Geert Deconinck is a full professor at KU Leuven , leading the Electrical Energy Systems and Applications (ELECTA) research group within the Department of Electrical Engineering (ESAT). He also serves as scientific leader of the EnergyVille research center's algorithms domain, focusing on smart electrical networks and thermal systems. M.Sc. and Ph.D. from KU Leuven Head of ELECTA since 2012 (10 professors, 8 postdocs, 70+ PhDs) Over 8 million EUR research budget in last 5 years 44 completed PhDs and 10 current advisees IEEE Transactions editorial board member His research spans smart grid architectures , distributed control , and cyber-physical security , with recent focus on EV-grid integration , renewable energy democratization , and multi-carrier energy systems . Current projects include: Smart Charging - E-Mobility meets Renewable Energy Early Detection and Defense Systems for Smart Grids Open-source P2P energy sharing platforms Microgrid control strategies for PV-battery systems Awarded IET Fellow and IEEE Senior Member status, his work combines machine learning with power systems engineering through both theoretical modeling and experimental validation . He has contributed over 575 publications with 9800+ Google Scholar citations.
Jered Haun is an Associate Professor at the Samueli School of Engineering , University of California, Irvine , with joint appointments in Biomedical Engineering , Chemical and Biomolecular Engineering , and Materials Science and Engineering . Based in 3107 Natural Sciences II , his research focuses on developing nanoengineering and molecular medicine technologies to improve disease diagnosis and treatment. Email: jered.haun@uci.edu Phone: (949) 824-1243 Research Interests include: Nanomaterial Probes for molecular profiling and disease detection Microfabricated Platforms for cell analysis and tissue processing Targeted Delivery Carriers that interact with unique disease molecules Technologies employed in his lab encompass: Fluorescence imaging and MRI enhancements Bioorthogonal chemistry and nanosensor development Micro-NMR for tumor analysis Quantum dot-based detection systems Awards include: NIH National Cancer Institute's Innovative Molecular Analysis Technologies Funding The Haun Laboratory actively recruits graduate and undergraduate students for projects related to nanoengineering, molecular medicine, and microfluidic device development.
Emilio Frazzoli is a Full Professor at ETH Zurich’s Department of Mechanical and Process Engineering. He leads the Institute for Dynamic Systems and Control and the Center for Sustainable Future Mobility, focusing on autonomous systems, robotics, and socio-technical control frameworks. Current affiliations: ETH Zurich (Dynamic Systems and Control, Sustainable Future Mobility) Research: Autonomous mobility-on-demand, game theory for resource allocation, and safety verification in multi-agent systems His work bridges robotics, control theory, and economics, with projects like the open-source AMoDeus simulation framework for autonomous taxis and karma-based resource allocation systems. Recent publications emphasize trustworthy AI, reproducibility in autonomous vehicle control, and human-robot interaction challenges. Notable projects include nuReality (VR-based pedestrian interaction studies) and CARSI II (context-driven vehicle interfaces).
Mark de Rond is Professor of Organisational Ethnography at Cambridge Judge Business School and Fellow of Darwin College, University of Cambridge. His work focuses on immersive ethnographic studies of human behavior in extreme contexts including war zones, high-stakes sports, and controversial social movements, revealing how individuals navigate challenging circumstances through compromise and sensemaking. His educational background includes a DPhil from the University of Oxford alongside advanced degrees in management and economics, photojournalism, documentary photography, biography, creative nonfiction, and prose fiction. This multidisciplinary training informs his unconventional methodological approach. De Rond's research centers on extreme context ethnography, examining how pressure-cooker environments expose fundamental organizational dynamics. His work spans military medicine (Camp Bastion fieldwork), elite sports (Cambridge Boat Race), Amazon river expeditions, and controversial practices like paedophile hunting. Key contributions explore negotiation, conflict resolution, serendipity, institutional persistence, and the emotional toll of fieldwork, often employing innovative methods like enactive ethnography and linguistic analysis of digital communities. His recent publications reveal a growing focus on digital vigilantism and the societal implications of extreme practices, with increasing interdisciplinary reach across organizational studies, criminology, medical anthropology, and sociology. The work demonstrates methodological creativity through linguistic analysis of Facebook groups, embodiment studies in extreme environments, and dream-based reflexive techniques. Scientific recognition includes: BEST ARTICLE AWARD FOR 2016 Favorite MBA Professor honors from Poets & Quants (2021, 2022) Guinness World Record for first unsupported Amazon row De Rond advises MBA students and delivers executive education to top law firms (Slaughter and May, Allen & Overy), professional services (McKinsey, KPMG), corporations (Sky, BT, Diageo), and NGOs (UNICEF, NHS). His negotiation training from Harvard Law School informs both academic work and university mediation practice. Current research involves collaborative teams studying paedophile hunting (featured in Sundance-nominated documentary Predators ) and high-performance sports organizations, with fieldwork requiring deep immersion in challenging environments. His research methodology involves embedded collaboration with diverse teams including medical personnel in conflict zones, elite rowers, and controversial activist groups, often pushing methodological boundaries through photojournalism and creative nonfiction approaches.
Dr. V.M. (Bala) Balasubramaniam is a Professor in the Department of Food Science and Technology at The Ohio State University. He holds editorial roles in food engineering journals and is a Fellow of IFT and IUFoST. His research focuses on clean food manufacturing technologies, particularly high-pressure and nonthermal methods, emphasizing microbial inactivation and nutrient preservation. He teaches unit operations in food engineering and contributes to industry via short courses and pilot plant demonstrations. Education: B.S. (Tamil Nadu Agricultural University), M.S. (Asian Institute of Technology), Ph.D. (Ohio State University). Research Interests: Thermal/nonthermal processing, food safety/quality modeling, and innovative applications of high-pressure technologies. Recent work includes ultra-shear technology development and superheated steam sanitation. Over 100 scientific papers, 20 book chapters, and co-edited books on high-pressure processing. Awards include the 2021 IFT Research & Development Award and 2017 Calvert L. Willey Award. Advising: Supervises graduate students like Liz Astorga Oquendo and Shruthy Seshadrinathan. Industrial outreach includes a USDA consortium for ultra-shear commercialization. Labs/teams focus on pilot-scale equipment testing and microbial efficacy studies.
Ira Lit is a Teaching Professor at the Graduate School of Education at Stanford University. They serve as Faculty Director of the Stanford Teacher Education Program (STEP) and are affiliated with the Stanford Woods Institute for the Environment . Their work bridges teacher education, elementary education, and educational equity. Key research areas include: Equity in education systems Design of schooling models Experiences of marginalized students Family-school partnerships Recent publications analyze teacher preparation programs, voluntary desegregation, and multilingual education. Lit has supervised doctoral students like Elena Darling Hammond and Nallely Aceves , with a focus on race, identity, and policy in California schools. They hold a Ph.D. from Stanford in Curriculum Studies and Teacher Education (2003) and have taught at Stanford since 1986.
Mennatallah El-Assady serves as Assistant Professor at ETH Zurich's Department of Computer Science, where she leads the Interactive Visualization and Intelligence Augmentation Lab (IVIA). Her academic trajectory includes research fellowships at ETH's AI Center and doctoral work at University of Konstanz and OntarioTech University, establishing her expertise at the intersection of visualization and artificial intelligence. Her research focuses on advancing responsible data-driven decision-making through human-centered analytics, with particular emphasis on explainable machine learning systems. Dr. El-Assady combines data mining techniques with visual interfaces to create transparent AI workflows, specializing in text data analysis. Her work bridges computational linguistics, digital humanities, and information visualization to develop tools that make complex AI processes interpretable for end users. Recent publications demonstrate growing focus on generative AI's impact on visualization practices and sophisticated frameworks for interactive machine learning. Her research consistently addresses the challenge of maintaining human agency in increasingly automated systems, with applications spanning political debate analysis, musicology, and healthcare data interpretation. The trend shows increasing sophistication in evaluation methodologies for visual analytics systems. Best Paper Award: Honorable Mention for 'Progressive Learning of Topic Modeling Parameters: A Visual Analytics Framework' Dr. El-Assady actively shapes her field through workshop leadership including ArgVis (Argument Visualization), Vis4DH (Visualization for Digital Humanities), and VISxAI (Visualization for AI Explainability). Her teaching includes 'Interactive Machine Learning- Visualization and Explainability' at ETH Zurich, training next-generation researchers in human-centered AI development. She maintains strong industry connections with coverage in Forbes and ETH News regarding human-AI collaboration frameworks. The Interactive Visualization and Intelligence Augmentation Lab (IVIA) develops cutting-edge tools including explAIner for transparent machine learning, LingVis for linguistic analysis, VisArgue for debate structure visualization, and VALIDA for political deliberation analysis. These projects share a common thread of enhancing human understanding through carefully designed visual interfaces that expose AI decision processes.
Eli Ben-Michael is an Assistant Professor jointly appointed in the Heinz College of Information Systems and Public Policy and the Department of Statistics & Data Science at Carnegie Mellon University. He is affiliated with the CMU-NIST AI Measurement Science & Engineering Cooperative Research Center (AIMSEC), contributing to cutting-edge research at the intersection of statistics, policy analysis, and artificial intelligence. His educational background includes a PhD in Statistics from U.C. Berkeley and undergraduate studies at Columbia University where he earned a dual degree in Computer Science and Statistics. Prior to his current position, he completed a postdoctoral fellowship at Harvard University's Institute for Quantitative Social Science and Department of Statistics. Ben-Michael's research focuses on developing innovative statistical and computational methods for causal inference and policy evaluation, with particular emphasis on integrating machine learning techniques to address complex problems in public policy and social science. His work bridges theoretical statistics with practical applications in healthcare, criminal justice, education, and social policy. Current research directions include safe policy learning, sensitivity analysis for clustered data, and methodological innovations for the synthetic control method. His publication record shows a strong trajectory in top-tier journals including Journal of the American Statistical Association, Journal of the Royal Statistical Society, and Proceedings of ICML. Recent work demonstrates increasing focus on policy-relevant applications including abortion legislation impacts, pre-trial risk assessment, and healthcare disparities, while maintaining methodological rigor in causal inference frameworks. Ben-Michael has developed open-source software tools including augsynth and multical R packages, which implement his methodological contributions for synthetic controls and multilevel calibration weighting. These packages have been adopted by researchers in multiple disciplines for causal inference applications.
Dr. Carolyn Conner Seepersad is a Woodruff Professor in the George W. Woodruff School of Mechanical Engineering at Georgia Institute of Technology. She leads the Digital Design and Manufacturing research group and previously founded the Center for Additive Manufacturing and Design Innovation at The University of Texas at Austin. Her research focuses on additive manufacturing, materials design, and process innovation. She holds editorial roles, including Editor-in-Chief of the ASME Journal of Mechanical Design, and has received numerous awards for research and teaching. Education: PhD, Mechanical Engineering, Georgia Tech, 2004 MS, Mechanical Engineering, Georgia Tech, 2001 BA, Philosophy, Politics, and Economics, Oxford University, 1998 BS, Mechanical Engineering, West Virginia University, 1996 Her research interests span design for additive manufacturing, simulation-based materials and structures, and metamaterials. She emphasizes manufacturing-aware design and sustainability. Key contributions include lattice structure optimization, negative stiffness composites, and process-aware manufacturing techniques. Her publications reflect advancements in additive manufacturing processes, materials characterization, and design methodologies. Awards include the ASME Design Automation Award and recognition as a University of Texas System Academy of Distinguished Teachers. Seepersad has advised on grants such as the LEAP-HI GOALI project and contributed to initiatives like the Solid Freeform Fabrication Symposium. Her work bridges academia and industry, emphasizing practical applications and innovation. Labs/Teams: Leads the Digital Design and Manufacturing group at Georgia Tech, previously directed the UT Austin Additive Manufacturing Center.
Associate Professor Chen Lou is a faculty member at the Wee Kim Wee School of Communication and Information, Nanyang Technological University (NTU), Singapore. Her research focuses on consumer psychology and advertising effects, exploring how marketing communications influence behavior through information processing mechanisms. She investigates topics like influencer marketing, content strategies, and the psychological impacts of brand communications. A mixed-methods researcher, she employs techniques such as eye-tracking, psychophysiological measures, and big data analytics (e.g., sentiment analysis). Dr. Lou holds editorial roles at journals including the International Journal of Advertising and Journal of Interactive Advertising , and serves on committees for organizations like the American Academy of Advertising (AAA) and the International Communication Association (ICA). She earned her Ph.D. from Michigan State University, an M.S. from Ohio University, and a B.A. from Wuhan University. Her research has been recognized with awards such as the 2021 Mary Alice Shaver Promising Professor Award (AAA) and multiple best article awards. She actively supervises student projects, including top-winning FYPs at NTU. Key research areas include: Influencer marketing dynamics and virtual influencers Social media platform affordances and advertising effectiveness Brand activism and message framing Psychological ownership in content marketing Consumer responses to ethical and cultural messaging
Anna Choromanska is an Associate Professor in the Department of Electrical and Computer Engineering at NYU Tandon School of Engineering, with affiliations to NYU Center for Data Science (CDS), NYU Center for Urban Science and Progress (CUSP), NYU Center for Advanced Technology in Communications (CATT), and the C2SMART Center. Her research focuses on deep learning optimization, generalization, and applications in autonomous driving and large-scale data analysis. She holds an Alfred P. Sloan Fellowship and NSF CAREER Award, and her work impacts industries like NVIDIA and Facebook. She directs the Learning Systems Laboratory (LSL), emphasizing interdisciplinary experimental/theoretical work. Research Interests: Machine Learning fundamentals, DL optimization, continual learning, autonomous vehicle systems, large data analysis. Her lab explores DNN learning dynamics, training architecture design, and scalable algorithms. Professional Impact: Over 50 invited talks, workshop organization for top ML conferences, and contributions to open-source projects like Vowpal Wabbit. Her algorithms are deployed in production systems at Facebook and Baidu. Awards: NSF CAREER Award Alfred P. Sloan Fellowship IBM Global University Program Academic Award (2x) Columbia University Presidential Fellowship Advising & Labs: Leads LSL, supervising interdisciplinary projects in optimization and autonomy. Actively involved in NYU's Modern AI seminar series and industry partnerships through CATT. Personal Interests: Accomplished pianist, salsa dancer, and fashion design enthusiast with notable performances and certifications in dance and music.
Sophie Sanchez is an Associate Professor at Uppsala University's Department of Organismal Biology, specializing in Evolution and Development. Her research focuses on vertebrate evolution, using advanced imaging techniques like synchrotron microtomography to study fossilized anatomy and developmental processes. She has contributed significantly to understanding sensory organ evolution in jawed vertebrates, early tetrapod development, and the structural adaptations of ancient fish. Key work includes studies on placoderms, osteostracans, and the molecular bases of evolutionary innovations. Her findings bridge paleontology with developmental biology, employing cutting-edge microscopy to reveal hidden biological details in fossils. Publications highlight her expertise in fossil musculature, bone histology, and genomic evolution, with high-impact contributions to journals like Nature and Science . Collaborative projects involve global teams analyzing Devonian and Permian vertebrates, emphasizing interdisciplinary approaches to evolutionary questions.
Magdy M. A. Salama is a Professor and University Research Chair at the University of Waterloo's Department of Electrical and Computer Engineering, Faculty of Engineering. He holds a P.Eng. license and is a Fellow of the IEEE. His research spans Energy Systems (Power Quality, Smart Grids, Renewable Energy) and Biomedical Engineering (Medical Imaging, Sleep Analysis). He has authored/co-authored over 460 publications and supervised numerous graduate students. Education: PhD (University of Waterloo), M.Sc. and B.Sc. (Cairo University). Awards include the IEEE Fellow distinction, University Research Chair, and multiple teaching/research awards from the University of Waterloo. Research trends in his articles focus on Smart Grid resiliency, renewable integration, cyber-physical security, and biomedical applications of AI. Notable projects include voltage sag mitigation, EV fleet electrification, and blockchain-based energy trading platforms. Scientific Awards: IEEE Fellow, University Research Chair, Teaching Excellence Award (2000) Grants/Consultation: Extensive industry and institutional collaborations on power systems and biomedical tech. Labs/Teams: Active in High Voltage Lab, Smart Grids Research Group, and Medical Image Processing Lab.
Soheil Salehi is a tenure-track Assistant Professor in the Department of Electrical and Computer Engineering at the University of Arizona, with a joint appointment in Systems and Industrial Engineering. He is the Director of the Privacy-preserving, Intelligent, and Secure Computing (PRISM) Lab, established in August 2022. Prior to this, he was an NSF-Sponsored Computing Innovation Fellow and Postdoctoral Research Fellow at the University of California, Davis. Ph.D., Electrical and Computer Engineering, University of Central Florida, 2020 M.S., Electrical and Computer Engineering, University of Central Florida, 2016 B.S., Isfahan University of Technology, Iran, 2014 Dr. Salehi's research focuses on the intersection of hardware, AI, and security. His work spans hardware and AI-enabled security in IoT , Generative AI for hardware design and security , neuromorphic and biologically-inspired AI hardware , emerging spin-based devices , reconfigurable architectures , low-power VLSI circuits , and digital twins and mixed reality for semiconductor workforce development . He also explores the application of Generative AI in personalized education . His recent publications, spanning 2023–2025, reveal a strong trend toward integrating AI and machine learning into hardware security and design. Key themes include automated secure IC design flows , AI-driven hardware obfuscation , firmware and side-channel attack analysis , security in neuromorphic and spiking neural networks , and educational frameworks using digital twins and generative models . His work appears in top venues like DAC, ICCAD, USENIX Security, IEEE TCAS-I, and ISCAS. Outstanding Reviewer Award, IEEE/ACM Design Automation Conference (DAC), 2023 Best Presentation of the Symposium Award, UC Davis Postdoctoral Research Symposium, 2021 UCF Excellence by a Graduate Teaching Assistant (University-Level), 2016 Nominated for 30-under-30 Award, UCF, 2020 Nominated for Postdoctoral Research Excellence Award, UC Davis, 2022 Dr. Salehi has secured significant research funding as PI and Co-PI, including a $300K NSF SaTC EAGER grant on Generative AI-based Personalized Cybersecurity Tutor, a $174,000 University of Arizona PIF Award, and multiple RII grants totaling over $198K. He has also received industry funding from CHEST. He actively mentors students and leads the PRISM Lab, which focuses on privacy-preserving and intelligent secure computing. His service includes roles as Technical Program Committee (TPC) Member and Session Chair at premier conferences such as DAC, ICCAD, CCS, NDSS, and GLSVLSI. The PRISM Lab, under his direction, conducts cutting-edge research in secure and intelligent hardware systems, with applications in IoT, edge computing, and workforce development. The lab emphasizes interdisciplinary collaboration and innovation in both research and education.
Tiancheng Zhao is a principal researcher at the Binjiang Institute of Zhejiang University and founder of the Om Artificial Intelligence Laboratory (Om AI Lab), dedicated to frontier open multimodal AGI research for building next-generation agents that transform work and life through advanced human-machine interaction. His academic credentials include: Ph.D. in Computer Science from Carnegie Mellon University (2016-2019) under Prof. Maxine Eskenazi, Prof. Louis-Philippe Morency, Prof. William W. Cohen, and Dr. Dilek Hakkani-Tur, with pioneering dissertation “Learning to Converse With Latent Actions” in end-to-end generative conversational models M.S. in Computer Science from Carnegie Mellon University (2014-2016) B.S. in Electrical Engineering from UCLA (2010-2014) with Summa Cum Laude, focusing on speech signal processing under Prof. Abeer Alwan Dr. Zhao’s research centers on multimodal foundation models and agents, tackling three core challenges: Multimodal Models for cross-modal representation learning in high-dimensional data, Learning to Learn for effective skill acquisition from diverse signals (supervised labels, rewards, meta-learning), and AI Agents for open-world understanding and complex decision-making. His work bridges computer vision, natural language processing, and real-world applications including healthcare analytics and remote sensing. Analysis of his 50+ publications reveals accelerating innovation in multimodal large language models (2024-2025), with emphasis on stable vision-language architectures (VLM-R1), agent orchestration frameworks, and domain-specific applications in geospatial analysis and healthcare. Key trends include solving long-tail distribution challenges in satellite imagery, developing human-like zooming capabilities for multimodal LLMs, and creating unified benchmarks for autonomous GUI testing. His scientific recognition includes: National Breakthrough Technology Award by Ministry of Science and Technology (2021) Microsoft Research Best & Brightest PhD (2018) BEST PAPER AWARD at SIGDIAL 2018 Best Paper Nomination at SIGDIAL 2016 Top 1 Outstanding Bachelor of Science Award at UCLA (2014) As Om AI Lab founder, Dr. Zhao leads research teams developing computational building blocks for human-AI collaboration. While specific student mentorship details aren’t public, his extensive publication record with junior co-authors indicates active research supervision. Current projects focus on practical system implementations for real-world multimodal agent deployment across diverse domains.
