Max M. Lang

About

I am a DPhil candidate at the University of Oxford's Department of Population Health and Statistics. My research examines the co-distribution of schistosomiasis and malaria, exploring shared risk factors and potential intervention strategies for these co-endemic diseases. This research is part of the Schistotrack Group, a cohort study in Uganda, and my project is supervised by Dr. Goylette F. Chami and Prof. Christl Donnelly.

In addition to my DPhil research, I work on new approaches to collecting high-quality data to answer important questions. This interest began during my time in Munich under the supervision of Prof. Frauke Kreuter, where I focused on survey methodology. More recently, my research has centered on automated dialogue systems that transform traditional data gathering methods.

This work has led to industry collaborations, particularly with 60 Decibels Inc., where I'm developing a fully automated LLM-powered telephone survey system. This system enables AI agents to collect impact research data via phone conversations that closely mimic human-led interviews.

I am always happy to discuss research ideas and look forward to new collaborations. Feel free to reach out via email if you'd like to connect.

Publications & Talks

Publications

2025

Telephone Surveys Meet Conversational AI: Evaluating a LLM-Based Telephone Survey System at Scale. Lang, M. M., Eskenazi, S., Accepted at 80th Annual AAPOR Conference 2025
Protocol for 'Clinical outcomes associated with schistosome infection and alcohol use: a systematic scoping review' , Lyne, B., Lang, M. M., Lewingtion, S., Chami, G. F.
Protocol for 'Shared Risk Factors for Malaria and Schistosoma Co-Infection: A Systematic Review and Meta-Analysis', Lang, M. M., Lyne, B., Donnelly, C. A., Chami, G. F.

2024

AI Conversational Interviewing: Transforming Surveys with LLMs as Adaptive Interviewers. Wuttke, A., Aßenmacher, M., Klamm, C., Lang, M. M., Würschinger, Q., & Kreuter, F., Accepted at 80th Annual AAPOR Conference 2025 & NAACL LaTeCH-CLfL2025
Pytector. Lang, M. M. A Python package for detecting prompt injection in text inputs using state-of-the-art transformer models.
Financial Times Chart Doctor Visual Vocabulary in German. Lang, M. M., & Smith.

2023

Time Series Analysis of Climatological and Hydrological Low Water Drivers in Bavaria - Climex II. Hobelsberger, C., Kleinlein, L., Lang, M. M., Meier, T., Paschan, N., Schernich, J., Böhnisch, A., Sasse, A., Funk, H., & Küchenhoff, H. Accepted at IWSM Dortmund 2023.

Talks

2025

Using AI Phone Agents to conduct impact research on a large scale, ID Insight

2024

Transforming Surveys with AI Conversational Interviewing, University of Zurich, Digital Society Initiative
Using Chatbots as Interviewers, New Directions: Bridging Natural Language Processing (NLP) and Survey Research at SurvAI-Day, University of Maryland
Featured: IASS Webinar 42: Leveraging AI for Survey Research by Frauke Kreuter

2023

Funding

My research is graciously funded by the Department of Population Health, University of Oxford.

Contact

Max Melchior Lang

Big Data Institute
Old Road Campus, University of Oxford
Oxford OX3 7LF, United Kingdom

Email: max.lang[at]stx.ox.ac.uk

ORCID: 0009-0004-6815-5321

Legal

Privacy Policy

This website does not collect any personal data from its visitors. It is hosted on GitHub Pages, which may collect some technical information such as IP addresses, browser types, and access times as part of its standard operation. This information is not used by the website owner.

GitHub may collect technical information as described in their Privacy Statement.

This website does not use cookies or any form of tracking technology.

Impressum (Legal Notice)

Angaben gemäß § 5 TMG:

Max Melchior Lang
Big Data Institute
Old Road Campus, University of Oxford
Oxford OX3 7LF, United Kingdom

Kontakt:
E-Mail: max.lang@stx.ox.ac.uk

Verantwortlich für den Inhalt nach § 55 Abs. 2 RStV:
Max Melchior Lang
Big Data Institute
Old Road Campus, University of Oxford
Oxford OX3 7LF, United Kingdom

Haftungsausschluss (Disclaimer):

Haftung für Inhalte
Die Inhalte dieser Seiten wurden mit größter Sorgfalt erstellt. Für die Richtigkeit, Vollständigkeit und Aktualität der Inhalte kann ich jedoch keine Gewähr übernehmen.

Haftung für Links
Diese Webseite enthält Links zu externen Webseiten Dritter, auf deren Inhalte ich keinen Einfluss habe. Deshalb kann ich für diese fremden Inhalte auch keine Gewähr übernehmen. Für die Inhalte der verlinkten Seiten ist stets der jeweilige Anbieter oder Betreiber der Seiten verantwortlich.

Copyright Notice

Disclaimer

The views and opinions expressed on this website are my own and do not necessarily reflect the official policy or position of any affiliated institution.