Craig Gutterman Electrical Engineering Department, Columbia University (732)

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1 Craig Gutterman Electrical Engineering Department, Columbia University (732) Education: Columbia University, Fu Foundation of Engineering and Applied Science, New York, NY Ph.D. Candidate in Electrical Engineering Sept Present Advisor: Prof. Gil Zussman GPA: 4.0 Columbia University, Fu Foundation of Engineering and Applied Science, New York, NY M.S. in Electrical Engineering (Tesla Scholar) Sept Feb Concentration: Wireless Communications, Networking GPA: 4.14 Rutgers University, School of Engineering, New Brunswick, NJ B.S. in Electrical Engineering, Minors: Economics, Math Sept May 2012 Concentration: Communications GPA: 4.0 Summa Cum Laude Honors & Awards: NYC Media Lab Combine Program Grant (9 groups selected out of 60 applicants 2016 for $25,000 funding to evaluate the commercialization potential of research results) Second Place Prize for a demo at the NYC Media Lab Summit 2015 NSF Graduate Research Fellowship 2014 NSF IGERT Fellowship, From Data To Solutions 2014 Columbia University Electrical Engineering M.S. Award of Excellence (given to less 2014 than 5% of the candidates for the M.S. degree to recognize outstanding achievement) Paper appeared in ACM MC2R as Best article from WinTECH 2012 workshop 2013 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 Eng.) 2012 James J. Slade Scholar (prepared, submitted, and presented honors thesis) 2012 James and Edna Noe Engineering Scholarship (for outstanding academic achievement) Research Experience: Columbia University, Wireless and Mobile Networking Lab Encrypted Video QoE Prediction (collaboration with Nokia Bell Labs) Jan Present Design system to identify video chunks in encrypted traffic and use machine learning to predict user Quality of Experience in real-time. Paper to appear in MMSys 19. Network Slice Automation (collaboration with Nokia Bell Labs) May Present Develop deep neural network to model and predict RAN congestion levels for individual slice instance and physical cell management. Dynamic Optical Networks Jan Present Develop Machine Learning algorithms to characterize and mitigate power excursions of EDFA systems. Implement QOS Management and topology reconfiguration OSDN applications for COSMOS testbed. The research resulted in papers that were accepted to ECOC 16, ACM SIGCOMM Workshop Big-DAMA 17, ECOC 18, and OFC 19. Adaptive Multicast in WiFi (collaboration with Alcatel Lucent Bell Labs) Sept Present Develop and evaluate rate adaption framework for efficiently sending multicast traffic in WiFi networks. The framework is based on selecting a subset of the nodes to report receiver channel quality. Implement

2 and evaluate the framework in the ORBIT testbed (400 WiFi nodes). Analyzing large datasets (tens of gigabytes) per experiment using Python and MATLAB. The research resulted in papers that were accepted to IEEE ICNP 13, IEEE INFOCOM 16, and IEEE INFOCOM 17. Received 2 nd place prize for a demo in the NYC Media Lab Summit. Selected to participate in the NYC Media Lab Combine. Energy Harvesting Active Networks Tags (EnHANTs) June Aug Analyze human motion data to determine the amount of kinetic energy that can be obtained by a harvester carried by people during their daily activities. Develope signal processing algorithms to determine when people move and how much energy can be harvested. Analyze the data to determine the type of networking applications that could be supported by energy harvested from human motion. Rutgers University, WINLAB Cognitive Radio Communication System Design Sept May 2012 Designed hardware and performed verification testing for the FPGA of the ORBIT Cognitive Radio kit (CRKIT). Designed the application test bench and communication applications (spectrum sensor and QPSK modulation receiver) for the WINLAB CRKIT framework using MATLAB/Simulink. Developed and implemented a rendezvous algorithm that allows two uncoordinated cognitive radios to establish communication in the presence of interference by opportunistically utilizing available spectrum. The research resulted in a paper presented in ACM WiNTECH 12 and published in ACM Mobile Computing and Communications Review. Visual MIMO May Aug Developed a mobile optical communication system which sends multiple optical signals (by the use of LEDs) and which receives the signals by multiple photo diodes (pixels on a camera sensor). Built and used hardware setup to complete experiments in order to confirm prior analytics. Employed real time object detection framework (Viola-Jones) in order to construct a software base for object (LED array) recognition. Rutgers University, Department of Electrical Engineering Coupling Light and Imaging the Optical Field Sept May 2010 Learned optical alignment techniques for nanophotonic devices. Aligned fiber optic cable with silicon chips to couple light through nanostructure. Analyzed experimental results of light recovered to determine the efficiency of coupling. Industry Experience: Gotenna Brooklyn, NY Network Engineer Jan Aug Designed proprietary mobile ad-hoc network (MANET) protocol for a new type of wireless device. Developed a protocol to allow for messaging within the user s local area when he/she does not have cell or data service (e.g., hiking, skiing, natural disaster). Built network simulation in Matlab to analyze network traffic and capacity under different scenarios. Designed scripts to plot experimental results on Google maps for visual analysis and extract data for system analysis. Raytheon BBN Technologies Cambridge, MA Advanced Networking Intern June Aug Explored the suitability of Android emulators, Virtual Machines, and Linux Containers for ad-hoc network emulation. Analyzed data synchronization overhead for Content Distributed Networks. The problem stems from a topology of a Content Distribution Network (CDN) defined by mobile ad hoc communities of nodes that are bound together and use cooperative storage in each community. Developed and simulated various data synchronization protocols for Content Distribution Network. Compared and contrasted results of alternative protocols to determine optimum use of network resources. Publications: 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 (to

3 appear), Jiakai Yu, Tingjun Chen, Craig Gutterman, Shengxiang Zhu, Gil Zussman, Ivan Seskar, and Dan Kilper, COSMOS: Optical architecture and prototyping, in Proc. OSA OFC 19 (invited) (to appear), Yousef Abdelmalek, Gil Zussman, DyMo: Dynamic Monitoring of large scale LTE-multicast systems, IEEE/ACM Transactions on Networking (to appear), Shengxiang Zhu, Craig Gutterman, Weiyang Mo, Yao Li, Gil Zussman, and Dan Kilper, Machine learning based prediction of erbium-doped fiber WDM line amplifier gain spectra, in Proc. ECOC 18, Weiyang Mo, Craig Gutterman, Yao Li, Shengxiang Zhu, Gil Zussman, Dan Kilper, Deep Neural Network Based Wavelength Selection and Switching in ROADM Systems, in Journal of Optical Communications and Networking, vol. 10, no. 10, pp. D1 D11, Oct Weiyang Mo, Craig Gutterman, Yao Li, Gil Zussman, and Dan Kilper, Deep neural network based dynamic resource reallocation of BBU pools in 5G C-RAN ROADM networks, in Proc. OSA OFC 18, Th1B.4, Varun Gupta, Craig Gutterman, Yigal Bejerano, and Gil Zussman, Experimental Evaluation of Large Scale WiFi Multicast Rate Control, in IEEE Transactions in Wireless Communications, vol. 17, no. 4, pp , Apr Yousef Abdelmalek, Gil Zussman, DyMo: Dynamic Monitoring of Large Scale LTE-Multicast Systems, (minor revision) in IEEE/ACM Transactions on Networking, Fast-tracked from IEEE INFOCOM 17 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 Yishen Huang, Craig L. Gutterman, Payman Samadi, Patricia B. Cho, Wiem Samoud, Cedric Ware, Mounia Lourdiane, Gil Zussman, Keren Bergman, Dynamic mitigation of EDFA power excursions with machine learning, in Optics Express, vol. 25, no. 3, pp , Feb 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 Best Paper Runner-Up Varun Gupta, Yigal Bejerano, Craig Gutterman, Jaime Ferragut, Katherine Guo, Thyaga Nandagopal, Gil Zussman, Light-weight feedback mechanism for WiFi multicast to very large groups experimental evaluation, in IEEE/ACM Transactions on Networking, vol. 24, no. 6, pp , Dec Yishen Huang, 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 VDE, Yigal Bejerano, Varun Gupta, Craig Gutterman, and Gil Zussman, AMuSe: Adaptive Multicast Services to very large groups Project overview, in Proc. ICCCN 16 (invited), 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

4 Yigal Bejerano, Jaime Ferragut, Katherine Guo, Varun Gupta, Craig Gutterman, Thyaga Nandagopal, and G. Zussman, Experimental evaluation of a scalable WiFi multicast scheme in the ORBIT testbed, in Proc. 3rd GENI Research and Educational Experiment Workshop (GREE 14), Mar Yigal Bejerano, Jaime Ferragut, Katherine Guo, Varun Gupta, Craig Gutterman, Thyaga Nandagopal, Gil Zussman, Scalable WiFi Multicast Services for Very Large Groups, in Proc. of the 21 st IEEE International Conference on Network Protocols (IEEE ICNP 13), Oct 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 Pg 30-39, Jan 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, in Proc. 7th ACM International Workshop on Wireless Network Testbeds, Experimental Evaluation, and Characterization (ACM WiNTECH 12), Istanbul, Turkey, Aug Demonstrations: V. Gupta, R. Norwitz, S. Petridis, C. Gutterman, G. Zussman, and Y. Bejerano, AMuSe: Large-scale WiFi Video Distribution - Experimentation on the ORBIT Testbed, Demo at IEEE INFOCOM 16, V. Gupta, R. Norwitz, S. Petridis, C. Gutterman, G. Zussman, and Y. Bejerano, WiFi multicast to very large groups experimentation on the ORBIT testbed, Demo at IEEE LCN 15, V. Gupta, R. Norwitz, S. Petridis, C. Gutterman, G. Zussman, and Y. Bejerano, WiFi multicast to very large groups experimentation on the ORBIT testbed, Demo at NYC Media Lab Summit, (2 nd place prize) Relevant Coursework: Information Theory (A), Probability and Random Processes (A), Stochastic Signals (A), Communication System Design (A), Wireless Communication Systems (A), Digital Signal Processing (A), Computer and Communication Networks (A), Wireless and Mobile Networks (A), Algebraic Coding Theory (A), MIMO Wireless Communications (A), Analysis of Algorithms I (A), Detection and Estimation Theory (A), Convex Optimization (A), Stochastic Models (A), Machine Learning (A), Optimization II (A), Bayesian Modeling For Machine Learning (A) Teaching Experience: Teaching Assistant, 5G Programmable Networks, Columbia University Spring 2018 Teaching Assistant, Computer Networks, Columbia University Spring 2017 Teaching Assistant, Computer Networks, Columbia University Summer 2016 Teaching Assistant, Computer Networks, Columbia University Summer 2015 Teaching Assistant, Networking Laboratory, Columbia University Fall 2013 Mentoring and Advising: Gharvhel Carre, Columbia University Undergraduate Researcher Siao-Ting Wang, Columbia University Master s Student Researcher Jenny Li, Columbia University Undergraduate Researcher Ido Michael, Columbia University Master s Student Researcher Fall 2017 Sarthak Arora, Columbia University Undergraduate Researcher Fall Present Andy Xu, Columbia University Undergraduate Researcher Summer Present Rodda John, High School Researcher Summer Present Bohan Wu, Duke University Undergraduate Researcher Summer 2016 Raphael Norwitz, Columbia University Undergraduate Researcher Summer 2015

5 Savvas Petridis, Columbia University Undergraduate Researcher Summer Fall 2015 Technical Skills: Programming Languages: Python, MATLAB Operating Systems: Linux, Mac OS, Windows