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. 

SoE Researchers win Best Paper Award at the 20th IEEE Annual International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT 2024)


ECE Professor Dario Pompili, ECE graduate students Songjun Huang and Chuanneng Sun, and CEE Assistant Professor Ruo-Qian (Roger) Wang have won the Best Paper Award at the 20th IEEE Annual International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT 2024), which was held in April 29–May 1, 2024, in Abu Dhabi (UAE), for their paper titled “Multi-Behavior Multi-Agent Reinforcement Learning for Informed Search via Offline Training”.

The conference intended since its creation to cover several aspects of distributed computing in smart systems such as high level abstractions and models, systematic design methodologies, signal and information processing, algorithms, analysis and applications. Starting from the 2023 event, DCOSS is broadening its scope beyond sensor networks to smart systems in general as well as the Internet of Things (IoT). The updated name is “Distributed Computing in Smart Systems and the Internet of Things” (DCOSS-IoT).

The winners were presented with an award certificate and a plaque. The abstract of the award winning paper is below.
 
Abstract: In modern informed search missions, Multi-Robot Systems (MRSs) are playing more and more important roles due to their flexibility in exploring environments. Reinforcement learning (RL) is now widely used as a decision-making method for MRS. However, existing RL-based and conventional model-based frameworks cannot deal with some challenges posed by the real-world environment. To address these challenges, a Multi-Behavior Multi-Agent Reinforcement Learning (MBMARL) framework via offline reinforcement learning method was developed. In this framework, each agent is deployed with multiple behavior policies to let the agent have choices on behaviors given a state. The proposed framework is compared with traditional reinforcement learning frameworks, including Multi-Agent Actor Critic (MAAC) and REINFORCE. The result shows that MBMARL outperforms others in both aspects of total reward and convergence time.
 

 

University Commencement

 

 

The 258th Anniversary Commencement at Rutgers University is on Sunday, May 12, 2024, at SHI Stadium, starting at 10 a.m. President Jonathan Holloway will preside over the ceremony, conferring degrees to graduates and honorary recipients.

2024 Commencement Speaker. Esteemed educator and STEM advocate Dr. Freeman A. Hrabowski III, President Emeritus of UMBC, has been announced as the 258th Anniversary Commencement Speaker on May 12 at SHI Stadium.

Guest Lecture - Reliable AI: From Legal Requirements to Neuromorphic Computing

Graduate Students and Faculty,
 
Professor Shirin Jalali is hosting Professor Gitta Kutyniok for a talk on Thursday May 16th at 10 am ET on Zoom. The talk is titled Reliable AI: From Legal Requirements to Neuromorphic Computing. Please see the poster below for more information. 
 
 

ECE undergraduate student Mukund Ramakrishnan won the Chancellor's Research Excellence Award 2024

Mukund Ramakrishnan will receive a Chancellor's Research Excellence Award during the 9th Annual Chancellor's Student Leadership Award Gala on Wednesday, May 1, 2024. The Chancellor's Student Leadership Awards honor undergraduate and graduate students who consistently demonstrate collaboration, leadership, and resourcefulness, have a tremendous passion for Rutgers, and go outside their responsibilities to achieve goals.

Mukund was an undergraduate student in Prof. Emina Soljanin's group under this year's James J. Slade Scholars Program. His oral presentation on his thesis was the ECE representative at the JJ Slade symposium. Mukund's NSF GRFP research proposal (based on his JJ Slade thesis) has received an NSF GRFP Honorable Mention. It concerns anonymous Internet communications. Mukund's extracurricular activities are equally impressive. To mention one last week, he conducted his original piece at a conducting class recital.

Mukund will attend graduate school. He has offers from UIUC, Purdue, NYU, and other PhD programs.

Congratulations to Mukund and Emina!

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