Mahnoosh Alizadeh
UC Santa Barbara · Electrical & Computer Engineering
Mahnoosh Alizadeh
Email
alizadeh [AT] ucsb.edu
Office
3151 Harold Frank Hall
Phone
(805) 893‑3981

Mahnoosh Alizadeh

UC Santa Barbara · Electrical & Computer Engineering

I am currently a Professor in the department of Electrical & Computer Engineering at the University of California Santa Barbara. I am also affiliated with the Institute for Energy Efficiency and the Center for Control, Dynamical Systems and Computation. My research group develops mathematically principled tools for real-time, robust decision-making in cyber-physical systems. Our work spans fundamental questions in networks, optimization, control, and artificial intelligence, as well as applications in smart grids and electric transportation systems.

We are especially interested in settings where safety, uncertainty, and limited information make online decision-making both mathematically challenging and practically consequential.

Prior to joining UC Santa Barbara in January 2017, I spent two years at Stanford University as a postdoctoral scholar. I received my PhD degree in Electrical and Computer Engineering from the University of California Davis in 2014. I was a recipient of the National Science Foundation CAREER award in 2019, and currently serve as Associate Editor of the IEEE Transactions on Control of Network Systems and IEEE Control Systems Magazine.

Control Learning Optimization Game Theory Energy Systems Cyber-Physical Systems

Research threads

Safe learning & online control

My lab's interest in safe learning originally grew out of demand response problems in power systems, where even a single exploratory action can be physically unacceptable. Motivated by these stage-wise safety constraints, which cannot be violated at any point during the learning process, we have studied stage-wise safety in bandits, online convex optimization, and learning-based control, with an emphasis on guarantees that are meaningful for safety-critical systems rather than only average-case performance.

Distributed optimization

A recurring thread in my lab's work is the design of distributed optimization and resource-allocation algorithms for large-scale multi-agent systems where privacy, scalability, and limited information make centralized control unrealistic. This includes resilient and communication-efficient optimization methods for networked decision-making, as well as more recent work on decentralized learning for modern machine learning systems. Across these problems, we are interested in how distributed computation, statistical efficiency, and system structure interact when learning and optimization must be carried out across many agents.

Game theory

Game theory is another core thread of my research, with a strong emphasis on theoretical questions in multi-agent systems and resource allocation. We are particularly interested in how incentives, timing, information structures, and asymmetries shape equilibrium behavior in competitive and cooperative settings. This has led to a body of work on General Lotto and related games, as well as broader questions involving coordination, strategic behavior, and equilibrium analysis in networked systems and electric vehicle applications.

Energy systems & transportation electrification

Energy systems remain the main application domain through which many of our theoretical questions are grounded. Our work spans electricity markets, demand response, smart grids, electric vehicle charging, and coupled power-transportation networks, including both foundational modeling contributions and deployable algorithms for charging and pricing.

Publications

A full list is also on Google Scholar

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✦ Hover over a paper title to see a brief summary of the problem and contribution.

Preprints
  • 2026
    Low-rank Adaptation is All You Need for Few-shot Adversarial Robustness in Vision–Language Models
    Problem: Vision-language models are vulnerable to adversarial perturbations, while standard robust fine-tuning can be too expensive for few-shot adaptation.
    Contribution: Uses low-rank adapters in an adversarial training objective to improve robustness while keeping the trainable footprint small.
    S. Ghiasvand, H. E. Oskouie, M. Alizadeh, R. Pedarsani
  • 2026
    Steady-state Based Approach to Online Non-stochastic Control
    Problem: Online non-stochastic control algorithms are usually compared against steady states attainable by constant inputs, which limits the benchmark class.
    Contribution: Achieves √T regret against the richer class of affine-controller steady states using an FPL-style optimization method with batching for stability.
    V. Hebbar, S. Hutchinson, M. Alizadeh, C. Langbort
  • 2026
    Pricing Electric Vehicle Charging and Station Access via Copositive Duality
    Problem: EV charging combines continuous charging schedules with binary station-access decisions, making traditional marginal pricing difficult.
    Contribution: Uses copositive duality to construct marginal prices for charging and station access, capturing EVSE congestion with revenue adequacy and user incentive guarantees.
    N. Jiang, Y. Zhou, J. A. Taylor, M. Alizadeh
  • 2026
    Near-Optimal Regret for the Safe Learning-based Control of the Constrained Linear Quadratic Regulator
    Problem: Adaptive LQR with unknown dynamics, unbounded disturbances, and state-input safety constraints lacks near-optimal regret guarantees.
    Contribution: Proves Õ(√T) regret with chance-constraint satisfaction using an optimistic SDP policy search followed by a safety-scaling step.
    S. Hutchinson, N. Jiang, M. Alizadeh
Journal Publications
Select CS-style Conference Proceedings
  • 2026
    MMLoP: Multi-modal Low-rank Prompting for Efficient Vision-Language Adaptation
    Problem: Deep multi-modal prompting improves VLM adaptation, but can require millions of trainable parameters and overfit in few-shot regimes.
    Contribution: Introduces low-rank vision and text prompts with consistency and alignment terms, achieving a strong accuracy-efficiency tradeoff with 11.5K trainable parameters.
    S. Ghiasvand, H. E. Oskouie, M. Alizadeh, R. Pedarsani
    European Conference on Computer Vision (ECCV)
  • 2026
    pFedMMA: Personalized Federated Fine-tuning with Multi-modal Adapter for Vision-Language Models
    Problem: Adapting vision-language models like CLIP to federated settings with heterogeneous data is challenging: existing personalized federated learning methods based on prompt tuning tend to achieve strong personalization but struggle to generalize to unseen classes or domains.
    Contribution: Proposes pFedMMA, a personalized federated learning framework using multi-modal adapters. Each client trains modality-specific adapter components locally while sharing a globally aggregated projection that aligns visual and textual features — achieving a strong personalization-generalization tradeoff while remaining communication-efficient.
    S. Ghiasvand, M. Alizadeh, R. Pedarsani
    14th Int'l Conference on Learning Representations (ICLR)
  • 2025
    Communication-efficient and Tensorized Federated Fine-tuning of Large Language Models
    Problem: Fine-tuning large language models on private data distributed across many devices requires balancing user privacy, communication efficiency, and robustness to the fact that different devices hold different data distributions.
    Contribution: Introduces FedTT and FedTT+, federated fine-tuning methods that use tensorized adapters to drastically cut communication costs while matching or exceeding existing approaches. FedTT+ further improves robustness to data heterogeneity by adaptively freezing parts of the tensor factors, achieving up to 10x reduction in communication overhead.
    S. Ghiasvand, Y. Yang, Z. Xue, M. Alizadeh, Z. Zhang, R. Pedarsani
    Findings of the Association for Computational Linguistics (ACL)
  • 2025
    Constrained Online Convex Optimization with Polyak Feasibility Steps
    Problem: Online optimization algorithms can achieve good long-term performance but may still choose unsafe actions along the way.
    Contribution: Introduces Polyak feasibility steps, which maintain constraint satisfaction at every round while preserving optimal regret guarantees.
    S. Hutchinson, M. Alizadeh
    42nd Int'l Conference on Machine Learning (ICML)
  • 2025
    Optimistic Safety for Linearly-constrained Online Convex Optimization
    Problem: In online optimization, safety constraints may be unknown, but the learner still needs to avoid unsafe decisions while learning.
    Contribution: Develops an optimistic safe online optimization method that learns the constraints, avoids violations, and achieves near-optimal regret.
    S. Hutchinson, T. Chen, M. Alizadeh
    Int'l Conference on Artificial Intelligence and Statistics (AISTATS)
  • 2024
    Directional Optimism for Safe Linear Bandits
    Problem: Safe linear bandit algorithms can be overly conservative when learning under uncertain constraints.
    Contribution: Introduces directional optimism to reduce conservatism, improve regret, and extend safe linear bandit methods to more general constraint settings.
    S. Hutchinson, B. Turan, M. Alizadeh
    Int'l Conference on Artificial Intelligence and Statistics (AISTATS)
  • 2022
    Parameter and Feature Selection in Stochastic Linear Bandits
    A. Moradipari, B. Turan, Y. Abbasi-Yadkori, M. Alizadeh, M. Ghavamzadeh
    39th Int'l Conference on Machine Learning (ICML) — Spotlight
  • 2020
    Stage-wise Conservative Linear Bandits
    A. Moradipari, C. Thrampoulidis, M. Alizadeh
    Advances in Neural Information Processing Systems (NeurIPS)
  • 2019
    Linear Stochastic Bandits under Safety Constraints
    S. Amani, M. Alizadeh, C. Thrampoulidis
    Advances in Neural Information Processing Systems (NeurIPS), pp. 9256–9266
Conference Proceedings
  • 2025
    Decentralized Low-rank Fine-tuning of Large Language Models
    Problem: Fine-tuning large language models in a fully decentralized setting, where devices collaborate directly without a central server, remains largely unexplored, and it is unclear how well existing methods like LoRA perform under such conditions.
    Contribution: Studies the behavior of LoRA in decentralized settings and proposes Dec-LoRA with provable convergence guarantees for nonconvex problems. Experiments on BERT and LLaMA-2 show competitive performance in homogeneous settings, with degradation under data heterogeneity highlighting open challenges.
    S. Ghiasvand, M. Alizadeh, R. Pedarsani
    1st Workshop for Research on Agent Language Models (REALM), pp. 334–345
  • 2025
    Online Nonstochastic Control with Convex Safety Constraints
    Problem: Online nonstochastic control (ONC) studies how to control a dynamic system when costs and disturbances can change adversarially over time, so performance is judged against the best policy in hindsight. Existing ONC methods often struggle to handle general convex safety constraints on states and inputs.
    Contribution: Extends ONC to general convex safety constraints. Proposes OGD-BZC, an online control method that uses buffer zones to keep the system safe at every step under bounded adversarial disturbances, while still achieving sublinear regret relative to the best safe linear policy in hindsight.
    N. Jiang, S. Hutchinson, M. Alizadeh
    American Control Conference (ACC)
  • 2025
    Stochastic Gradient Descent with Strategic Querying
    Problem: In many settings, obtaining a stochastic gradient requires querying a user, and each query can be costly. Standard SGD typically samples users uniformly, which can use a limited query budget inefficiently when gradients from different users are not equally informative.
    Contribution: Introduces strategic querying as a way to replace uniform sampling in SGD with a rule that queries the user whose gradient offers the largest expected one-step improvement. Proposes OGQ as an oracle benchmark and SGQ as a practical approximation, with provable early-stage improvements.
    N. Jiang, H.-T. Wai, M. Alizadeh
    IEEE Conference on Decision and Control (CDC)
  • 2025
    The Price of Simplicity: Analyzing Decoupled Policies for Multi-location Inventory Control
    Y. John, V. Shah, J. A. Preiss, M. Alizadeh, J. R. Marden
    IEEE Conference on Decision and Control (CDC)
  • 2025
    The Safety-Privacy Tradeoff in Linear Bandits
    Problem: In multi-agent bandit setups with a central decision-maker and global safety constraints, the privacy of each agent can be a concern, making privacy preservation essential in bandit algorithms.
    Contribution: Develops a private multi-agent version of the safe-LinUCB algorithm and studies the effect of each agent's privacy requirements on regret. Introduces unilaterally unimprovable privacy parameters for agents under a given regret budget for a group of polytopic constraint sets.
    A. Zibaie, S. Hutchinson, R. Pedarsani, M. Alizadeh
    IEEE Int'l Symposium on Information Theory (ISIT)
  • 2024
    Robust Decentralized Learning with Local Updates and Gradient Tracking
    Problem: In distributed learning systems like Federated Learning, models trained across many devices face two compounding challenges: data across nodes is statistically different (heterogeneous), and the system must remain robust against adversarial attacks, all while keeping communication costs low.
    Contribution: Introduces Dec-FedTrack, a decentralized minimax optimization algorithm that combines local updates (to reduce communication overhead) and gradient tracking (to handle data heterogeneity), enabling adversarially robust training with provable convergence for nonconvex-strongly-concave minimax problems.
    S. Ghiasvand, A. Reisizadeh, M. Alizadeh, R. Pedarsani
    60th Annual Allerton Conference on Communication, Control, and Computing
  • 2024
    Safe Online Convex Optimization with First-order Feedback
    Problem: Safe online optimization requires learning good decisions while maintaining hard constraint satisfaction using only local first-order information.
    Contribution: Develops a first-order safe online optimization method that achieves sublinear regret while avoiding constraint violations.
    S. Hutchinson, M. Alizadeh
    American Control Conference (ACC)
  • 2024
    Safe Online Convex Optimization with Multi-point Feedback
    Problem: In many safe online optimization problems, the learner only observes function values rather than gradients, making safe learning harder.
    Contribution: Uses multi-point feedback and gradient estimation to achieve zero constraint violation with sublinear regret.
    S. Hutchinson, M. Alizadeh
    6th Annual Learning for Dynamics & Control Conference (L4DC), PMLR, pp. 168–180
  • 2024
    Safe Dynamic Pricing for Nonstationary Network Resource Allocation
    B. Turan, S. Hutchinson, M. Alizadeh
    6th Annual Learning for Dynamics & Control Conference (L4DC), PMLR, pp. 155–167
  • 2023
    The Impact of the Geometric Properties of the Constraint Set in Safe Optimization with Bandit Feedback
    Problem: In safe optimization with bandit feedback, the shape of the feasible set can strongly affect how difficult it is to learn safely.
    Contribution: Introduces a geometric sharpness measure for constraint sets and uses it to characterize when safe learning can achieve sublinear regret.
    S. Hutchinson, B. Turan, M. Alizadeh
    5th Annual Learning for Dynamics and Control Conference (L4DC), PMLR
  • 2023
    Analyzing Pre-commitment Strategies in General Lotto Games
    K. Paarporn, R. Chandan, D. Kovenock, M. Alizadeh, J. Marden
    American Control Conference (ACC)
  • 2022
    The Art of Concession in General Lotto Games
    Problem: In adversarial resource-allocation games, it is unclear whether a player can ever benefit by voluntarily weakening its own position through budget or contest concessions.
    Contribution: Shows that budget concessions never help, but value concessions can improve performance in coalitional Lotto games; characterizes when such concessions are optimal and the equilibria that emerge when both players can concede.
    R. Chandan, K. Paarporn, D. Kovenock, M. Alizadeh, J. R. Marden
    Game Theory for Networks (GameNets)
  • 2022
    Multi-environment Meta-learning in Stochastic Linear Bandits
    A. Moradipari, M. Ghavamzadeh, C. Thrampoulidis, M. Alizadeh
    IEEE Int'l Symposium on Information Theory (ISIT)
  • 2022
    Real-time Electric Vehicle Smart Charging at Workplaces: A Real-world Case Study
    N. Tucker, G. Cezar, M. Alizadeh
    IEEE Power and Energy Society General Meeting
  • 2022
    Strategic Investments in Multi-stage General Lotto Games
    R. Chandan, K. Paarporn, M. Alizadeh, J. R. Marden
    IEEE Conference on Decision and Control (CDC)
  • 2022
    A Safe Pricing Mechanism for Distributed Resource Allocation with Bandit Feedback
    Problem: Infrastructure operators need pricing mechanisms that steer demand safely without knowing users' response models in advance.
    Contribution: Develops a safe learning-based pricing mechanism that estimates user response from feedback while respecting network constraints.
    S. Hutchinson, B. Turan, M. Alizadeh
    IEEE Conference on Decision and Control (CDC)
  • 2022
    Safe Dual Gradient Method for Network Utility Maximization Problems
    B. Turan, M. Alizadeh
    IEEE Conference on Decision and Control (CDC)
  • 2022
    A Deployable Online Optimization Framework for EV Smart Charging with Real-world Test Cases
    N. Tucker, M. Alizadeh
    13th IEEE Int'l SmartGridComm
  • 2022
    Predicting Parameters for Modeling Traffic Participants
    A. Moradipari, S. Bae, M. Alizadeh, E. Moradi-Pari, D. Isele
    23rd Int'l Conference on Intelligent Transportation Systems (ITSC)
  • 2022
    Collaborative Multi-agent Stochastic Linear Bandits
    A. Moradipari, M. Ghavamzadeh, M. Alizadeh
    American Control Conference (ACC)
  • 2022
    The Importance of Randomization in Resource Assignment Problems
    K. Paarporn, R. Chandan, M. Alizadeh, J. R. Marden
    American Control Conference (ACC)
  • 2021
    Regret Bounds for Safe Gaussian Process Bandit Optimization
    S. Amani, M. Alizadeh, C. Thrampoulidis
    IEEE Int'l Symposium on Information Theory (ISIT)
  • 2021
    On Robustness of the Normalized Subgradient Method with Randomly Corrupted Subgradients
    B. Turan, C. A. Uribe, H.-T. Wai, M. Alizadeh
    American Control Conference (ACC)
  • 2021
    The Division of Assets in Multiagent Systems: A Case Study in Team Blotto Games
    K. Paarporn, R. Chandan, M. Alizadeh, J. R. Marden
    51st IEEE Conference on Decision and Control (CDC)
  • 2021
    On Robustness of Normalized Block Coordinate Descent Method for Non-convex Optimization
    B. Turan, C. A. Uribe, H.-T. Wai, M. Alizadeh
    51st IEEE Conference on Decision and Control (CDC)
  • 2020
    Generalized Linear Bandits with Safety Constraints
    S. Amani, M. Alizadeh, C. Thrampoulidis
    IEEE Int'l Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 3562–3566
  • 2020
    Regret Bounds for Safe Gaussian Process Bandit Optimization
    S. Amani, M. Alizadeh, C. Thrampoulidis
    Learning for Dynamics and Control (L4DC), PMLR, pp. 158–159
  • 2020
    An Aggregate Model of the Flexible Energy Demand of Thermostatically Controlled Loads with Explicit Outdoor Temperature Dependency
    K. Hreinsson, A. Scaglione, M. Alizadeh
    HICSS-53
  • 2020
    Linear Thompson Sampling under Unknown Linear Constraints
    A. Moradipari, M. Alizadeh, C. Thrampoulidis
    IEEE Int'l Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 3392–3396
  • 2020
    Mobility-aware Smart Charging of Electric Bus Fleets
    A. Moradipari, N. Tucker, T. Zhang, G. Cezar, M. Alizadeh
  • 2019
    An Online Pricing Mechanism for Electric Vehicle Parking Assignment and Charge Scheduling
    N. Tucker, B. Ferguson, M. Alizadeh
    American Control Conference (ACC), pp. 5755–5760
  • 2019
    Online Charge Scheduling for Electric Vehicles in Autonomous Mobility on Demand Fleets
    N. Tucker, B. Turan, M. Alizadeh
    IEEE Intelligent Transportation Systems Conference (ITSC), pp. 226–231
  • 2019
    Smart Charging Benefits in Autonomous Mobility-on-Demand Systems
    B. Turan, N. Tucker, M. Alizadeh
    IEEE Intelligent Transportation Systems Conference (ITSC), pp. 461–466
  • 2019
    Risk and Security Tradeoffs in Graphical Coordination Games
    K. Paarporn, M. Alizadeh, J. R. Marden
    IEEE 58th Conference on Decision and Control (CDC), pp. 4409–4414
  • 2019
    Characterizing the Interplay between Information and Strength in Blotto Games
    K. Paarporn, R. Chandan, M. Alizadeh, J. R. Marden
    IEEE 58th Conference on Decision and Control (CDC), pp. 5977–5982
  • 2019
    Resilient Distributed Optimization Algorithms for Resource Allocation
    C. A. Uribe, H.-T. Wai, M. Alizadeh
    IEEE 58th Conference on Decision and Control (CDC), pp. 8341–8346
  • 2018
    Optimal Planning of Workplace Electric Vehicle Charging Infrastructure with Smart Charging Opportunities
    B. Ferguson, V. Nagaraj, E. C. Kara, M. Alizadeh
    21st Int'l Conference on Intelligent Transportation Systems (ITSC), pp. 1149–1154
  • 2018
    Learning to Dynamically Price Electricity Demand Based on Multi-armed Bandits
    A. Moradipari, C. Silva, M. Alizadeh
    IEEE Global Conference on Signal and Information Processing (GlobalSIP), pp. 917–921
  • 2018
    On the Interaction Between Autonomous Mobility-on-Demand Systems and the Power Network: Models and Coordination Algorithms
    F. Rossi, R. Iglesias, M. Alizadeh, M. Pavone
    Robotics: Science and Systems (RSS)
  • 2018
    Online Pricing Mechanisms for Electric Vehicle Management at Workplace Charging Facilities
    N. Tucker, M. Alizadeh
    56th Annual Allerton Conference on Communication, Control, and Computing, pp. 351–358
  • 2018
    The Impact of Informed Adversarial Behavior in Graphical Coordination Games
    B. Canty, P. N. Brown, M. Alizadeh, J. R. Marden
    IEEE Conference on Decision and Control (CDC), pp. 1923–1928
  • 2018
    Pricing Differentiated Services in an Electric Vehicle Public Charging Station Network
    A. Moradipari, M. Alizadeh
    IEEE Conference on Decision and Control (CDC), pp. 6488–6494
  • 2017
    Wholesale Electricity Pricing in the Presence of Geographical Load Balancing
    M. A. Abdelghany, H. Mohsenian-Rad, M. Alizadeh
    51st Asilomar Conference on Signals, Systems, and Computers, pp. 653–658
  • 2017
    Marginal Charging Station Pricing in an Intelligent Electric Transportation System
    M. Alizadeh, H.-T. Wai, A. Goldsmith, A. Scaglione
    American Control Conference (ACC), pp. 3438–3444
  • 2017
    Optimal Electricity Pricing for Societal Infrastructure Systems
    M. Alizadeh, H.-T. Wai, A. Goldsmith, A. Scaglione
    50th Hawaii Int'l Conference on System Sciences (HICSS)
  • 2017
    Congestion Control and Pricing in a Network of Electric Vehicle Public Charging Stations
    P. Wong, M. Alizadeh
    55th Annual Allerton Conference on Communication, Control, and Computing, pp. 762–769
  • 2015
    The Perils of Dynamic Electricity Pricing Tariffs in the Presence of Retail Market Imperfections
    M. Alizadeh, A. Goldsmith, A. Scaglione
    49th Asilomar Conference on Signals, Systems and Computers, pp. 683–688
  • 2014
    Capturing Aggregate Flexibility in Demand Response
    M. Alizadeh, A. Scaglione, A. Goldsmith, G. Kesidis
    53rd IEEE Conference on Decision and Control (CDC), pp. 6439–6445
  • 2014
    Optimized Path Planning for Electric Vehicle Routing and Charging
    M. Alizadeh, H.-T. Wai, A. Scaglione, A. Goldsmith, Y. Y. Fan, T. Javidi
    52nd Annual Allerton Conference on Communication, Control, and Computing, pp. 25–32
  • 2013
    On Modeling and Marketing the Demand Flexibility of Deferrable Loads at the Wholesale Level
    M. Alizadeh, T.-H. Chang, A. Scaglione
    46th Hawaii Int'l Conference on System Sciences (HICSS), pp. 2177–2186
  • 2013
    Clustering Consumption in Queues: A Scalable Model for Electric Vehicle Scheduling
    M. Alizadeh, G. Kesidis, A. Scaglione
    Asilomar Conference on Signals, Systems and Computers, pp. 374–378
  • 2013
    Least Laxity First Scheduling of Thermostatically Controlled Loads for Regulation Services
    M. Alizadeh, A. Scaglione
    IEEE Global Conference on Signal and Information Processing, pp. 503–506
  • 2013
    Scalable Model Predictive Control of Demand for Ancillary Services
    M. Alizadeh, A. Scaglione, G. Kesidis
    IEEE Int'l Conference on Smart Grid Communications (SmartGridComm), pp. 684–689
  • 2013
    Incentive Design for Direct Load Control Programs
    M. Alizadeh, Y. Xiao, A. Scaglione, M. Van Der Schaar
    51st Annual Allerton Conference on Communication, Control, and Computing, pp. 1029–1036
  • 2012
    Grid Integration of Distributed Renewables through Coordinated Demand Response
    M. Alizadeh, T.-H. Chang, A. Scaglione
    51st IEEE Conference on Decision and Control (CDC), pp. 3666–3671
  • 2012
    The Emergence of Deferrable Energy Requests and a Greener Future: What Stands in the Way?
    M. Alizadeh, T.-H. Chang, A. Scaglione, C. Chen, S. Kishore
    5th Int'l Symposium on Communications, Control and Signal Processing
  • 2012
    On the Market Effects of Queueing Energy Requests as an Alternative to Storing Electricity
    M. Alizadeh, Z. Wang, A. Scaglione, C. Chen, S. Kishore
    IEEE Power and Energy Society General Meeting
  • 2012
    Coordinated Home Energy Management for Real-time Power Balancing
    T.-H. Chang, M. Alizadeh, A. Scaglione
    IEEE Power and Energy Society General Meeting
  • 2012
    A Cournot Game Analysis on Market Effects of Queuing Energy Request as Demand Response
    C. Chen, S. Kishore, Z. Wang, M. Alizadeh, A. Scaglione
    IEEE Power and Energy Society General Meeting
  • 2012
    How Will Demand Response Aggregators Affect Electricity Markets? — A Cournot Game Analysis
    C. Chen, S. Kishore, Z. Wang, M. Alizadeh, A. Scaglione
    5th Int'l Symposium on Communications, Control and Signal Processing
  • 2012
    Queuing Models for Providing Quality of Service to Transactive Loads
    A. Scaglione, M. Alizadeh, R. J. Thomas
    IEEE PES Innovative Smart Grid Technologies (ISGT)
  • 2011
    Direct Load Management of Electric Vehicles
    M. Alizadeh, A. Scaglione, R. J. Thomas
    IEEE Int'l Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 5964–5967
  • 2011
    Information Infrastructure for Cellular Load Management in Green Power Delivery Systems
    M. Alizadeh, A. Scaglione, R. J. Thomas, D. Callaway
    IEEE Int'l Conference on Smart Grid Communications (SmartGridComm), pp. 13–18
  • 2011
    Demand Side Management Trends in the Power Grid
    M. Alizadeh, Z. Wang, A. Scaglione
    4th IEEE Int'l Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), pp. 141–144
  • 2011
    On the Impact of Smart Grid Metering Infrastructure on Load Forecasting
    M. Alizadeh, A. Scaglione, Z. Wang
    48th Annual Allerton Conference on Communication, Control, and Computing, pp. 1628–1636
Book Chapter
  • 2012
    New Models for Networked Control in Smart Grid
    A. Scaglione, Z. Wang, M. Alizadeh
    in Smart Grid Communications and Networking, Cambridge University Press

Group

Current members & alumni
Note to Prospective Students

I am currently looking for new PhD students. I tend to work best with students who have a strong mathematical background and are interested in interdisciplinary problems. If you are interested in joining the group, please apply to the ECE graduate program at UCSB and list me as a potential faculty advisor.

Current Ph.D. Students
Spencer Hutchinson
Spencer Hutchinson
Since Oct 2021
B.Sc. Colorado School of Mines
Arghavan Zibaei
Arghavan Zibaei
Since Jan 2023 · B.Sc. Sharif University
co-advised with R. Pedarsani
Nanfei Jiang
Nanfei Jiang
Since Oct 2023
B.Sc. SUSTech
Sajjad Ghiasvand
Sajjad Ghiasvand
Since Oct 2023 · B.Sc. Sharif University
co-advised with R. Pedarsani
Irmak Fitoz
Irmak Fitoz
Since Oct 2025
B.Sc. Bilkent University
Current Undergraduate Researchers
Lily Chen
Since Sep 2024
Yi Zhou
Since Jan 2025
Samuel Zhou
Since Feb 2026
Alumni

Ph.D.

  • Berkay Turan · Dec 2023 First position: Uber
  • Ahmadreza Moradipari · Dec 2022 First position: Toyota InfoTech Labs
  • Nathaniel Tucker · Sep 2022 First position: Apple

Postdoctoral

  • Keith Paarporn · 2018–2021 Next position: Assistant Professor, University of Colorado Colorado Springs

M.S.

  • Rey Yue · 2021–2024 First position: Resonant Inc.
  • Brian Canty · 2017–2019 First position: CACI International
  • Varun Nagaraj · 2017–2018 First position: Qualcomm

Undergraduate

  • Cody Silva · 2017 Currently: Scientist, General Motors
  • Bryce Ferguson · 2017–2018 Currently: Assistant Professor, Dartmouth College
  • Tuo Zhang · 2019–2020 Currently: Applied Scientist, Amazon
  • Kelly Lin · 2020 Currently: Software Engineer, Netflix
  • Gil Marc Sia · 2021 Freshman research intern
  • Arthur Wang · 2021–2022 Capstone project & honors thesis
  • Jovanny Ramirez · May–Aug 2025 UC LEADS summer research intern (UC Berkeley)

Teaching

Courses at UCSB