Matthew Louis Mauriello is an Assistant Professor in the Department of Computer and Information Sciences at the University of Delaware , where he directs the Sensify Lab . He holds a PhD in Computer Science from the University of Maryland (2018) and completed postdoctoral work at Stanford University (School of Medicine, 2020; School of Public Policy & Environmental Engineering Department, 2019). Research interests span Human-Computer Interaction (HCI), Ubiquitous Computing, and User-Centered Design with applications in: Sustainability Human-Building Interaction Wearable Technology Personal Informatics Educational Game Design Mental Health Interventions Recent publications highlight trends in stress monitoring (skin-like biosensors, workplace wellbeing), energy auditing (thermography systems), and educational technology (block-based programming tools for teachers). His work appears in ACM CHI, ACM Human-Computer Interaction, and Building and Environment. Scientific awards include: Best Paper Honorable Mention, CHI 2016 Best Paper Honorable Mention, CHI 2015 Best of WebSci'18 Teaching at University of Delaware encompasses courses like Educational Game Design Operating Systems Advanced Web Technologies Computing for Social Good with a focus on project-based learning and systems thinking.
Prof. Christian Holz is an Associate Professor at the Department of Computer Science and Deputy Head of the Institute of Intelligent Interactive Systems at ETH Zürich. His work focuses on advancing human-computer interaction through innovations in wearable technologies, mixed reality systems, and sensor-driven applications. Key research areas include motion capture, physiological signal processing, and adaptive user interfaces. Holz leads the SIPLab (siplab.ethz.ch), producing influential work at the intersection of computer science and biomedical engineering. His research explores cutting-edge topics such as egocentric vision systems, wearable health monitoring devices, and VR/AR applications. Recent studies investigate cybersickness detection via EEG, heart rate estimation from eye-tracking cameras, and scalable motion capture using inertial/UWB sensors. Holz's work emphasizes practical applications in healthcare, education, and human-centered computing. Publications reflect a strong focus on interdisciplinary solutions, combining machine learning with sensor data analysis. Notable contributions include the EgoSim multi-view simulator, WildPPG biomedical dataset, and MiBOT cardiovascular modulation device. His research bridges theoretical advancements with real-world usability in domains like emergency response training, chronic disease monitoring, and immersive education.
David Mazières is a Professor at Stanford University , affiliated with the School of Engineering and the Department of Computer Science . He serves as a software engineer at the Stellar Development Foundation . His work bridges academic research and industry applications in distributed systems and security. University: Stanford University Academic Rank: Professor Email: dm@scs.stanford.edu Research Interests: David Mazières' research focuses on distributed systems , cryptocurrencies , and computer security . Key areas include consensus protocols (e.g., Stellar ), low-latency scheduling ( Syrup , Shinjuku ), and cryptographic techniques for privacy and security ( SafetyPin , CCFI ). Key Achievements: His notable works include: TCP-ENO (RFC 8547) for secure transport protocols Stellar Consensus Protocol for decentralized finance SOSP 1999 Best Paper for 'Separating key management from file system security' Teaching Contributions: He has taught core and advanced courses at Stanford since 2005, including CS212 (Operating Systems) , CS240h (Functional Systems in Haskell) , and CS251 (Cryptocurrencies and Blockchain Technologies) . Previously taught courses at NYU (2001-2005).
Anca Dragan is an Associate Professor in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley, where she runs the InterACT Lab focused on algorithms for human-AI and human-robot interaction. Currently on leave from Berkeley, she leads AI Safety and Alignment at Google DeepMind, overseeing safety for Gemini models and preparing for future advancements. Dragan has been a co-PI of the Center for Human-Compatible AI and served on the steering committee for the Berkeley AI Research (BAIR) Lab. B.Sc. in Computer Science from Jacobs University Bremen, Germany Ph.D. from Carnegie Mellon University Dr. Dragan's research focuses on enabling AI agents to work effectively with, around, and in support of people. Her work bridges robotics, machine learning, and game theory to create systems that better understand human preferences and coordinate with users. Key areas include AI alignment (ensuring AI does what people actually want), learning reward functions from diverse human feedback forms, and developing algorithms for human-AI collaboration across domains like autonomous vehicles, brain-machine interfaces, and recommender systems. Her research emphasizes maintaining uncertainty about human preferences and accounting for the plurality of human values. Dr. Dragan's recent publications reveal a strong focus on addressing fundamental challenges in AI safety and alignment. Her work spans theoretical foundations of reward learning, practical implementations for human-AI coordination, and critical examinations of limitations in current approaches. There's a clear trajectory toward more robust, safe, and value-aligned AI systems that can handle complex human preferences while avoiding both present-day harms and potential catastrophic risks. IEEE RAS Early Academic Career Award in Robotics and Automation (2021) McEntyre Award for Excellence in Teaching (2020) PECASE (Presidential Early Career Award for Science and Engineering) (2019) Sloan Fellowship (2018) NSF CAREER Award (2017) Okawa Foundation Award (2017) MIT Tech Review 35 Innovators Under 35 (2017) Multiple best paper awards at top robotics and AI conferences Dr. Dragan has mentored numerous successful students who have gone on to faculty positions at MIT, Stanford, CMU, and Princeton, as well as industry roles at DeepMind, Waymo, and Meta. Her advising philosophy emphasizes both technical rigor and consideration of broader societal impacts. She has secured significant research funding including NSF CAREER, ONR Young Investigator, and Okawa Foundation awards, supporting work on human-AI interaction and alignment. Dragan has also consulted for Waymo for six years, helping develop roadmaps for deploying increasingly learning-based safety-critical systems. Dr. Dragan leads the InterACT Lab at UC Berkeley, which has produced influential work on Cooperative Inverse Reinforcement Learning, Inverse Reward Design, and other foundational concepts in human-AI interaction. The lab's research has significantly shaped the field of AI alignment, with applications spanning autonomous vehicles that coordinate with human drivers, brain-machine interfaces that adapt to user needs, and language models that better understand human preferences. Current work focuses on scaling safety approaches as AI capabilities advance, ensuring alignment keeps pace with technological progress.
Celestine Mendler-Dünner is a Principal Investigator at the ELLIS Institute in Tübingen, co-affiliated with the Max Planck Institute for Intelligent Systems and the Tübingen AI Center. She leads the Algorithms and Society research group, focusing on machine learning in social contexts and the role of prediction in digital economies. Her work bridges theoretical machine learning with practical societal impact, developing tools for safe, reliable, and equitable AI ecosystems. Her educational background includes a PhD from ETH Zurich in collaboration with IBM Research, followed by an SNSF postdoctoral fellowship at UC Berkeley hosted by Moritz Hardt. She was previously a group leader at the Max Planck Institute for Intelligent Systems before joining the ELLIS Institute. Mendler-Dünner's research spans several interconnected themes including performative prediction (where predictions change the behavior they aim to predict), algorithmic collective action (how participants can steer AI systems toward common goals), and the role of LLMs in social science research. Her work combines theoretical foundations with practical implementations, addressing challenges in interactive machine learning, optimization in dynamic environments, and context-specific evaluation of AI systems. She particularly examines how algorithmic predictions mediate services and platforms at societal scale, exploring concepts of economic power in digital markets. Her publication record shows a clear evolution from system-aware machine learning algorithms (including foundational work on IBM Snap ML) toward increasingly sociotechnical questions at the intersection of machine learning, economics, and policy. Recent work focuses on measuring performative power in digital economies, evaluating LLMs as risk scores, and developing frameworks for algorithmic collective action in recommender systems and labor markets. Among her notable recognitions are the ETH Medal for her dissertation, the IBM Research Division Award, the Fritz Kutter Award, and the IBM Eminence and Excellence Award. She is an ELLIS Scholar, a fellow of the Elisabeth-Schiemann-Kolleg, and affiliated with several prestigious research programs including the International Max Planck Research School for Intelligent Systems and the Max Planck ETH Center for Learning Systems. ETH Medal (dissertation award) IBM Research Division Award Fritz Kutter Award IBM Eminence and Excellence Award SNSF Early Postdoc Mobility Fellowship Mendler-Dünner actively mentors the next generation of researchers, advising PhD student Patrik Wolf and supervising research interns including Joachim Baumann, Haiqing Zhu, and Anna Badalyan, as well as Master's student Dorothee Sigg. She serves as core faculty for the International Max Planck Research School and associated faculty for the Max Planck ETH Center for Learning Systems. Her group has secured significant research funding through fellowships and institutional support, enabling work on projects like Powermeter (measuring search engine influence) and Snap ML (resource-efficient machine learning library with over 1 million PyPI downloads). She leads the Algorithms and Society research group, which examines machine learning as part of broader sociotechnical ecosystems. The group explores human-population interactions with algorithmic systems and incorporates these insights into learning system fundamentals. Current projects include investigating economic incentives in digital platforms, developing tools for systematic LLM evaluation in social science contexts, and creating frameworks for collective action in algorithmic systems. Mendler-Dünner also co-organizes the Algorithmic Collective Action workshop at NeurIPS 2025, demonstrating her leadership in emerging research directions at the AI-society interface.
Eytan Adar is a Professor of Information and Computer Science at the University of Michigan, holding dual appointments in the School of Information and the College of Engineering's Electrical Engineering and Computer Science department. His work sits at the intersection of human-computer interaction and artificial intelligence, focusing on large-scale systems analysis and novel interface design. Adar's research spans multiple domains including social media analysis (Twitter, Reddit), academic citation networks, meme propagation, information extraction, and political networks. His methodological expertise includes data mining, data visualization, and graph-based network analysis. His work often operates at internet scale, examining language, creativity, social network dynamics, and text production. His recent publications reveal a strong focus on AI-human collaboration, with particular attention to generative AI interfaces, visualization techniques for complex data, and ethical considerations in AI systems. His work shows a consistent pattern of bridging theoretical insights with practical system implementations. Best Paper Award at ProtoAI: Model-Informed Prototyping for AI-Powered Interfaces, IUI'21 Honorable Mention Award at CHI'24 for feminist interaction techniques research Best Paper Award at ICWSM'18 for Wikipedia language edition analysis Best Paper Award at ICML 2016 Workshop for neural language model visualization Best Student Paper at WSDM'09 for web dynamics research Best of CHI at CHI 2008 for web revisitation patterns analysis Adar has advised numerous PhD students who have gone on to prominent positions at organizations including Google, Apple, RAND, Northwestern University, and Stanford. His research is generously supported by the NSF, IARPA, NIH, the Education Department, and major technology companies including Adobe, Microsoft, Facebook, Google, and Yahoo. He is also a founder of the International Conference on Web and Social Media (ICWSM) and has served as Co-General Chair for WSDM and Co-Program Chair for UIST.
Adriana Tapus is a Full Professor at ENSTA Paris, affiliated with Institut Polytechnique de Paris, leading the Autonomous Systems and Robotics Laboratory (SAR) within the Computer Science and Systems Engineering Unit (U2IS). She holds an HDR (Habilitation) and a PhD from EPFL, Switzerland, with postdoctoral experience at USC. Her research focuses on socially assistive robotics, human-robot interaction (HRI), and personalized therapy for individuals with physical/cognitive impairments. She directs the IP Paris Doctoral School and coordinates national/international projects like the EU-funded ENRICHME and SWEET. Education: PhD in Mobile Robotics, EPFL (2005) Habilitation (HDR), ENSTA Paris (2011) M.S. Computer Science, University Joseph Fourier Engineer, Politehnica University of Bucharest Research Interests: Tapus pioneers socially assistive robotics, integrating machine learning, human modeling, and multimodal communication (verbal/non-verbal/para-verbal). Her work addresses adaptive therapies for vulnerable populations using robotics, physiological data interpretation, and context-aware interaction. Key themes include: Human-robot cooperation and trust Emotion recognition and expression Personalized rehabilitation systems AI ethics and human-centered design Publications: Over 150 articles, with recent work exploring humor in HRI, teleoperation trust models, and cross-cultural intelligent vehicles. Notable 2025 contributions include studies on robot laughter efficacy and multimodal facial expression frameworks. Awards: 2025: 4 IROS papers accepted 2016: 25 Women in Robotics recognition 2010: Romanian Academy Award Multiple conference best paper awards (RO-MAN, ICRA, etc.) Advising & Grants: Supervised over 20 PhD students and led projects like EU Horizon 2020 ENRICHME. Current students focus on teleoperation dynamics, robot humor, and haptic interfaces. Active in editorial roles (IJSR, THRI) and conference organization (HRI General Chair 2019). Labs/Teams: Founder of RoboticsByDesign lab and co-initiator of the Hi! Paris interdisciplinary AI center. The SAR lab develops systems for healthcare, education, and human-robot collaboration.
James McCann is an Associate Professor at the Carnegie Mellon Robotics Institute, where he leads the Carnegie Mellon Textiles Lab. He has been a faculty member since May 2017 after working at Disney Research Pittsburgh. McCann's academic journey includes a PhD from Carnegie Mellon advised by Nancy Pollard, followed by a postdoc at Adobe Research and a period developing video games. McCann's research focuses on building creative tools that operate in real-time and build user intuition, with particular emphasis on textiles fabrication and machine knitting. His work spans computer-aided fabrication, simulation, graphics, and creative tools development. He has pioneered systems for machine knitting design, including compilers for knitting instructions and tools for automatic conversion of 3D meshes to knitting patterns. His recent publications demonstrate a strong trend toward computational textiles, with significant contributions to knitting semantics, deployable textile structures, and applications of machine knitting in healthcare and robotics. McCann's work bridges computer science, robotics, and textile arts, creating practical systems for once-off manufacturing with industrial knitting machines. McCann actively mentors students, with current PhD candidates working on solid knitting machines, knit calibration, and assistive devices. His teaching portfolio includes courses on Real-Time Graphics, Algorithmic Textiles Design, and Game Programming. He has taught at CMU since 2017, developing innovative courses that blend computer science with physical fabrication. As director of the Textiles Lab, McCann oversees research projects spanning machine knitting, robotic painting, and real-time graphics systems. His lab develops practical tools for creators, emphasizing intuitive interfaces and real-time feedback that lower barriers to advanced fabrication techniques.
Changxi Zheng is an Associate Professor in the Department of Computer Science at Columbia University's School of Engineering and Applied Science (SEAS). He directs Columbia's Computer Graphics Group (C2G2) within the Columbia Vision and Graphics Center (CVGC). After receiving his PhD from Cornell University, he joined the faculty of Computer Science Department at Columbia, where he has established himself as a leading researcher in computer graphics and scientific computing. Dr. Zheng's research spans multiple areas of applied computer science with a particular focus on computer graphics and scientific computing. His work centers around developing numerical models for simulating physical phenomena involving complex motions such as fluids, bubbles, and thin rods, along with their resulting acoustic waves. Leveraging computational insights from these models, he devises methods for improving tangible object creation, enabling novel human-computer interactions, and developing software tools for acoustic and photonic devices. His research has attracted significant public interest and media coverage, including projects like FontCode, AirCode, and Computational Metallophone Design. His recent publications reveal a strong interdisciplinary approach, bridging computer graphics, physics simulation, machine learning, and hardware design. His work demonstrates consistent innovation in computational methods for simulating physical phenomena and applying these techniques to practical problems in 3D printing, acoustic modeling, and interactive systems. The breadth of his research spans from fundamental physics-based simulations to practical applications in industry. Columbia SEAS Dean's Fellow (for advised students) NSF Graduate Research Fellow (for Ruilin Xu) Snap Research Fellow (for Rundi Wu) CKGSB Fellow (for Yun Fei) Adobe Research Fellow (for Gabriel Cirio) Marie Sklodowska-Curie Individual Fellow (for Rundi Wu) Best Paper Award at ACM International Conference on Multimedia (ACMMM), 2019 Dr. Zheng actively mentors a diverse group of students, including current PhD candidates and postdoctoral researchers. His research group has received support from various sources that enable their innovative work in computational graphics and physics-based simulation. He has supervised numerous successful students who have gone on to positions at leading technology companies including Adobe, Tencent, Facebook, and academic institutions. As director of Columbia's Computer Graphics Group (C2G2) within the Columbia Vision and Graphics Center (CVGC), Dr. Zheng leads a vibrant research team focused on advancing the state of the art in computer graphics, physics-based simulation, and their applications. The group maintains strong collaborations with industry partners and academic institutions worldwide, fostering an environment of innovation and practical application of theoretical concepts.
Andrew Head is an Assistant Professor at the University of Pennsylvania in the Computer and Information Science department. His research focuses on human-computer interaction, programming, and reading, particularly in developing technology for interactive reading and reasoning. He advises PhD students Alyssa Hwang, Hita Kambhamettu, Litao Yan, Jeffrey Tao, and Jessica Shi, and co-leads the Penn Human-Computer Interaction (Penn HCI) group with Danaé Metaxa. His work is published in top venues like ACM CHI, UIST, and ICSE. Research Interests: Andrew's work bridges interactive systems with programming environments, aiming to enhance how scientists and programmers interact with their tools. Key areas include AI-assisted code understanding, math notation accessibility, and medical note interpretation through interactivity. He employs user studies to identify needs and builds interactive systems to address them. Recent Article Trends: His publications emphasize systems-centric HCI approaches, integrating AI into code and document interfaces. Topics span code explanation (e.g., Ivie), property-based testing (e.g., Tyche), math notation augmentation (e.g., FreeForm), and medical informatics (e.g., Explainable Notes). Recent work also explores notebook environments (e.g., Bolt-on, Tyche) and literate programming (e.g., Colaroid). Scientific Awards: Distinguished Paper Award, ICSE 2024 Best Paper Awards at CHI (2024, 2023, 2022, 2019), UIST (2023, 2018), and others. Nominated for Best Paper at CHI 2023 and VL/HCC 2015. Advising & Grants: Andrew advises multiple PhD students and has secured significant grants, including a $1M NSF award for 'Property-based Testing for the People' (2024). His group collaborates with Penn’s MindCORE center and teams like PLClub and PennNLP. Labs & Teams: He co-leads the Penn HCI group, which works closely with other Penn research teams and labs. The group focuses on creating interactive tools that enhance scientific and programming workflows, supported by grants and cross-institutional partnerships.
Karthik R. Narasimhan is a Professor at Princeton University's School of Engineering and Applied Science in the Department of Computer Science. Previously, he earned his PhD from MIT under Regina Barzilay and served as a visiting research scientist at OpenAI during 2017-18. His research focuses on the intersection of language and decision-making, building autonomous agents that learn from both experience and human knowledge. His research spans multiple high-impact areas including language agents (Text-DQN, CALM, ReAct, Tree of Thoughts), reinforcement learning (h-DQN, Multi-Objective RL), and AI safety (Toxicity in ChatGPT, DataMUX). He has developed critical datasets and benchmarks such as WebShop, InterCode, SWE-bench, and SILG that have become standard evaluation tools in the field. Current work emphasizes agent capabilities, software engineering automation, and multimodal interaction. His publication trends show strong focus on practical agent deployment (SWE-agent, Tree of Thoughts), safety evaluation (Probing AI Safety), and efficiency improvements (DataMUX). Recent work increasingly addresses real-world challenges in software engineering, security, and human-AI collaboration through rigorous benchmarking. Co-author of foundational GPT (2018) paper Key developer of Text-DQN (2015), CALM (2020), ReAct (2022), Tree of Thoughts (2023) Creator of influential benchmarks: WebShop (2022), SWE-bench (2023), InterCode (2023) He actively advises students through Princeton's computer science program, with research supported by multiple grants focused on autonomous agent development and language-based decision systems. His GitHub repositories (nlp-datasets, text-world-player) demonstrate strong community engagement in open-source research tools. Current projects include advancing language agent capabilities through Reflexion (2023) and Tree of Thoughts (2023) frameworks while addressing critical safety and efficiency challenges.
Prof. Dr. Gülşen Eryiğit is a Professor at Istanbul Technical University within the Faculty of Computer and Informatics , Department of Artificial Intelligence and Data Engineering . She founded and directs the ITU Natural Language Processing Group , Turkey's leading team in Turkish-language NLP, and serves as Senior Action Editor for ACL RR , Director of ITU TÖMER (Turkish Language Teaching Center), and Co-Chair of the EU UniDive Cost Action WG3. Education: PhD in Computer Engineering from ITU (2007), MSc and BSc from ITU and Marmara University Research: Focuses on Natural Language Processing for Turkish, including dependency parsing , coreference resolution , multiword expressions , and language education technology Her recent work involves multilingual transfer learning , LLM applications for Turkish text simplification, and gamification for morphology education. She has received prestigious awards like the Siemens Excellence Award and TÜBİTAK's Above Threshold Award . Her research has produced 65+ publications and 29+ projects funded by EU, TÜBİTAK, and industry partners. Scientific Awards: Siemens Excellence Award (2007) Above Threshold Award (TÜBİTAK, 2015) Certificate of Appreciation (EU 7th Framework, 2012) Thank You Plaque (ITU, 2017) The ITU NLP Group under her leadership has developed Turkey's first licensed NLP software exported internationally. She collaborates with European institutions through COST actions and participates in ACL, CoNLL, and LREC conferences. Her lab focuses on language technology for Turkish , including sign language processing and social media normalization.
Alenda Y. Chang serves as an Assistant Professor in Film and Media Studies at the University of California, Santa Barbara. With a multidisciplinary background spanning biology, literature, and film, she integrates ecocritical theory with contemporary media analysis. Her scholarly work appears in journals including Interdisciplinary Studies in Literature and Environment , Qui Parle , and electronic book review , focusing on sustainable media practices and ecological frameworks for digital engagement. Her educational background includes: M.A. and Ph.D. in Rhetoric from the University of California, Berkeley M.A. in English Language and Literature from the University of Maryland Chang specializes in environmental media and game studies, developing theoretical frameworks for ecological game design. Her research examines how digital games model environmental systems and foster player engagement with climate change through concepts like "slow violence" and "rambunctious play." She advocates for sustainable design patterns that address gaming's carbon footprint while creating immersive ecological narratives. Analysis of her 15 most recent publications reveals a consistent trajectory from foundational ecological game theory (2013-2018) to current work on thermal contexts of play and sustainable infrastructure (2022-2024). Her scholarship increasingly bridges data-driven activism with critical game design, emphasizing multispecies entanglements and infrastructural play as tools for environmental awareness. As co-founder of Wireframe—a collaborative media studio at UCSB—Chang cultivates critical game design practices through initiatives like growinggames.net . The studio supports data-driven global media art, environmental activism, and experimental pedagogy, serving as a hub for translating ecological theory into tangible digital interventions that challenge anthropocentric perspectives in gaming.
Peter A. Flach is Professor of Artificial Intelligence at the Intelligent Systems Laboratory, School of Computer Science, University of Bristol, where he has been faculty since 1997 and was promoted to Professor in 2003. He currently directs the UKRI AI Centre for Doctoral Training in Practice-Oriented Artificial Intelligence and serves as Vice-President of the European Association for Data Science (EuADS), having previously served as its President. His research centers on rigorous evaluation and improvement of machine learning systems, with seminal contributions to classifier calibration (including Beta Calibration and Precision-Recall-Gain curves), explainable AI frameworks, and data science methodology. He pioneered the extension of CRISP-DM to data science trajectories and developed Explainability Fact Sheets for systematic assessment of XAI approaches. His work bridges theoretical foundations with practical deployment in real-world systems. Analysis of his recent publications reveals a dominant focus on interpretable and reliable machine learning, with strong emphasis on performance evaluation metrics, model calibration techniques, and human-centered explainability. His research increasingly addresses healthcare applications through projects like SPHERE and clinical decision support systems, while maintaining core contributions to fundamental ML theory. His scientific recognition includes prestigious fellowships: Fellow of the European Lab for Learning and Intelligent Systems (ELLIS) Fellow of the European Association for Artificial Intelligence (EurAI) Professor Flach has secured major research funding including UKRI Centre for Doctoral Training grants and EU network funding (TAILOR). He leads extensive collaborations across Engineering, Population Health Science, and international institutions (Monash University, Polytechnic University of Valencia), plus over 20 industry partners in the Practice-Oriented AI CDT including LV= and QinetiQ. His work with the SPHERE project demonstrates successful translation of AI research into residential healthcare settings. He leads the Intelligent Systems Laboratory at Bristol, which develops influential open-source tools including the FAT Forensics Python toolbox for algorithmic fairness and the Explainability Fact Sheets framework. His lab maintains strong connections with the European AI community through ELLIS and EurAI, positioning Bristol as a hub for human-centered and methodologically rigorous AI research.
Dr. Ly Fie Sugianto is an Associate Professor in the Department of Accounting at Monash Business School, Monash University. Her research focuses on the integration of data analytics, artificial intelligence, and machine learning in accounting and business systems, with applications in the energy sector and organizational behavior. Monash Business School, Monash University Department of Accounting Specialization: Accounting Information Systems, Data Analytics, AI Her research interests span Accounting Information Systems , Agent-Based Simulation , Decision Support Systems , and Technology Adoption . She applies computational methods to study competitive dynamics in deregulated electricity markets and the impact of digital tools on employee well-being and organizational resilience. The recent publications reflect a strong trend in using AI and simulation to analyze complex socio-technical systems, particularly in energy markets and leadership dynamics. Keywords across her work include agent-based modeling , data analytics , servant leadership , and enterprise social media , indicating interdisciplinary research at the intersection of information systems, management, and public policy. Her scientific awards include competitive grants from the ARC (SPIRT/Linkage) , the Australia Indonesia Governance Research Partnership (AIGRP) , and the Sumitomo Foundation . ARC Grant: Dispatch Optimisation in the Australian National Electricity Market Sumitomo Foundation: Technology Use and Employee Well-Being AIGRP: Governance and MSME Resilience during Pandemic Dr. Sugianto has advised research projects and collaborated with industry partners such as Western Power , Ecogen Energy , and AEMO . She is currently accepting PhD students and leads externally funded research initiatives. Her work contributes to UN Sustainable Development Goals related to industry innovation and responsible consumption. She is affiliated with research teams focusing on intelligent decision support systems and digital transformation in business , with active collaborations in Australia and Indonesia.
