Columbia University

LinkedinIconCraig Gutterman

Ph.D. Student

Electrical Engineering
Columbia University

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

Craig Gutterman 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. He is currently working towards his Ph.D. at Columbia University. His interests include mobile and wireless networks, optimization algorithms, and data science. His research focuses on analyzing data and developing algorithms to improve the performance of networked systems.

For more information regarding, please be directed to Craig’s resume.

News

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

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

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

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

[6/12/2016]: Paper got 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 got 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, Stochastic Modeling, 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

  • 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

  • Craig Gutterman, Weiyang Mo, Shengxiang Zhu, Yao Li, Daniel C. Kilper, Gil 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]
  • Yishen Huang, Craig Gutterman, Payman Samadi, Patricia. Cho, Wiem Samoud, CedricWare, Mounia Lourdiane, Gil Zussman, and Keren Bergman, “Dynamic mitigation of EDFA power excursions with machine learning,” Optics Express, vol. 25, no. 3, pp. 2245–2258, Feb. 2017. [pdf]
  • Varun Gupta, Yigal Bejerano, Craig Gutterman, Jaime Ferragut, Katherine Guo, Thyaga Nandagopal, and Gil 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]
  • Yigal Bejerano, Chandru Raman, Chun-Nam Yu, Varun Gupta, Craig Gutterman, Tomas Young, Hugo Infante, Yousef Abdelmalek, Gil Zussman, “DyMo: Dynamic Monitoring of Large Scale LTE-Multicast Systems,” to appear in Proc. IEEE International Conference on Computer Communications (IEEE INFOCOM’17), Apr. 2017. Best Paper Runner-Up [pdf]
  • Huang, Yishen, Wiem Samoud, Craig L. Gutterman, Cedric Ware, Mounia Lourdiane, Gil Zussman, Payman Samadi, Keren 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]
  • Yigal Bejerano, Varun Gupta, Craig Gutterman, and Gil Zussman, “AMuSe: Adaptive Multicast Services to very large groups Project overview”, in Proc. ICCCN’16 (invited), 2016. [pdf]
  • Varun Gupta, Craig Gutterman, Yigal Bejerano, and Gil Zussman, “Experimental evaluation of large scale WiFi multicast rate control,” in Proc. IEEE International Conference on Computer Communications (IEEE INFOCOM’16), Apr. 2016. [pdf]
  • Yigal Bejerano, Jaime Ferragut, Katherine Guo, Varun Gupta, Craig Gutterman, Thayga Nandagopal, Gil 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.
  • Yigal Bejerano, Jaime Ferragut, Katherine Guo, Varun Gupta, Craig Gutterman, Thyaga Nandagopal, Gil Zussman, “Scalable Wifi Multicast Services for Very Large Groups,” Proc. of the 21st IEEE International Conference on Network Protocols (IEEE ICNP), Oct. 2013. [pdf]
  • Khanh Le, Prasanthi Maddala, Craig Gutterman, Kyle Soska, Aveek Dutta, Dola Saha, Peter Wolniansky, Dirk Grunwald, and Ivan 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.
  • Khanh Le, Prasanthi Maddala, Craig Gutterman, Kyle Soska, Aveek Dutta, Dola Saha, Peter Wolniansky, Dirk Grunwald, and Ivan 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.

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.