H M Sajjad Hossain

✉ hhossain aT microsoft doT com

✎ Resume

Hi I'm Sajjad!

I am a Machine Learning Scientist at Microsoft. My interest lies in decision making under uncertainty (Active Learning & Reinforcement Learning).

I completed my PhD in Information Systems from University of Maryland Baltimore County in 2019. I was supervised by Dr Nirmalya Roy in Mobile, Pervasive & Sensor Computing Lab. My thesis was focused on how we can leverage Active Learning in Activity Recognition domain to mitigate the manual effort needed for data labeling and select annotator opportunistically depending on the user context.

Before joining UMBC, I completed my BSc in Computer Science & Engineering from Bangladesh University of Engineering & Technology. After completing my bachelors I worked as Software Engineer for almost two years.

Apart from work, I love music and watching movies. I am a big fan of Tool. If you like to know my taste in music, checkout my Last.fm profile. Time to time I also love to read philosophical stuff and poetry.

Publications

Weihao Tan, David Koleczek, Siddhant Pradhan, Nicholas Perello, Vivek Chettiar, Nan Ma, Aaslesha Rajaram, Vishal Rohra, Soundar Srinivasan, H M Sajjad Hossain, Yash Chandak. On Optimizing Interventions in Shared Autonomy, 36th AAAI Conference on Artificial Intelligence (AAAI) 2022.
Weihao Tan, David Koleczek, Siddhant Pradhan, Nicholas Perello, Vivek Chettiar, Nan Ma, Aaslesha Rajaram, Vishal Rohra, Soundar Srinivasan, H M Sajjad Hossain, Yash Chandak. Intervention Aware Shared Autonomy, Workshop on Human-AI Collaboration in Sequential Decision-Making (HumanAI@ICML21).
Remmelt Ammerlaan, Gilbert Antonius, Marc Friedman, H M Sajjad Hossain, Alekh Jindal, Peter Orenberg, Hiren Patel, Shi Qiao, Vijay Ramani, Lucas Rosenblatt, Abhishek Roy, Irene Shaffer, Soundarajan Srinivasan, Markus Weimer. PerfGuard: Deploying ML-for-Systems without Performance Regressions, Almost!, Proc. VLDB Endow. 14, 2021.

Remmelt Ammerlaan, Gilbert Antonius, Marc Friedman, H M Sajjad Hossain, Alekh Jindal, Peter Orenberg, Hiren Patel, Shi Qiao, Vijay Ramani, Lucas Rosenblatt, Abhishek Roy, Irene Shaffer, Soundarajan Srinivasan, Markus Weimer. PerfGuard: Deploying ML-for-Systems without Performance Regressions, MLOps Systems 2020, Austin, USA.

H. M. Sajjad Hossain, Nirmalya Roy: Active Deep Learning for Activity Recognition with Context Aware Annotator Selection. KDD 2019

H M Sajjad Hossain, MD Abdullah Al Haiz Khan, and Nirmalya Roy. 2018. DeActive: Scaling Activity Recognition with Active Deep Learning. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT) (Presented at UbiComp 2018).
H M Sajjad Hossain, Sreenivasan R. Ramamurthy, Md Abdullah Al Hafiz Khan and Nirmalya Roy. An Active Sleep Monitoring Framework Using Wearables, ACM Transactions on Interactive Intelligent Systems (TiiS).
H M Sajjad Hossain, Nirmalya Roy. SocialAnnotator: Annotator Selection Using Activity and Social Context. AAAI Fall Symposium: Reasoning and Learning in Real-World Systems for Long-Term Autonomy 2018.
Abu Zaher Md Faridee, Sreenivasan Ramasamy Ramamurthy, H M Sajjad Hossain, Nirmalya Roy. HappyFeet: Recognizing and Assessing Dance on the Floor. ACM Workshop on Mobile Computing Systems and Applications (HotMobile) 2018.
Md Abdullah Al Hafiz Khan, Nirmalya Roy and H M Sajjad Hossain. Wearable Sensor-based Location-specific Occupancy Detection in Smart Environments, Mobile Information Systems (2018).

H M Sajjad Hossain, Md Abdullah Al Hafiz Khan, Nirmalya Roy. SoccerMate: A Personal Soccer Attribute Profiler using Wearables. First International Workshop on Behavioral Implications of Contextual Analytics (co-located with PerCom 2017)
Md Abdullah Al Hafiz Khan, H M Sajjad Hossain, Nirmalya Roy. COAR: Collaborative and Opportunistic Human Activity Recognition, International Conference on Distributed Computing in Sensor Systems (DCOSS) 2017.
Joseph Taylor, H M Sajjad Hossain, Mohammad Arif Ul Alam, Md Abdullah Al Hafiz Khan, Nirmalya Roy, Elizabeth Galik, Aryya Gangopadhyay. SenseBox: A Low-Cost Smart Home System, In Proceedings of the IEEE International Conference on Pervasive Computing and Communications Demonstrations (PerCom) 2017.

H M Sajjad Hossain, Nirmalya Roy, Md Abdullah Al Hafiz Khan. Active Learning Enabled Activity Recognition, IEEE International Conference on Pervasive Computing and Communications (PerCom) 2016.
H M Sajjad Hossain, Nirmalya Roy and Md Abdullah Al Hafiz Khan. Active Learning Enabled Activity Recognition, Pervasive and Mobile Computing (PMC) Journal, Elsevier 2016.

H M Sajjad Hossain, Nirmalya Roy, Md Abdullah Al Hafiz Khan. Sleep Well: A Sound Sleep Monitoring Framework for Community Scaling. IEEE International Conference on Mobile Data Management (MDM) 2015.
MD Abdullah Al Hafiz Khan, H M Sajjad Hossain, Nirmalya Roy. SensePresence: Infrastructure-less Occupancy Detection for Opportunistic Sensing Applications, International Workshop On Human Mobility Computing And Privacy (HuMoComP) 2015.
Md Abdullah Al Hafiz Khan, H M Sajjad Hossain, Nirmalya Roy. Infrastructure-less Occupancy Detection and Semantic Localization in Smart Environments, International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services (MobiQuitous) Portugal. July, 2015.
H M Sajjad Hossain, Sheikh Al-Amin, Mahmuda Naznin. Energy Efficient Routing in Wireless Sensor Network for Multi-commodity Based Network, Workshop on Wireless Network and Communication (WNC). BUET, Dhaka. April, 2013.

Patents

FormsAI - Generative AI assistant for Microsoft Forms*, Filed November 2023, Co-inventor. This pending patent pertains to a FormsAI assistant system designed to enhance the user experience on Microsoft Forms, enabling customers to create engaging and comprehensive surveys and quizzes with ease and efficiency.
System and method for machine learning for system deployments without performance regressions*, Issued September 2023, US Patent 11748350, Co-inventor/Inventor. This patent involves a performance safeguard system which is used to design pre-production experiments for determining the production readiness of learned models based on a pre-production budget.

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