MSc THESIS

Open Thesis Topics

1. Fairness in Recommendation Systems (MSc) with Stanford University

Goal of this project is to evaluate currently available food recommender systems and build a recommender system that takes into consideration healthy food as a recommendation set. Knowledge of Computer Vision (image recognition) and recommender system is useful for this project.

2. Deep Learning for Disease Diagnosis (MSc) with Stanford University

Augmentation of disease diagnosis and decision-​making in health care with machine learning algorithms is gaining much impetus in recent years. In particular, in the current epidemiological situation caused by COVID-​19 pandemic, swift and accurate prediction of disease diagnosis with machine learning algorithms could facilitate identification and care of vulnerable clusters of population, such as those having multi- morbidity conditions. In order to build a useful disease diagnosis prediction system, advancement in both data representation and development of machine learning architectures are imperative. In this project we seek to build deep learning pipeline for efficient and accurate diagnosis of diseases.

Literature: See here

3. Deep Learning on Text Documents and Knowledge Graphs (MSc) with Stanford University

Deep learning methods have given rise to a variety of models to learn representations (or embeddings) from both structured and unstructured data. In this project we will evaluate some existing methods and develop new techniques to learn embeddings from text documents and knowledge graphs. Especifically, the goal is to extract information from text documents, so that they can be populated into knowledge bases. Working on this project requires experience with both structured and unstructured datasets, and of the mathematical models to represent them. Coursework on NLP, ML, or Statistics will be helpful. Experience with Tensorflow and/or Keras, or the readiness to learn them will be expected.

Relevant literature:

– Weston, Jason, et al. “Connecting language and knowledge bases with embedding models for relation extraction.” (2013).
– Zhang, Wen, et al. “Interaction Embeddings for Prediction and Explanation in Knowledge Graphs.” (2019).

See also here

For more information or if you want to apply for the thesis, please contact me or Bibek Paudel (bibekp@stanford.edu)

4. Organizations and Algorithms

During the last two decades, the scientific advancements in artificial intelligence (AI) and ML algorithms in the field of computer science—including findings from research on deep neural networks and the development of hardware such as graphics processing units (GPUs) and tensor processing units ( TPUs) that makes training such complex nonlinear models feasible—has led to the development of predictive technologies capable of undertaking tasks that previously required human judgment and decision making. Such, AI- and ML-​based predictive technologies have been successfully applied to automate knowledge work and decision making (eg, in dynamic pricing in e-​commerce websites and high-​frequency trading and recommender systems). When well integrated, firms can benefit from partially automated/fully automated decision making because (in certain scenarios) it reduces coordination costs and frees up human attention. However, given the opacity, lack of accountability, and embedded bias of these algorithms, managers might lack the required expertise to better leverage predictive technologies in their organizations. Given their idiosyncratic features, there is a need to rethink organizational designs when integrating these predictive technologies into our organizations. This gives rise to the grand challenge, namely, how to redesign organizations to efficiently benefit from the automation of knowledge work and decision making. The focus of work in this thesis should aim to understand how organizations and organization designs are being shaped by algorithms as both tools and agents. Working on this project requires experience with both structured and unstructured datasets, and of the mathematical models to represent them. Coursework on NLP, ML, or Statistics and those on strategy and organization theories will be helpful.

Relevant literature:

– von Krogh, G. (2018). Artificial Intelligence in Organizations: New Opportunities for Phenomenon-​Based Theorizing. Academy of Management Discoveries, 4(4), 404-​409.

– Shrestha, YR, Ben-​Menahem, S., & von Krogh, G (2019). Organizational Decision-​Making Structures in the Age of Artificial Intelligence. California Management Review.


For Application: Please send a short description on your potential project idea, CV and transcripts to me

Past Master Thesis Supervision @ETH Zurich

19. Claudio Zihlmann (2022)
Topic: Market access strategy for a new value product in the oral market for biomaterials MAS Excellence Award 2022

18. Lydia Pagani (2022)
Topic: Stylistic production: How the stylistic choices of the occupational community of the Swiss independent watchmakers influence their company’s strategy

17. Savindu Herath (2021)
Topic: Leaveraging data-driven decision-making in co-creation business models to improve firm performance: Evidence from online fashion retailing Received ETH Medal Award 2022 (top 2%)

16. Tobias Motz (2021)
Topic: Artificial Intelligence and Organizations: Paradigms of action for a successful integration

15. Anastasios Papageorgiou (2021)
Topic: Deep Learning for disease diagnosis

14. Leopold Franz (2020)
Topic: Managing Disease Diagnoses with Structured and Unstructured Clinical Data\

13. Sebastian Windeck Otto (2020)
Topic: Knowledge graphs for strategic business applications

12. Martin Buttenschön (2018)
Topic: Data for AI: How well structured data empowers business to benefit from machine learning

11. Apurva Maduskar (2017)
Topic: When Corporate Agile Meets Open Source: Contrasting Knowledge Integration and Documentation Practices Recieved MAS Excellence Award 2018

10. Chun-Hui Kuo (2017)
Topic: From startup to scaleup: Two-phase searching for human resource acquisition inearly-stage spin-offs

9. Matthias Stenske (2017)
Topic: Open Source Strategy for Swiss Telecommunication infrastructure industry:Impact on strategic resources

8. Mikko Leimio (2017)
Topic: The Impact of an Open Source Hardware Strategy for 5G Technology to the Telecom Industry and a major Swiss Telecommunications Provider

7. Matteo Frondoni (2016)
Topic: Competing with giants: Artificial Intelligence as a threat or an opportunity for the swiss TIME industry?
Recieved Student Prize 2016 from the SEW-EURODRIVE Foundation and ETH Medal 2017 (top 2%)

6. David Roegiers (2016)
Topic: Start-up acquisitions in the open source software space: What is the effect oncommunity dynamics ?

5. Christoph Hirnschall (2016)
Topic: Online learning of user preferences with applications for online marketplaces
Recieved Willie Studer Prize 2017 (best student in each ETH Zurich Master’s degree programme)

4. Matthias Auf der Mauer (2015)
Topic: Business Model Analysis of Intuitive Surgical Inc.and Strategic Implications for Companies in the Robotic Assisted Ablation Catheter Industry

3. Pascal Mages (2015)
Topic: IT Outsourcing for Small and Medium Enterprises

2. Remo Hug (2015)
Topic: Sharing economy in the market for kid’s goods in Switzerland

1. Fotini Traka (2015)
Topic: OSS Licenses and project sustainability