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Sisällön tarjoaa Kyle Polich. Kyle Polich tai sen podcast-alustan kumppani lataa ja toimittaa kaiken podcast-sisällön, mukaan lukien jaksot, grafiikat ja podcast-kuvaukset. Jos uskot jonkun käyttävän tekijänoikeudella suojattua teostasi ilman lupaasi, voit seurata tässä https://fi.player.fm/legal kuvattua prosessia.
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Data Skeptic
Merkitse kaikki (ei-)toistetut ...
Manage series 49487
Sisällön tarjoaa Kyle Polich. Kyle Polich tai sen podcast-alustan kumppani lataa ja toimittaa kaiken podcast-sisällön, mukaan lukien jaksot, grafiikat ja podcast-kuvaukset. Jos uskot jonkun käyttävän tekijänoikeudella suojattua teostasi ilman lupaasi, voit seurata tässä https://fi.player.fm/legal kuvattua prosessia.
The Data Skeptic Podcast features interviews and discussion of topics related to data science, statistics, machine learning, artificial intelligence and the like, all from the perspective of applying critical thinking and the scientific method to evaluate the veracity of claims and efficacy of approaches.
…
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562 jaksoa
Merkitse kaikki (ei-)toistetut ...
Manage series 49487
Sisällön tarjoaa Kyle Polich. Kyle Polich tai sen podcast-alustan kumppani lataa ja toimittaa kaiken podcast-sisällön, mukaan lukien jaksot, grafiikat ja podcast-kuvaukset. Jos uskot jonkun käyttävän tekijänoikeudella suojattua teostasi ilman lupaasi, voit seurata tässä https://fi.player.fm/legal kuvattua prosessia.
The Data Skeptic Podcast features interviews and discussion of topics related to data science, statistics, machine learning, artificial intelligence and the like, all from the perspective of applying critical thinking and the scientific method to evaluate the veracity of claims and efficacy of approaches.
…
continue reading
562 jaksoa
Kaikki jaksot
×In this episode, Dave Bechberger, principal Graph Architect at AWS and author of "Graph Databases in Action", brings deep insights into the field of graph databases and their applications. Together we delve into specific scenarios in which Graph Databases provide unique solutions, such as in the fraud industry, and learn how to optimize our DB for questions around connections, such as "How are these entities related?" or "What patterns of interaction indicate anomalies?" This discussion sheds light on when organizations should consider adopting graph databases, particularly for cases that require scalable analysis of highly interconnected data and provides practical insights into leveraging graph databases for performance improvements in tasks that traditional relational databases struggle with.…
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Data Skeptic
In this episode, Adam Machowczyk, a PhD student at the University of Leicester, specializes in graph rewriting and its intersection with machine learning, particularly Graph Neural Networks. Adam explains how graph rewriting provides a formalized method to modify graphs using rule-based transformations, allowing for tasks like graph completion, attribute prediction, and structural evolution. Bridging the worlds of graph rewriting and machine learning, Adam's work aspire to open new possibilities for creating adaptive, scalable models capable of solving challenges that traditional methods struggle with, such as handling heterogeneous graphs or incorporating incremental updates efficiently. Real-life applications discussed include using graph transformations to improve recommender systems in social networks, molecular research in chemistry, and enhancing IoT network analysis.…
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Data Skeptic
In this episode, the data scientist Wentao Su shares his experience in AB testing on social media platforms like LinkedIn and TikTok. We talk about how network science can enhance AB testing by accounting for complex social interactions, especially in environments where users are both viewers and content creators. These interactions might cause a "spillover effect" meaning a possible influence across experimental groups, which can distort results. To mitigate this effect, our guest presents heuristics and algorithms they developed ("one-degree label propagation”) to allow for good results on big data with minimal running time and so optimize user experience and advertiser performance in social media platforms.…
Alex Bisberg, a PhD candidate at the University of Southern California, specializes in network science and game analytics, with a focus on understanding social and competitive success in multiplayer online games. In this episode, listeners can expect to learn from a network perspective about players interactions and patterns of behavior. Through his research on games, Alex sheds light on how network analysis and statistical tests might explain positive contagious behaviors, such as generosity, and explore the dynamics of collaboration and competition in gaming environments. These insights offer valuable lessons not only for game developers in enhancing player experience, engagement and retention, but also for anyone interested in understanding the ways that virtual interactions shape social networks and behavior.…
In this episode we discuss the GitHub Collaboration Network with Behnaz Moradi-Jamei, assistant professor at James Madison University. As a network scientist, Behnaz created and analyzed a network of about 700,000 contributors to Github's repository. The network of collaborators on GitHub was created by identifying developers (nodes) and linking them with edges based on shared contributions to the same repositories. This means that if two developers contributed to the same project, an edge (connection) was formed between them, representing a collaborative relationship network consisting of 32 million such connections. By using algorithms for Community Detection, Behnaz's analysis reveals insights into how developer communities form, function, and evolve, that can be used as guidance for OSS community managers.…
We are joined by Abhishek Paudel, a PhD Student at George Mason University with a research focus on robotics, machine learning, and planning under uncertainty, using graph-based methods to enhance robot behavior. He explains how graph-based approaches can model environments, capture spatial relationships, and provide a framework for integrating multiple levels of planning and decision-making.…
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Data Skeptic
We are joined by Maciej Besta, a senior researcher of sparse graph computations and large language models at the Scalable Parallel Computing Lab (SPCL). In this episode, we explore the intersection of graph theory and high-performance computing (HPC), Graph Neural Networks (GNNs) and LLMs.
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Data Skeptic
In this episode, we sit down with Yuanyuan Tian, a principal scientist manager at Microsoft Gray Systems Lab, to discuss the evolving role of graph databases in various industries such as fraud detection in finance and insurance, security, healthcare, and supply chain optimization.
Our new season "Graphs and Networks" begins here! We are joined by new co-host Asaf Shapira, a network analysis consultant and the podcaster of NETfrix – the network science podcast. Kyle and Asaf discuss ideas to cover in the season and explore Asaf's work in the field.
Join us for our capstone episode on the Animal Intelligence season. We recap what we loved, what we learned, and things we wish we had gotten to spend more time on. This is a great episode to see how the podcast is produced. Now that the season is ending, our current co-host, Becky, is moving to emeritus status. In this last installment we got to spend a little more time getting to know Becky and where her work will take her after this. Did Data Skeptic inspire her to learn more about machine learning? Tune in and find out.…
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Data Skeptic
David Obembe, a recent University of Tartu graduate, discussed his Masters thesis on integrating LLMs with process mining tools. He explained how process mining uses event logs to create maps that identify inefficiencies in business processes. David shared his research on LLMs' potential to enhance process mining, including experiments evaluating their performance and future improvements using Retrieval Augmented Generation (RAG).…
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Data Skeptic
Our guest today is Risa Shinoda, a PhD student at Kyoto University Agricultural Systems Engineering Lab, where she applies computer vision techniques. She talked about the OpenAnimalTracks dataset and what it was used for. The dataset helps researchers predict animal footprint. She also discussed how she built a model for predicting tracks of animals. She shared the algorithms used and the accuracy they achieved. She also discussed further improvement opportunities for the model.…
This episode features an interview with Mélisande Teng, a PhD candidate at Université de Montréal. Her research lies in the intersection of remote sensing and computer vision for biodiversity monitoring.
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Data Skeptic
In this interview with author Deborah Gordon, Kyle asks questions about the mechanisms at work in an ant colony and what ants might teach us about how to build artificial intelligence. Ants are surprisingly adaptive creatures whose behavior emerges from their complex interactions. Aspects of network theory and the statistical nature of ant behavior are just some of the interesting details you'll get in this episode.…
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Data Skeptic
This season it’s become clear that computing skills are vital for working in the natural sciences. In this episode, we were fortunate to speak with Madlen Wilmes, co-author of the book "Computing Skills for Biologists: A Toolbox". We discussed the book and why it’s a great resource for students and teachers. In addition to the book, Madlen shared her experience and advice on transitioning from academia to an industry career and how data analytic skills transfer to jobs that your professionals might not always consider. Join us and learn more about the book and careers using transferable skills.…
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