News & Highlights Details

2019 CS Conference on Scalability and Diversity

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Rutgers is proud to announce the 4th annual NJ Computer Science Summit on Diversity and Scalability on November 1st, 2019.   Prior summits have provided opportunities for stakeholders from a variety of backgrounds to participate in a dialogue regarding the state of computer science education in New Jersey. As we continue to see growth in undergraduate CS programs, and increased interest in secondary CS, this summit offers an opportunity for conversations among New Jersey 4- and 2-year college faculty, K-12 teachers and administrators, representatives from industry, the NJ Department of Education and other government offices. As in past summits, we plan on a schedule that includes dynamic guest speakers, discussion panels, and breakout sessions. 

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Date:               November 1

Time:               8:00AM to 4:30PM 

Location:         Hill Center at Rutgers Busch Campus in New Brunswick

Cost:               FREE and lunch provided thanks to the Rutgers Computer Science Department

Registration:   Please register here by October 15th. Space is limited, so register early.

Keynote Speakers:

  • Director Michael Geraghty – NJ Chief Information Security Officer 
  • Dr. Leigh Ann Delyser –Co-founder and Executive Director of CSforAll

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Sampling of some of the the breakout sessions.   The schedule is still being finalized and more to be added based on interest by the registrants.

Working Partnerships (Higher Ed and K-12) for CS Education

Engaging student in larger classes  

CS 0/1: Where are we now? Where are we going?

CS Minors for non-STEM majors?

Building cybersecurity programs

Free standards-based curricula for 9-12

Strategic Planning Tool for School Districts

CS Professionals as Volunteers in the Classroom

Big Data, Data Science: Where does it fit? Who is it for?

Hiring process and competencies - Google

Teaching CS to ELL (English Language Learner) students

College Board: AP CS Principles and AP CS A Updates

CS initiatives in large school districts (NYC and Newark)

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2019 NJ CS Summit at Rutgers  - Please share this flier

Register here:  http://bit.ly/NJCSSummit2019  

If you have any questions contact This email address is being protected from spambots. You need JavaScript enabled to view it.

HackRU Fall 2019

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HackRU Fall 2019 registration is open now at www.hackru.org!

HackRU is a 24-hour hackathon at Rutgers University. We welcome hundreds of students to join us in building awesome tech projects. Industry experts and mentors help foster an atmosphere of learning through tech-talks and one-on-one guidance. We encourage all students, no matter their experience level or educational background, to challenge themselves and expand their creative, technical, and collaboration skills at HackRU.

HackRU Fall 2019

International Multi-Robot Systems (MRS 2019) Conference at Rutgers

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MRS 2019 will be held on August 22-23 at the Rutgers Academic Building in New Brunswick, NJ, USA.

MRS is a new initiative of the IEEE RAS Technical Committee on Multi-Robot Systems. The goal of the conference is to bring together researchers who are in the field of multi-robot systems (MRS) and multi-agent systems (MAS), both directly and indirectly, to cross-fertilize ideas. Typically MRS/MAS research is spread across large conferences, and this makes it difficult for us to keep up to date on new findings and meet others in the area. The intent of the conference is to bring those researchers together with a high-quality symposium to highlight the best in the field. We would like to see the top advances in multi-robot and multi-agent research represented at MRS 2019.

Visit the MRS 2019 website for more information.

The conference scope will include any research related to multi-robot and multi-agent systems, an inherently diverse community. Several competences are needed in this field, ranging from control systems to mechanical design, coordination, cooperation, estimation, perception and interaction. The fields of interest include the following general fields, but are not limited to:

  • Modeling and Control of MRS/MAS
  • Optimal Control and Optimization Methods for MRS/MAS
  • Motion and Path Planning for MRS
  • Bio-Inspired MRS and Swarm Intelligence/Robotics
  • Distributed Perception and Estimation in MRS/MAS
  • Planning and Decision Making for MRS/MAS
  • Physical Interaction in/with MRS/MAS
  • Cooperative/Collective Learning in MRS/MAS
  • AI of Large Scale Systems
  • Applications of MRS
  • Technological and Methodological Issues
  • MRS for Cooperative Manipulation
  • Micro/Nano Scale MRS
  • Operating Systems and Cloud Technology for MRS
  • Communication in MRS/MAS
  • Performance Evaluation and Benchmarking in MRS/MAS
  • Human-robot and Human-agent interaction
  • Game theoretic approaches for MAS/MRS
  • Teamwork, team formation, teamwork analysis

Prof. Kostas Bekris is the General Chair of the event while Prof. Jingjin Yu is the Local Arrangement Chair. Rahul Shome and Han Shuai, PhD students in the Computer Science department, are serving as Website and Registration Chair, respectively.

 

MRS 2019 website

CS Conference 2019 a Success!

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The CS Department held its second Conference on January 31, 2019. The Conference included a keynote talk, given by Mike Davies, The Director of Neuromorphic Division at Intel Labs, a panel discussion on "Brain-integrative AI" and poster presentations.

More than 200 people had the chance to listen to Mike Davies, the leading researcher behind Intel's Loihi chip, who showcased the disruptive potential of the neuromorphic computing technology. Calling for a bottom-up rethinking of computing, Mike Davies announced that Prof. Michmizos's group will be one of the first groups in the world to receive the "Kapoho Bay", a chip that emulates how neurons learn in the brain and brings neuromorphism into real-world robotic applications.

In the panel discussion, the audience had also a rather unique opportunity to listen to leading researchers discussing how AI can use the brain both as an inspiration and a target. The panel spanned Computer Science (Dimitri Metaxas), Neuromorphic Computing (Mike Davies), Medical School (Steven Silverstein) and Philosophy (Brian McLaughlin). Further interest in the topic was spurred by the comments of the panel moderator, Prof. Casimir Kulikowski. 

52 undergradute and graduate students presented their research posters.  The best poster awardees, scored for their originality, clarity and potential impact, were:

  • Joseph Boyle, an undergradute student in CS, advised by Prof. Nguyen. 
  • Mona Rassouli, an undergraduate pre-med student double majoring in CS and Biomathematics, advised by Prof. Michmizos.
  • Valia Kalokyri, a PhD student, advised by Prof. Marian.

Congratulations to all who presented. Planning for the 2020 CS Conference has begun!

 

 

 

5 New Tenure-track Faculty Members

Referenced People:
  • Ahn, Sungjin
  • Bernstein, Aaron
  • Deng, Dong
  • Kannan, Sudarsun
  • Narayana, Srinivas
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    The Department of Computer Science is excited to welcome five new tenure-track faculty members.

    Sungjin Ahn received his Ph.D. at the University of California, Irvine under the supervision of Prof. Max Welling. He was then a postdoc under Prof. Yoshua Bengio at the University of Montreal and MILA. His research area is machine learning, particularly in the areas of deep learning, Bayesian learning, reinforcement learning, and neuro & cognitive science-inspired learning. His current research focus is probabilisitic generative models, meta learning, and representation learning to advance agent/robot learning. His goal is to make a general AI agent that can learn like humans in complex environments.

    Aaron Bernstein is interested in the design and analysis of algorithms, with a focus on algorithms that are able to handle the challenges posed by massive data sets. Prior to joining Rutgers, he did a postdoc with Martin Skutella at the Berlin University of Technology, funded by the Einstein Fellowship. He received his PhD from Columbia University, where he was advised by Clifford Stein, and supported by the NSF graduate fellowship and a fellowship from the Simons Institute. His thesis was on algorithms for maintaining information in graphs that evolve over time. He has received multiple best paper awards from top conferences in theoretical computer science, such as Symposium on Theory of Computing and Symposium on Discrete Algorithms.

    Dong Deng is interested in database and data science research, with a special focus on building systems and developing algorithms for data curation, data integration, and data cleaning. Before joining Rutgers, he conducted a postdoc research with Mike Stonebraker and Sam Madden in the Computer Science and Artificial Intelligence Lab (CSAIL) at MIT. Dong obtained his PhD degree from Tsinghua University with the highest doctoral dissertation award. He also received the Siebel Scholarship, Google PhD Fellowship, and Microsoft PhD Fellowship during his PhD study. He has been regularly publishing in top database venues like SIGMOD, PVLDB, and ICDE.

    Sudarsun Kannan is interested in operating systems, with a focus towards heterogeneous resource (memory, storage, and compute) management challenges and understanding their impact on large-scale applications. Before joining Rutgers, he was a postdoc at the University of Wisconsin-Madison's CS department advised by Prof. Andrea Arpaci-Dusseau and Prof. Remzi Arpaci-Dusseau where he worked on designing device-level file systems. Prior to his postdoc, he graduated from the College of Computing, Georgia Tech, where he was advised by the late Prof. Karsten Schwan and Prof. Ada Gavrilovska. His thesis explored methods to extend virtual memory support for heterogeneous memory technologies.

    Srinivas Narayana's research aims to design computer networks that are highly programmable and easy to manage. Programming abstractions enable network operators to diagnose poor application performance, developers to build high-performance applications, and hardware architects to design mechanisms that allocate resources efficiently. Srinivas's research combines insights from network protocol design, programming languages, compilers, operating systems, computer architecture, and databases. Srinivas received his Ph.D. in Computer Science from Princeton University and joined the Department of Computer Science at Rutgers after a postdoctoral position at Massachusetts Institute of Technology.



     

    2018 CS Summit on Scalability and Diversity

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    Rutgers is proud to announce the 3rd annual NJ Computer Science Summit on Diversity and Scalability on May 18th, 2018.   Prior summits have provided opportunities for stakeholders from a variety of backgrounds to participate in a dialogue regarding the state of computer science education in New Jersey. As we continue to see growth in undergraduate CS programs, and increased interest in secondary CS, this summit offers an opportunity for conversations among New Jersey 4- and 2-year college faculty, K-12 teachers and administrators, representatives from industry, the NJ Department of Education and other government offices. As in past summits, we plan on a schedule that includes dynamic guest speakers, discussion panels, and breakout sessions. 

    The Summit will be held on BUSCH CAMPUS, Computing Research and Education Building (CoRE). Parking is available in lots 60A, 60B, and 64. Lots are very close to CoRE. Directions to the parking lot for your GPS device would be best (94 Brett Road, Piscataway, NJ). You will see the lots marked. You will see CoRE (the tall brown building) as you near the lots from Brett Rd. You do not need a parking permit.

    Complete the Registration Form so we can better organize the day and order the appropriate amount of food!

    Summit Agenda (subject to change)

    (Details on sessions will be available at a later time)

    8:00-8:30             Breakfast  (CoRE lobby)

    (CoRE Auditorium)

    8:30-8:40     Welcome --- Fran Trees and Thu Nguyen 

    8:40 - 9:00   State of the State -- Daryl Detrick

    Bio: Daryl is a computer science teacher at Warren Hills Regional High School in Washington, NJ, where he has seen the program grow from 30 students to over 200 with 39% female participation.  He is the past president of the Computer Science Teachers Association(CSTA) of Central NJ and a member of CSTA National Advocacy Committee.  He received the NCWIT NJ Educator Award, the CSTA National Advocate of Year and is a Google CS4HS Ambassador..  Daryl believes that computer science education can positively change the lives of our students, particularly those in low income situations, by opening doors of opportunity.

    9:00 - 9:50   Keynote Speaker: Pat Yongpradit, Chief Academic Officer, Code.org.

    Bio: Pat is the Chief Academic Officer for Code.org, a nonprofit dedicated to promoting computer science education. As a national voice on K-12 computer science education, his passion is to bring computer science opportunities to every school and student in the United States. Throughout his career as a computer science teacher, he inspired students to create mobile games and apps for social causes, and implemented initiatives to broaden participation in computer science among underrepresented groups. As a result, enrollment in his school’s computer science program doubled, the number of girls taking advanced computer science tripled, and many of his students went on to majors and careers in computing. Pat has also written and consulted on technology curricula at the local, state and national level and in 2010 was recognized as a Microsoft Worldwide Innovative Educator.

    9:50 - 9:55           Summary of Breakout sessions and logistics

    9:55 - 10:05         Break

    10:05- 11:00 Breakout session 1 (Refer to table below)

    11:05-12:00  Breakout session 2 (Refer to table below)

    12:00 - 1:00 LUNCH (CoRE Lobby)

     

    (CoRE Auditorium)

    1:00 - 1:30 Keynote Speaker:  Chris Kay, Partnerships Bureau Chief within the New Jersey Cybersecurity & Communications Integration Cell (NJCCIC)

    Bio: Lieutenant Chris Kay (NJSP) is responsible for leading a multi-disciplined outreach team towards developing partnerships with public and private sector organizations and assisting these organizations improve their digital resilience. Prior to his current assignment, Lt. Kay served for nine years during three gubernatorial administrations on the NJ State Police’s Executive Protection Unit. Lt. Kay is a 17-year member of the NJSP and a US Army veteran.

    1:30 - 2:20 Panel discussion- Industry: Industry leaders and working College Grads. (Facilitator: John Hajdu)

    Panelists:

    Jeff Lane, Strategic Initiatives manager for Apple 

    Aaron Kuderka, Google Software Engineer

    Michael  Brodack, FBI, Assistant Special Agent in Charge , Newark Field Office

    Archana Jain, VP and CIO of Verizon Partnership Solutions

    Aimee Rosato, Regional Manager, TEALS K-12

     

    2:20 - 2:30 Break

    2:30 - 3:25  Breakout Session 3 (Refer to table below)

    (CoRE Auditorium)

    3:30 - 3:45 Wrap up, Next Steps

    3:45 - 5:00 CSTA NJ-North and NJ-Central Chapter Meeting (If you are not a member, join!)

     

    Breakout 1 (10:05 - 11:00)

    Breakout 2 (11:05 - 12:00)

    Breakout 3 (2:30 - 3:25)

    NJ Statewide Action Plan: What should it look like?  

     

    CoRE 431

    CS Teacher Certification/Endorsement

     

    CoRE 431

    Grants/Funding Opportunities for CS Education Research: Hints for writing successful grants

     

    CoRE 431

    CyberSecurity in CS Education

     

    CoRE 301

    Hackathons/Contests? What is important?

     

     

    CoRE 301

    CyberSecurity in CS Education

     

    CoRE 301

    Scalability  and Diversity: How and what  are we doing?

     

    CoRE 305

    Undergraduate CS Curriculum: Is it (or should it be) changing? A look at CS minors?

     

    CoRE 305

    Game Design

     

    CoRE 305

    Makerfest

     

    Hill  350

    Professional Development What do we have? What do we need?

      

    Hill 350

    CS 4 NJ: Industry working with Educators

     

    Hill 350

     

    College Board

    AP CSP and AP CSA

     

    CoRE 229

    Cyber concerns: Why we shouldn't just sign our students for the next hot edtech initiative, but ask, is this necessary?What info are we leaking of our students?

     

    CoRE  229

    Near Peer Mentoring-utilizing students for engagement and mentorship

     

     CoRE  229

     

     

    Rutgers CS Conference 17

    Referenced People:
  • Yu, Jingjin
  • de Melo, Gerard
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    The CS Conference Awards Committee announced the best poster Awards, which recognize the research posters that shined in the inaugural CS Conference, held on December 13, 2017 at CoRE lobby, Busch campus.

    The CS Conference Awards honored excellence in two special categories – Undergraduate Poster and Graduate Poster. Posters were judged based on technical merit, originality, potential impact on their respective field and society at large, as well as clarity and quality of the poster presentation.

    Congratulations to:

    1] Rupesh Chinta, and his advisor, Prof. Jingjin Yu, for winning the Undergraduate Award, for their poster "Pattern following for miniature autonomous robots,"

    2] Monica Bansal, and her advisor, Prof. Gerard de Melo, for winning the Graduate Award, for their poster "Rutgers schedule of classes v/s bus schedule."

    The CS Conference serves as an additional tool to empower both our Graduate and Undergraduate students with the skills to 1) express their initial curiosity, 2) systematically find an answer to their question, and 3) communicate their discoveries effectively. It also helps our students to develop a better understanding of the different CS branches that innervate society, and a deeper confidence in themselves.

    See you all next year!

    New Co-director of the Professional MS Program in Data Science joins CS

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    The Department of Computer Science is excited to announce that Dr. George Moustakides will be joining its ranks and will be co-directing the Professional MS Program in Data Science:

    Dr. George V. Moustakides received the diploma in Electrical and Mechanical Engineering from the National Technical University of Athens, Greece in 1979, the M.Sc in Systems Engineering from the Moore School of Electrical Engineering, University of Pennsylvania in 1980 and the Ph.D in Electrical Engineering and Computer Science from Princeton University in 1983. From 1991 until 2017 he was a Professor with the departments of Computer Engineering and Informatics and Electrical Engineering of the University of Patras, Greece. He also held long-term appointments as Junior and Senior Researcher with INRIA, France and numerous positions as Visiting Scholar and Adjunct Professor with Princeton University, University of Pennsylvania, Columbia University, University of Maryland, Georgia Institute of Technology, University of Southern California, University of Illinois at Urbana-Champaign and Rutgers University. Since 2017, Prof. Moustakides is a Full Professor of Professional Practice with the Computer Science department at Rutgers University, co-directing the Professional Masters Program in Data Science.

    3 New Faculty Members in Machine Learning, Data Science, Physical Computing

    Referenced People:
  • Aanjaneya, Mridul
  • Ahn, Sungjin
  • Zhang, Yongfeng
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    The Department of Computer Science is excited to announce that the following three faculty members will be joining its ranks:

    Dr. Mridul Aanjaneya joined the Department of Computer Science at Rutgers University as an Assistant Professor starting Sep. 1, 2017. His research lies at the intersection of Computer Graphics, High-performance Computing, Computational Physics, and Applied Mathematics. Some of his recent work has focused on pushing the limits of computational fluid dynamics by designing novel data structures and algorithms that can accommodate over a billion degrees of freedom on heterogeneous (multi CPU-GPU) workstations. He obtained his PhD in Computer Science from Stanford University in 2013, and was a postdoctoral researcher at the University of Wisconsin - Madison from 2014 - 2017. While at Stanford, he was also a consultant in the Spatial Technologies team at the Nokia Research Center for two years.

    Dr. Yongfeng Zhang's research focuses on the intersection of Economic Data Science, Recommender Systems, and Information Retrieval. His recent research includes 1) Economic Data Science, including the application and analysis of economic theories in web applications such as recommendation, search, and sharing economy; 2) Explainable machine learning and its application to recommendation and search systems; 3) Conversational search, recommendation, and question answering systems. Dr. Zhang received his PhD (2016) and BE (2011) degrees in Computer Science from Tsinghua University, and the BS (2016) degree in Economics from Peking University. During 2015-2016 he was an Assistant Specialist in the School of Engineering at UC Santa Cruz, and in 2016-2017 he was a Postdoc Research Associate in CICS at UMass Amherst. Dr. Zhang is a Siebel Scholar and Baidu Scholar, as well as Microsoft and IBM PhD Fellowship winner. Dr. Zhang will be formally joining the department as Assistant Professor in January of 2018.

    Dr. Sungjin Ahn is currently a Research Scientist at Element AI. Prior to this, he was a postdoctoral researcher at the University of Montreal working with Prof. Yoshua Bengio on deep learning and its applications. He received his Ph.D. in Computer Science from the University of California, Irvine, under the supervision of Prof. Max Welling. During his Ph.D. program, his research interest was on scalable approximate Bayesian inference. He co-developed the Stochastic Gradient MCMC algorithms and awarded best paper awards from the International Conference on Machine Learning in 2012 and the ParLearning in 2016, respectively. His current research interests include deep learning, deep reinforcement learning, robot learning, brain-inspired learning algorithms, and applying these methodologies to cognitive agent learning in complex environments. Dr. Ahn will be formally joining the department as Assistant Professor in September of 2018.

     

    HackHERS

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    HackHERS is the annual women-centric and beginner oriented hackathon held every Spring right here at Rutgers University! Our goal is to create a space in which women can explore tech culture and be empowered to create with code. All genders are allowed to participate because closing the gender gap requires everyone's contribution. For 24 hours, students from various Universities come together to learn and to create some wonderful and useful applications. Some examples of projects that were demoed are an app that taught programming using art, combining the creative and the technical side! Another example was an app to manage your budget!

    As a beginner oriented hackathon we also offer various workshops like HTML/CSS, javascript,git, Intro to APIs, etc to help beginners get started with programming. Not only this, there is constant help throughout the event provided through our mentors and company volunteers and students also get the opportunity to network with the sponsor companies' in a casual setting,play games, enjoy de-stressing activities and of course FREE food!It is a great way to initiate a social change by encouraging minority towards technical fields,making new friends, learning about internship opportunities and building something cool!


    aw
    The Android Workshop



    iapiw
    The Intro to API Workshop


    hh2
    Let the Hacking Begin!


    pd
    Project Demos

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    wcs

    WiCS
     

       It is a widely known fact that men far outnumber women in the field of computer science. Simply step into any of the numerous computer science courses offered at Rutgers, and more likely than not, one will find that more than 80% of the students are male. Women in Computer Science (WiCS) was created as a result of this gender gap, to encourage the participation of women in computer science as well as to help the women that are enrolled in computer courses and provide support.

    WiCS is open to any students, male or female, at Rutgers interested in computer science and supportive of women in this field. As a member of WiCS, you will be able to network with other computer science undergraduate students as well as computer science graduate students and faculty during our events throughout the year. Also, you will be alerted to opportunities such as scholarships, internships, research, job events, and more as they arise through our mailing list.

    2017 CS Summit on Scalability and Diversity

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    DATE: Friday, May 12th, 2017
    Location: Rutgers, Busch Campus, CoRE building Auditorium (Directions)
    Time:  8:00am - 4:30pm

    Please register here (free) so that we can better organize the day.

    Tentative Summit Agenda (under construction)
    8:00am - 8:30am:     Continental Breakfast
    8:30am - 8:45am:     Welcome and Brief Introductions

    8:45am - 10:15am (90 minutes):    

    • State of our State 
      • An update and current status of K-12 CS in NJ and the current state-wide K-12 initiatives 
    • First Course for Majors
    • Panel (4 members): CS @ 4 yr and, 2 yr institutions, and AP programs in our local area
      • A panel of representatives from local 1 College, 1 University,  1 Community College, and 1 HS  will compare and contrast their curricula. 


    10:15am - 10:30am break

    10:30am - 12:00 pm Breakout Sessions: See below for descriptions.

    • 10:30 - 11:15
    • 11:20- 12:05


    12:05 pm - 1:00 pm: Lunch with groups brainstorming on suggested topics.

    1:00pm - 1:45pm: Flash Talks and Q&A on AP CS Principles Curriculum (9-12, post-secondary level) There are many College Board Endorsed curricula for AP CS Principles. Participants in this session will present a 5-minute overview of a curriculum they use (or have created themselves). This "flash talk" will be followed by Q&A from summit attendees. 

    1:45pm - 2:30pm: 

    • Gender and Ethnic Diversity
    • Code.org State of other States
    • Panel: Industry Partners - Who are we hiring, and why?


    2:30pm - 2:45pm Break

    2:45pm - 3:30pm Breakout Sessions: See below for descriptions.


    3:30pm - 4:30pm: 

    • Next Steps
    • Bringing it all together
    • Next year


    Breakout topics for the three sessions.

    • CS1/AP CS A: What gets credit? Does AP CSA prepare students for CS2?
    • AP CS Principles: Follow-up on Flash Talks. What gets credit at colleges? How to go about getting your university to give credit.
    • Information for and discussions among school administrators: How to start a CS program at your school
    • CS Teaching Endorsements: what do colleges have to know and do?
    • Grant writing/funding for educational research: Where to start, opportunities available, involvement of K-12 CS
    • Curriculum resources, available technology, and tools for CS:
      • K-8
      • 9-12
      • post secondary
    • Makerspaces/Hackerspaces

    In Memory of Prof. Michael Grigoriadis

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    Professor Michael D. Grigoriadis graduated from Robert College in Istanbul in 1958 with a degree in Civil Engineering. He emigrated to US in 1958 to study at Lehigh University, PA. He was awarded a fellowship from IBM to study for his Ph.D  in Computer Science, which he completed in 1970 at University of Wisconsin, Madison. His thesis was on structured nonlinear programming, written under the supervision of Professor J.B. Rosen, who was well-known for his work on gradient projection method.  

    Professor Grigoriadis's research interests were optimization algorithms, especially on structured and network problems. He joined Rutgers University's Computer Science Department in 1980 as a Professor II. Prior to Rutgers, he worked at the IBM Corporation, Data Processing Division in White Plains, New York. At Rutgers, he introduced two courses on optimization theory that became standard courses: linear programming,  and network and combinatorial optimization algorithms.  He was influential in expanding the optimization group at the Computer Science department of Rutgers University. He interacted with RUTCOR and DIMACS when these centers were being formed at Rutgers and continued his interactions throughout.  He recruited Bahman Kalantari, who was also a student of J.B. Rosen to the Rutgers CS department with whom they wrote several  articles on approximation algorithms. Grigoriadis also wrote several articles on the maximum flow problem with co-authors, who included Robert Tarjan, a Turing Award winner.  

    In 1990, Professor Grigoriadis was influential in attracting Leonid Khachiyan to Rutgers, a world renowned mathematician and computer scientist, who was the recipient of the Fulkerson Prize by the American Mathematical Society and Mathematical Programming Society for his work on linear programming.  Grigoriadis and Khachiyan wrote several joint articles, including on fast approximation algorithms for multicommodity flows, matrix games, and block angular convex programming problems. He retired from Rutgers in the Summer of 2015.

    Profesor Grigoriadis is remembered as a very kind person by his colleagues and students. He is survived by his wife Mary, an artist;  his daughter Vanessa, a journalist; and two grandchildren, Olympia and Apollo.

     

    In memory of Prof. Liviu Iftode: 1959-2017

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    The Department of Computer Science is deeply saddened to report that Prof. Liviu Iftode passed away on February 16, 2017. Liviu was a beloved and highly respected member of the Rutgers CS faculty, and mentored numerous students over the years.

    Liviu was born and raised in Cristian, Sibiu County, Romania. He graduated from the Gheorghe Lazar High School in Sibiu, and subsequently obtained his BS in Electrical Engineering and Computer Science (with Highest Honors) from the Technical University of Bucharest in Romania in 1984. He then worked for several years at Research Institute for Computer Technology, Bucharest, where he was the head of the System U development team. He honed his expertise in operating system kernel development during his time there. Liviu was a graduate student in the Department of Computer Science at Princeton University between 1991 and 1997, from where he earned a Ph.D. under the supervision of Prof. Kai Li. The work that he did for his Ph.D. thesis as part of Princeton's Scalable High-performance Really Inexpensive Multi-Processor (SHRIMP) project remains to this day a pioneering piece of work on distributed shared memory.

    Liviu joined the Rutgers Computer Science department in 1998 and has remained here ever since, except for a brief stint on the faculty of the Department of Computer Science at the University of Maryland, College Park, in 2002-03. During his time at Rutgers, he worked on a wide variety of projects in computer systems, pervasive computing, and computer security. He was among an early cohort of researchers to recognize the power of mobile computing and vehicular computing, and led a number of influential projects in these areas. Liviu's work has had an immense impact on these areas, and landed him multiple awards, research grants, and lead to many collaborative efforts, both at Rutgers and beyond.

    Of all his accomplishments, Liviu was proudest of his legacy as a teacher and mentor. At Rutgers, he frequently taught courses on operating systems and distributed systems. His unique teaching style for operating systems courses, where he delved into the guts of operating system code during class, instilled precision and rigor into his students. He directly supervised the Ph.D. thesis of ten students, in addition to mentoring dozens of people ranging from high-school students to junior faculty colleagues, both at Rutgers and beyond. Liviu's presence on the Rutgers CS faculty drew a number of current and former faculty members to join the department.

    Liviu is survived by his wife of 33 years, Cristina Iftode. A memorial service was held on February 19th at Princeton Abbey, where a number of students, colleagues and friends shared their remembrances and paid tribute to him.

    Liviu has left a deep impact on many people, and will be sorely missed.

     

    MS program in Data Science

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    While other analytical degree programs adapt to the advent of Big Data, the MSDS program within the Computer Science department is designed from the ground up to focus on the latest systems, tools, and algorithms to store, retrieve, process, analyze, visualize, and synthesize large data.This special two years MSDS professional program consists of 6 foundational classes and 6 Elective Classes. Every student is required to complete before graduation a competitive one semester Capstone Project. A central goal of the program is to build systems that integrate in a coherent manner the full data cycle- from data gathering to data visualization and data synthesis aided by computer-human interaction. The six foundational classes expose students to the identification of questions whose answers can be aided by data retrieval, data cleaning and data modeling tools, plus specialized algorithmic and statistical processing, machine learning, pattern recognition and interactive visualization tools. A faculty supervised CapStone class is dedicated to building a prototype system where students exercise the skill set acquired in the other foundational classes.

    National-Scale Vehicular Networking

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    The real-time data feeds from these two large-scale platforms present unprecedented opportunities for us to study vehicular networking at extraly-large scale in a real-time fashion. For example, real-time traffic modeling at national scale is essential to many applications, but its calibration is extremely challenging due to its large spatial and fine temporal coverage. The existing work mostly is focused on urban-scale calibration with complete field data from single data sources (e.g., loop sensors or taxis), which cannot be generalized to national scale, because complete single-source field data at national scale are almost impossible to obtain. To address this challenge, we start a project called MultiCalib, a model calibration framework to optimize traffic models based on multiple incomplete data sources at national scale in real time. Instead of naively combining multi-source data, we theoretically formulate a multi-source model calibration problem based on real-world contexts and multi-view learning. In particular, we design (i) convex multi-view learning to integrate multi-source data by quantifying biases of data sources, and (ii) context-aware tensor decomposition to infer incomplete multi-source data by extracting real-world contexts. More importantly, we implement and evaluate MultiCalib with the above nationwide vehicle networks to infer traffic conditions on 36 expressways and 119 highways, along with 4 cities across China.

    Learning to Manipulate Unknown Objects

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    Summary of the project:

    Outdoor robots, such as search-and-rescue robots and planetary rovers, often need to grasp and push objects such as debris and rocks that have irregular shapes. While most object manipulation techniques require mechanical or geometric models of the objects, objects that are encountered in outdoor environments do not typically have any known models.

    To grasp unknown novel objects, data-driven approaches are becoming increasingly popular. These approaches learn from examples statistical models that predict the success of grasping or pushing actions. The biggest drawback of statistical models is perhaps their inherent inaccuracies, the predicted values are always subject to an error unless the objects used during testing are identical to some of the objects used for training.

    To solve these issues, we explore in this project two self-supervisory learning techniques that allow the robot to adapt to new objects by correcting online the predicted values. Using these techniques, we show how a robot can efficiently learn on the fly and in real-time to push and grasp novel objects. We test both techniques on the task of autonomously clearing piles of natural and man-made objects. 

     

    • The first technique is used for clearing piles wherein grasping actions alone are enough for clearing the pile. The outcomes of the grasping actions are modeled as a Gaussian Process, and an entropy-guided method is used in order to learn where the best grasp is most likely to be found.
    • The second technique is used for tight piles wherein grasping actions alone are not effective, the robot first needs to push obstacles away in a particular way that helps grasping by creating empty space around them to insert the robot’s fingers. We use a reinforcement learning approach that we use for selecting the best sequence of pushing and grasping actions to execute in order to clear a given pile.

    In this project, we also develop a perception technique that is used, along with the online adaptation techniques, to build a fully autonomous system. 

    Relevent publications: 

     

     

    HackRU Fall 2017 - October 14-15

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    HackRU is the biannual, 24-hour hackathon at Rutgers University. It is the largest educational event hosted at Rutgers, and the second longest-running student-run hackathon in the nation. Hosted by the Undergraduate Student Alliance of Computer Scientists (USACS), HackRU welcome hundreds of students of all skill levels and backgrounds to the Banks for a weekend dedicated to building awesome software and hardware projects. Industry experts and mentors come from all over the country to foster an environment for learning through tech talks and one-on-one guidance, while also providing an opportunity for students to network and inquire about career opportunities.

    The Cave

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    The CAVE has ten iLab computers, couches, chairs, a ten person bungee conference room table, large amounts of whiteboard space and a 60" LCD HDTV for gaming and presentations.  There's also a PS3 for gaming, two rolling whiteboards, a projector for presentations and an eighty-eight inch SMART board.  It's one part collaborative computer lab, one part CS student lounge and one part recitation/presentation space.  The CAVE acts as the unofficial home of USACS, RUSLUG and WCS. 

    The CAVE now encompasses Hill 252 and Hill 250.  The main iLab hall now consists of the Hack-R-space, two rooms of the CAVE and the command center in 248.  The CAVE is a staffed facility and is available to the students from 1pm to 11pm Monday thru Thursday, Friday from 1pm to 6pm as well as Sundays from 3-11.  We hold recitations on Tuesdays, Wednesdays and Fridays in the AM and Friday features Hacker Hour from 6pm to 7pm

    NOTE : Code Red is no longer held in the CAVE.  It has been moved to CoRE 301 on Wednesdays from 5pm to 8pm and Thursdays from 2pm to 5pm.

    If you have any questions about the CAVE please drop Lars (see below for email) a line.

    Contact Lars Sorensen at This email address is being protected from spambots. You need JavaScript enabled to view it.