printer friendly pageCOMPMED 290: Laboratory Animal Science Professional Development and Career Exploration
Focus is on career development for graduate students and trainees enrolled in a trainee program in the Department of Comparative Medicine. Seminar topics include career pathways in laboratory animal science, resume preparation, manuscript preparation and authorship, life in academics, life in industry and biopharma, regulatory agencies, veterinary and medical school. Speakers include faculty, speakers from industry and pharmaceutical companies, veterinary school and medical school graduates, regulatory and compliance professionals, research scientists, and animal research program/laboratory managers. Students may choose to shadow veterinary clinical faculty or rotate through basic science laboratory, by special arrangement. The objective is to introduce students to the multiple career pathways available to individuals with advanced training in laboratory animal science. May be taken up to six quarters.
Terms: Aut, Win
| Units: 1
| Repeatable
6 times
(up to 6 units total)
Instructors:
Green, S. (PI)
;
Juarez, V. (SI)
COMPMED 291: Masters Research Presentations
Students enrolled in Masters of Laboratory Animal Science Graduate Program will present their Masters research project to the department. The first few sessions of the course are designed to assist students with development of their scientific talk and presentation skills. All students will receive feedback and evaluations from the audience, including faculty and fellow trainees. These talks are intended to help students be prepared for job talks as they seek employment in biotech, academia, and professional school. This course will meet the research presentation requirement of the MLAS degree program.
Last offered: Spring 2023
| Repeatable
4 times
(up to 4 units total)
COMPMED 299: Directed Reading in Comparative Medicine
Prerequisite: consent of instructor. (Staff)
Terms: Aut, Win, Spr, Sum
| Units: 1-18
| Repeatable
for credit
COMPMED 370: Medical Scholars Research
Provides an opportunity for student and faculty interaction, as well as academic credit and financial support, to medical students who undertake original research. Enrollment is limited to students with approved projects.
Terms: Aut, Win, Spr, Sum
| Units: 4-18
| Repeatable
for credit
COMPMED 399: Graduate Research
Investigations sponsored by individual faculty members.Opportunities are available in comparative medicine and pathology, immuno-histochemistry, electron microscopy, molecular genetics, quantitative morphometry, neuroanatomy and neurophysiology of the hippocampus, pathogenesis of intestinal infections, immunopathology, biology of laboratory rodents, anesthesiology of laboratory animals, gene therapy of animal models of neurodegenerative diseases, and development and characterization of transgenic animal models. Prerequisite: consent of instructor.
Terms: Aut, Win, Spr, Sum
| Units: 1-18
| Repeatable
for credit
COMPMED 801: TGR Project
Terms: Aut, Win, Spr, Sum
| Units: 0
| Repeatable
for credit
CS 58: You Say You Want a Revolution (Blockchain Edition)
This project-based course will give creative students an opportunity to work together on revolutionary change leveraging blockchain technology. The course will provide opportunities for students to become operationally familiar with blockchain concepts, supported by presentation of blockchain fundamentals at a level accessible to those with or without a strong technical background. Specific topics include: incentives, ethics, crypto-commons, values, FOMO 3D, risks, implications and social good. Students will each discover a new possible use-case for blockchain and prototype their vision for the future accordingly. Application and impact areas may come from medicine, law, economics, history, anthropology, or other sectors. Student diversity of background will be valued highly.
Last offered: Winter 2019
CS 102: Working with Data - Tools and Techniques
Aimed at non-CS undergraduate and graduate students who want to learn a variety of tools and techniques for working with data. Many of the world's biggest discoveries and decisions in science, technology, business, medicine, politics, and society as a whole, are now being made on the basis of analyzing data sets. This course provides a broad and practical introduction to working with data: data analysis techniques including databases, data mining, machine learning, and data visualization; data analysis tools including spreadsheets, Tableau, relational databases and SQL, Python, and R; introduction to network analysis and unstructured data. Tools and techniques are hands-on but at a cursory level, providing a basis for future exploration and application. Prerequisites: comfort with basic logic and mathematical concepts, along with high school AP computer science,
CS106A, or other equivalent programming experience.
Last offered: Spring 2020
| UG Reqs: WAY-AQR
CS 224V: Conversational Virtual Assistants with Deep Learning
Generative AI, and in particular Large Language Models (LLMs), has already changed how we work and study. But this is just the beginning, as it has the potential of assisting and perhaps eventually automating knowledge workers in all areas, from law, medicine, to teaching and mental health therapists. This course will focus on the general principles and the latest research on methodologies and tools that can be applied to all domains. This is a project-oriented course, where students will gain hands-on experience in either methodology research or applying the concepts to create useful assistants for a domain of their choice. Topics include: (1) growing LLMs' knowledge through a combination of manual supervised learning and self-learning, (2) stopping LLMs from hallucination by grounding them with external corpora of knowledge, which is necessary for handling new, live, private as well as long-tail data, (3) handling external data corpora in different domains including structured and un
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Generative AI, and in particular Large Language Models (LLMs), has already changed how we work and study. But this is just the beginning, as it has the potential of assisting and perhaps eventually automating knowledge workers in all areas, from law, medicine, to teaching and mental health therapists. This course will focus on the general principles and the latest research on methodologies and tools that can be applied to all domains. This is a project-oriented course, where students will gain hands-on experience in either methodology research or applying the concepts to create useful assistants for a domain of their choice. Topics include: (1) growing LLMs' knowledge through a combination of manual supervised learning and self-learning, (2) stopping LLMs from hallucination by grounding them with external corpora of knowledge, which is necessary for handling new, live, private as well as long-tail data, (3) handling external data corpora in different domains including structured and unstructured data, (4) experimentation and evaluation of conversational assistants based on LLMs, (5) controlling LLMs to achieve tasks, (6) persuasive LLMs, (7) multilingual assistants, and (8) combining voice and graphical interfaces. Prerequisites: one of
LINGUIST 180/280,
CS 124,
CS 224N,
CS 224S, 224U.
Terms: Aut
| Units: 3-4
Instructors:
Lam, M. (PI)
;
Bruzzese, T. (TA)
;
Jandaghi Semnani, S. (TA)
...
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Instructors:
Lam, M. (PI)
;
Bruzzese, T. (TA)
;
Jandaghi Semnani, S. (TA)
;
Singh, A. (TA)
;
Sridhar, A. (TA)
CS 231N: Deep Learning for Computer Vision
Computer Vision has become ubiquitous in our society, with applications in search, image understanding, apps, mapping, medicine, drones, and self-driving cars. Core to many of these applications are visual recognition tasks such as image classification and object detection. Recent developments in neural network approaches have greatly advanced the performance of these state-of-the-art visual recognition systems. This course is a deep dive into details of neural-network based deep learning methods for computer vision. During this course, students will learn to implement, train and debug their own neural networks and gain a detailed understanding of cutting-edge research in computer vision. We will cover learning algorithms, neural network architectures, and practical engineering tricks for training and fine-tuning networks for visual recognition tasks.Prerequisites: Proficiency in Python - All class assignments will be in Python (and use numpy) (we provide a tutorial here for those who
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Computer Vision has become ubiquitous in our society, with applications in search, image understanding, apps, mapping, medicine, drones, and self-driving cars. Core to many of these applications are visual recognition tasks such as image classification and object detection. Recent developments in neural network approaches have greatly advanced the performance of these state-of-the-art visual recognition systems. This course is a deep dive into details of neural-network based deep learning methods for computer vision. During this course, students will learn to implement, train and debug their own neural networks and gain a detailed understanding of cutting-edge research in computer vision. We will cover learning algorithms, neural network architectures, and practical engineering tricks for training and fine-tuning networks for visual recognition tasks.Prerequisites: Proficiency in Python - All class assignments will be in Python (and use numpy) (we provide a tutorial here for those who aren't as familiar with Python). If you have a lot of programming experience but in a different language (e.g. C/C++/Matlab/Javascript) you will probably be fine.College Calculus, Linear Algebra (e.g.
MATH 19,
MATH 51) -You should be comfortable taking derivatives and understanding matrix vector operations and notation. Basic Probability and Statistics (e.g.
CS 109 or other stats course) -You should know basics of probabilities, gaussian distributions, mean, standard deviation, etc.
Terms: Spr
| Units: 3-4
Instructors:
Adeli, E. (PI)
;
Li, F. (PI)
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