https://mashhadi.squarespace.com/covid19 daily 0.75 2020-04-15 https://images.squarespace-cdn.com/content/v1/5e23fb29f9e62c2c01212863/1586295295713-VVSVFIYA8K0D4EP6XPGL/IMG_A16759DBBC11-1.jpg Social Distancing Impact https://images.squarespace-cdn.com/content/v1/5e23fb29f9e62c2c01212863/1586296264747-59FVLEWFLOVC47VLV2LQ/Screen+Shot+2020-04-07+at+2.50.17+PM.png Social Distancing Impact https://mashhadi.squarespace.com/capstone daily 0.75 2021-06-07 https://images.squarespace-cdn.com/content/v1/5e23fb29f9e62c2c01212863/1623099450568-ZWLE6UZWS1U2RI4NU17L/Poster.jpg Capstone https://images.squarespace-cdn.com/content/v1/5e23fb29f9e62c2c01212863/1623099585845-9ULWLAVK0Z1SU0H8YTDM/pptA42E.png Capstone https://images.squarespace-cdn.com/content/v1/5e23fb29f9e62c2c01212863/1623100011727-Y09WPW4B0D7NVGT5MIWZ/CSS497-Poster-MTaing.jpg Capstone https://mashhadi.squarespace.com/gallery-1-2 daily 0.75 2023-01-17 https://images.squarespace-cdn.com/content/v1/5e23fb29f9e62c2c01212863/1f0a0183-3e21-4549-92c1-a618bc6115fc/Screen+Shot+2023-01-17+at+11.03.46+AM.png RaceAfterTech Chapter 1 https://images.squarespace-cdn.com/content/v1/5e23fb29f9e62c2c01212863/1673982512936-MXRDM4HAVCGPEFUFSRZX/Screen%2BShot%2B2023-01-17%2Bat%2B11.05.53%2BAM.jpg RaceAfterTech Chapter 2 https://images.squarespace-cdn.com/content/v1/5e23fb29f9e62c2c01212863/1673983152602-BPRGLO0VQWJSPJTWTYIL/Screen%2BShot%2B2023-01-17%2Bat%2B11.15.07%2BAM.jpg RaceAfterTech Chapter 5, Retooling Solidarity, Reimagining Justice https://mashhadi.squarespace.com/home daily 1.0 2026-03-16 https://images.squarespace-cdn.com/content/v1/5e23fb29f9e62c2c01212863/85a35cc2-9ac0-44ba-aa1f-4e74043bb2d6/tempImageX4FB0w.jpg Home At EPFL 2023- PETs Conference https://mashhadi.squarespace.com/home/pacific-northwest-bzca3 monthly 0.5 2026-02-05 https://images.squarespace-cdn.com/content/v1/5e23fb29f9e62c2c01212863/1580507582720-AJNIH75FRV0YJ710O7L6/DSC03564.jpg Home - Publications https://images.squarespace-cdn.com/content/v1/5e23fb29f9e62c2c01212863/1580529521487-MSBR4LR2GJCON1ZUL7C8/image-asset.jpeg Home - Publications https://images.squarespace-cdn.com/content/v1/5e23fb29f9e62c2c01212863/1580528628276-AYPQ9OYO5NKF06CFY6XU/image-asset.jpeg Home - Publications https://mashhadi.squarespace.com/home/northeast-c4n8z monthly 0.5 2020-02-01 https://images.squarespace-cdn.com/content/v1/5e23fb29f9e62c2c01212863/1631555501893-FI1JX075DTXJRS2FY2PQ/Screen+Shot+2021-09-13+at+10.51.30+AM.png Home - Research Statement https://images.squarespace-cdn.com/content/v1/5e23fb29f9e62c2c01212863/1631555735431-NHFRF4FQXJVDN0Q08S5K/Screen+Shot+2021-09-13+at+10.55.20+AM.png Home - Research Statement https://images.squarespace-cdn.com/content/v1/5e23fb29f9e62c2c01212863/1631556298863-EZ4JPADTNETNKU50LFGO/image.jpg Home - Research Statement https://images.squarespace-cdn.com/content/v1/5e23fb29f9e62c2c01212863/1580525898709-JFY2AFMU14BQ5NILU6NV/pexels-photo-799443.jpeg Home - Research Statement My general area of research is Machine learning and Responsible AI where I am interested in developing mathematical and computational models that leverage the proliferation of data and breakthroughs in machine learning to design human-centric systems that accommodate for human needs, capabilities and behaviors. Specifically my research focus is on sensing, modeling, understanding and predicting human behavior using the “digital traces” that are generated daily in our online and offline lives. In particular my research has focused on viewing “digital traces” in two ways (1) firstly as distinctive realms in which much of human experience now resides. That is studying socio-technical systems and modeling social dynamics of human behavior in these platforms. (2) to view digital data platforms as generalizable microcosms of society and thus develop mathematical and computational models to better understand societies and social phenomenas at different spatial scales. By analyzing the use of technology at these different scales, from the micro level, such as person-to-person or person-to-system interactions, to the macro level, such as population dynamics, I aim to build systems based on better models of human behavior that can help us construct more powerful human centric services. https://images.squarespace-cdn.com/content/v1/5e23fb29f9e62c2c01212863/1580509779959-WLNZIQTPOYLQ5GWRX31U/Big-Ben.jpg Home - Research Statement https://images.squarespace-cdn.com/content/v1/5e23fb29f9e62c2c01212863/1580509876424-J5U3R3I9EFFMM5MC4KIM/shutterstock_717478675.jpg Home - Research Statement https://images.squarespace-cdn.com/content/v1/5e23fb29f9e62c2c01212863/1580509955776-9UH7QVV1X8KRMX983A70/27255253547_5ac6061b3f_b.jpg Home - Research Statement https://mashhadi.squarespace.com/home/southwest-8cbnw monthly 0.5 2020-02-01 https://images.squarespace-cdn.com/content/v1/5e23fb29f9e62c2c01212863/1580531206630-2U0P4DOAAJJURQ3L2BM9/adalovelacehero.jpg Home - Scholarly Services - Women In computing: I currently serve as a member of Diversity, Equity and Inclusion in STEM for University of Washington Bothell. I served as a fellowship board for N2Women for a period of 2 years. I am part of Richard Tapia Celebration of Diversity Board. I have served as chair of Broadening Participation Workshop for Ubicomp 2017. https://mashhadi.squarespace.com/home/ubicomp-lab monthly 0.5 2021-09-13 https://images.squarespace-cdn.com/content/v1/5e23fb29f9e62c2c01212863/1585770630280-2FJR1LYM9WA3A9UEZP0S/image-asset.jpeg Home - Research Lab https://images.squarespace-cdn.com/content/v1/5e23fb29f9e62c2c01212863/1585770799118-BOOGDIAG1FK9PVU4R1UE/Large+JPG-20140228_Trade+151_0046.jpg Home - Research Lab https://images.squarespace-cdn.com/content/v1/5e23fb29f9e62c2c01212863/8f8a06d9-b431-46e7-9c4c-8f3e91b541ed/Screen+Shot+2022-02-07+at+12.36.14+PM.png Home - Research Lab - Make it stand out Whatever it is, the way you tell your story online can make all the difference. https://mashhadi.squarespace.com/home/southeast-f3ntk monthly 0.5 2022-03-16 https://images.squarespace-cdn.com/content/v1/5e23fb29f9e62c2c01212863/1615487345462-BK2IHE0KM6HVMXSVZHCD/2scholars.jpg Home - Teaching and Mentoring - Mary Gates Fellows 2020-2021 Joshua Sterner (right)- fellow Joshua Sterner is working on how to preserve the privacy of data used to train a machine-learning model while also keeping the model private. He has previously worked on the implication of federated deep embedded clustering and has published multiple papers with our research team. Ali Jahangirnezhad (left)- fellow Ali is developing a model that accounts for the properties and dynamics of sound. More specifically he is designing an unsupervised representation learning model based on the LSTM and applying deep embedded techniques. Applications of his work include detecting sounds of marine animals using data from hydrophones (in collaboration with Dr Shima Abadi) Inkar Kapen- fellow 2023 https://images.squarespace-cdn.com/content/v1/5e23fb29f9e62c2c01212863/b3151754-219f-4e9f-9dab-4f823224bf4c/DSC_7630.jpg Home - Teaching and Mentoring - Make it stand out Mary Gates Fellows 2021-2022 Project: Independent Measurement Platform for Federated Learning Models on Android Devices Machine learning is a powerful tool that allows us to use data to make predictions and decisions about the world, but it requires expensive centralized hardware and data, is prone to algorithmic biases, and has privacy concerns surrounding the use of required data. In contrast, Federated Learning (FL) allows users to collaboratively train a shared model under a central server while keeping personal data on their devices. This ability potentially addresses problems of traditional machine learning by using widely available mobile devices to increase accessibility to mainstream users and leverages decentralized user data and computational resources to train machine learning models more efficiently. However, this emerging field requires established processes for training and measuring the efficiency of FL models on edge devices. This research provides an inclusive framework to federatively train models on Android devices and analyze their computational and energy efficiency. On the mobile devices, I leveraged a terminal application to install dependencies and natively train machine learning models on the device. Then, I analyzed the device’s efficiency by measuring the computational, energy, and network resources through terminal applications. This flexible framework can deploy diverse machine learning models and datasets for training on Android devices. In preliminary experiments, I used this framework to measure efficiency for a PyTorch obstacle detection model and a Tensorflow abnormal heartbeat detection algorithm. These experiments showed that training machine learning models on mobile phones makes efficient use of CPU, memory, and bandwidth, and it uses minimal energy consumption compared to centralized machine learning systems. With little to no examples of FL on Android devices, this framework provides a novel plug-and-play solution for native FL on mobile devices. Applications of this research will also demonstrate novel methods for using FL techniques to address topics of accessibility, privacy, algorithmic bias, and hardware limitations for machine learning.