The concentration was developed under the leadership of A. Russell Chandler III Chair and Professor Guanghui (George) Lan, who served as the named lead on the proposal to the Institute Undergraduate Curriculum Committee, working with a group of ISyE faculty across the AI, optimization, and statistics areas.
The curriculum draws on a portfolio of seven undergraduate courses taught by ISyE faculty, spanning the mathematical and algorithmic foundations of modern AI. Among them is Foundations of AI for Decision Systems, developed and taught by Assistant Professor Johannes Milz, which opens the black box of large language models. Students learn what is actually happening inside the AI systems they use every day from tokenization and attention mechanisms to the training processes that shape model behavior. They will graduate able to explain why outputs vary, why models hallucinate, and when an AI response should be trusted. As Milz has observed, students who enter thinking of AI outputs as either right or wrong leave the course recognizing that hallucination is not a random error but a predictable consequence of how these systems work, a more durable form of AI literacy than any tool-based introduction could provide.
Foundations of Modern Data Science, taught by Gerald D. McInvale Early Career Professor and Assistant Professor Ashwin Pananjady, takes a "looking under the hood" approach to data science — developing the probabilistic modeling, statistical inference, and optimization foundations that make modern data methods coherent. Students engage with everything from generative modeling and Bayesian inference to A/B testing and causal inference, learning to evaluate methods critically rather than apply them as black boxes.
The concentration also draws on Foundations and Applications of Machine Learning (ISYE 4600), developed by Coca-Cola Foundation Chair and Professor Yao Xie and now taught by her and several other faculty. The course introduces senior undergraduates to the core methods of modern machine learning – supervised and unsupervised learning, classification, regression, neural networks, feature selection, and ensemble methods – with an emphasis on mathematical foundations, algorithmic understanding, and practical implementation.
Two additional courses launch alongside the concentration this fall: Responsible AI, taught by Assistant Professor Juba Ziani, which grounds questions of fairness, accountability, and human-aware decision-making in the mathematical tools of machine learning and optimization; and Optimization Foundations for Machine Learning and AI, taught by Coca-Cola Foundation Chair and Professor Katya Scheinberg.
Together with existing courses on modern data science, machine learning, reinforcement learning, online learning, and advanced stochastic systems, the concentration prepares students to understand AI systems from the inside, evaluate their outputs critically, and deploy them responsibly in the complex operational settings where ISyE graduates work: supply chain, healthcare, manufacturing, finance services, and logistics.
The concentration's launch coincides with two new program-wide requirements for all BSIE students taking effect Fall 2026: a Systems Design requirement, ensuring every graduate has experience designing solutions at the systems level rather than optimizing components in isolation; and a Human Factors overlay, reflecting the growing centrality of human-AI interaction across every domain where ISyE graduates contribute.
"The methods that power modern AI from optimization, stochastic modeling, statistical inference to sequential decision-making are the methods ISyE has taught for decades," said Dima Nazzal, Associate Chair for Academic Administration. "What is new is the intentionality with which we are making that connection explicit, and the depth of preparation we are offering students who want to lead in AI-driven industries."
The concentration was developed under the leadership of Professor George Lan, working with a group of ISyE faculty including Ashwin Pananjady, Yao Xie, Katya Scheinberg, Johannes Milz, Joel Sokol, and Juba Ziani, whose collective expertise in optimization, machine learning, and statistical modeling shaped the curriculum.
]]>As ISyE’s A. Russell Chandler III Professor, Huo’s theoretical research focuses on explaining why modern deep-learning methods preform so well. He also uses statistics, machine learning, and data science to better to better understand the reliability and fairness of learning systems.
Huo has authored more than 15 refereed journal articles since 2023 and currently has 10 papers under review. His career includes 71 journal articles, 41 conference papers, 10 book chapters, and an edited volume.
“What I find most rewarding is that rigorous theory and useful tools are not in tension – the mathematical questions that fascinate me most often turn out to be the ones that help others make sense of their data,” Huo said. “I’m deeply honored to receive this award and especially grateful to my students and collaborators, who have been at the heart of this work from the very beginning.”
Huo’s first algorithm for distance covariance remains a standard tool for testing statistical dependence and is reproduced in widely used statistical software. He co-directs the Georgia Clinical and Translational Science Alliance's Biostatistics, Epidemiology and Research Design program, which is supported by the National Institutes of Health. He also serves as a co-principal investigator on the $20 million NSF AI Institute for Agent-based Cyber Threat Intelligence and Operation.
Huo said he is proud of the researchers he has trained. In the most recent recruiting cycle, his doctoral graduates earned tenure-track faculty offers from Georgetown University and the University of Florida. Earlier advisees hold faculty positions at the City University of Hong Kong, Korea Advanced Institute of Science and Technology, and Seoul National University of Science and Technology, while other former students are researchers at companies that include Apple, Citadel, and JP Morgan.
His recently published work includes two 2024 papers in the Journal of Machine Learning Research (JMLR). He and his students established learning guarantees for deep neutral networks, including minimax-optimal convergence rates of neural-network classifiers was accepted in 2026 by IEEE Transactions on Information Theory, and his work on the universal consistency of wide and deep networks appeared at the International Conference on Machine Learning.
Huo’s work on the fairness of learning systems is reflected in a 2025 Journal of the American Statistical Association paper that characterizes the asymptotic behavior of the adversarial-training estimator, complementing his JMLR work on distributionally robust estimation. At the Conference on National Information Processing Systems (NeurIPS) 2025, Huo and collaborators introduced a kernel-based quantification of the accuracy fairness trade-off in representation learning, along with a new diffusion method for imbalanced text-to-image generation, while a 2026 International Conference on Learning Representations paper advanced policy optimization for large-language-model reasoning.
]]>The award recognizes division members who have made sustained and exceptional contributions to research and applications in energy systems, demonstrated through distinguished scholarly achievement. Gebraeel was honored for more than two decades of research advancing how energy systems are monitored, maintained, operated, and protected. His work has also been shaped by collaborations with major industrial partners and organizations, including Southern Company, GE, Siemens, Pratt & Whitney, and the Electric Power Research Institute (EPRI).
Much of Gebraeel’s work builds on his early contributions to Prognostics and Health Management. PHM is a field that uses sensor data and predictive analytics to assess asset condition, forecast degradation, and support operational decisions. In the energy sector, this research has developed along three connected directions: monitoring individual energy assets, optimizing power network operations and maintenance, and improving the security of increasingly digital energy systems.
Gebraeel’s research group developed predictive analytics methods for monitoring gas turbines, drawing in part on experimental capabilities at Georgia Tech’s Ben T. Zinn Combustion Laboratory, directed by Professor Tim Lieuwen and the START labs at Penn State University. His group also demonstrated that smart meters already deployed in homes can be used to monitor the health of transformers, at negligible additional cost, in collaboration with Georgia Tech’s Center for Distributed Energy, led by Professor Deepak Divan.
The second direction extends from individual assets to the broader power network. Gebraeel and his team developed optimization models that utilize real-time information about generator health to guide both operational and maintenance decisions. These models determine how generators should be dispatched and maintained while satisfying electricity demand and respecting the topology and transmission limits of the power network. His group also worked on scalable solutions that help translate these models from academic studies to large, practical power networks.
The third direction addresses power system cybersecurity, drawing on expert collaboration with Professor A.P. “Sakis” Meliopoulos of Georgia Tech’s School of Electrical and Computer Engineering and his Power Systems Control and Automation Laboratory. Gebraeel’s group developed methods to detect stealthy cyberattacks that falsify sensor readings, distinguish those attacks from ordinary equipment faults, and identify compromised regions of the network using only normal operating data. His group also developed a blockchain-based framework that allows utilities to jointly detect attacks across interconnected systems while preserving the privacy of each company’s operational data.
“This award reflects a journey shared with remarkable students, collaborators, and industry partners,” said Gebraeel. “I owe a special debt to Georgia Tech’s Strategic Energy Institute and to its director at the time, Tim Lieuwen, whose vision and support helped draw my research into data science and energy systems and opened the door to the partnerships that shaped this work.”
Gebraeel, who served as associate director of the Strategic Energy Institute from 2016 to 2021, continues to advance research in decentralized industrial intelligence, including federated learning methods that allow energy companies to develop privacy-preserving diagnostic models, as well as uncertainty-aware optimization models that remain robust under noisy data and feature uncertainty.
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A few months ago, I caught up with a former colleague at an industry event. He is a senior leader at a large global company with a well-regarded supply chain organization. His team had been through a lot since we last talked. Port closures. Tariff escalations. Freight volatility. Inventory repositioning. The kinds of disruptions that used to arrive once in a cycle had become an avalanche, and this was before Hormuz!
His people had responded well. They had adapted. But now he was thinking about something harder to solve: what it would take to keep them in the game longer term. The experience his team had gained came at a cost and he worried some would look for roles that were not on the “firing line”. He also wondered whether he could still attract the best and brightest in the next generation of talent who would be looking at this field and asking whether the complexity was worth it.
That conversation has stayed with me. Supply chain and logistics has always been a field of cycles — demanding, but navigable. What has changed is that the field has moved from cyclical difficulty to something more structural: a persistent state of volatility where the curveballs do not stop coming.
That conversation was in the back of my mind as I developed a recent talk on logistics trends from 2026 to 2030 for GT SCL Industry Partner Manhattan’s annual Momentum conference. The brief was to look ahead and be a bit provocative. What follows builds on that talk, but with a broader point in view: if the curveballs keep coming, leaders need a clearer sense of which shifts matter most and what they should do about them.
Logistics has entered a structurally more volatile era, not a groundbreaking insight given the last four years. Several things changed at roughly the same time, and they have not changed back:
Several shifts hit the industry at once, and none of them have meaningfully reversed. Geopolitics is now a supply chain design variable, not something to catch up on in a podcast. Strategic decoupling between China and the United States, instability in the Middle East, and the long shadow of the Russia-Ukraine conflict have pushed energy, sourcing, and network design into the same conversation. What once sat in the news feed now needs to be in the nominal scenario during business planning.
At the same time, customer expectations have permanently shifted. Amazon reset the standard for visibility, precision, and speed, and that standard now applies even more as Amazon is emerging as an open source 3PL. Labor and energy costs have also changed the economics of physical logistics in ways that will not self-correct. Demographic pressure, wage inflation, and energy volatility have altered the baseline cost structure calling into question existing network locations.
Meanwhile, AI and automation have moved out of the experimental category and into the realm of near-term value creation. The tools are real, and organizations that understand where to apply them are making materially better decisions than those that do not. That matters because networks now have to optimize for two things at once: cost and recovery. Efficiency still matters, but a network that performs well in steady state and fails under disruption no longer meets the standard.
There is also a macro pattern worth calling out: the industry is in a longer-duration rebalancing cycle than many executives expected. We examined the Hormuz disruption and its downstream effects in a recent SCL Spotlight piece. The short version is that energy pass-through effects, freight volatility, and extended planning uncertainty will impact costs and capacity well into 2027. Executives planning around a near-term return to normal are making a strategic error.
The next decade will reward adaptable logistics networks more than simply optimized ones.
Amazon's logistics operation is not just something to amaze us as packages arrive at our doorstep consistently with compressed lead times. It is a capability demonstration that has redefined what customers consider normal — same-day expectations, ETA precision, real-time visibility, low-friction returns. The important implication is not that every organization needs to replicate Amazon's infrastructure. It is that Amazon-shaped expectations are now the standard against which every supply chain is measured, whether or not Amazon is a direct competitor. Amazon’s recent announcement that it is making its capabilities available to all only raises the bar.
Organizations that understand this have shifted their strategic question from "how do we improve our operations" to "where will we compete, where will we leverage others' capabilities, and where will we differentiate on something Amazon cannot replicate." The benchmark is no longer functional excellence alone. It is well oiled end-to-end execution.
There is a version of the automation conversation that focuses on “wow” demos — autonomous vehicles, lights-out warehouses, robotics showcases. That version makes for compelling conference content. It is also not where most of the real value is being created today.
The highest-value wins tend to be quieter: fewer errors, fewer touches, fewer injuries, fewer claims. Computer vision that catches a loading error before a truck leaves the dock. Sensor verification that eliminates a reconciliation step. An alert from a Machine Learning model that prevents a cascading service failure. These are error-proofing stories, and they are compelling because the ROI is measurable in terms operations leaders understand.
The reason automation is scaling in these areas is not novelty — it is because the math finally works, driven by labor scarcity, safety pressure, and the compounding cost of variability. My own view informed by industry contacts and academic researchers is that computer vision may become one of the most quietly transformative technologies of this decade, not because it is the most advanced, but because it applies to so many high-variability, human-intensive touchpoints across logistics operations.
That said, a high percentage of large-scale automation efforts still fail. Many of the reasons are well known and tackling this issue is critical for those who do not yet have a model for success.
The next margin pool may come more from consistency and reliability than from flashy robotics demonstrations.
This theme generated significant discussion at the Manhattan Associates' Momentum conference this spring — enough that we are dedicating our July SCL webinar to it directly. If your organization is navigating automation decisions, the session is worth your time.
Autonomous vehicle technology has generated significant hype and its share of missed timelines. A more realistic view is emerging. Autonomy scales first where variability is lowest, economics are clearest, and environments are most constrained — yard operations, middle-mile freight on repetitive lanes, internal shuttles, port drayage, and warehouse orchestration. This amounts to millions of miles and load counts that are increasing daily.
The organizations watching this most carefully are not asking when full autonomy will arrive. They are asking which specific lanes and operations have the cost structure where autonomy pays out today. One dynamic worth watching: the scaling of urban robotaxi operations is building safety data, insurance frameworks, and regulatory precedent that may indirectly accelerate confidence in middle-mile freight and warehouse applications.
The shift that matters is not from no autonomy to full autonomy. It is from technology demonstrations to lane economics — and that is the transition that creates real operating decisions for logistics leaders.
The common reality in most logistics organizations today includes AI copilots, workflow assistance tools, exception management support, improved ETA prediction, and document automation. These are useful. They are also early.
What is still uncommon: autonomous execution, fully integrated AI decisioning across functions, self-optimizing networks, and end-to-end agentic orchestration. Those capabilities exist in pilots and in forward-leaning early adopters. They are not yet standard operating practice in most organizations.
The framing that I keep coming back to is this: start with a broken logistics workflow, then apply the lightest AI capable of clearing a hard ROI threshold. I got a text from a mentee today that showed a picture of a Microsoft Co-pilot Studio agent he built that automates a daily inventory check on a critical SKU. Organizations that start by selecting the most impressive tool and then look for a process to apply it to are making the investment in the wrong order.
There is another structural shift worth highlighting. The industry has moved out of data scarcity and is living in decision overload. The challenge is not access to information — it is building the discipline to convert that information into insight and informed decisions at the right time to impact action.
Logistics first, AI second. Start with the broken workflow. Then apply the lightest tool that clears a hard ROI threshold.
Back to the conversation I opened with. The concern was not that my friend’s team lacked technical skills — it was sustaining engagement and attracting talent to a field that had become genuinely exhausting. That challenge is real, and it is connected to something missed in the automation conversation.
As AI automates more routine work — reporting, documentation, tracking, reconciliation — the work that remains becomes more demanding in different ways. The value shifts toward judgment: escalation management, cross-functional orchestration, interpreting second-order consequences, maintaining trust when the data is ambiguous. The organizations that will attract and retain the strongest professionals are not necessarily those with the most advanced tools. They are the ones that create conditions where smart people make consequential decisions and continue to grow.
As AI capabilities become more democratized across the industry, the differentiating capabilities will increasingly be leadership, communication, collaboration, and the kind of critical thinking that no tool can fully replicate.
These are the shifts I believe deserve more attention than they are getting in most leadership conversations:
I want to close by coming back to my colleague's concern — and to the question he was really asking: how do we help our people process all of this and remain effective?
Here is my honest answer. The field is not going to slow down. What staying current requires is not reading every article or attending every conference. It requires developing a point of view on the shifts that matter most for your specific context, and then actively deciding how you will act and adjust. Passive awareness is not enough. The question is not whether you know what is changing. It is what you have decided to do about it.
For organizations, that means investing in conditions that allow talented people to keep learning. For individuals, it means resisting the temptation to treat busyness as a substitute for development. The professionals who remain most valuable will be those who continue to understand what is changing and develop the judgment to translate that understanding into better decisions.
You cannot sit still. The question is not whether you know what is changing. It is what you have decided to do about it.
I want to end where I began — with empathy for everyone in this field who is carrying a lot right now. Fatigue is real. The complexity is real. The ongoing intensity is real.
And so is the opportunity.
Logistics is no longer just moving product. It is becoming a resilience system, a customer experience system, a technology system, an energy system, and a real-time decision system simultaneously. The professionals who learn to navigate that complexity — who develop both technical fluency and human judgment — will be among the most valuable people in any organization.
The future arrives not as one dramatic breakthrough, but as a sequence of operational readthroughs: decisions made well, workflows redesigned thoughtfully, capabilities built deliberately. That is hard work. It is also genuinely exciting work. And I believe the best of it is still ahead.
Why Do So Many Automation Projects Fail?
Automation in logistics is accelerating — but so is the gap between what is promised and what is delivered. Systems get sized on optimistic assumptions. Hidden dependencies become single points of failure. Technology that shines in the demo struggles under real operating conditions.
Leaders from Georgia Tech's Supply Chain and Logistics Institute join industry practitioners to dig into the root causes of automation underperformance — and the design, evaluation, and implementation practices that build more resilient, effective operations.
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“The IISE Sustainable Development Teaching Award confirms sustainable development as a strength of industrial engineering,” said Professor Thomas, who also has a joint appointment in Georgia Tech’s Jimmy and Rosalynn Carter School of Public Policy. “Receiving this award brings ISyE’s sustainable development offerings to the attention of the wider industrial engineering community.”
The award was developed by IISE’s Sustainable Development Division to recognize the contribution of an individual advancing the knowledge and practice of sustainability in the field of industrial engineering.
Thomas has an extensive history of high-quality teaching and leadership in developing interdisciplinary, sustainability focused courses at both the undergraduate and graduate level. The quality and effectiveness of her sustainability-focused teaching efforts have consistently yielded high evaluations in student feedback.
The IISE award committee also highlighted Thomas’ teaching impact beyond courses and classrooms, through global learning experiences in Rwanda focused on sustainable development, and her scholarly contributions in sustainability education.
Thomas has created three sustainability-focused courses at Georgia Tech, one at the undergraduate level and two at the graduate level, and a study abroad program.
Thomas’ undergraduate course, Energy, Efficiency and Sustainability (ISYE 4501), is offered annually. This course focuses on technology cost analysis and environmental modelling. Students learn how to assess efficiency and impacts of industrial systems such as power generation, transportation, manufacturing, and building operation.
At the graduate level, Thomas collaborated with Georgia Tech Jimmy and Rosalynn Carter School of Public Policy Professor Marilyn Brown to develop an interdisciplinary course, Energy Technology and Policy (ISYE/PUBP 6701), that draws graduate students from ISyE, Public Policy, College of Engineering, and from throughout Georgia Tech, with both an in-person section and a distance-learning section. The distance-learning section draws students from online master’s programs in Industrial Engineering, Mechanical Engineering and more. To support this course, Brown and Thomas co-wrote a textbook, “Energy Technology and Policy Innovation.”
“I’ve had the privilege of teaching Energy Technology and Policy with Valerie once a year for 20 years. We used to do major marketing with flyers and videos, but soon the course caught on, moved to larger classrooms, and doubled in numbers with an on-line section. Our course focuses on core scientific principles and practical tools to support fact-based energy system decision-making. We never could find a textbook with all the material we wanted students to master. So we created a book from our lecture notes - all 610 pages and 44 chapters - that was just published by Springer-Nature,” said Professor Brown, Regents' and Brook Byers Professor of Sustainable Systems. “Teaching beside Valerie for twenty years has been both a joy and an education in itself.”
Also at the graduate level, Thomas created a new course on life cycle assessment (LCA); this course draws graduate students from across Georgia Tech. Students gain hands-on experience with a comprehensive set of LCA methods and tools, including the life cycle process-based method, energy and greenhouse gas calculations, cost analysis, allocation and system expansion, economic input–output LCA, hybrid LCA, the life cycle process matrix method, consequential LCA, and life cycle impact assessment. The course culminates in a substantial, project-based life cycle assessment, providing students with conceptual understanding and practical expertise.
In addition to academic courses, Professor Thomas has developed a Rwanda Study Abroad program that powerfully complements classroom learning. In this program, students work on sustainable development projects during the spring semester at Georgia Tech and then travel to Rwanda during spring break.
]]>Grajczak currently serves as a Managing Director in Accenture's Supply Chain & Engineering practice. He is a leader in the firm's Private Equity practice, where he advises operating partners on developing and executing high-impact value creation plans across their investment portfolios.
"We are thrilled to welcome Conrad to our Industry Advisory Board," said SCL Managing Director Chris Gaffney. "His extensive background as a management consultant specializing in margin improvement and cost reduction for private equity-backed companies will be invaluable. We look forward to leveraging his insights to drive our strategic initiatives forward."
Throughout his career, Grajczak has partnered closely with Private Equity Operating Partners to deliver bottom-line value through strategic sourcing. He routinely leads pre-purchase and post-acquisition initiatives to establish a value creation roadmap and implement transformative cost programs across Consumer, Industrial, and Healthcare clients.
Prior to Accenture, Grajczak was part of Insight Sourcing for 11 years and promoted to Partner. Insight Sourcing was a boutique procurement consulting firm where he focused on Private Equity. Insight Sourcing was acquired by Accenture in 2024.
Before consulting, Grajczak started his career out at Merck & Co. in Vaccine Manufacturing Science and Commercialization.
A proud Georgia Tech alumnus, Grajczak earned his B.S. in Chemical and Biomolecular Engineering with a minor in Business through the Technology & Management Program. He also holds an MBA from Emory University’s Goizueta Business School.
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