Vaneet Aggarwal

18 Pages·2017·0.18 MB·English

by  Vaneet Aggarwal

Vaneet Aggarwal
Purdue University, Email: [email protected]
West Lafayette, IN 47907 Homepage: http://web.ics.purdue.edu/∼vaneet
Research Interests
Machine Learning, Reinforcement Learning, Networking and Communications, ML in Autonomous Trans-
portation, Quantum Machine Learning.
Education
PhD. Princeton University, Princeton, New Jersey - July 2010
Major: Electrical Engineering - GPA 4.0/4.0
Thesis: Decisions in Distributed Wireless Networks with Imprecise Information
Minors: Machine Learning and Computational Perception, Computer Science
Advisor: Prof. A. Robert Calderbank
Dissertation Reading Committee: Prof. Ashutosh Sabharwal, Prof. Vincent Poor
Dissertation Oral Committee: Prof. Sergio Verdu, Prof. Paul Cuff, Prof. Peter Ramadge
MA. Princeton University, Princeton, New Jersey - June 2007
Major: Electrical Engineering - GPA 4.0/4.0
Bachelor of Technology. Indian Institute of Technology, Kanpur - May 2005
Major: Electrical Engineering - GPA 9.6/10.0
Senior Thesis Advisor: Prof. R. K. Bansal
Work Experience
Purdue University, West Lafayette - Jan. 2015 - Current: Faculty in the Schools of Industrial Engineer-
ing and Electrical and Computer Engineering (by courtesy) (Assistant Professor: 2015-2019, Associate
Professor: 2019-2022, Full Professor: 2022-Current.), Leading Purdue CLAN (Machine Learning, and
Networking Research) Labs, Affiliated Faculty Center of Intelligent Infrastructure (CII) with thrust lead
on Artificial Intelligence Application, Affiliated Faculty Computational Science and Engineering Program
(CSE), Affiliated Faculty Energy Center, Affiliated Faculty in the Center for Education and Research in
Information Assurance and Security (CERIAS), Affiliate Faculty in the Center for Resilient Infrastructures,
Systems, and Processes (CRISP), Affiliate Faciulty in the Center for Innovation in Control, Optimization,
andNetworks(ICON),AffiliateFacultyinthePurdueQuantumScienceandEngineeringInstitute(PQSEI).
KAUST, Saudi Arabia - May 2022 - Current: Visiting Professor in the Department of Computer Science.
IIIT Delhi - Jan 2022 - Current: Adjunct Professor in the Department of Computer Science.
Indian Institute of Science (IISc) Bangalore - May 2018 - Apr 2019. VAJRA Adjunct Faculty of Electrical
Communications Engineering (Distinguished Visiting Professor)
AT&T Labs Research, NJ - Aug. 2010 - Dec. 2014 (Florham Park, NJ till Sept. 2013 and Bedminster, NJ
from Oct. 2013): Senior Member of Technical Staff-Research
Research on erasure coded distributed storage, network virtualization, cloud based multimedia, machine
Vaneet Aggarwal 2
learning, energy optimization, interference networks, geographical routing, cellular and WiFi systems.
Columbia University, New York, NY - Aug. 2013 - Dec 2014: Adjunct Assistant Professor of Electrical
Engineering
Key Leadership Experience
ACM Journal of Transportation Systems, co-Editor-in-Chief, 2022-Current
Director of Potential NSF AI Institute on Human-AI Decision Making at Scale, 2021-current (went through
university selection and NSF pre-proposal)
Founding Technical Lead Purdue-TVS Advanced AI Program, 2021-Current
AI Thrust Lead in Purdue CII, 2019-Current
Lead of Purdue IE Seminar Committee, 2020-Current
Founding Technical Lead Purdue-Intel Design For Security Badge Program, 2017-2019
Lead of AT&T Centre Recruiting, 2013-2014
Lead of Princeton Student Wireless Communication Reading Group, 2008-2010
Key Publications
Machine Learning
1. JiayuChen,JingdiChen,TianLan,andVaneet Aggarwal,“Multi-agentCoveringOptionDiscovery
based on Kronecker Product of Factor Graphs,” in Proc. Neurips, Dec 2022.
2. Qinbo Bai, Amrit Singh Bedi, Mridul Agarwal, Alec Koppel, and Vaneet Aggarwal, “Achieving
Zero Constraint Violation for Constrained Reinforcement Learning via Primal-Dual Approach,” in
Proc. AAAI, Feb 2022 (15% acceptance rate, 1349/9251).
3. Washim Uddin Mondal, Mridul Agarwal, Vaneet Aggarwal, and Satish Ukkusuri, “On the Approxi-
mationofCooperativeHeterogeneousMulti-AgentReinforcementLearning(MARL)usingMeanField
Control (MFC),” Journal of Machine Learning Research, Mar 2022 (received Best Paper Award in
Proc. NeurIPS Workshop on Cooperative AI, Dec. 2021)
4. MridulAgarwal,VaneetAggarwal,ChristopherJ.Quinn,andAbhishekUmrawal,“DART:aDaptive
AcceptRejecTfornon-lineartop-Ksubsetidentification,”inProc. AAAI,Feb2021(21%acceptance
rate, 1692/7911, earlier versions of the paper appeared in ALT 2021 and ACM/IMS Transactions on
Data Science, Nov 2021.)
5. WenqiWang,Vaneet Aggarwal,andShuchinAeron,“EfficientLowRankTensorRingCompletion,”
in Proc. ICCV, Oct 2017 (28.9% acceptance rate) (Further use of tensor rings for compression of
neural networks appeared in CVPR, Jun 2018)
Federated/Fog Learning
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1. Anis Elgabli, Chaouki Ben Issaid, Amrit Singh Bedi, Ketan Rajawat , Mehdi Bennis, and Vaneet
Aggarwal, “FedNew: A Communication-Efficient and Privacy-Preserving Newton-Type Method for
Federated Learning,” in Proc. ICML, Jul 2022.
2. Seyyedali Hosseinalipour, Sheikh Shams Azam, Christopher G. Brinton, Nicolo Michelusi, Vaneet
Aggarwal, David J. Love, Huaiyu Dai, “Multi-Stage Hybrid Federated Learning over Large-Scale
Wireless Fog Networks,” Accepted to IEEE/ACM Transactions on Networking, Jan 2022.
3. Mridul Agarwal, Bhargav Ganguly, and Vaneet Aggarwal, “Communication Efficient Parallel Rein-
forcement Learning,” in Proc. UAI, Jul 2021 (26.5% acceptance rate, 206/777).
4. SeyyedaliHosseinalipour,ChristopherG.Brinton,VaneetAggarwal,HuaiyuDai,andMungChiang,
“From Federated Learning to Fog Learning: Towards Large-Scale Distributed Machine Learning in
Heterogeneous Networks,” IEEE Communications Magazine, vol. 58, no. 12, pp. 41-47, December
2020, doi: 10.1109/MCOM.001.2000410.
5. AnisElgabli,JihongPark,AmritS.Bedi,MehdiBennis,andVaneetAggarwal,“GADMM:Fastand
CommunicationEfficientFrameworkforDistributedMachineLearning,”JournalofMachineLearning
Research, 21(76): 1-39, Mar 2020.
Networking and Communications
1. Vaneet Aggarwal and Tian Lan, “Modeling and Optimization of Latency in Erasure-coded Storage
Systems,” Now Foundations and Trends in Communication and Information Theory, Vol. 18, No. 3,
pp 380-525, Jul 2021.
2. DivijaSwethaGadiraju,V.Lalitha,andVaneetAggarwal,“SecureRegeneratingCodesforReducing
Storage and Bootstrap Costs in Sharded Blockchains,” in Proc. IEEE International Conference on
Blockchain, Nov 2020 (16% acceptance rate, 36/225).
3. Anis Elgabli, Muhaman Felemban, and Vaneet Aggarwal, “GroupCast: Preference-Aware Coopera-
tive Video Streaming with Scalable Video Coding,” IEEE/ACM Transactions on Networking, vol. 27,
no. 3, pp. 1138-1150, June 2019 (Best Paper Award at Infocom HotPost Workshop).
4. Abubakr Alabbasi, Vaneet Aggarwal, and Moo-Ryong Ra, “Multi-tier Caching Analysis in CDN-
based Over-the-top Video Streaming Systems,” IEEE/ACM Transactions on Networking, vol. 27, no.
2, pp. 835-847, April 2019.
5. Melissa Duarte, Ashutosh Sabharwal, Vaneet Aggarwal, Rittwik Jana, Kadangode Ramakrishnan,
Chris Rice, and N. K. Shankar, “Design and Characterization of a Full-duplex Multi-antenna System
for WiFi networks,” IEEE Transactions on Vehicular Tech., vol. 63, no. 3, pp. 1160-1177, March
2014. (2017 Jack Neubauer Memorial Award recognizing the Best Systems Paper published in
the IEEE Transactions on Vehicular Technology)
ML in Autonomous Transportation
1. Jiayu Chen, Abhishek K. Umrawal, Tian Lan, and Vaneet Aggarwal, “DeepFreight: A Model-free
Deep-reinforcement-learning-based Algorithm for Multi-transfer Freight Delivery,” in Proc. ICAPS,
Jun 2021 (29% acceptance rate).
2. Marina Haliem, Vaneet Aggarwal, and Bharat Bhargava, “AdaPool: A Diurnal-Adaptive Fleet
ManagementFrameworkusingModel-FreeDeepReinforcementLearningandChangePointDetection,”
Accepted to IEEE Transactions on Intelligent Transportation Systems, Aug 2021. (presented in part
at ACM Buildsys 2020)
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3. KaushikManchella,AbhishekK.Umrawal,andVaneet Aggarwal,“FlexPool: ADistributedModel-
Free Deep Reinforcement Learning Algorithm for Shared Passengers and Goods Delivery,” IEEE
Transactions on Intelligent Transportation Systems, vol. 22, no. 4, pp. 2035-2047, April 2021, doi:
10.1109/TITS.2020.3048361. (presented at ACM Computer Science in Cars Symposium (CSCS), Dec
2020, and extended version in Neurips Workshop 2020).
4. Abubakr Alabbasi, Arnob Ghosh, and Vaneet Aggarwal, “DeepPool: Distributed Model-free Algo-
rithm for Ride-sharing using Deep Reinforcement Learning,” IEEE Transactions on Intelligent Trans-
portation, vol. 20, no. 12, pp. 4714-4727, Dec. 2019 (Featured also as a ICAPS 2020 Journal Track
Paper).
5. Arnob Ghosh and Vaneet Aggarwal, “Electric Vehicle Charging with Menu-Based Pricing,” IEEE
Transactions on Smart Grid, vol. 9, no. 6, pp. 5918-5929, Nov. 2018.
Quantum Machine Learning
1. Mohammad Ali Javidian, Vaneet Aggarwal, and Zubin Jacob, “Quantum Causal Inference in the
Presence of Hidden Common Causes: an Entropic Approach,” Accepted to Physical Review A, Nov
2022.
2. DebanjanKonar,AdityaDasSarma,SohamBhandary,SiddharthaBhattacharyya,AttilaCangia,and
Vaneet Aggarwal, “A Shallow Hybrid Classical-Quantum Spiking Feedforward Neural Network for
Noise-Robust Image Classification,” Applied Soft Computing, vol. 136, paper 110099, Mar 2023
3. Dheeraj Pedirredy, Vipul Bansal, Zubin Jacob, and Vaneet Aggarwal, “Tensor Ring Parametrized
Variational Quantum Circuits for Large Scale Quantum Machine Learning,” NeurIPS Workshop on
Quantum Tensor Networks in Machine Learning, Dec. 2021
4. Mohammad Ali Javidian, Vaneet Aggarwal, and Zubin Jacob, “Quantum Entropic Causal Infer-
ence,” UAI Workshop, Jul 2021.
5. Vaneet AggarwalandA.RobertCalderbank,“BooleanFunctions,ProjectionOperators,andQuan-
tumErrorCorrectingCodes,”IEEETrans. InformationTheory, vol.54, no.4, pp.1700-1707, Apr2008.
Visiting Research Experience
KAUST, Saudi Arabia - May 2022-current
IIIT Delhi, India - May 2022
IISc Bangalore, India - May-Aug 2018
Air Force Research Lab, Rome NY - Jun-Aug 2015
Investigated placement of radar antennas for target localization
Swiss Federal Institute of Technology (EPFL), Lausanne
2009: Research on compressive sensing with Prof. Suhas Diggavi
2004: Developing a virtual operating system based on the MIPS R3000 architecture
Rice University, Houston, Texas - Summer 2007 and Spring 2009
Developing multi-round wireless protocols with Prof. Ashutosh Sabharwal
Summer Internship: AT&T Labs Research, Florham Park, NJ - 2008
Developing multicast scheduling strategies for IPTV
Vaneet Aggarwal 5
Summer Internship: Indian Institute of Technology Kanpur - Summer 2004
Accelerating OSPF convergence and introducing resilience to single node failures. Forensic analysis of 5km
WLAN 802.11b link between IIT Kanpur and Mandhana Village leading to improved system design.
Key Research Metrics as of 10 Mar 2023
Source: https://scholar.google.com/citations?user=Tu4lmGwAAAAJ
Citations: 5996
h-index: 38
i10-index: 131
Honors & Awards
2021 NeurIPS Workshop Best Paper Award for paper [C128]
2020 Most Impactful Faculty Innovator, Purdue University.
2018 Infocom Workshop Best Paper Award for paper [C71]
2018 Visiting Advanced Joint Research (VAJRA) Award from Science and Engineering Research
Board (SERB), India, for research in summers 2018 at IISc Bangalore
2017 Jack Neubauer Memorial AwardfortheBest SystemsPaperpublishedintheIEEETransactions
on Vehicular Technology for paper [J18]
Best-in-Session-Presentation Award in technical session“Cloud Storage” in IEEE Infocom 2017 for pa-
per [C62]
AT&T, Virtual University Research Initiative (VURI) Award, “Video Streaming over Cloud,” Mar 2016.
Elevated to IEEE Senior Member, 2015
AirForceResearchLab,ResearchExtensionAward,“LocalizationwithMIMORadar/Multi-Dimensional
Data Completion Algorithms/Joint Subspace Clustering and Data Completion,” Aug 2015.
Visiting Faculty Research Program Award, Air Force Research Laboratory, Summer 2015.
Senior Vice President Team Excellence Award - AT&T, 2014: forcoordinatinganaggressiveCentre
recruiting program. The program was transparent, thorough and highly professional, engaging the entire
organization in a positive activity that positions AT&T Labs Research (now AT&T Labs Advanced Tech-
nologies) for the future.
Finalist for Fierce Innovation Awards 2013: My work in small cell propagation models was chosen as
a finalist for the industry innovation awards.
COMSOC Multimedia Communications Technical Committee R-letters: for paper “Optimizing
Cloud Resources for Delivering IPTV Services through Virtualization,” IEEE Transactions on Multimedia,
Vaneet Aggarwal 6
June 2013, edited by Carl James Debono. The Review Board for R-letters aims at recommending recent
state-of-the-art and emerging publications in the literature.
AT&T Key Contributor Award, 2013: for technical contribution in AT&T.
Vice President Excellence Award - AT&T, 3Q 2012: for experimenting with different models for
small cell indoor RF propagation and recommending a model, which was later developed by Labs Analysis
and Optimization Organization on their Hetnet Analysis and Resource Planning tool (currently in use by
business).
The Innovation Pipeline Executive Challenge Second Prize - AT&T, 2011: for solution to “How
can AT&T leverage our network and technology to provide Pharmaceutical and Clinical Research Organi-
zations with advanced tools for clinical trials in both the US and abroad?”
Porter Ogden Jacobus Honorific Fellowship - 2009: The highest honor awarded by the Graduate
School at Princeton. Four awards are made each year to recognize exceptional scholarly excellence and this
award was made to Electrical Engineering student after thirteen years.
Excellence in Teaching Award - 2008: Departmental award recognizes contributions to instruction of
ELE 485 Signal Analysis and Communication Systems
Princeton Research Symposium Poster Award - 2009: Princeton University community and the
general public evaluates the research poster presentation at an Annual event
Sridhar Memorial Prize - 2005: Top graduating student at the end of 7 semesters in Electrical Engi-
neering at IIT Kanpur, separately recognized for academic excellence every year (2001-2004)
Princeton University Fellowship - 2005
Research Funding
CISCO,“ProficientReinforcementLearningandExplorationforOnlineNetworkManagement,”Apr2023-
Mar 2024, Co-PI, Amount: $100,000 (PI: Tian Lan, GWU)
Meta Research Award, “Reinforcement Learning for Online Service Placement in Cloud Management,”
Jan 2023-Dec 2023, Co-PI, Amount: $300,000 (PI: Tian Lan, GWU)
NSF,“FW-HTF-R:SensingtoEnableExpertDecision-MakinginHealthcare,”Nov2022-Oct2026,Co-PI,
Amount: $300,000 (PI: Shweta Singh, Purdue)
NSF, “FMRG: Eco: Cyber Enabled Transformation to Circular Supply Chains for Sustainable Pharmaceu-
tical Manufacturing Networks ,” Oct 2021-Sept 2025, Co-PI, Amount: $200,000 (PI: Denny Yu, Purdue)
NSF,“CIF:Small: SequentialDecisionMakingUnderUncertaintyWithSubmodularRewards,”Feb2022-
Jan 2025, PI, Amount: $250,000 (Co-PI: Chris Quinn, Iowa State)
Facebook Research Award, “Reinforcement Learning for Online Service Placement in Cloud Manage-
ment,” Jan 2022-Dec 2022, Co-PI, Amount: $50,000 (PI: Tian Lan, GWU)
NSF, “FW-HTF-R: Biometrics and AI to Support Expert Nurse Decision-Making,” Oct 2021-Sept 2025,
Co-PI, Amount: $500,000 (PI: Denny Yu)
Vaneet Aggarwal 7
CISCO, “Deeplace: Deep-Learning Online Service Placement in Edge Computing under Peak and Average
Constraints,” Sept 2021-Aug 2024, PI, Amount: $100,000 (Co-PI: Tian Lan, GWU)
DARPA, “Adaptive Quantum Perceptronics ,” Oct 2020-Sep 2021, Co-PI, Amount: $100,000 (PI: Zubin
Jacob, Purdue)
DARPA,“QuantumEntropicCausalInference,”Aug2020-Sep2021, Co-PI,Amount: $100,000(PI:Zubin
Jacob, Purdue)
NSF, “Collaborative Research: SWIFT: Small: Cross-Layer Interference Management: Bringing Interfer-
enceAlignmenttoReality,”Oct2020-Sept2023,SeniorPersonnel,Amount: $30,000(PI:AlirezaVahid,UC
Denver)
DARPA,“GeneratingNoveltyinOpen-worldMulti-agentEnvironments(GNOME),”Nov. 2019-May2023,
Co-PI, Amount: $350,000 (PI: Mayank Kejriwal, ISI)
CISCO, Faculty Research Award, “Rethinking Erasure Codes for Cloud Storage: A Quantitative Frame-
work for Latency, Reliability, and Cost Optimization,” PI, Jun 2019 - Dec 2020, Amount: $80,000
DARPA, “A Fundamental Theory for Dexterous Surgical Skills Transfer to Medical Robots,” Oct. 2018-
Sept. 2021, Amount: $250,000 (PI: Juan Wachs, Purdue)
National Science Foundation, “CIF: Small: Collaborative Research: Communications with Energy Har-
vesting Nodes,” REU Supplement, PI, Aug. 2017-Aug. 2020, Amount: $7,800
AT&T,VirtualUniversityResearchInitiative(VURI)Award,“VideoStreamingoverCloud,”PI,Mar2016,
Amount: $20,000
National Science Foundation, “CSR: Small: Collaborative Research: Rethinking Erasure Codes for
Cloud Storage: A Quantitative Framework for Latency, Reliability, and Cost Optimization,” PI, Oct. 2016-
Sept. 2021, Amount: $250,000
National Science Foundation, “CIF: Small: Collaborative Research: Communications with Energy Har-
vesting Nodes,” Award 1527486, PI, Sept. 2015-Aug. 2020, Amount: $229,504
AirForceResearchLab,ResearchExtensionAward,“LocalizationwithMIMORadar/Multi-Dimensional
DataCompletionAlgorithms/JointSubspaceClusteringandDataCompletion,”PI,Aug-Dec2015,Amount:
$10,000
Teaching Experience
Purdue University:
IE69000 Mathematics of Data Science, Spring 2022.
IE59000 Security Applications, Spring 2022.
IE59000 Predictive Models, Spring 2022.
IE47400 Industrial Control Systems, Fall 2021.
Vaneet Aggarwal 8
IE53600 Stochastic Models in Operations Research, Spring 2021.
IE47400 Industrial Control Systems, Fall 2020.
Purdue IMPACT-X (Instruction Matters: Purdue Academic Course Transformation) Fellow, Summer 2020
for redesigning courses to create student-centered teaching and learning environments.
IE69000 Mathematics of Data Science, Fall 2019.
IE59000 Security Applications, Fall 2019.
IE53600 Stochastic Models in Operations Research, Spring 2019.
IE49000 Introduction to Data Science, Spring 2019.
IE59000 Security Applications, Spring 2019.
IE69000 Scheduling in Computer Systems, Fall 2018.
IE49000 Introduction to Data Science, Spring 2018.
Purdue IMPACT (Instruction Matters: Purdue Academic Course Transformation) Fellow, Fall 2017 for re-
designing courses to create student-centered teaching and learning environments.
IE33600 Operations Research - Stochastic Models, Fall 2017.
IE53600 Stochastic Models in Operations Research, Spring 2017.
IE69000 Mathematics of Data Science, Fall 2016.
IE33600 Operations Research - Stochastic Models, Fall 2016.
IE53600 Stochastic Models in Operations Research, Spring 2016.
IE33600 Operations Research - Stochastic Models, Fall 2015.
IE53600 Stochastic Models in Operations Research, Spring 2015.
KAUST:
CS 394V: Contemporary Topics in Reinforcement Learning, Fall 2022.
Columbia University:
ELEN E6713 Topics in Comm: Cooperative Wireless Communication Systems, Fall 2013.
Princeton University:
ELE 485 Signal Analysis and Communication Systems: Assisted Prof. Stuart C. Schwartz (2006 and 2007)
and Adjunct Prof. Paul Henry (2008) by organizing labs, giving precepts to undergraduates, setting home-
work and exams.
Vaneet Aggarwal 9
Direction of Research
Advisor for Postdoc Researchers
Debanjan Konar, 2023-current
Mohammad Perdramfar, 2023-current
Washim Uddin Mondal, 2021-current (joint with Prof. Satish Ukkusuri)
Rakhi Pratihar, 2022-2023 (joint with Prof. Anuradha Sharma, IIITD) Mohammad Ali Javidian, 2020-2022
(joint with Prof. Zubin Jacob)
Fanglin Bao, Purdue University, 2019-2020 (joint with Prof. Zubin Jacob)
Arnob Ghosh, Purdue University, 2016-2019
Ke Liu, Purdue University, 2017-2019
Thesis Advisor for PhD students (Current)
Guangchen Lan, Management, Purdue University, 2023-current
Fares Fourati, ECE, KAUST, 2022-current (joint with Prof. M.S. Alouini)
Dipesh Tamboli, ECE, Purdue University, 2022-current
Jiayu Chen, IE, Purdue University, 2021-current
Bhargav Ganguly, IE, Purdue University, 2021-current
Mudit Gaur, Stats, Purdue University, 2021-current
Qinbo Bai, ECE, Purdue University, 2019-Current
Dheeraj Pedireddy, IE, Purdue University, 2019-current
Abhishek Umrawal, IE, Purdue University, 2019-current (awarded VIP Graduate Student Mentor Award
2019; Bilsland Dissertation Fellowship Award 2022)
Chang-Lin Chen, ECE, Purdue University, 2019-current
Thesis Advisor for PhD students (Graduated Students)
Mridul Aggarwal, ECE, Purdue University, 2018-2022
AbubakrAlabassi, IE,PurdueUniversity, 2016-2019(AwardedRossFellowshipAward, 2016andHonorable
Mention Outstanding Graduate Student Research Award 2019)
Anis Elgabli, ECE, Purdue University, (joint with Prof. Mark Bell) 2015-2018
Wenqi Wang, IE, Purdue University, 2015-2018 (Awarded Bilsland Dissertation Fellowship Award, 2017)
Mehdi Ashraphijuo, ECE, Columbia University, (joint with Prof. Xiaodong Wang), 2012-2016 (awarded
Qualcomm Innovation Fellowship, 2014)
Yu Xiang, ECE, George Washington University, (joint with Prof. Tian Lan), 2012-2015
Mentoring/ co-supervising PhD students while at AT&T:
Achaleshwar Sahai, Rice University (Feedback in Interference Channels, 2009-2010)
Alireza Vahid, Cornell University (Local View in Interference Channels, 2010-2011)
Kanes Sutuntivorakoon, Cornell University (Local View in Interference Channels, 2010-2011)
Pedro Santacruz, Rice University (Local View in Interference Channels, 2010-2013)
Melissa Duarte, Rice University (Full Duplex wireless systems, 2011-2012)
Khawla Alnajjar, Columbia University (Interference Alignment, 2011-2012)
Dinesh Bharadia, Stanford University (Full Duplex wireless systems, 2012-2013)
Robert Margolies, Columbia University (Cellular Signal Prediction, 2012-2013)
Tingting Sun, Rutgers University (Full Duplex wireless systems, 2012-2013)
Zhe Wang, Columbia University (Energy Harvesting for Wireless Networks, 2012-2014)
Fraida Fund, Polytechnic Institute of NYU, (Device to Device Communications, Summer 2013)
Uri Livnat, Columbia University, (Optimization for Small Cells, 2013-2014)
Hongyao Ma, Harvard University, (Tensor Completion at multiple resolutions, Summer 2013)
Rajarajan Sivaraj, UC Davis, (LTE Heterogenous Networks, 2013-2014)
Shayan Saeed, UIUC (Realtime Storage Systems, Summer 2014)
Xiao-Yang Liu (Adaptive Tensor Completion for RF Fingerprinting, 2014-2015)
Vaneet Aggarwal 10
Thesis Advisor for MS Research students (Current)
Veni Goyal, Purdue University, 2022-current
Pulkit Mundra, Purdue University, 2022-current
Thesis Advisor for MS Research students at Purdue (Graduated)
Madhu Lekha Guntaka, Purdue University, 2020-2021 (joint with Prof. Dharmendra Saraswat)
Zequn Li, Purdue University, 2020-2021 (joint with Prof. Hua Cai)
Anirudh Shankar, Purdue University, 2019-2021
Kaushik Manchella, Purdue University, 2019-2020
Ashutosh Singh, Purdue University, 2019-2020
Mayank Gupta, Purdue University, 2016-2018
Vineeth CR, Purdue University, 2016- 2018
Zijian He, Purdue Universty, 2015-2016
Jingxian Fan, Purdue University, 2015-2016
Advisor for Undergraduate Research (Current)
Kathy Niu, ECE, Purdue University
Advisor for Undergraduate Research (Previous)
Aaryan Garg, ECE, Spring 2021, Summer 2021 (funded through Purdue SummerStay)
Shivangi Agarwal, CS, Summer 2020 (funded through Purdue SummerStay)
Olamidotun Folajimi Akinnola, ECE, Fall 2019
Aarushi Bannerjee, CS, Fall 2019, Spring 2020
Siyuan Cao, IE (Independent Study, Fall 2017)
Lingjun Chen, Statistics (Independent Study, Fall 2017, Spring 2018)
Ved Rajesh Dave, ECE, Fall 2019, Spring 2020
Kosei William Dohi, Physics and Statistics, Summer 2022 (funded through Purdue SummerStay )
Chufan Gao, Computer Science (funded through Purdue SummerStay, Summer 2017, Independent Study,
Fall 2017, Spring 2018)
Zaid Al Haddadin, Computer Science (funded through Purdue SummerStay, Summer 2016)
He Huang, Computer Science (Independent Study, Fall 2016, Spring 2017)
Jae Joong Li, Computer Science (Independent Study, Spring 2017, Fall 2017, Spring 2018)
Nanyi Jiang, Computer Science, Summer 2022 (funded through Purdue SummerStay )
Jeremiah S Johnson, Computer Science (funded through REU supplement, Fall 2017, Spring 2018)
Nolan Lewis, AAE (funded through Purdue SummerStay, Summer 2017)
Deeptanshu Malik, Computer Engineering (funded through Purdue SummerStay, Summer 2016)
Kartikeya Mishra, ECE (funded through Purdue SummerStay, Summer 2017)
Mahira Morris, ECE, Fall 2019
Daniyaal Shoaib Rasheed, CS, Fall 2019
Austin Sale, ECE, Fall 2019, Spring 2020
Anurag Shah, CS, Spring 2020, Summer 2020 (funded in part through Purdue SURF)
AbhimanyuSharma,ComputerScience(fundedthroughREUsupplement,Fall2017,Spring2018,Fall2018)
Pratyaksh Sharma, Computer Engineering (funded through Purdue SummerStay, Summer 2017)
Raymond Susilo, Industrial Engineering (Summer 2016)
Haobo Wang, CS, Summer 2019, Fall 2019, Spring 2020
Wenqin Yi, Industrial Engineering (Independent Study, Spring 2017)
Haozhe Zhou, CS, Summer 2020 (funded through Purdue SummerStay)
Mentoring Undergraduate Research(at Princeton): Led the ELE Senior Thesis writing group and guided a
physics senior thesis in quantum computing (David Zaslavsky in 2008)