Spring 2023
Automated Detection of Disease Using Expressive Human Signals
Dr. Mary Pietrowicz
Presented on: May 16, 2023
Abstract:
Many disorders, particularly disorders in psychiatry, neurology, gastroenterology, pulmonology, cardiology, and speech and language are underdiagnosed or are incorrectly diagnosed, causing both financial burden and unnecessary suffering. To cite a specific example, prior to the pandemic, about 18% and 7% of the population suffered from Anxiety Disorders (AD) and Major Depressive Disorder (MDD) respectively; yet only 37% of people with AD and 50% of people with MDD received treatment. Post-pandemic, over 30% of the population is affected by AD and MDD. Left untreated, AD/MDD can result in the inability to hold a job, financial problems, broken relationships, and even suicide. Barriers to diagnosis for these and other disorders include low self-perception of need, attitudinal barriers such as stigma and lack of trust in the health care system, structural barriers such as cost and low availability or accessibility of doctors, and subtlety of early symptoms that are often confused with other disorders. This work addresses the problem by developing accessible, inexpensive, online, automated disease screening techniques based on highly-available human signals, particularly speech and language. This talk will introduce this exploratory field, present a few recent projects, discuss challenges and barriers to this research, and discuss next steps and longer-term goals
Spring 2022
Exploiting parallelism IN LARGE SCALE DEEP LEARNING MODEL TRAINING: FROM CHIPS TO SYSTEMS TO ALGORITHMS
Saurabh Kulkarni
VP and GM, Graphcore (North America)
Presented on: 04/04/2022
GRAPH ENGINE TO TACKLE THE TOUGHEST GRAPH ANALYTICS CHALLENGES
Janice McMahon
Lucata
Presented on: 04/18/2022

The Cerebras Wafer-Scale Engine (WSE-2) – A new architecture for ML and HPC Workloads
Cindy Orozco Bohorquez & Adam Lavely
Product team Technical Staff, Cerebras
Presented on: 05/02/2022
Fall 2021

Evolution of Memory: From Basic Foraging Decisions to Cognitive Map Construction
Ekaterina GribkovaPostdoctoral Research Associate, Coordinated Science Laboratory, University of Illinois Urbana-Champaign11/15/2021
The Principles of Deep Learning Theory
Dan RobertsResearch Affiliate, Center for Theoretical Physics, Massachusetts Institute of Technology11/08/2021
Machine learning and Inverse Problems in Scientific Imaging
Zhizhen ZhaoAssistant Professor, Electrical and Computer Engineering, University of Illinois Urbana-Champaign11/01/2021
Secure Learning in Adversarial Environment
Bo LiAssistant Professor, Computer Science, University of Illinois Urbana-Champaign10/18/2021
Survival Analysis Based on Survey and Test Data on the UIUC Campus
Weihao GeResearch Scientist, National Center for Supercomputing Applications, University of Illinois Urbana-Champaign10/11/2021
Looking behind-the-Seen in Order to Anticipate
Alexander SchwingAssistant Professor, Electrical and Computer Engineering, University of Illinois Urbana-Champaign10/04/2021
Domain-guided Machine Learning for Health Analytics
Yogatheesan VaratharajahAssistant Research Professor, Bioengineering, University of Illinois Urbana-Champaign09/27/2021
Practical Predictions: Models for Operational Impact
Rebecca SmithAssociate Professor, Veterinary Medicine, University of Illinois Urbana-Champaign09/20/2021Spring 2021

A Startup Foundry Approach to Increasing Research Impact
Sanjay PatelActing Co-Director IBM-Illinois Center for Cognitive Computing Systems Research, Professor of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign05/03/2021
Dataflow Optimized Systems for AI
Kunle OlukotunCadence Design Professor of Electrical Engineering and Computer Science, Stanford University04/26/2021
NCSA Industry Overview with Computational Breakthroughs and Synergies with Artificial Intelligence
Brendan McGinty and Seid KorićDirector of Industry, NCSA and Technical Director, NCSA04/19/2021
Data Science and the Valley of Death! How the National Labs Help You Bridge the Gap
Wayne MillerDeputy Director, Lawrence Livermore National Laboratory04/12/2021
Tensor Methods for Deep Learning with TensorLy and PyTorch
Jean KossaifiSenior Research Scientist, NVIDIA04/05/2021
AI for Food Security
Sebastian AhnertSenior Research Fellow and Lecturer, The Alan Turing Institute and University of Cambridge03/22/2021
So You Built an AI Model, Now What?
Brian MartinResearch Fellow, Head of AI, R&D IR, AbbVie03/08/2021

Data Science and Historical Texts: Modeling Meaning Change from Ancient Greek to Web Archives
Barbara McGillivrayResearch Fellow, The Alan Turing Institute/Cambridge University02/22/2021Fall 2020

Data Heterogeneity and Disease Surveillance: Impacts on Precision and Accuracy
Rebecca SmithAssistant Professor, University of Illinois at Urbana-Champaign11/16/2020
Chemical Imaging for an Expanded View of the Pathologic Basis of Disease: A Challenge for AI
Rohit BhargavaProfessor, University of Illinois at Urbana-Champaign11/09/2020
Demystifying Hardware Acceleration for Machine Learning
Matthew KrafczykResearch Programmer, National Center for Supercomputing Applications11/02/2020
Topological Obstructions to Autoencoders
Yoni KahnAssistant Professor, University of Illinois at Urbana-Champaign10/26/2020
The Geometry of Data: An Interpretation of the Challenges of Training Models at Scale
Aaron SaxtonData Engineer, National Center for Supercomputing Applications10/19/2020
Convolutional Tensor-Train LSTM for Spatio-Temporal Learning
Wonmin ByeonResearcher, NVIDIA10/12/2020
Machine Learning for Quantum Computing and Quantum Matter
Bryan ClarkAssistant Professor, University of Illinois at Urbana-Champaign10/05/2020
Design and Control of Soft Musculoskeletal Architectures
Mattia GazzolaAssistant Professor, University of Illinois at Urbana-Champaign09/21/2020