The Institute for AI-Driven Discovery and Innovation hosts Dr. Mary
Simoni for a talk on her music and its intersection with AI, as part
of the Music and AI Seminars series.
The event will be held on Thursday, December 10, 2020, at 3:00 PM.
Abstract: Mary Simoni, Dean of Humanities, Arts & Social Sciences at
Rensselaer Polytechnic Institute will discuss her research in the use
of computer algorithms and technology in the composition and
performance of music. The talk will feature compositions inspired by
Augmented Transition Networks (ATNs), employ motion tracking to
control synthesis parameters, and a work in progress that employs
machine learning using training data that juxtaposes classical music
with COVID-19. During this talk, participants will be introduced to
several technologies that support music information retrieval, machine
learning, and algorithmic composition such as jSymbolic, Weka, and
Common Music.
Zoom details below:
https://stonybrook.zoom.us/j/98236706900?pwd=bDFEZFZtaHBWU0cyL0wxK3UrdUpIdz09
Meeting ID: 982 3670 6900
Passcode: 133945
Simoni for a talk on her music and its intersection with AI, as part
of the Music and AI Seminars series.
The event will be held on Thursday, December 10, 2020, at 3:00 PM.
Abstract: Mary Simoni, Dean of Humanities, Arts & Social Sciences at
Rensselaer Polytechnic Institute will discuss her research in the use
of computer algorithms and technology in the composition and
performance of music. The talk will feature compositions inspired by
Augmented Transition Networks (ATNs), employ motion tracking to
control synthesis parameters, and a work in progress that employs
machine learning using training data that juxtaposes classical music
with COVID-19. During this talk, participants will be introduced to
several technologies that support music information retrieval, machine
learning, and algorithmic composition such as jSymbolic, Weka, and
Common Music.
Zoom details below:
https://stonybrook.zoom.us/j/9
Meeting ID: 982 3670 6900
Passcode: 133945
Subject: RADIOLOGY GRAND ROUNDS CT Colonography: An Effective Test for Colorectal Cancer Screening- Judy Yee, M.D.
When: Wednesday, May 12, 2021 12:00 PM-1:00 PM (UTC-05:00) Eastern Time (US & Canada).
Where: JOIN ZOOM MEETING
Judy Yee, MD
Chair, Department of Radiology
Professor, Department of Radiology
Abdominal Imaging
Join Zoom Meeting
https://einsteinmed.zoom.us/j/
Meeting ID: 977 8219 0723
Passcode: 101083
Climate Uncertainty, Decision Making, and AI for Earth System Predictability Dr. Nathan Urban, Brookhaven National Laboratory
Bio: Nathan Urban is the group leader of the Optimal Experimental Design & Uncertainty Quantification group in the Applied Mathematics Department at Brookhaven National Laboratory's Computing & Data Sciences directorate (CDS). He holds a Ph.D. in theoretical condensed matter physics from Penn State, and has previously held research positions at Los Alamos National Laboratory, Princeton, and Penn State. His research interests include Bayesian inference and spatiotemporal statistics, probabilistic prediction and forecasting, multi-model / model-form / model structural uncertainty quantification, reduced order modeling, scientific machine learning and hybrid physical-data driven modeling, in-situ/streaming data analysis at scale, information fusion, decision making under uncertainty and optimal experimental design, and integrated multiscale computational frameworks for decision support.
Location: IACS Seminar Room
Lunch will be provided
Bio: Nathan Urban is the group leader of the Optimal Experimental Design & Uncertainty Quantification group in the Applied Mathematics Department at Brookhaven National Laboratory's Computing & Data Sciences directorate (CDS). He holds a Ph.D. in theoretical condensed matter physics from Penn State, and has previously held research positions at Los Alamos National Laboratory, Princeton, and Penn State. His research interests include Bayesian inference and spatiotemporal statistics, probabilistic prediction and forecasting, multi-model / model-form / model structural uncertainty quantification, reduced order modeling, scientific machine learning and hybrid physical-data driven modeling, in-situ/streaming data analysis at scale, information fusion, decision making under uncertainty and optimal experimental design, and integrated multiscale computational frameworks for decision support.
Location: IACS Seminar Room
Lunch will be provided
Join Zoom Meeting
https://stonybrook.zoom.us/j/91945227869?pwd=emhoZDFWVTV0MVdPWW5uVk43MjQzUT09
Meeting ID: 919 4522 7869
Passcode: 452304
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https://stonybrook.zoom.us/j/
Meeting ID: 919 4522 7869
Passcode: 452304
One tap mobile
+16468769923,,91945227869# US (New York)
+13126266799,,91945227869# US (Chicago)
Dial by your location
+1 646 876 9923 US (New York)
+1 312 626 6799 US (Chicago)
+1 301 715 8592 US (Germantown)
+1 669 900 6833 US (San Jose)
+1 253 215 8782 US (Tacoma)
+1 346 248 7799 US (Houston)
+1 408 638 0968 US (San Jose)
Meeting ID: 919 4522 7869
Find your local number: https://stonybrook.zoom.us/u/
The SUNY AI Symposium brings together AI experts from across the state, in Western New York and around the country.
This two-day event showcases AI thought leaders, SUNY researchers, students and companies of all sizes who leverage AI to produce positive outcomes--with scientific discovery, business innovation and economic impact. Come curious, explore the fascinating world of AI and leave with connections to those at the forefront of innovation.
Technical Interview Prep for Undergraduate Students by Google
Come learn the ins and outs of the technical interviews so you can nail your next one! Google software engineers will pull back the curtains on the interview process and give awesome tips & practice questions too.
RSVP at: https://stonybrook.joinhandshake.com/events/426175
Come learn the ins and outs of the technical interviews so you can nail your next one! Google software engineers will pull back the curtains on the interview process and give awesome tips & practice questions too.
RSVP at: https://stonybrook.joinhandshake.com/events/426175
This event brings together people with interests in Computer Vision theory and techniques and examines current research issues in the field.
Each seminar consists of multiple short talks (around 15 minutes) by several students.
Join Zoom Meeting:
https://stonybrook.zoom.us/j/93547152068?pwd=WVpoRVgzelBXeloxdXVEakNSb2M5UT09
Meeting ID: 935 4715 2068 | Passcode: 481832
Each seminar consists of multiple short talks (around 15 minutes) by several students.
Join Zoom Meeting:
https://stonybrook.zoom.us/j/93547152068?pwd=WVpoRVgzelBXeloxdXVEakNSb2M5UT09
Meeting ID: 935 4715 2068 | Passcode: 481832
Zoom Link: https://github.com/giorgianb/spdhackspring2021/blob/main/bit.ly/spdhack2021
ΣΦΔ Hack Spring 2021 is ΣΦΔ's first annual machine learning hackathon. ΣΦΔ Hack Spring 2021 aims to introduce Stony Brook students to the rich and challenging field of machine learning, and develop the skills necessary to build sophisticated machine learning models on their own.
More info here: https://github.com/giorgianb/spdhackspring2021/blob/main/README.md
ΣΦΔ Hack Spring 2021 is ΣΦΔ's first annual machine learning hackathon. ΣΦΔ Hack Spring 2021 aims to introduce Stony Brook students to the rich and challenging field of machine learning, and develop the skills necessary to build sophisticated machine learning models on their own.
More info here: https://github.com/giorgianb/spdhackspring2021/blob/main/README.md
CSE 656 Seminars in Computer Vision - Wednesdays 11:30am-12:50pm, Room NCS 120
The overall purpose of this seminar is to bring together people with interests in Computer Vision theory and techniques and to examine current research issues. This course will be appropriate for people who already took a Computer Vision graduate course or already had research experience in Computer Vision. To enroll in this course, you must either: (1) be in the PhD program or (2) receive permission from the instructors.
Each seminar will consist of multiple short talks (around 10 minutes) by multiple people. Students can register for 1 credit for CSE656. Registered students must attend and present a minimum of 2 or 3 talks. Everyone else is welcome to attend. Fill in https://forms.gle/pCVXovgfMfQwGqG38 to subscribe to our mailing list for further announcement.
The first meeting will be Wed Jan 29 at 11.30am, room 120 New CS. The meeting will deal with organizational matters and we will start right away with some presentations. Send David Paredes Merino <dparedesmeri@cs.stonybrook.edu> an email if you are interested but cannot attend the first meeting. Please forward to people outside the CS department that you think might be interested.
The overall purpose of this seminar is to bring together people with interests in Computer Vision theory and techniques and to examine current research issues. This course will be appropriate for people who already took a Computer Vision graduate course or already had research experience in Computer Vision. To enroll in this course, you must either: (1) be in the PhD program or (2) receive permission from the instructors.
Each seminar will consist of multiple short talks (around 10 minutes) by multiple people. Students can register for 1 credit for CSE656. Registered students must attend and present a minimum of 2 or 3 talks. Everyone else is welcome to attend. Fill in https://forms.gle/pCVXovgfMfQwGqG38 to subscribe to our mailing list for further announcement.
The first meeting will be Wed Jan 29 at 11.30am, room 120 New CS. The meeting will deal with organizational matters and we will start right away with some presentations. Send David Paredes Merino <dparedesmeri@cs.stonybrook.edu> an email if you are interested but cannot attend the first meeting. Please forward to people outside the CS department that you think might be interested.