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1. Promoting Inclusivity in Computing (PINC) via Computing Application Minor

   Yoon, I ; Pennings, P. ; Kulkarni, A. ; Okada, K ; Domingo, C. ( January 2018 , 2018 CoNECD - The Collaborative Network for Engineering and Computing Diversity Conference)

   We aimed to build a new educational pathway that would provide basic training in computer science for women and students from underrepresented (UR) groups who otherwise may not take computer science classes in college. Specifically, this on-going project focused on creating a 2-year Computer Science (CS) program consisting of exciting new courses aimed at biology majors. Biology traditionally attracts large numbers of women, a significant number of students from UR groups, and has compelling needs for CS technology. The interdisciplinary program is training the next generation of innovators in the biological sciences who will be prepared to cross disciplinary boundaries. The program consists of the following: (1) computer science courses with content related to biology, (2) cohorts of students that progress through the program together, and (3) a small group peer mentoring environment, and (4) facilitated interdisciplinary research projects. Graduates from this program, referred to as "PINC" - Promoting INclusivity in Computing - will receive a “Minor in Computing Applications” in addition to their primary science degree in Biology. The program is now in its second year and thus far 60 students have participated. Among them, 73% are women and 51% are underrepresented minorities (URM). The majority of students inmore »the PINC program stated that they would not have taken CS courses without the structured support of the PINC program. Here we present the data collected during this two year period as well as details about the Computing Application minor and programmatic components that are having a positive impact on student outcomes.« less
2. STEM Scholars Engaging in Local Problems

   Tian, E. ; Kishbaugh, T. ; Showalter, D. ; Barge, S. ( July 2022 , ASEE Annual Conference proceedings)

   Eastern Mennonite University received a 5-year S-STEM award for their STEM Scholars Engaging in Local Problems (SSELP) program. The goal of this place-based, interdisciplinary scholarship program is to increase the number of academically talented, low-income students who graduate in STEM fields and either pursue immediate employment in STEM careers or STEM-related service or continue their STEM education in graduate school. In 2018 and 2019, two cohorts of seven students were recruited to major in biology, chemistry, engineering, computer science, mathematics, or environmental science. A key part of recruitment involved on-campus interviews, during a February Scholarship Day, between STEM faculty and potential scholars. As the yield rate for the event is high (54-66%), the university has continued this practice, funding additional STEM scholarships. In order to retain and graduate the scholars in STEM fields, the SSELP faculty designed and carried out various projects and activities to support the students. The SSELP Scholars participated in a first-year STEM Career Practicum class, a one-credit course that connected students with regional STEM practitioners across a variety of fields. The scholars were supported by peer tutors embedded in STEM classes, and now many are tutors themselves. They participated in collaborative projects where the cohorts workedmore »to identify and solve a problem or need in their community. The SSELP scholars were supported by both faculty and peer mentors. Each scholarship recipient was matched with a faculty mentor in addition to an academic advisor. A faculty mentor was in a related STEM field but typically not teaching the student. Each scholar was matched with a peer mentor (junior or senior) in their intended major of study. In addition, community building activities were implemented to provide a significant framework for interaction within the cohort. To evaluate the progress of the SSELP program, multiple surveys were conducted. HERI/CIRP Freshman Survey was used in the fall of 2018 for the first cohort and 2019 for the second cohort. The survey indicated an upward shift in students’ perception of science and in making collaborative effort towards positive change. Preliminary data on the Science Motivation Questionnaire showed that the SSELP scholars began their university studies with lower averages than their non-SSELP STEM peers in almost every area of science motivation. After over three years of implementation of the NSF-funded STEM Scholars Engaging in Local Problems program, the recruitment effort has grown significantly in STEM fields in the university. Within the two cohorts, the most common majors were environmental science and engineering. While 100% of Cohorts 1 and 2 students were retained into the Fall semester of the second year, two students from Cohort 1 left the program between the third and fourth semesters of their studies. While one student from Cohort 2 had a leave of absence, they have returned to continue their studies. The support system formed among the SSELP scholars and between the scholars and faculty has benefited the students in both their academic achievement as well as their personal growth.« less
3. Implementing the CURE: Combining Wet-Lab Protein Biochemistry with Computational Analysis to Provide Gains in Student Learning in the Biochemistry Teaching Lab

   Pikaart, Michael J ( April 2018 , The FASEB journal)

   Most undergraduates studying biochemistry and molecular biology get their broadest exposure to wet-lab techniques in protein and nucleic acid chemistry (and, increasingly, computer/visualization) in their upper-level laboratory courses. These tend to be juniors and seniors with well-defined career goals. Some of these students will have already have a research background in a traditional one-to-one (or one-to-few) research mentoring setting, for example a summer research program. This approach has proved effective at increasing student learning and persistence in the sciences. At the same time, extended full-time PI-directed research is limited in the number of students served, and can even present a barrier. To broaden the impact of teaching through research, many practitioners have adopted a CURE, or Course-based Undergraduate Research Experience, approach.This presentation reports on “BASIL” (Biochemical Authentic Scientific Inquiry Laboratory), a team of faculty who have worked to bring computational and wet-lab protein science to the biochemistry teaching lab. Together, we have developed a protein biochemistry CURE to determine enzymatic function of proteins of unknown activity. This work leverages the results of the Protein Structure Initiative, a fifteen-year NIH-funded effort which concluded in 2015 with the publication and distribution of more than 5000 previously uncharacterized proteins. The great majority ofmore »these are “orphans,” with high quality structures and pre-cloned expression plasmids available, but no research on their enzymatic function or role in native organisms. The BASIL consortium of undergraduate biochemistry faculty and students seeks to identify functional properties of a subset of these uncharacterized proteins, seeking to unify structure and function relationships. Currently, implementable modules are available for faculty who wish to adopt them, and expected student results will be presented.Support or Funding InformationSupported by NSF IUSE 1709278This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.« less
4. The SEECRS Scholar Academy at Whatcom Community College: Three Cohorts of S-STEM Scholarships Later

   Babcock, M. J. ; Vannelli, T. A. ; Hanley, D. ; Davishahl, E. ( July 2021 , 2021 ASEE Virtual Annual Conference)

   The STEM Excellence through Engagement in Collaboration, Research, and Scholarship (SEECRS) project at Whatcom Community College is in year four of a five-year NSF S-STEM funded program aiming to support academically talented students with demonstrated financial need in biology, chemistry, geology, computer science, engineering, and physics. This program offered financial, academic, and professional support to three two-year cohorts of students and is in the final year of the third and final cohort of the currently funded grant cycle. The SEECRS project aimed to utilize a STEM-specific guided pathways approach to strengthen recruitment, retention, and matriculation of STEM students at the community college level. Over the course of the program 39 individuals received scholarship support. The program supported scholarship recipients through participation in the SEECRS Scholars Academy, a multi-pronged approach to student support combining elements of community building, faculty mentorship, targeted advising activities, authentic science practice, and social activities. Key elements of the program are: a required two-credit course that emphasized STEM identity development, course-based undergraduate research experiences (CUREs) in Biology, Chemistry and Engineering courses, funded summer research opportunities, and paring of each scholar with a faculty mentor. This paper presents data from the first four years of the program includingmore »participant outcomes and feedback on their experiences. Results from project evaluation activities such as pre and post surveys, focus groups, exit interviews, and faculty surveys are also presented and analyzed to compare how gains reported by program participants regarding such attributes as their STEM identities and sense of belonging compare to responses from a control group of students who did not participate in the program. Preliminary identification of some program best practices will also be presented.« less
5. The Deep Learning Epilepsy Detection Challenge: Design, Implementation, and Test of a New Crowd-Sourced AI Challenge Ecosystem

   Kiral, Isabell ; Roy, Subhrajit ; Mummert, Todd ; Braz, Alan ; Tsay, Jason ; Tang, Jianbin ; Asif, Umar ; Schaffter, Thomas ; Mehmet, Eren ; Picone, Joseph ; et al ( April 2019 , Challenges in Machine Learning Competitions for All (CiML))

   The DeepLearningEpilepsyDetectionChallenge: design, implementation, andtestofanewcrowd-sourced AIchallengeecosystem Isabell Kiral*, Subhrajit Roy*, Todd Mummert*, Alan Braz*, Jason Tsay, Jianbin Tang, Umar Asif, Thomas Schaffter, Eren Mehmet, The IBM Epilepsy Consortium◊ , Joseph Picone, Iyad Obeid, Bruno De Assis Marques, Stefan Maetschke, Rania Khalaf†, Michal Rosen-Zvi† , Gustavo Stolovitzky† , Mahtab Mirmomeni† , Stefan Harrer† * These authors contributed equally to this work † Corresponding authors: rkhalaf@us.ibm.com, rosen@il.ibm.com, gustavo@us.ibm.com, mahtabm@au1.ibm.com, sharrer@au.ibm.com ◊ Members of the IBM Epilepsy Consortium are listed in the Acknowledgements section J. Picone and I. Obeid are with Temple University, USA. T. Schaffter is with Sage Bionetworks, USA. E. Mehmet is with the University of Illinois at Urbana-Champaign, USA. All other authors are with IBM Research in USA, Israel and Australia. Introduction This decade has seen an ever-growing number of scientific fields benefitting from the advances in machine learning technology and tooling. More recently, this trend reached the medical domain, with applications reaching from cancer diagnosis [1] to the development of brain-machine-interfaces [2]. While Kaggle has pioneered the crowd-sourcing of machine learning challenges to incentivise data scientists from around the world to advance algorithm and model design, the increasing complexity of problem statements demands of participants to be expert datamore »scientists, deeply knowledgeable in at least one other scientific domain, and competent software engineers with access to large compute resources. People who match this description are few and far between, unfortunately leading to a shrinking pool of possible participants and a loss of experts dedicating their time to solving important problems. Participation is even further restricted in the context of any challenge run on confidential use cases or with sensitive data. Recently, we designed and ran a deep learning challenge to crowd-source the development of an automated labelling system for brain recordings, aiming to advance epilepsy research. A focus of this challenge, run internally in IBM, was the development of a platform that lowers the barrier of entry and therefore mitigates the risk of excluding interested parties from participating. The challenge: enabling wide participation With the goal to run a challenge that mobilises the largest possible pool of participants from IBM (global), we designed a use case around previous work in epileptic seizure prediction [3]. In this “Deep Learning Epilepsy Detection Challenge”, participants were asked to develop an automatic labelling system to reduce the time a clinician would need to diagnose patients with epilepsy. Labelled training and blind validation data for the challenge were generously provided by Temple University Hospital (TUH) [4]. TUH also devised a novel scoring metric for the detection of seizures that was used as basis for algorithm evaluation [5]. In order to provide an experience with a low barrier of entry, we designed a generalisable challenge platform under the following principles: 1. No participant should need to have in-depth knowledge of the specific domain. (i.e. no participant should need to be a neuroscientist or epileptologist.) 2. No participant should need to be an expert data scientist. 3. No participant should need more than basic programming knowledge. (i.e. no participant should need to learn how to process fringe data formats and stream data efficiently.) 4. No participant should need to provide their own computing resources. In addition to the above, our platform should further • guide participants through the entire process from sign-up to model submission, • facilitate collaboration, and • provide instant feedback to the participants through data visualisation and intermediate online leaderboards. The platform The architecture of the platform that was designed and developed is shown in Figure 1. The entire system consists of a number of interacting components. (1) A web portal serves as the entry point to challenge participation, providing challenge information, such as timelines and challenge rules, and scientific background. The portal also facilitated the formation of teams and provided participants with an intermediate leaderboard of submitted results and a final leaderboard at the end of the challenge. (2) IBM Watson Studio [6] is the umbrella term for a number of services offered by IBM. Upon creation of a user account through the web portal, an IBM Watson Studio account was automatically created for each participant that allowed users access to IBM's Data Science Experience (DSX), the analytics engine Watson Machine Learning (WML), and IBM's Cloud Object Storage (COS) [7], all of which will be described in more detail in further sections. (3) The user interface and starter kit were hosted on IBM's Data Science Experience platform (DSX) and formed the main component for designing and testing models during the challenge. DSX allows for real-time collaboration on shared notebooks between team members. A starter kit in the form of a Python notebook, supporting the popular deep learning libraries TensorFLow [8] and PyTorch [9], was provided to all teams to guide them through the challenge process. Upon instantiation, the starter kit loaded necessary python libraries and custom functions for the invisible integration with COS and WML. In dedicated spots in the notebook, participants could write custom pre-processing code, machine learning models, and post-processing algorithms. The starter kit provided instant feedback about participants' custom routines through data visualisations. Using the notebook only, teams were able to run the code on WML, making use of a compute cluster of IBM's resources. The starter kit also enabled submission of the final code to a data storage to which only the challenge team had access. (4) Watson Machine Learning provided access to shared compute resources (GPUs). Code was bundled up automatically in the starter kit and deployed to and run on WML. WML in turn had access to shared storage from which it requested recorded data and to which it stored the participant's code and trained models. (5) IBM's Cloud Object Storage held the data for this challenge. Using the starter kit, participants could investigate their results as well as data samples in order to better design custom algorithms. (6) Utility Functions were loaded into the starter kit at instantiation. This set of functions included code to pre-process data into a more common format, to optimise streaming through the use of the NutsFlow and NutsML libraries [10], and to provide seamless access to the all IBM services used. Not captured in the diagram is the final code evaluation, which was conducted in an automated way as soon as code was submitted though the starter kit, minimising the burden on the challenge organising team. Figure 1: High-level architecture of the challenge platform Measuring success The competitive phase of the "Deep Learning Epilepsy Detection Challenge" ran for 6 months. Twenty-five teams, with a total number of 87 scientists and software engineers from 14 global locations participated. All participants made use of the starter kit we provided and ran algorithms on IBM's infrastructure WML. Seven teams persisted until the end of the challenge and submitted final solutions. The best performing solutions reached seizure detection performances which allow to reduce hundred-fold the time eliptologists need to annotate continuous EEG recordings. Thus, we expect the developed algorithms to aid in the diagnosis of epilepsy by significantly shortening manual labelling time. Detailed results are currently in preparation for publication. Equally important to solving the scientific challenge, however, was to understand whether we managed to encourage participation from non-expert data scientists. Figure 2: Primary occupation as reported by challenge participants Out of the 40 participants for whom we have occupational information, 23 reported Data Science or AI as their main job description, 11 reported being a Software Engineer, and 2 people had expertise in Neuroscience. Figure 2 shows that participants had a variety of specialisations, including some that are in no way related to data science, software engineering, or neuroscience. No participant had deep knowledge and experience in data science, software engineering and neuroscience. Conclusion Given the growing complexity of data science problems and increasing dataset sizes, in order to solve these problems, it is imperative to enable collaboration between people with differences in expertise with a focus on inclusiveness and having a low barrier of entry. We designed, implemented, and tested a challenge platform to address exactly this. Using our platform, we ran a deep-learning challenge for epileptic seizure detection. 87 IBM employees from several business units including but not limited to IBM Research with a variety of skills, including sales and design, participated in this highly technical challenge.« less