Hilton Pharma Merit Scholar
ne08092@st.habib.edu.pk
Passionate about the intersection of AI and telecommunications, I aim to leverage my background in machine learning and network systems to build intelligent, innovative connectivity solutions for the future.<\/p>\n
ne08092@st.habib.edu.pk
nahyanjaved1996@gmail.com
https:\/\/www.linkedin.com\/in\/nahyanjaved
Portfolio<\/p>\n
+923312139873<\/p>\n
Dhanani School of Science and Engineering
BS Electrical Engineering<\/p>\n
2026
Data Analysis & Visualization|
\nFPGA & SoC Implementation|
\nIoT & Cloud Integration|
\nMachine Learning|
\nNetwork Simulation & Configuration|
\nPython Programming|
Drive for Results; Planning; Takes Initiative
AI Engineer, AI- Modeling ; Telecommunication Engineer; UI\/UX Designer, Software;
N\/A
Ee Representative|
\nMember,Entrepreneurship Club|
Student Researcher,
\n Summer Tehqeeq Research Program – Student Researcher
\n (June \u2013 August 2025)
\n|<\/p>\n
HBL,
\n Compliance Automation
\n (July \u2013 August 2024)
\n|
Publication \u2013 Biosensing-Driven Framework for Assistive Therapy: A Proof-of-Concept Study (2nd IEEE Karachi Humanitarian Technology Conference (KHI-HTC) 2026) (Under-Review)|
\nPublication \u2013 BiLSTM-Based Sequential Acoustic Crash Detection for Urban Road Safety (2nd IEEE Karachi Humanitarian Technology Conference (KHI-HTC) 2026) (Under-Review)|
\nPublication \u2013 HU at SemEval Task 10: Psycholinguistic Extraction and Classification for Conspiracy Narratives (SemEval 2026) (Under Review)|
Car Crash Detection Using Acoustic Signature
This project aims to enhance road safety and accelerate emergency response times by developing an automated car crash detection system based on acoustic signatures. Utilizing a machine learning approach, the system processes audio data such as collisions, skids, and ambient background noise and converts it into spectrogram images.<\/p>\n
An RNN-LSTM model is then trained to accurately classify these acoustic events, reliably distinguishing actual crashes from normal traffic sounds. To enable real-time, low-latency detection at the edge, the architecture is planned for hardware deployment on a Zynq SoC platform. By instantly identifying accidents the moment they occur, this system provides a critical solution to reduce emergency dispatch delays and improve outcomes in life-threatening situations.<\/p>\n
<\/p>\n","protected":false},"excerpt":{"rendered":"
Hilton Pharma Merit Scholarne08092@st.habib.edu.pkPassionate about the intersection of AI and telecommunications, I aim to leverage my background in machine learning and network systems to build intelligent, innovative connectivity solutions for the future. ne08092@st.habib.edu.pknahyanjaved1996@gmail.comhttps:\/\/www.linkedin.com\/in\/nahyanjavedPortfolio +923312139873 Dhanani School of Science and EngineeringBS Electrical Engineering 2026Data Analysis & Visualization| FPGA & SoC Implementation| IoT & Cloud Integration| Machine […]<\/p>\n","protected":false},"author":9,"featured_media":8582,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"single-graduate-01.php","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"program":[30],"school":[42],"class":[44],"minor":[],"competencies":[61,58,59],"class_list":["post-8147","graduate","type-graduate","status-publish","format-standard","has-post-thumbnail","hentry","program-bsee","school-dhanani-school-of-science-and-engineering","class-class-of-2026","competencies-drive-for-results","competencies-planning","competencies-takes-initiative"],"acf":{"scholarship":"Hilton Pharma Merit Scholar","aspiration_statement":"Passionate about the intersection of AI and telecommunications, I aim to leverage my background in machine learning and network systems to build intelligent, innovative connectivity solutions for the future.","video_profile_link":"","habib_email":"ne08092@st.habib.edu.pk","email":"nahyanjaved1996@gmail.com","linkedin_link":"https:\/\/www.linkedin.com\/in\/nahyanjaved","portfolio_Title":"Portfolio","portfolio_link":"","personal_cell_1":"+923312139873","personal_cell_2":"","core_skills":[{"skill":"Data Analysis & Visualization"},{"skill":"FPGA & SoC Implementation"},{"skill":"IoT & Cloud Integration"},{"skill":"Machine Learning"},{"skill":"Network Simulation & Configuration"},{"skill":"Python Programming"}],"academic_awards":[{"academic_record":"N\/A"}],"leadership-experiences":[{"leadership":"Ee Representative"},{"leadership":"Member, Entrepreneurship Club"}],"work-experience":[{"experience":"Student Researcher, Summer Tehqeeq Research Program - Student Researcher (June \u2013 August 2025)"},{"experience":"HBL, Compliance Automation (July \u2013 August 2024)"}],"publications-projects":[{"publications_title":"","publications":"Publication \u2013 Biosensing-Driven Framework for Assistive Therapy: A Proof-of-Concept Study (2nd IEEE Karachi Humanitarian Technology Conference (KHI-HTC) 2026) (Under-Review)"},{"publications_title":"","publications":"Publication \u2013 BiLSTM-Based Sequential Acoustic Crash Detection for Urban Road Safety (2nd IEEE Karachi Humanitarian Technology Conference (KHI-HTC) 2026) (Under-Review)"},{"publications_title":"","publications":"Publication \u2013 HU at SemEval Task 10: Psycholinguistic Extraction and Classification for Conspiracy Narratives (SemEval 2026) (Under Review)"}],"project_title":"Car Crash Detection Using Acoustic Signature","fyp_desc":"This project aims to enhance road safety and accelerate emergency response times by developing an automated car crash detection system based on acoustic signatures. Utilizing a machine learning approach, the system processes audio data such as collisions, skids, and ambient background noise and converts it into spectrogram images.\r\n\r\nAn RNN-LSTM model is then trained to accurately classify these acoustic events, reliably distinguishing actual crashes from normal traffic sounds. To enable real-time, low-latency detection at the edge, the architecture is planned for hardware deployment on a Zynq SoC platform. By instantly identifying accidents the moment they occur, this system provides a critical solution to reduce emergency dispatch delays and improve outcomes in life-threatening situations.","project_pictures":[{"project_pic":8802}],"project-video":""},"yoast_head":"\n