Raazesh Sainudiin
Universitetslektor vid Matematiska institutionen; Statistik, AI och data science
- Telefon:
- 018-471 31 53
- E-post:
- raazesh.sainudiin@math.uu.se
- Besöksadress:
- Ångströmlaboratoriet, Lägerhyddsvägen 1
- Postadress:
- Box 480
751 06 UPPSALA
Universitetslektor vid Matematiska institutionen; Sannolikhetsteori och kombinatorik
- Telefon:
- 018-471 31 53
- E-post:
- raazesh.sainudiin@math.uu.se
- Besöksadress:
- Ångströmlaboratoriet, Lägerhyddsvägen 1
- Postadress:
- Box 480
751 06 UPPSALA
Mer information visas för dig som medarbetare om du loggar in.
Biografi
Denna text finns inte på svenska, därför visas den engelska versionen.
Raazesh Sainudiin (Raaz) is a Researcher in applied mathematics and statistics at Department of Mathematics, Uppsala University. His interdisciplinary work focuses on applying custom-built mathematical and statistical models to solve real-world problems. He enjoys experiments in the kitchen that challenge culinary conventions with his wife and daughters, field plant taxonomy, poetry, pluralisic histories, comparative philosophy, and tongue twisters in English, Sanskrit and Tamil.
Raaz obtained a BS in Mathematics and Biology (summa cum laude) at Minnesota State University, an MS in Biometrics and a PhD in Statistics at Cornell University. He was a Research Fellow of the Royal Commission for the Exhibition of 1851 in the Mathematical Genetics Group, Department of Statistics, University of Oxford and a Senior Lecturer in the School of Mathematics and Statistics, University of Canterbury, Christchurch, New Zealand. Raaz has over two years of experience in the data industry and enjoys research collaborations between academia and industry.
Forskning
Denna text finns inte på svenska, därför visas den engelska versionen.
Raaz likes to work at the interface of mathematics, computing and statistics. This inter-disciplinary research aims broadly to use computers to solve real-world problems using custom-built mathematical and statistical models.
He has worked on theoretical and practical aspects of a range of real-world decision problems from population genetics, phylogenetics, ecological genetics, air-traffic management, identification and control of gas turbines, natural language processing, collaborative filtering, terrorism studies, statistical physics of complex fluids, cross-domain data-fusion and knowledge-graph extractions for security-related applications, nonparametric self-exciting point processes on networks for crime-risk prediction, scalable geospatial computing of GPS trajectories on OpenStreetMaps, models of transmissions on networks and their inference for meme evolution from Twitter streams, classical problems in nonparametric density estimation and applied interval analysis. See http://lamastex.org/ for more details.
Raaz is currently passionate about models of meme evolution based on analysing real data collected from the twitterverse. Such models can shed light on how extremist ideologies spread and echo-chambers emerge and persist in social media.
Publikationer
Senaste publikationer
- Distributed training and scalability for the particle clustering method UCluster (2021)
- Swapping trajectories with a sufficient sanitizer (2020)
- Minimum distance histograms with universal performance guarantees (2019)
- Characterizing the Twitter network of prominent politicians and SPLC-defined hate groups in the 2016 US presidential election (2019)
- Full likelihood inference from the site frequency spectrum based on the optimal tree resolution (2018)
Alla publikationer
Artiklar
- Distributed training and scalability for the particle clustering method UCluster (2021)
- Swapping trajectories with a sufficient sanitizer (2020)
- Minimum distance histograms with universal performance guarantees (2019)
- Characterizing the Twitter network of prominent politicians and SPLC-defined hate groups in the 2016 US presidential election (2019)
- Full likelihood inference from the site frequency spectrum based on the optimal tree resolution (2018)
- A nonlinear dynamical system approach for the yielding behaviour of a viscoplastic material (2017)