Fall, 2021 Lectures

For more details about the meeting access, click here.

Date Speaker TitleLecture Recording
10/13 Dr. Edward Sazonov, James R. Cudworth endowed Professor, Electrical and Computer Engineering, the University of Alabama From Wearable Sensors to Behavioral Informatics: Frontiers in Digital HealthClick here for the recording.
10/29 Dr. Jie Liang, Richard and Load Hill Professor, Biomedical Engineering, University of Illinois at Chicago Computational prediction of disease effects of genetic variants and altered 3D chromosome folding patternsClick here for the recording.
11/12 Dr. Parisa Rashidi,  Associate professor, Biomedical Engineering, the University of FloridaChallenges of Developing Intelligent Critical Care Systems
12/3 Dr. Julien Penders, Co Founder &COO, BloomlifeImproving prenatal health with medical grade wearables and large scale data analysisClick here for the recording.
12/15 Dr. Byung-Jun Yoon, Associate Professor,  Electrical and Computer Engineering, Texas A&M UniversityMachine Learning for Computational Network Biology
All eastern time at 12pm

Dec. 15, 12pm ET

Speaker:

Dr. Byung-Jun Yoon,

Associate Professor,

Department of Electrical and Computer Engineering
Texas A&M University

Title:
Machine Learning for Computational Network Biology

Abstract:


Real-world biomedical problems often involve complex biological systems that consist of numerous interacting entities. Such systems can be conveniently represented as networks, where the nodes represent the constituent entities (such as genes or proteins) and the edges reflect the interactions between the connected nodes. Analysis of these networks can lead to novel insights that are invaluable for understanding the structure and functional organization of the complex biological systems of interest. Furthermore, network representations of biological systems can be extremely useful for analyzing the high-dimensional data that originate from such systems, for manifold learning, information propagation, denoising, and various other tasks. In this talk, we will present a range of machine learning models and techniques that can be used for effective analysis of large-scale biological networks and robust network-based analysis of high-dimensional omics data that originate from complex biological systems.

Biosketch:

Prof. Byung-Jun Yoon received his B.S.E. (summa cum laude) degree from the Seoul National University (SNU), Seoul, Korea, in 1998, and the M.S. and Ph.D. degrees from the California Institute of Technology (Caltech), Pasadena, CA, in 2002 and 2007, respectively, all in Electrical Engineering. Since 2008, he has been with the Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, USA, where he is currently an Associate Professor. Dr. Yoon holds a joint appointment at Brookhaven National Laboratory (BNL), Upton, NY, where he is a Scientist in Computational Science Initiative (CSI), Applied Mathematics group. He received the National Science Foundation (NSF) CAREER Award, the Best Paper Award at the 9th Asia Pacific Bioinformatics Conference (APBC), the Best Paper Award at the 12th Annual MCBIOS Conference, and the SLATE Teaching Excellence Award from the Texas A&M University System. Dr. Yoon’s main theoretical interests lie in Scientific AI/ML, optimal experimental design (OED), and objective-based uncertainty quantification. He is actively working on the development of these methods and their application to various scientific domains, including computational biology and materials science.

Speaker:

Dr. Julien Penders,

Co-founder and COO,

Bloomlife

Title:
Challenges of Developing Intelligent Critical Care Systems

Abstract:

We are witnessing a maternal health crisis with increasing rates of maternal death, preterm births and widening of disparities in care globally. This maternal health crisis is due to a combination of increasing high risk pregnancies, shortage of care providers, and limited tools/data to predict and manage pregnancy complications.

To transform prenatal care we need better data, and to get better data we need better technology. Bloomlife is building a remote prenatal care platform to empower moms, increase access to care, and improve birth outcomes. We combine medical grade wearables with artificial intelligence to provide unprecedented insights to expectant mom and her care team, to identify and address modifiable risk factors, and to predict and prevent pregnancy complications. At the heart of our solution is the first at home clinical grade wearable that tracks critical health parameters of mom and her unborn baby, information so far restricted to prenatal visits.

In this talk, we will show how our data driven approach helps to shed light on poorly understood links between physiological changes and pregnancy outcomes. We will discuss opportunities arising from crowdsourcing clinical research, by providing consumers with clinical grade tools and data, and analyzing such data at a scale beyond what is possible in regular clinical studies. We will take specific examples from Bloomlife’s research on maternal health, fetal wellbeing and labor detection.

Biosketch:

Julien Penders is co-founder and COO at Bloomlife where he’s building the future of prenatal health using wearable technologies and predictive analytics. Julien is a passionate entrepreneur with 15-year experience in R&D and product development for the medical device and digital health industry. He led international teams through the development of wearable and digital health products covering hardware, software, analytics and clinical validation. He has (co-) authored over 60 papers and 14 patents. He is a TEDx and Creativity World Forum speaker. He serves on the IEEE Technical Committee on Biomedical Health Informatics and sits on the Program Committees for several international conferences. Julien was a 2004/2005 fellow of the Belgian American Educational Foundation. He holds a M.Sc. degree in Systems Engineering from University of Liege, Belgium (2004) and a M.Sc. degree in Biomedical Engineering from Boston University MA (2006).

Nov. 12, 12pm ET

Speaker:

Dr. Parisa Rashidi,

Associate Professor,

Department of Biomedical Engineering, University of Florida

Title:
Challenges of Developing Intelligent Critical Care Systems

Abstract:

Today’s ICUs face many critical barriers to achieving continuous monitoring of the patients. First, essential information such as functional status is not captured automatically but repetitively assessed by overburdened ICU nurses. Second, critical severity-of-illness scores for predicting patient trajectory are sparsely assessed and have limited accuracy, leading to alarm fatigue and missing early interventions.  This presentation will explore solutions to these issues using AI techniques customized to the critical care setting. More specifically, this presentation will examine how pervasive sensing technology and machine learning can be used for monitoring patients and their environment in the ICU. Additionally, this presentation will explore how AI techniques can analyze the wealth of data in the ICU, including clinical data, lab results, and physiological signals. It will discuss the issues and challenges in this space and point to future opportunities for novel research directions.

Biosketch:

Dr. Parisa Rashidi received her Ph.D. in computer science with an emphasis on machine learning. She is currently an associate professor at the J. Crayton Pruitt Family Department of Biomedical Engineering (BME) at the University of Florida (UF). She is also affiliated with the Electrical & Computer Engineering (ECE) and Computer & Information Science & Engineering (CISE) departments. She is the director of the “Intelligent Health Lab” (i-Heal), and the co-diretcor of the Intelligent Critical Care Center (IC3).  Her research aims to bridge the gap between machine learning and patient care.

Dr. Rashidi is a National Science Foundation (NSF) CAREER awardee, the National Institute of Health (NIH) Trail Blazer Awardee, Herbert Wertheim College of Engineering Assistant Professor Excellence Awardee, and a recipient of the UF term professorship. She is also a recipient of UF’s Provost excellence award for assistant professors; with more than 500 tenure-track assistant professors at UF, Dr. Rashidi is one of only 10 to receive this award. She was invited by the National Academy of Engineering (NAE) as one of only 38 outstanding US engineers under 45 to participate in the 2017 EU-US Frontiers of Engineering (FOE) Meeting. To date, she has authored 120+ peer-reviewed publications. She has chaired six workshops and symposiums on intelligent health systems and has served on the program committee of 20+ conferences. Dr. Rashidi’s research has been supported by local, state, and federal grants, including awards from the National Institutes of Health (NIBIB, NCI, and NIGMS) and the National Science Foundation (NSF).

Oct. 29, 12pm ET

Speaker:

Dr. Jie Liang,

Professor, Department of Bioengineering

University of Illinois at Chicago

Title:
Computational prediction of disease effects of genetic variants and altered 3D chromosome folding patterns

Abstract:
With the rapid progress of genome studies, many missense single-nucleotide polymorphisms (SNPs) in populations of somatic cells of different diseases such as cancer have been identified.  In addition, 3D chromosome studies are emerging as a major source of information on transcription machineries important for disease development.  However, it is challenging to identify and understand the implications of disease-related variants. In addition, deciphering the relationship between disease associated variants and 3D chromosome folding is still in its infancy.  Here we describe recent progress in developing computational methods to assess the effects of missense mutations and chromosome folding of genomic regions enriched with cancer-variants.  By integrating protein sequence information, structural and topological properties of protein conformations, we show machine-learning based predictions of pathological effects of mutations can be made, with improved performance compared to current state-of-the-art methods on challenging data sets. We discuss how key relevant residues, including previously unreported cancer-variants, can be identified from a large number of background variants found in cancer patients.  Furthermore, we discuss how cancer-related variants can be uncovered through identification of higher-order cooperative units of clusters of residues that function collectively and cooperatively.  Finally, we discuss insight gained from deciphering the folding mechanism of 3D genome, and results on differential patterns of chromatin folding of genomic regions enriched in cancer variant mutations.

Biosketch:
Dr. Jie Liang is Richard and Loan Hill professor in the Dept of Bioengineering at the University of Illinois at Chicago (UIC), and UIC Distinguished Professor.  He received BS (Biophysics, 1986) from Fudan University, MCS and Ph.D. (Biophysics, 1994) from the University of Illinois at Urbana-Champaign. He was an NSF CISE postdoctoral fellow (1994-1996) at the Beckman Institute and National Center for Supercomputing and its Applications (NCSA). He was a fellow at the Institute of Mathematics and Applications (IMA) at Minneapolis during 1996-97, an Investigator at SmithKline Beecham Pharmaceuticals during 1997-98.  He joined UIC in 1999 and became a full professor in 2007. He was a recipient of the NSF CAREER award (2003), a fellow of the American Institute of Medicine and Biological Engineering (2007), and a
University Scholar (2010). Dr. Liang’s research interests include structural bioinformatics, 3D chromosome, molecular stochastic networks, cellular pattern formation, and topological data analysis.  His recent work can be
found at (gila.bioe.uic.edu/liang/liang_pub.html).

Oct. 13, 12pm ET

Speaker:

Dr. Edward Sazonov,

Professor, Dept. of Electrical and Computer Engineering

University of Alabama

Title:
From Wearable Sensors to Behavioral Informatics: Frontiers in Digital Health

Abstract:
The emergence of wearable sensor technology paves the way for objective, sensor-driven assessment of health-related behaviors, which in modern society act as the major determining factors of life expectancy and quality of life. The modern sensor technology carries the promise to objectively measure and quantify complex human behaviors such as physical activity, food intake patterns, addictions, sleeping patterns, and social interactions. Furthermore, real-time recognition of the behavior enables novel approaches for just-in-time behavior modification. The recognition, characterization and interpretation of behaviors form sensor data presents a challenging problem due to complexity and variability of real-life behaviors as well as the indirect manner in which events of interest are inferred from behavioral and physiological manifestations registered by the sensors. The talk will provide an overview of our work on wearable sensors for monitoring of food intake in adults and infants, monitoring of cigarette smoking and smoke exposure, as well as monitoring of physical activity and energy expenditure. Special attention will be paid to the sensor solutions for monitoring of food intake, which are of particular interest for understanding and treatment of related medical conditions, such as obesity and eating disorders.

Biosketch:
Edward Sazonov (IEEE M’02, SM’11) received the Diploma of Systems Engineer from Khabarovsk State University of Technology, Russia, in 1993 and the Ph.D. degree in Computer Engineering from West Virginia University, Morgantown, WV, in 2002. Currently he is a James R. Cudworth endowed Professor in the Department of Electrical and Computer Engineering at the University of Alabama, Tuscaloosa, AL and the head of the Computer Laboratory of Ambient and Wearable Systems (http://claws.eng.ua.edu). His research interests span wearable devices, sensor-based behavioral informatics and methods of biomedical signal processing and pattern recognition. Devices developed in his laboratory include a wearable sensor for objective detection and characterization of food intake (AIM – Automatic Ingestion Monitor); a highly accurate physical activity and gait monitor integrated into a shoe insole (SmartStep, winner of Bluetooth Innovation WorldCup 2009); a wearable sensor system for monitoring of cigarette smoking (PACT); and others. The research in his lab was recognized by several awards, including best paper awards, President’s research award at the University of Alabama and others. In 2020 Dr. Sazonov served as a Fulbright Distinguished Chair at the University of Newcastle, Australia. His research has been supported by the National Institutes of Health, National Science Foundation, National Academies of Science, as well as by state agencies, private industry and foundations. Dr. Sazonov serves as an Specialty Chief Editor for Wearable Electronics,  Frontiers In Electronics and Associate Editor for several IEEE journals.


Zoom Meeting connection details

Please click the link below to join the webinar: https://pitt.zoom.us/j/9630638972

Meeting ID: 963 063 8972

Or One tap mobile
+12678310333,9630638972# US (Philadelphia)
8778535247,9630638972# US Toll-free

Dial by your location
+1 267 831 0333 US (Philadelphia)
877 853 5247 US Toll-free

Meeting ID: 963 063 8972

Find your local number: https://pitt.zoom.us/u/a53rHRou5
Join by SIP 9630638972@zoomcrc.com
Join by H.323
162.255.37.11 (US West)
162.255.36.11 (US East)
115.114.131.7 (India Mumbai)
115.114.115.7 (India Hyderabad)
213.19.144.110 (Amsterdam Netherlands)
213.244.140.110 (Germany)
103.122.166.55 (Australia Sydney)
103.122.167.55 (Australia Melbourne)
149.137.40.110 (Singapore)
64.211.144.160 (Brazil)
149.137.68.253 (Mexico)
69.174.57.160 (Canada Toronto)
65.39.152.160 (Canada Vancouver)
207.226.132.110 (Japan Tokyo) 149.137.24.110 (Japan Osaka)