Teaching & supervisions
I supervise a number of undergraduate courses. I am also keen on supervising final year (Part II) projects in HCI, information visualisation, and applied data mining / machine learning. I have a number of ideas for projects for Part III / MPhil ACS students which can be supervised by a senior faculty member. Here is a list of project ideas. If interested, get in touch.
In early 2017 I wrote a short book on the Cambridge supervision system, called 'How to define an elephant', which you might find interesting.
Guidelines for supervisions
- Venue: supervisions will be at the Computer Laboratory (in the William Gates Building). We will meet in front of reception.
- Deadline: work is due 48 hours before the supervision. Late work may not be marked. Excellent work submitted slightly late is preferable to timely but inadequate work. Work not handed in is immediately added to the work due for the subsequent supervision.
- Work is best handed in through email as a PDF attachment. I intensely dislike Word/OpenOffice/etc documents.
- Do not send me links to hosted files.
- Leave a margin of at least one inch on every side.
- Any code or pseudocode must be in a monospaced typeface such as Courier or Monaco.
- If your work is handwritten, a scanned copy (or legible pictures) is acceptable, but pay close attention to the next point.
- Keep your total file size small (less than 0.5MB).
- If you must hand in a hard copy, email me after you have put it in the blue box, otherwise I won't pick it up.
- The following must be clearly visible on the front page: your name, my name, the full name of the course, and the number of the supervision (e.g. "Artificial Intelligence II Supervision 2 — John Doe for Advait Sarkar").
- Support: if you have questions or difficulties related to the course, email me a few days in advance of the supervision with the query or difficulty to give me the best chance of addressing it well.
1advised: I contributed nontrivially to the supervision of the student, but was not the primary supervisor.
Laszlo Makk. Undergraduate dissertation, 2017.
Automated captioning of visualised data.
Ben P.W. Catterall. Undergraduate dissertation, 2016.
Parallelized Deep Learning for Convolutional Networks on the Intel Xeon Phi.
Tamas Stzanka-Toth. Undergraduate dissertation, 2016.
Cached Bug Prediction for Python Repositories on GitHub.
Ana Šemrov. Undergraduate dissertation, 2016.
Interactive exploration of latent semantic spaces.
🏆 Best poster award at OWCSC 2016
Tanvi S. Potdar. Undergraduate dissertation, 2016.
Semantic Markup of Heterogeneous Networks for Concept Evolution.
Lawrence F. Dior. Undergraduate dissertation, 2016 (advised1).
Visualising uncertainty of approximate database queries through sketchy rendering.
Mariana Mărășoiu. M.Phil. dissertation, 2015 (advised1).
Consultative sketching of statistical hypotheses.
🏆 Honourable mention at Eurographics/IEEE VGTC EuroVis 2016
Neil Satra. M.Eng. dissertation, 2015 (advised1).
Sketching Statistical Queries.
Maria Gorinova. Undergraduate dissertation, 2015.
Interactive Development Environment for Probabilistic Programming.
★ Paper presented at ACM CHI 2016
🏆 Young researcher prize at OWCSC 2016
Abhishek Chander. Undergraduate dissertation, 2015.
Dynamic Visualisation of Data Based on Eye-Tracking.
★ Paper presented at PPIG 2016
Roman Kolacz. Undergraduate dissertation, 2015.
Time-Lapse Based Weather Classification.
Neil Satra. Undergraduate dissertation, 2014 (advised1).
Mario Carreon. M.Phil. dissertation, 2014.
A study of the usage of the R statistical programming language based on mining a source code corpus.
- Co-designing and delivering Interaction with machine learning, a course for the MPhil in advanced computer science at the University of Cambridge, forthcoming 2018.
- Invited lecturer for Designing Decisions, a second-year "live project'' module in the BA design course at Goldsmiths, University of London (2017)
- Honorary demonstrator for An introduction to digital design for graduate students in the humanities and social sciences, an experimental course run in collaboration by the Computer Lab and the Cambridge Centre for Research in Arts, Social Science and Humanities (CRASSH), 2014.
© Advait Sarkar