01:198:405 - Seminar in Computers and Society

CS, In Collaboration with Rutgers Graduate School of Education, Awarded $4 Million USDOE Grant To Expand Participation in Computer Science-Focused STEM Education

Congratulations to Dr. Fran Trees for receiving a $4 million dollar Education Innovation Research (EIR) award in collaboration with Dr. Cynthia Blitz from Rutgers Graduate School of Education’s Center for Effective School Practices (CESP). The grant is one of only 28 awards nationwide and the only award to a recipient from New Jersey. The “Extending the Computer Science Pipeline” project will support efforts to broaden participation of high-needs students in STEM education – specifically with a computer science focus – through Technical Assistance (TA) for middle school educators in grades 5-8. Over the course of the grant period, the team will refine and implement a computer science TA Framework that involves tailored, purposeful TA to build capacity of middle school educators, as well as enhance educator engagement and collaboration through a researcher-practitioner partnership. This project will continue the successful and sustained partnership between Dr. Trees and the CS Department with Dr. Blitz and the Graduate School of Education to broaden participation of underrepresented students in STEM by increasing their access to and engagement with rigorous and relevant computer science education to subsequently increase interest, self-efficacy, and achievement.

Michmizos Lab receives 2 Best Paper Awards on Neuro-AI

Two papers on neuro-AI published by Michmizos Lab won the 2nd Best Paper Awards in two prominent conferences, in Brain Informatics and Robotics. The first paper presents for the first time a principled theoretical analysis of the practical advantages for enhancing neural networks with astrocytes. It demonstrates how astrocytes can learn memory sequences in associative networks. The second paper introduces the notion of informing neural networks through neuroscience that results to brain-optimized structures that minimize training. It demonstrates how a spiking neural network emulating the brain's oculomotor system can be used as a bio-inspired algorithm to control an in-house built robotic head.  The presentations and the papers for both papers follow.  1. Sequence Learning in Associative Neuronal-Astrocytic Networks Authors: Leo Kozachkov, Konstantinos Michmizos 2020 ACM Proceedings of the International Conference on Brain Informatics Place: 2nd Best Paper Award Presentation Link: https://youtu.be/4rSNQqvnROc Paper: https://link.springer.com/chapter/10.1007/978-3-030-59277-6_32   2. A Spiking Neural Network Emulating the Structure of the Oculomotor System Requires No Learning to Control a Biomimetic Robotic Head Authors: Praveenram Balachandar, Konstantinos Michmizos 2020 IEEE Proceedings of the International Conference on Biomedical Robotics and Biomechatronics (BioRob) Place: 2nd Best Paper Award Presentation Link: https://www.youtube.com/watch?v=4dMlFg3HiFw&pbjreload=101  Paper: https://arxiv.org/pdf/2002.07534.pdf

CS Ph.D. student Fangda Han's research on AI & Nutrition featured in Vice

CS Ph.D. student Fangda Han's research linking AI, computer vision, and nutrition was recently featured in a news article by Vice.  "These Pizzas Do Not Exist"  highlights the unique computational approach Fangda created to synthesize photo-realistic images of pizzas from the textual description of its ingredients.   MPG, the Multi-ingredient Pizza Generator combines state-of-the-art deep neural network models, StyleGANs, with conditional deep encoders to precisely control the content and appearance of synthetic pizza images. This advanced research will make it possible to forge new links between AI and nutrition.  The technology described in Fangda's work will help create educational games, assisting children and adults alike in learning about food, meal preparation, and the food's impact on health and wellness. Fangda's research work is described in a recent Arxiv report, but is also available for everyone to enjoy through the technical demo on http://foodai.cs.rutgers.edu.  Congratulations to Fangda and his collaborators!

Prof Hui Xiong named AAAS Fellow

Hui Xiong, a professor in the management science and information systems department at Rutgers Business School-Newark and New Brunswick has been named a fellow of of the American Association for the Advancement of Science (AAAS) The association cited Xiong for “distinguished contributions to the fields of data mining and mobile computing.”

Anastasis Sathopoulos wins Best Student paper Award

Title: Deception Detection in Videos using Robust Facial FeaturesAuthors: Anastasis Stathopoulos, Ligong Han, Norah Dunbar, Judee Burgoon, Dimitris MetaxasConference: Future Technologies Conference (FTC) 2020   

Prof. Yongfeng Zhang receives NSF grant to develop explainable search systems

Prof. Yongfeng Zhang received an NSF IIS grant for project titled "Scrutable and Explainable Information Retrieval with Model Intrinsic and Agnostic Approaches" for a total amount of $500,000, covering a three-year period starting from 10/01/2020. As a collaborative project with researchers from University of Utah, the team will develop explainable machine learning algorithms to provide transparent and explainable results in search engines that people use in their daily life. Information Retrieval (IR) systems are important for people for information access. For example, intelligent search engines are widely used in Web-based services such as web search, product search, and job search. Recently, sophisticated data and complicated black-box models have made modern IR systems less transparent to users. However, as more and more people rely on IR systems to guide their daily life and decision making, there has been growing needs of explainable search results, both for technical communities and the general public, so that they understand why certain search results are provided. Meanwhile, governmental agencies are demanding IR systems to provide not only high-quality results, but also reasonable justifications, so as to enhance the trustworthiness of the systems. This project focuses on developing algorithms and frameworks to improve the scrutability, explainability, and transparency of modern IR systems. It will inspire large-scale academic-industry collaboration, which benefits billions of users by facilitating the development of reliable and explainable information access services. This project will develop general and reusable frameworks for scrutable and explainable IR. Research in this project will be performed on two directions. The first direction aims at new retrieval models for model-intrinsic explanation. This includes developing transparent inference process and decision boundaries for retrieval actions, scrutable functions that support result exploration with user feedback, and traceable information flow to distinguish the contribution of model inputs. The second direction aims at building analytical and simulative framework for model-agnostic explanation. This includes post-hoc explanation systems with external knowledge, and a simulation framework over black-box retrieval models with explainable outputs. Besides model-intrinsic and model-agnostic approaches, this project will also investigate crowd-sourcing tasks and systematic metrics to compare the effectiveness of intrinsic and agnostic explanations. The research outcomes will include multiple public benchmark datasets and evaluation platforms for explainable IR, which will contribute to the research community for sustainable and reproducible future studies. More details can be found on the National Science Foundation's webpage at https://www.nsf.gov/awardsearch/showAward?AWD_ID=2007907 and https://www.nsf.gov/awardsearch/showAward?AWD_ID=2007398.

Profs. Pavlovic and Kapadia win NSF grant to study Deep Learning methods for Crowd Behavior-Environment Interactions

  Professors and Mubbasir Kapadia, together with Prof. Sejong Yoon from TCNJ (a Rutgers CS alumnus) have received a $1M grant from the NSF IIS Robust Intelligence directorate to develop new deep learning models & algorithms for understanding of interactions between human crowds and the environments they occupy: Project NUCLEUM: Learning Joint Crowd-Space Embeddings for Cross-Modal Crowd Behavior Prediction This award will support, over the next four years, a unique research project that aims to bring together state-of-the-art in probabilistic multimodal Machine Learning, crowd & behavior modeling, and building design. The project will address the deficiencies of traditional physical simulation-based crowd models by devising a novel way of modeling the inherent relationship between the structure and semantics of complex environments, and the presence and behavior of its human occupants, from small groups to dense crowds. The main goal is to accurately predict crowd behavior, from microscopic motion to aggregate crowd dynamics, in novel, never-before-seen environment configurations using Neuro-Cognitive Modeling of Environments and Humans (NUCLEUM) to replace the computationally expensive yet often mismatched-with-reality physical simulations. Find out more about this project at https://www.nsf.gov/awardsearch/showAward?AWD_ID=1955404 and at https://project-nucleum.github.io.  

Domingo, David

Complaint & Appeals Process - MS

Complaints and Appeals Policy MS Program Department of Computer Science November, 2020 The university and the department have safeguards in place to protect members of the community both as students and as employees, and to maintain standards of responsibility and professionalism in graduate education.  Here is the graduate school code of responsible conduct and professionalism in graduate education, for faculty, students, mentors and mentees: https://gsnb.rutgers.edu/code-responsible-conduct-and-professionalism-graduate-education We expect and encourage: Honesty and integrity Respect and tolerance Sensitivity to differences among individuals Professionalism Attention to goals and responsibilities Timely and constructive feedback Acceptance of constructive feedback Inappropriate behaviors: Mistreatment, abuse, bullying, or harassment, whether by actions or language Unprofessional criticism Requests for personal services Assigning tasks as punishment or retribution Sexual assault or sexual harassment Discrimination Indifference to inappropriate behaviors that are witnessed University Resources: School of Graduate Studies (SGS) Problem Resolution: http://gsnb.rutgers.edu/student-services/problem-resolution Code of Student Conduct: http://studentconduct.rutgers.edu/disciplinary-processes/university-code-of-student-conduct/ Office of Violence Prevention and Victim Assistance: http://vpva.rutgers.edu/ Title IX, to report complaints http://compliance.rutgers.edu/title-ix/ CAPS: https://sasundergrad.rutgers.edu/academic-standing/student-services/1895-caps University ethics and compliance: https://uec.rutgers.edu/programs/ethics/ We expect you to fulfill your part here.  But we also are ready to assist you if you feel you are not being treated fairly by the people you are working with in the program, which includes your advisor or any staff members you are interacting with.  The above listed university resources are here to help you. If you don’t know or are unsure what university resource to reach out to, please do not hesitate to contact Dr. Barbara Bender (bbender@grad.rutgers.edu), Senior Associate Dean of the School of Graduate Studies. She will get you in touch with the appropriate university resources. Goals and Scope of this Document The goal of this document is to clarify the complaint and appeals process for the MS program in the Department of Computer Science. For simplicity, we will refer to a complaint or appeal as simply a “complaint”. If you experience any form of inappropriate behavior including harassment, bullying, or retaliation, you should reach out to Dr. Ulrich Kremer (uli@cs.rutgers.edu), the Graduate Program Director, or any university resources as listed above. Any member of the University who is aware of any harassment, bullying, or retaliation is strongly encouraged to report such inappropriate behavior. Any such behaviors will not be tolerated. There are separate processes and procedures in place to deal with these situations. The scope of this document is limited to complaints addressing academic decisions or situations, including your study plan, course grades, thesis, essays, independent study, and CPT. Sometimes graduate students need another avenue for dealing with problems that cannot be resolved within their graduate programs or other Rutgers office. If you have been unable to solve a problem or if you do not feel comfortable addressing a concern within your graduate program, you may wish to contact Dr. Barbara Bender (bbender@grad.rutgers.edu), Senior Associate Dean of the School of Graduate Studies, who is available to confidentially assist students and faculty in addressing a wide variety of concerns. Dean Bender will help students and faculty identify options, consider avenues to pursue, and seek alternate resolutions to disputes. When and How to File a Complaint As a general rule for academic decisions, you first need to clarify a decision or try to resolve a disagreement with the faculty member who made the decision. This may be your instructor who graded your homework or exam, your thesis or essay advisor, or the MS program director who approves your CPT. The root of many conflicts is a misunderstanding of a particular decision or situation, or a miscommunication about the expectation of a deliverable. If you made a good faith effort to resolve the situation, but still feel that you are treated unfairly or that a decision is not justified, you may file a complaint. Keep in mind that being unhappy about a grade or a decision is not a sufficient reason to file a complaint. For example, the fact that an instructor could have given you more points for an exam problem is not a valid reason for a complaint as long your score is consistent with the grading policy used for the particular exam problem, and the grading policy has been applied evenly and fairly. Where to send your complaint? If the complaint is about a grader or teaching assistant (TA), you should send the complaint to the instructor of the class. If your complaint is about the instructor of the class, you should file the complaint with the MS Program Director, assuming that the MS Program Director is not the instructor. If you have a complaint about the MS Program Director, you should file a complaint with the Graduate Program Director. If the complaint is about the Graduate Program Director, you should file it with the Graduate Program Committee. For any complaint beyond this level you should reach out to the Dean’s office or general university administration. What is the format of a complaint? The format of a complaint is an email message. You should clearly state the reasons for your complaint, list all individuals involved, and describe your failed, good faith effort to resolve the conflict in question. Be respectful and do not leave out any relevant details even if they may seem to weaken your case. What to expect after filing a complaint? You can expect to receive a response to your complaint via email within 10 working days from sending your complaint email. Please note that you may be asked to clarify some aspects of your complaint in order to allow a fair and informed decision. If you believe that the resulting decision is unfair or unjustified, you may escalate the complaint to the next level (see “Where to send your complaint?”). Please note that any escalation has to be justified and is not just another opportunity to be successful with your complaint. Sample Situations Where Conflicts May Arise This is not meant to be a complete list. (a) Study Plan Your study plan is an essential part of our MS program. You will need to meet with your advisor to discuss and develop your study plan. Your advisor will monitor and guide your progress towards successfully finishing your degree requirements. If you and your advisor cannot mutually agree in a timely fashion on a study plan that reflects your interests and is consistent with the MS program requirements, please contact the MS Program Director. If the MS Program Director is your advisor, please contact the Graduate Program Director. (b) Course Grades You can appeal your grade in a course. You should first contact the grader or instructor, (the latter for classes without graders or TAs) stating the grade you received, the grade you think you deserved, and your justification. In many cases, the grader or instructor will agree with you, apologize for the error, and make the change.  (Remember how quickly grading has to be done—errors unfortunately occur.) In other cases, the grader or instructor will explain their reasons for sticking with the original grade.  Be open to the possibility that there may be areas where you fell short in the class that you didn’t appreciate, in terms of technical skills or specific tasks and requirements. If you are still unsatisfied, you have the option of initiating the complaint process. (c) MS Theses, Essays, and Independent Studies Some aspects of our MS program involve a negotiation between you, the student, and a faculty advisor. Examples include the MS thesis, MS essay, and an independent study course (198:60X). Here, you and your faculty advisor will have to agree on the scope of the work required to successfully accomplish your deliverables. The negotiation of these required deliverables should be done in advance, resulting in an agreement between you and your advisor. However, since some of this work requires research or implementation work, things are unpredictable in nature, i.e., things may not work out, or are harder (or sometimes easier!) than expected. In these cases, a “re-negotiation” is often required which should involve your advisor and all members of your thesis committee. Your thesis committee members are an important resource who will be able to guide you and help with solving conflicts. Do not hesitate to reach out to them. If you believe that your advisor’s and committee’s expectations with respect to workload and timeline to produce results is unreasonable, and your advisor and committee disagree with your assessment and are not willing to change their expectations, you may file a complaint. Please be aware that an MS thesis counts for 6 credits, i.e., involves a significant amount of independent work. Most likely, there will be stumbling blocks on the way, which is the expected rather than the exceptional case when conducting research or implementing a larger software system. You should inform your advisor as soon as possible about such stumbling blocks in order to get your advisor’s help to solve your issues, or allow your advisor to adjust your “deliverables” if these issues cannot be solved within a reasonable time. It is your responsibility to keep your advisor informed in a timely fashion, and it is your advisor’s responsibility to guide you around these stumbling blocks. The same expectation holds for essays and independent studies. (d) CPT Curricular Practical Training (CPT) is another important aspect of your MS education. If you are an international student, you are allowed to spend up to 20 hours per week during the fall and spring semesters at an industrial internship covered by the CPT. During the summer, the CPT can be up to 40 hours per week. Your CPT has to be approved by the MS Program Director. The CPT has to match your study plan, and will only be approved if you are making good progress towards your study plan’s goals. The CPT should not be a distraction, but a contribution to your educational and professional goals. Typically, up to 10 - 15 hours per week may be considered reasonable, but based on your academic performance, you may only be able to work fewer hours, or not take a CPT at all. You may appeal any CPT decision. Other Matters Students can reach out to the Graduate Program Director about all matters concerning their status in the MS program, including any paperwork that requires approval from our MS program office, or a request for advice how to proceed with a potential complaint. In addition, students may contact Dr. Barbara Bender, Senior Associate Dean of the School of Graduate Studies, about any issues related to our MS program.

Complaint & Appeals Process - PhD

Complaints and Appeals Policy PhD Program Department of Computer Science November, 2020 The university and the department have safeguards in place to protect members of the community both as students and as employees, and to maintain standards of responsibility and professionalism in graduate education.  Here is the graduate school code of responsible conduct and professionalism in graduate education, for faculty, students, mentors and mentees: https://gsnb.rutgers.edu/code-responsible-conduct-and-professionalism-graduate-education We expect and encourage: Honesty and integrity Respect and tolerance Sensitivity to differences among individuals Professionalism Attention to goals and responsibilities Timely and constructive feedback Acceptance of constructive feedback Inappropriate behaviors: Mistreatment, abuse, bullying, or harassment, whether by actions or language Unprofessional criticism Requests for personal services Assigning tasks as punishment or retribution Sexual assault or sexual harassment Discrimination Indifference to inappropriate behaviors that are witnessed University Resources: School of Graduate Studies (SGS) Problem Resolution: http://gsnb.rutgers.edu/student-services/problem-resolution Code of Student Conduct: http://studentconduct.rutgers.edu/disciplinary-processes/university-code-of-student-conduct/ Office of Violence Prevention and Victim Assistance: http://vpva.rutgers.edu/ Title IX, to report complaints http://compliance.rutgers.edu/title-ix/ CAPS: https://sasundergrad.rutgers.edu/academic-standing/student-services/1895-caps University ethics and compliance: https://uec.rutgers.edu/programs/ethics/ We expect you to fulfill your part here.  But we also are ready to assist you if you feel you are not being treated fairly by the people you are working with in the program, which includes your advisor or any staff members you are interacting with.  The above listed university resources are here to help you. If you don’t know or are unsure what university resource to reach out to, please do not hesitate to contact Dr. Barbara Bender, Senior Associate Dean of the School of Graduate Studies. She will get you in touch with the appropriate university resources. Goals and Scope of this Document The goal of this document is to clarify the complaint and appeals process for the PhD program in the Department of Computer Science. For simplicity, we will refer to a complaint or appeal as simply a “complaint”. If you experience any form of inappropriate behavior including harassment, bullying, or retaliation, you should reach out to Dr. Ulrich Kremer (uli@cs.rutgers.edu), the Graduate Program Director, or any university resources as listed above. Any member of the University who is aware of any harassment, bullying, or retaliation is strongly encouraged to report such inappropriate behavior. Any such behaviors will not be tolerated. There are separate processes and procedures in place to deal with these situations. The scope of this document is limited to complaints addressing academic decisions or situations, including your course grades, TA and GA-ship , PhD thesis, independent study, and CPT. Sometimes graduate students need another avenue for dealing with problems that cannot be resolved within their graduate programs or other Rutgers office. If you have been unable to solve a problem or if you do not feel comfortable addressing a concern within your graduate program, you may wish to contact Dr. Barbara Bender, Senior Associate Dean of the School of Graduate Studies, who is available to confidentially assist students and faculty in addressing a wide variety of concerns. Dean Bender will help students and faculty identify options, consider avenues to pursue, and seek alternate resolutions to disputes. When and How to File a Complaint As a general rule, you first need to clarify a decision or try to resolve a disagreement with the faculty member who made the decision. This may be your instructor who graded your homework or exam, your faculty and thesis (research) advisor, the instructor you TA for, or the Graduate Program Director who approves your CPT and monitors your progress through the PhD program. The root of many conflicts is a misunderstanding of a particular decision or situation, or a miscommunication about the expectation of a deliverable. If you made a good faith effort to resolve the situation, but still feel that you are treated unfairly or that a decision is not justified, you may file a complaint. Keep in mind that being unhappy about a grade or a decision is not a sufficient reason to file a complaint. For example, the fact that an instructor could have given you more points for an exam problem is not a valid reason for a complaint as long your score is consistent with the grading policy used for the particular exam problem, and the grading policy has been applied evenly and fairly. Where to send your complaint? If the complaint is about a grader or teaching assistant (TA), you should send the complaint to the instructor of the class. If your complaint is about the instructor of the class, or your academic or thesis advisor, you should file the complaint with the Graduate Program Director, assuming that the Graduate Program Director is not the instructor or the advisor. If the complaint is about the Graduate Program Director, you should file your complaint with the Graduate Program Committee. For any complaint beyond this level you should reach out to the Dean’s office or general university administration. What is the format of a complaint? The format of a complaint is an email message. You should clearly state the reasons for your complaint, list all individuals involved, and describe your failed, good faith effort to resolve the conflict in question. Be respectful and do not leave out any relevant details even if they may seem to weaken your case. What to expect after filing a complaint? You can expect to receive a response to your complaint via email within 10 working days from sending your complaint email. Please note that you may be asked to clarify some aspects of your complaint in order to allow a fair and informed decision. If you believe that the resulting decision is unfair or unjustified, you may escalate the complaint to the next level (see “Where to send your complaint?”). Please note that any escalation has to be justified and is not just another opportunity to be successful with your complaint. Sample Situations Where Conflicts May Arise This is not meant to be a complete list. (a) TA Workload As a TA, you are expected to spend an average of 15 hours per week to support course instruction.  You must expect some crunch times where work is higher, especially around exam times.  This is because Rutgers gives very little time for grading, especially in the spring semester—students need their grades for graduation just a couple days after the exam period ends.  In addition, no matter what other teaching obligations there are, recitations and similar fixed sessions associated with the class must always meet as scheduled.  Although we know conflicts may arise because of considerations such as research travel, illness, and family emergencies, we expect you to notify the instructor as soon as possible and arrange for someone to cover for you (for example, swapping duties with another TA for the class).  Over the semester, the occasional weeks of heavy load should average out.  However, if you find that you are consistently spending more than the allocated 15 hours per week, you should prepare a short memo itemizing your duties for the class and the time that you are spending on each. Present it to your instructor for discussion and problem solving.  Be prepared for a range of outcomes that let you focus your time back on your own coursework and research: The instructor hires additional staff to assist with problematic tasks, such as grading. The instructor adjusts aspects of the course implementation so that TA duties are reduced. Small changes with few pedagogical ramifications – like making a difficult-to-grade program component extra credit – can sometimes lighten TA workload substantially. The instructor offers mentoring advice to improve your efficiency. Ineffective email strategies can be extremely time consuming. In grading, it’s usually not worth agonizing about details or writing long comments to students.   Being a successful teacher means finding the point of diminishing returns and stopping (maybe sooner than you’re comfortable with).  Write up your understanding of your discussion and share it with the instructor; keep track of how your time demands change in the week or two that follow.  If you still feel the burden is too large, or you feel that the tasks that you are asked to do fall outside what’s appropriate, you should file a complaint.  We will expect you to include in your complaint both your summary describing your initial problem with the class and the outcome of your meeting with the instructor to attempt a resolution.  Keep your messages short and to the point.  We are obligated to find a way to reduce your hours to the contractual amount. (b) GA Workload A GA also involves 15 hours per week of work, on average.  It’s important to be clear on what that means, however.  Research, like teaching, has crunch times where you will need to sprint—conference deadlines, project meetings, deliverable due dates.  It should average out.  Also, there is not always a clear divide between GA duties and your own research.  You need to have realistic expectations—finishing a PhD is a full-time job, and you should expect to work diligently but consistently on your own research ideas. Bottom line: If you are a GA, you should not be asked regularly to spend more than 15 hours per week on tasks that do not contribute to your research portfolio in terms of your dissertation work, publications, or other measures of research productivity and visibility (e.g., releasing open source software or data sets, participating in a high-profile competitive evaluation).  Such tasks might include building software infrastructure for others’ experiments, maintaining general lab software or equipment, managing or mentoring other teammates, or supporting external collaborators and assisting in technology transfer. If you find that you are consistently spending more than the allocated 15 hours per week on such tasks, you should prepare a short memo itemizing your duties and the time that you are spending on each. Present it to your supervisor for discussion and problem solving.  Again, be prepared for a range of good outcomes, including not only bringing on additional assistance but learning to work more effectively or finding ways to formalize the contributions the work brings to your own research.  Write up your understanding of your discussion and share it with your supervisor; keep track of how your time demands change in the weeks that follow.  If you still feel the burden is too large, or you feel that the tasks that you are asked to do fall outside what’s appropriate, you may file a complaint with the Graduate Program Director. We will expect you to include in your complaint both your summary describing your initial problem and the outcome of your meeting with your supervisor to attempt a resolution.  Keep your messages short and to the point.  We are obligated to find a way to reduce your hours to the contractual amount. (c) Course Grades You can appeal your grade in a course. You should first contact the grader or instructor, (the latter for classes without graders or TAs) stating the grade you received, the grade you think you deserved, and your justification. In many cases, the grader or instructor will agree with you, apologize for the error, and make the change.  (Remember how quickly grading has to be done—errors unfortunately occur.) In other cases, the grader or instructor will explain their reasons for sticking with the original grade.  Be open to the possibility that there may be areas where you fell short in the class that you didn’t appreciate, in terms of technical skills or specific tasks and requirements. If you are still unsatisfied, you have the option of initiating the complaint process. (d) PhD Theses and Independent Studies Major aspects of our PhD program involve a negotiation between you, the student, and a faculty or thesis advisor. This includes your PhD thesis topic, your PhD thesis, and independent study courses (198:60X). Here, you and your faculty advisor will have to agree on the scope of the work required to successfully accomplish your deliverables. The negotiation of these required deliverables should be done in advance, resulting in an agreement between you and your advisor. However, since some of this work requires research or implementation work, things are unpredictable in nature, i.e., things may not work out, or are harder (or sometimes easier!) than expected. In these cases, a “re-negotiation” is often required which should involve your advisor and all members of your thesis committee. Your thesis committee members are an important resource who will be able to guide you and help with solving conflicts. Do not hesitate to reach out to them. If you believe that your advisor’s and committee’s expectations with respect to workload and timeline to produce results is unreasonable, and your advisor and committee disagree with your assessment and are not willing to change their expectations, you may file a complaint. Most likely, there will be stumbling blocks on the way, which is the expected rather than the exceptional case when conducting research or implementing a larger software system. You should inform your advisor as soon as possible about such stumbling blocks in order to get your advisor’s help to solve your issues, or allow your advisor to adjust your “deliverables” if these issues cannot be solved within a reasonable time. It is your responsibility to keep your advisor informed in a timely fashion, and it is your advisor’s responsibility to guide you around these stumbling blocks. (e) CPT Curricular Practical Training (CPT) is another possible aspect of your PhD education. If you are an international student, you are allowed to spend up to 20 hours per week during the fall and spring semesters at an industrial internship covered by the CPT. During the summer, the CPT can be up to 40 hours per week. Your CPT has to be approved by the Graduate Program Director. The CPT has to match your research plan, and will only be approved if you are making good progress towards finishing your degree. The CPT should not be a distraction, but a contribution to your educational and research goals. Typically, up to 10 - 15 hours per week may be considered reasonable, but based on your academic performance or research progress, you may only be able to work fewer hours, or not take a CPT at all. For students with a TA or GA-ship, getting an approval for a CPT during the academic year is rather rare, and if granted, is likely to be restricted to 5 hours a week or less. You may appeal any CPT decision. More details related to CPT and OPT (Optional Practical Training) can be found here: https://global.rutgers.edu/international-scholars-students/students/current/employment/practical-training/cpt       and https://global.rutgers.edu/international-scholars-students/students/current/employment/practical-training/opt Other Matters Students can reach out to the Graduate Program Director about all matters concerning their status in the PhD program, including any paperwork that requires approval from our PhD program office, or a request for advice how to proceed with a potential complaint. In addition, students may contact Dr. Barbara Bender, Senior Associate Dean of the School of Graduate Studies, about any issues related to our PhD program.

Dissertation Committee & Proposal

Dissertation committee and proposal requirements: - By the end of your fourth year or within one year of passing the depth requirement, whichever is later, you must have formed your dissertation committee and have your proposal approved. - The dissertation committee consists of a) the advisor of record (a full member of the CS graduate faculty), b) two other inside members (full or associate members of the CS graduate Faculty), and c) the outside member, meeting School of Graduate Studies requirements and approved by the Graduate Program Director in Computer Science. - Once the committee has been formed and recorded, it can be changed only by the mutual agreement of the student, current committee members, proposed (replacement) committee members, and the Graduate Program Director. - You have to prepare a two to five page proposal document. The proposal should summarize the findings, results and planned work for the dissertation, and describe the relationships among your published and/or submitted papers. Your proposal must be approved by your dissertation committee. - You will need to complete a form to list the names of your committee members along with your title and abstract. Your advisor must sign off that he or she approves. Such form can be obtained by contacting the Program Coordinator.

Gerasoulis, Apostolos

Tenure-Track Assistant Professor Position

The Computer Science Department at Rutgers University invites applications for a Tenure-Track Assistant Professor position in Theoretical Computer Science. We welcome candidates working on computational complexity theory but outstanding applicants in all areas of TCS will be considered. Consistent with the aims of the Simons Junior Faculty Fellows program, which provides partial funding, the department also welcomes applicants who are most affected by the COVID-19 pandemic: postdocs and new PhDs. Requirements: Successful completion of a PhD or equivalent in Computer Science or a closely related field is required by the start of September 1, 2021. Responsibilities include: research in the area of Theoretical Computer Science, supervision of PhD students, and teaching undergraduate and graduate level courses in Computer Science. Pursuit of external research funding is expected. How to apply: Applicants should submit their CV, a research statement addressing both past and future work, a teaching statement, and contact information for three references. Applications received by January 15, 2021 will be given priority.  Click here to go to apply. Timeline: Applications received by January 15, 2021 will be given priority. Comtact info:  martin@farach-colton.com Rutgers Policies: The CS Department is strongly committed to increasing the diversity of our faculty and welcomes applications from women, dual-career couples, historically underrepresented populations and candidates with disabilities. Offer is contingent upon successful completion of all pre-employment screenings. Rutgers is an affirmative action/equal opportunity employer.  

Best Paper Award for Zachary Daniels

Congratulations to Zachary Daniels for winning aBest paper award at the recently held Info Symbiotics /DDDAS2020 Conference, MIT, Cambridge, MA, October 2-4, 2020 for his paper:“Active Scene Classification via Dynamically Learning Prototypical Views” Zachary A. Daniels and Dimitris N. Metaxas

Prof Metaxas wins NSF Convergence Accelerator Award.

Rutgers Researchers (Metaxas PI and D’Imperio Linguistics) withcollaborators from BU (Neidle) and RIT (Huenerfauth) win a 1M$ NSF Convergence Accelerator Award Rutgers University - Center for Accelerated Real Time Analytics

Khalid, Baber

Prof Desheng Zhang wins NSF grant

Prof Desheng Zhang wins a 2.3 Million dollar NSF award to provide smart city functionalities in the city of Newark Smart city services are deeply embedded in modern cities aiming to enhance various aspects of citizens' lives, including safety, wellness, and quality of life. Examples include intelligent traffic control and air quality control. Given these services, monitoring a city's safety and performance collectively is crucial, yet also challenging due to many potential conflicts among the number increasing of services deployed. Researchers have accumulated abundant knowledge on how to design these services independently. However, underlying expected or unexpected couplings among services due to complex interactions of social and physical activities are under-explored, which leads to potential service conflicts. Developing approaches of reducing conflicts is essential for ensuring social inclusion and equity of city services because when conflicts occur, their impacts are likely to be concentrated in some sub-communities (e.g., specific geographic locations, specific user groups like patients with respiratory illness, etc.) meaning that some citizens will experience lower quality services than others due to the diversity. Put differently, service conflicts contribute to a digital divide in service provision.The key intellectual merit of the proposed project is the development of a socially aware conflict management theory and its deployment for smart cities, consisting of 5 sequential components as follows. (1) a novel, template-based requirements specification component/tool that integrates social and technical requirements to formally define a conflict; (2) a social diversity aware detection approach that utilizes machine learning and conflict correlations to detect conflicts in practice; (3) a multi-objective yet equity-centric resolution method that accounts for socially acceptable trade-offs, behavioral models, and control theory to resolve existing conflicts; (4) a participant-based conflict prevention solution that employs Game Theory and Reinforcement Learning in a scalable, decentralize fashion to prevent future conflicts; (5) a social intervention approach based on education outreach and professional training to disseminate the proposed technology to empower the community. The real-world implementation of this theory by working with the city partners in Newark NJ will show its effectiveness and broader impacts on a diverse set of stakeholders of conflict management from city operators, to service providers, to average citizens. 

Michmizos receives grant to develop a neuromorphic robotic nurse

Michmizos receives a global research award grant from Intel to develop a robotic nurse. This work is based on a new method developed this year in Michmizos's Lab, to co-train deep and spiking neural networks. This is a $150K, single-PI award for 2 years. Abstract. The Circolo Hospital in Varese, a city in the northern Lombardy region that was the first epicentre of the covid-19 outbreak in Italy, welcomed the addition of robot nurses tirelessly helping flesh-and-blood doctors and nurses. We propose to fast develop Loihi-controlled mobile robots, controlled by an end-to-end hybrid spiking and deep neural network that can co-learn to navigate in highly dynamic real-world environment. The project will allow the fast deployment of a Loihi-controlled robot nurse in hospitals, and the evaluation of its ability to deliver effective healthcare to people in need, from monitoring vital signs and transporting medication, to increasing communication avenues for patients and carrying medical supplies to their bedside. Other relevant applications aiming to decrease in-person interaction are straightforward, including service robots for warehouses, supermarkets, nursing homes, and schools.
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