Past Speakers

2020-2021 Education and Workforce Working Group's Speakers

2020-2021 Speakers

Friday, August 6

 

Presenters:

 

 

Dr. Jian Tao

Talk Title: DS+X: an Immersive and Interdisciplinary Approach for Data Science Education

Bio: Dr. Jian Tao is a Research Scientist / Computational Scientist / Adjunct Professor at Texas A&M Engineering. He is affiliated with the Texas A&M High Performance Research Computing, the Texas A&M Institute of Data Science, and the Department of Electrical & Computer Engineering at Texas A&M University. He received his Ph.D. in Physics (Computational Astrophysics) from Washington University in St. Louis in 2008. He is currently the Associate Director of the Scientific Machine Learning Laboratory at the Texas A&M Institute of Data Science. He is a contributor to the SPEC CPU 2017 benchmark suite. He is also a University Ambassador of the NVIDIA Deep Learning Institute and an XSEDE Campus Champion at Texas A&M University. His research interests include high performance computing, numerical algorithms, image processing, data analytics, machine learning, and workflow management.

 

 

Dr. Arko Barman

Talk Title: Case study: Applied Data Science & Machine Learning Capstone designed for externally-funded projects

Bio: Dr. Arko Barman is an Assistant Teaching Professor at the Data to Knowledge Lab with a joint appointment in the ECE department at Rice University. Dr. Barman has been involved in curriculum design and teaching data science courses for several years. Prior to joining Rice University, Dr. Barman was a postdoctoral research fellow at the University of Texas Health Science Center (UTHealth), where he developed introductory data science and statistics courses for postdoctoral fellows in biology and medicine. He received the Excellence in Teaching Award and the inaugural Postdoctoral Service Award at UTHealth for his teaching and service activities respectively. Dr. Barman received his Ph.D in Computer Science from the University of Houston, Masters in Signal Processing from the Indian Institute of Science, and his Bachelors in Electrical Engineering from Jadavpur University, India. His research interests include machine learning, deep learning, biomedical image analysis, computer vision, data mining, and heuristic optimization. Dr. Barman has also worked in the industry for several years at Broadcom Corporation and the Palo Alto Research Center (Xerox PARC).

 

Friday, July 2

Presenters:

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Dr. Julia Stoyanovich

Talk Title: Teaching Responsible Data Science

Bio: Julia Stoyanovich is an Assistant Professor at New York University in the Department of Computer Science and Engineering at the Tandon School of Engineering, and the Center for Data Science.  Julia's research focuses on responsible data management and analysis practices: on operationalizing fairness, diversity, transparency, and data protection in all stages of the data acquisition and processing lifecycle. She established the Data, Responsibly consortium (https://dataresponsibly.github.io/), and served on the New York City Automated Decision Systems Task Force, by appointment from Mayor de Blasio. Julia developed and is teaching courses on Responsible Data Science at NYU (https://dataresponsibly.github.io/courses/spring20/).  In addition to data ethics, Julia works on management and analysis of preference data, and on querying large evolving graphs. She holds M.S. and Ph.D. degrees in Computer Science from Columbia University, and a B.S. in Computer Science and in Mathematics and Statistics from the University of Massachusetts at Amherst.  Julia's work has been funded by the NSF, BSF and by industry. She is a recipient of an NSF CAREER award and of an NSF/CRA CI Fellowship.

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Dr. Raghu Machiraju

Talk Title: Creating novel academic programming: Combining data analytics with design thinking.

Bio: Raghu Machiraju serves as a Professor of Biomedical Informatics, Computer Science and Engineering, Pathology at The Ohio State University.  Currently, he serves as the Associate Chair for Growth in the Department of  Computer Science and Engineering and as the Principal Data Scientist at the Translational Data Analytics Institute (TDAI). At TDAI he also served as the Interim Faculty Lead for more than 3 years during which he architect a Professional Master’s degree in Translational Data Analytics.  This degree is unique in its curriculum in that it includes design thinking as a mainstay in addition to the usual suspects of computer science and statistics.  Raghu’s research focus includes developing machine learning and visualization methods towards  the diagnosis of  cancer and chronic diseases and the automation of  the experimental laboratory for reproducible research.

 

June 4, 2021 

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Dr. Joyce Malyn-Smith

Talk Title: Tools for Developing New Data Pathways in Community Colleges

Bio: 

Joyce Malyn-Smith is a national expert on STEM career development and workforce education. She leads a body of work that explores how people develop skills and knowledge in and out of school then translate those into productive and rewarding careers. Her projects develop industry/ education connections in computational thinking, big data, artificial intelligence (AI), and other advanced technologies.  Malyn-Smith’s research focuses on the future of work and its implications for lifelong learning. She investigates the skills and dispositions humans will need as they partner with machines in problem solving, what it means to be human in the age of AI, and the foundational skills K–20 students need to prepare for work at the Human-Technology Frontier. Malyn-Smith collaborates with community colleges to advance knowledge of the expertise needed in emerging occupations and in occupations that are changing dramatically due to new technologies. She also develops standards-based tools and resources to ensure schools align their programs and curricula with changing industry demand.  Malyn-Smith hold an EdD from Boston University in business/career education and bilingual education leadership.

 

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Dr. Jianwu Wang

Talk Title:

Team-Based Online Multidisciplinary Education on Big Data + High-Performance Computing + Atmospheric Sciences

Bio:

Dr. Jianwu Wang is an Assistant Professor at the Department of Information Systems, University of Maryland, Baltimore County (UMBC). He is also an affiliated faculty at the Joint Center for Earth Systems Technology (JCET), UMBC. He received his Ph.D. degree in Computer Science from Institute of Computing Technology, Chinese Academy of Sciences in 2007. His research interests include Big Data Analytics, Scientific Workflow, Distributed Computing, Service Oriented Computing. He has published 100 papers with more than 1500 citations (h-index: 20). He is/was associate editor or editorial board member of four international journals, conference organization committee member of eight conferences and co-chair of eight related workshops. He is also program committee member for over 40 conferences/workshops, and reviewer of over 15 journals or books. Since joining UMBC in 2015, he has received multiple grants as PI funded by NSF, NASA, DOE, State of Maryland, and Industry. He is also an NSF CAREER awardee. His current research interests include Big Data Analytics, Distributed Computing and Scientific Workflow with application focuses on climate and manufacturing.

May 7, 2021

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Dr. R.N. Uma

Talk Title: Data Science for Social Justice: An Approach to Broaden Participation

BioDr. R. N. Uma is a Professor of Computer Science in the Department of Mathematics and Physics at NC Central University, Durham, NC, USA. Her research interests include data science, scheduling and resource allocation with applications to cloud computing, robotics, wireless sensor networks, multimedia networking, and large logistics problems. Her research has spanned from purely theoretical to experimental and simulations. She has published several papers in leading conferences and journals, and a book, in her areas of expertise. On the education front, she is interested in increasing the enrollment and retention of minority students, particularly in the Mathematical Sciences including Computer Science, and towards this end has implemented several federally funded projects. She has served her professional community in a variety of roles including guest editor, TPC Co-Chair, and Publications Chair in addition to routine roles such as TPC Member, Reviewer and Session Chair. She received her BSc degree in Mathematics from the University of Madras, Chennai, India, her ME degree in Computer Science from the Indian Institute of Science, Bangalore, India, and her PhD degree in Computer Science from NYU Tandon School of Engineering, New York. She is a senior member of IEEE.

 

April 2, 2021

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Dr. David Beck

Talk Title: Domain Specific Graduate Training in Data Science in the Context of a University Wide Approach

Bio: David Beck is a Research Associate Professor in Chemical Engineering at the University of Washington (UW), a Senior Data Science Fellow and Director of Research and Education for the eScience Institute.  Founded in 2008, UW’s eScience Institute serves to advance data-intensive discovery in all fields from the humanities to science, engineering and medicine.  Dr. Beck’s research explores the intersection of molecules and data science, or molecular data science, and its applications to energy, environment and health.  Beck is the associate director of the NSF Research Traineeship Data Intensive Research Enabling Clean Tech (DIRECT) and co-director of the Engineering Data Science Institute (EDSI) at UW.  He is an adjunct faculty in the Paul G. Allen School for Computer Science and the Department of Environmental and Occupational Health Sciences and coordinates the UW Data Science Seminar.

 

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Dr. David Gutman

Talk Title: The Human Tumor Atlas Network Data Coordinating Center

Bio: Dr. David Gutman received his PhD in Neuroscience and MD from Emory University School of Medicine and is now an associate professor of Neurology.  Dr. Gutman is also a staff Physician at the Atlanta VA Medical Center on the Psychiatric Inpatient Unit.  Dr. Gutman's research focuses on developing informatics technologies to process and analyze large imaging data sets, and is leading the imaging informatics efforts to integrate data for the Human Tumor Atlas Network.

 

 

March 5, 2021

Presenters:

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Dr. Jon Schwabish

Talk Title: Teaching Data Visualization to Kids

Bio: Dr. Jonathan Schwabish is an economist, writer, teacher, and creator of policy-relevant data visualizations. He is considered a leading voice for clarity and accessibility in how researchers communicate their findings. His book Better Presentations: A Guide for Scholars, Researchers, and Wonks helps people improve the way they prepare, design, and deliver data-rich content and his edited book, Elevated the Debate: A Multilayered Approach to Communicating Your Researchhelps people develop a strategic plan to communicating their work across multiple platforms and channels. His latest book, Better Data Visualizations: A Guide for Scholars, Researchers, and Wonks details essential strategies to create more effective data visualizations. He is on Twitter @jschwabish

 

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Dr. Ben Marwick

Talk Title: Data Skills and Data Studies: A New Interdisciplinary Minor in Data Science for Arts, Humanities and Social Sciences

Bio: Ben is an Associate Professor of Archaeology, with other local affiliations including the eScience Institute, the Burke Museum, the Center for Statistics and Social Sciences, the Quaternary Research Center, and the Southeast Asia Center. His research interests include hominin dispersals into mainland Southeast Asia, forager technologies and ecology in Australia, mainland Southeast Asia and elsewhere.  Ben is interested in techniques and methods for reproducible research and open science, and is the program chair for the UW eScience Institute's Reproducible Research Special Interest Group. He is the inaugural director of the UW Interdisciplinary Data Science Minor.

 

 

 

Friday, February 5, 2021

 

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Dr. Karl Schmidt Talk Title:  Keeping Academic Data Science On-Track: What to Assess and How

Karl is engaged in a variety of Data Science Educational, most recently by serving as a member of the ACM Data Science Task Force to develop their curricular recommendations for Data Science. He was the founding director of Valparaiso University's undergraduate Data Science degree and ran Valpo's Masters in Analytics and Modeling program for 5 years. Karl is now the Program Coordinator for Data Analytics at Trinity Christian College and is part of implementing the new program there, alongside a new Data Center.  Karl specializes in data science as applied to networks and graphs. He’s done work with applying network algorithms to improve genome assembly and published fundamental work in understanding K-Dense graphs. He’s also very interested in finding ways to connect data science with social good, especially through the classroom and experiential learning.  Karl has taught designed courses in Optimization, Data Mining, and both Introductory and Advanced Data Visualization.  He’s also designed and implemented an Introduction to Data Science course targeted at students with minimal programming experience that centers around a data-driven service-learning project.

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Dr. Hunter Schafer | Talk Title: Designing CSE 163, an Intermediate Data Programming course

Hunter Schafer is an Assistant Teaching Professor in the Paul G. Allen School of Computer Science & Engineering at the University of Washington. His work prioritizes integrating evidence-based teaching practices to achieve equitable learning outcomes for all students, regardless of their primary field of study. Hunter teaches classes on introductory programming, data structures and algorithms, data science, and machine learning. In his first year on the Allen School faculty, he designed ‘CSE 163 - Intermediate Data Programming’, a core programming course taken by students pursuing data science options in majors across the university. His goal is to enable students from a variety of fields to appreciate the accessibility and relevance of these courses to their studies.

 

 

Friday, January 8, 2021

 

Dr. Mandy HeringTalk Title:  Navigating Undergraduate Research Projects Fully Online
 
Amanda Hering obtained her Ph.D. from Texas A&M University in Statistics in 2009. She joined the Department of Applied Mathematics and Statistics at Colorado School of Mines in Golden, Colorado in 2009 as an Assistant Professor and then the Department of Statistical Science at Baylor University in 2016 as an Associate Professor.  She has over 55 peer-reviewed papers in both statistics and subject matter journals.  Her research focus is on methods and applications to problems in natural and engineered water environments. In 2019, she was selected for The International Environmetrics Society (TIES) Abdel El-Shaawari Early Investigator Award. She is the PI of an NSF Data Science Corps project titled “Modernizing Water and Wastewater Treatment through Data Science Education and Research.” 

 

Dr. Adam LaMee | Talk Title: Getting into K12 Classrooms: a large-scale partnership between UCF Physics and local schools integrating data science into core middle grades curriculum with free, open source materials

As the PhysTEC Teacher-in-Residence at the University of Central Florida department of Physics, Adam LaMee coordinates undergraduate laboratories, the Learning Assistant program, and partnerships with K12 schools. For the past five years that has included integrating Python and Jupyter into undergraduate physics curriculum and large-scale data science initiatives in middle and high schools. Mr. LaMee is also a Teaching & Learning Fellow for the NSF-funded Quarknet Program whose activities include mentoring K12 teachers and students in particle physics research and creating K12 curriculum that exposes students to current research in a range of physics disciplines. He has 15 years of experience teaching at the high school and university levels, has led workshops around the country for hundreds of K12 teachers, and was a developer of Florida’s secondary science course standards.

Friday, November 6, 2020 at 11AM EST

 

Dr. Cheryl A. Swanier | Talk Title: How Secure Is Your Data?

Bio: Dr. Cheryl A. Swanier is an Associate Professor of Computer Science at Claflin University where she was named the Henry N. and Alice Carson Tisdale Endowed Professor and the former Department Chair of Mathematics and Computer Science.  Dr. Swanier is the founder and CEO of Swanier Consulting, LLC. Dr. Swanier conducts research in Human Computer Interaction with an emphasis in visual programming of educational simulations with end user programming and educational gaming technologies.  Dr. Swanier works with outreach initiatives to improve computer science education at all levels. One of these initiatives is the ARTSI Alliance, Advancing Robotics, Technology for Societal Impact. Another initiative is the STARS (Students & Technology in Academia, Research & Service) Alliance, regional partnerships among academia, industry, K-12, and the community to strengthen local BPC programs by focusing on K-12 outreach, community service, student leadership and computing diversity research. Dr. Swanier is a member of the NCWIT Academic Alliance and has served as a NCWIT Pacesetter. NCWIT awarded Swanier a $10,000 Seed Fund Award for the Kewl Girlz Kode summer learning program during 2016-17.  She also conducts outreach activities to organizations such as Girls, Inc., Delta Sigma Theta Sorority, Inc. Delta Academy and Delta GEMS, and Links, Inc. in a concerted effort to broaden participation in computing for underrepresented minorities and girls.

 

 

Dr. Eliu Huerta | Talk Title: Training the Next Generation of AI Practitioners 

Dr. Eliu Huerta is the director of the Center for Artificial Intelligence Innovation at the University of Illinois at Urbana-Champaign. He completed a Master of Advanced Study and a PhD in Applied Mathematics and Theoretical Astrophysics at the University of Cambridge. His research interests are at the interface of artificial intelligence, theoretical astrophysics, extreme scale computing and mathematics. He is the head of the Gravity Group at the National Center for Supercomputing Applications, where he leads a vibrant, interdisciplinary, and multi-institutional research program funded by NSF and DOE.

 

Friday, October 2, 2020 at 11AM EST.

 

Dr. Debra F. Laefer | Talk Title: Remote Sensing and Data Science: Opportunities and Challenges

Bio: With degrees from the University of Illinois Urbana-Champaign (MS, PhD), NYU (MEng), and Columbia University (BS, BA), Prof. Debra Laefer has a wide-ranging background spanning from geotechnical and structural engineering to art history and historic preservation. Not surprisingly, Prof. Laefer’s work often stands at the cross-roads of technology creation and community values such as devising technical solutions for protecting architecturally significant buildings from sub-surface construction. As the density of her aerial remote sensing datasets continues to grow exponentially with time, Prof. Laefer and her Urban Modeling Group pioneer computationally efficient storage, querying, and visualization strategies that both harness distributed computing-based solutions and bridge the gap between data availability and its usability for the engineering community. Prof. Laefer has authored over 180 peer-reviewed publications, been awarded 4 patents, and has supervised 15 doctoral and 20 Masters theses. Among many honors from IEEE, ISPRS, her portrait was commissioned by the Royal Irish Academy as one of eight researchers selected for the Women on Walls project to celebrate Irish women in science and engineering.  

Dr. Le Xie | Talk Title: A Cross-Domain Approach to Analyzing the Short-Run Impact of COVID-19 on the U.S. Electricity Sector 

Dr. Le Xie is a Professor and Chancellor EDGES Fellow in the Department of Electrical and Computer Engineering at Texas A&M University, and the Assistant Director-Energy Digitization at Texas A&M Energy Institute. He received B.E. in Electrical Engineering from Tsinghua University in 2004, S.M. in Engineering Sciences from Harvard in 2005, and Ph.D. in Electrical and Computer Engineering from Carnegie Mellon in 2009. His industry experience includes ISO-New England and Edison Mission Energy Marketing and Trading. His research interest includes modeling and control in data-rich large-scale systems, grid integration of clean energy resources, and electricity markets. Dr. Xie received the U.S. National Science Foundation CAREER Award, and DOE Oak Ridge Ralph E. Powe Junior Faculty Enhancement Award. He was awarded the 2017 IEEE PES Outstanding Young Engineer Award. He was recipient of Texas A&M Dean of Engineering Excellence Award, ECE Outstanding Professor Award, and TEES Select Young Fellow. He serves or have served on the Editorial Board of IEEE Transactions on Smart Grid, IET Transaction on Smart Grid, and Foundations and Trends in Electric Energy Systems. He is the founding chair of IEEE PES Subcommittee on Big Data & Analytics for Grid Operations. His team received the Best Paper awards at North American Power Symposium 2012, IEEE SmartGridComm 2013, HICSS 2019, IEEE Sustainable Power & Energy Conference 2019, and IEEE PES General Meeting 2020. 

 

Friday, September 4, 2020 at 11AM EST.

 

Aryya Gangopadhyay is a Professor in the Department of Information Systems at UMBC. He is also an Affiliate Professor of Computer Science and Electrical Engineering at UMBC. Dr. Gangopadhyay received his PhD in Computer Information Systems from Rutgers University in 1993. He has published over 125 peer-reviewed articles. His research has been supported by grants from NSF, NIST, DoED, IBM, and other places. His research areas are in deep learning, cybersecurity, and smart cities. Dr. Gangopadhyay has graduated 16 PhD students and has been a member of many additional committees.

 

 

Dr. Mantravadi is a Heath Economist with expertise in Data  Science education, and Big Data.

Talk Title: Quality Matters Based Best Practices for Data Science and Health Informatics Teaching 

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