Renata Medeiros de Carvalho is an Assistant Professor at Eindhoven University of Technology (TU/e), affiliated with the Process Analytics and EAISI Health groups. She holds a PhD in Computer Science from Federal University of Pernambuco (Brazil), an MSc and BSc in Computer Engineering from University of Pernambuco, and has conducted postdoctoral research at UQAM (Canada). Her research focuses on adaptive and declarative business processes, with particular emphasis on healthcare and data privacy. Education: PhD in Computer Science, Federal University of Pernambuco (2015) MSc in Computer Engineering, University of Pernambuco BSc in Computer Engineering, University of Pernambuco Research Interests: Flexible business processes and Process Mining Declarative modeling (e.g., OCBC language) Healthcare process optimization GDPR compliance frameworks Key Projects: PATIENCE 2 : Patient-centric healthcare through nomadic sensing BPR4GDPR : GDPR compliance toolkit Awards: Xerox University Affairs Committee Grant NSERC Engage Grant Teaching & Leadership: Local coordinator for EIT Digital Data Science and Erasmus Mundus BDMA master programs Teaches courses like Advanced Process Mining and DBL Data Challenge
Mohammad T. Khasawneh is a SUNY Distinguished Professor and Director of the School of Systems Science and Industrial Engineering at Binghamton University. He leads the Watson Institute for Systems Excellence (WISE) and the Healthcare Systems Engineering Center. His roles include Director of the Manhattan Graduate Program in Health Systems. Education: BS and MS in Mechanical Engineering from Jordan University of Science and Technology (1998, 2000), PhD in Industrial Engineering from Clemson University (2003). Research focuses on healthcare systems engineering, operations management, and data science. His work optimizes healthcare systems for improved patient outcomes and cost efficiency. Key areas include predictive analytics, hospital resource utilization, and clinical performance improvement. He has generated over $15M in external funding and led projects with U.S. hospital systems. Notable achievements include developing the Executive Master of Science in Health Systems and an MS in Healthcare Systems Engineering. His research has produced 60+ journal articles and 120+ conference papers. Awards include SUNY Chancellor’s Awards for Teaching (2011) and Scholarship (2021), University Awards for Graduate Director (2015) and International Education (2016). He is an IISE Fellow and holds honorary visiting professorships at Hebei University of Technology (China) and Vellore Institute of Technology (India). Grants and funding: $2.5-3M annually via WISE, $39M in software/equipment grants. Lab/Initiatives: Healthcare Systems Engineering Center, WISE, and multiple hospital partnerships.
Markus Keller is a **Château Ste. Michelle Distinguished Professor** in the **Department of Horticulture and Landscape Architecture** at **Washington State University (WSU)**, affiliated with the **Prosser Irrigated Agriculture Research and Extension Center (IAREC)**. His research focuses on grapevine physiology, viticulture production systems, and environmental stress management. He holds a **Ph.D. (1995)** and **M.S. (1989)** in Agricultural Engineering from the **Federal Institute of Technology (ETH Zurich), Switzerland**. **Research Interests**: Dr. Keller’s work addresses crop physiology, irrigation strategies, cold hardiness, berry shrivel, and mechanized viticulture. His projects include developing deficit irrigation frameworks, understanding water movement in berries, and predicting frost damage risks using machine learning. He collaborates with industry partners like the USDA and Washington wine producers. **Key Contributions**: His studies on grapevine water relations, berry development disorders, and cold tolerance have advanced sustainable viticulture practices. He leads teams investigating precision agriculture tools for vineyards, including hyperspectral imaging and decision-support systems. **Grants & Funding**: Supported by grants from USDA, Washington wine tax, and juice processors. His work integrates applied research with grower education on frost protection and yield optimization. **Lab & Collaborations**: Directs research at WSU’s Viticulture and Enology program, collaborating with international teams on climate resilience and grapevine health. Mentors Ph.D. and M.S. students in experimental design and field studies.
Zhen Xie is an Assistant Professor in the Department of Computer Science at Binghamton University (SUNY), serving as Director of the Parallel Computing and Intelligent System (PCIS) Lab. He holds a PhD from the Chinese Academy of Sciences and a BA from Wuhan University of Technology. His research focuses on high-performance computing (HPC), machine learning, and their intersections, particularly optimizing performance for HPC and AI/DL applications across heterogeneous architectures. Research Highlights: Dr. Xie’s work emphasizes system-level performance optimization for ML and HPC, including GPU acceleration, memory optimization, and AI accelerator selection. His team has won the ACM Gordon Bell Special Prize (2022) for their GenSLMs project predicting SARS-CoV-2 evolution. Recent grants include a 2024 gift from OpenAI for AI testbed initiatives. Awards: ACM Gordon Bell Special Prize (2022), Impact Argonne Awards (2023) Lab: PCIS Lab explores middleware for parallel computing, targeting scientific simulations and big data analytics. Collaborations include Argonne National Lab and Lawrence Berkeley National Lab. Teaching: Teaches Distributed Systems (CS 457/557) and oversees independent studies. Previously trained researchers at Argonne’s ATPESC program. Grants & Collaborations: Subcontract with Lawrence Berkeley Lab (HEVI-LOAD), Argonne testbed expeditions, and OpenAI-funded projects. Active in DOE labs like Summit and Aurora supercomputers.
Dr. Katie McConky is a Professor and Department Head of Industrial and Systems Engineering at Rochester Institute of Technology (RIT). She holds a Ph.D. in Industrial Engineering from SUNY Buffalo and prior experience as a research scientist at CUBRC Inc., where she worked on military and data mining projects. Her research focuses on operations research, machine learning, and energy systems optimization, addressing challenges in combinatorial optimization, cyberattack forecasting, and sustainable energy management. She has secured funding from agencies like ONR, AFRL, NASA, and NYSERDA. Education: BS and MS (RIT), Ph.D. (SUNY Buffalo) Key Roles: Department Head, Faculty Member, Research Scientist (CUBRC) Her work spans applications such as kidney exchange optimization, rover mission planning, and energy demand forecasting. She emphasizes interdisciplinary collaboration in her research, integrating machine learning with traditional optimization techniques. Dr. McConky’s publications highlight advancements in forecasting methodologies for cyber threats, energy systems, and transportation logistics. She teaches courses including Operations Research and Forecasting Methods, emphasizing practical software tools like Gurobi. Her contributions include patents on remote activity detection and energy storage optimization. Her research is supported by grants from federal agencies and industry partners, reflecting her expertise in both academic and applied domains.
Brenda Gannon, Ph.D., is an Assistant Professor in the Department of Pharmacology and Toxicology at the University of Arkansas for Medical Sciences (UAMS), within the College of Medicine. Her research focuses on the abuse-related effects of novel psychoactive substances (NPS), including synthetic cannabinoids, cathinones, and opioids. She also investigates regulatory policy and drug-drug interactions. Dr. Gannon’s work employs behavioral pharmacology techniques such as intravenous self-administration, drug discrimination, and telemetry-based physiological monitoring. Education: Ph.D., Interdisciplinary Toxicology, UAMS (2015) Graduate Certificate in Regulatory Sciences, UAMS (2014) Postdoctoral Fellowship at University of Texas Health Science Center-San Antonio (2015–2018) Research Interests: Dr. Gannon’s research bridges preclinical pharmacology and regulatory science, with emphasis on understanding the neurochemical and behavioral profiles of emerging drugs. Her lab evaluates abuse liability, polypharmacology, and translational strategies for drug development. Key areas include synthetic cathinones, psychedelics, and opioid alternatives. Grant Activity: She serves as Lab Manager (Co-Investigator equivalent) on NIH-funded projects (IDs 15DDHQ24A00000020 and 15DDHQ24A00000027) investigating hallucinogens and stimulants’ abuse potential using in vivo assays. Labs/Teams: Her laboratory focuses on translational drug research, combining behavioral and neurochemical analyses to inform regulatory policy and therapeutic design.
Marianne Winslett is a Professor at the University of Illinois' Siebel School of Computing and Data Science, affiliated with the Department of Computer Science since 1987. Her research focuses on data security, information management, and privacy in cyber-physical systems. She co-led the TrustBuilder project, advancing access control and authentication in open computing environments, and directed the Advanced Digital Sciences Center (ADSC) in Singapore from 2009–2013, addressing challenges in data analytics and smart grids. Her work includes pioneering methods to ensure privacy in biomedical data analysis. Education: Earned her doctorate in Computer Science from Stanford University and worked at Bell Labs before joining Illinois. Awards: ACM Fellow (2006), NSF Presidential Young Investigator (1989), University Scholar, and Stanley H. Pierce Award for advising. She has supervised 24 PhD theses and mentored numerous graduate students, particularly supporting female scholars. Research Interests Secure data management in distributed systems Privacy-preserving techniques for biomedical data Adversarial attack detection in cyber-physical systems like smart grids Elastic resource scheduling in cloud environments Query optimization under differential privacy constraints Key Contributions Developed frameworks for self-supervised learning in smart grid cybersecurity Pioneered causal mechanism transfer networks for mechanical system domain adaptation Advanced auto-scaling strategies for real-time stream processing (DRS/Elasticutor systems) Labs & Teams Former Director of the Advanced Digital Sciences Center (ADSC), a University of Illinois research outpost in Singapore focusing on data analytics and IoT applications.
Stella Kapodistria is an Associate Professor at Eindhoven University of Technology's Department of Mathematics and Computer Science, specializing in Stochastic Operations Research. She holds roles as EAISI High Tech Systems Associate Professor and editorial board member of journals like MCAP and PEIS. Her research focuses on data-driven decision-making, stochastic systems optimization, and maintenance policies, with applications in renewable energy, critical infrastructure, and cryptocurrency networks. She has secured grants including NWA-ORC, NWO Big Data, and TKI WoZ, and collaborates with industry partners in the Brainport region. Education: BSc (2003), MSc (2006, Hons.), and PhD (2009, summa cum laude) in Mathematics from the University of Athens. Postdoc at TU/e, followed by roles at Groningen University and TU/e's Stochastic Operations Research group. Teaching includes courses on Optimal Decision Making, Stochastic Performance Modeling, and Financial Mathematics. Research interests emphasize real-time learning, system resilience, and scalable algorithms for complex networks. Recent work addresses maintenance logistics, blockchain confirmation times, and wind energy prediction. She has published over 40 peer-reviewed articles and contributed to the 4TU Resilience Engineering Center. Awards include editorial leadership roles and grant funding. Advised 32 academic works and oversees industrial projects bridging theory and practice. Her labs and collaborations focus on adaptive systems, predictive analytics, and sustainable engineering solutions.
Krzysztof Czarnecki is a Professor at the University of Waterloo's Department of Electrical and Computer Engineering, with a cross-appointment to the School of Computer Science. He serves as leader of the Waterloo Intelligent Systems Engineering Lab and holds the title of University Research Chair. His research focuses on generative software development, model-driven engineering, and autonomous systems, particularly in automotive cybersecurity and perception safety. Education: Doctorate in Computer Science, Technical University of Ilmenau (1999) Master of Science in Computer Science, Technical University of Ilmenau (1995) Bachelor of Science in Computer Science, California State University (1994) Research Interests: Dr. Czarnecki's work spans generative programming, software product lines, and safety-critical AI for autonomous vehicles. Recent projects address robust perception systems, uncertainty quantification in neural networks, and strategic driving behavior modeling. He co-authored Generative Programming (Addison-Wesley, 2000), a foundational text in the field. Publications Trends: Recent work emphasizes multimodal AI integration (e.g., LEO-MINI), 3D object detection improvements (OV-SCAN), and safety assurance frameworks for autonomous systems. His research bridges theoretical software engineering with applied robotics challenges. Awards: Premier’s Research Excellence Award (2004) British Computing Society’s Upper Canada Award (2008) University Research Chair, University of Waterloo (2023) Teaching & Leadership: Teaches courses like ECE 495 (Autonomous Vehicles) and ECE 651 (Software Engineering Foundations). Oversees WatCAR initiatives and collaborates on industry projects through the NSERC Bank of Nova Scotia Industrial Research Chair (previous). Labs & Teams: Directs the Waterloo Intelligent Systems Engineering Lab, focusing on AI-driven solutions for autonomous systems and safety-critical software. Active in cross-disciplinary collaborations with automotive and robotics partners.
Per Gustavsson is a Senior Professor at the Institute of Environmental Medicine, part of Karolinska Institutet in Stockholm, Sweden. He leads research in the Unit of Occupational Medicine and is affiliated with Jenny Selander's research group focusing on chemical and physical work environment exposures. His primary role involves epidemiological studies on occupational health risks, particularly linking workplace exposures to chronic diseases like cancer and cardiovascular issues. Education: He holds a Docent degree from Karolinska Institutet (1996). His research emphasizes methodological advancements in exposure assessment, including job-exposure matrices and expert evaluations. Key topics include motor exhaust effects, particulate matter toxicity, and occupational carcinogens in professions like chimney sweeps and drivers. Research Interests: His work spans occupational epidemiology, environmental health, and exposure-response relationships. Current studies investigate respirable crystalline silica, diesel exhaust, and shift work impacts. He collaborates internationally on large-scale analyses like the SYNERGY project for lung cancer etiology. Grant Activities: His research has been supported by grants (detailed in his profile). He contributes to public health policy through studies on labor market reforms and occupational disease recognition. Labs/Teams: Active in the Unit of Occupational Medicine and cross-disciplinary groups like the EPHOR Mega Cohort project. He advises on exposure assessment and collaborates with multiple European institutions.
Eric Balkanski serves as Assistant Professor of Industrial Engineering and Operations Research at Columbia Engineering, Columbia University, and is an Affiliated Member of the Foundations of Data Science Institute. His academic home integrates theoretical computer science with operations research methodologies. Balkanski earned his PhD in Computer Science from Harvard University, establishing foundational expertise in algorithmic theory before joining Columbia's faculty. His research pioneers algorithms with predictions —a transformative paradigm blending machine learning insights with classical optimization. Key thrusts include exponentially faster submodular optimization for data summarization and recommendation systems, strategyproof mechanism design incorporating predictive advice, and fairness-aware online algorithms . This work bridges theoretical guarantees with real-world applications in network analysis and decision-making under uncertainty, often yielding breakthroughs in computational efficiency. Recent publications (2022-2025) reveal a dominant trend toward prediction-augmented frameworks across scheduling, correlation clustering, and facility location. His submodular optimization advances enable orders-of-magnitude speedups, while fairness-oriented work introduces novel cost-free fairness models for online settings. The consistent focus on theoretical foundations of learning-augmented algorithms positions him at the forefront of this emerging field. His accolades demonstrate exceptional scholarly impact: ACM SIGecom Doctoral Dissertation Honorable Mention Award Google PhD Fellowship Smith Family Graduate Science and Engineering Fellowship Best Paper Award at CIAA 2013 Andrew Carnegie Society Scholar Balkanski co-founded Robust Intelligence (an AI security startup), translating theoretical work into practical cybersecurity applications. His NSF-funded collaborative research on 'Mechanisms with Predictions' indicates active grant leadership, though specific student advising details remain unpublicized. The absence of formal lab descriptions suggests integration within Columbia's broader data science and operations research ecosystems. As an early-career researcher, Balkanski demonstrates remarkable productivity with 15+ high-impact publications since 2022, primarily in top-tier venues like STOC, NeurIPS, and EC. His trajectory suggests continued leadership in bridging algorithmic theory with machine learning applications.
Esther Maier is an Associate Professor at the Lazaridis School of Business and Economics, Wilfrid Laurier University. Her work focuses on the intersection of organizational processes, media production, and corporate sustainability. She specializes in analyzing how budgeting, creativity, and control intersect in large-scale creative projects, particularly in television and digital media industries. Her research explores topics such as sustainability reporting in corporate crises, media framing of scandals, and the economic mechanisms behind cultural production. She has published extensively on creative industry management, including studies on budget allocation in film/TV production and the role of calculative practices in artistic projects. Dr. Maier’s work bridges sociology and media studies, offering critical insights into how organizations navigate the tension between artistic vision and financial constraints. Her recent research emphasizes the ethical dimensions of corporate sustainability claims, particularly in the wake of environmental disasters like the Deepwater Horizon oil spill. Her articles reflect a consistent focus on interdisciplinary methodologies, combining organizational behavior theory with media production case studies. No specific awards or grants are listed in the provided materials, though her active publication record indicates sustained academic engagement.
Donald Sull is a Professor of the Practice at MIT Sloan School of Management, where he directs the Strategic Agility Project and Culture 500 initiatives. He specializes in competitive strategy, strategy execution, and organizational culture. Formerly a professor at Harvard Business School and London Business School, Sull holds degrees from Harvard University. His research focuses on leadership, corporate culture transformation, and the interplay between strategy and execution. He co-founded CultureX, leveraging AI to measure and improve corporate culture, and advises global organizations including Fortune 500 companies and startups like Betterworks and eToro. Recognized as a leading management thinker, Sull’s work on 'active inertia' has shaped business theory, and he has authored five books and over 100 articles, including best-selling Harvard Business Review pieces. Education: Bachelor’s, Master’s, and Doctorate from Harvard University. Prior Experience: Strategy consultant at McKinsey & Company, management-investor at Clayton, Dubilier & Rice. Affiliations: Chairman of FilmFish, advisor to startups, and contributor to MIT Sloan Management Review. Research Interests: Sull’s work centers on organizational agility, corporate culture dynamics, and leadership strategies. His projects like CultureX apply AI-driven analytics to diagnose and enhance culture. He emphasizes practical frameworks for strategy execution, addressing challenges like toxic workplace environments and aligning culture with strategic goals. Recent studies explore AI’s role in organizational self-awareness and data-driven decision-making. Advisory & Grants: Advised over 50 Fortune Global 500 companies and non-profits like the Bill & Melinda Gates Foundation. His executive education programs, such as the Global CEO Program (MIT Sloan-IESE alliance), focus on leadership and global business strategy. Labs/Teams: Leads the Strategic Agility Project and Culture 500, collaborating with industry partners to analyze organizational resilience and cultural health.
Dr. Yiqun Pan is a Special Faculty at Carnegie Mellon University's Center for Building Performance and Diagnostics, and a Visiting Professor at Lawrence Berkeley National Laboratory. With 25+ years of experience, she specializes in building performance simulation, energy efficiency, and sustainable design. Her work integrates machine learning and big data to enhance building performance and occupant well-being. Research focuses include low-carbon building technologies, energy flexibility optimization, and carbon reduction strategies. She has led projects funded by the China National Science Foundation and U.S. Energy Foundation. Dr. Pan has authored six books and over 150 publications, including 42 English journal papers. Teaching includes courses on LEED certification, green infrastructure, HVAC systems for low-carbon buildings, and building energy systems integration. Awards include IBPSA Fellow and ASHRAE Membership. She chaired the 2023 Building Simulation Conference, demonstrating global leadership in building science. Her contributions span tool development (e.g., DeST 3.0 simulation platform) and interdisciplinary collaborations. Current work bridges academic research with practical applications, advancing sustainable urban development and zero-carbon building practices.
Alfred Hero is the John H. Holland Distinguished University Professor of Electrical Engineering and Computer Science and the R. Jamison and Betty Williams Professor of Engineering at the University of Michigan. He is currently on leave from the University of Michigan as a Program Director in the CISE Directorate at the National Science Foundation. His primary appointment is in the Department of Electrical Engineering and Computer Science (EECS) with secondary appointments in the Department of Biomedical Engineering and the Department of Statistics. He is affiliated with multiple research centers including the UM Center for Computational Medicine and Bioinformatics (CCMB), the UM Graduate Program in Applied and Interdisciplinary Mathematics (AIM), the UM Applied Physics Program, and the Michigan Institute for Data Science (MIDAS). Hero's research focuses on data science, developing theory and algorithms for multimodality data collection, fusion, analysis and visualization using statistical machine learning and distributed optimization. His work has applications in wearable technologies for personalized health and predictive medicine, spatio-temporal networks in biology, climate, and social discourse, anomaly detection, and data analysis for international security. His research group has produced numerous PhD students who have gone on to prominent academic and industry positions. His recent publications show a strong focus on high-dimensional statistical methods, machine learning theory, network analysis, and applications in biomedical domains. The research trends indicate increasing emphasis on multimodal data fusion, robust learning algorithms, and applications to complex systems in biology and security domains. His work bridges theoretical foundations with practical implementations across diverse application areas. Fellow of the Institute of Electrical and Electronics Engineers (IEEE) Fellow of the Society for Industrial and Applied Mathematics (SIAM) Fourier Award in Signal Processing from the IEEE Hero has advised numerous PhD, MS, and undergraduate students who have gone on to successful careers in academia and industry. His research has been supported by various grants, though specific grant details are not provided in the source material. His lab collaborates extensively across disciplines with researchers in statistics, biomedical engineering, and computational medicine. The Hero Research Group maintains active collaborations with institutions worldwide and participates in major conferences in machine learning, signal processing, and data science.
