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Somayeh Sojoudi Associate Professor Electrical Engineering and Computer Sciences University of California, Berkeley Email: sojoudi (at) berkeley.edu Office: 5114 Etcheverry Hall |
Brief Biography
I am an Associate Professor in the Electrical Engineering and Computer Sciences and Mechanical Engineering departments at UC Berkeley. I work on interdisciplinary problems in optimization theory, control theory, machine learning, and power systems.
News
March 2024: Our paper “Improving the Accuracy-Robustness Trade-Off of Classifiers via Adaptive Smoothing” has been accepted for publication in SIAM Journal on Mathematics of Data Science (SIMODS).
February 2024: New paper: Transport of Algebraic Structure to Latent Embeddings.
February 2024: New paper: MixedNUTS: Training-Free Accuracy-Robustness Balance via Nonlinearly Mixed Classifiers.
February 2024: New paper: Pausing Model Learning in Real-world Reinforcement Learning.
January 2024: New paper: The computation of approximate feedback Stackelberg equilibria in multi-player nonlinear constrained dynamic games.
January 2024: New paper: Distributionally Robust Joint Chance-Constrained Optimal Power Flow using Relative Entropy.
January 2024: Our paper “Absence of spurious solutions far from ground truth: A low-rank analysis with high-order losses” has been accepted for oral presentation at the 26th International Conference on Artificial Intelligence and Statistics, AISTATS 2024.
December 2023: My Ph.D. student, Brendon Anderson, won first place in INFORMS OR/MS Tomorrow Poster Competition for the paper Asymmetric Certified Robustness via Feature-Convex Neural Networks.
December 2023: I will serve as an Area Chair for ICML 2024.
November 2023: The blog post by my PhD student, Samuel Pfrommer, has been published on the BAIR blog: Asymmetric Certified Robustness via Feature-Convex Neural Networks.
October 2023: Awarded a grant by the UC Noyce Initiative to enhance power grid security against cyberattacks.
October 2023: I gave an online distinguished lecture (for the IEEE Systems Council) on computational methods for solving non-convex machine learning problems.
September 2023: Three papers to appear in NeurIPS 2023: 1) Asymmetric Certified Robustness via Feature-Convex Neural Networks; 2) Algorithmic Regularization in Tensor Optimization: Towards a Lifted Approach in Matrix Sensing; 3) Tempo Adaptation in Non-stationary Reinforcement Learning.
September 2023: The paper “Projected Randomized Smoothing for Certified Adversarial Robustness” to appear in Transactions on Machine Learning Research.
August 2023: I will serve as an Area Chair for ICLR 2024.
August 2023: The paper “A New Complexity Metric for Nonconvex Rank-one Generalized Matrix Completion” to appear in Mathematical Programming.
July 2023: 6 papers have been accepted at the 62nd IEEE Conference on Decision and Control: 1) Tight Certified Robustness via Min-Max Representations of ReLU Neural Networks; 2) Initial State Interventions for Deconfounded Imitation Learning; 3) Meta-Learning Parameterized First-Order Optimizers using Differentiable Convex Optimization; 4) Leveraging the Physics of AC Power Flow in Support Vector Regression to Identify Power System Topology; 5) Distributionally Robust Optimization for Nonconvex QCQPs with Stochastic Constraints; 6) Scenario-Game ADMM: A Parallelized Scenario-Based Solver for Stochastic Noncooperative Games.
May 2023: Our paper “Mixing Classifiers to Alleviate the Accuracy-Robustness Trade-Off” ranked second on Robust-Bench.
April 2023: Elizabeth Glista has defended her Ph.D. thesis titled “Optimizing for the future smart grid: Efficient methods for nonconvex AC power flow problems” (dissertation slides).
April 2023: Our paper “Over-parametrization via Lifting for Low-rank Matrix Sensing: Conversion of Spurious Solutions to Strict Saddle Points” has been selected for a live presentation at the 2023 International Conference on Machine Learning (ICML).
March 2023: I will serve as a Senior Associate Editor for the IEEE Systems Journal.
March 2023: We are organizing the 5th annual NorCal Control Workshop at UC Berkeley.
March 2023: Two of my PhD students (Brendon Anderson and Ziye Ma) received the 2023 Outstanding Graduate Student Instructor Award.
March 2023: The 2nd Workshop on Formal Verification of Machine Learning has been accepted for ICML 2023.
March 2023: Our paper “Geometric Analysis of Noisy Low-rank Matrix Recovery in the Exact Parameterized and the Overparameterized Regimes” to appear in INFORMS Journal on Optimization.
March 2023: I will serve as an Area Chair for NeurIPS 2023.
February 2023: Our paper “Noisy Low-rank Matrix Optimization: Geometry of Local Minima and Convergence Rate” has been selected for oral presentation at the 25th International Conference on Artificial Intelligence and Statistics, AISTATS 2023, (top 6% of accepted papers).
January 2023: The paper “Cost Inference for Feedback Dynamic Games from Noisy Partial State Observations and Incomplete Trajectories” has been accepted for publication at the 22nd International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2023).
December 2022: I will serve as an Area Chair for ICML 2023.
November 2022: The paper “Semidefinite Programming versus Burer-Monteiro Factorization for Matrix Sensing” to appear in Thirty-Seventh AAAI Conference on Artificial Intelligence.
November 2022: The paper “An Efficient Homotopy Method for Solving the Post-contingency Optimal Power Flow to Global Optimality” to appear in IEEE Access.
October 2022: Our paper “Efficient Global Optimization of Two-layer ReLU Networks: Quadratic-time Algorithms and Adversarial Training” has been accepted for publication in SIAM Journal on Mathematics of Data Science (SIMODS).
October 2022: The paper “Projected Randomized Smoothing for Certified Adversarial Robustness” received the 2022 INFORMS Data Mining Best Student Paper Award Runner-Up.
September 2022: I gave a semi-plenary talk at the 8th IFAC Symposium on System Structure and Control (SSSC).
September 2022: I gave a Distinguished Lecture at Concordia University in Montreal.
August 2022: The paper “Distributed Power System State Estimation Using Graph Convolutional Neural Networks” to appear in the 2023 Hawaii International Conference on System Sciences (HICSS-56).
August 2022: Eli Brock and Lucas Hyunin Lee joined our group as Ph.D. students.
August 2022: The paper “An Overview and Prospective Outlook on Robust Training and Certification of Machine Learning Models” to appear in the 2022 IFAC Symposium on System Structure and Control (SSSC).
July 2022: Our paper “A MILP for Optimal Measurement Choice in Robust Power Grid State Estimation” has won the Best-of-the-Best Conference Paper Award of the 2022 Power & Energy Society General Meeting (as the top paper in the area of Power System Modeling & Analysis).
July 2022: I gave an invited talk at the 2022 ICML Workshop on Formal Verification of Machine Learning.
July 2022: Three papers accepted to 61st IEEE Conference on Decision and Control: “LQR Control with Sparse Adversarial Disturbances”, “A Sequential Greedy Approach for Training Implicit Deep Models”, and “Distributed Optimal Control of Graph Symmetric Systems via Graph Filters”.
June 2022: The paper “Data-Driven Certification of Neural Networks with Random Input Noise" to appear in IEEE Transactions on Control of Network Systems.
June 2022: The paper “Towards Optimal Randomized Smoothing: A Semi-Infinite Linear Programming Approach" to appear in ICML 2022 Workshop on Formal Verification of Machine Learning.
May 2022: I have been appointed as a Systems Council Distinguished Lecturer.
May 2022: I will give a semi-plenary talk at IFAC SSSC 2022.
April 2022: New paper: “LQR Control with Sparse Adversarial Disturbances”.
April 2022: New paper: “A Sequential Greedy Approach for Training Implicit Deep Models”.
April 2022: New paper: “A Unified Complexity Metric for Nonconvex Matrix Completion and Matrix Sensing in the Rank-one Case”.
March 2022: Our joint project with Caltech, KTH and EPRI on cybersecurity using artificial intelligence has received funding from C3.ai Digital Transformation Institute.
March 2022: The paper “Internally Hankel k-positive systems” to appear in the SIAM Journal on Control and Optimization.
March 2022: Two papers to appear in the 2022 Learning for Dynamics & Control Conference (L4DC): “Safe Reinforcement Learning with Chance-constrained Model Predictive Control” and “Certified Robustness via Locally Biased Randomized Smoothing”.
February 2022: The paper “Node-Variant Graph Filters in Graph Neural Networks” to appear in the 2022 IEEE Data Science & Learning Workshop (IEEE DSLW).
February 2022: The paper “Distributed Linear-Quadratic Control with Graph Neural Networks” has been accepted for publication in Signal Processing.
January 2022: Two papers to appear in the American Control Conference (ACC 2022): “Practical Convex Formulations of One-hidden-layer Neural Network Adversarial Training” and “Control and Uncertainty Propagation in the Presence of Outliers by Utilizing Student-t Process Regression”.
January 2022: The paper “A MILP for Optimal Measurement Choice in Robust Power Grid State Estimation” to appear in the 2022 Power & Energy Society General Meeting.
January 2022: The paper “Factorization Approach for Low-complexity Matrix Completion Problems: Exponential Number of Spurious Solutions and Failure of Gradient Methods” to appear in International Conference on Artificial Intelligence and Statistics (AISTATS 2022).
January 2022: Christian Grussler has joined the Technion as an Assistant Professor.
December 2021: New paper: “Efficient Global Optimization of Two-layer ReLU Networks: Quadratic-time Algorithms and Adversarial Training”
December 2021: New paper: “Safe Reinforcement Learning with Chance-constrained Model Predictive Control”
November 2021: The paper “Sharp Restricted Isometry Property Bounds for Low-rank Matrix Recovery Problems with Corrupted Measurements” to appear in AAAI Conference on Artificial Intelligence (AAAI-22).
November 2021: The paper “On the absence of spurious local trajectories in time-varying nonconvex optimization” to appear in IEEE Transactions on Automatic Control.
October 2021: The paper “Efficient Global Optimization of Two-layer ReLU Networks: Adversarial Training and Quadratic-time Algorithms” by Yatong Bai, Tanmay Gautam, and myself received the INFORMS Data Mining Best Student Paper Award Runner-Up.
October 2021: New paper on matrix completion:“Factorization Approach for Low-complexity Matrix Completion Problems: Exponential Number of Spurious Solutions and Failure of Gradient Methods.”
August 2021: Samuel Pfrommer has joined my group as a Ph.D. student.
July 2021: The paper “Augmented Lagrangian Method for Instantaneously Constrained Reinforcement Learning Problems” accepted for presentation at the 2021 IEEE Conference on Decision and Control.
July 2021: The paper “A Sequential Framework Towards an Exact SDP Verification of Neural Networks” accepted for presentation at the 2021 IEEE International Conference on Data Science and Advanced Analytics (DSAA 2021).
July 2021: The paper “Spectral Graph Attention Network with Fast Eigen-approximation” accepted for presentation at 30th ACM International Conference on Information and Knowledge Management (CIKM 2021).
June 2021: The paper “Sample Complexity of Block-Sparse System Identification Problem” to appear in IEEE Transactions on Control of Network Systems (TCNS).
June 2021: Professor Javad Lavaei and I have received a joint grant from the C3.ai Digital Transformation Institute.
May 2021: I received Office of Naval Research Young Investigator Award.
April 2021: I will serve as an Area Chair for NeurIPS 2021.
March 2021: Our paper “Graph Neural Networks for Distributed Linear-Quadratic Control” has been accepted for oral presentation at the 2021 Learning for Dynamics & Control Conference (L4DC).
March 2021: New paper: “Distributed Linear-Quadratic Control with Graph Neural Networks”.
March 2021: New paper: “Internally Hankel k-positive systems”.
February 2021: New paper on robust neural network training: “Convex Formulation of Robust Two-layer Neural Network Training”.
January 2021: The paper “No Spurious Solutions in Non-convex Matrix Sensing: Structure Compensates for Isometry” accepted for presentation at the 2021 American Control Conference.
January 2021: New paper on neural networks: “Partition-Based Convex Relaxations for Certifying the Robustness of ReLU Neural Networks”.
January 2021: I received the NSF CAREER Award.
December 2020: Brendon Anderson has been awarded the 2021 John and Janet McMurtry Fellowship.
December 2020: The papers “Power up! Robust Graph Convolutional Network via Graph Powering” and “Improving Fairness and Privacy in Selection Problems” accepted to the 35th AAAI Conference on Artificial Intelligence (AAAI-21).
November 2020: New paper on graph neural networks and control: “Graph Neural Networks for Distributed Linear-Quadratic Control”.
October 2020: The paper “Role of Sparsity and Structure in the Optimization Landscape of Non-convex Matrix Sensing” to appear in Mathematical Programming.
October 2020: New papers on robustness analysis of neural networks: “Strengthened SDP Verification of Neural Network Robustness via Non-Convex Cuts” and “Certifying Neural Network Robustness to Random Input Noise from Samples”.
October 2020: New paper on neural networks: “Data-Driven Assessment of Deep Neural Networks with Random Input Uncertainty”.
September 2020: The paper “Implicit Graph Neural Networks” to appear in 34th Conference on Neural Information Processing Systems (NeurIPS 2020).
September 2020: The paper "Boundary Defense Against Cyber Threat for Power System State Estimation has been accepted (with minor revisions) for IEEE Transactions on Information Forensics & Security.
September 2020: Dr. Fernando Gama has joined my group as a postdoctoral researcher.
August 2020: Video of my plenary talk for the IEEE Conference on Control Technology and Applications (CCTA), 2020.
August 2020: The paper “Absence of Spurious Local Trajectories in Time-Varying Optimization: A Control-Theoretic Perspective” has won an outstanding student paper award of the IEEE Conference on Control Technology and Applications (CCTA), 2020.
July 2020: The paper “Efficient Learning of Distributed Linear-Quadratic Controllers” to appear in SIAM Journal on Control and Optimization.
July 2020: The papers “Tightened Convex Relaxations for Neural Network Robustness Certification” and “Convex model to evaluate worst-case performance of local search in the Optimal Power Flow problem” to appear in IEEE Conference on Decision and Control, 2020.
July 2020: The paper Homotopy Method for Finding the Global Solution of Post-contingency Optimal Power Flow has won the Best Student Paper Award of 2020 American Control Conference.
May 2020: Nan Tian successfully defended his Ph.D. thesis entitled “Cloud-Edge Hybrid Robotic Systems for Physical Human Robot Interactions”.
May 2020: Salar Fattahi successfully defended his Ph.D. thesis entitled “Structure-Aware Methods in Large-Scale Computational Problems: Machine Learning, Optimization, and Control”.
April 2020: The paper “Absence of Spurious Local Trajectories in Time-Varying Optimization: A Control-Theoretic Perspective” to appear in the IEEE Conference on Control Technology and Applications (CCTA), 2020.
April 2020: Mohammad Mahdi Khalili will join University of Delaware as an Assistant Professor in Fall 2020.
April 2020: New paper on neural networks: “Tightened Convex Relaxations for Neural Network Robustness Certification”.
April 2020: New paper on power systems: “Convex model to evaluate worst-case performance of local search in the Optimal Power Flow problem”.
March 2020: Elizabeth Glista won an NSF Graduate Research Fellowship.
March 2020: The paper “Exact Guarantees on the Absence of Spurious Local Minima for Non-negative Rank-1 Robust Principal Component Analysis” to appear in the Journal of Machine Learning Research.
February 2020: Salar Fattahi will join University of Michigan as an Assistant Professor in Fall 2020.
February 2020: The paper Homotopy Method for Finding the Global Solution of Post-contingency Optimal Power Flow has been selected as a best student paper finalist for the American Control Conference 2020.
February 2020: I have been invited to participate in the National Academy of Engineering's 2020 Japan-America Frontiers of Engineering Symposium.
January 2020: I will give a plenary talk at the CCTA 2020.
January 2020: The paper Homotopy Method for Finding the Global Solution of Post-contingency Optimal Power Flow to appear in the 2020 American Control Conference.
January 2020: The paper Conic Relaxations of Power System Optimization: Theory and Algorithms to appear in European Journal of Operational Research.
January 2020: Dr. Christian Grussler joined my group as a postdoctoral researcher.
January 2020: Dr. Mohammad Mahdi Khalili has joined my group as a postdoctoral researcher.
September 2019: New paper on learning and control: “Efficient Learning of Distributed Linear-Quadratic Controllers”.
September 2019: New paper on homotopy and optimization: "Homotopy Method for Finding the Global Solution of Post-contingency Optimal Power Flow”.
August 2019: The paper “Global Optimality Guarantees for Nonconvex Unsupervised Video Segmentation” to appear in 57th Annual Allerton Conference on Communication, Control, and Computing, 2019.
July 2019: The paper “Motion Segmentation and Synthesis for Latency Mitigation in a Cloud Robotic Tele-Operation System” to appear in The International Symposium on Robotics Research (ISRR), 2019.
July 2019: The paper “Learning Sparse Dynamical Systems from a Single Sample Trajectory” to appear in IEEE Conference on Decision and Control, 2019.
July 2019: New paper on learning: “Global Optimality Guarantees for Nonconvex Unsupervised Video Segmentation”.
June 2019: The paper “Sharp Restricted Isometry Bounds for the Inexistence of Spurious Local Minima in Nonconvex Matrix Recovery” to appear in the Journal of Machine Learning Research.
May 2019: New paper on robust learning: “Power up! Robust Graph Convolutional Network against Evasion Attacks based on Graph Powering”.
May 2019: New paper on online optimization: “Absence of spurious local trajectories in time-varying optimization”.
April 2019: Cedric Josz will join Columbia University as an Assistant Professor in Fall 2019.
March 2019: New paper on Fog Robotics: “Mitigating Network Latency for Fog Robotics using Motion Segmentation and Synthesis”.
March 2019: New paper on sparse system identification: “Learning Sparse Dynamical Systems from a Single Sample Trajectory”.
January 2019: The paper “Mathematical Models of Physiological Responses to Exercise” to appear in the 2019 American Control Conference.
January 2019: The paper “A Fog Robotic System for Dynamic Visual Servoing” to appear in the 2019 International Conference on Robotics and Automation (ICRA).
December 2018: New paper on non-smooth optimization for learning: “Exact Guarantees on the Absence of Spurious Local Minima for Non-negative Robust Principal Component Analysis”.
December 2018: The paper “Transient Stability Analysis of Power Systems via Occupation Measures” to appear in IEEE PES Innovative Smart Grid Technologies Conference, 2019.
December 2018: The paper “Graphical Lasso and Thresholding: Equivalence and Closed-form Solutions” to appear in the Journal of Machine Learning Research.
December 2018: The paper “Linear-Time Algorithm for Learning Large-Scale Sparse Graphical Models” to appear in IEEE Access.
December 2018: I received a grant from Air Force Office of Scientific Research (AFOSR).
November 2018: The paper “Conic Optimization for Control, Energy Systems, and Machine Learning: Applications and Algorithms” to appear in Annual Reviews in Control.
November 2018: Our paper “Graphical Lasso and Thresholding: Equivalence and Closed-form Solutions” won the INFORMS 2018 Data Mining Best Paper Award.
October 2018: The Berkeley team led by me has been selected to receive funding to participate in the ARPA-E $4M Cash Prize Grid Optimization (GO) Competition (link 1, link 2).
September 2018: Video of my talk on conic optimization and graphical models: “Learning Large-Scale Sparse Graphical Models: Theory, Algorithm, and Applications”
September 2018: I received a seed grant from Siebel Energy Institute.
September 2018: I received a grant from NSF.
September 2018: New paper: “A Fog Robotic System for Dynamic Visual Servoing”
September 2018: Two papers to appear in 32nd Annual Conference on Neural Information Processing Systems (NIPS): “How Much Restricted Isometry is Needed In Nonconvex Matrix Recovery?” and “A Theory on the Absence of Spurious Optimality”.
August 2018: Elizabeth Glista has joined my group as a PhD student.
August 2018: Brendon Anderson has joined my group as a PhD student.
August 2018: New paper on statistical analysis of system identification: Sample Complexity of Sparse System Identification Problem.
August 2018: New tutorial paper: Conic Optimization With Applications to Machine Learning and Energy Systems.
August 2018: The paper Data-Driven Sparse System Identification to appear in 56th Annual Allerton Conference on Communication, Control, and Computing, 2018.
July 2018: I have been appointed as an Associate Editor of IEEE Access.
July 2018: The paper Non-Asymptotic Analysis of Block-Regularized Regression Problem to appear in IEEE Conference on Decision and Control, 2018.
June 2018: I received a grant from the Office of Naval Research.
May 2018: New paper on spurious local minima for nonsmooth optimization: “A Theory on the Absence of Spurious Optimality”
May 2018: New paper on spurious local minima for nonconvex optimization: “How Much Restricted Isometry is Needed In Nonconvex Matrix Recovery?”
May 2018: The paper A Cloud-Based Robust Semaphore Mirroring System for Social Robots has been accepted for the 2018 IEEE International Conference on Automation Science and Engineering.
May 2018: The paper Large-Scale Sparse Inverse Covariance Estimation via Thresholding and Max-Det Matrix Completion has been accepted for the 2018 International Conference on Machine Learning (ICML).
May 2018: I will teach a 16-hour short course on Optimization and Machine Learning in Shenzhen, China, May 8-13.
April 2018: Salar Fattahi won the Katta G. Murty Best Paper Prize in Optimization from Berkeley IEOR for the paper Graphical Lasso and Thresholding: Equivalence and Closed-form Solutions.
April 2018: My group has organized a session on sparse semidefinite programming for 23rd International Symposium on Mathematical Programming.
March 2018: New paper on sparse system identification: Data-Driven Sparse System Identification .
March 2018: New paper on system identification for physiology: Mathematical Models of Physiological Responses to Exercise .
March 2018: New paper on statistical learning: Non-Asymptotic Analysis of Block-Regularized Regression Problem .
March 2018: New paper on stability of power systems: Transient Stability Analysis of Power Systems via Occupation Measures .
March 2018: New paper on numerical algorithm for learning graphical models: Large-Scale Sparse Inverse Covariance Estimation via Thresholding and Max-Det Matrix Completion.
February 2018: The paper Sparse Inverse Covariance Estimation for Chordal Structures has been accepted for the 2018 European Control Conference.
January 2018: The paper “Closed-form Solution and Sparsity Path for Inverse Covariance Estimation Problem” has been selected as one of the five best student award finalists for the American Control Conference 2018.
January 2018: The papers “Closed-form Solution and Sparsity Path for Inverse Covariance Estimation Problem” and “Conic Optimization Theory: Convexification Techniques and Numerical Algorithms” have been accepted for the 2018 American Control Conference.
January 2018: Our tutorial session “Optimization and Machine Learning: Theory, Algorithms, and Applications” has been accepted for the 2018 American Control Conference (contributors: Sam Coogan, Murat Arcak, Javad Lavaei, Richard Zhang, Cedric Josz, and Somayeh Sojoudi).
December 2017: The paper Convexification of Generalized Network Flow Problem to appear in Mathematical Programming.
December 2017: Video of my talk at GraphXD seminar series: Data-Driven Methods for Learning Sparse Graphical Models.
December 2017: I have given talks at McGill University, University of Melbourne and UC Berkeley's GraphXD seminar series on learning sparse graphical models.
November 2017: New paper on structure learning: Sparse Inverse Covariance Estimation for Chordal Structures.
November 2017: I have become an affiliate faculty member of the Tsinghua-Berkeley-Shenzhen-Institute.
October 2017: New tutorial paper on conic optimization: Conic Optimization Theory: Convexification Techniques and Numerical Algorithms
September 2017: I have been appointed as an Associate Editor of Systems & Control Letters.
September 2017: New paper on graphical models: Graphical Lasso and Thresholding: Equivalence and Closed-form Solutions.
August 2017: Dr. Cedric Josz has joined my group as a postdoctoral scholar.
August 2017: I will give a talk on machine learning at INFORMS 2017.
August 2017: I gave a seminar at Hong Kong University of Science and Technology.
July 2017: I have joined the Departments of Electrical Engineering and Computer Sciences and Mechanical Engineering as an Assistant Professor in Residence.
June 2017: I have been appointed as a Conference Editorial Board (CEB) Associate Editor of the IEEE Control Systems Society.
May 2017: I have given talks at University of Michigan (EECS), UC Berkeley (IEOR), Georgia Institute of Technology (CSE), UCLA (EE), Rice (EE and CAAM), Ohio State University (ISE), UIUC (ECE), Georgia Institute of Technology (ISyE and ECE) and UC Berkeley (EECS and ME) in the Spring semester.
December 2016: The paper Finding Low-rank Solutions of Sparse Linear Matrix Inequalities using Convex Optimization to appear in SIAM Journal on Optimization.
October 2016: I will give talks at UC San Diego, UT Austin, UC Berkeley, UCLA, Caltech and Stanford University in the Fall semester.
September 2016: Our paper Convex Relaxation for Optimal Power Flow Problem: Mesh Networks has received the 2016 INFORMS ENRE Energy Best Publication Award.
August 2016: I have been appointed as an Associate Editor of IEEE Transactions on Smart Grid.
August 2016: I will give the tutorial talk “Data-Driven Computational Methods for Sparse Network Estimation” at IEEE Multi-Conference on Systems and Control 2016.
July 2016: The paper Graphical Lasso and Thresholding: Conditions for Equivalence to appear in 55th IEEE Conference on Decision and Control 2016.
July 2016: I will give a talk at INFORMS Annual Meeting 2016 on machine learning.
June 2016: I will give a talk at International Conference on Continuous Optimization on machine learning.
June 2016: Our article Semidefinite Relaxations for Nonlinear Optimization Problems has appeared in the INFORMS Optimization Society Newsletter 2016.
May 2016: The paper “Equivalence of Graphical Lasso and Thresholding for Sparse Graphs” to appear in Journal of Machine Learning Research.
March 2016: I will give a talk at INFORMS International Conference on data analytics and graphical lasso.
February 2016: New paper on machine learning : Graphical Lasso and Thresholding: Conditions for Equivalence
October 2015: I am a co-recipient of the INFORMS Optimization Society Prize for Young Researchers.
August 2015: Slides of my talks at Princeton, Johns Hopkins University and UC Berkeley: Computational Methods for Complex Systems: From Brain to Power Networks
June 2015: Two new papers on brain connectivity using ECoG: Causal Cortical Interactions in Speech Processing: Event Related Analysis of Human ECoG and Large-Scale Cortical Interactions During Auditory, Visual and Audiovisual Speech Processing: Event Related Analysis of Human ECoG
May 2015: The paper Corpus Callosum Area and Brain Volume in Autism Spectrum Disorder: Quantitative Analysis of Structural MRI from the ABIDE Database to appear in Journal of Autism and Developmental Disorders.
January 2015: Video of my talk at CMU: Computational Methods for the Analysis and Modeling of Brain Networks
December 2014: I am a co-author of a best student paper award finalist for the 53rd IEEE Conference on Decision and Control.
December 2014: Tutorial paper for the 53rd IEEE Conference on Decision and Control: Graph-Theoretic Algorithms for Polynomial Optimization Problems
October 2014: I chaired the session “Control Applications and Nonlinear Control” for IEEE 52nd Annual Allerton Conference on Communication, Control and Computing, 2014.
October 2014: I co-chaired the session ‘‘Stochastic Systems and Control" for IEEE 52nd Annual Allerton Conference on Communication, Control and Computing, 2014.
October 2014: Two papers accepted in Allerton: Study of the Brain Functional Network Using Synthetic Data and Equivalence of Graphical Lasso and Thresholding for Sparse Graphs
October 2014: New paper on low-rank optimization: “Finding Low-rank Solutions of Sparse Linear Matrix Inequalities using Convex Optimization”
July 2014: The paper Exactness of Semidefinite Relaxations for Nonlinear Optimization Problems with Underlying Graph Structure to appear in SIAM Journal on Optimization
July 2014: The paper “Low-Rank Solutions of Matrix Inequalities With Applications to Polynomial Optimization and Matrix Completion Problems” to appear in IEEE Conference on Decision and Control 2014.