- Ai - Engineering Mailing Lists

AI Alignment Reading Group Meeting
by Pope, Quintin 06 May '22

06 May '22

Hello everyone, There will be an AI alignment reading group meeting at 2 PM PST on Friday. We’ll be discussing “Superrational Agents Kelly Bet Influence! <https://www.alignmentforum.org/posts/7EupfLrZ63pbdyb9J/superrational-agents…>” It’s a short post on how cooperative agents with different utility ought to negotiate a consensus in regards to the degrees of influence different agents have on collective decision making. Please feel free to join if you’re interested! Join Zoom Meeting https://oregonstate.zoom.us/j/95843260079?pwd=TzZTN0xPaFZrazRGTElud0J1cnJLU… Password: 961594 Phone Dial-In Information +1 971 247 1195 US (Portland) +1 253 215 8782 US (Tacoma) +1 301 715 8592 US (Washington DC) Meeting ID: 958 4326 0079 All the best, Quintin

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AI seminar: May 11, 2022
by Mangannavar, Rajesh Devaraddi 05 May '22

05 May '22

Dear all, Our next AI seminar on *"Graphical Models for Handling Data that may be Incomplete or Non-iid" *by professor Karthika Mohan and Chi Zhang is scheduled to be on May 11th, 1-2 PM PST (Add to google calendar <https://calendar.google.com/event?action=TEMPLATE&tmeid=YXExYTZuY2NiZXRhbHJ…> ). It will be followed by a 30-minute Q&A session by the graduate students. Zoom Link: https://oregonstate.zoom.us/j/93591935144?pwd=YjZaSjBYS0NmNUtjQzBEdzhPeDZ5U… *Graphical Models for Handling Data that may be Incomplete or Non-iid* *Karthika Mohan *Computer Science Oregon State University * Chi Zhang* Computer Science University of California, Los Angeles *Abstract:* Majority of modern machine learning algorithms assume that data are iid and missingness/incompleteness are random. However, these assumptions rarely hold in real world datasets. For instance, the wealthy are less likely to reveal their income (income causes its own missingness) and an individual’s decision to get vaccinated is influenced by his family, friends and work colleagues. In this talk we will discuss the following, (i) modeling non-iid and missing data generation processes using causal Bayesian networks, (ii) determining whether or not a quantity of interest is recoverable under missingness, (iii) identifying conditions under which bias is to be expected given non-iid data and finally (iv) algorithm for eliminating bias given non-iid data. *Speaker Bio:* *Karthika Mohan* is an Assistant Professor of Computer Science in the School of EECS at Oregon State University. Before joining Oregon State University she was a postdoctoral scholar in the Computer Science department at University of California, Berkeley mentored by Stuart Russell. Karthika received her PhD in Computer Science (Artificial Intelligence) from University of California, Los Angeles (UCLA) where she was advised by Judea Pearl. Her research is of an interdisciplinary nature and her areas of interest include Causal Inference, Graphical Models and AI Safety. She was awarded the Google Outstanding Graduate Research Award, 2017 which is a UCLA Commencement Award. Currently she serves on the editorial board of the Journal of Causal Inference. *Chi Zhang* is a graduate student advised by Judea Pearl at the Computer Science Department in University of California, Los Angeles. Her research is mainly about causality, with a focus on causal inference under interference. *Please watch this space for future AI Seminars :* * https://eecs.oregonstate.edu/ai-events <https://eecs.oregonstate.edu/ai-events>* Rajesh Mangannavar, Graduate Student Oregon State University ---- AI Seminar Important Reminders: -> For graduate students in the AI program, attendance is strongly encouraged.

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[CoRIS-List] OSU Robotics Seminar 05/06: Shai Revzen, "Faster Models for Multi-Contact Motion"
by Ross L. Hatton 05 May '22

05 May '22

[Updated to include correct picture of speaker] Hi All, This week's OSU Robotics seminar will be presented by Shai Revzen from the University of Michigan, on "Faster Models for Multi-Contact Motion". The seminar will be from 10-11am, followed by an 11-11:30 student-only Q&A session. The seminar will be an *in person* event, simulcast over Zoom, and will be immediately followed by a half-hour students-only Q&A session with the speaker. May 6, 10am LINC 268 https://oregonstate.zoom.us/j/93908195212?pwd=cEdoQVdac0JxMUpOSS9xVzZjVG5xU… <https://oregonstate.zoom.us/j/93908195212?pwd=cEdoQVdac0JxMUpOSS9xVzZjVG5xU…> ----------------- Seminar Title: Faster Models for Multi-Contact Motion Abstract: Multi-legged robots have many potential applications, but their motion planning presents a challenge - e.g. when slipping is involved, and when multi-contact collisions can occur. We present two recent advances that promise both a deeper insight into the physics of these systems, and large computational gains in their modeling. The first advance relates to the realization that simple "Stokesian" (first order) equations of motion govern multi-legged locomotion. Using this observation we created fast learning algorithms that identify highly predictive models for the interaction physics of multi-contact gaits from a few dozen cycles of motion. We have also produced simulation codes that are remarkably simple computationally, yet fit our experimental observations to 90% accuracy or better. The second advance relates to the resolution of multi-contact collisions. We have shown that although they are discontinuous, such collisions often admit a first order approximation that can be computed thousands of times faster than conventional collision detection methods. Combined, these methods may open up a new era of autonomous multi-legged and multi-fingered robotic systems. The work presented was funded by the NSF CMMI 1825918, ARO W911NF-14-1-0573, ARO W911NF-17-1-0306, and the D. Dan and Betty Kahn Michigan-Israel Partnership for Research and Education Autonomous Systems Mega-Project. Bio: Shai Revzen is an Associate Professor of Electrical Engineering and Computer Science in the College of Engineering of the University of Michigan, Ann Arbor. He also holds a courtesy faculty appointment in the Department of Ecology and Evolutionary Biology, and positions in the Robotics Institute and the Applied Physics Program. Currently, he is also a Visiting Associate Professor in Mechanical Engineering in the Technion, Israel. He received his PhD in Integrative Biology from Berkeley, and a B.Sc. and M.Sc. from Hebrew University in Jerusalem. He did his postdoctoral work here, in the GRASP Laboratory of the University of Pennsylvania. Shai is also CSO of Acculine, an Israeli biomedical technology start-up. In his spare time he is in the second year of law school, studying toward a JD at Wayne State University. =========== Ross L. Hatton Associate Professor, Robotics and Mechanical Engineering Collaborative Robotics and Intelligent Systems Institute Oregon State University coris.oregonstate.edu <http://coris.oregonstate.edu/> research.engr.oregonstate.edu/lram/ <http://research.engr.oregonstate.edu/lram/> rosslhatton.com ross.hatton(a)oregonstate.edu

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[CoRIS-List] OSU Robotics Seminar 05/06: Shai Revzen, "Faster Models for Multi-Contact Motion"
by Ross L. Hatton 05 May '22

05 May '22

Hi All, This week's OSU Robotics seminar will be presented by Shai Revzen from the University of Michigan, on "Faster Models for Multi-Contact Motion". The seminar will be from 10-11am, followed by an 11-11:30 student-only Q&A session. The seminar will be an *in person* event, simulcast over Zoom, and will be immediately followed by a half-hour students-only Q&A session with the speaker. May 6, 10am LINC 268 https://oregonstate.zoom.us/j/93908195212?pwd=cEdoQVdac0JxMUpOSS9xVzZjVG5xU… <https://oregonstate.zoom.us/j/93908195212?pwd=cEdoQVdac0JxMUpOSS9xVzZjVG5xU…> ----------------- Seminar Title: Faster Models for Multi-Contact Motion Abstract: Multi-legged robots have many potential applications, but their motion planning presents a challenge - e.g. when slipping is involved, and when multi-contact collisions can occur. We present two recent advances that promise both a deeper insight into the physics of these systems, and large computational gains in their modeling. The first advance relates to the realization that simple "Stokesian" (first order) equations of motion govern multi-legged locomotion. Using this observation we created fast learning algorithms that identify highly predictive models for the interaction physics of multi-contact gaits from a few dozen cycles of motion. We have also produced simulation codes that are remarkably simple computationally, yet fit our experimental observations to 90% accuracy or better. The second advance relates to the resolution of multi-contact collisions. We have shown that although they are discontinuous, such collisions often admit a first order approximation that can be computed thousands of times faster than conventional collision detection methods. Combined, these methods may open up a new era of autonomous multi-legged and multi-fingered robotic systems. The work presented was funded by the NSF CMMI 1825918, ARO W911NF-14-1-0573, ARO W911NF-17-1-0306, and the D. Dan and Betty Kahn Michigan-Israel Partnership for Research and Education Autonomous Systems Mega-Project. Bio: Shai Revzen is an Associate Professor of Electrical Engineering and Computer Science in the College of Engineering of the University of Michigan, Ann Arbor. He also holds a courtesy faculty appointment in the Department of Ecology and Evolutionary Biology, and positions in the Robotics Institute and the Applied Physics Program. Currently, he is also a Visiting Associate Professor in Mechanical Engineering in the Technion, Israel. He received his PhD in Integrative Biology from Berkeley, and a B.Sc. and M.Sc. from Hebrew University in Jerusalem. He did his postdoctoral work here, in the GRASP Laboratory of the University of Pennsylvania. Shai is also CSO of Acculine, an Israeli biomedical technology start-up. In his spare time he is in the second year of law school, studying toward a JD at Wayne State University. =========== Ross L. Hatton Associate Professor, Robotics and Mechanical Engineering Collaborative Robotics and Intelligent Systems Institute Oregon State University coris.oregonstate.edu <http://coris.oregonstate.edu/> research.engr.oregonstate.edu/lram/ rosslhatton.com ross.hatton(a)oregonstate.edu

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AI seminar: May 4, 2022
by Mangannavar, Rajesh Devaraddi 03 May '22

03 May '22

Dear all, Our next AI seminar on *"Probabilistic Planning through the Lens of Approximate Inference" *by Roni Khardon is scheduled to be on May 4th(Tomorrow), 1-2 PM PST (Add to google calendar <https://calendar.google.com/event?action=TEMPLATE&tmeid=YXExYTZuY2NiZXRhbHJ…> ). It will be followed by a 30-minute Q&A session by the graduate students. Zoom Link: https://oregonstate.zoom.us/j/93591935144?pwd=YjZaSjBYS0NmNUtjQzBEdzhPeDZ5U… *Probabilistic Planning through the Lens of Approximate Inference* Roni Khardon Professor Department of Computer Science Indiana University, Bloomington *Abstract:* Planning to control an agent in a probabilistic environment requires reasoning about actions, their outcomes and their effects on the overall utility of the agent. The talk will emphasize the role of approximate probabilistic inference as a tool for understanding and developing new planning algorithms. I will first discuss planning through a differentiable symbolic form of belief propagation, and our corresponding system SOGBOFA that yields state of the art performance in a range of problems with large combinatorial action spaces. I will then discuss a more general framework that combines inference style and approximation style to derive old and new planning algorithms and show some preliminary results on their empirical performance. Time permitting I will show how these ideas can be extended for control in continuous spaces, with applications for reinforcement learning and robotics. *Speaker Bio:* Roni Khardon is a professor in the Department of Computer Science at Indiana University, Bloomington. He holds a Ph.D. in Computer Science from Harvard University, and M.Sc. and B.Sc. degrees from the Technion. Prior to moving to Bloomington he held faculty positions at the University of Edinburgh (1997-2000) and at Tufts University (2000-2018). His research interests are in developing agents that can learn from data, build representations of their world, use such knowledge for reasoning and decision making, and act in their environment so as to optimize their objectives. His recent work spans topics in AI (probabilistic planning, knowledge representation), machine learning (graphical models, approximate inference, computational learning theory) and the connections between these areas. This talk will be followed by a talk on the topic "*Graphical Models for Handling Data that may be Incomplete or Non-iid*" by professor Karthika Mohan and Chi Zhang on 11th May (Wednesday) 1-2 PM, 2022. *Please watch this space for future AI Seminars :* * https://eecs.oregonstate.edu/ai-events <https://eecs.oregonstate.edu/ai-events>* Rajesh Mangannavar, Graduate Student Oregon State University ---- AI Seminar Important Reminders: -> For graduate students in the AI program, attendance is strongly encouraged.

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CBEE Seminar on machine learning + materials with Taylor Sparks 5/2 at 4 PM Johnson 102
by Tadepalli, Prasad 02 May '22

02 May '22

Hello all, Some of you might find this CBEE seminar interesting (May 2, 4 pm). Please attend if you can. Prasad Tadepalli ________________________________ From: Mills, Tina K <tina.mills(a)oregonstate.edu<mailto:tina.mills@oregonstate.edu>> Sent: Thursday, April 28, 2022 9:40 AM To: CBEE All Faculty and Staff <cbee-all(a)oregonstate.edu<mailto:cbee-all@oregonstate.edu>>; CBEE Postdocs <cbee-postdocs(a)oregonstate.edu<mailto:cbee-postdocs@oregonstate.edu>>; cbee-grads(a)engr.orst.edu<mailto:cbee-grads@engr.orst.edu> <cbee-grads(a)engr.orst.edu<mailto:cbee-grads@engr.orst.edu>>; Engineering, All Faculty <all-faculty(a)engr.orst.edu<mailto:all-faculty@engr.orst.edu>> Subject: REMINDER: CBEE Seminar with Taylor Sparks Dear CBEE Graduate Students, Faculty, and Postdocs, A CBEE seminar is scheduled for Monday, May 2. Taylor Sparks, Associate Professor of Materials Science and Engineering at University of Utah will join us at 4 p.m. to deliver his talk, titled "Materials Informatics: Moving Beyond Screening Via Generative Learning Machine Learning Models." An abstract and brief speaker bio is provided below. Please try to attend if you are able. Spring Term Seminars are in-person in Johnson 102, but can also be accessed through the Zoom link provided below. Monday, May 2 4-4:50 p.m. Johnson 102 Zoom ID: 938 0547 9867 <https://oregonstate.zoom.us/j/93805479867?pwd=SkNkZElEVGx3Y0VWbUdLL2ZEcmZXd…> Passcode: cstr Materials informatics: Moving beyond screening via generative machine learning models ABSTRACT Technology progresses only as fast as the development of new, advanced materials. Materials discovery has never been more important, but it is far too slow and expensive. Materials informatics has accelerated materials development, but primarily allows us to screen known materials as opposed to truly discover new materials. Here, I will describe our efforts to generate new periodic crystalline materials by predicting crystallographic information file data using generative adversarial networks in conjunction with the newly published DiSCoVeR algorithm that combines a chemical distance metric, density-aware dimensionality reduction, clustering, and a regression model. ABOUT THE PRESENTER Dr. Sparks is an Associate Professor of Materials Science and Engineering at the University of Utah. He holds a BS in MSE from the UofU, MS in Materials from UCSB, and PhD in Applied Physics from Harvard University. He was a recipient of the NSF CAREER Award and a speaker for TEDxSaltLakeCity. When he's not in the lab you can find him running his podcast "Materialism" or canyoneering with his 4 kids in southern Utah. Tina Mills Assistant to CBEE School Head Chemical, Biological and Environmental Engineering GO BEAVS!

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Alignment reading group canceled today
by Pope, Quintin 29 Apr '22

29 Apr '22

Hello everyone, I'm again canceling the reading group for this week. Sorry about the inconsistent schedule. I hope to bring things back to more consistency within the next few weeks. All the best, Quintin Pope

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[CoRIS-List] OSU Robotics Seminar 4/29: ICRA 2022 practice talks
by Balasubramanian, Ravi 29 Apr '22

29 Apr '22

Folks: This week's OSU Robotics seminar will be presented by our own robotics students. The seminar will be an *in person* event and simulcast over Zoom. Friday April 29, 10—11 AM LINC 268 https://oregonstate.zoom.us/j/93908195212?pwd=cEdoQVdac0JxMUpOSS9xVzZjVG5xU… Here are the speakers and the topics: Helei Duan Time required: 4 min + questions Title: Sim-to-Real Learning of Footstep-Constrained Bipedal Dynamic Walking  Recently, work on reinforcement learning (RL) for bipedal robots has successfully learned controllers for a variety of dynamic gaits with robust sim-to-real demonstrations. In order to maintain balance, the learned controllers have full freedom of where to place the feet, resulting in highly robust gaits. In the real world however, the environment will often impose constraints on the feasible footstep locations, typically identified by perception systems. Unfortunately, most demonstrated RL controllers on bipedal robots do not allow for specifying and responding to such constraints. This missing control interface greatly limits the real-world application of current RL controllers. In this paper, we aim to maintain the robust and dynamic nature of learned gaits while also respecting footstep constraints imposed externally. We develop an RL formulation for training dynamic gait controllers that can respond to specified touchdown locations. We then successfully demonstrate simulation and sim-to-real performance on the bipedal robot Cassie. In addition, we use supervised learning to induce a transition model for accurately predicting the next touchdown locations that the controller can achieve given the robot's proprioceptive observations. This model paves the way for integrating the learned controller into a full-order robot locomotion planner that robustly satisfies both balance and environmental constraints.  Alexander You Time required: 4 min + questions Precision fruit tree pruning using a learned hybrid vision/interaction controller Robotic tree pruning requires highly precise manipulator control in order to accurately align a cutting implement with the desired pruning point at the correct angle. Simultaneously, the robot must avoid applying excessive force to rigid parts of the environment such as trees, support posts, and wires. In this paper, we propose a hybrid control system that uses a learned vision-based controller to initially align the cutter with the desired pruning point, taking in images of the environment and outputting control actions. This controller is trained entirely in simulation, but transfers easily to real trees via a neural network which transforms raw images into a simplified, segmented representation. Once contact is established, the system hands over control to an interaction controller that guides the cutter pivot point to the branch while minimizing interaction forces. With this simple, yet novel, approach we demonstrate an improvement of over 30 percentage points in accuracy over a baseline controller that uses camera depth data. Capprin Bass Time required: 4min + questions Title: Characterizing Error in Noncommutative Geometric Gait Analysis Authors: Capprin Bass and Ross L. Hatton Abstract: A key problem in robotic locomotion is in finding optimal shape changes to effectively displace systems through the world. Variational techniques for gait optimization require estimates of body displacement per gait cycle; however, these estimates introduce error due to unincluded high order terms. In this paper, we formulate existing estimates for displacement, and describe the contribution of low order terms to these estimates. We additionally describe the magnitude of higher (third) order effects, and identify that choice of body coordinate, gait diameter, and starting phase influence these effects. We demonstrate that variation of such parameters on two example systems (the differential drive car and Purcell swimmer) effectively manages third order contributions. Jeremy Dao Time required: 4 min + questions Title: Sim-to-Real Learning for Bipedal Locomotion Under Unsensed Dynamic Loads Authors: Jeremy Dao, Kevin Green, Helei Duan, Alan Fern, Jonathan Hurst Abstract: Recent work on sim-to-real learning for bipedal locomotion has demonstrated new levels of robustness and agility over a variety of terrains. However, that work, and most prior bipedal locomotion work, have not considered locomotion under a variety of external loads that can significantly influence the overall system dynamics. In many applications, robots will need to maintain robust locomotion under a wide range of potential dynamic loads, such as pulling a cart or carrying a large container of sloshing liquid, ideally without requiring additional load-sensing capabilities. In this work, we explore the capabilities of reinforcement learning (RL) and sim-to-real transfer for bipedal locomotion under dynamic loads using only proprioceptive feedback.  We show that prior RL policies trained for unloaded locomotion fail for some loads and that simply training in the context of loads is enough to result in successful and improved policies.  We also compare training specialized policies for each load versus a single policy for all considered loads and analyze how the resulting gaits change to accommodate different loads. Finally, we demonstrate sim-to-real transfer, which is successful but shows a wider sim-to-real gap than prior unloaded work, which points to interesting future research.  Tim Player Time required: 10 min + questions Title: Closed-Loop Generative Grasping with Temperospatial Sparse Convolution Authors: Tim Player, Dongsik Chang, Fuxin Li, Geoffrey Hollinger Abstract: Robotic grasp synthesis on unseen objects is a crucial skill that enables the application of generalized robotics to manufacturing, home service, and scientific applications. While state-of-the-art robotic grasping systems achieve reliable performance grasping previously unseen objects in laboratory conditions, robots deployed in real-world environments must continue to operate when visual conditions are degraded or when environments are dynamic. Motivated by the problem of underwater sample collection, we propose a novel algorithm that identifies 6-DOF grasp poses in a streaming depth video in real time, enabling operation in dynamic environments through closed loop control. Our technique uses temperospatial sparse convolution to process information from consecutive depth images in parallel, improving robustness to temporary occlusions. Our preliminary results indicate that the proposed grasp synthesis method is suitable for closed loop grasping. ----- Ravi Balasubramanian (he/him/his) Associate Professor Collaborative Robotics and Intelligent Systems (CoRIS) Institute School of Mechanical, Industrial, and Manufacturing Engineering Oregon State University Corvallis, OR 97331. ravi.balasubramanian(a)oregonstate.edu<mailto:ravi.balasubramanian@oregonstate.edu> <mailto:ravi.balasubramanian@oregonstate.edu><mailto:ravi.balasubramanian@oregonstate.edu> Graf Hall 315 Ph#: 541-737-4267 http://web.engr.oregonstate.edu/~balasubr/ Zoom: https://oregonstate.zoom.us/j/7036813309 Meeting password: robotics OSU Robotics: http://coris.oregonstate.edu/

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AI seminar: May 4, 2022
by Mangannavar, Rajesh Devaraddi 28 Apr '22

28 Apr '22

Dear all, Our next AI seminar on *"Probabilistic Planning through the Lens of Approximate Inference" *by Roni Khardon is scheduled to be on May 4th, 1-2 PM PST (Add to google calendar <https://calendar.google.com/event?action=TEMPLATE&tmeid=YXExYTZuY2NiZXRhbHJ…> ). It will be followed by a 30-minute Q&A session by the graduate students. Zoom Link: https://oregonstate.zoom.us/j/93591935144?pwd=YjZaSjBYS0NmNUtjQzBEdzhPeDZ5U… *Probabilistic Planning through the Lens of Approximate Inference* Roni Khardon Professor Department of Computer Science Indiana University, Bloomington *Abstract:* Planning to control an agent in a probabilistic environment requires reasoning about actions, their outcomes and their effects on the overall utility of the agent. The talk will emphasize the role of approximate probabilistic inference as a tool for understanding and developing new planning algorithms. I will first discuss planning through a differentiable symbolic form of belief propagation, and our corresponding system SOGBOFA that yields state of the art performance in a range of problems with large combinatorial action spaces. I will then discuss a more general framework that combines inference style and approximation style to derive old and new planning algorithms and show some preliminary results on their empirical performance. Time permitting I will show how these ideas can be extended for control in continuous spaces, with applications for reinforcement learning and robotics. *Speaker Bio:* Roni Khardon is a professor in the Department of Computer Science at Indiana University, Bloomington. He holds a Ph.D. in Computer Science from Harvard University, and M.Sc. and B.Sc. degrees from the Technion. Prior to moving to Bloomington he held faculty positions at the University of Edinburgh (1997-2000) and at Tufts University (2000-2018). His research interests are in developing agents that can learn from data, build representations of their world, use such knowledge for reasoning and decision making, and act in their environment so as to optimize their objectives. His recent work spans topics in AI (probabilistic planning, knowledge representation), machine learning (graphical models, approximate inference, computational learning theory) and the connections between these areas. This talk will be followed by a talk on the topic "*Graphical Models for Handling Data that may be Incomplete or Non-iid*" by professor Karthika Mohan and Chi Zhang on 11th May (Wednesday) 1-2 PM, 2022 *Please watch this space for future AI Seminars :* * https://eecs.oregonstate.edu/ai-events <https://eecs.oregonstate.edu/ai-events>* Rajesh Mangannavar, Graduate Student Oregon State University ---- AI Seminar Important Reminders: -> For graduate students in the AI program, attendance is strongly encouraged.

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(fwd) Internship and Full time positions at Oracle
by Weng-Keen Wong 27 Apr '22

27 Apr '22

My contact at Oracle sent this ad to me for internship and full-time opportunities. Please respond directly to Oracle if interested. Weng-Keen --------------- Job Title: Data Scientist Short description Designs, develops and programs methods, processes, and systems to consolidate and analyze unstructured, diverse "big data" sources to generate actionable insights, Machine Learning Models and solutions for CX Marketing products. Job Description Our future success depends on hiring outstanding, early in career talent who are looking to power next-generation services and solutions. We are hiring bachelor's, master's, and PhD graduates with a diverse set of skills and experiences to work in and across multiple teams within Oracle. Our program is intended to enhance your overall learning experience, help build your network, and accelerate your chance to make an impact. Create the future with us! The Company: Oracle is the world's leading provider of business software. With a presence in over 175 countries, we are one of the biggest technology companies in the world. We're using innovative emerging technologies to tackle real-world problems today. From advancing energy efficiency to reimagining online commerce, the work we do is not only redefining the world of business-it's helping advance governments, power nonprofits, and giving billions of people the tools they need to outpace change. For more information about Oracle (NYSE:ORCL), visit us at oracle.com. What You'll Do: Data Scientist responsibilities may include, but are not limited to: * Work on multiple stages of data science projects, including data pipeline, aggregation, feature engineering, modeling and visualization. * Develop models to build solutions to tackle real-world business problems * Interact with product and service teams to identify questions and issues for data analysis and experiments * Communicate your analysis findings to product, service, and business managers We are seeking to grow the data science capabilities with forward-thinking and diverse individuals with exceptional technical ability. This is an exciting role that will stretch your knowledge and curiosity, while at the same time is an excellent opportunity to learn new skills and work within an unusually talented, global community at Oracle. What You'll Bring (Objective Minimum Qualifications): To be considered for a Data Scientist position, you must possess the Objective Minimum Qualifications (OMQs) below. Please ensure that your application clearly indicates that you meet these OMQs, either on your resume and/or by uploading additional documents such as a transcript. * Have at least a Bachelor's degree in an applied business or quantitative field such as Statistics, Operations Research, Applied Mathematics, Computer Science, Economics, Business Analytics, or equivalent field by August 2022. * Have no more than 12 months of professional full-time work experience in the technology field (excluding internships, research and/or teaching assistant roles, and military experience). * Have academic coursework, projects, internships, and/or research experience with tools needed to execute the more technical aspects of statistical data analysis, including proficiency (e.g., can complete projects without any assistance) in two or more of the following programming languages: o Python, Java, R, SQL, PL/SQL. * Have experience through coursework, projects, internships, and/or research with one or more of the following libraries: o Numpy, Scipy, Matplotlib, SciKit, TensorFlow, PyTorch, Spark * Have completed coursework, projects, internships, and/or research in three or more of the following: o Algorithms o Artificial Intelligence o Big Data Query o Data Mining o Data Modeling o Machine Learning o Statistical Inference/Analysis * Have completed at least one internship, research assistantship, teaching assistantship, or equivalent practical experience in one of the Data Science areas listed above. * Reside in the United States and/or attend a university in the US. * Authorized to work in the US in 2022. Life at Oracle: An Oracle career can span industries, roles, countries and cultures, giving you the opportunity to tackle new roles and challenges, while blending work and life. Oracle has thrived through 40+ years of change by innovating and operating with integrity while delivering for the top companies in almost every industry. To nurture the talent that makes this happen, we work hard to build a vibrant and inspiring workplace that celebrates diverse, hardworking teams where everyone can contribute. We take care of each other, and value giving back to the community. We have flexible work arrangements and offer benefits including generous paid parental leave and comprehensive premium medical insurance. Designs, develops and programs methods, processes, and systems to consolidate and analyze unstructured, diverse "big data" sources to generate actionable insights and solutions for client services and product enhancement. Interacts with product and service teams to identify questions and issues for data analysis and experiments. Develops and codes software programs, algorithms and automated processes to cleanse, integrate and evaluate large datasets from multiple disparate sources. Identifies meaningful insights from large data and metadata sources; interprets and communicates insights and findings from analysis and experiments to product, service, and business managers. Duties and tasks are standard with some variation. Completes own role largely independently within defined policies and procedures. 2 years relevant experience and BA/BS degree preferred. If you are a Colorado resident, Please Contact us or Email us at oracle-salary-inquiries_us(a)oracle.com <mailto:oracle-salary-inquiries_us@oracle.com> to receive compensation and benefits information for this role. Please include this Job ID: 126881 in the subject line of the email.

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