Patricio Vela is a Professor at the School of Electrical and Computer Engineering , Georgia Institute of Technology , specializing in geometric perspectives for control theory and computer vision. His research focuses on computer vision integration for semi-autonomous systems, nonlinear control of robotic systems, and biologically inspired mechanics. Education: B.S. (1998) and Ph.D. (2003) from Caltech Research Areas: Autonomy, Robotics, Computer Vision, Control Theory Key Contributions: Geometry-based control systems, visual navigation frameworks, SLAM benchmarking Recent publications highlight advances in vision-based motion planning , 6D pose tracking , and safe navigation policies for autonomous robots. His work bridges geometric mechanics with deep learning for robust perception and control in dynamic environments. Awards: HENAAC Most Promising Engineer (2005) Contact: pvela@gatech.edu | Office: TSRB 441 | Phone: 404.894.8749
Santiago Grijalva serves as Georgia Power Distinguished Professor and Director of the Advanced Computational Electricity Systems (ACES) Laboratory at Georgia Institute of Technology's School of Electrical and Computer Engineering. He also holds the position of Associate Director for Electricity Systems at the Strategic Energy Institute (SEI). His educational background includes an Electrical Engineer degree from EPN-Ecuador (1994), M.S. in Information Systems from ESPE-Ecuador (1997), and M.S./Ph.D. in Electrical and Computer Engineering from University of Illinois at Urbana-Champaign (1999/2002). Grijalva pioneers research in decentralized power system architectures, renewable energy integration, and grid cybersecurity. His work spans smart grid technologies, electricity markets design, AI applications for power systems, and ultra-reliable grid architectures. The ACES Laboratory under his direction focuses on real-time power system control, informatics, and economics. His recent publications demonstrate strong trends in photovoltaic integration, multi-agent control systems, voltage stability analysis, and prosumer-based grid architectures, reflecting the industry's shift toward distributed energy resources and decentralized control paradigms. Georgia Tech ECE Outstanding Faculty Award Great Minds in STEM National Achievement Award ARPA-E More Than Smart Grid Disruptor Award IBM Faculty Award As principal investigator for major research projects funded by U.S. Department of Energy, ARPA-E, EPRI, PSERC, NSF, and industry sponsors, Grijalva leads initiatives modernizing electrical infrastructure. His past roles include Senior Software Developer at PowerWorld Corporation and Department Head at Ecuador's National Center for Energy Control (CENACE). He serves as Associate Editor for IEEE Transactions on Industrial Informatics and has published over 250 peer-reviewed papers. The Advanced Computational Electricity Systems (ACES) Laboratory operates at the intersection of power systems engineering and computational science, developing next-generation grid technologies for renewable integration and cyber-physical security.
Professor Yasamin Mostofi is a faculty member in the Department of Electrical and Computer Engineering at the University of California, Santa Barbara. She is also affiliated with the Department of Computer Science and the Center for Control, Dynamical Systems and Computation. Her work bridges wireless systems, robotics, and machine learning. PhD, Stanford University MS, Stanford University MS, Sharif University of Technology Research Interests : Her lab focuses on wireless systems and autonomous agents , developing novel mathematical models for RF sensing and communication-aware robotics. Current directions include WiFi/millimeter wave/6G-based imaging and analytics, networked robotics for cellular systems, human-robot collaboration, and machine learning integration. Recent Publications : Her work spans through-wall crowd analytics , robot-assisted connectivity , and vision-aided wireless sensing , with applications in smart health, security, and retail optimization. Key trends include cross-modal integration (WiFi and vision), synthetic data generation, and real-world clinical validation. Scientific Recognition : Presidential Early Career Award for Scientists and Engineers (PECASE) Antonio Ruberti Prize, IEEE Control Systems Society NSF CAREER Award IEEE Region 6 Outstanding Engineer Award IEEE Fellow (2020) Advising and Leadership : She mentors PhD students in systems research, with graduates joining leading companies like Qualcomm and Google. She co-founded NPJ Wireless Technology (Nature Portfolio) and serves on editorial boards including IEEE Transactions on Control of Network Systems .
Ira Kemelmacher-Shlizerman is a Full Professor of Computer Science at the Paul G. Allen School of Computer Science & Engineering at the University of Washington and Director of the UW Reality Lab. She also serves as a Principal Scientist at Google, where she leads the Shopping Gen AI visuals teams focusing on Virtual Try-On, 3D, and product videos. Her research spans computer vision, computer graphics, and Generative AI, with particular contributions to virtual try-on technology, 3D modeling, and augmented reality applications. Professor Kemelmacher-Shlizerman's research interests focus on Generative AI applications in visual computing. Her work bridges the gap between theoretical computer vision and practical applications, particularly in e-commerce and virtual reality. She has made significant contributions to virtual try-on technology, 3D editing with generative models, and AI applications for shopping experiences. Her research combines deep learning with traditional computer vision techniques to solve challenging problems in image and video synthesis. Her recent publications demonstrate a strong trend toward Generative AI applications for visual shopping experiences, virtual try-on technology, and 3D content creation. The work spans multiple top conferences including CVPR, SIGGRAPH, and ICCV, with a focus on practical applications of computer vision and graphics. Her research has evolved from foundational work in face reconstruction and aging to current applications in virtual shopping and 3D content generation. Google faculty award Madrona prize GeekWire Innovation of the Year Award Covers of CACM and SIGGRAPH Best student paper honorable mention at CVPR'21 Best demo runner up MobiSys'22 Senior member of IEEE Distinguished Member of ACM Professor Kemelmacher-Shlizerman has successfully tech-transferred multiple research projects to industry. She founded Dreambit, a startup acquired by Meta, and previously built and launched the Face Movies feature at Google. She currently leads Google's Shopping Gen AI visuals teams, focusing on 10x improvements to shopping journeys. Her UW Reality Lab serves as a hub for AR/VR research with industry partnerships. She has mentored numerous PhD students who have become researchers in both academia and industry, with several publications featuring student co-authors receiving recognition at top conferences. Professor Kemelmacher-Shlizerman leads the Graphics and Imaging Laboratory (GRAIL) and the UW Reality Lab, which focuses on augmented and virtual reality research with industry partnerships including Google. The labs work on cutting-edge projects in virtual try-on, 3D modeling, and immersive experiences, bridging academic research with real-world applications.
Mani Golparvar Fard is a Professor at the University of Illinois at Urbana-Champaign, holding joint appointments in the Siebel School of Computing and Data Science and the Department of Civil and Environmental Engineering. He also contributes to the Technology Entrepreneur Center. His research focuses on integrating artificial intelligence, computer vision, and data analytics to advance construction management, infrastructure monitoring, and automation. Key areas include BIM integration, reality capture systems, and deep learning-based progress tracking. His work emphasizes automated construction progress monitoring through semantic segmentation, vision-language models, and UAV-based data collection. He has pioneered methods like Scan2BIM-NET for converting point clouds into BIM models and developed frameworks for worker safety analysis using machine learning. Awards: Walter L. Huber Civil Engineering Research Prize (2018) Daniel W. Halpin Award for Scholarship (2016) Advising & Grants: While no specific grant details are provided, his research is supported by collaborations with industry and government initiatives, such as the Japanese national bridge inspection project. He advises a team focused on AI-driven construction solutions and maintains active partnerships with engineering firms. Labs & Teams: Leads research groups in vision-based construction analytics, automated scheduling systems, and BIM integration. His work is disseminated through platforms like the VisualSiteDiary system and the InstaDam open-source platform for structural damage analysis.
Yang Liu is a tenured Associate Professor at the Department of Management and Engineering , Linköping University, Sweden, and an Adjunct Professor at the University of Oulu, Finland. His expertise spans smart manufacturing, clean energy transition, and Industry 4.0 applications. He holds an M.Sc. and D.Sc. from the University of Vaasa, Finland. Research & Awards: Liu's work focuses on sustainable systems, decision support systems, and AI-driven energy efficiency. He has authored over 140 Web of Science publications, including top 0.1% ESI Hot Papers. He is ranked among the world's top 2% scientists (Stanford-Elsevier) and leads globally in 'big data analytics in manufacturing' and 'Industry 4.0-driven circular economy' research. Leadership & Projects: He leads projects like FlexSUS (EU Horizon 2020) and PERSEUS, developing tools for smart urban energy planning and 15-minute city models. He serves as Editor-in-Chief of Cleaner Engineering and Technology and Guest Editor for multiple journals. His research emphasizes bridging data science with sustainability challenges in manufacturing and energy systems. Key Achievements: Top-ranked in global citations, ESI Highly Cited Papers, and industry-driven sustainability frameworks. Grants: Leads EU-funded projects and collaborates with Siemens Energy on energy transition solutions. Labs & Teams: Part of the Environmental Technology and Management (MILJÖ) division and Unit for Product Service Innovation (MILJOPSI) at Linköping.
Dr. Dmytro Matsypura is an Associate Professor in the Discipline of Business Analytics at the University of Sydney Business School. He holds a BA (Hons) from Kyiv Polytechnic Institute (KPI), an MS (Hons) from KPI, and a PhD from the University of Massachusetts Amherst. His research focuses on optimization methodologies, network science, and their applications in finance, transportation, ecology, and graph theory. He is a recipient of multiple teaching awards, including the Wayne Lonergan Outstanding Teaching Award (Early Career) in 2010. Education: PhD in Management Science, University of Massachusetts Amherst (2006) MS (Hons) in Information Systems, Kyiv Polytechnic Institute (2000) BA (Hons) in Business Administration, Kyiv Polytechnic Institute (1998) Research Interests: Dr. Matsypura’s work spans operations research and management science, with a focus on mathematical optimization and network science. His methodological contributions include developing efficient optimization algorithms, while his applied research addresses real-world challenges in finance, engineering, and ecology. Notable applications include wildfire fuel management, portfolio margining, and credit card fraud detection via graph-based models. Awards and Recognition: Teaching Excellence Award (2008, 2013, 2018) Wayne Lonergan Outstanding Teaching Award (Early Career) (2010) Grants and Projects: Current projects include Bushfire Analytics: Optimization of Fuel Reduction (2023, ARC Discovery Project). His research frequently integrates interdisciplinary collaborations, such as applying graph theory to biomedical problems and cybersecurity. Labs/Teams: Active in the Sydney Environment Institute, contributing to projects at the intersection of analytics and sustainability. Collaborates with industry on fraud detection and supply chain optimization.
Heewon Chang, Ph.D., is a Professor and Chair in the PhD in Organizational Leadership Department at Eastern University's College of Business and Leadership. She specializes in autoethnography and qualitative research methods, with notable works including Autoethnography as Method (2008). Her research focuses on multicultural education, leadership development, and collaborative learning within marginalized communities. Dr. Chang has contributed to advancing methodologies like Collaborative Autoethnography (CAE) and the Transformative Autoethnography Model (TAM). Her academic journey includes an MA in Education and Anthropology from the University of Oregon (1984) and Pennsylvania Elementary Teaching Certification (2000). She has served in leadership roles, emphasizing equity and inclusion in higher education. Her work bridges personal narratives with sociocultural analysis, impacting fields like organizational behavior and pedagogy. Key research themes include BIPOC doctoral student experiences, online leadership program efficacy, and systems thinking in education. Dr. Chang’s publications span journals and edited volumes, reflecting her commitment to democratizing knowledge through participatory research. She has also contributed to journal editing and community service, advocating for educational equity and mentorship for underrepresented scholars.
Heather Zheng is the Neubauer Professor of Computer Science at the University of Chicago, co-directing the SAND Lab (Security, Algorithms, Networking and Data) with Prof. Ben Y. Zhao. She holds IEEE (2015) and ACM (2023) Fellowships, and was recognized as MIT TR35 (2005) for cognitive radio research. Her work bridges mobile/IoT security, adversarial ML, and generative AI ethics. Zheng earned her PhD in Electrical and Computer Engineering from University of Maryland in 1999, with prior roles at Bell-Labs, Microsoft Research Asia, and UCSB. Education: PhD in Electrical and Computer Engineering, University of Maryland, College Park (1999) Research Focus: Explores cutting-edge challenges in: Security implications of mobile/IoT sensors (e.g., bracelet-of-silence countermeasures) Adversarial machine learning defense mechanisms (e.g., Blacklight attack detection) Generative AI governance (Glaze, NightShade copyright tools) Awards: ACM Fellow (2023) IEEE Fellow (2015) World Technology Network Fellow Labs & Collaborations: Leads SAND Lab focusing on security, ML, and networked systems. Active in UChicago's Systems Group exploring cloud/edge computing architectures.
Furkan Alaca is an Assistant Professor at Queen's University's School of Computing, part of the Faculty of Arts and Science. His research focuses on user authentication systems, addressing security and usability challenges. He holds a Ph.D. (2018) in Computer Science from Carleton University, an M.A.Sc. (2012) in Electrical and Computer Engineering, and a B.Eng. (2010) in Communications Engineering, all from Carleton University. His academic career includes teaching roles at Queen's University and the University of Toronto Mississauga, where he taught courses such as Cryptography, Cybersecurity, and Discrete Mathematics. He is affiliated with Queen's Security Research Group and Computer Security Research Lab. Research interests include computer and internet security, usable security, authentication mechanisms, and systems security. He has contributed to advancements in web authentication frameworks, malware analysis, and privacy-preserving technologies. His work spans conferences like IEEE and ACM, with notable publications in cybersecurity, machine learning, and network efficiency. Current teaching includes CISC 447 (Introduction to Cybersecurity) and CISC 468 (Cryptography). He has advised on courses ranging from undergraduate programming to graduate-level security topics.
Matthew Payne is an Associate Professor in the Department of Film, Television, and Theatre at the University of Notre Dame. His affiliations include roles as Director of Graduate Studies and regional director for the Learning Games Initiative, a multi-institute research collective focused on game archiving and education. His research spans video game studies, media literacy, military entertainment, and the cultural history of video games. He explores play theory, media and war, and the intersection of media production with new technologies. The 15 most recent articles highlight his focus on video game studies, transmedia narratives, and the cultural implications of gaming. These works cover topics from Nintendo Amiibo to slow game time mechanics in Red Dead Redemption 2, with recurring themes of military representation and ludic storytelling. Matthew Payne has co-edited notable works including How to Play Video Games and Joystick Soldiers , emphasizing collaborative approaches to game studies and critical analysis of military gaming.
Bettina Migge is a Full Professor in the School of Languages, Cultures and Linguistics at University College Dublin, where she has been a faculty member since 2004. She previously held academic positions at Goethe University Frankfurt and earned her PhD in Linguistics from The Ohio State University. She served as Head of School from 2016 to 2021 and has held leadership roles in research centers and committees, including the Royal Irish Academy and the Society for Pidgin and Creole Linguistics, of which she is President until 2023. She is Co-Editor of the Journal of Pidgin and Creole Languages and actively contributes to COST Action LITHME, focusing on language and technology. Her educational background includes studies at Universität Hamburg, Université de Cameroun, Freie Universität Berlin, and The Ohio State University, culminating in a PhD focused on African languages in creole genesis, with fieldwork in Suriname and Benin. She is fluent in Dutch, English, French, German, and Ndyuka/Pamaka. Bettina Migge's research centers on sociolinguistics, language contact, and creole languages, with a focus on multilingual contexts undergoing rapid social change such as urbanization and migration. Her work spans French Guiana, Suriname, and Ireland, examining structural and socio-pragmatic aspects of language use, linguistic landscaping, and computer-mediated communication. She has led significant projects like the trilingual DicoNenge(e) dictionary and has explored the role of AI in reshaping language practices. Her recent publications reflect a critical engagement with the impact of AI and machine learning on language, revealing colonial continuities in language technologies and questioning dataist ideologies. She investigates how language is materially produced in both colonial and digital contexts, emphasizing power, authenticity, and control. She has received research funding from the National Science Foundation (USA), IRCHSS, IRC, Ulysses grants, and French research units like SeDyL. Her collaborative projects include work on complementation in creoles, South Dublin English, and language practices in multilingual border zones. Bettina Migge supervises MA and PhD students in sociolinguistics, linguistic landscaping, and World Englishes. She is involved in numerous editorial and professional associations, including the Society for Caribbean Linguistics and The Global Council on Anthropological Linguistics. She has coordinated modules such as World Englishes, Sociolinguistics, and Research in Creole Languages. She leads and participates in public engagement activities, including workshops on AI in linguistics, multilingualism in Ireland, and dictionary launches. Her research team includes collaborators from Europe and beyond, and she is committed to participatory and ethnographically grounded approaches in language documentation and analysis.
Xiaoxiao Long is a Tenure-Track Associate Professor at the School of Intelligence Science and Technology, Nanjing University. He joined NJU as an associate professor in February 2024. Previously, he earned his Ph.D. from the University of Hong Kong (HKU) under the supervision of Prof. Wenping Wang (IEEE & ACM Fellow) and Prof. Taku Komura. His educational background includes: Ph.D. in Computer Science from University of Hong Kong Bachelor's degree in Control Science & Engineering from Zhejiang University Dr. Long's research focuses on computer graphics and 3D computer vision, with particular emphasis on 3D Vision, Physical AI, and World Models. His long-term goal is to develop General-Purpose AI with spatial capabilities. His work bridges theoretical understanding of 3D spaces with practical implementations of spatial AI systems, with applications spanning robotics, virtual reality, and augmented environments. He employs innovative neural network approaches and geometric constraints to advance 3D scene understanding and reconstruction. His publication record shows strong momentum with multiple papers accepted to top-tier conferences including CVPR (5 papers in 2025 alone), ICML, ICLR, ECCV, and TPAMI. His research demonstrates a clear progression from foundational geometric estimation techniques (ASN++) toward more comprehensive spatial AI systems. His scientific recognition includes: Excellent Young Scholars Fund (Overseas) from NSFC Dr. Long has successfully mentored numerous students who have published at major venues and gone on to pursue advanced degrees at prestigious institutions including USTC, Beihang University, HKU, UCAS, Virginia Tech, and HKUST. He is currently recruiting Ph.D. and master's students for Fall 2026, seeking candidates interested in pushing the boundaries of 3D computer vision and spatial AI. His laboratory focuses on developing advanced techniques for 3D scene understanding, neural rendering, and physical AI. Current projects span Gaussian-based representations, neural radiance fields, and geometric estimation, with applications in robotics, virtual environments, and spatial reasoning systems.
Rachel Cummings is an Associate Professor in the Department of Industrial Engineering and Operations Research (IEOR) at Columbia University, with a courtesy appointment in the Department of Computer Science. She serves as Co-chair of the Cybersecurity Research Center at Columbia’s Data Science Institute. Previously, she was faculty at Georgia Tech’s School of Industrial and Systems Engineering (ISyE), holding a courtesy appointment in Computer Science. She holds a Ph.D. in Computing and Mathematical Sciences from Caltech, with research visits at UPenn, Hebrew University, Microsoft Research, and the Simons Institute. Her research focuses on differential privacy, integrating tools from machine learning, algorithm design, economics, optimization, statistics, HCI, usable security, and public policy. She emphasizes practical applications of theoretical privacy-preserving methods. Key roles include Managing Editor for the Journal of Privacy and Confidentiality , service on the ACM U.S. Technology Policy Council, IEEE Standards Association, and Future of Privacy Forum’s Advisory Board. She has advised on the U.S. Census Bureau’s Scientific Advisory Council and served as a Fellow at the Center for Democracy & Technology. Recent work includes papers on privacy elasticity, synthetic control methods, and differential privacy under class imbalance. Her awards include NSF CAREER, DARPA Young Faculty Award, and Best Paper recognitions at DISC, CCS, and SaTML. She actively chairs conferences (e.g., DEF CON Crypto) and mentors students like Tingting Ou (PhD 2025) and Peihan Liu (PhD 2024–present). Her lab explores privacy-preserving technologies, policy implications, and interdisciplinary collaborations.
Clark Olson is a Professor in the Division of Computing & Software Systems at the University of Washington Bothell, part of the School of Science, Technology, Engineering & Mathematics. He earned his Ph.D. in Computer Science from UC Berkeley (1994), M.S. in Electrical Engineering (1990), and B.S. in Computer Engineering (1989) from the University of Washington, Seattle. Education: Ph.D. in Computer Science (2017) from University of California, Berkeley M.S. in Electrical Engineering (1990) from University of Washington, Seattle B.S. in Computer Engineering (1989) from University of Washington, Seattle His research focuses on computer vision, robot navigation, and clustering algorithms. He has developed techniques for Mars rover terrain mapping, subspace clustering, and geometric feature matching. His work bridges theory and application in autonomous systems and image analysis. Analysis of his publications reveals expertise in computer vision (8 papers), clustering algorithms (4 papers), and robotics (5 papers). Key subtopics include Mars exploration (3 papers), Hough transforms (3 papers), and probabilistic methods (3 papers). Professor Olson teaches courses ranging from introductory programming (CSS 161-162) to advanced topics in computer vision (CSS 487-587) and algorithm design (CSS 549). He also advises on the CSSE Capstone (CSS 497) projects requiring rigorous prerequisites and structured evaluation criteria.
