Dr. Philip Odonkor is an Assistant Professor at Stevens Institute of Know-how’s Charles V. Schaefer, Jr. College of Engineering and Science, main the Design Informatics Lab. He earned his PhD and MS in Mechanical Engineering from the College at Buffalo, SUNY. Dr. Odonkor’s analysis encompasses city informatics, design optimization, cyber-physical programs, and sustainability. He obtained the distinguished NSF CAREER award in 2024 for advancing vitality fairness in city areas. A co-founder of Grid Discovery, he’s an energetic member of IEEE, ASME, and ACM. His work has been featured in Time Journal and on the TEDx stage.
Odonkor mentioned the intersection of informatics, sensible cities, and sustainability. He emphasised that cities are complicated socio-technical programs with inherent inefficiencies, notably in vitality use as a result of traditionally piecemeal infrastructure growth. Odonkor detailed how information science and machine studying, particularly reinforcement studying, can optimize vitality consumption and enhance city residing. He highlighted challenges like balancing privateness with information assortment and integrating cultural values into algorithmic fashions. Odonkor famous various international privateness attitudes and projected fast electrical and autonomous transportation developments by 2025. He careworn the significance of long-term, adaptive city planning.
Scott Douglas Jacobsen: At this time, we’re right here with Dr. Philip Odonkor, and we’ll talk about informatics, sensible cities, and sustainability—a spread of matters he focuses on. Let’s start with the fundamentals. What’s the hyperlink between informatics and sustainability in cities?
Dr. Philip Odonkor: That’s an insightful query. Cities are inherently complicated environments the place individuals work together each day. Whereas we sometimes discover the seen points of cities—residents, buildings, and transportation programs—analyzing cities intently reveals them as intricate socio-technical programs.
This viewpoint is central to my work. Advanced programs typically include many inefficiencies. In most cities, you’ll be able to observe inefficiencies in useful resource administration, public companies, and vitality use. Cities have developed over many many years, typically including infrastructure piecemeal to fulfill rising calls for, main to numerous inefficiencies.
As cities increase, vitality programs are retrofitted to help the elevated load, which can lead to unsustainable practices and better emissions. That is the place informatics and information science—my focus areas—play an important position. We are able to higher perceive how cities operate and determine methods to enhance effectivity utilizing information. For example, I analyze how buildings use electrical energy and search strategies to optimize vitality use, making certain that every unit of electrical energy contributes extra successfully than it presently does. We are able to improve sustainability and different essential city metrics by addressing these inefficiencies.
Jacobsen: How do democratic programs influence long-term metropolis planning? Democratic societies typically function inside election cycles, limiting the flexibility to plan long-term tasks. The give attention to election phrases can prohibit the scope of planning, resulting in infrastructure tasks which may be segmented or delayed, leading to inefficiencies as they progress by way of completely different political agendas. How do you contemplate this when redesigning vitality programs and infrastructure?
Odonkor: I undertake a long-term perspective when in search of options in my work. Quite than focusing solely on short-term outcomes, I contemplate what we would like cities to appear to be in 20 or 30 years. With this imaginative and prescient as a goal, I work backward to find out the steps wanted to bridge the hole between present programs and the specified future state. This strategy helps information decision-making and prioritize enhancements. Though the method is complicated and gradual, short-term methods are built-in into this bigger imaginative and prescient to make incremental progress towards a sustainable and environment friendly city future.
There are two completely different scopes you’ll be able to have a look at this from. I strategy it from a long-term perspective, which helps easy out among the points that is perhaps obvious when it from a short-term view.
Jacobsen: We dwell in an period of huge information and programs that may course of huge quantities of knowledge. It could be helpful to know make sense of all of it. Moreover, sensible individuals construct algorithms that may analyze and interpret this information. How do you collect information a couple of society or a metropolis, and the way do you make it comprehensible so you should utilize it successfully?
Odonkor: These are nice questions. How do I collect the info? Wanting round your private home or metropolis, you’ll discover many units capturing varied metrics. For example, some cameras, akin to ring doorbells or surveillance cameras, file video. Throughout cities, now we have sensors that seize information like temperature and noise, offering insights into metropolis capabilities at any given time. This assortment of sensors is a part of the Web of Issues (IoT). IoT sensors are usually small, low-power units that seize real-time information.
In recent times, there was a major improve within the variety of these units, making information assortment a lot simpler than earlier than. Nevertheless, the principle problem lies in dealing with this information. Simply having information doesn’t routinely result in options. It typically requires extracting insights from the info or combining a number of datasets to derive invaluable info. That is the place my analysis is available in—understanding what forms of information to mix, when, and on what time scale to attract significant insights.
A method I strategy that is by way of machine studying and synthetic intelligence. I exploit machine studying extensively in my work, notably a way often known as reinforcement studying. Reinforcement studying permits us to program an algorithm to investigate information and perceive the decision-making processes that generated it. For instance, now we have information a couple of residence. In that case, the algorithm tries to find out what management actions led to sure patterns of electrical energy use.
Programming these algorithms goals to learn the way a house makes use of vitality. As soon as we obtain that, we are able to modify the algorithm to experiment with completely different actions and determine which modifications might enhance vitality consumption inside the residence.
That approach, I can begin automating capabilities inside your private home in order that it behaves as you’d management it, however extra effectively. I perceive what you favor and don’t, and I can modify issues so every thing seems regular. Nevertheless, behind the scenes, processes are occurring extra effectively.
Jacobsen: How do the niches inside cities issue into this? For instance, take Vancouver, which is close to the place I dwell. It has pockets of various subcultures, actions, and establishments, like universities scattered all through town. Do machine studying and AI algorithms naturally contemplate these variations a part of their course of?
Odonkor: No, not naturally. You may consider machine studying as an open canvas—it’s essential to inform it what to give attention to, prioritize, and its targets. Some machine-learning variations can try to determine issues out independently. Nonetheless, on this area, we information the algorithms all through the method.
It’s attention-grabbing that you just carry this up as a result of a part of my work entails tuning algorithms to contemplate points past vitality effectivity. For instance, we design algorithms that stability effectivity whereas contemplating vitality fairness points. Suppose we optimize vitality utilization in a single space. Can the algorithm assess the demographics and determine the place to prioritize vitality distribution to stability general utilization? We additionally overlay this strategy with issues for entry to renewable vitality sources.
You may focus these algorithms on completely different matters. One among our key targets is to get these algorithms to include vitality fairness points, not simply effectivity. If we solely give attention to effectivity, we create environments that is perhaps environment friendly however not essentially nice or livable. Individuals need to dwell in areas which are environment friendly, gratifying, and appropriate for his or her wants.
Jacobsen: Totally different cultures and subcultures worth sure points otherwise, together with the acceptability of inefficiency or the aesthetic and really feel of an area. That may differ by nation, state, and even county. When inefficiencies, do you contemplate them as constructive utilities associated to native values? It’s not about inefficiency being inherently unfavorable however understanding the path and context of these values. Is there a option to fine-tune machine studying to account for these variations?
Odonkor: That’s an intriguing query, and whereas we haven’t totally carried out this but, it’s a major level. Totally different cultures certainly have assorted views on what constitutes inefficiency. For instance, within the Western world, energy outages are seen as wholly unfavorable, and we attempt to reduce them. Nevertheless, in creating international locations, individuals might have tailored to energy outages as a part of each day life. Whereas they could be inconvenient, they aren’t seen as catastrophic in the identical approach that they’re right here.
As a substitute of telling the machine studying algorithm that minimizing energy outages ought to be the first goal, we might program it extra flexibly. Energy outages could also be tolerable below sure circumstances, or the algorithm can shift energy distribution to account for variables like weaker infrastructure.
You enable the machine studying algorithm to tolerate inefficiencies in the event that they end in a collective profit. These tweaks appear necessary, particularly contemplating points like vitality fairness. This flexibility throughout societies and cities may very well be advantageous if correctly carried out.
We give attention to sustaining optimum circumstances in most analysis. Nevertheless, as you talked about, there will be utility in so-called inefficiencies, and exploring how machine studying algorithms can leverage these for the larger good is one thing we’re investigating.
Jacobsen: What do individuals throughout completely different cultures sometimes worth in relation to their imaginative and prescient of a sensible metropolis? Clear air, clear water, inexperienced areas—what are the first and secondary issues?
Odonkor: That’s a wonderful query, and a part of the problem is that there must be a universally accepted definition of a sensible metropolis. A sensible metropolis can imply various things to completely different individuals, influenced by what they’ve heard or skilled. Actually, once I train a category on sensible cities, a lot of my college students begin out needing to be taught what the time period actually encompasses.
One recurring theme is the will for effectivity. Individuals envision sensible cities as environment friendly and livable locations. Livability normally means having clear air, dependable transportation, lowered homelessness, and different related components. Nevertheless, one vital concern that comes up repeatedly is privateness.
Balancing privateness and information assortment is a posh situation. As I discussed, my analysis depends closely on information captured by varied sensors, which is crucial for coaching machine studying algorithms. The extra information now we have, the higher the outcomes. Nevertheless, producing all that information requires compromising privateness. Individuals need the benefits of a sensible metropolis, however not at the price of their privateness. Discovering a center floor stays an open query we’re nonetheless making an attempt to unravel.
We’ve seen some sensible metropolis initiatives battle or fail as a result of they couldn’t adequately handle privateness considerations. A notable instance is Sidewalk Labs. I’m undecided when you’re aware of it—it was a Google-affiliated firm trying to construct a sensible metropolis mission in Canada. It confronted vital pushback as a result of considerations about privateness and information safety, and in the end, it didn’t transfer ahead as a result of it couldn’t provide ample ensures that folks’s privateness could be protected.
Jacobsen: When contemplating the digitization of every thing and the mixing of sensors in every single place—visible or in any other case—how do encryption, safety, and privateness play into these programs? If the sidewalk has a bodily sensor, how do you guarantee these are extremely encrypted to handle privateness considerations? In societies the place privateness isn’t a given proper, have some tasks infringed on that privateness? This turns right into a cybersecurity situation. How can we defend all this digital infrastructure? It might be an open query.
Odonkor: It’s certainly an open query. The truth is that we might by no means attain some extent the place these programs are 100% safe. We see this even with essentially the most trusted digital programs, akin to banking programs, which solely assure partial safety. Nevertheless, there have been vital developments in encrypting information. The options won’t be restricted to sensible cities alone; they may possible contain broader purposes, akin to defending banking or delicate well being information. These applied sciences will proceed to evolve and be utilized in varied sensible metropolis contexts.
The principle problem is that sensible cities are extremely complicated. They include a number of interconnected programs, and any time you may have such a distributed system with quite a few shifting components, one weak hyperlink is sufficient to create vital points. Advanced programs like this may inherently have weak factors, a significant problem for sensible cities. Nevertheless, there’s numerous analysis on this house. Whereas I don’t concentrate on cybersecurity, I’m assured that progress will lead us to a degree the place we’re “secure sufficient” inside sensible cities.
Jacobsen: Concerning privateness, in some international locations, how is that this approached?
Odonkor: Sure, that’s an necessary consideration. In international locations like China, for instance, applied sciences that monitor residents are already in place. Whereas monitoring can have protecting and safety advantages, it’s additionally true that these applied sciences have twin makes use of. As a result of governmental construction in such international locations, it’s simpler to put in and function these programs.
In distinction, in america, for instance, implementing widespread programs like facial recognition typically requires extra help. So, the worldwide panorama varies relying on societal constructions and attitudes towards privateness and governance. We’ll proceed to see these variations in how sensible metropolis applied sciences are deployed primarily based on societal norms and authorized frameworks.
Jacobsen: What’s the most possible growth in sensible cities by the tip of 2025?
Odonkor: One thrilling space is transportation, particularly the electrification of transport. We’re seeing a surge in micro-mobility options, akin to electrical bikes and scooters. On a bigger scale, we’re seeing the mixing of electrical buses and the gradual emergence of autonomous autos. An attention-grabbing problem is merging electrical autonomous transportation with conventional transportation and navigating the ensuing effectivity good points and challenges. This space is evolving shortly, and I’m excited to see how insurance policies adapt to those developments. Cities are caught off guard by this shift towards electrical micro-mobility, so watching how they reply is fascinating.
Jacobsen: Glorious. Philip, thanks a lot to your time and insights at the moment.
Odonkor: You’re welcome, Scott.
Jacobsen: Cool, thanks. It was good assembly you.
Odonkor: Thanks, Scott.
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Scott Douglas Jacobsen is the Founding father of In-Sight Publishing and Editor-in-Chief of In-Sight: Impartial Interview-Primarily based Journal (ISSN 2369–6885). He’s a Freelance, Impartial Journalist with the Canadian Affiliation of Journalists in Good Standing, a Member of PEN Canada, and a Author for The Good Males Undertaking. E mail: [email protected].
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