I am broadly interested in designing practical algorithms with theoretical guarantees, and analyzing existing heuristics by providing guarantees for them under real-world assumptions. I like learning new things and skills.
If you think you are working on a problem that I might find interesting, do not hesitate to reach out to me to chat about it!
| Title | Authors | Venue | Year | Links |
|---|---|---|---|---|
| Learning and Testing Latent-Tree Ising Models Efficiently | Yuval Dagan, Constantinos Daskalakis, Anthimos-Vardis Kandiros, Davin Choo | Conference on Learning Theory (COLT) | 2023 | Conference link, COLT, arXiv |
| Adaptivity Complexity for Causal Graph Discovery | Davin Choo, Kirankumar Shiragur | Uncertainty in Artificial Intelligence (UAI) | 2023 | UAI, Code, arXiv, Video |
| New metrics and search algorithms for weighted causal DAGs | Davin Choo, Kirankumar Shiragur | International Conference on Machine Learning (ICML) | 2023 | Conference link, ICML, Code, arXiv |
| Active causal structure learning with advice | Davin Choo, Themistoklis Gouleakis, Arnab Bhattacharyya | International Conference on Machine Learning (ICML) | 2023 | Conference link, ICML, Code, arXiv |
| Subset verification and search algorithms for causal DAGs | Davin Choo, Kirankumar Shiragur | International Conference on Artificial Intelligence and Statistics (AISTATS) | 2023 | AISTATS, Code, arXiv, Video |
| Verification and search algorithms for causal DAGs | Davin Choo, Kirankumar Shiragur, Arnab Bhattacharyya | Conference on Neural Information Processing Systems (NeurIPS) | 2022 | Conference link, NeurIPS, Code, arXiv, Slides, Longer slides |
| Learning Sparse Fixed-Structure Gaussian Bayesian Networks | Arnab Bhattacharyya, Davin Choo, Rishikesh Gajjala, Sutanu Gayen, Yuhao Wang | International Conference on Artificial Intelligence and Statistics (AISTATS) | 2022 | AISTATS, Code, arXiv |
| The Complexity of Sparse Tensor PCA | Davin Choo, Tommaso d'Orsi | Conference on Neural Information Processing Systems (NeurIPS) | 2021 | Conference link, NeurIPS, arXiv, Video, Slides |
| Massively Parallel Correlation Clustering in Bounded Arboricity Graphs | Mélanie Cambus, Davin Choo, Havu Miikonen, Jara Uitto | International Symposium on Distributed Computing (DISC) | 2021 | DISC, arXiv, Video |
| k-means++: few more steps yield constant approximation | Davin Choo, Christoph Grunau, Julian Portmann, Václav Rozhoň | International Conference on Machine Learning (ICML) | 2020 | Conference link, ICML, arXiv, Video, Slides |
| BOSPHORUS: Bridging ANF and CNF Solvers | Davin Choo, Mate Soos, Kian Ming A. Chai, Kuldeep S. Meel | Proceedings of Design, Automation, and Test in Europe (DATE) | 2019 | DATE, Paper, arXiv, Code, Kuldeep's website link |
| Chemical Structure Elucidation from Mass Spectrometry by Matching Substructures | Jing Lim, Joshua Wong, Minn Xuan Wong, Lee Han Eric Tan, Hai Leong Chieu, Davin Choo, Neng Kai Nigel Neo | Machine Learning for Molecules and Materials (NeurIPS Workshop) | 2018 | arXiv |
| Title | Remarks | Links |
|---|---|---|
| Learning bounded degree polytrees with samples | Manuscript. Joint work with Joy Qiping Yang, Arnab Bhattacharyya, and Clément L. Canonne. | Coming soon |
| Causal discovery under off-target interventions | Manuscript. Joint work with Kirankumar Shiragur and Caroline Uhler. | Coming soon |
| The Sharp Power Law of Local Search on Expanders | Manuscript. Joint work with Simina Brânzei and Nicholas Recker. | arXiv |
| Template for NUS SoC QE talk slides | - | Overleaf (Read-only) |
| Template for NUS SoC QE report | - | Overleaf (Read-only) |
| Scribe notes for entire course | Massively Parallel Algorithms (Spring 2019) Lecturer: Mohsen GHAFFARI |
Course webpage, Notes |
| Scribe notes for entire course | Advanced Algorithms (Fall 2018) Lecturer: Mohsen GHAFFARI |
Course webpage, Notes |
| Event | Date (DD.MM.YYYY) | Topic / Talk title | Location / Remarks | Links |
|---|---|---|---|---|
| MPI EI Tea talks | 10.08.2023 | Recovering causal graphs with adaptive interventions | N 4.022 | Slides |
| NUS AlgoTheory Seminar | 17.04.2023 | Learning causal DAGs using adaptive interventions | Seminar Room @ LT19 (BIZ 2) | Slides |
| NUS SoC AlgoTheory Group Meeting | 24.03.2023 | Solving problems using imperfect advice | COM3-02-59 | Slides |
| CS6235 Paper Presentation | 08.03.2023 | Partitioning Friends Fairly | LT19 Seminar Room | Paper, Slides |
| Computing Research Week - Open House 2023 | 24.02.2023 | Learning Causal DAGs using Adaptive Interventions | Multipurpose Hall 1 (COM3-01-26) | Slides |
| CS6220 Paper Presentation | 02.02.2022 | Triad Constraints for Learning Causal Structure of Latent Variables | Zoom talk and discussion. | Paper, Slides |
| CS6101 Paper Presentation | 03.09.2021 | Online Algorithms with Advice: A Survey | Zoom talk and discussion. | Paper, Slides |
| Aalto CS Theory Seminar | 29.07.2020 | k-means++: few more steps yield constant approximation | Zoom talk. | arXiv |
| MADZ Group Meeting talk | 04.05.2020 | k-means++: few more steps yield constant approximation | Zoom talk. | arXiv |
| Reading Group on Discrete and Distributed Algorithms | 23.05.2019 | Dynamic Algorithms for the Massively Parallel Computation Model | Whiteboard talk. Attached are some pictures. The paper talks about maintaining an approximate MST but I think an exact MST should be doable. See write-up for a sketch. Update: There's a SPAA 2020 paper related to this! | arXiv, pic1, pic2, pic3, pic4, pic5 |
| DSO technical sharing |
02.02.2017 | 2^{To be, or not to be?}: A look at boolean satisfiability | State-of-the-art methods building upon DPLL and CDCL are covered. An alternative solving method (Stalmarck's method) is also discussed. Animations and some slides removed. | Slides |
| DSO technical sharing |
10.11.2016 | A gentle introduction to community detection | Common methods such as graph partitioning and spectral clustering are discussed. Talk is mainly based off a survey by Santo Fortuno. Animations and some slides removed. | Slides |
| Place | Course | Year | Links |
|---|---|---|---|
| National University of Singapore (NUS) |
GET1031 / GEI1000 (Cross listed): Computational Thinking
Lecturers: LEONG Hon Wai and LEOW Wee Kheng |
AY 2021/2022 Sem 1 | NUSMODS |
| National University of Singapore (NUS) |
CS3230: Design and Analysis of Algorithms
Lecturer: LEE Wee Sun |
AY 2015/2016 Sem 1 | NUSMODS |
| National University of Singapore (NUS) |
GET1031: Computational Thinking
Lecturers: LEONG Hon Wai and LEOW Wee Kheng |
AY 2015/2016 Sem 2 | NUSMODS |
| National University of Singapore (NUS) |
CS2020: Data Structures and Algorithms (Accelerated)
Lecturer: Seth GILBERT |
AY 2014/2015 Sem 2,
AY 2015/2016 Sem 2 |
NUSMODS |
| National University of Singapore (NUS) |
CS1101S: Programming Methodology
Lecturers: Martin HENZ and LOW Kok-Lim |
AY 2014/2015 Sem 1 | NUSMODS |
| National University of Singapore (NUS) |
CS1231: Discrete Structures
Lecturers: Stéphane BRESSAN and Bryan Kian Hsiang LOW |
AY 2014/2015 Sem 1 | NUSMODS |
| Temasek Junior College (TJC) | Initialized and conducted a 12-week student outreach course on Computer Science | Jan 2014 - May 2014 | Some old, partial teaching material |
| Remarks | Links | |
|---|---|---|
| IBM Ponder This puzzles | Solutions to some IBM's monthly Ponder This puzzles that I attempted. | Github |
| Neopets Shapeshifter solver |
Used this game as a practice while learning about constraint solvers.
Wrote a JavaScript to extract the puzzle from Neopets, which can be solved by either of my 2 solvers --- One using Google's ortools, one using the MiniZinc constraint solver. |
Directory |
| Telegram Chess Bot | Learnt about the existence of Telegram bots worked. Hacked up a chess bot for fun.
Note: It requires server hosting to work. |
Github |
| Threshold Secret Sharing Schemes | Explored and implemented 3 secret sharing schemes. | Github |
| Cryptopals | Solutions to some of the Cryptopals challenges that I attempted. | Github |
| Cipher encodings | A collection of cipher encodings. | Github |
I am currently a PhD student (started Aug 2021) happily supervised by Arnab Bhattacharyya in the School of Computing (SoC) of National University of Singapore, under the AISG PhD Fellowship Programme. Between my undergraduate and Masters, I worked for a while as an applied defence scientist at DSO National Laboratories on projects that lie in the intersection of A.I. and security.
Awards (that I am grateful for receiving)
