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Affinity Workshop
LatinX in AI
Maria Luisa Santiago · Juan Banda · CJ Barberan · MIGUEL GONZALEZ-MENDOZA · Caio Davi · Sara Garcia · Jorge Diaz · Fanny Nina Paravecino · Carlos Miranda · Gissella Bejarano Nicho · Fabian Latorre · Andres Munoz Medina · Abraham Ramos · Laura Montoya · Isabel Metzger · Andres Marquez · Miguel Felipe Arevalo-Castiblanco · Jorge Mendez · Karla Caballero · Atnafu Lambebo Tonja · Germán Olivo · Karla Caballero Barajas · Francisco Zabala

Mon Nov 28 06:00 AM -- 04:00 PM (PST) @ Room 298 - 299
Event URL: https://www.latinxinai.org/neurips-2022 »

Author Information

Maria Luisa Santiago (Accel AI)
Juan Banda (Georgia State University)
CJ Barberan (Microsoft)
MIGUEL GONZALEZ-MENDOZA (Tecnologico de Monterrey)

Miguel González Mendoza holds a PhD degree and a Postdoc in Artificial Intelligence from INSA and LAAS-CNRS Toulouse, France, in 2003 and 2004 respectively. Since 2004 he works as research professor at Tecnologico de Monterrey, Mexico. Miguel González Mendoza’s research activities are focused on machine learning, semantic web and big data applications, areas in which has supervised 9 PhD and 21 MSc. Theses, published more than 100 peer reviewed scientific publications, participated and conducted more than 20 national (CONACYT founded) and international (European founded) research and innovation projects, and chaired 4 international Congresses. President of the Mexican Society for Artificial Intelligence (2017-2018), Member of the Mexican National Research System (SNI) rank II (Jan 2016), member since 2006. Head of the Graduate Programs on Computer Sciences at Tecnologico de Monterrey, Mexico 2005-2016. Invited as Young Scientist at the World Economic Forum for New Champions in Tianjin China in September 2012.

Caio Davi (Texas A&M University)
Sara Garcia (Coventry University)

Tiny person with big dreams

Jorge Diaz
Fanny Nina Paravecino (Microsoft)

Dr. Nina-Paravecino is currently a Senior Researcher at the AI and Advance Architectures group in Microsoft, where she leads different efforts to improve performance of Deep Learning workloads. Previously, Dr. Nina-Paravecino was part of Intel Corporation as a Research Scientist to push Intel’s ground-breaking volumetric reconstruction technology using Deep Learning. In the past, her work has contributed to efficiently exploit GPU architectures and enabled identification of bottlenecks on a myriad of applications that includes image processing and video analytics. Dr Nina-Paravecino received her Ph.D. in Computer Engineering from Northeastern University, her M.Sc. in Computer Engineering from University of Puerto Rico at Mayaguez Campus, and her B.S. in System and Informatics Engineering from University of San Antonio Abad of Cusco – Peru. She has been PC-member/Reviewer of different Journals/Conferences/Workshops such as IEEE Transactions on Image Processing 2017, JPDC 2017, CF 2018, PPoPP 2018, SC 2018, GPGPU 2018, PARCO 2018, IA^3 2019, SC 2019, DAC 2020, ICCD 2020, HPCA 2021. Most recently, Dr. Nina was co-chair of the Video Analytics mini-track at HICSS 2020

Carlos Miranda (Buawei)
Gissella Bejarano Nicho (Baylor University)
Fabian Latorre (EPFL)
Andres Munoz Medina (Google)
Abraham Ramos (Accel AI Institute)
Laura Montoya (Accel AI)
Isabel Metzger (Latinx In AI)
Andres Marquez (Rivian)
Miguel Felipe Arevalo-Castiblanco (Rice University)
Jorge Mendez (MIT)
Karla Caballero (Pandora SiriusXM)
Atnafu Lambebo Tonja (Instituto Politécnico Nacional)
Atnafu Lambebo Tonja

I am a PhD student in Computer Science at Centro de Investigación en Computación, Instituto Politécnico Nacional (IPN). My specialization lies in Natural Language Processing (NLP) and my broad research areas are:- Machine Translation, NLP for low resource languages, MT for Low resource languages, Text Processing, Named-Entity Recognition, Sentiment Analysis, Offensive Language Identification, Author Profiling, Clinical NLP and Code-Mixed Texts.

Germán Olivo (Accel AI)
Karla Caballero Barajas (Sirius XM/ Pandora)
Francisco Zabala (LatinX in AI/Amazon AWS)
Francisco Zabala

I love developing hardware and software to build datasets that I can use to train and test deep neural nets. 🧮

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