Daniel G. Brown

Ph.D. (Computer Science), Cornell, 2000
S.B. (Mathematics with Computer Science), MIT, 1995

Me, with chickenpox spots, February 2007. Not pleasant.
An ever-growing set of astonishingly adorable photos of our dog River, who came home in August, 2021
Many cute photos of our previous dog Rover, who died in January 2018 after a life full of wagging and snuggling
A formal photo of me from early 2005, which I really liked; I don't look much like this anymore
A photo from when I was in grad school. People sometimes are surprised that this photo is here, but I think it's fun to remember where we come from.

I perform research on computational creativity, music information retrieval and bioinformatics. I also tend not to pay attention to disciplinary boundaries and just work on whatever amuses me and my students.

You may find my CV in .pdf form here. My Google Scholar page lists most of my publications, though some are missing. The summaries below don't link to papers, but they should all be in my Scholar record; if it's confusing, let me know.

Research projects

• Computational creativity
  • Computational creativity is the study of computer programs that create artifacts that, if created by a human, would be perceived to be creative artifacts. Most of my early work in this area was with my former PhD student Carolyn Lamb (co-supervised with Charlie Clarke), who studied how to make computer programs that make poetry, and what it means to make a good computer poet. Among the outcomes of that work are a quite detailed tutorial of different ways to understand creativity and how that impacts on the ways of evaluating it, and several papers on news poets of different sorts that we built.
  • Since Carolyn graduated, I have focused on using algorithmic information theory to model what creativity is, in a series of papers with Tiasa Mondol (my former MMath student, who has now moved on to study something even more complicated, namely theory of quantum computing,for her PhD), and most recently with Max Peeperkorn, a student at University of Kent, in the UK.
  • It's also the wave of generative AI, and suddenly, computers building art and text and music and everything is exciting. So I've studied poetry generation, copyright, jokes, games, ethics, and more, all with a lens built from my focus on ethics, equity, and labour rights.
  • Oh, and these days, I'm also General Chair of the International Conference on Computational Creativity, which will happen soon at Waterloo.
• Bioinformatics
  • Phylogenetics: QTree and LSHtree are two very fast algorithms for phylogenetic tree reconstruction that I designed with Jakub Truszkowski.

    QTree uses a complex search structure and a neat random walk approach to add new taxa to a growing tree, with O(n log n) runtime in a quartet framework.

    LSHtree adds new taxa to a growing tree by using hashing to approximately locate the taxon, and then doing quartet queries to place the taxon. It offers the first sub-quadratic runtime for an algorithm that reconstructs trees in a Markov model of evolution.

    As part of proving one of the theorems in the first QTree paper, I needed a tail inequality for negative bionomial random variables. I couldn't find any good links for "negative binomial Chernoff", so I spent forever proving something that should've been obvious. Now you don't need to. (And yes, this is sort-of in the Dubhashi/Panconesi book, but not with the emphasis it might have had.)

  • Robustness to outliers: Margareta Ackerman and David Loker and I adapted some of their theoretical work on clustering to look at what kinds of properties various phylogenetic algorithms satisfy.
  • Sequence correction: I have done two separate projects in this domain.

    Pollux is a sequence-correction method for large sets of second-generation sequence reads, developed by Eric Marinier and Brendan McConkey and me.

    PANDASeq2 is an alarmingly heavily-cited, very fast algorithm for fixing overlapping reads sequenced from, for example, ribosomal genes. Because the method is so fast, and because Andre Masella's code is very solid, it is easily incorporated without pain into other pipelines for metagenomic analysis.

  • I have also worked on alignment, gene finding, haplotype inference, motif finding, HMM decoding, HIV subtype detection, kinship discovery, and a host of other bioinformatics problems. If it's NP-hard and a sequence or tree problem from 2003 to 2013, there's a good chance I have studied why it's easy to solve in practice, and I might have developed a surprisingly fast algorithm for it.
• Music and lyrics
  • RhymeAnalyzer: My former MMath student Hussein Hirjee and I developed an algorithm for detecting rhymes in rap music lyrics. They're interesting because they tend to be long, internal, and imperfect. The program is on SourceForge; Hussein has finished his MD (yes, really) at the University of Western Ontario, and is now a resident in psychiatry in Toronto.
  • Rhyme complexity: Abhishek Singhi noticed that the RhymeAnalyzer can be used to predict which songs are successful. Gratifyingly, the more successful songs have more complex rhyme patterns, according to the RhymeAnalyzer.
  • Alignment: With a group in France, I adapted BLAST-style bioinformatics seeding of alignment to the cover song detection problem. With a group in the Netherlands, we are also exploring use of multiple alignment in this same domain.
  • We've also studied some cross-cultural aspects of music lyrics and their understanding, and also whether poetry and lyrics differ.
• Problem gambling
  • Largely because of my concerns about casino gambling, I spent a few years affiliated with the Gambling Research Lab at Waterloo. Our work culminated in a project to (essentially) put calorie labels on slot machines, and then my main co-author retired. We have also studied a variety of other gambling methods, like scratch tickets and bingo.

Teaching

These are the courses I have taught at UW:

• Computing and Discrimination (CS 497 and CS 492), a course I co-developed with my colleague Maura Grossman, which we have taught in Winter 2022 (2 sections) and Winter 2023 (2 sections), and will teach in Winter 2024.
• Social Implications of Computing (CS 492), which I taught in a "vanilla" style (well, I put a paper about furries and a paper about queer truck drivers on the syllabus...) in Fall 2022.
• Introduction to Computer Science 2 (CS 116), a course I developed and co-ordinated, and taught in its initial offering in Winter 2009.
• Computational techniques in biological sequence analysis (CS 482/682), a course I developed and co-ordinate, which I have taught eight times.
• Designing Functional Programs (CS 135)
• Algorithms (CS 341)
• Theory of computation (CS 360), which I have taught five times.
• Algorithmic methods in phylogenetics (CS 798)
• Markov Chains, Mixing Times and Concentration (CS 798)
• Advanced topics in bioinformatics: large-scale sequence analysis (CS 882)

Regrettably, I haven't taught much in the past several years; see the next section for the primary reason why I haven't. (As of 2023, I am teaching a lot more, and that's okay too.)

Service

• For the 2020-2021 pandemic year, I served as president of the Faculty Association (FAUW). Prior to being FAUW president, I was treasurer for four years. I was also FAUW's board member at the Ontario Confederation of Universiy Faculty Associations (OCUFA), which is a bit ironic considering that six years ago, I was Waterloo's Academic Colleague to the Council of Ontario Universities. I similarly served as FAUW's representative to the Canadian Association of University Teachers (CAUT).
• From July to December of 2018, I served as Acting Director of the School, chairing numerous School committees, interacting with external groups and funders, representing CS within the university and Math Faculty, and providing direction to staff and (sometimes) to faculty.
• For over four years, in three chunks between July 2013 and August 2019, I served as Director of Undergraduate Studies for CS. As DUS, I chaired the curriculum committee and was responsible for undergraduate operations. I also directed the undergraduate staff, working closely with their manager, Fenglian Qiu. A few weeks ago, I was at a meeting with my collegue Kate Larson (who succeeded me as Undergrad Director, and who was in that role during the worst parts of the COVID-19 pandemic), and I said, "well, Kate and I ran the undergrad program in CS for most of the past decade." And that was true.
• From July, 2011 to June, 2013, I was Associate Director of the School, which means that I acted as Director when the School's Director, David Taylor, was away, and also meant that I chaired a variety of School committees.
• From July 2006 to June 2009, I was the School's Director of First-Year Studies. As a role without much portfolio, this mostly meant I served in several School and Faculty committees, with a particular eye on first-year curricula.
• From February 2013 until June 2014, I was Waterloo's Academic Colleague to the Council of Ontario Universities, which means I met most of the university presidents in Ontario, more than any other thing.
• From 2008 to 2014, I served on the University Senate and (in various years) its Finance and Executive Committees. As FAUW president, I again served on Senate and Senate Executive, ex officio.
• From 2010 to 2014, and for the first half of 2016 and of 2018, I was a member of the Faculty Association's Status of Women and Equity Committee, recently renamed the Equity Committee; I was also the representative from FAUW or from SWEC to a number of diversity-related committees on campus.

Non-work-related things

And finally, I am not the bestselling author Dan Brown, nor are we related. I don't like his writing. We have corresponded in the past, but that was long ago. Please don't send me fan mail or threats.

last updated 23 May 2023