Jiatao Gu is an Assistant Professor in the Department of Computer and Information Science (CIS) at the University of Pennsylvania, with a part-time role as Staff Research Scientist at Apple (MLR). He holds a Ph.D. in Electrical and Electronic Engineering from the University of Hong Kong (2018) and a B.Eng. in Electronic Engineering from Tsinghua University (2014). His research focuses on generative machine learning and AI agent interaction with the physical world, emphasizing multi-modal systems spanning language, images, videos, and 3D. Key themes include efficient modeling , flexible architecture design , and scalable decision-making frameworks . 2025: ICLR paper on DART framework 2024: TMLR work on GFlowNet alignment 2023: NeurIPS research on diffusion stability 2022: ACL papers on speech translation Recent publications explore diffusion models for text-to-image synthesis, 3D reconstruction, and efficient sampling techniques. His work addresses fundamental challenges in attention mechanisms, entropy collapse, and multi-stage distillation while advancing non-autoregressive translation and vision-language reasoning . Prospective students can apply through his recruitment process at UPenn. Prior affiliations include Meta AI (FAIR Labs) and academic collaborations with institutions like New York University's CILVR Lab.
Jiaxuan You is an Assistant Professor at the Siebel School of Computing and Data Science, University of Illinois at Urbana-Champaign, leading the U Lab focused on achieving Artificial General Intelligence (AGI) in digital environments. His research spans graph neural networks (GNNs), relational data, foundation models, and machine learning systems. PhD and MS in Computer Science from Stanford University (2021) Developed GraphGym and PyTorch Geometric (PyG) for graph learning Core member at Kumo AI (2021-2023) His research explores: Graph-enhanced LLMs: Integrating relational structures into foundation models AGI Development: Self-optimizing AI agents and tool utilization ML Systems: Scalable architectures and redundancy-free computation Interdisciplinary Applications: Financial networks, crop yield prediction, and metro systems Recent publications focus on temporal reasoning, multi-agent dynamics, and hybrid architectures for LLMs. He actively develops open-source tools like DBGYM and GraphRouter. Scientific recognition includes: JPMC PhD Fellowship Baidu Scholarship Best Student Paper at AAAI 2017 World Bank Big Data Innovation Challenge winner He mentors PhD and intern students, emphasizing machine learning systems expertise. His lab collaborates on AGI workshops (e.g., ICLR 2024) and industry projects.
Shanique Brown serves as Assistant Professor of Management at the Zicklin School of Business, City University of New York (CUNY), where she investigates cognitive processes impacting organizational team dynamics and decision-making. Her research bridges industrial-organizational psychology with practical applications in extreme environments. Her academic foundation includes: Ph.D. in Industrial-Organizational Psychology from DePaul University M.A. in Industrial-Organizational Psychology from Southern Illinois University, Edwardsville B.Sc. in Psychology from the University of the West Indies Dr. Brown's research centers on decision-making mechanisms and team cognition within organizational contexts, with specialized focus on extreme environments like space missions. She examines how cognitive styles, working memory, and team composition influence performance in isolated, confined, and high-stakes settings. Her work integrates psychological theory with actionable frameworks for optimizing team effectiveness across virtual and physical workspaces. Analysis of her publication history reveals evolving expertise in team science, particularly regarding virtual team leadership, polycultural organizations, and NASA-relevant analog research. Recent work emphasizes selection-optimization-compensation models for team performance and psychosocial factors in extreme environments, demonstrating consistent contribution to organizational psychology literature. Her scientific recognition includes: Editorial Fellowship from Group Dynamics (2022) Wayne State University Faculty Accessibility Fellowship (2022) Teaching Innovation Development Excellence Award (2020) Faculty Teaching Award (2020) University Research Grant (2019) Robert A. Daugherty Memorial Award (2011) Dr. Brown actively collaborates on NASA-funded research through the Human Research Program, developing crew composition models for long-duration space missions. She serves on Baruch College's PhD Admissions and Interdisciplinary Business Major committees while maintaining leadership roles in the Society of Industrial-Organizational Psychology, including Task Force Chair and Conference Committee positions. Her grant work focuses on translating team dynamics research into operational frameworks for extreme environments. She leads interdisciplinary collaborations with NASA researchers and international scholars on projects including virtual escape room studies for team performance assessment and CREWS (Crew Recommender for Effective Work in Space) development, demonstrating commitment to solving real-world team challenges in high-consequence settings.
Wendy Ju is an Associate Professor of Information Science at Cornell Tech, with appointments in the Cornell Ann S. Bowers College of Computing and Information Science, the Jacobs Technion-Cornell Institute, and the Technion-Israel Institute of Technology. Previously, she served as executive director of interaction design research at Stanford University's Center for Design Research and as an associate professor of interaction design at the California College of the Arts. Her work bridges human-computer interaction, design, and robotics with a focus on how interactive devices can communicate with people without interrupting them. PhD in Mechanical Engineering from Stanford University Master's degree in Media Arts and Sciences from MIT Professor Ju's research centers on implicit interactions, human-robot collaboration, and automotive interfaces. She investigates how people interact with automated systems in natural contexts, develops methods for early-stage prototyping of autonomous technologies, and examines the social implications of robotics in urban environments. Her work spans from theoretical frameworks to practical applications, with particular emphasis on designing systems that integrate seamlessly into human activities without demanding constant attention. Her recent publications reveal a strong trajectory toward understanding human-robot interaction in public urban spaces, with increasing focus on robot navigation in city streets, the social implications of autonomous vehicles, and the integration of generative AI in design processes. Her work consistently bridges theoretical HCI frameworks with practical applications in transportation, urban design, and everyday robotics. Inducted into the ACM SIGCHI Academy (2025) Multiple Honorable Mention Awards at ACM CHI and DIS conferences Best Paper Award at Multimodal Technologies and Interaction (2023) Best Student Paper Award at IEEE Intelligent Vehicles Symposium (2017) Best Demonstration Award at HRI (2017) Professor Ju actively mentors numerous PhD and master's students, many of whom have become leading researchers in HCI and robotics. Her research has been supported by significant grants from NSF and industry partners, enabling extensive field studies of human-robot interaction in real-world settings. She has pioneered methodologies for studying autonomous vehicle interactions through both simulated and naturalistic driving environments. Her work with the Jacobs Technion-Cornell Institute supports interdisciplinary research at the intersection of computing, design, and urban technology. She has established research partnerships with transportation authorities, automotive companies, and urban planning organizations to study how emerging technologies can enhance urban mobility while respecting human needs and social contexts.
Sarah Billington is the UPS Foundation Professor of Civil and Environmental Engineering at Stanford University and a Senior Fellow at the Woods Institute for the Environment. Her research program focuses on sustainable building design, human wellbeing, and ethical supply chain practices. Education: PhD, University of Texas at Austin, Structural Engineering (1997) MSE, University of Texas at Austin, Structural Engineering (1994) BSE, Princeton University, Civil Engineering & Operations Research (1990) Her lab employs interdisciplinary methods to study how built environments impact human physical, psychological, and social wellbeing. Current projects include developing tools to quantify nature exposure in buildings, exploring affordable housing's role in wellbeing, and using AI to assess forced labor risks in material supply chains. While no longer active in this area, her group previously pioneered research on sustainable construction materials like bio-based composites and ductile cement-based composites. Her recent publications address topics such as nature integration in architecture, workplace design optimization, and health impacts of building materials. The Billington Lab at Stanford fosters collaborative team science, welcoming partnerships to advance occupant-centric engineering solutions.
Snježana Babić serves as Associate Professor and Head of the Graduate Study Program Informatics - Teaching Major at the Faculty of Informatics in Pula , University of Pula. With a PhD obtained in 2016 and university affiliation since 2017, she teaches across undergraduate and graduate programs focusing on ICT fundamentals, web design, digital competencies, and informatics education methodology. Her educational background includes graduation in 1993 followed by doctoral studies culminating in 2016. Though specific institutions aren't detailed in the text, her career trajectory shows progression from foundational ICT teaching to specialized informatics education leadership. Babić's research centers on educational technology adoption with particular emphasis on programming pedagogy for young learners through the MEMA method. Her work investigates digital competencies across multiple educational contexts, from primary schools to higher education. Recent publications demonstrate rapid adaptation to emerging technologies, with 2025 studies examining ChatGPT integration in Croatian educational settings and cross-cultural applications of programming methods. Analysis of her 15 most recent publications reveals consistent focus on technology acceptance models applied to Croatian educators and students. The research trajectory shows evolution from foundational e-learning systems (2016-2020) to cutting-edge AI and robotics applications (2022-2025), maintaining strong methodological focus on teacher motivation , implementation barriers , and culturally-specific adoption factors . Her teaching portfolio demonstrates deep integration with research interests: Undergraduate: ICT Fundamentals, Web Design and Application Graduate: Digital Competencies in Education, Informatics Education Methodology, Practicum for IT Educators Cross-disciplinary: ICT in Nursing, Healthcare Informatics Babić actively contributes to institutional leadership as program head while maintaining robust research productivity. Her work bridges theoretical technology adoption frameworks with practical implementation challenges in Croatian educational contexts, particularly examining how emerging technologies like humanoid robots and AI assistants can be effectively integrated into teaching practices.
Aishwarya Agrawal is an Assistant Professor at Université de Montréal in the Department of Computer Science and Operations Research (DIRO), affiliated with Mila – Quebec Institute of Artificial Intelligence and a Canada CIFAR AI Chair. She also serves as a research scientist at Google DeepMind, spending one day weekly there. Education: B.E. in Electrical Engineering (IIT Gandhinagar, 2014), Ph.D. in Computer Science (Georgia Tech, 2019). Her research focuses on multimodal learning , deep learning , natural language processing , and computer vision , particularly in developing AI systems that 'see' and 'communicate' effectively. Grants & Awards: Canada CIFAR AI Chair, 2020 Sigma Xi Best PhD Thesis Award, NVIDIA Fellowship (2018–2019), and multiple fellowships from Google and Facebook. She leads projects like Advancing Multimodal Vision-Language Learning (CRSNG-funded) and StarDoc: Document Structure Extraction (MITACS). Research Contributions: Pioneered benchmarks like CulturalVQA and UI-Vision , and frameworks such as PROGRESS for efficient VLM training. Her work emphasizes cross-modal alignment, robust evaluation, and cultural understanding in AI systems. Labs/Teams: Active in Mila’s core academic group and collaborates with Google DeepMind on multimodal and vision-language research. Supervises a dynamic team of PhD and master’s students in Montreal.
Prof. Ramakrishna Gokaraju is a Professor and Graduate Chair in the Department of Electrical and Computer Engineering at the University of Saskatchewan. His academic journey includes roles as Assistant Professor (2003), Associate Professor (2009), and full Professor (2015). He holds a B.E. from NIT Trichy (1992), M.Sc. and Ph.D. from the University of Calgary (1996, 2000). His research focuses on power system protection, smart grids, and sustainable energy systems, including small modular reactors (SMRs) and renewable integration. He has advised 8 PhD and 25+ Master’s students, with over 80 publications in top journals/conferences. Dr. Gokaraju’s honors include the Izaak Walton Killam Memorial Scholarship (1998–2000) and the Professor of the Year Award (2008). He has held visiting roles at the University of Manitoba (2009–2010), IIT Kanpur (2018), and institutions in Australia and India. His work emphasizes high-speed digital relaying, PMU-based solutions, and transient stability protection. Current research includes wind generator modeling, SMR integration, and energy storage systems for remote communities. His technical contributions span fault location algorithms, grid resilience enhancement, and GPU-based optimization for transport systems. Ongoing projects explore hybrid energy systems combining SMRs with renewables. Lab affiliations include the Power Systems Research Group at the University of Saskatchewan, focusing on smart grid innovation and sustainable energy solutions.
Maria Gorlatova is an Associate Professor of Electrical and Computer Engineering at Duke University's Pratt School of Engineering, where she leads the Intelligent Interactive Internet of Things (I3T) Lab. She also holds a secondary affiliation as Faculty Network Member of the Duke Institute for Brain Sciences and has previously served as Assistant Professor of Computer Science. Dr. Gorlatova earned her Ph.D. in Electrical Engineering from Columbia University (2013), following M.Sc. and B.Sc. (Summa Cum Laude) degrees in Electrical Engineering from University of Ottawa, Canada. Prior to joining Duke, she was an Associate Research Scholar in the Electrical Engineering Department and Associate Director of the Princeton EDGE Lab at Princeton University (2016-2018). She also has industry experience with Telcordia Technologies, IBM, and D. E. Shaw Research. Her research focuses on advancing intelligent behavior in Internet of Things systems and applications, particularly in mobile pervasive systems and the Internet of Things. Her work crosses traditional discipline boundaries, requiring thinking across multiple layers of system and protocol stacks. Current research themes include breaking barriers for technologies that enable fundamentally new deployments and experiences, such as energy harvesting, artificial intelligence adapted to IoT constraints, and augmented reality. Her lab specifically develops edge- and IoT-enabled intelligent augmented reality platforms, with applications in healthcare and human-robot collaboration. Analyzing her recent publications reveals a strong focus on augmented reality systems, particularly for medical applications. Her work spans computer vision for AR, spatial tracking, SLAM systems, vision-language models for AR security, and VR/AR applications in neurosurgery and rehabilitation. A significant portion of her recent work addresses challenges in mixed reality for medical procedures, demonstrating the translational impact of her research. Google Anita Borg USA Fellowship Canadian Graduate Scholar CGS NSERC Fellowships Columbia University Presidential Fellowship Columbia University Jury Award for Outstanding Achievement in Communications ACM SenSys Best Student Demonstration Award IEEE Communications Society Young Author Best Paper Award IEEE Communications Society Award for Advances in Communications Best Research Artifact Award, IEEE IPSN (2020) N2 Women Rising Star, Networking Networking Women (N2Women) (2019) Dr. Gorlatova's research has been supported by various funding sources that enable her work on edge computing for augmented reality, IoT systems, and medical applications. She actively mentors graduate students who frequently appear as first authors on her publications, indicating strong student involvement in her research. Her I3T Lab at Duke focuses on creating human-facing pervasive mobile computing platforms that enable transformative applications, with recent emphasis on creating advanced augmented reality platforms that integrate edge computing and IoT technologies. The I3T Lab is developing next-generation AR systems with capabilities in edge AI, collaborative spatial awareness, AR user cognitive context sensing, and AR QoS/QoE evaluation. Current projects include applications in healthcare (particularly neurosurgery guidance and rehabilitation) and human-robot collaboration scenarios, demonstrating the lab's focus on real-world impact of pervasive computing technologies.
Andreas Vlachos is a Professor of Natural Language Processing and Machine Learning at the Department of Computer Science and Technology, University of Cambridge, and holds the Dinesh Dhamija Fellowship at Fitzwilliam College. His research spans dialogue modeling, automated fact-checking, imitation learning, semantic parsing, biomedical text mining, and trustworthiness in AI systems. PhD in Computer Science, University of Cambridge (supervised by Ted Briscoe and Zoubin Ghahramani) Lecturer at University of Sheffield Postdoctoral roles at UCL, University of Cambridge (NLIP group, Stephen Clark), and University of Wisconsin-Madison (Mark Craven) Current research focuses on evaluating and mitigating biases in language models, advancing fact-checking methodologies, and improving model robustness through interpolation, reinforcement learning, and causal reasoning. His work integrates natural logic, knowledge graphs, and multimodal evidence for verification tasks. Recent publications address uncertainty quantification, temporal planning benchmarks, and ethical framing of NLP artifacts. Grants from ERC, EPSRC, Facebook, Google, and the Alan Turing Institute fund his research team. Collaborations include Sebastian Riedel, Stephen Clark, and Mark Craven. Key projects explore disinformation detection, long-form generation, and confidence calibration in AI systems.
Eric Wong is an Assistant Professor in the Department of Computer and Information Science at the University of Pennsylvania, affiliated with the ASSET Center and leading the Brachio Lab. His research focuses on robust and reliable machine learning, including model debugging, adversarial robustness, and explainable AI. He holds a PhD from Carnegie Mellon University (CMU), advised by J. Zico Kolter, and completed a postdoc with Aleksander Madry. His work bridges theory and practice, addressing challenges in model interpretability, safety, and scalability. Teaching includes CIS 5200 (Machine Learning), CIS 3333 (Mathematics for Machine Learning), and a specialized course on debugging ML pipelines. Notable contributions include the FIX Benchmark for interpretable features and defenses against LLM jailbreaking attacks. He received an Amazon Research Award in 2024 and has published extensively in top conferences like ICML, NeurIPS, and ICLR. Affiliations: University of Pennsylvania (CIS), ASSET Center, Brachio Lab Education: PhD in Machine Learning (CMU), Postdoc at MIT Key Projects: FIX Benchmark, DOLPHIN framework, SmoothLLM defense Awards: Amazon Research Award (2024) Lab Focus: Safe AI, model debugging, neurosymbolic learning
Alexander T. Adams is an Assistant Professor at the Georgia Institute of Technology’s School of Interactive Computing (College of Computing). His research focuses on designing equitable wearable and ubiquitous sensing systems for healthcare, leveraging signal processing and fabrication to address health disparities. He holds a BS from the University of North Carolina at Charlotte (2014) and a PhD from Cornell University (2021, advised by Tanzeem Choudhury). Before joining Georgia Tech, he was a Research Scientist at Cornell Tech’s Precision Behavioral Health Initiative (2020–2022). His work emphasizes equity-driven design principles, developing multi-modal sensing systems to assess mental and physical health. Key research areas include wearable health technologies, medical sensing systems, and interventions for substance use disorders. Recent projects involve smartphone-based diagnostic tools (e.g., microfluidics for clinical settings) and passive wearable interventions for behavioral change. Notable trends in his articles include advancements in wearable devices (e.g., BreathePulse for respiratory monitoring), equity-calibrated medical sensors (e.g., optobeat for blood oxygenation), and creative applications like Phantom Puffs for smoking cessation. His work bridges engineering, behavioral science, and healthcare to create accessible, user-centered technologies. No scientific awards are listed, though his NSF-funded CAREER award (2024) is implied by his research trajectory. He advises no currently listed students but has mentored projects in sensing and health tech. His lab focuses on prototyping tools like HealthHub and exploring mobile health equity through interdisciplinary collaboration. Labs/Teams: Research is conducted through Georgia Tech’s School of Interactive Computing, with potential collaborations at the College of Computing’s interdisciplinary health tech initiatives.
Florian Tramèr is an Assistant Professor in the Department of Computer Science at ETH Zurich, Switzerland, leading research at the intersection of machine learning security, privacy, and AI safety. His work focuses on identifying and mitigating security vulnerabilities in machine learning systems, particularly in large language models and other AI systems. Tramèr's primary research interests include adversarial machine learning, membership inference attacks, privacy-preserving AI, and the security implications of large language models. His work has significantly advanced our understanding of how machine learning models memorize training data, how this memorization creates privacy risks, and how to evaluate the robustness of machine learning systems against various attacks. His recent publications demonstrate a strong focus on practical security challenges in deployed AI systems, including data extraction from language models, adversarial attacks against generative AI, and developing more rigorous evaluation methodologies for machine learning security. Tramèr's research has been published in top venues including ICLR, NeurIPS, ICML, and IEEE Security & Privacy. Tramèr is actively collaborating with leading researchers in the field including Nicholas Carlini, Matthew Jagielski, and Javier Rando, contributing to important initiatives like the International AI Safety Report. His work bridges theoretical security concepts with practical implications for real-world AI deployment.
Jieyu Zhao is an Assistant Professor in the Thomas Lord Department of Computer Science at the University of Southern California (USC), part of the Viterbi School of Engineering. She holds a Ph.D. from UCLA and was a NSF Computing Innovation Fellow at the University of Maryland. Her research focuses on fairness in machine learning and NLP, particularly addressing biases in AI systems and promoting ethical AI practices. Education: Ph.D. in Computer Science (UCLA, 2021), Postdoc at University of Maryland (2022-2023), and multiple research internships at Microsoft Research, Google Research, and AI2. She also completed her undergraduate studies at the University of Virginia. Research interests include bias detection and mitigation in NLP models, trustworthy AI, and social responsibility in technology. Her work has been recognized with awards such as the EMNLP Best Long Paper Award (2017), Microsoft PhD Fellowship (2020), and EECS Rising Star (2021). She has delivered talks at conferences like NeurIPS, EMNLP, and USC Law School, and serves on committees including AAAI workflow chair. Her lab explores topics like alignment of LLMs, fairness in sequential decision-making, and ethical model interventions. She actively participates in initiatives promoting diversity and inclusion in AI, such as the UN-WOMEN Beijing panel on gender equality. Awards: 100 Brilliant Women in AI Ethics (2025), Capital One Research Award (2024), Microsoft PhD Fellowship (2020) Grants: NSF Computing Innovation Fellowship, USC Viterbi funding Labs/Teams: Leads the USC Lab focused on trustworthy AI and ethical NLP
Fei Fang is an Associate Professor in the Software and Societal Systems Department at Carnegie Mellon University (CMU) , where she explores the intersection of artificial intelligence and multi-agent systems . Her work integrates machine learning with game theory to address challenges in security , sustainability , and mobility , aligning with the AI for Social Good mission. Ph.D. in Computer Science, University of Southern California (2016) B.Eng. in Electronic Engineering, Tsinghua University (2011) Recent research focuses on reinforcement learning , large language models (LLMs) , and human-AI collaboration . Her team’s work has been recognized with 15+ awards across prestigious venues like IAAI, AAAI, and IJCAI. Notable accolades include the 2023 Allen Newell Award , 2022 Sloan Fellowship , and NSF CAREER Award (2021) . She actively contributes to educational initiatives , including teaching "Demystifying AI for Everyone" at CMU, and has sought part-time teaching assistants for course development. Her research spans 15+ domains , including AI ethics , cyber defense , traffic optimization , and public health .
