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The University will offer a Bachelors of Science in Data Science for the first time the degree had previously only been available as a minor in the recently-founded School of Data Science. First years will be able to apply to the major starting this spring semester.

The Data Science Institute was founded in 2013, with the current School of Data Science created in 2019 following a $120 million gift from the Quantitative Foundation the largest private donation in University history.

The new Data Science major will focus on four domains systems, analytics, value and design. It is an interdisciplinary major bringing elements of computer science, statistics, ethics and mathematics together.

Phil Bourne, founding Data Science dean and Biomedical Engineering professor, said the process to get the major approved consisted of getting the proposal through the Provosts Office and then to the Faculty Senate, where it was sent to the State Council of Higher Education for Virginia.

The major is looked at relative to what's going on in other universities, Bourne said. Everybody wants to do data science right now, its unbelievable.

Applicants must have either completed or be currently enrolled in two prerequisite courses Foundations of Data Science and Programming for Data Science. The programming requirement can also be fulfilled by a variety of entry-level computer science courses, Advanced Placement or International Baccalaureate credit.

Professor of Data Science Dr. Aaron Abrams joined the University's data science faculty this past year as the new major has increased the need for professors. Abrams said while Data Science incorporates content from comparable majors, such as computer science or statistics, there are other aspects that make it unique.

It also incorporates elements from design and ethics, Abrams said. To be an effective data scientist, you need some exposure to all those different domains.

Students who are currently in their second year or beyond can not apply for the major given the three-year course load. Computer science is a highly popular alternative.

Second-year College student Daniel Brock, majoring in computer science, said that a data science degree is valuable because of its rising popularity in the workforce.

If I had started off at U.Va. with the option to major in Data Science it very well may have been an option I would choose, Brock said. The market for computer scientists is not very great at the moment and the field is becoming increasingly competitive, whereas data science is continuing to grow in popularity with increasing demand making the market less competitive.

According to the U.S. Bureau of Labor Statistics, employment of data scientists is projected to grow 35 percent from 2022 to 2032, meaning the major is increasing in demand and value and about 17,700 openings for data scientists are projected each year.

According to U.Va. Data Science News, over 600 undergraduate students across 50 majors were actively pursuing a data science minor as of last spring.

Although planning to major in commerce, first-year College student Lynn Rizk, is considering a minor in Data Science as opposed to computer science.

Data Science equips you with analytical skills that help you draw conclusions based on information that you're studying, Rizk said. I'm more interested in analyzing information that will specifically help me with consumer and market related trends and using that to analyze businesses.

The University has offered a minor in Data Science for the past three years this past April, the University was ranked 11 in top 20 public universities for highest-paying jobs in Data Science. They have now joined 160 other universities that offer Data Science majors and related courses.

We did a survey last year of freshmen coming into U.Va., and almost half of them expressed some interest in potentially doing the major, Bourne said. The fact students are even interested [in the major] tells you that they are thinking, when I graduate from here in four years, what are the jobs going to be?

In light of the growing interest in data science among first years, Bourne's insights shed light on the University's forward-thinking approach. In January of 2019, the University announced the construction of the School of Data Science on Emmet Street, with an amphitheater and pond between the building and the street. The $35 million, 60,000-square-foot building is expected to open in the spring of 2024. Data science classes are currently being held across Grounds, but primarily in the Dell.

U.Va. School of Data Science will be holding Datapalooza 2023: The Future on Nov. 10 in Newcomb Hall. This event will focus on the future of data science, AI in education and the launch of the Futures Initiative.

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U.Va approves new Data Science B.S. - University of Virginia The Cavalier Daily

The Advanced Undergraduate Research Opportunities Program, or SuperUROP, is celebrating a significant milestone: 10 years of setting careers in motion.

Originally mapped out by Dean Anantha Chandrakasan (then the head of the Department of Electrical Engineering and Computer Science, SuperUROP is designed to act as a launching pad for careers in research and industry, allowing juniors and seniors to experience an authentic and authentically challenging research experience. Students begin their year-long effort by identifying a project and building a relationship with a faculty member or senior research scientist, before spending many hours per week engaged in closely focused research on a specific question; writing a high-quality research paper and bringing it through the review process; and finally, presenting their findings in a scientific poster conference attended by key stakeholders, including faculty, peers, and generous supporters of the program.

Unlike most homework or exams, which usually have a highly structured result, SuperUROP research is frequently very open-ended, morphing into graduate theses, startup plans, or industry positions as students continue their work well past the semesters close.

Research, especially as an undergraduate, is always very challenging, says Chelsea Finn '14, an alumna of SuperUROP who is now an assistant professor at Stanford University working on robotic interaction. Doing research as an undergraduate student is the best way to get a flavor of the ambiguity, challenge, and thrill that comes from trying to solve problems that no one has solved before. SuperUROP is super useful for figuring out if a career in research is a good fit.

Students come to SuperUROP to get ahead not only on research skills, but on the entrepreneurial skills theyll need for careers in startups and industry. A SuperUROP scholar in 2015-16, Eric Dahlseng '17 went on to co-found Empo Health, a medical device company. At its core, I think the SuperUROP program teaches undergraduates how to create things that dont exist (whether that be processes, ideas, technologies, etc.) and share those creations with the world effectively, says Dahlseng, whose company has introduced a device used to remotely monitor patients at risk of dangerous diabetic complications. This is an important set of skills for research and academia, but also an immensely important set of skills for entrepreneurship.

Dahlseng also found that SuperUROP stretched his communications abilities I took the communication portion of my SuperUROP very seriously, recalls the entrepreneur, who received the Ilona Karmel Writing and Humanistic Studies Prize for Engineering Writing award for the paper portion of his project. As I advance in my career, and especially as Empo Health grows, the importance of good scientific communication is only expanding. I find my role focusing more and more on the communication pieces as I work on growing the team and establishing strong collaboration amongst everyone, sharing our learnings with key stakeholders, and highlighting what were creating for end customers and users.

Luis Voloch '13, SM '15 can also testify to the power of SuperUROP to transform strong students into strong scientific communicators. When Voloch was enrolled in SuperUROP, in 2012-13, he investigated how sources of information, including viruses, can be concealed or revealed in computer networks. He is now the co-founder of Immunai, an AI-driven cancer immunotherapy biotech company based in New York City which employs over 140 people and develops technologies at the intersection of AI, genomics, big data, and immunology. In addition to his career at Immunai, Voloch lectures within the Stanford Graduate School of Business on management and entrepreneurship topics in data science and AI-heavy companies. In both roles, the communications skills he acquired during his SuperUROP experience help him connect with students. In my SuperUROP, I started to learn how to do better scientific communications, which I built up further during my graduate research work and beyond. Communicating clearly is a core professional and research skill, and Im thankful we got started with it that early.

As careers change and grow, those core skills can flex to meet new challenges. Jennifer Madiedo '19, MNG '20, a senior software engineer in Industry Solutions Engineering at Microsoft, credits her SuperUROP experience with developing her skills in scientific communication and storytelling. How do you introduce your work to someone who may understand the overarching concepts of your field but not all the details? How do you figure out what background work is relevant and pull it into a cohesive backstory? How do you explain your methodology without losing your reader in too many details? It's all about the communication; learning to communicate deeply technical ideas in a way peers can understand was a whole new challenge I hadn't really encountered before at MIT.

Madiedo started her career in a half-engineering, half-research natural language processing team at Microsoft, and now works directly with customers and their engineering teams to solve multifaceted problems. I'm completely out of a lab setting now, but the skills I learned in undergraduate research truly form the bedrock of how I communicate with my teammates and peers.

Again and again, the alumni of SuperUROP stress that communication often regarded as a soft skillwas one of the most important abilities to be tested and developed by the program. Communication is important in many areas, but is truly an essential part of science, says Chelsea Finn, who balances her research and teaching responsibilities at Stanford with a role on the Google Brain team. The ultimate outcome of science is knowledge, and that knowledge is not very useful if it is not communicated to others! Finn credits much of her passion for science communication to the infectious passion of her SuperUROP advisor, the late Seth Teller: Seth instilled in me the importance of conveying enthusiasm for things that I am excited about, especially when talking to students and mentees.

With 10 years of enthusiastic alumni now engaged in groundbreaking work across many fields, that legacy of enthusiasm continues to pull new scientists into the lab, and new students into a productive year of critical thinking, communicating, and creating through SuperUROP.

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Reflecting on a decade of SuperUROP at MIT - MIT News

Chi-Ren Shyu

Nov. 3, 2023

Chi-Ren Shyu has been named a Fellow of the American College of Medical Informatics (ACMI).

Shyu is the Paul K. and Dianne Shumaker Professor in electrical engineering and computer science and serves as the director of theMU Institute for Data Science and Informatics.

As one of 23 new ACMI Fellows elected by the 473-member body, Shyu represents excellence from academia, government and industry. Inductees are the best and brightest stars in the field demonstrating thought leadership, stellar experience and established scholarship, according to the ACMI.

Dr. Shyu has a sustained record in informatics research developing innovative technologies and explainable AI methods with significant impacts on industry, research community, and federal agencies, the ACMIs historical biography says. He is also recognized for building the National Institutes of Health and National Security Agency funded data science and informatics programs with trainees making notable contributions in the informatics community.

I am honored to have been elected as a Fellow of ACMI, Shyu said. It is a privilege to stand among such esteemed colleagues in the field of biomedical informatics within the College, all united in the mission to pioneer innovation in informatics research and education aimed at enhancing human health.

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Chi-Ren Shyu named a Fellow of the American College of Medical ... - University of Missouri

The School of Engineering has selected 13 new Takeda Fellows for the 2023-24 academic year. With support from Takeda, the graduate students will conduct pathbreaking research ranging from remote health monitoring for virtual clinical trials to ingestible devices for at-home, long-term diagnostics.

Now in its fourth year, the MIT-Takeda Program, a collaboration between MITs School of Engineering and Takeda, fuels the development and application of artificial intelligence capabilities to benefit human health and drug development. Part of the Abdul Latif Jameel Clinic for Machine Learning in Health, the program coalesces disparate disciplines, merges theory and practical implementation, combines algorithm and hardware innovations, and creates multidimensional collaborations between academia and industry.

The 2023-24 Takeda Fellows are:

Adam Gierlach

Adam Gierlach is a PhD candidate in the Department of Electrical Engineering and Computer Science. Gierlachs work combines innovative biotechnology with machine learning to create ingestible devices for advanced diagnostics and delivery of therapeutics. In his previous work, Gierlach developed a non-invasive, ingestible device for long-term gastric recordings in free-moving patients. With the support of a Takeda Fellowship, he will build on this pathbreaking work by developing smart, energy-efficient, ingestible devices powered by application-specific integrated circuits for at-home, long-term diagnostics. These revolutionary devices capable of identifying, characterizing, and even correcting gastrointestinal diseases represent the leading edge of biotechnology. Gierlachs innovative contributions will help to advance fundamental research on the enteric nervous system and help develop a better understanding of gut-brain axis dysfunctions in Parkinsons disease, autism spectrum disorder, and other prevalent disorders and conditions.

Vivek Gopalakrishnan

Vivek Gopalakrishnan is a PhD candidate in the Harvard-MIT Program in Health Sciences and Technology. Gopalakrishnans goal is to develop biomedical machine-learning methods to improve the study and treatment of human disease. Specifically, he employs computational modeling to advance new approaches for minimally invasive, image-guided neurosurgery, offering a safe alternative to open brain and spinal procedures. With the support of a Takeda Fellowship, Gopalakrishnan will develop real-time computer vision algorithms that deliver high-quality, 3D intraoperative image guidance by extracting and fusing information from multimodal neuroimaging data. These algorithms could allow surgeons to reconstruct 3D neurovasculature from X-ray angiography, thereby enhancing the precision of device deployment and enabling more accurate localization of healthy versus pathologic anatomy.

Hao He

Hao He is a PhD candidate in the Department of Electrical Engineering and Computer Science. His research interests lie at the intersection of generative AI, machine learning, and their applications in medicine and human health, with a particular emphasis on passive, continuous, remote health monitoring to support virtual clinical trials and health-care management. More specifically, He aims to develop trustworthy AI models that promote equitable access and deliver fair performance independent of race, gender, and age. In his past work, He has developed monitoring systems applied in clinical studies of Parkinsons disease, Alzheimers disease, and epilepsy. Supported by a Takeda Fellowship, He will develop a novel technology for the passive monitoring of sleep stages (using radio signaling) that seeks to address existing gaps in performance across different demographic groups. His project will tackle the problem of imbalance in available datasets and account for intrinsic differences across subpopulations, using generative AI and multi-modality/multi-domain learning, with the goal of learning robust features that are invariant to different subpopulations. Hes work holds great promise for delivering advanced, equitable health-care services to all people and could significantly impact health care and AI.

Chengyi Long

Chengyi Long is a PhD candidate in the Department of Civil and Environmental Engineering. Longs interdisciplinary research integrates the methodology of physics, mathematics, and computer science to investigate questions in ecology. Specifically, Long is developing a series of potentially groundbreaking techniques to explain and predict the temporal dynamics of ecological systems, including human microbiota, which are essential subjects in health and medical research. His current work, supported by a Takeda Fellowship, is focused on developing a conceptual, mathematical, and practical framework to understand the interplay between external perturbations and internal community dynamics in microbial systems, which may serve as a key step toward finding bio solutions to health management. A broader perspective of his research is to develop AI-assisted platforms to anticipate the changing behavior of microbial systems, which may help to differentiate between healthy and unhealthy hosts and design probiotics for the prevention and mitigation of pathogen infections. By creating novel methods to address these issues, Longs research has the potential to offer powerful contributions to medicine and global health.

Omar Mohd

Omar Mohd is a PhD candidate in the Department of Electrical Engineering and Computer Science. Mohds research is focused on developing new technologies for the spatial profiling of microRNAs, with potentially important applications in cancer research. Through innovative combinations of micro-technologies and AI-enabled image analysis to measure the spatial variations of microRNAs within tissue samples, Mohd hopes to gain new insights into drug resistance in cancer. This work, supported by a Takeda Fellowship, falls within the emerging field of spatial transcriptomics, which seeks to understand cancer and other diseases by examining the relative locations of cells and their contents within tissues. The ultimate goal of Mohds current project is to find multidimensional patterns in tissues that may have prognostic value for cancer patients. One valuable component of his work is an open-source AI program developed with collaborators at Beth Israel Deaconess Medical Center and Harvard Medical School to auto-detect cancer epithelial cells from other cell types in a tissue sample and to correlate their abundance with the spatial variations of microRNAs. Through his research, Mohd is making innovative contributions at the interface of microsystem technology, AI-based image analysis, and cancer treatment, which could significantly impact medicine and human health.

Sanghyun Park

Sanghyun Park is a PhD candidate in the Department of Mechanical Engineering. Park specializes in the integration of AI and biomedical engineering to address complex challenges in human health. Drawing on his expertise in polymer physics, drug delivery, and rheology, his research focuses on the pioneering field of in-situ forming implants (ISFIs) for drug delivery. Supported by a Takeda Fellowship, Park is currently developing an injectable formulation designed for long-term drug delivery. The primary goal of his research is to unravel the compaction mechanism of drug particles in ISFI formulations through comprehensive modeling and in-vitro characterization studies utilizing advanced AI tools. He aims to gain a thorough understanding of this unique compaction mechanism and apply it to drug microcrystals to achieve properties optimal for long-term drug delivery. Beyond these fundamental studies, Park's research also focuses on translating this knowledge into practical applications in a clinical setting through animal studies specifically aimed at extending drug release duration and improving mechanical properties. The innovative use of AI in developing advanced drug delivery systems, coupled with Park's valuable insights into the compaction mechanism, could contribute to improving long-term drug delivery. This work has the potential to pave the way for effective management of chronic diseases, benefiting patients, clinicians, and the pharmaceutical industry.

Huaiyao Peng

Huaiyao Peng is a PhD candidate in the Department of Biological Engineering. Pengs research interests are focused on engineered tissue, microfabrication platforms, cancer metastasis, and the tumor microenvironment. Specifically, she is advancing novel AI techniques for the development of pre-cancer organoid models of high-grade serous ovarian cancer (HGSOC), an especially lethal and difficult-to-treat cancer, with the goal of gaining new insights into progression and effective treatments. Pengs project, supported by a Takeda Fellowship, will be one of the first to use cells from serous tubal intraepithelial carcinoma lesions found in the fallopian tubes of many HGSOC patients. By examining the cellular and molecular changes that occur in response to treatment with small molecule inhibitors, she hopes to identify potential biomarkers and promising therapeutic targets for HGSOC, including personalized treatment options for HGSOC patients, ultimately improving their clinical outcomes. Pengs work has the potential to bring about important advances in cancer treatment and spur innovative new applications of AI in health care.

Priyanka Raghavan

Priyanka Raghavan is a PhD candidate in the Department of Chemical Engineering. Raghavans research interests lie at the frontier of predictive chemistry, integrating computational and experimental approaches to build powerful new predictive tools for societally important applications, including drug discovery. Specifically, Raghavan is developing novel models to predict small-molecule substrate reactivity and compatibility in regimes where little data is available (the most realistic regimes). A Takeda Fellowship will enable Raghavan to push the boundaries of her research, making innovative use of low-data and multi-task machine learning approaches, synthetic chemistry, and robotic laboratory automation, with the goal of creating an autonomous, closed-loop system for the discovery of high-yielding organic small molecules in the context of underexplored reactions. Raghavans work aims to identify new, versatile reactions to broaden a chemists synthetic toolbox with novel scaffolds and substrates that could form the basis of essential drugs. Her work has the potential for far-reaching impacts in early-stage, small-molecule discovery and could help make the lengthy drug-discovery process significantly faster and cheaper.

Zhiye Song

Zhiye Zoey Song is a PhD candidate in the Department of Electrical Engineering and Computer Science. Songs research integrates cutting-edge approaches in machine learning (ML) and hardware optimization to create next-generation, wearable medical devices. Specifically, Song is developing novel approaches for the energy-efficient implementation of ML computation in low-power medical devices, including a wearable ultrasound patch that captures and processes images for real-time decision-making capabilities. Her recent work, conducted in collaboration with clinicians, has centered on bladder volume monitoring; other potential applications include blood pressure monitoring, muscle diagnosis, and neuromodulation. With the support of a Takeda Fellowship, Song will build on that promising work and pursue key improvements to existing wearable device technologies, including developing low-compute and low-memory ML algorithms and low-power chips to enable ML on smart wearable devices. The technologies emerging from Songs research could offer exciting new capabilities in health care, enabling powerful and cost-effective point-of-care diagnostics and expanding individual access to autonomous and continuous medical monitoring.

Peiqi Wang

Peiqi Wang is a PhD candidate in the Department of Electrical Engineering and Computer Science. Wangs research aims to develop machine learning methods for learning and interpretation from medical images and associated clinical data to support clinical decision-making. He is developing a multimodal representation learning approach that aligns knowledge captured in large amounts of medical image and text data to transfer this knowledge to new tasks and applications. Supported by a Takeda Fellowship, Wang will advance this promising line of work to build robust tools that interpret images, learn from sparse human feedback, and reason like doctors, with potentially major benefits to important stakeholders in health care.

Oscar Wu

Haoyang Oscar Wu is a PhD candidate in the Department of Chemical Engineering. Wus research integrates quantum chemistry and deep learning methods to accelerate the process of small-molecule screening in the development of new drugs. By identifying and automating reliable methods for finding transition state geometries and calculating barrier heights for new reactions, Wus work could make it possible to conduct the high-throughput ab initio calculations of reaction rates needed to screen the reactivity of large numbers of active pharmaceutical ingredients (APIs). A Takeda Fellowship will support his current project to: (1) develop open-source software for high-throughput quantum chemistry calculations, focusing on the reactivity of drug-like molecules, and (2) develop deep learning models that can quantitatively predict the oxidative stability of APIs. The tools and insights resulting from Wus research could help to transform and accelerate the drug-discovery process, offering significant benefits to the pharmaceutical and medical fields and to patients.

Soojung Yang

Soojung Yang is a PhD candidate in the Department of Materials Science and Engineering. Yangs research applies cutting-edge methods in geometric deep learning and generative modeling, along with atomistic simulations, to better understand and model protein dynamics. Specifically, Yang is developing novel tools in generative AI to explore protein conformational landscapes that offer greater speed and detail than physics-based simulations at a substantially lower cost. With the support of a Takeda Fellowship, she will build upon her successful work on the reverse transformation of coarse-grained proteins to the all-atom resolution, aiming to build machine-learning models that bridge multiple size scales of protein conformation diversity (all-atom, residue-level, and domain-level). Yangs research holds the potential to provide a powerful and widely applicable new tool for researchers who seek to understand the complex protein functions at work in human diseases and to design drugs to treat and cure those diseases.

Yuzhe Yang

Yuzhe Yang is a PhD candidate in the Department of Electrical Engineering and Computer Science. Yangs research interests lie at the intersection of machine learning and health care. In his past and current work, Yang has developed and applied innovative machine-learning models that address key challenges in disease diagnosis and tracking. His many notable achievements include the creation of one of the first machine learning-based solutions using nocturnal breathing signals to detect Parkinsons disease (PD), estimate disease severity, and track PD progression. With the support of a Takeda Fellowship, Yang will expand this promising work to develop an AI-based diagnosis model for Alzheimers disease (AD) using sleep-breathing data that is significantly more reliable, flexible, and economical than current diagnostic tools. This passive, in-home, contactless monitoring system resembling a simple home Wi-Fi router will also enable remote disease assessment and continuous progression tracking. Yangs groundbreaking work has the potential to advance the diagnosis and treatment of prevalent diseases like PD and AD, and it offers exciting possibilities for addressing many health challenges with reliable, affordable machine-learning tools.

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2023-24 Takeda Fellows: Advancing research at the intersection of ... - MIT News