changes/
🔍 Review policies: 2025.emnlp.org/reviewer-pol...

@aclmeeting.bsky.social @dirkhovy.bsky.social @christos-c.bsky.social @carolynrose.bsky.social @tanmoy-chak.bsky.social @violetpeng.bsky.social

10.05.2025 17:26 — 👍 6    🔁 2    💬 0    📌 0

Volunteer to join EMNLP 2025 Senior Area Chair (SAC) Committee Dear NLP Community, Please use this form to express your interest in joining the EMNLP 2025 SAC committee. SACs will be responsible for overseeing the overall review process in their track, following ...

EMNLP is currently seeking additional Senior Area Chairs (SACs). If you would like to help, please fill out this form, and share the link with others!
docs.google.com/forms/d/e/1F...

01.03.2025 16:27 — 👍 10    🔁 6    💬 0    📌 0

How to Upscale Neural Networks with Scaling Law? A Survey and Practical Guidelines Neural scaling laws have revolutionized the design and optimization of large-scale AI models by revealing predictable relationships between model size, dataset volume, and computational resources. Ear...

Check out our recent work

arxiv.org/abs/2502.12051

21.02.2025 23:43 — 👍 1    🔁 0    💬 0    📌 0