Data Skeptic podcast

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Inhalt bereitgestellt von Kyle Polich. Alle Podcast-Inhalte, einschließlich Episoden, Grafiken und Podcast-Beschreibungen, werden direkt von Kyle Polich oder seinem Podcast-Plattformpartner hochgeladen und bereitgestellt. Wenn Sie glauben, dass jemand Ihr urheberrechtlich geschütztes Werk ohne Ihre Erlaubnis nutzt, können Sie dem hier beschriebenen Verfahren folgen https://de.player.fm/legal.

The So What from BCG

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Re-Recruiting and Other New HR Strategies You Can’t Ignore 18:09

vor 8 Tagen18:09

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18:09

HR is no longer just about managing people—it’s about shaping the future of work. Jens Baier, BCG’s HR transformation expert, discusses how AI and shifting employee expectations are forcing companies to rethink talent strategies. From re-recruiting to upskilling employees, HR must adapt to a rapidly changing landscape. Learn More: Jens Baier: https://on.bcg.com/41ca7Gv BCG on People Strategy: https://on.bcg.com/3QtAjro Decoding Global Talent: https://on.bcg.com/4gUC4IT…

Data Skeptic

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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.

566 Episoden

#Datamining #Datascience #Machinelearning #Statistics #Kyle Polich #Data Science #Science #Math #Tech #Skeptic

Data Skeptic

794 subscribers

updated vor 2 Tagen

Serie-Home•Feed

566 Episoden

#Datamining #Datascience #Machinelearning #Statistics #Kyle Polich #Data Science #Science #Math #Tech #Skeptic

Alle Folgen

Data Skeptic

1
Organizational Network Analysis 44:15

vor 2 Tagen44:15

44:15

In this episode, Gabriel Petrescu, an organizational network analyst, discusses how network science can provide deep insights into organizational structures using OrgXO, a tool that maps companies as networks rather than rigid hierarchies. Listeners will learn how analyzing workplace collaboration networks can reveal hidden influencers, organizational bottlenecks, and engagement levels, offering a data-driven approach to improving effectiveness and resilience. Key insights include how companies can identify overburdened employees, address silos between departments, and detect vulnerabilities where too few individuals hold critical knowledge. Real-life applications range from mergers and acquisitions, where network analysis helps assess company dynamics before an acquisition, to restructuring efforts that improve workflow and team collaboration. Gabriel’s work highlights how organizations can shift from traditional hierarchical thinking to a network-based perspective, leading to smarter decision-making and more adaptable companies.…

Data Skeptic

1
Organizational Networks 27:48

vor 8 Tagen27:48

27:48

Is it better to have your work team fully connected or sparsely connected? In this episode we'll try to answer this question and more with our guest Hiroki Sayama, a SUNY Distinguished Professor and director of the Center for Complex Systems at Binghamton University. Hiroki delves into the applications of network science in organizational structures and innovation dynamics by showing his recent work of extracting network structures from organizational charts to enable insights into decision-making and performance, He'll also cover how network connectivity impacts team creativity and innovation. Key insights include how the structure of organizational networks—such as the depth of hierarchy or proximity to leadership—can influence corporate performance and how sparse network connectivity fosters more diverse and innovative ideas than fully connected networks.…

Data Skeptic

1
Networks of the Mind 43:16

vor 15 Tagen43:16

43:16

A man goes into a bar… This is the beginning of a riddle that our guest, Yoed Kennet, an assistant professor at the Technion's Faculty of Data and Decision Sciences, uses to measure creativity in subjects. In our talk, Yoed speaks about how to combine cognitive science and network science to explore the complexities and decode the mysteries of the human mind. The listeners will learn how network science provides tools to map and analyze human memory, revealing how problem-solving and creativity emerge from changes in semantic memory structures. Key insights include the role of memory restructuring during moments of insight, the connection between semantic networks and creative thinking, and how understanding these processes can improve problem-solving and analogical reasoning. Real-life applications span enhancing creativity in the workplace, building tools to combat cognitive rigidity in aging, and improving learning strategies by fostering richer, more flexible mental networks. ------------------------------- Want to listen ad-free? Try our Graphs Course? Join Data Skeptic+ for $5 / month of $50 / year https://plus.dataskeptic.com…

Data Skeptic

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LLMs and Graphs Synergy 34:47

vor 23 Tagen34:47

34:47

In this episode, Garima Agrawal, a senior researcher and AI consultant, brings her years of experience in data science and artificial intelligence. Listeners will learn about the evolving role of knowledge graphs in augmenting large language models (LLMs) for domain-specific tasks and how these tools can mitigate issues like hallucination in AI systems. Key insights include how LLMs can leverage knowledge graphs to improve accuracy by integrating domain expertise, reducing hallucinations, and enabling better reasoning. Real-life applications discussed range from enhancing customer support systems with efficient FAQ retrieval to creating smarter AI-driven decision-making pipelines. Garima’s work highlights how blending static knowledge representation with dynamic AI models can lead to cost-effective, scalable, and human-centered AI solutions. ------------------------------- Want to listen ad-free? Try our Graphs Course? Join Data Skeptic+ for $5 / month of $50 / year https://plus.dataskeptic.com…

Data Skeptic

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A Network of Networks 46:27

vor 30 Tagen46:27

46:27

In this episode, Bnaya Gross, a Fulbright postdoctoral fellow at the Center for Complex Network Research at Northwestern University, explores the transformative applications of network science in fields ranging from infrastructure to medicine, by studying the interactions between networks ("a network of networks"). Listeners will learn how interdependent networks provide a framework for understanding cascading failures, such as power outages, and how these insights transfer to physical systems like superconducting materials and biological networks. Key takeaways include understanding how dependencies between networks can amplify vulnerabilities, applying these principles to create resilient infrastructure systems, and using network medicine to uncover relationships between diseases, potential drug repurposing and the process of aging. ------------------------------- Want to listen ad-free? Try our Graphs Course? Join Data Skeptic+ for $5 / month of $50 / year https://plus.dataskeptic.com…

Data Skeptic

1
Auditing LLMs and Twitter 40:26

vor 5 weeks40:26

40:26

Our guests, Erwan Le Merrer and Gilles Tredan, are long-time collaborators in graph theory and distributed systems. They share their expertise on applying graph-based approaches to understanding both large language model (LLM) hallucinations and shadow banning on social media platforms. In this episode, listeners will learn how graph structures and metrics can reveal patterns in algorithmic behavior and platform moderation practices. Key insights include the use of graph theory to evaluate LLM outputs, uncovering patterns in hallucinated graphs that might hint at the underlying structure and training data of the models, and applying epidemic models to analyze the uneven spread of shadow banning on Twitter. ------------------------------- Want to listen ad-free? Try our Graphs Course? Join Data Skeptic+ for $5 / month of $50 / year https://plus.dataskeptic.com…

Data Skeptic

1
Fraud Detection with Graphs 37:23

vor 6 weeks37:23

37:23

In this episode, Šimon Mandlík, a PhD candidate at the Czech Technical University will talk with us about leveraging machine learning and graph-based techniques for cybersecurity applications. We'll learn how graphs are used to detect malicious activity in networks, such as identifying harmful domains and executable files by analyzing their relationships within vast datasets. This will include the use of hierarchical multi-instance learning (HML) to represent JSON-based network activity as graphs and the advantages of analyzing connections between entities (like clients, domains etc.). Our guest shows that while other graph methods (such as GNN or Label Propagation) lack in scalability or having trouble with heterogeneous graphs, his method can tackle them because of the "locality assumption" – fraud will be a local phenomenon in the graph – and by relying on this assumption, we can get faster and more accurate results. ------------------------------- Want to listen ad-free? Try our Graphs Course? Join Data Skeptic+ for $5 / month of $50 / year https://plus.dataskeptic.com…

Data Skeptic

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Optimizing Supply Chains with GNN 38:04

vor 7 weeks38:04

38:04

Thibaut Vidal, a professor at Polytechnique Montreal, specializes in leveraging advanced algorithms and machine learning to optimize supply chain operations. In this episode, listeners will learn how graph-based approaches can transform supply chains by enabling more efficient routing, districting, and decision-making in complex logistical networks. Key insights include the application of Graph Neural Networks to predict delivery costs, with potential to improve districting strategies for companies like UPS or Amazon and overcoming limitations of traditional heuristic methods. Thibaut’s work underscores the potential for GNN to reduce costs, enhance operational efficiency, and provide better working conditions for teams through improved route familiarity and workload balance.…

Data Skeptic

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The Mystery Behind Large Graphs 47:47

vor 8 weeks47:47

47:47

Our guest in this episode is David Tench, a Grace Hopper postdoctoral fellow at Lawrence Berkeley National Labs, who specializes in scalable graph algorithms and compression techniques to tackle massive datasets. In this episode, we will learn how his techniques enable real-time analysis of large datasets, such as particle tracking in physics experiments or social network analysis, by reducing storage requirements while preserving critical structural properties. David also challenges the common belief that giant graphs are sparse by pointing to a potential bias: Maybe because of the challenges that exist in analyzing large dense graphs, we only see datasets of sparse graphs? The truth is out there… David encourages you to reach out to him if you have a large scale graph application that you don't currently have the capacity to deal with using your current methods and your current hardware. He promises to "look for the hammer that might help you with your nail".…

Data Skeptic

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Customizing a Graph Solution 38:07

vor 11 weeks38:07

38:07

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.…

Data Skeptic

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Graph Transformations 32:48

vor 12 weeks32:48

32:48

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.…

Data Skeptic

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Networks for AB Testing 36:42

vor 14 weeks36:42

36:42

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.…

Data Skeptic

1
Lessons from eGamer Networks 37:52

vor 15 weeks37:52

37:52

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.…

Data Skeptic

1
Github Collaboration Network 42:24

vor 16 weeks42:24

42:24

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.…

Data Skeptic

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Graphs and ML for Robotics 41:59

vor 17 weeks41:59

41:59

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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