Certificate Program: Socially Cognizant Robotics

The Certificate in Socially Cognizant Robotics creates a new vehicle for graduate training and research that integrates the technology domains of robotics, machine learning and computer vision, with social and behavioral sciences (psychology, cognitive science and urban policy planning). The Certificate program was created under the (NSF National Research Traineeship NRT entitled “Socially Cognizant Robotics for a Technology Enhanced Society” (SOCRATES, PI: Kristin Dana, co-PIs: Kostas Bekris, Clinton Andrews, Jacob Feldman, Jingang Yi). For a program description see https://robotics.rutgers.edu/.  The goal of the program is to train a new type of reflective practitioner, through the convergence of the socially aware technologists and the technology-aware social scientist.

 

 

Certificate in Socially Cognizant Robotics  

I. Overview 

Emerging applications of robotics are certain to bring significant changes in individuals' lives and profound social impacts, including the future workforce of the nation. The traditional objective of robotics research has been to provide automated platforms that operate at high-speed, accurately and consistently, such as in the context of manufacturing systems. As robots are being deployed in a wider variety of domains, it becomes important to consider other aspects, such as safety, adaptability to human desires, ethical considerations, and societal impacts. Key questions are: What are the societal impacts of robotic technology? How can these impacts be predicted and evaluated in order to influence next-generation technology? How can robotics be developed in a socially cognizant manner? While the potential of robotics is often postulated, the realization of ubiquitous robot assistants augmenting an individual's productivity and quality of life has not been realized.  

 

The Certificate in Socially Cognizant Robotics creates a new vehicle for graduate training and research that integrates the technology domains of robotics, machine learning and computer vision, with social and behavioral sciences (psychology, cognitive science and urban policy planning). The Certificate program was created under the (NSF National Research Traineeship NRT entitled “Socially Cognizant Robotics for a Technology Enhanced Society” (SOCRATES, PI: Kristin Dana, co-PIs: Kostas Bekris, Clinton Andrews, Jacob Feldman, Jingang Yi). For a program description see https://robotics.rutgers.edu/.  The goal of the program is to train a new type of reflective practitioner, through the convergence of the socially aware technologists and the technology-aware social scientist. The key to fostering such interdisciplinary expertise is an intentional cross-listed training program that creates meaningful collaborations among constituent disciplines. Through a set of core interdisciplinary courses, students will receive training in 7 subdisciplines: Robot Embodiment, Control and Planning, Computer Vision, Language and Dialogue, Cognitive Science, Urban Planning and Policy Development.  The School of Engineering (SOE), the School of Arts and Sciences (SAS) and the Edward J. Bloustein School of Planning and Public Policy (Bloustein) have developed this Certificate Program in order to offer graduate students throughout Rutgers a unique interdisciplinary learning opportunity.  

  

The certificate is comprised of a three-course sequence developed by the SOCRATES NRT faculty:  

  1. Robotics and Society (16:332:640 and cross-listings): examines the interplay of technology and society, giving students an understanding of the ethics, unintended consequences, and social implications of robotics. A sequence of foundational lectures will be constructed to provide both technical and social science students with the core prerequisite skills for their cross-disciplines 
  2. Socially Cognizant Robotics (16:332:590 and cross-listings)  where students will be exposed to the foundations of robotics and state-of-the-art developments to learn the expected trajectory of robot capabilities that will impact individuals and society. 
  3. Design Methods in Socially Cognizant Robotics (16:332:595 and cross-listings) graduate where students  gain hands-on experience on a practical project, working together with students from other participating disciplines. 

 

The Certificate is open to Rutgers graduate students. The departments that the course draws primarily from are: Electrical and Computer Engineering (ECE), Mechanical and Aerospace Engineering (MAE) departments from the School of Engineering, Computer Science (CS), and Psychology from the School of Arts and Sciences, Urban Planning and Policy Development from the Bloustein School.    

 

Optional Activity: N2E Robotics Club  

The SOCRATES NRT has also created a new robotics club ( N2E Robotics Club ) with the goal of broadening participation from both STEM and social science students. N2E Robotics Club has student-taught 1-hour sessions on robotics and robotics coding. Graduate students of the certificate program are encouraged (but not required) to participate and teach in these short workshops with a target audience of undergraduates and first-year graduate students throughout Rutgers. 

 

 
 

Certificate Program: Machine Learning for Electrical and Computer Engineers

In an era where data-driven decision-making is integral to the world's most influential industries, the Department of Electrical and Computer Engineering (ECE) at Rutgers University-New Brunswick is proud to present a timely and crucial offering: a 12-credit graduate certificate program in Machine Learning. 

The applications of Machine Learning are vast and rapidly evolving, powering innovations in sectors such as technology, healthcare, finance, transportation, manufacturing, agriculture, telecommunications, education, energy, and even creative industries like music and film, among others. The ability to interpret complex datasets and forecast trends is not just a highly sought-after skill, but a necessity in today's data-centric world. 

At Rutgers ECE, we recognize this seismic shift and have meticulously curated a certificate program that goes beyond mere theory. Our mission is to equip our students with a robust understanding of machine learning techniques, enabling them to address real-world engineering problems and navigate various software packages used across numerous electrical and computer engineering applications. In essence, we aim to transform learners into practitioners. 

Our department boasts an exceptional faculty with both academic and industry experience, a diverse and comprehensive range of courses, and a learning environment that fosters collaboration, innovation, and excellence. Our program emphasizes both statistical learning theory and deep learning, integral courses in our renowned ECE graduate program, providing students with a distinct edge in their professional pursuits. 

Eligibility: This opportunity isn't confined to just Rutgers students or those enrolled in the ECE department. We have extended the scope of this program to benefit learners across various disciplines such as Computer Science, Mechanical Engineering, Biomedical Engineering, Data Science, Mathematics, Physics, Information Technology, Statistics, and even non-traditional fields where data analysis is crucial, like Business, Economics, and Social Sciences. Whether you're pursuing a PhD, MS, or simply aiming to secure a certificate, our Machine Learning program provides an enriching and versatile learning path. Moreover, some requirements for the certificate may also fulfill your existing graduate degree requirements, providing further academic flexibility. 

Admission requirements 

Current ECE Students: Graduate students (non-matriculated, MS. or PhD) from the ECE department interested in the Machine Learning certificate do not need to apply separately. Interested students should simply notify the ECE Graduate Program Office at ecegradprogram@soe.rutgers.edu of their intention to pursue the certificate, along with their intended course selection. 

Current non-ECE Rutgers Students: Graduate students (MS or PhD) from departments other than ECE should submit a copy of their Rutgers transcripts, a letter of recommendation, and the intended course selection to ECE Graduate Program Office at ecegradprogram@soe.rutgers.edu. Once notified by the ECE Graduate Program Office of the approval of their application, the students can start earning credits towards the certificate. 

 

Curriculum: In order to receive the certificate, students must complete four courses, equivalent to 12 credits, maintaining a GPA of at least 3.0. This coursework must include a minimum of two courses from List A, and a maximum of two courses from List B: 

List A: 

14:332:443 – Machine Learning for Engineers (or its graduate-level equivalent course) 

16:332:515 – Reinforcement Learning for Engineers 

16:332:530 – Introduction to Deep Learning 

16:332:549 – Detection & Estimation Theory: Inference & Machine Learning for Engineers 

16:332:561 – Machine Vision 

 

List B: 

16:332:509 – Convex Optimization 

16:332:518 – Mobile Embedded Systems and On-Device AI 

16:332:525 – Optimum Signal Processing 

16:332:531 – Probabilistic Methods for Large Scale Signal Processing and Learning 

16:332:532 – Multimodal Machine Learning for Sensing Systems 

16:332:533 – Machine Learning for Inverse Problems 

 

We understand that the landscape of Machine Learning is dynamic, and therefore our graduate curriculum is updated regularly to keep pace with the latest advancements. In light of this, additional courses beyond those mentioned above could be accepted towards the completion of this certificate, subject to the sole discretion of the ECE Graduate Program Director and subsequent approval from the School of Graduate Studies. This ensures our program remains flexible, current, and responsive to the evolving needs of the field. 

Smartphone Interruptions: Are Yours Relentless and Annoying?

Rutgers study reveals that personality traits influence and help predict receptiveness to smartphone notifications

Does your smartphone spew a relentless stream of text messages, push alerts, social media messages and other noisy notifications?

Well, Rutgers experts have developed a novel model that can predict your receptiveness to smartphone interruptions. It incorporates personality traits and could lead to better ways to manage a blizzard of notifications and limit interruptions – if smartphone manufacturers get on board.

“Ideally, a smartphone notification management system should be like an excellent human secretary who knows when you want to be interrupted or left alone,” said Janne Lindqvist, an assistant professor in the Department of Electrical and Computer Engineering in Rutgers’ School of Engineering. “We know that people struggle with time management all the time, so a smartphone, instead of being a nuisance, could actually help with things.”

Currently, smartphone users can limit interruptions by turning off their ringers, but no system figures out when you want to receive notifications. “Preferably, your smartphone would recognize your patterns of use and behavior and schedule notifications to minimize interruptions,” said Lindqvist, who leads a research group focusing on human-computer interaction and security engineering.

Studies have shown that inappropriate or untimely smartphone interruptions annoy users, decrease productivity and affect emotions, he said. So it’s important to choose the right time to interrupt people.

Lindqvist began thinking about how to reduce smartphone distractions several years ago, so he and his doctoral students, Fengpeng Yuan and Xianyi Gao, conducted a peer-reviewed study: “How Busy Are You? Predicting the Interruptibility Intensity of Mobile Users.” The pioneering study will be formally published in May at the ACM CHI Conference on Human Factors in Computing Systems in Denver, Colorado. It’s the premier international conference on human-computer interaction.

For their study, the researchers developed and evaluated a two-stage model to predict the degree to which people are interruptible by smartphones. The first stage is aimed at predicting whether a user is available at all or unavailable. The second stage gauges whether people are not interruptible, highly not interruptible, highly interruptible, interruptible or neutral toward interruptions, according to Lindqvist.

They collected more than 5,000 smartphone records from 22 participants at Rutgers University over four weeks, and they were able to predict how busy people were. That’s important because people can respond to different kinds of interruptions based on their level of busyness.

In a first, the researchers used major personality traits to help predict how interruptible people were. Study participants took a standard test to see how their personalities aligned with the “Big Five” personality traits in psychological theory – extroversion, agreeableness, conscientiousness, neuroticism and openness.

In addition to building a model for interruptibility, the researchers studied the situations when participants’ interruptibility varied. When participants were in a pleasant mood, they were likely to be more interruptible than if they were in an unpleasant mood, the study showed. The study also found that participants’ willingness to be interrupted varied based on their location. A few participants were highly interruptible at locations such as health care and medical facilities, possibly because they were waiting to see doctors. But participants were reluctant to be interrupted when they were studying and, compared with other activities, were less interruptible when exercising.

Lindqvist and his team are working on next steps that could lead to smarter smartphone notifications.

“We could, for example, optimize our model to allow smartphone customization to match different preferences, such as always allowing someone to interrupt you,” he said. “This would be something an excellent human secretary would know. A call from your kids or their daycare should always pass through, no matter the situation, while some people might want to ignore their relatives, for example.”

“Ideally, smartphones would learn automatically,” he said. “As it is today, the notification management system is not smart or only depends on a user’s setting, such as turning on or off certain notifications.  Our model is different because it collects users’ activity data and preferences. This allows the system to learn automatically like a ‘human secretary,’ so it enables smart prediction.”

Story by Rutgers science communicator Todd B. Bates at tbates@ucm.rutgers.edu or 848-932-0550.

For more Research News, click here.

 

ECE Welcomes New Faculty

The Department of Electrical and Computer Engineering welcomes two new faculty members to the Rutgers engineering. These new instructors bring extensive experience to Rutgers, having performed research in several exciting fields. We look forward to their continued growth and innovation as part of the Rutgers Engineering faculty.
 
 
 
Assistant Professor
Electrical and Computer Engineering
PhD, Electrical and Computer Engineering, 2012
McGill University
Maryam Dehnavi recently worked as a postdoctoral researcher at the Massachusetts Institute of Technology, where she worked on machine learning and stencil computations. Her research included improving previous methods for finite-element method computations and other algorithms. Her goal is to create more efficient methods by which data-heavy or otherwise large-scale computations are performed by parallel systems. Previously she worked at Qualcomm Incorporated, where she developed and optimized code to improve applications. She has also been a visiting scholar at the University of California Berkeley and Irvine, where she likewise studied methods of improving software to work more effectively with hardware. She has been granted multiple scholarships and grants by the Natural Sciences and Engineering Research Council of Canada, and most recently earned a postdoctoral fellowship by the Quebec Research Fund.
 
Assistant Professor
Electrical and Computer Engineering
PhD, Electrical Engineering, 2010
University of Maryland

Vishal Patel’s key research interests are in machine learning, signal/image processing, computer vision processing, and security and privacy. He is a co-principal investigator on a Defense Advanced Research Projects Agency (DARPA) study analyzing fingerprints as a security feature for mobile devices. After earning his doctorate studying visual and atomic representations of signals, he worked and taught at the University of Maryland’s Center for Automation Research. Other research areas include approximation theory with wavelets, face recognition by computers, and biometrics. He is the principal investigator on a LADAR imaging study hosted by an Army Research Laboratory with General Dynamics. He was the recipient of a 2015 Computer Vision and Pattern Recognition Outstanding Reviewer Award.

ECE Faculty.jpg

Certificate Program: Cybersecurity in Electrical and Computer Engineering

In an increasingly interconnected world where data breaches, cyber threats, and digital espionage have escalated, the demand for robust cybersecurity measures has never been more paramount. Recognizing the urgent need for expertise in this critical domain, the Department of Electrical and Computer Engineering (ECE) at Rutgers University-New Brunswick is excited to introduce a comprehensive 12-credit graduate certificate program in Cybersecurity. 

The field of Cybersecurity, instrumental in safeguarding our digital ecosystems, finds its significance across a multitude of sectors such as banking, healthcare, education, military, government, e-commerce, and more. The task of securing cyber systems, their communication links, and the connected physical systems has become a primary concern for national economy, industry, health systems, and individual safety. As a crucial component of the nation's cyber infrastructure, the development and employment of apt software and devices to ensure cyber resilience calls for a deep and extensive understanding of cybersecurity principles and practices. 

Our Cybersecurity certificate program at Rutgers ECE is designed to fill this gap. This program has been carefully crafted, not just to impart theoretical knowledge, but to provide students with the practical skills required to secure various cyber systems. Our courses cover a broad spectrum of topics including internet security, personal and mainframe computers security, security of communication devices and systems, and security of cyber-physical systems.  

The exceptional faculty at Rutgers ECE, boasting extensive academic and industry experience, alongside a diverse and innovative range of courses, promises a rich and comprehensive learning experience. Whether you're a PhD, MS student, a non-matriculating student, or a professional seeking to specialize in Cybersecurity, this program offers an enriching pathway to augment your capabilities. 

Eligibility : The program is not limited to ECE or Rutgers students alone. We welcome learners from a wide range of disciplines including Computer Science, Electrical Engineering, Information Technology, Data Science, Business and Management, Public Policy, Law Enforcement, Health Informatics, and even non-traditional disciplines that interface with digital technologies and data security, expanding the reach of this essential knowledge. Moreover, some requirements for the certificate may even fulfill existing graduate degree requirements, offering added academic flexibility. 

 

Admission Requirements 

Current ECE Students: Graduate students (non-matriculated, MS. or PhD) from the ECE department interested in the Cybersecurity certificate do not need to apply separately. Interested students should simply notify the ECE Graduate Program Office at ecegradprogram@soe.rutgers.edu of their intention to pursue the certificate, along with their intended course selection. 

 Current non-ECE Rutgers Students: Graduate students (MS or PhD) from departments other than ECE should submit a copy of their Rutgers transcripts, a letter of recommendation, and the intended course selection to ECE Graduate Program Office at ecegradprogram@soe.rutgers.edu. Once notified by the ECE Graduate Program Office of the approval of their application, the students can start earning credits towards the certificate. 

 

Curriculum : In order to receive the certificate, students must complete four courses, equivalent to 12 credits, maintaining a GPA of at least 3.0. This coursework must include a minimum of two courses from List A, and a maximum of two courses from List B:  

List A:   

16:332:506 – Applied Controls  

16:332:507 – Security Engineering  

16:332:530 – Introduction to Deep Learning  

16:332:543 – Communication Networks I  

16:332:548 – Error Control Coding  

16:332:579:04 – Advanced Topics in Computer Engineering – Hardware and Systems Security  

   

List B:   

16:332:501 – Systems Analysis  

16:332:542 – Information Theory and Coding  

16:332:544 – Communication Networks II  

16:332:557 – Quantum Computing and Communications Algorithms  

16:332:561 – Machine Vision 

Recognizing the fast-paced and ever-evolving nature of the Cybersecurity landscape, we ensure that our graduate curriculum is routinely updated to stay abreast of the latest breakthroughs and challenges in the field. Consequently, courses beyond the ones explicitly mentioned may be recognized towards the completion of this certificate. These will be considered at the sole discretion of the ECE Graduate Program Director and subject to approval from the School of Graduate Studies. This commitment to flexibility and relevance ensures our program remains adaptive, current, and responsive to the multifaceted demands of the cybersecurity arena. 

2024 ECE Capstone Expo Award Winners

Dear Students and Colleagues:

We had a very successful ECE Capstone EXPO yesterday with 52 teams presenting their capstone projects. A panel of 50 judges from industry and academia joined us to select the top projects. In addition, three special awards (best in research, best in impact, and best in commercialization) were selected. 

Speaking with the judges, I would like to report that they were very impressed with the quality of the capstone projects this year!  The judges talked very highly about the complexity and innovation of the capstone projects and the enthusiasm with which they were presented. As a result, the judges decided to give awards to the top 15 teams this year instead of the usual top 10. I would like to thank our judges for their effort and time taken from their busy schedules to support our capstone program and celebrating our students' achievements!
 
Special thanks to the ECE Staff:  Pam, Kevin, John, Arletta, Katie and Chris for helping to make this year’s EXPO such a success! This would not have been possible without their hard work and dedication, and months of planning in advance. My special thanks also to Prof. Demetrios Lambropoulos who has worked tirelessly from Fall 2023 to help and support the Capstone program.  Many thanks also to all the students who volunteered in this event! 

I would like to congratulate this year’s senior students who participated in the ECE capstone program and their advisors from inside and outside Rutgers ECE who helped guide their projects. Your help and support of our students are essential to the success of the Capstone Program! 
 
I would like to acknowledge the support of the following industry sponsors: 7x24 Exchange Metro New York Chapter, Novo Nordisk, Lockheed Martin and L3Harris.  
 
Here is the list of award recipients and their advisors: 

Top 15 projects and Awardees of Best in Research, Commercialization and Social Impact:  

1st Place and Tie in Best in Research Award 
Project S24-48: Radar Based Vital Sign Monitoring with Automated Beam Steering
Team members: Daniel Gore, Gavin Young, Felipe Valencia, Daniel Petronchak, Nithish Warren
Adviser: Dr. Athina Petropulu
 
2nd Place and Tie in Best in Research Award 
Project S24-41: Levity
Team members: Daniel Maevsky, Yashovardhan Bamalwa
Advisors: Dr. Richard Howard and Dr. Narayan Mandayam

3rd Place 
Project S24-02:  RailVision: Overgrowth Detection Drone
Team members: Osmin Nolasco, James Sullivan, Steeve Cantave, Dhruv Patel
Advisors: Dr. Daniel Burbano Lombana & Dr. Sasan Haghani 
  
4th Place
Project S24-24: Accessible Video Game Controller for One-Handed Individuals
Team members: Andrew Chacko, Teerth Patel, Mayank Barad, Georgiy Aleksanyan, Marco Ghabrial
Advisor: Dr. Jorge Ortiz

5th Place
Project S24-49: Cloud-Connected Automation and Optimization for Drones
Team members: Ravi Raghavan, Atharva Pandhare, Aryan Patil, Shreyas Ramachandran, Vijay Chandhar
Advisor: Dr. Maria Striki

6th Place Tie and Best in Commercialization
Project S24-32: BlazeGuard : An Automated Fire Prevention Plug for Electronics and Appliances
Team members: Devesh Kaloty, Jason Peake, Taylor Scheuering
Advisor: Dr. Sasan Haghani and Don Bachman

6th Place Tie and Tie in Best in Social Impact
Project S24-07: RINSIGHT - Real-time Interactive Neural Sensory Integration Glasses for Hearing Technology
Team members: William Ching, Jonathan Romero, Aliza Ezrapour, Aman Saxena, Matthew Gravatt
Advisor: Dr. Sheng Wei

6th Place Tie
Project S24-54: HomeBud: Small-Scale Smart Watering System for Indoor Plants
Team members:  Kasey Tian, Izabela Bigos, Victoria Chen, Kristina Jokic, Billie Liang
Advisor: Dr. Sasan Haghani

7th Place
Project S24-27: Cascaded-UNet: Medical Image Security Enhancement Through AI-Powered Digital Watermarking and Visual Cryptography
Team members: Fiona Wang, Zachary Asis, Yesmina Hammouda, Irina Mukhametzhanova, Thomas Trieu
Advisor: Dr. Dario Pompili and  Tingcong Jiang

8th Place and Tie in Best Social Impact
Project S24-37: Human Motion Estimation for Interactive Rehabilitation
Team members: Ronan John, Daniel Gameiro, Marco Garcia-Palma
Advisor: Dr. Daniel Burbano Lombana

9th Place
Project S24-10: Design and Application of a Battery Management System for a Formula-Style Car
Team members: Mukund Ramakrishnan, Dana Fabiano, Ryan Billings, Thomas Troy Forzani,  Daniel McCormack
Advisor: Dr. Michael Caggiano

10th Place
Project S24-31: SensoryNav
Team members: Ramya Ramabhadran, Raveena Gupta, Hima Nukala, Nandana Pai
Advisor: Dr. Jorge Ortiz

11th Place
Project S24-42: Hand ANalyzed Dynamics
Team members: Samuel Marran, James Artuso
Advisor: Dr. Sasan Haghani

12th Place
Project S24-03: Drone-Assisted Replication Training
Team members: Jinam Modasiya, Aaron Yagudaev, Keyur Rana, Ryan Meegan, Sean Maniar
Advisor: Dr. Laleh Najafizadeh and Dr.  Laurent Burlion

13th Place
Project S24-29: 
Team members: Oliver Rzepecki, Vraj Panchal, Isaiah Pajaro, Ryan Elizondo-Fallas
Advisor: Dr. Hang Liu

14th Place
Project S24-08: Ten(Sen)sor
Team members: Gurveer Grewal,  Danial Fahim
Advisor: Demetrios Lambropoulos

15th Place
Project S24-46: 
Team members: Samuel Marran, James Artuso
Advisor: Dr. Sasan Haghani, Demetrios Lambropoulos & Jingang Yi

Congratulations to all the students and their advisors!

Best Regards,
Sasan Haghani
Undergraduate Program Director
Department of Electrical and Computer Engineering
Rutgers, The State University of New Jersey
94 Brett Rd, Piscataway, NJ, 08854
e-mail: sasan.haghani@rutgers.edu

ECE faculty Bo Yuan awarded a Presidential Fellowship for Scholarly Excellence for the academic year 2023-2024

The Office of the Executive Vice President for Academic Affairs (EVPAA) awarded a Presidential Fellowship for Scholarly Excellence for the academic year 2023-2024 to Bo Yuan as one of the university's most distinguished young faculty members. This award is bestowed in recognition of his outstanding scholarly accomplishments in his years at Rutgers, as documented in the evaluation that has led to his recent recommendation for promotion to Associate Professor, subject to approval by the Board of Governors. The award will be presented to him at a reception to be held at the University President's house in early May.

We are very proud of Bo for his achievements and high recognition. Congratulations!

ECE faculty Umer Hassan awarded a Presidential Fellowship for Teaching Excellence for the academic year 2023-2024

The Office of the Executive Vice President for Academic Affairs (EVPAA) awarded a Presidential Fellowship for Teaching Excellence for the academic year 2023-2024 to Umer Hassan as one of the university's most distinguished young faculty members. This award is bestowed in recognition of his outstanding teaching accomplishments in his years at Rutgers, as documented in the evaluation that has led to his recent recommendation for promotion to Associate Professor, subject to approval by the Board of Governors. The award will be presented to him at a reception to be held at the University President's house in early May. 

We are very proud of Umer for his achievements and high recognition. Congratulations!

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