Columbia University

LinkedinIconCraig Gutterman

Ph.D. Student

Electrical Engineering
Columbia University

Office: 801 CEPSR
Email: clg2168[at]columbia.edu

I am  currently working towards my Ph.D. at Columbia University. I graduated with a B.S. degree in Electrical Engineering from Rutgers University in May 2012 and an M.S. degree in Electrical Engineering from Columbia University in February 2014.

My research focuses on improving the performance of future networks and systems, by developing machine learning based network systems and data driven network algorithms. In particular, I have worked on tools for traffic prediction, traffic classification, and resource management with applications for multimedia streaming, optical networks, network slicing, wireless multicast, and edge computing. In addition, I assist in the design and deployment of the NSF PAWR COSMOS testbed.

I am on the job market!
[resume][Google Scholar]

Recent News

[8/16/2019]: Paper was accepted to IEEE ICNP’19 Workshop on Midscale Education and Research and Research Infrastructure and Tools (MERIT’19)

[5/25/2019]: Oral presentation was accepted to ACM SIGCOMM’19 Workshop on Optical Systems Design (OptSys 2019)

[5/21/2019]: I presented the COSMOS tutorial on Optical Path Management in the ORBIT/COSMOS Testbed at the COSMOS Experimenters Workshop held at Rutgers University.

[3/30/2019]: Our paper, “RAN resource usage prediction for a 5G slice broker” was accepted to ACM MobiHoc 2019.

[1/1/2019]: Paper was accepted to ACM MMSys 2019.

[7/10/2018]: Paper was accepted to ECOC 2018.

[4/13/2018]: Paper was accepted to Journal of Optical Communications and Networking.

[12/27/2017]: Paper was accepted to IEEE Transactions on Wireless Communications.

[12/8/2017]: Paper was accepted to OFC 2018.

[5/30/2017]: Paper was accepted to Sigcomm Workshop Big-DAMA.

[4/18/2017]: INFOCOM’17 paper awarded best paper runner-up award.

[1/23/2017]: Paper was accepted to Optics Express 2017.

[11/25/2016]: Paper was accepted to IEEE INFOCOM 2017.

[6/12/2016]: Paper was accepted to IEEE ECOC 2016.

[12/23/2015]: I was selected to participate in the NYC Media Lab Combine program. [news item]

[11/30/2015]: Paper was accepted to IEEE INFOCOM 2016.

[9/25/2015]: Demo received the second place prize in the NYC Media Lab 2015 Summit. [news item]

Education

Columbia University

  • Ph.D. Candidate, Electrical Engineering Fall 2014 – Present, GPA: 4.0
    • Research Interests: Wireless and Mobile Networking, Optimization, Machine Learning
    • Advisor: Prof. Gil Zussman

Columbia University

  • M.S. Electrical Engineering, September 2012 – Feb. 2014, Final GPA:4.14

Rutgers University

  • B.S. Electrical Engineering, Minors: Economics and Math , September 2008 – May 2012
  • Final GPA:4.0, Summa Cum Laude

Awards & Honors

  • Top Prize, Enabling Technology, for a demo at the NYC Media Lab’s Annual Summit (2019)
  • INFOCOM’17 paper awarded best paper runner-up award. (2017)
  • Second Place Prize for demo in the NYC Media Lab Summit (2015)
  • Columbia Electrical Engineering Master of Science Award of Excellence (2014)
  • NSF Graduate Research Fellowship Recipient (2014)
  • NSF Integrative Graduate Education and Research Traineeship Fellowship, From Data to Solutions (2014)
  • Columbia University Tesla Scholar (for top incoming Electrical Engineering M.S. Students) (2012)
  • Rutgers University John B. Smith Memorial Prize (highest ranking graduating senior in Dept. of Electrical Engineering) (2012)

Publications

Conference Proceedings

  • Craig Gutterman, Edward Grinshpun, Sameer Sharma, and Gil Zussman, “RAN resource usage prediction for a 5G slice broker,” in Proc. ACM MobiHoc’19, 2019. [pdf][slides]
  • Craig Gutterman, Katherine Guo, Sarthak Arora, Xiaoyang Wang, Les Wu, Ethan Katz-Bassett, Gil Zussman, “Requet: Real-Time QoE Detection for Encrypted YouTube Traffic,” in Proc. ACM MMSys’19, 2019. [pdf][data][slides]
  • J. Yu, T. Chen, C. Gutterman, S. Zhu, G. Zussman, I. Seskar, and D. Kilper, “COSMOS: Optical architecture and prototyping,” in Proc. OSA OFC’19 (invited), 2019. [pdf] [slides][COSMOS website]
  • S. Zhu, C. Gutterman, W. Mo, Y. Li, G. Zussman, and D. Kilper, “Machine learning based prediction of erbium-doped fiber WDM line amplifier gain spectra,” in Proc. ECOC’18, 2018. [pdf][slides]
  • W. Mo, C. Gutterman, Y. Li, G. Zussman, and D. Kilper, “Deep neural network based dynamic resource reallocation of BBU pools in 5G C-RAN ROADM networks,” in Proc. OSA OFC’18, Th1B.4, 2018. [pdf] [slides]
  • Y. Bejerano, C. Raman, C. Yu, V. Gupta, C. Gutterman, Tomas Young, Hugo Infante, Yousef Abdelmalek, Gil Zussman, “DyMo: Dynamic Monitoring of Large Scale LTE-Multicast Systems,” in Proc. IEEE International Conference on Computer Communications (IEEE INFOCOM’17), Apr. 2017. Best Paper Runner-Up [pdf]
  • Y. Huang, W. Samoud, C. Gutterman, C. Ware, M. Lourdiane, G. Zussman, P. Samadi, K. Bergman, “A Machine Learning Approach for Dynamic Optical Channel Add/Drop Strategies that Minimize EDFA Power Excursions,” in ECOC 2016; 42nd European Conference on Optical Communication; Proceedings of, pp. 1-3. VDE, 2016. [pdf]
  • Y. Bejerano, V. Gupta, C. Gutterman, and G. Zussman, “AMuSe: Adaptive Multicast Services to very large groups Project overview”, in Proc. ICCCN’16 (invited), 2016. [pdf]
  • V. Gupta, C. Gutterman, Y. Bejerano, and G. Zussman, “Experimental evaluation of large scale WiFi multicast rate control,” in Proc. IEEE International Conference on Computer Communications (IEEE INFOCOM’16), Apr. 2016. [pdf]
  • Y. Bejerano, J. Ferragut, K. Guo, V. Gupta, C. Gutterman, T. Nandagopal, G. Zussman, “Scalable Wifi Multicast Services for Very Large Groups,” Proc. of the 21st IEEE International Conference on Network Protocols (IEEE ICNP), Oct. 2013. [pdf]

Journal Publications

  • Y. Bejerano, C. Raman, C. Yu, V. Gupta, C. Gutterman, T. Young, H. Infante, Y. Abdelmalek, G. Zussman, “DyMo: Dynamic Monitoring of large scale LTE-multicast systems,” in IEEE/ACM Transactions on Networking, vol. 27, no. 1, pp 258-271, Feb. 2019. Fast tracked from IEEE INFOCOM’17 [pdf] [slides]
  • W. Mo, C. Gutterman, Y. Li, S. Zhu, G. Zussman, and D. Kilper, “Deep neural network based wavelength selection and switching in ROADM systems,” Journal of Optical Communications and Networking, vol. 10, no. 10, pp. D1–D11, Oct. 2018. [pdf]
  • V. Gupta, C. Gutterman, Y. Bejerano, and G. Zussman, “Experimental evaluation of large scale WiFi multicast rate control,” IEEE Transactions on Wireless Communications, vol. 17, no. 4, pp. 2319–2332, Apr. 2018. [pdf]
  • Yi. Huang, C. Gutterman, P. Samadi, P. Cho, W. Samoud, C. Ware, M. Lourdiane, G. Zussman, and K. Bergman, “Dynamic mitigation of EDFA power excursions with machine learning,” Optics Express, vol. 25, no. 3, pp. 2245–2258, Feb. 2017. [pdf]
  • V. Gupta, Y. Bejerano, C. Gutterman, J. Ferragut, K. Guo, T. Nandagopal, and G. Zussman, “Light-weight feedback mechanism for WiFi multicast to very large groups – experimental evaluation,” IEEE/ACM Transactions on Networking, vol. 24, no. 6, pp. 3826–3840, Dec. 2016. [pdf]

Workshops

  • C. Gutterman, A. Minakhmetov, J. Yu, M. Sherman, T. Chen, S. Zhu, I. Seskar, D. Raychaudhurti, D. Kilper, and G. Zussman, “Programmable Optical x-Haul Network in the COSMOS Testbed,” in Proc. IEEE ICNP’19 Workshop on Midscale Education and Research and Research Infrastructure and Tools (MERIT’19), 2019. [pdf]
  • C. Gutterman, A. Minakhmetov, M. Sherman, J. Yu, T. Chen, S. Zhu, G. Zussman, I. Seskar, D. Raychaudhurti, and D. Kilper, “COSMOS: Optical Architecture and Protoyping,” in . ACM SIGCOMM’19 Workshop on Optical Systems Design (Optsys’19), 2019.
  • C. Gutterman, W. Mo, S. Zhu, Y. Li, Daniel C. Kilper, G. Zussman, “Neural Network Based Wavelength Assignment in Optical Switching,” in Proc. of Big Data Analytics and Machine Learning for Data Communication Networks (Big-DAMA ’17), Aug. 2017. [pdf] [slides]
  • Y. Bejerano, J. Ferragut, K. Guo, V. Gupta, C. Gutterman, T. Nandagopal, G. Zussman, “Experimental Evaluation of a Scalable WiFi Multicast Scheme on the ORBIT Testbed,” Invited paper, Proc. GENI Research and Educational Experiment Workshop (GREE14), Atlanta, GA, Mar. 2014.
  • K. Le, P. Maddala, C. Gutterman, K. Soska, A. Dutta, D. Saha, P. Wolniansky, D. Grunwald, and I. Seskar, “Cognitive Radio Kit Framework: Experimental Platform for Dynamic Spectrum Research,” Proc. 7th ACM International Workshop on Wireless Network Testbeds, Experimental Evaluation, and Characterization (ACM WiNTECH’12), Istanbul, Turkey, Aug. 2012.

Posters and Demonstrations  (Peer Reviewed)

  • V. Gupta, L. Xu, B. Wu, C. Gutterman, Y. Bejerano, and G. Zussman, “Evaluating Video Delivery over Wireless Multicast,” in Demo at IEEE INFOCOM’17, 2017. [pdf]
  • V. Gupta, R. Norwitz, S. Petridis, C. Gutterman, G. Zussman, and Y. Bejerano, “AMuSe: Large-scale WiFi Video Distribution – Experimentation on the ORBIT Testbed,” in Demo at IEEE INFOCOM’16, 2016. [pdf]
  • V. Gupta, R. Norwitz, S. Petridis, C. Gutterman, G. Zussman, and Y. Bejerano, “WiFi multicast to very large groups – experimentation on the ORBIT testbed,” in Demo at IEEE LCN’15, 2015. [pdf]
  • K. Le, P. Maddala, C. Gutterman, K. Soska, A. Dutta, D. Saha, P. Wolniansky, D. Grunwald, and I. Seskar, “Cognitive Radio Kit Framework: Experimental Platform for Dynamic Spectrum Research,” ACM Mobile Computing and Communications Review (ACM MC2R) Vol. 17 No. 1 PP 30-39, Jan. 2013. Selected as Best article from WinTECH 2012 workshop

Technical Reports

  • V. Gupta, C. Gutterman, Y. Bejerano, and G. Zussman, “Experimental evaluation of large scale WiFi multicast rate control,” in arXiv:1601.06425 [cs.NI], Jan. 2016. [pdf]
  • Y. Bejerano, C. Raman, C.-N. Yu, V. Gupta, C. Gutterman, T. Young, H. Infante, Y. Abdelmalek, and G. Zussman, “DyMo: Dynamic Monitoring of large scale LTE-multicast systems,” in arXiv:1701.02809 [cs.NI], Jan. 2017 [pdf].

 

Industry Experience

Intern, Raytheon BBN Technologies

  • Advanced Network Intern, June 2013 – August 2013
    Explored the suitability of Android emulators, Virtual Machines, and Linux Containers for ad-hoc network emulation. Researched data synchronization overhead for Content Distributed Network. The problem stems from a topology of a Content Distributed Network defined by mobile ad hoc communities of nodes that are bound together and used cooperative storage in each community. Developed and simulated various data synchronization protocols for Content Distributed Network. Compared and contrasted results of alternative protocols to determine optimum use of network resources.