Past events
BICB Colloquium: Shijia Zhu
Dr. Shijia Zhu is presenting as part of the nomination process for new BICB faculty. He is an Assistant Professor in the Department of Laboratory Medicine and Pathology at the Medical School.
Title
Abstract
Dr. Zhu integrates diverse multi-omics data in his studies, including the spatial transcriptomics, Pacbio SMRT-seq long read, and traditional bulk sequencing technologies. Meanwhile, he designed the novel computational methods of multi-omics data analysis tailored for disease studies. In this presentation, he will talk about how to align the spot-level spatial transcriptomics to the nuclear morphology to achieve the single-cell level spatial data analysis, therefore enabling the real cell-cell interaction in disease tissues rather than the current spot-spot interaction. In addition, he will talk about how to combine the long read and short read sequencing to accurately characterize the pathogenic alternative splicing isoforms, which further contribute to the novel therapeutic strategy. Last, he will talk about how to screen the disease driving factors from a very small sample size via aggregating the downstream targets to minimize the false positives and improve the discovery power.
Biography
In 2012, Dr. Zhu obtained his Ph.D. degree in Computer Science and Bioinformatics from Harbin Institute of Technology, in China. He designed the novel methods of regulatory network among epigenetic marker, gene expression and alternative splicing. In 2013-2017, he did his postdoc at Icahn School of Medicine at Mount Sinai, at New York. During this period, he was working on the Pacbio SMRT-seq to study both novel form of DNA methylation in the eukaryotes genome and the alternative splicing isoforms. Since 2018, he is a research-track Assistant Professor at University of Texas Southwestern Medical Center. He is utilizing multi-omics data to study the digestive liver disease and liver cancer.
Computer Science Learning Abroad Online Info Session
This virtual info session is for Computer Science students who are interested in studying abroad to learn more about program opportunities. We will highlight programs featured on the Computer Science Major Advising Page for Learning Abroad and help both CLA and CSE Computer Science students to learn more about the process. Programs in many regions of the world will be featured to help Computer Science students better understand how to gain a global perspective and take advantage of learning abroad opportunities.
CS&E Colloquium: Socially-Informed Human-Centered AI
The computer science colloquium takes place on Mondays from 11:15 a.m. - 12:15 p.m. This week's speaker, Chan Young Park (Carnegie Mellon University), will be giving a talk titled "Socially-Informed Human-Centered AI."
Abstract
Biography
Graduate Programs Online Information Session
During each session, the graduate staff will review:
- Requirements (general)
- Applying
- Prerequisite requirements
- What makes a strong applicant
- Funding
- Resources
- Common questions
- Questions from attendees
Students considering the following programs should attend:
CS&E Colloquium: Decoding Abusive Adversaries for Safer Digital Systems
The computer science colloquium takes place on Mondays from 11:15 a.m. - 12:15 p.m. This week's speaker, Rosanna Bellini (Cornell Tech), will be giving a talk titled "Decoding Abusive Adversaries for Safer Digital Systems."
Abstract
People today face threats to their digital safety that most computing systems were never designed to protect them from: those closest to them. Abusive adversaries take ample advantage of standard user interfaces and ineffective anti-abuse mechanisms, leveraging their close social and physical proximity to their target to stalk, harass, and control. In this talk, I describe my research focused on intimate partner violence where I: (1) pioneer approaches to engaging with abusive adversaries first hand across online and in-person contexts, (2) design and deploy bespoke systems to challenge abusive behaviors via community-based interventions; and (3) develop new frameworks for building abuse-resilient technologies. I outline my research vision to achieve digital safety for all people across critical domains, including finance, healthcare, and research.
Biography
Rosanna Bellini is a Postdoctoral Associate at Cornell Tech in New York City. Her research develops data-driven and engaged research methods to tackle complex societal challenges, such as technology-enabled harms. Her work has been published in top-tier human-computer interaction (HCI) and computer security venues, including USENIX Security, IEEE S&P, CHI, and CSCW, and featured on the BBC World Service. She has received multiple Best Paper awards from CHI and CSCW, as well as Distinguished Paper awards from USENIX Security. Her research has helped to prompt legislative changes and improvements to consumer-facing financial applications, benefiting tens of millions of customers. She also helps to lead the Clinic to End Tech Abuse, a frontline service for survivors of technology-facilitated abuse, and has personally helped over 150 survivors reclaim their privacy, security, and financial freedom.
CS&E Colloquium: Towards a Future Powered By Safe and Human-Centered AI and ML
The computer science colloquium takes place on Mondays from 11:15 a.m. - 12:15 p.m. This week's speaker, Anshuman Chhabra (UC-Davis), will be giving a talk titled "Towards a Future Powered By Safe and Human-Centered AI and ML."
Abstract
With the growing adoption of ML and AI in society, there is an increasing need to ensure that models (and systems employing these models) are centered around human safety so as to minimize harm to end users. In this talk, I will motivate this problem through multiple examples, and delve into my works on the safety risks associated with security, robustness, fairness, and accuracy of model performance. I will describe my efforts along two thrusts: (1) advancing the science behind safe ML/AI, and (2) improving real-world systems to minimize harm. These efforts offer both theoretical guarantees and empirical efficacy, illustrating the synergy of theory and pragmatic application. To conclude, I will discuss two exciting directions for future work in this domain: (1) accelerating Generative AI alignment efforts, and (2) utilizing Generative AI for social good.
Biography
Anshuman Chhabra is a recent Ph.D graduate (September 2023) at the University of California, Davis advised by Prof. Prasant Mohapatra. His research seeks to safeguard users from harm by curbing the negative behavior of foundational ML/AI models as well as real-world systems employing these models. He received the UC Davis Graduate Student Fellowship in 2018, and has held research positions at Lawrence Berkeley National Laboratory (2017), the Max Planck Institute for Software Systems, Germany (2020), and the University of Amsterdam, Netherlands (2022). His research has been funded by the NSF, Army Research Laboratory, and Robert N. Noyce Foundation.
CS&E Colloquium: Domain-guided Machine Learning for Healthcare
The computer science colloquium takes place on Mondays from 11:15 a.m. - 12:15 p.m. This week's speaker, Yogatheesan Varatharajah (University of Minnesota), will be giving a talk titled "Domain-guided Machine Learning for Healthcare."
Abstract
Recent advances in wearables, brain implants, and sensing technology have enabled us to design systems that continuously monitor patients' brain health and ascertain individualized treatments for neurological diseases. However, there is a lack of efficient methods that translate continuous physiological data streams into meaningful biological models of underlying diseases, relate them to existing clinical knowledge and biomarkers, and provide actionable treatment parameters. Machine learning (ML) holds great promise in tackling these challenges; however, the mainstream black-box-ML approaches have proven to be untrustworthy because of label inconsistencies, spurious correlations, and the lack of deployment robustness. My goal is to ensure trustworthiness in ML for healthcare, particularly neurology, via a novel framework known as “Domain-guided Machine Learning” or “DGML” that merges machine learning with clinical domain expertise. In this talk, I will discuss the need for trustworthy ML in healthcare, how to leverage clinical domain knowledge to engineer trustworthy ML models, and several real-world applications of DGML in neurological care and decision making.
Biography
Dr. Yoga Varatharajah is an Assistant Professor in Computer Science & Engineering at the University of Minnesota and a Visiting Scientist at the Mayo Clinic, Rochester. He leads the Health Intelligence Laboratory, where an interdisciplinary team of researchers work closely to develop novel ML-based solutions to improve disease diagnosis, clinical decision making, review of patient data, and discovery of new clinical knowledge. He obtained his Ph.D. in Electrical and Computer Engineering at the University of Illinois at Urbana-Champaign. Over the past seven years, he has been working closely with domain experts at Mayo Clinic and Cleveland Clinic to develop, evaluate, and deploy domain-guided ML models to inform clinical decisions related to neurological diseases. His research has been published at reputed engineering conferences (e.g., Neurips, ML4H, BIBM, ISBI, EMBC, NER) and medical journals (e.g., Scientific Reports, Journal of Neural Engineering, Brain Communications, Epilepsia, Neuroimage), has contributed to an ongoing clinical trial in neuromodulation for epilepsy, and has resulted in a joint patent between Mayo and Illinois. He also received several honors, including a CSL Ph.D. Thesis Award, a Mayo-Clinic-Illinois Alliance Fellowship, an American Epilepsy Society Young Investigator Award, an NSF CRII Research Initiation Award, NCSA Faculty Fellowship, and several best paper awards and nominations.
BICB Colloquium: Kyungsoo Yoo
Kyungsoo Yoo is presenting as part of the nomination process for new BICB faculty. He is a Professor in the Department of Soil, Water, and Climate at the University of Minnesota
Title
Abstract
All earthworms in Minnesota are invasive. Much of the Northern Hemisphere was under ice sheets or cold periglacial environments that wiped out native earthworms. As the climate warmed in the early Holocene epoch, the glaciers melted, and the newly exposed lands evolved into the temperate, boreal, and arctic ecosystems that we see today. The natural dispersal rates of earthworms are slow. As a result, these vast ecosystems have evolved without earthworms for the past ~10,000 years. However, this status quo has been rapidly altered by the human-mediated introduction of European earthworms over the past decades and centuries and, more recently, Asian earthworms. I will present my research group's efforts to characterize the dramatic ecological and biogeochemical impacts that these invasive earthworms have had on formerly glaciated ecosystems. I want to highlight the earthworm invasion as a serious global change issue. At the same time, I will invite biologists and data scientists to consider the potential collaborative opportunities that invasive earthworms and soils present.
Code Freeze 2024 - AI for SE for AI
Thursday, Jan. 11, 2024, 8 a.m. through Thursday, Jan. 11, 2024, 5:30 p.m.
Robert H. Bruininks Hall
222 Pleasant Street SE
Minneapolis, MN 55455
Pricing:
- General Attendance: $139
- Students/CS&E Alumni: $99
- U of M Staff/Faculty: $99
General Attendee: Join the Waitlist
Student/Alumni Registration
There are only a few tickets for Students/Alumni!
Registration Includes breakfast, lunch & snacks.
(Note: Parking is not included)
Platinum Sponsor:
Gold Sponsor:
Code Freeze is a symposium organized by the University of Minnesota Software Engineering Center, partnered with various co-sponsors, that features best practices in software engineering and development. Since 2006, this annual winter event has provided a forum for innovators in the field to share their ideas, experiences, successes and challenges via thought-provoking talks and interactive workshops.
This topical full day symposium affords attendees the opportunity for networking, collaboration, exchange of knowledge and fresh insight into the field of software engineering.
Join us at our next Code Freeze event!
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Computer Science Ph.D. application deadline for fall
Computer Science Ph.D. application deadline for fall.
We offer fall admission only and do not admit for the spring semester.