EP09 - The Thinking Process of LLMs. With Sara Marjanovic

EP10 - Quantum Computing and Memory. With Dr. Alessandro Berti

Talking about Quantum Computing always makes me feel like I am wearing a pair of glasses that let me see straight into the future. Join me in this episode of targz with Alessandro Berti [https://www.linkedin.com/in/aleberti/], Postdoc at University of Pisa [https://www.linkedin.com/school/unipisa/], where we talk about Quantum Algorithms and specifically the importance of (quantum) memory! If you want to dig further in the topic, you can read more in the paper "Efficient Quantum State Preparation with Bucket Brigade QRAM [https://arxiv.org/pdf/2510.16149]".

EP09 - The Thinking Process of LLMs. With Sara Marjanovic

Have you ever thought about the thinking process of LLMs? Well, Sara Marjanovic [https://www.linkedin.com/in/sara-vera-marjanovic], PhD student at University of Copenhagen [https://www.linkedin.com/school/university-of-copenhagen/], did it and she shares her research with us in this episode of targz! If you want to dig further in the topic, you can read more in the paper "DeepSeek-R1 Thoughtology: Let’s think about LLM reasoning [https://openreview.net/pdf?id=BZwKsiRnJI]".

EP08 - Fairness and Relevance in Recommender Systems. With Dr. Theresia Veronika Rampisela

Fairness and relevance may be diverging goals in recommender systems. Can we find a way to achieve both? We talk about it with Dr. Theresia Veronika Rampisela [https://www.linkedin.com/in/theresia-rampisela/] in this episode of targz! Do you want to dig more? Checkout the paper: "Joint Evaluation of Fairness and Relevance in Recommender Systems with Pareto Frontier [https://arxiv.org/abs/2502.11921]"

EP07 - Efficient Neural Search: Rethinking Inverted Indexes for Learned Sparse Representations. With Dr. Franco Maria Nardini

In this episode of targz, Franco Maria Nardini [https://www.linkedin.com/in/fmnardini/], Research Director at ISTI-CNR [https://www.linkedin.com/company/cnr-isti/], explains Seismic, a two-level inverted index for fast retrieval over learned sparse representations. It beats graph-based state-of-the-art methods by up to 3.5x in speed with comparable memory, and opens new directions in inference-free and edge retrieval. Want to know more? checkout the paper: Efficient Inverted Indexes for Approximate Retrieval over Learned Sparse Representations [https://arxiv.org/abs/2404.18812]

EP06 - Does Fair Ranking Lead to Fair Recruitment? With Dr. Carlos Castillo

Everyone would like a fair recruitment process, but unfortunately the reality is way more complex than just fixing some sorting algorithm. In this episode of targz Dr. Carlos Castillo, aka ChaTo, from ICREA describes the research conducted by his group to address the issue. Want to know more? checkout the paper: https://www.sciencedirect.com/science/article/pii/S0306457325004479 [https://www.sciencedirect.com/science/article/pii/S0306457325004479] Here you can also find more information about the project: http://findhr.eu/ [http://findhr.eu/]