Cueport: A local-first music player for DJs
Local-First Conf 2026, July 12, 2026
🎥 video, 🎞️ slides, 🌐 website

Abstract. A DJ’s Serato library represents years of work in crates, cue points, tags, and prep notes. It all lives on the gig laptop, but DJs want to prepare sets on trains, flights, and long walks. This talk is about Cueport, a local-first mobile player for music you own, but away from that one machine. The talk covers when to use a CRDT, how Cueport’s LAN sync works, and the seven local-first ideals in practice.

The role of B2B data collaboration in marketing
Marketing Analytics & Data Science (MADS), October 22, 2024
🎞️ slides

Abstract. This presentation explores the concept of data collaboration and its use cases in marketing. We’ll review how marketers leverage data collaboration to drive decision-making, enhance customer experiences, and achieve business outcomes. We’ll also discuss current practices, challenges, and potential solutions for streamlining data sharing and enabling seamless collaboration across organizations.

A new era in B2B data collaboration
MDS Fest 2.0, April 10, 2024
🎞️ slides

Abstract. Businesses collaborate through data — every contract includes a data sharing or transfer clause. However, data collaboration tools have a long way to go to serve modern enterprise needs. In this talk, we will discuss some of the macro trends and practices impacting products in the data collaboration space. Some of these topics remain open and evolving debates.

General Folders: The first AI-powered data logistics company
Demo Day: Techstars San Diego Powered by SDSU, December 7, 2023
🎞️ slides, 📰 coverage 1, 2, 3, 4

Abstract. Join us at Snapdragon Stadium for the first ever Techstars San Diego powered by San Diego State University Demo Day. Meet the incredible cohort of companies as they showcase their progress.

Cross-company data exchange for the cloud
Scale By the Bay: Code and Data in the Age of AI, November 15, 2023
🎥 video, 📰 coverage 1, 2

Abstract. Cross-company data pipelines are often built as one-off integrations that are difficult to test, monitor, secure, and audit. In this talk, we examine the challenges of moving data between organizations across different clouds and data platforms, including fragmented tooling, limited visibility, changing requirements, and the difficulty of maintaining security and compliance across company boundaries.

Rethinking B2B data exchange and collaboration
Crunch Conference Budapest, October 6, 2023
🎥 video, 📰 coverage

Abstract. Business data collaboration takes many forms, including file transfers, shared databases, APIs, and data clean rooms. In this talk, we compare these methods across security, simplicity, and speed, then consider how companies can collaborate without requiring their partners to adopt the same tools or technical architecture.

The state of cross-company data exchange
Data Council Austin, March 30, 2023
🎥 video, 🎞️ slides, 📃 blog post

Abstract. Data exchange is integral to every business relationship. Yet data exchange practices are highly manual, prone to leaks, difficult to validate, impossible to monitor, and costly to audit. In this talk, we present an overview of the methods enterprises use to exchange data and the outstanding challenges. We conclude by enumerating the properties of a good solution.

Making an impact with data
with Gorkem Yurtseven, Britt Allen, moderated by Elizabeth Dlha
Data Mash #2, June 2, 2022
🎞️ slides

Abstract. After introducing General Folders, we’ll review three impactful data projects. First, the design of OKRs to encourage collaboration among product teams at Twitter; second, the feature creation pipeline for fraud detection at Paytm; and finally, sales enablement at Carbon Health via risk quantification.

Data transfer challenges in evaluating AI platforms
apply(meetup), February 10, 2022
🎥 video, 📃 blog post, 📰 coverage

Abstract. Not so long ago, I met with over 30 AI companies to learn of their workflows at the very first step in the evaluation process — that of data collection and transfer. I had a hunch this part of the pipeline posed challenges. In this talk, I review the myriad roadblocks faced by companies in providing access to their data. Then I discuss potential solutions.

Data Science for tech-enabled healthcare
with Rebekkah Ismakov
The AI Summit, October 1, 2020
🎥 video, 📃 blog post, 📊 data, 🎙️ discussion

Abstract. The first part of the talk is an overview of the Data Science team roadmap and infrastructure decisions, with a tour of the clinical decision support system and covidclinicaldata.org. The second part is a review of our efforts for the COVID-Ready program. We report on recommendations that can be made to employers, based on simulations surfacing how testing cadence and other policies affect outbreaks in the workplace.

DJing and the art of audio signal processing
Twitter HQ, September 6, 2017

Abstract. In this talk, we review concepts from the audio signal processing field. We then show how familiarity with these concepts allows for a better understanding of DJing tools and techniques, and vice versa.

Techstars San Diego powered by SDSU Founder Panel: What it Takes to Get Into and Through a Top Accelerator
with Alexander Kurtynin, Sean Braacx, moderated by Misti Cain
Vancouver Startup Week (VSW), May 29, 2025
📷 photos

Building teams and culture that support ML innovation
with Ziad Asghar, Ameen Kazerouni, moderated by Sam Charrington
TWIMLcon, January 22, 2021
🎥 video

Abstract. Traditional approaches to managing technical projects can be at odds with achieving success with machine learning. In this session, we discuss how ML and AI executives can build effective teams, support them with the right processes and tools, and shift the broader organizational culture in ways that reinforce innovation in machine learning.

Making an impact in data science: when traditional methods fail
with Eric Glover, Halim Abbas, Kevin Stumpf, and Sean McPherson
Branch HQ, February 27, 2020
🎥 video

Abstract. In this meetup, we hear about data science projects that succeeded in spite of the limitations of existing methodology.

Culture & organization for effective ML at scale
with Eric Colson and Jennifer Prendki, moderated by Maribel Lopez
TWIMLcon, September 27, 2019
🎥 video

Abstract. Hear from people that have experienced startups and large corporations in a range of industries reveal tips to work faster, more efficiently, and create an org-wide culture that supports effective ML.

Women in Data Science meetup: Growing a career in data science
with Laura Pruitt, Kasia Rachuta, and Alexandra Johnson, moderated by Chloe Tseng
Airbnb HQ, March 8, 2018

Abstract. Meet women in data science from all over the Bay Area at this WiDS post-conference screening. The event will be an opportunity to meet like-minded women as well as listen to the great lineup of panelists.

Pardis Noorzad of General Folders: Transforming B2B Data Collaboration
with Jake Villarreal
Born in Silicon Valley by Match Relevant, September 6, 2023
🎙️ podcast episode

Abstract. Join us for an engaging conversation with Pardis Noorzad, Founder and CEO of General Folders. Learn how she is revolutionizing B2B data collaboration and transforming the way businesses handle data logistics.

Making Cross-Company Data Exchange Easy
with Kostas Pardalis and Eric Dodds
The Data Stack Show, September 6, 2023
🎙️ podcast episode

Abstract. The conversation includes the importance of data collaboration and sharing, the challenges and complexities of data sharing in various industries, the need for efficient and secure solutions, and the underlying definitions and dimensions of the data exchange problem—including infrastructure, security, economics, user needs, and more!

Modeling the Facebook social network: The memoryless GEO-P graph model
SOGMSC, May 21, 2014
🎞️ slides

Abstract. Online social networks are ubiquitous graphs. To test algorithms that scale with the size and order of these networks, we require synthetic samples. In this talk, we go over several methods for generating random graphs representative of online social networks. We are especially interested in the M-GEOP model (Bonato et al., 2014), and in assessing the fit of these models to the Facebook dataset.

Efficient classification based on sparse regression
AUT, July 17, 2012
🎞️ slides

Abstract. Master’s thesis defense slides.

SPARROW: SPARse appROximation Weighted regression
UdeM, March 12, 2012 and SUT, February 22, 2012
🎞️ slides, 📰 UdeM-McGill-mPrime ML seminar ad

Abstract. We propose sparse approximation weighted regression (SPARROW), a nonparametric method of regression that takes advantage of the sparse linear approximation of a query point. SPARROW employs weights based on sparse approximation in the context of locally constant, locally linear, and locally quadratic regression to generate better estimates than for e.g., k-nearest neighbor regression and more generally, kernel-weighted local polynomial regression. Our experimental results show that SPARROW performs competitively.

Sparse coding and dictionary learning
SUT, October 5, 2011
🎞️ slides

Abstract. Sparse coding is achieved by solving an under-determined system of linear equations under sparsity constraints. We briefly look at several algorithms that solve the resulting optimization problem (exactly or approximately). We then see how this optimization principle can be applied in both a supervised and unsupervised context: multiclass classification and feature learning, respectively. Next, we talk about dictionary learning and some of its well-known instances. Applications of dictionary learning include image denoising and inpainting.

Feature learning with deep networks for image classification
SUT, May 18, 2011
🎞️ slides

Abstract. An image can be represented at different levels, starting from pixels, going on to edges, to parts, to objects, and beyond. Over the years, many attempts have been made at engineering useful descriptors that are able to extract low-to-high level features from images. But what if we could make this process automatic? What if we could “learn” to detect layer after layer of features of increasing abstraction and complexity? After all, it would be impossible for us to foresee and hard-code all the kinds of invariances necessary to build features for our ever more complicated tasks. In this talk, we go over several unsupervised feature learning methods that have been in the making since 2006.

Computational learning theory
AUT, April 26, 2011
🎞️ slides

Abstract. A review of computational learning theory fundamentals.

Parametric density estimation using GMMs
AUT, April 12, 2011
🎞️ slides

Abstract. A review of Gaussian mixture models and the EM algorithm.

High dimensional data and dimensionality reduction
AUT, February 1, 2011
🎞️ slides

Abstract. A review of dimensionality reduction techniques with applications.

The split Bregman method for total variation denoising
AUT, May 30, 2010
🎞️ slides

Abstract. This is an overview of the split Bregman method for solving an $\ell_1$-regularized problem arising from TV denoising.

Efficient classification based on sparse regression
MSc Thesis, Amirkabir University of Technology, July 2012
📔 thesis, 📕 translation, 🎞️ slides

Regression with sparse approximations of data
with Bob L. Sturm
European Signal Processing Conference (EUSIPCO), 2012
📃 paper, 📰 poster

On automatic music genre recognition by sparse representation classification using auditory temporal modulations
with Bob L. Sturm
Computer Music Modeling and Retrieval: Lecture Notes in Computer Sciences (LNCS). Springer, 2012
📃 paper