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DePaul University’s Jarvis College of Computing and Digital Media were the proud sponsors of DePaul at the Women in Data Science Chicago. 

WiDS: Chicago was an exciting conference showcasing women data scientists, which took place on Friday, March 24th from 5:00-8:30 PM CDT at Roosevelt University located at 430 S. Michigan Ave. This free event was community-led and offered attendees the opportunity to expand their professional network, gain knowledge from their peers and experienced professionals, and learn from outstanding women doing outstanding work in the field of data science.

The conference was organized by Mena Whalen, Angelina Leigh, Maggie Wolff, Natalie Rubenstein, and Devanshi Verma as part of the annual WiDS Worldwide conference and an estimated 200 WiDS Regional Events worldwide. WiDS: Chicago was an independent event that was open to everyone, and attendees could participate in all WiDS conference and WiDS Datathon Workshop events.

The DePaul University Jarvis College of Computing and Digital Media was one of the sponsors of the event and had set up a desk giving out information about the various programs that the college had to offer and the inclusive nature of these programs. The college representatives were available to answer any questions that attendees may have had about their programs and initiatives.

Overall, the conference was a great success, providing a unique chance to connect with other data science professionals, learn from experts in the field, and discover new insights and trends in the world of data science. Attendees left the event feeling inspired and empowered to continue their work in the field of data science.

 

DePaul representation at WiDS Chicago.

 

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Applications of Data Science in Non-Technology Oriented Companies

Data science is taking the storm now everywhere around the world and is not constrained to just academia. It used to be that data science and machine learning was just used by technology companies like Microsoft and Google, but now it is being used in every industry and by almost every company. In this talk, I will provide examples of projects where I have used data science and machine learning in non-technology companies in the mobile and telecom industry, vehicle manufacturing industry and transportation logistics industry, to illustrate how data science can help solve complex problems, provide competitive insights to drive product development, and improve user experience.

When: Thursday, May 25, 2023, at 4 pm central time.

RSVP Link: https://forms.gle/7cAsKjs64Grj5du67

Speaker:

Dr. Alvin Chin

Dr. Alvin Chin Dr. Alvin Chin is currently Principal Data Scientist at RXO in Chicago where he is leading a Data Science team for creating and deploying data science and machine learning pricing models in production. He is also an Adjunct Professor at DePaul University’s Jarvis College of Computing and Digital Media teaching data science courses. Previously, he worked for BMW as Innovation Engineer for BMW Manufacturing; AI and Emerging Technology Researcher at BMW Tech Office USA; and Senior Machine Learning Researcher at BMW Technology Corporation. During his work at BMW, he worked on causal machine learning, neuromorphic and quantum computing, big data and machine learning for improving driving behavior and enabling intelligent driving, and applying AI to improve vehicle diagnostics, manufacturing and BMW’s internal processes. Dr. Chin has authored more than 30 publications and 10 patents, including pending. He received a PhD in Computer Science from the University of Toronto, MASc in Computer Engineering from the University of Waterloo and BASc from the University of Waterloo. He has previously worked for Nokia Research Center, Nokia and Microsoft in Beijing, China. Dr. Chin is active in the community in data science and machine learning meetups, as member of various program committees of academic conferences, and has given more than 50 talks to universities, meetups and conferences.

Interactive Machine Learning: Leveraging Strengths of Human and Machine.

Working with data has become a critical skill for today’s workforce as the cohort of businesses and organizations looking to cash in on the promise of big data grows. Despite their efforts, CEOs report failing to fulfill this promise. One reason is the unfair expectation that someone with a lifetime of expertise in another subject should quickly be able to master data science, a field that requires a lifetime to master in its own right. Instead, the tooling should come to the analysts where they already are. This goal means different things to different domains, but there are common threads. Careful design and implementation can leverage the best of human and machine, each performing its role. Human decision makers remain in control of interfaces curated to support their insight workflow, while behind-the-scenes machine learning augments human efforts. In this talk, the speaker discusses his work on these interactive machine learning (IML) systems, including with domain collaborators from diverse fields like biotechnology, medical informatics and journalism. In addition, he explains our recent drive to bring this technology to a suite of common data science problems.

When: Thursday, April 27, 2023, at 4 pm CST.

Speaker:

Eli T Brown

Eli T. Brown is an Associate Professor in the College of Computing and Digital Media (CDM) at DePaul University. He earned his B.A. from Cornell University in Computer Science and Math, and his Ph.D. and M.S. in Computer Science from Tufts University. His teaching is focused in the Data Science Program, where he teaches data visualization and machine learning. His research revolves around integrating the two disciplines together for more effective data analytics. He directs the Laboratory for Interactive Human-Computer Analytics (LIHCA; lihca.io), which develops new interactive machine learning technology to bring the power of machine learning to a wider audience with diverse problems to solve, including through collaborators in a variety of fields like biomedical, biotechnology and journalism.

Ensemble Labelling towards scientific information extraction.

Scientific publications constitute an extremely valuable repository of knowledge and collection of facts crucial to the advancement of science and development of applications, which grows as researchers learn from previous works and scientists use results in the literature to design and create. With the exponential growth of available publications, reading and extracting this wealth of information has become impractical for humans. Despite great progress in natural language processing, machine-learned solutions require large amounts of carefully annotated data for good performance. This is especially true in the context of accurately labeling and extracting complex scientific data. Towards our ultimate goal of extracting scientific facts from the literature, we first aim to identify blobs of text that contain all of the facts in a publication to be later automatically extracted or scrutinized by experts. Our previous work identified some facts missed by experts yet missed others due to the assumption that the target relation—here, a polymer and its glass transition temperature—would be contained within the same sentence. We set out to enhance our approximate labeling system to look back and ahead for missing information and successfully achieved 100% recall of scientific facts while reducing the full-text publication to 6% of its original size. Moreover, we assign confidence scores to sentences to further assist expert curators in identifying important sentences and facts locked in unstructured text.

When: Thursday, April 13, 2023, at 4 pm CST.

Speaker:

Roselyne Tchoua

Roselyne Tchoua is an Assistant Professor in the School of Computing, DePaul University. Her interests have always gravitated around making seemingly inaccessible technology or unmanageable amounts of data more reachable. She joined the DePaul Center for Data Science to continue working in the fascinating space between data science and other science fields (e.g., medicine), extracting insight from data using machine learning and natural language processing techniques. She received her PhD in computer science from the University of Chicago, focusing on Hybrid Human Machine Scientific Information Extraction and working with materials science data specifically. During her graduate studies, she collaborated with scientists at the UChicago Institute of Molecular Engineering and the National Institute of Standards and Technology (NIST) to extract polymer names and properties from the literature. Before going to the University of Chicago, she was a scientist at Oak Ridge National.

Machine Learning in Medical Informatics.

This talk covers a number of different applications of machine learning as applied to medical informatics.  It also introduces the problem of diagnosing chronic fatigue syndrome from survey data, lung nodule malignancy from radiologist-annotated computed tomography imaging, traumatic brain injury from magnetic resonance imaging and, separately, from free text data, and, finally, breast cancer malignancy from pathology slides.  The talk discusses commonalities and dissimilarities, and tailoring a machine learning strategy for each of the different tasks.

When: Thursday, March 13 2023, at 4 pm CST.

Speaker:

Jacob Furst

Jacob Furst is an Professor in the Jarvis College of Computing and Digital Media (JCDM) at DePaul University. His research interests are in medical informatics with applications of machine learning and data mining to medical image processing and computer vision. Dr. Furst earned his PhD in computer science from UNC Chapel Hill; he has a master’s degree in education and a bachelor’s degree in English literature.

 

Afternoon Tea at the TEAPOT LAB.

In this talk, the speaker describes several of the projects developed at TEAPOT (The Educational and Professional Online Training) Lab on a diverse range of topics: psychology research methods, the Lisp programming language, and deep understanding of argumentative texts. He also describes some of the Natural Language Processing techniques that support these systems and other systems that could be developed in the future. Because testing the performance of these systems is important, and because it was a data science-type talk, he also describe the data used to develop these systems and test how well they help learners learn.

When: Thursday, Feb 9, 2023, at 4 pm CST

Speaker: 

Peter Hastings

Peter Hastings has been at DePaul since 2001. For his PhD dissertation at the University of Michigan, he studied how people could learn the meanings of words from context. Afterwards, he was a postdoc at University of Memphis, working on interactive educational systems, one for helping 5th graders write stories, and another for college students learning the basics of computing. Before coming to DePaul, he taught Cognitive Science and Artificial Intelligence for two years at the University of Edinburgh. He teaches courses in Human Computer Interaction, Artificial Intelligence, and sometimes, Computational Neuroscience.

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Even if you have never participated in a hackathon or even think that hackathons are not for you…now is your time to learn that hackathons are a great way to test your entrepreneurial spirit; collaborate with data scientists and other technologists.

Hack4Space a global virtual hackathon

Build your skills and help make a difference

Strut your non-technical skills like product management, project management, marketing, and business leadership to solve wicked problems in a practical way that also make the world a better place. IBM Z and the DePaul Center for Data Science bring to you Hack4Space, a hackathon geared toward leveraging space data to solve a problem your team identifies using space data. We will provide training, systems access, and help you identify a team if you haven’t established one before the deadline. Expect to learn more about Design Thinking, a free cloud solution from IBM Z that’s open to all students from across the globe, data science, space technology, and pitching like a pro in a half-day virtual training (also free). Expect to leave inspired by your fellow classmates and experts from IBM focused on startup acceleration, space technology, data science, cloud computing, and much more.

Dates for the Hack4Space

Award Ceremony

The Hack4Space Awards Ceremony will be on May 27 from 4PM – 5:30 PM.

Keynote speakers will include:
– Kevin Murphy, Chief Data Scientist from NASA
– Michaela Musilova, Director of HI-SEAS, International Moonbase Alliance (IMA)
– Naeem Altaf, Distinguished Engineer, CTO – Space Tech from IBM
– Melissa Sass, Chief Penguin, IBM Z, Student & Entrepreneur Experience

Subscribe to IBM Expert TV at https://ibm.biz/experttv, then tune in and watch the live or on-demand episode: https://techtv.bemyapp.com/#/conference/60958224fb4332001b8e6065

Agenda for the Hackathon:

7th May 2021

click here to learn more about the speakers’ professional accomplishments and contributions

Who are the participants?

DePaul students, alumni and their friends are invited to participate in this exciting hackathon. Participants will work in teams of 4-6 people. You don’t need to be a developer and/or data scientist to participate in this hackathon, teams also need entrepreneurs, project managers, scientists, creative designers, etc…
We will allow up to 25 teams to participate in the immersive learning and hackathon journey. So register early! You can register with your team if you already have one. If you don’t have a team, that’s not a problem. You can register as an individual and you will be able to form a team on the day of the hackathon.

You don’t need to be a developer and/or data scientist to participate in this hackathon, teams also need entrepreneurs, project managers, creative designers, scientists, and artists!

Challenge Theme

Teams must leverage earth or space science data to identify a problem and propose a solution  using data.

The NASA Science Data Overview page provides inspiration for your project. Here some themes you can explore:

• Earth’s climate change and the impact on the environment
• The effect of the Sun on Earth and its impact on the rest of the solar system
• Using space data to learn about biological and physical systems on Earth.
• Is there life on other planets?

The NASA ‘s Science Data Archives page includes a comprehensive list of Space and Earth science datasets that you can use for your project:

• Earth Data Pathfinder (https://earthdata.nasa.gov/learn/pathfinders) 
• NASA COVID 19 Dashboard (https://earthdata.nasa.gov/covid19/) 
• NASA Worldview app (https://worldview.earthdata.nasa.gov/) 
• Planetary science (https://science.nasa.gov/planetary-data-system)
• Astrophysics (https://science.nasa.gov/astrophysics/astrophysics-data-centers)
• Space weather (https://science.nasa.gov/heliophysics/focus-areas/space-weather) 
• Atmosphere data  (https://earthdata.nasa.gov/learn/discipline/atmosphere) 
• Heliophysics (https://science.nasa.gov/heliophysics/heliophysics-data)
• Space Biology (https://science.nasa.gov/biological-physical/programs/space-biology),
• NASA GeneLab(https://genelab.nasa.gov)

Prizes

The top team will present their project and will have a chance to win mentorship sessions with employees from IBM Z and NASA. The winners will be announced at the Hack4Space Awards Ceremony on May 27th, 2021 at 4-5:30pm.

Sharing on social media

Don’t forget to join the IBM Z Global Student Hub at http://ibm.biz/student-hub and post that you are participating in Hack4Space. Below is a social media asset you can use; however, feel free to create something yourself that talks about why you are passionate about data science and participating in hackathons. Do not forget to tag IBM Z, DePaul, @_ilyas_ustun_ and @mentorafrika in Twitter (DePaul, Ilyas Ustun and Melissa Sassi in LinkedIn) to help get the content elevated. You can use the #Hack4Space in your social media posts.

Submission Instructions

Final submissions must include the following:

• a link to a video presentation (max 5 minute long) with a “demo” of your project. 
• a 500 word problem statement describing the challenge, the solution, and why it is important
• a link to source code/technical documentation on github or other public repository.

Please note: all links must be public and working properly. (They should not require permission or registration for our judges to access them.)

Scoring Rubric

Your submission will be scored based on the following criteria:

• Problem Statement: the problem statement was clearly identified and evidence clearly supports the problem statement.
• Relevance: the problem statement and solution are relevant and very well executed – you used space data and are solving a problem involving space data.
• Audience / Target Market: you have clearly defined who is facing the problem and who will utilize your solution – bonus point for having an early advocate.
• Solution Quality: you have created a solution using data science fundamentals that has been executed in an excellent fashion.
• Feasibility: Your solution is feasible and has been integrated into the data you have elected to use that solves the problem you have identified.
• Team Credibility / X-Factor: you have an excellent and well-rounded, diverse, and inclusive team and your presentation inspires the judges.
• LinuxONE Community Cloud Utilization: you received training from IBM Z on the LinuxONE Community Cloud on May 7. If you elect to use the LinuxONE Community Cloud in your solution, you will be awarded a bonus point during judging. It’s not mandated; however, you can decide if you want to take advantage of the opportunity to gain a bonus point!

Issue escalation

If you have any questions, please reach out to us. Do not be shy!
Ask any questions, and no question is dumb or silly. If you need to escalate any immediate issues, please use the Hack4Space Slack channel.

If you have any questions feel free to email us at dsc@depaul.edu

The post Hack4Space 2021 – IBM Z & DePaul Center for Data Science appeared first on DePaul Center for Data Science.

When: Friday, February 19, 2021, 4:00 pm CST – 5:30 pm CST

Agenda:

• Welcome and Introductions (10 min)
• Alumni Presentation (10 min presentation + 5 min Q&A per person)
  1. Aaren Stubberfield, Manager of Data Governance and AI from Ingredion Incorporated
  2. Ke Feng, Data Scientist from Aon
  3. Laura Floyd, Marketing Science Partner from Facebook
  4. Taihua (Ray) Li, Data Scientist from AWS
• General Q&A (20 min)

RSVP: https://forms.gle/J6JQAssxm6uTANQo6 by February 16, the zoom link will be emailed after RSVP.

Speakers:

Aaren Stubberfield: Voted by his employer Ingredion Inc. as 2019 Professional of the Year in an organization of over 11,000 employees, Aaren Stubberfield is a Data Science professional and online course author. With a diverse background, including Sales, R&D, Procurement, and Supply Chain, Aaren now holds the title of Manager of Data Governance and AI. He is responsible for playing a pivotal role in Ingredion’s strategy around data governance and leading a team of developers to deliver AI and machine learning projects. After receiving a master’s in Predictive Analytics from DePaul in 2015, Aaren used his experience and expertise to develop courses in “Supply Chain Analytics” and “Joining Data with pandas” on the online learning platform for Data Science, DataCamp.com. Over 14,000 students have participated in these courses. LinkedIn: https://www.linkedin.com/in/aaren-stubberfield/.

Ke Feng: Ke Feng currently works as a Data Scientist at Aon in Chicago. Before joining Aon, she completed her M.S. in Data Science at DePaul and holds a B.A. degree in Political Science and Media Studies. She developed her interests in computer programming and Data Science through DePaul’s program and gained her practical training through an internship course. Her current work involves extensive modeling on patent data, visualization, and NLP. LinkedIn: https://www.linkedin.com/in/fengke910/.

Laura Floyd: Laura Floyd is a Marketing Science Partner at Facebook, working with the platform’s top advertisers in adopting better measurement as a way to improve business outcomes. Prior to joining Facebook, Laura worked in data science and analytics within the advertising and market research industries at Leo Burnett and Kantar TNS. She is a “Double Demon,” having earned both her undergraduate (Marketing) and masters (Predictive Analytics) degrees from DePaul University. Additionally, she was the inaugural recipient of the DePaul University Department of Marketing “Young Alum of the Year” award in 2015. LinkedIn: https://www.linkedin.com/in/laurafloyd/.

Ray Li: Ray Li attained a Master of Science degree in Predictive Analytics from DePaul University and is currently a data scientist at Amazon Web Services (AWS). At AWS, he helps customers to realize desired business outcomes using machine learning, such as computer vision, natural language processing and time series forecasting. Prior to AWS, he worked on computer vision applications in the auto insurance and the automotive space where he helped develop the first AI-enabled vehicle collision estimate technology in the world. Outside of work, Ray enjoys taking fitness classes and reading non-fiction literatures. You can learn more about Ray at https://taihuali.com. LinkedIn: https://www.linkedin.com/in/taihuali/.

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When: Monday, November 9, 2020, 3:00 pm CST – 5:00 pm CST

Agenda:

• Welcome and Introductions (10 min)
  • Dr. Daniela Raicu, Director of DePaul Center for Data Science
  • Dr. Ilyas Ustun, Associate Director of DePaul Center for Data Science
• Projects Showcase (15 min presentation + 5 min Q&A per project)

1. 1. Computer-Aided Prognosis of Age-Related Macular Degeneration
      • Yiyang (Ian) Wang, PhD. Student in Data Science
      • Hao Wu, Graduate Student in Data Science
   2. COVID-19 Biomarker Detection in Chest X-rays
      • Mirtha Lucas, PhD. Student in Computer Science
   3. Does the User Have A Theory of the Recommender?
      • Muheeb Faizan Ghori, PhD. Student in Computer Science
   4. The Impact of COVID-19 on Transportation in Austin, Texas
      • Bhooma Shivakumar, Graduate Student in Data Science
   5. Analyzing COVID-19’s impact on Melbourne, Australia’s transportation network
      • Jennifer Franks, Graduate Student in Data Science

• Closing Remarks (10 min)
  • Dr. Daniela Raicu, Director of DePaul Center for Data Science
  • Dr. Ilyas Ustun, Associate Director of DePaul Center for Data Science

The event is open to anyone interested in research.

Please RSVP on the following link: https://forms.gle/GZapFgN8mU9Zq7Bc8

The post CDS Research Showcase Event appeared first on DePaul Center for Data Science.

Electronic word of mouth (eWoM) messages are increasingly consequential for consumers’ decisions regarding products/services. This has led to a large body of scholarly research on factors affecting eWoM message adoption. Adoption of an eWoM message refers to accepting the information and recommendations contained in an eWoM message, which consequently influence consumers’ cognitive and behavioral tendencies toward pertinent products/services. Notwithstanding the contributions of prior eWoM studies, we observe inconsistent findings across these studies that make a consensus difficult to reach. Lack of consistency is also evident among eWoM service providers in the selection and presentation order of the pertinent factors on their sites. To address this gap, we draw on the heuristic-systematic model in a meta-analytic structural equation modeling study to test a nomological eWoM adoption model that assesses the factors affecting the adoption of an eWoM message. Our meta-analysis of 87 eWoM studies, comprising 105,318 observations, sheds light on eight factors toward eWoM message adoption. Our findings unravel the multitude of ways in which these factors can influence eWoM message adoption and show the relative importance of these factors based on their total effects on eWoM message adoption. This enables the eWoM service providers to enhance the inconsistencies in the selection and presentation order of important factors toward eWoM message adoption on their sites.

When: Friday, March 6, 1-2:00pm

Where: CDM Theater 708

Who: HAMED QAHRI-SAREMI, Assistant Professor, DePaul University

Now the colloquium talks are live-streamed and available on YouTube!

Speaker bio: HAMED QAHRI-SAREMI is an Assistant Professor of Information Systems at DePaul University in Chicago, IL. He holds a Ph.D. in business administration with a concentration on information systems from the DeGroote School of Business, McMaster University. His research interests lie at the intersection of social and technological systems, including the positive and negative impacts of information systems and computer-mediated communications. His research papers have appeared in various journals, such as Journal of Management Information Systems, Journal of Strategic Information Systems, Information & Management, New Media & Society, Internet Research, Computers & Education, Expert Systems with Applications, Journal of Organizational Computing and Electronic Commerce, and European Financial Review, and are presented in various IS and management conferences. He has served in various capacities as an associate editor, a track chair, a reviewer for different journals and conferences in information systems.

The post Research Colloquium talk – Factors Affecting the Adoption of an Electronic Word of Mouth Message: A Meta-Analytic Structural Equation Modeling Study appeared first on DePaul Center for Data Science.

Peer monitoring refers to when peers, voluntarily, noticing and responding to others’ violations of organizational information security policies (ISP). This study explores how peer monitoring reduces employee ISP violations (or facilitates their compliance). Drawing upon theories in management, we propose that, peer monitoring discourages one to violate the ISP. Moreover, trust plays an important role. Not only does it facilitate peer monitoring, but also it moderates the effect of peer monitoring on employee ISP violations. Otherwise, collective responsibility leads to peer monitoring as well. The proposed research model is validated using data gathered via two waves of survey two weeks apart with 254 employees in the United States. We measured the dependent variable, one’s intention to violate the ISP, with four scenarios in the second wave, while all other constructs in the first wave. The results suggest that peer monitoring decreases one’s intention to violate the ISP. Furthermore, both collective responsibility and trust contribute to peer monitoring. Finally, trust amplifies the effect of peer monitoring on employee intention to violate the ISP. We discuss the theoretical contributions and practical implications.

When: Friday, February 28, 1-2:00pm

Where: CDM Theater 708

Who: Adel Yazdanmehr, Assistant Professor, Baruch College, The City University of New York

Now the colloquium talks are live-streamed and available on YouTube!

Speaker bio: Adel Yazdanmehr is an Assistant Professor of Information Systems at Baruch College, The City University of New York. He received his PhD in Management Information Systems from The University of Texas at Arlington, MS in Business Analytics from the University of Texas at Dallas, MBA from Mazandaran University of Science and Technology, and BS in Software Engineering from the University of Isfahan. His current research interests are mainly in the behavioral aspect of information assurance and Identity theft. His work has appeared in Information Systems Journal, Decision Support Systems journal as well as International Conference on Information Systems, and Dewald Roode Workshop on Information Systems Security Research (IFIP).

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Digitization or Industry 4.0 is currently the center of attention within commercial supply chains. The humanitarian sector, however, follows the commercial sector from far behind in terms of data management and computing capabilities, infrastructure and system architecture. There is tremendous room for new research from the fields of OR/AI/IT/CS in humanitarian supply chains. We will review what has been done so far and identify research gaps, needs and trends in this context.

When: Friday, February 21, 1-2:00pm

Where: CDM Theater 708

Who: Nezih Altay, Professor, Driehaus College of Business

Now the colloquium talks are live-streamed and available on YouTube!

Speaker bio: Nezih Altay is a Professor in the Department of Management & Entrepreneurship at the Driehaus College of Business. He holds a Ph.D. in Operations Management from Texas A&M University. Altay’s research specializes in humanitarian supply chains, disruption management, and socially responsible operations. He is the first recipient of the American Logistics Aid Network’s Academic Contributions Award. He has published over 30 papers and co-edited two books: Service Parts Management: Demand Forecasting and Inventory Control (2011) and Advances in Managing Humanitarian Operations (2016) both published by Springer. He currently serves as the Co-Editor-in-Chief of the Journal of Humanitarian Logistics & Supply Chain Management, and Senior Editor of Production and Operations Management. Altay is also the current President of the Humanitarian Operations and Crisis Management group within the Production and Operations Management Society. At DePaul, Dr. Altay serves as the faculty director of the M.S. in Supply Chain Management program. Altay just has been awarded a Fulbright Scholarship to visit Finland in 2021 to conduct research on aid logistics within conflict zones at the HumLog Institute.

The post Research Colloquium talk – Digitization in Humanitarian Supply Chains: A Rough Review of Research Needs appeared first on DePaul Center for Data Science.

In future, when the robots will surround us, there will be a need for the robots to communicate with us as effectively as we humans do with each other through various means–among all, through movement. As humans, when we walk around, our movement profile can be indicative of our inner state of mind, our emotions and affinity towards others. To make the robots more acceptable to people, they should be able to non-verbally communicate with humans as well. For this goal, this talk discusses a recipe for designing verified walking styles in two-legged robots using tools from trajectory optimization. The generated gaits are then verified for specific labels through user studies with lay human viewers. Overall this work is a step in the direction of making robots execute context-aware movement behaviors.

When: Friday, February 14, 1-2:00pm

Where: CDM Theater 708

Who: Umer Huzaifa, Visiting Assistant Professor, Rose-Hulman Institute of Technology

Now the colloquium talks are live-streamed and available on YouTube!

Speaker bio: Umer Huzaifa is a Visiting Assistant Professor in the Department of Electrical and Computer Engineering at Rose-Hulman Institute of Technology. His research interests are in making machines more expressive by generating variable behaviors in articulated robots, leveraging embodied movement analysis and practice in this pursuit. He completed his M.S. in mechanical engineering from UIUC and B.S. in electrical engineering from National University of Sciences and Technology (NUST) in 2016 and 2012, respectively. He was a finalist for the Best Poster Award at the 6th IEEE RAS/EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob 2016). He is also a recipient of the Mavis Future Faculty Fellowship (MF3) and Graduate Teaching Fellowship at UIUC.

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