Artificial intelligence (AI) has become part of how nearly every industry gets its work done, from diagnosing patients and detecting fraud to editing news and processing benefits claims. Companies across diverse fields are competing for the same limited pool of people who can build, train, and manage AI systems, but the talent isn't keeping up with the demand.

The catch is that "a career in AI" describes dozens of different jobs. Someone who trains machine learning models, someone who studies the ethics of automated decisions, and someone who programs robots all work in AI, yet their days look nothing alike. Each job also has its own entry point, usually a specific graduate degree, so the decision starts with knowing which kind of AI work fits you.

Key Takeaways

• AI careers include at least six distinct specializations, from machine learning and data science to robotics, computer vision, AI software engineering, and AI ethics, with each suited to a different set of strengths.
• Demand and pay far exceed the national average for all jobs, with data scientist roles projected to grow 34 percent through 2034 and median wages from $102,320 to $140,910 across the occupations AI work draws on, according to the U.S. Bureau of Labor Statistics.
• Your specialization points to a specific type of graduate degree: Applied AI for deployment, the MS in AI for research, computer science for software engineering, and data science for analytics.
• Pace University's Seidenberg School offers all four in New York City, with online options in computer science and data science.

Areas of AI Specialization

Federal wage and growth data shows why AI skills command attention. Across the occupations that AI work draws on, median wages far exceed the national average of $49,500, and several rank among the fastest-growing jobs the U.S. Bureau of Labor Statistics tracks.

The table below lists the occupations most relevant to AI careers; because many AI roles are too new for their own category, these serve as the closest available benchmarks rather than exact figures, and published estimates for specialized roles often climb higher.

Underneath those headline numbers are half a dozen distinct areas of AI work. Each one draws on a different mix of skills, appeals to a different kind of thinker, and lines up with a specific graduate degree.

Machine Learning

Machine learning is the foundation of modern AI. Engineers in this area develop the systems that learn patterns from data and sharpen their predictions over time, from recommendation engines to fraud detection to medical imaging tools. The work blends software engineering with applied statistics, so you write production code while also choosing algorithms, tuning models, and deciding what "accurate enough" means for a given problem.

Natural Language Processing

Natural language processing (NLP) is the branch of AI that teaches machines to work with human language, whether reading it, interpreting it, or generating it. Every time a chatbot answers a question, a search engine interprets a vaguely worded query, or a tool summarizes a long document, NLP is what makes that possible. The rise of large language models (LLMs) has pushed demand for this area sharply upward, because the companies deploying those models need people who can fine-tune them, evaluate their output, and keep them accurate in production.

Data Science and Analytics

Data science turns raw information into decisions. Professionals in this area collect, clean, and model large datasets to answer questions a business cannot settle by intuition alone: which customers are about to leave, where a supply chain will bottleneck, or what a medical trial's results actually mean. In an AI context, data scientists also assemble the training datasets that machine learning models depend on, which makes this one of the most direct routes into applied AI.

Robotics

Robotics gives AI a physical form. Engineers in this field build machines that sense their surroundings and respond to them, from warehouse systems that pick and sort inventory to surgical assistants to autonomous drones. The work extends past software into control systems, sensor integration, and hardware, so a robotics engineer has to understand both the code that makes a decision and the physical motors that execute it.

Researchers in human-robot interaction focus on how people and machines work together. At Pace University, Sara Falcone, PhD, an assistant professor of computer science whose work combines human-robot interaction and embodied AI, earned an Alfred P. Sloan Foundation grant to make hybrid conferences, lab visits, and talks more accessible for remote participants. “We’re building lightweight wearable haptic prototypes worn on the forearms and shoulders that deliver haptic cues as a low-disruption, private communication channel alongside audio/video,” Falcone said. “The goal is to support things like attention management, timing and turn-taking, navigation and awareness in physical spaces, and to smooth social coordination in busy hybrid settings.”

Computer Vision

Computer vision teaches machines to interpret what they see. A trained model can read handwritten forms, flag a manufacturing defect a human eye would miss, guide a self-driving car around obstacles, or scan medical images for early signs of disease. As cameras and sensors turn up in more products, demand for people who can develop this kind of perception has grown across healthcare, automotive, retail, and security.

AI Software Engineering

Behind every AI product is the engineering that makes it usable: the APIs, pipelines, and infrastructure that move a model from a researcher's notebook into an application millions of people can open on their phones. AI software engineers write that connective code. They integrate models into products, scale systems to handle traffic, and keep everything stable once it ships.

AI Ethics and Governance

As AI systems decide who gets a loan, how a résumé is ranked, or what a medical algorithm flags, organizations need people who can judge whether those systems are fair, transparent, and legally compliant. AI ethics and governance is the youngest of these specialties, and one of the fastest to gain ground as regulation catches up with the technology.

The people in these roles translate legal requirements and ethical principles into standards that engineering teams can follow. It’s also active research territory at Pace, where Professor Christelle Scharff, PhD, and her students expanded an image dataset to include African fashion so AI systems would stop misclassifying non-Western garments.

Soheyla Amirian, PhD, an assistant professor of computer science who leads the Applied Machine Intelligence Initiatives and Education Laboratory, brings the same scrutiny to AI in healthcare. “The objective is to develop algorithms that minimize bias related to sex, ethnicity, or socioeconomic status, ensuring equitable healthcare outcomes,” Amirian said. “This work is guided by the principle that AI should benefit everyone, not just a privileged few.”

Because the specialty is so new, federal wage data has yet to catch up. Technical responsible-AI work is closely related to the research-scientist and data-science figures above, while policy-facing governance roles align more with compliance and management occupations.

Matching an AI Specialization to a Pace Graduate Degree

Once you know which kind of AI work suits you, the next step is choosing a program that trains you for it. Pace University's Seidenberg School of Computer Science and Information Systems has taught AI for more than 30 years, with over 15 AI faculty experts and more than 40 courses that build AI across its programs. Across the school, 93 percent of Seidenberg students are employed, continuing their education, or pursuing service within one year of graduation.

Four graduate degrees anchor Pace’s AI offerings, with one built for applied work, one for research, one for core computing and software, and one for data science.

MS in Applied Artificial Intelligence

The Master of Science in Applied AI is the most hands-on of the four. You learn to design, deploy, and manage AI systems that produce real results, building things like retrieval-augmented chatbots, forecasting pipelines, and decision dashboards along the way. A required ethics course also makes it a solid route into responsible-AI and governance work.

The program offers three concentrations that enable you to tailor the degree around your goals:

• Human-centric AI for user experience and human-computer interaction
• Data-centric AI for large-scale data work
• Computational Intelligence for machine learning, NLP, and computer vision

If you want to put AI into production across a field such as healthcare, finance, or media, this is the program for you.

MS in Artificial Intelligence

Where the Applied AI degree points toward production, the Master of Science in AI points toward research. You go deep into machine learning, natural language processing, robotics, computer vision, and generative AI, then put the theory to work on faculty-led projects at the Pace AI Lab, the Robotics Lab, and the Augmented Intelligence Lab.

Courses including Pattern Recognition and Advanced AI get into the methods behind visual recognition and reinforcement learning. Anyone drawn to the research end of the field will find a fit here, whether the goal is a research scientist role, an NLP or computer vision specialty, or doctoral study later on.

MS in Computer Science

Pace’s computer science master’s degree helps you build the engineering foundation that AI software work depends on. The core includes algorithms, cloud and web computing, and database systems, with AI and data science available through electives.

Flexibility sets this one apart. You can study on campus or fully online, full or part time, and a single Java bridge course opens the door to people without a computing background. For anyone targeting AI software engineering, machine learning infrastructure, or a systems-level role, it offers the deepest grounding in how software gets built.

MS in Data Science

The data science master’s program teaches you how to turn large, unorganized datasets into decisions. You work in Python, R, TensorFlow, Spark, and Hadoop, learn the statistics and modeling that data roles demand, and graduate with a portfolio built on real data.

Courses in artificial intelligence and deep learning connect the analytics work to modern AI methods. Like computer science, the program is offered on campus and online, which suits working professionals. If your target is data science, analytics, or machine learning modeling, this one is the ideal program.

At a Glance

Each degree fits some specializations better than others, though several overlap in practice.

How to Move Into an AI Career in 5 Steps

The path to breaking into AI looks different for a working developer than it does for someone crossing over from another field. Still, most successful moves share the same handful of steps.

Step 1: Choose a specialization

The areas above reward different strengths, so start by matching one to your background and interests. A statistics-minded analyst may take to data science quickly, while someone who loves systems and scale is drawn to AI software engineering. Picking a direction early keeps your coursework, projects, and job search pointed at the same target.

Step 2: Learn the fundamentals

Every AI specialty needs the same common base:

• Programming (usually Python)
• Statistics and linear algebra
• Working knowledge of how machine learning models are trained and evaluated

A graduate program builds these, though self-study and bootcamps can fill gaps for people who already work in tech. Career-changers without a computing background often start with a bridge course, which several Pace programs offer.

Step 3: Build real experience

Employers hire on evidence, and a portfolio of projects can outweigh a list of courses. Contribute to open-source tools, enter Kaggle competitions, or build an application end to end and put it where a hiring manager can see it. Graduate programs help here through capstone projects, faculty research, and industry partnerships that enable you to produce work you can actually show.

Step 4: Earn credentials that signal skill

Certifications from cloud and AI platform providers can strengthen a résumé, especially for career-changers who need to prove hands-on ability. A graduate degree is often required for advanced and research roles, many of which list a master's as a baseline. The right education depends on where you want to go. Those looking to enter the field may only need a certificate to get started, while those looking to fill advanced AI roles will need a degree.

Step 5: Network where the work happens

Many AI roles are filled through referrals and professional connections before they ever appear on a job board. Attend meetups and conferences, join AI communities, and connect with people already doing the work you want. Location helps: being near a tech hub like New York City puts you close to the employers, alumni, and events that turn conversations into offers.

Advance Your AI Career at Pace

The AI field has room for many kinds of talent, and the surest way in is a graduate program matched to the work you want to do. Pace University's Seidenberg School of Computer Science and Information Systems offers four routes into it:

• MS in Applied Artificial Intelligence for building and deploying AI systems
• MS in Artificial Intelligence for research and advanced technical work
• MS in Computer Science for AI software engineering
• MS in Data Science for analytics and modeling

Seidenberg is based in the heart of New York City, one of the world's densest tech economies. Students can work in labs like the Pace AI Lab and the Robotics Lab, take on real projects through the NYC Design Factory, and connect with an alumni network of more than 161,000 people at companies from Google to OpenAI. That proximity is a major advantage, and it frequently helps Pace students turn their coursework into internships, and internships into job offers. The in-person programs put you in the middle of it, while the online options in computer science and data science open the same faculty and network to students anywhere.

Whichever path fits your goals, the next step is a short conversation. Request information to compare the programs, ask about admission and bridge courses, and find the one that will help you get where you want to go.

Frequently Asked Questions

Do you need a computer science degree to work in AI?
No, you do not always need a computer science degree to work in AI. Many AI roles value demonstrated skill, a strong project portfolio, and the right technical foundation over a specific undergraduate major. Career-changers from math, engineering, or other quantitative fields regularly move into AI, often by completing a graduate program with a bridge course that fills any gaps in programming or theory before the core coursework begins.

Is a master's in AI worth it?
Yes, a master’s in AI is worth it, especially for advanced, research, and leadership roles. Many senior AI positions list a master's as a baseline expectation, and a graduate degree can raise both your earning potential and the range of jobs open to you. For entry-level work, a strong portfolio may be enough, so the value of a degree grows with how far you want to advance.

What is the difference between an MS in AI and an MS in Computer Science?
The difference between an MS in AI and an MS in Computer Science is scope. A computer science master's is broader, covering algorithms, systems, and software engineering, with AI as one focus area among several. An AI master's concentrates on intelligent systems, machine learning, natural language processing, and computer vision. Choose computer science for flexibility across the tech field, or AI for depth in the science of intelligent systems.

How long does a master's in AI take?
Most of Pace's Seidenberg graduate programs typically require the completion of 30 to 34 credits and take about two years of full-time study. Part-time schedules extend that timeline but let working professionals stay employed while they study. Computer science and data science also offer online options, which give students flexibility in how quickly they move through the coursework.

Can you study AI online?
Yes, you can study AI online, at least in part. Pace offers its MS in Computer Science and MS in Data Science both on campus and fully online, with the same curriculum and faculty in each format. The MS in Applied Artificial Intelligence and MS in Artificial Intelligence are in-person programs, since both draw heavily on lab work and faculty-led research on the New York City campus.

In McKinsey's 2025 "State of AI" survey, 88 percent of organizations reported regularly using artificial intelligence (AI) in at least one business function, up from 78 percent the year before. With adoption now near-universal, the question has shifted from whether to use AI to how to make it deliver, and that is the work of applied AI.

Applied AI is what that delivery actually looks like. It takes models that already work and gives them a job, whether that is spotting a fraudulent transaction, reading an X-ray, or predicting equipment failure before it happens. Where theoretical AI research works to expand what these systems can do, applied AI is measured by a narrower standard, which is whether a real problem gets solved.

For anyone weighing a career in the field, the applied side rewards a particular kind of skill, the ability to turn a capable model into a working system. Understanding that work starts with a clearer picture of what applied AI includes.

Key Takeaways

• Applied AI puts artificial intelligence to work on real-world problems. Success is measured by whether the problem gets solved, not by how advanced the model is.
• Theoretical AI asks what systems can do. Applied AI turns those systems into dependable, working tools for fields like healthcare, finance, and manufacturing.
• A strong model is only the start. Applied AI also depends on solid data, infrastructure, governance, ethics, and ongoing monitoring, since a system's accuracy fades without upkeep.
• AI skills command a 62 percent wage premium, and those jobs are growing nearly eight times faster than the broader market, according to PwC's 2026 Global AI Jobs Barometer.
• Applied AI is already everywhere, from FDA-cleared medical imaging to the fraud detection and recommendation systems people rely on daily.

What Is Applied AI?

Applied artificial intelligence is the practice of building AI systems that solve specific, real-world problems. Instead of advancing AI theory for its own sake, applied AI aims existing techniques at a concrete task, whether that means helping healthcare workers read scans faster, give bankers a better shot at preventing fraud, or tightening demand forecasts for retail stakeholders. The point is a measurable result, not a more sophisticated model.

Purpose is what separates applied AI from research-oriented AI. Research pushes at the boundaries of what these systems can do, while applied AI starts from what already works and asks how to make it useful somewhere specific, an emphasis that influences the skills a professional builds and the kind of problems they take on. The two paths diverge from there.

Most applied AI draws on a handful of core technologies, each suited to a different kind of problem:

• Machine learning (ML) trains systems to find patterns in data and improve their predictions over time, which powers everything from credit scoring to demand forecasting.
• Computer vision interprets images and video, so a system can read a medical scan, inspect a part on an assembly line, or guide an autonomous vehicle.
• Natural language processing (NLP) allows systems to understand and generate human language, behind the customer service chatbots, document search, and translation tools people use daily.
• Automation and robotics connect AI decisions to physical or digital action, from routing a delivery fleet to executing steps in a business workflow.

What ties these together is deployment. A model that performs well in a research paper has proven a concept. Applied AI is the discipline of getting that model into production, where it has to be reliable, maintainable, and accountable to the people who depend on it.

Benefits of Applied AI

Applied AI pays off both inside the organizations that put it to use and in the careers of the people who build it.

For organizations, the return is operational. A well-built system takes on work that would swamp a human team, screening millions of transactions for fraud, watching equipment for the early signs of failure, or sorting customer requests so specialists spend their time where it counts. Decisions get faster, costs come down, and errors drop in the places that hurt most. The consulting firm PwC, which tracks AI's effect on the global economy, found that companies in the most AI-exposed industries saw 34 percent productivity growth from 2018 to 2025, against 24 percent for the least exposed.

For professionals, applied AI has become one of the most valuable skill sets in the job market. PwC's 2026 Global AI Jobs Barometer put the average wage premium for AI skills at 62 percent over comparable roles without them, up from 57 percent a year earlier, with those jobs growing almost eight times faster than the market overall. The demand points to a genuine shortage. Plenty of people can explain what a model does, and far fewer can put one into production and keep it running.

Closing that gap is where applied AI expertise proves its worth. Building a system that holds up outside a controlled environment takes judgment earned through practice, knowing how to scope a problem, choose the right tools, weigh the tradeoffs, and rework an approach when the first attempt falls short. An applied AI education is built to develop exactly that judgment, which is why employers pay so well for it.

What Does It Take to Build Applied AI Systems?

Getting an AI system to work in a demonstration is one thing, but keeping it accurate, secure, and trustworthy once real people and real data depend on it is harder. That success centers on a set of practical concerns that shape every applied AI project. Below are the considerations a skilled practitioner weighs at every stage, from first build to live operation.

Data Management

Every AI model learns from data, and the quality of that data sets the ceiling on how well the model can perform. Applied AI work depends on collecting, cleaning, labeling, and organizing large datasets and then keeping them current as conditions change. Poorly managed data introduces bias, gaps, and errors that a model will faithfully reproduce at scale, which is why data engineering is among the most in-demand skills in the field.

Network Infrastructure

AI systems need somewhere to run, and the demands are steep. Training and serving models calls for substantial computing power, storage, and bandwidth, whether that lives in the cloud, on local servers, or in some combination of the two. Decisions about infrastructure can affect how fast a system responds, how much it costs to operate, and how well it handles growth in users or data volume. Matching the infrastructure to the problem is part of designing a system that can actually be maintained.

Model Training

Training is where a model learns to do its job, a process that rarely happens just once. A practitioner selects an approach, feeds the model prepared data, evaluates how it performs against known outcomes, and adjusts until the results hold up. As new data arrives or conditions shift, models are retrained to keep them accurate. This cycle of building, testing, and refining is the core technical work of applied AI, and it demands both statistical judgment and a clear sense of what "good enough" means for the problem at hand.

Governance

Governance refers to the policies and accountability structures that determine how an AI system is built, approved, and used. The National Institute of Standards and Technology (NIST) AI Risk Management Framework, the leading US reference on the subject, treats governance as a continuous practice woven throughout the whole lifecycle of a system rather than a one-time sign-off. In an organization, that means clear ownership, documented decisions, and defined limits on what a system is permitted to do.

Ethics

Applied AI systems make or inform decisions that affect people, from who gets a loan to how a patient is triaged, ensuring fairness, transparency, and privacy as working requirements rather than afterthoughts. A model trained on biased data can quietly reproduce that bias against thousands of people before anyone notices. Building AI responsibly means testing for those harms, explaining how a system reaches its conclusions, and protecting the data of the people it serves.

Monitoring

A model's accuracy is highest the day it launches and slips from there without regular upkeep. A fraud detection model trained on last year's schemes, for instance, will miss the latest tactics invented by criminals. Applied AI systems need ongoing monitoring to catch that decline along with errors and unexpected behavior. In McKinsey's 2025 survey, 51 percent of organizations using AI reported at least one negative consequence, with inaccuracy the most common. Sustained oversight, including well-defined points where a human reviews the system's output, is what keeps a deployed model reliable over time.

Applied AI in the Real World

The clearest way to understand applied AI is to look at where it already operates. Across industries, the same underlying techniques adapt to very different problems, but regardless of those nuances, applied AI is always aimed at a specific, measurable outcome.

Healthcare is one of the most active fields for applied AI, and medical imaging is where the field has boomed. When a patient gets a CT scan or mammogram, an AI system trained on millions of prior images can scan the results in seconds, marking areas that warrant a closer look, a suspicious nodule, a small fracture, or the early signature of a stroke, for example. The radiologist still makes the final call, but the system triages the queue, pushing urgent cases to the top and giving the physician a second set of eyes on the rest. In practice, that means faster diagnoses for patients who need them most and fewer findings that slip through the cracks.

The footprint is already substantial. By the end of 2025, the US Food and Drug Administration had authorized 1,451 AI-enabled medical devices, roughly three-quarters of them in radiology. Beyond imaging, hospitals also use applied AI to predict which patients are at risk of deterioration, match clinical trials to eligible candidates, and speed the early stages of drug discovery.

Emergency care is typically a tougher setting for these tools, since paramedics and other first responders make decisions quickly and often before a patient reaches a hospital. At Pace University, Zhan Zhang, PhD, an associate professor and director of the Human Centered Design program, earned a Best Paper Award at the 2026 IEEE International Conference on Health Informatics. The paper, co-authored with Pace student researchers, maps where AI decision support can help emergency medical services providers. “Our research focuses on understanding how AI can support their decision-making in ways that fit naturally into their workflow,” Zhang said.

What works in a hospital works anywhere an organization faces a repetitive, high-volume decision and has enough data to inform it. Here's how applied AI plays out across a range of industries:

In customer service, people often judge a chatbot by how the conversation feels as well as by the answer it gives. Youngsoo Shin, PhD, an assistant professor in Pace’s Seidenberg School of Computer Science and Information Systems, examined how users perceive empathy in chatbots and virtual assistants. He found that active listening and personalization consistently improved the experience, while more emotionally expressive systems could have unintended effects on users.

The common thread across these examples is the discipline behind them. The underlying models are often similar from one industry to the next, so the challenge is rarely the technology itself. The hard part is fitting a general capability to a particular job and making it dependable enough that a business, a hospital, or a driver can rely on it.

Earn Your Master's in Applied AI at Pace

The people who thrive in applied AI are the ones who have built systems and watched them succeed or fail in the real world. Pace University's Master of Science in Applied Artificial Intelligence is built around that kind of hands-on practice. The STEM-designated program trains you to design, deploy, and manage AI systems that work outside a controlled setting.

Offered through the Seidenberg School of Computer Science and Information Systems, the 30-credit degree takes two years to complete and centers on project work rather than lectures alone. Its core features include:

• A project-driven curriculum where you train and evaluate machine learning models, bring large language models into real workflows, and build working tools for fields like healthcare, finance, and cybersecurity.
• Courses in Applied AI, Ethical Issues in AI, and Human-AI Interaction, covering the practical side of the field from data and governance to monitoring a system once it is live.
• Three concentrations—human-centric AI, data-centric AI, and computational intelligence—so you can point the degree toward the career you want.
• Foundation courses in coding and database management that build computing fundamentals before the applied AI core.

Location is a real advantage here. Pace is located in the middle of one of the world's largest technology job markets, where finance, healthcare, media, and a deep startup scene all put applied AI to work. That proximity turns into access for Pace students: internships during the program, a 161,000-strong alumni network, and recruiting relationships with employers such as Google, Meta, IBM, JPMorgan Chase, and OpenAI, where Pace graduates already work. Across the Seidenberg School, 91 percent of students are employed, continuing their education, or pursuing service within a year of graduating.

Still weighing the applied path against the research side? Our article, “MS in AI vs. MS in Applied AI: Which Is Better?” breaks down the difference. You can also request information about the Applied AI MS directly.

Frequently Asked Questions

Do you need to know how to code to work in applied AI?
Some coding ability helps, but you do not need to be an expert programmer to start. Applied AI work relies most on Python and familiarity with common frameworks, and many practitioners come from analytics, engineering, or domain backgrounds and build coding skills as they go. Graduate programs often include foundation courses in coding and databases so students from non-technical fields can catch up before advanced work.

Is applied AI the same as machine learning?
No, machine learning is one technology that applied AI uses, not the whole field. Machine learning is a method for training systems to learn from data, while applied AI is the broader practice of putting that method, along with computer vision, natural language processing, and automation, to work on a real problem. A machine learning model becomes applied AI once it is built into something people actually use.

How much math does a career in applied AI require?
Applied AI leans more on statistics and probability than on advanced calculus, since the work centers on evaluating models and interpreting their output rather than deriving new algorithms. A working knowledge of linear algebra and statistics is genuinely useful, but the applied side generally demands less theoretical math than research-focused AI, which is one reason career changers find it approachable.

What are some everyday examples of AI most people already use?
Most people rely on applied AI several times a day without noticing. The recommendations on a streaming service, the spam filter in an email inbox, predictive text on a phone, the route a maps app chooses, and the fraud alert from a bank are all applied AI at work. Each takes a trained model and points it at one specific, repeated task.

Which industries hire the most applied AI talent?
Technology, finance, and healthcare hire heavily for applied AI, followed by retail, manufacturing, and logistics. The common factor is scale: organizations that handle large volumes of data and repetitive decisions have the most to gain from automating and improving them. Roles range from machine learning engineer and data engineer to AI product manager, depending on how technical the work is.

For survivors of domestic violence, leaving an abusive relationship can be only the beginning of a complicated legal journey. Questions about child custody, financial support, housing, and personal safety can make an already difficult situation feel impossible. And for many, the cost of hiring an attorney puts legal help out of reach.

For 35 years, the Pace Women’s Justice Center (PWJC) has worked to change that reality.

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Founded in 1991, the Center provides free civil legal services to survivors of domestic violence, sexual assault, and elder abuse in Westchester and Putnam Counties. Today, its team of approximately 35 professionals, including 26 attorneys, serves roughly 3,500 clients annually. Through courthouse offices, legal consultations, a helpline, and partnerships with the Elisabeth Haub School of Law, PWJC helps survivors understand their rights and navigate a legal system that can be difficult to access.

But as the Center marks its 35th anniversary during Domestic Violence Awareness Month in October, its leaders acknowledge that significant challenges remain.

Cindy Kanusher, JD, PWJC's executive director, has spent nearly three decades working to expand access to legal representation for survivors. She joined the organization when it had just three attorneys and has helped guide its evolution into one of the region's established legal advocacy organizations.

We spoke with Kanusher about the financial and legal barriers survivors encounter, where the justice system continues to fall short, and why the Center's work remains as urgent as ever.

What are the biggest legal challenges facing survivors of domestic violence today?

One of the biggest challenges is that survivors simply cannot afford legal help. So many of the people who come to us don't have the financial means to hire an attorney. Sometimes that's because financial abuse is part of their situation. Other times, they just don't have the resources to pay for legal representation, which can be extraordinarily expensive.

That's why being able to provide our services for free is so important.

But affordability is only one barrier. Access is another. We have offices in the family courthouses in White Plains, Yonkers, and New Rochelle, where someone can walk in without an appointment and speak with an attorney. We also have a legal helpline and a walk-in clinic where people can receive consultations, including remotely.

Someone might need an emergency order of protection, or they might be worried about custody of their children or how they're going to support their family. They can come to us and begin understanding their options immediately.

We try to make legal help available in ways that work for people's lives. That might mean speaking to an attorney during a lunch break or from the privacy of their car.

How does a lack of information about legal rights keep people in abusive situations?

It's an enormous barrier. Many survivors don't understand their legal rights, and sometimes that's because they've been deliberately told they don't have any.

Someone might be told that if they leave, they'll automatically lose custody of their children. Or that they have no right to financial support or access to the family's money. Those beliefs can have a tremendous impact on someone's decisions.

Being able to sit down with an attorney and learn that leaving doesn't automatically mean losing custody of your children, or that there may be ways to obtain financial support, can make an enormous difference.

Sometimes people aren't ready to take legal action. They just want to understand their rights and what the process might look like. Giving them that information allows them to make decisions about their own lives and their families.

What does meaningful legal advocacy look like beyond just securing an order of protection?

It's much more comprehensive than simply providing legal representation!

Someone might come to us because they're considering divorce, seeking custody of their children, or trying to obtain an order of protection. We explain how the legal system works, what they can expect, and what steps they might need to take.

But we also talk about safety, because pursuing legal action can create very real safety concerns for someone living in an abusive relationship.

We work with community partners who provide counseling, advocacy, safety planning, and other services. Sometimes the immediate concern is housing. Someone may want to leave an abusive situation but have nowhere to go, so we help connect them with shelter or other housing resources.

It's really a support system. The legal component is essential, but it's only one part of what someone may need to move forward safely.

After so many years of advocacy at the Center, in your opinion, where does the legal system still fall short for survivors?

That's a difficult question, because we've made tremendous progress, but there are still significant problems. The legal system, whether we're talking about family court or criminal court, is fundamentally adversarial. And that doesn't always align with what survivors want or need.

Not every survivor wants to pursue criminal charges. Not everyone wants their abuser arrested. Sometimes they simply want the abuse to stop. They want to live in peace. But the options available through the legal system don't always reflect those needs.

We need to continue thinking about innovative approaches that make the system more responsive to survivors. There have been conversations about restorative justice and mediation, although both raise serious concerns in domestic violence cases because of the power imbalances involved.

We also need continued education for judges, attorneys, and everyone who works within the justice system. Understanding the dynamics of domestic violence and the trauma survivors experience is essential to responding appropriately.

There isn't one simple answer. But we have to keep asking how the system can work better for the people it's supposed to protect.

As the Pace Women's Justice Center celebrates its 35th anniversary, which accomplishments are you most proud of?

When I joined the Center 28 years ago, I was its third attorney. Today, we have approximately 26 attorneys and a staff of 35. That growth has allowed us to expand our services tremendously.

When I first started, much of our work focused on training and education, particularly helping police and prosecutors understand domestic violence and sexual assault cases. Over time, we began providing direct legal representation in family court, helping survivors with custody, support, divorce, and other matters.

One of our most innovative programs is our Family Court Legal Program, which places attorneys directly in courthouses so people can receive help without an appointment. We also work closely with Haub Law students, who gain hands-on legal experience while helping us serve more people. That program alone assists approximately 500 to 600 people annually.

We've also built a substantial pro bono network. We have more than 50 volunteers, many of them attorneys, who contribute approximately 10,000 hours of service annually. That represents an extraordinary contribution to our clients and our mission.

And one of our newest initiatives, our walk-in legal clinic, provides about 800 consultations each year.

We've expanded our work to address elder abuse and the legal needs of sexual assault survivors as well. I'm proud of how much we've grown and how many different ways we've found to reach people who need help.

How does the Pace Women’s Justice Center prepare the next generation of attorneys while expanding access to justice?

One of the things that makes our work so special is the opportunity to involve current Haub Law students directly in serving survivors. Through our Family Court Legal Program, law students work alongside experienced attorneys, gaining hands-on legal experience while helping people navigate some of the most difficult moments of their lives.

It's a win-win. Students receive valuable training and learn what it means to advocate for clients facing real challenges, while their involvement allows us to serve more people. That program alone helps approximately 500 to 600 people every year, and we simply couldn't do that without our students.

Being part of the Elisabeth Haub School of Law also gives us a strong foundation for this work. Pace University's support has been instrumental in helping the Center grow and remain stable over the years. Together with our volunteers and community supporters, that foundation allows us to continue providing free legal services to thousands of people who need them.

What gives you hope for the future of domestic violence advocacy, and what do you want people to understand about PWJC?

I often joke that I hope one day the Pace Women's Justice Center will be out of business because our services won't be needed anymore.

But that hasn't happened. In fact, we're probably receiving more calls now than ever before. My hope is that we'll continue to be here for as long as people need us.

And the most important thing I want people to understand is that they are not alone.

If someone is experiencing domestic violence, has been sexually assaulted, or is experiencing elder abuse, there are resources available. There's somewhere they can go to talk to someone, understand their options, and get help.

We want people to know that they'll be believed and treated with respect. We'll work with them to understand what they want to do and help them make decisions that keep themselves and their families safe.

Most importantly, people need to know we're here. Because if they don't know about us, we can't be a resource for them.

Access Legal Help. Support the Mission.

The Pace Women's Justice Center provides free legal services and resources for survivors of domestic violence, sexual assault, and elder abuse in Westchester and Putnam Counties.

Community members, legal professionals, and students can also support the Center's work through volunteer opportunities, pro bono service, and financial contributions.

Learn more about the Pace Women's Justice Center and how to get involved.

The Center for Undergraduate Research Experiences (CURE) supports faculty in advancing research and creative inquiry through collaboration with undergraduate students. Explore upcoming events, professional development webinars, and funding opportunities designed to help you engage student researchers, strengthen your projects, and expand undergraduate research at Pace.

Funding Opportunities Faculty Webinar Series Undergraduate Research Presentations

For more information, visit the CURE website or contact Norma Quiridumbay, CURE director of operations, at nquiridumbay@pace.edu, or Maria Iacullo-Bird, PhD, assistant provost for research, at miacullobird@pace.edu.

2026–2027 Funding Opportunities

2026–2027 Undergraduate Research Assistantship Program Funded by Federal Work Study (FWS)

Looking for support for your research or creative inquiry project? The Undergraduate Research Assistantship Program provides opportunities for faculty to recruit student research assistants through Federal Work Study (FWS). The program also gives students valuable hands-on research experience while working alongside faculty mentors.

Faculty are invited to submit proposals for research assistantship positions tied to specific projects. Proposals will receive priority consideration through October 15, 2026. After that date, applications will be accepted on a rolling basis until funding is exhausted.

Undergraduate Research Assistantship Position Application

Fall 2026 Faculty Undergraduate Research Webinar Series

Join CURE for the Fall 2026 Faculty Undergraduate Research Webinar Series, featuring Maria Iacullo-Bird, PhD, assistant provost for research. These virtual sessions will help faculty explore undergraduate research resources, develop grant-writing skills, incorporate research into their courses, and pursue external funding opportunities.

Webinars will be held during Tuesday common hours, from 3:25 p.m. to 4:25 p.m:

All webinars take place on Tuesdays from 3:25 p.m. to 4:25 p.m.:

• September 29, 2026: Council on Undergraduate Research (CUR) Opportunities, Resources, and Advocacy
• October 13, 2026: Getting Started in Grant Writing and Incorporating Undergraduate Research
• November 10, 2026: Developing Course-Based Undergraduate Research Experiences (CUREs)
• December 8, 2026: Applying for a National Science Foundation (NSF) Research Experience for Undergraduates (REU) Program Site or Supplement

Register for Webinars

Fall 2026 Undergraduate Research Presentation Series

Join CURE for the Fall 2026 Undergraduate Research Presentation Series, a series of virtual events showcasing the work of Pace University student scholars. Held during 12:10 p.m.–1:10 p.m. common hours from September 23 through November 9, the presentations highlight research and creative projects supported by University and external undergraduate research programs. Faculty are encouraged to attend, celebrate student achievements, and support the next generation of scholars.

• Wednesday, September 23, 2026: Computer Science
• Monday, September 28, 2026: Creative Arts
• Wednesday, September 30, 2026: Economics
• Monday, October 5, 2026, noon–1:00 p.m.: Humanities
• Monday, October 12, 2026: Psychology and Biology
• Friday, October 16, 2026: Social Justice and Environmental Studies
• Monday, October 19, 2026: Mathematics and Statistics
• Wednesday, October 21, 2026: Social Sciences
• Monday, October 26, 2026: Creative Arts
• Monday, November 2, 2026: Biology and Chemistry
• Monday, November 9, 2026: Humanities

Register for Presentation Series

For More Information

For more information, visit the CURE website or contact Norma Quiridumbay, CURE director of operations, at nquiridumbay@pace.edu, or Maria Iacullo-Bird, PhD, assistant provost for research, at miacullobird@pace.edu.

Looking to get involved in undergraduate research at Pace? The Center for Undergraduate Research Experiences (CURE) connects students with opportunities to collaborate with faculty, develop research skills, and showcase their work. Explore upcoming events, webinars, funding opportunities, and resources to take your research experience to the next level.

Presentation Series Webinar Series Other Opportunities

For more information, visit the CURE website or contact Norma Quiridumbay, CURE director of operations, at nquiridumbay@pace.edu, or Maria Iacullo-Bird, PhD, assistant provost for research, at miacullobird@pace.edu.

Fall 2026 Undergraduate Research Presentation Series

Join CURE for the Fall 2026 Undergraduate Research Presentation Series, a series of virtual events showcasing the work of Pace University student scholars. Held during 12:10 p.m.–1:10 p.m. common hours from September 23 through November 9, the presentations will highlight research and creative projects supported by University and external undergraduate research programs. Come discover the exciting work our students are doing and support the next generation of scholars!

• Wednesday, September 23, 2026: Computer Science
• Monday, September 28, 2026: Creative Arts
• Wednesday, September 30, 2026: Economics
• Monday, October 5, 2026, noon–1:00 p.m.: Humanities
• Monday, October 12, 2026: Psychology and Biology
• Friday, October 16, 2026: Social Justice and Environmental Studies
• Monday, October 19, 2026: Mathematics and Statistics
• Wednesday, October 21, 2026: Social Sciences
• Monday, October 26, 2026: Creative Arts
• Monday, November 2, 2026: Biology and Chemistry
• Monday, November 9, 2026: Humanities

Register for Presentation Series

Fall 2026 Student Undergraduate Research Webinar Series

Join CURE for the Fall 2026 Student Undergraduate Research Webinar Series, featuring Maria Iacullo-Bird, PhD, assistant provost for research. These virtual sessions will help students explore undergraduate research opportunities, learn about conference funding, develop essential research skills, and discover ways to get involved.

Webinars will be offered during Thursday common hours (3:25 p.m.–4:25 p.m.) and Friday common hours (12:10 p.m.–1:10 p.m.).

Student Conference Travel Funds and External Conferences, Including the National Conference on Undergraduate Research (NCUR)

• Thursday, September 24, 2026, 3:25 p.m.–4:25 p.m.
• Friday, September 25, 2026, 12:10 p.m.–1:10 p.m.

Peer Undergraduate Research Leaders (PURLs) and Getting Started in Research

• Thursday, October 15, 2026, 3:25 p.m.–4:25 p.m.
• Friday, October 16, 2026, 12:10 p.m.–1:10 p.m.

Social Justice, the Responsible Conduct of Research, and CITI Training

• Thursday, November 5, 2026, 3:25 p.m.–4:25 p.m.
• Friday, November 6, 2026, 12:10 p.m.–1:10 p.m.

Applying for a Summer 2026 National Science Foundation (NSF) Research Experience for Undergraduates (REU)

• Thursday, December 10, 2026, 3:25 p.m.–4:25 p.m.
• Friday, December 11, 2026, 12:10 p.m.–1:10 p.m.

Register for Thursday Webinars Register for Friday Webinars

After registering, you will receive a confirmation email with instructions for joining the webinar.

Other Opportunities

2026–2027 Student Academic Conference Travel Fund for Undergraduate Presentations

Pace University students presenting scholarly research or artistic projects at external academic conferences may be eligible for travel funding. To qualify, students must be presenters at the conference and be listed in the conference program. Applications are accepted on a rolling basis, subject to funding availability.

Apply for the Student Academic Conference Travel Fund

Council on Undergraduate Research (CUR) Membership

Pace University offers faculty, students, and staff access to the Council on Undergraduate Research (CUR), a leading organization dedicated to advancing undergraduate research. Through the University's Enhanced Institutional Membership, members of the Pace community can access CUR resources and professional development opportunities.

Activate Your CUR Membership

CUR Governance Nominations and Elections

Interested in helping shape the future of undergraduate research? The CUR governance nominations and elections process is open, offering opportunities to get involved in the organization's leadership.

Learn More About CUR Nominations and Elections

Looking to build your resume, explore AI, or develop new career-ready skills? You can do it at no cost through Pace University.

Through the Google AI for Education Accelerator, the Pace Community has access to Google's professional certificates and AI training courses, designed to help them develop in-demand skills and prepare for the workforce.

Explore courses in cybersecurity, data analytics, digital marketing, project management, IT support, and more. Students can also take advantage of Google's AI training programs to learn how to use emerging technologies in their studies and future careers.

The best part? Courses are online, flexible, and available at no cost.

How to Get Started

Ready to build your skills? Getting started with the Pace University Google Learning Program is easy. Follow these steps to access your courses:

1. Visit the Pace University Google Learning Program on Coursera and select Log In.
2. Choose Single Sign-On (SSO). Select Log in with Pace University rather than creating a separate Coursera account.
3. Enter your Pace credentials. Sign in using your standard Pace username and password.
4. Complete your first-time login. If prompted to create a new password, choose one that is different from your Pace password.
5. Start learning! Once logged in, you'll be directed to the Coursera dashboard, where you can explore available courses and begin your training.

Need help? Visit the Pace University Google Learning Program Helpdesk guide for detailed instructions and screenshots, or contact acadtech@pace.edu for assistance.

As surveillance technologies become increasingly sophisticated, where do we draw the line between public safety and personal privacy? For Amy Freedman, PhD, professor and chair of Political Science, that question is a key aspect of her research on democracy and individual freedoms in Southeast Asia. In this Q+A, Freedman explores the intersection of technology, political power, and civil liberties, and shares how her research informs her teaching and inspires student collaboration.

Your research interests relate to trends toward and away from democracy, and particularly in Southeast Asia. How did you become interested in this topic?

In countries such as Indonesia, Malaysia and Singapore, individual rights and freedoms are not a given, and there is a lot of variation within Southeast Asia and over time in rights protections and individual liberties and freedoms. I became interested in these questions in graduate school and in my dissertation research on ethnic Chinese communities, who are an ethnic minority in Indonesia and Malaysia and have often suffered persecution. I was interested in how they were able to organize and assert their interests, and this led to other related questions about democracy, rights, and freedoms.

How do surveillance and freedom in particular intersect with your research on Southeast Asia?

Autocratic countries, like many in Southeast Asia (Vietnam, Thailand, and Cambodia, for example), and semi-democratic countries such as Malaysia, Indonesia, and the Philippines, use surveillance as a way of perpetuating state control. While not as rigidly controlled or censored as China, almost all countries in Southeast Asia have limitations on things like a free press, free assembly, and what you can say or post on the internet. Laws purporting to address misinformation or ‘fake news’ often are used against critics of leaders and opposition forces; and, these laws often go hand in hand with state surveillance of all citizens’ online and in-real-life activities.

While surveillance varies around Southeast Asia; several Southeast Asian countries have heavily invested in AI-driven public tracking, frequently importing low-cost facial recognition and smart-city software from Chinese tech giants, as well as developing fairly extensive surveillance technologies themselves.

Were any of your research findings surprising or challenged assumptions?

Surveillance isn’t that surprising in autocratic states, but what has been interesting is how countries which used to be very suspicious of China (in terms of their foreign policy), have shifted their position and become much closer to China, welcoming investment and deals for infrastructure and technology. This includes countries like Indonesia, Malaysia, Singapore.

It’s both an election year in the U.S. and a time with much debate surrounding Flock cameras and data centers. As a political scientist, what are your thoughts on the intersection of these?

I think the focus of this election cycle on Flock cameras and data centers is really useful as a way for people to become better informed about how technology and surveillance are already present in our lives. But I am frankly not hopeful that either political party is going to do much to change the status quo. Democrats, if and when they regain control in Washington, might put some safeguards in place to better protect rights of privacy and curtail some of the worst abuses of state power, but it is really local governments and police forces who are buying and using facial recognition equipment and allowing construction of data centers.

How has your research informed your work with students and teaching?

As part of the Provost Office’s Summer 2026 Dyson College Student-Faculty Undergraduate Research Initiative, I collaborated with Idris Johnson ’27, a Political Science major, on the topic of surveillance and individual freedom, and he is much more knowledgeable than I am on the implications for democracy here in the US.

The courses I teach that relate to this topic are Modern Chinese Politics, Politics of Southeast Asia, and Freedom in the World. Certainly, in the classroom, we discuss individual rights and freedoms, both at a theoretical level and in application to the real world. Philosopher and social theorist Jürgen Habermas’s work is interesting in how he thinks about the importance of community and technology, and I often assign journalist and historian Anne Applebaum’s work on growing authoritarianism around the world. In addition, my students examine Freedom House reports on levels of freedom globally. (for many years, I wrote the Freedom in the World reports for Malaysia and Singapore).

Curious about the forces shaping freedom and democracy around the world? Explore how a degree in Political Science from Pace University’s Dyson College of Arts and Sciences can help you understand global challenges, question the status quo, and turn your curiosity into meaningful research and real-world impact.

When Christina Tram ’27 was considering a career, her experience with illness during high school influenced her thinking. In and out of hospitals as a teenager, Tram said she rarely felt motivated to heal, and often identified more with her illness than her potential.

Then, one day, a nurse changed her outlook on life.

“It was the first time in a long time that someone treated me like a person and not just a patient,” said Tram, a Pace University nursing student. “It was through her kindness that I realized how much of an impact a nurse can have, and this helped me see a professional future in something meaningful.”

Today, Tram is a nursing student scientist at Memorial Sloan Kettering Cancer Center and the president of the Pace Nursing Student Association. She’s also among the more than 1,000 nursing students currently pursuing degrees at Pace’s Lienhard School of Nursing (LSN) within the College of Health Professions, which marks its 60th anniversary this year.

“I’m so grateful that the contributions of my professors and clinical instructors and the knowledge I gained in Lienhard prepared me to excel in my internships,” Tram said.

From pioneering nurse-led healthcare to advanced clinical simulation, Lienhard has for decades been at the vanguard of training the next generation of nurses. More than 12,800 alumni have earned their nursing degrees at Lienhard.

“I’m so grateful that the contributions of my professors and clinical instructors and the knowledge I gained in Lienhard prepared me to excel in my internships,” Tram said.

In 1966, when Pace University welcomed nursing students to the Westchester Campus, it marked a turning point for healthcare education in the region, opening new pathways into a profession that would become increasingly central to the region's healthcare system.

Along the way, Pace's school of nursing has grown into a national powerhouse for nursing education, research, and clinical innovation, preparing nurses to serve patients, lead healthcare teams, and respond to changing community needs.

The need for nurses has never been greater. While many industries are contracting amid a wave of technical disruption, health care remains an economic brightspot. A recent US Bureau of Labor Statistics jobs report found that healthcare employment is surging, and projections for future growth in healthcare occupations remain robust.

Brian Goldstein, PhD, dean of the College of Health Professions, said the goal at Pace is to ensure that graduates are ready to serve from day one.

“Through education by exemplary faculty who are scholar-practitioners in their areas of expertise and advanced simulation experiences in our accredited Center for Excellence in Healthcare Simulation, we’re preparing students to align with workforce demands,” said Goldstein. “With strong teaching, scholarship, and clinical and practice partnerships, our model of education directly benefits students, faculty, and the communities we serve.”

A Legacy of Firsts

Innovation has been part of Lienhard's identity from the beginning.

In 1977, Pace established what became University Health Care, the first nurse-managed academic health center in the United States. Initially supported by the Robert Wood Johnson Foundation, the initiative demonstrated how nurses could lead in healthcare delivery while expanding access to essential services, which continue to be offered at the Westchester campus.

Throughout the decades, the school continued breaking new ground. It established a nursing research center in 1982 and expanded advanced nursing education to include a nurse practitioner master's programs, a Doctor of Nursing Practice, and then a Doctor of Philosophy in Nursing.

"We are poised to lead, honoring our legacy while embracing innovation, advancing nursing education, and preparing the next generation of nurses to shape the future of healthcare.”

The school's evolution also helped lay the foundation for Pace's College of Health Professions, established in 2010.

“As we celebrate 60 years of the Lienhard School of Nursing, we recognize that we stand on the shoulders of generations of dedicated nursing educators, visionary leaders, and colleagues whose commitment and passion have built the exceptional nursing program we are today,” said Esma Paljevic, EdD, RN, CPNP, FNYAM, senior associate dean and chief nursing officer at the College of Health Professions.

“Our rich history is not only a source of pride but also the foundation that informs and inspires our future,” said Paljevic. “As healthcare continues to transform through technological innovation, artificial intelligence, and new models of care, we are poised to lead, honoring our legacy while embracing innovation, advancing nursing education, and preparing the next generation of nurses to shape the future of healthcare.”

Preparing Nurses for What's Next

Today, Lienhard combines industry experience with contemporary approaches to learning.

Through partnerships with leading healthcare institutions, students gain clinical experience in settings such as NewYork-Presbyterian, Mount Sinai, Northwell Health, and Westchester Medical Center. The school's Center for Excellence in Healthcare Simulation provides opportunities to practice clinical decision-making, develop technical skills, and navigate complex patient-care scenarios in a controlled environment.

From undergraduate nursing education to master's and doctoral programs, Lienhard prepares nurses for roles in direct patient care, advanced practice, research, and healthcare leadership.

“I know the feeling of looking at other students and viewing them as extraordinarily talented or intelligent—beyond any perceived capacity I would ever achieve—and thinking that there was no way I could ever be so capable,” said Tram. “And yet, through trial and error, and not being afraid to say yes to opportunities (or being afraid and still saying yes), I am the student I once looked up to.”

On Sunday, October 18, alumni, faculty, clinical partners, and friends will gather at Pace's Westchester Campus for the Lienhard School of Nursing's 60th Anniversary Celebration. Register to attend.