Srijan Kumar is an Assistant Professor in the School of Computational Science and Engineering at Georgia Institute of Technology's College of Computing. His research focuses on data science, AI for security, and online safety, addressing challenges in detecting malicious users, misinformation, and enhancing AI robustness. His work has been deployed in platforms like Flipkart and Wikipedia, and recognized through awards such as the NSF CAREER and Forbes 30 Under 30. Education: B.Tech from Indian Institute of Technology, Kharagpur Ph.D. in Computer Science from University of Maryland, College Park Postdoctoral training at Stanford University Research Interests: Multi-modal/multi-lingual detection of harmful content and users Adversarial robustness of AI models Graph and network analysis for early detection Responsible recommender systems Awards & Grants: NSF CAREER Award (2023) Kavli Fellow (2022) Facebook/Adobe Faculty Awards NSF Convergence Accelerator Phase II grant ($5M) Advising & Labs: Leads the CLAWS Lab, advising over 20 students. Active in mentoring through conferences and NSF-funded projects.
Elisa Ricci is a Full Professor at the Department of Information Engineering and Computer Science (DISI) at the University of Trento and serves as Head of the Research Unit Deep Visual Learning at Fondazione Bruno Kessler. She coordinates the Doctoral Program in Information Engineering and Computer Science at the University of Trento and holds prestigious fellowships from ELLIS and IAPR. Her research focuses on advancing computer vision and deep learning systems capable of operating in open-world environments. Key interests include domain adaptation, continual learning, and self-supervised learning for visual and multi-modal data processing. Her work addresses critical challenges in enabling machines to adapt to new domains without forgetting prior knowledge, with applications spanning robotics perception, medical imaging, and privacy-preserving AI systems. Recent publications reveal a dominant trend toward leveraging vision-language models for open-vocabulary tasks, training-free adaptation methods, and federated learning architectures. Significant research thrusts include machine unlearning for privacy, robustness against bias in visual classifiers, and novel class discovery using foundation models—particularly evident in 2025 publications addressing medical imaging, 3D segmentation, and collaborative generative systems. Her major recognitions include: ELLIS Fellow IAPR Fellow As Doctoral Program Coordinator at the University of Trento, she oversees PhD training while leading the Deep Visual Learning unit at Fondazione Bruno Kessler. Her research group secures competitive grants in AI-driven perception systems, though specific funding sources aren't detailed in the source material. The Deep Visual Learning Research Unit specializes in open-world computer vision challenges, developing frameworks for domain adaptation, continual learning, and multi-modal perception. Current projects integrate generative models with robotics applications while addressing privacy concerns in vision-language systems through unlearning techniques.
Lin Tan is a Professor of Computer Science at Purdue University , holding the Mary J. Elmore New Frontiers Professorship . She joined Purdue in 2019 after serving as a Canada Research Chair and associate professor at the University of Waterloo. She is an ACM Distinguished Member and IEEE Senior Member . Education: PhD in Computer Science, University of Illinois Urbana-Champaign BS in Computer Science and Technology, Zhejiang University Research Interests: Professor Tan’s research lies at the intersection of software engineering , artificial intelligence , and security . Her work focuses on software-AI synergy , software dependability , defect detection & repair , and software text analytics . She leverages machine learning and natural language processing to enhance software reliability, and conversely uses software techniques to improve the dependability of AI systems. Her recent projects include building binary foundation models (Nova), evaluating large language models for code generation and repair, and developing interactive debugging tools that reduce debugging time by one-third. She also explores robot task planning with LLMs and automated front-end development . Awards & Honors: Best Paper Award Finalist, ICRA 2025 ELATES Fellow, 2024-2025 ACM SIGSAC Distinguished Paper Award, CCS 2024 J.P.Morgan AI Faculty Research Awards (2020, 2021, 2022) ACM SIGSOFT Distinguished Paper Awards (ASE 2020, MSR 2018, FSE 2016) Canada Research Chair (2017) Ontario Early Researcher Award (2015) NSERC Discovery Accelerator Supplements Award (2015) Google Faculty Research Awards (2010, 2014) IEEE Micro Top Picks (2006) Advising & Funding: Professor Tan currently advises eight PhD students and has graduated 20+ PhD and Master’s students now thriving in academia (York University, Concordia University, University of Alberta) and industry (Microsoft, Meta, Amazon, Google). Her group is generously supported by NSF , Meta/Facebook Research Awards , J.P.Morgan AI Faculty Awards , and NSF REU programs. Labs & Teams: She leads the Software Reliability & AI Lab at Purdue, recruiting postdocs, PhD, MS, and undergraduate researchers year-round. Lab interests span binary recovery , LLM-based program repair , testing deep-learning libraries , and data-free model extraction .
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.
Dr. Arno Solin is a tenured Associate Professor in Machine Learning at Aalto University's Department of Computer Science and an Academy of Finland Research Fellow . He leads the Aalto machine learning research group and serves as Director of the Finnish Doctoral Program Network in AI (AI-DOC) . His work bridges probabilistic modeling with practical applications in sensor fusion and real-time inference. ELLIS Scholar (European Laboratory for Learning and Intelligent Systems) Adjunct Professor at Tampere University Member of Young Academy Finland (2021–2025) Research Interests focus on data-efficient machine learning with probabilistic methods for real-time inference and sensor fusion. Key areas include Gaussian processes, diffusion models, stochastic differential equations, and uncertainty quantification in deep learning. His group develops methods that combine structural constraints with adaptive learning for deployment on resource-limited hardware. Publication Trends show consistent output in top venues (NeurIPS, ICML, ICLR, AISTATS) with emphasis on diffusion models , 3D scene reconstruction , and real-time probabilistic modeling . Recent works explore physics-informed learning, heterophily-aware graph models, and compressed representations for world models in reinforcement learning. Scientific Recognition : Awarded AI Researcher of the Year 2024 by AI Finland Teacher of the Year 2023 at Aalto CS ISIF Jean-Pierre Le Cadre Best Paper Award (2018) MLSP Schizophrenia Classification Challenge Winner (2014) NeurIPS/ICML Reviewer Awards Research Leadership includes coordinating Finland's AI Center of Excellence program and directing the Finnish Doctoral Program Network in AI (AI-DOC). He supervises 15+ doctoral/postdoctoral researchers and has spun off Spectacular AI , a company commercializing sensor fusion technology.
Christopher Kanan is a tenured Associate Professor of Computer Science at the University of Rochester, leading the AI Initiative within the Hajim School of Engineering & Applied Sciences. He holds secondary appointments in Brain and Cognitive Sciences, the Goergen Institute for Data Science and AI (GIDS-AI), and the Center for Visual Science. His research focuses on deep learning systems for artificial general intelligence (AGI), including continual learning, medical computer vision, and visual question answering. Previously, he was an Associate Professor at RIT’s Carlson Center for Imaging Science and a leader at Paige.AI, contributing to the FDA-cleared Paige Prostate system. Kanan earned his PhD from UC San Diego, completed postdoctoral work at Caltech, and worked at NASA JPL. Education: PhD in Computer Science, UC San Diego MS in Computer Science, University of Southern California Bachelor’s in Philosophy and Computer Science, Oklahoma State University Research Interests: Kanan’s work spans foundational AI capabilities like continual learning, medical imaging (pathology and radiology), multi-modal reasoning, and cognitive science-inspired models. His lab develops bias-robust AI systems and applies deep learning to healthcare and fusion research. Articles Trends: His recent work emphasizes out-of-distribution generalization, foundation models in pathology, and stability in continual learning. Key themes include AI applications in healthcare, model robustness, and neuroscience-inspired algorithms. Awards: NSF CAREER Award Senior Member, AAAI and IEEE DoE and NSF grants totaling $5M+ DARPA/ARL awards Advising & Grants: Mentored over 10 PhD students, including Robik Shrestha and Usman Mahmood. Secured grants for AI in nuclear fusion and medical imaging. Led RIT’s Center for Human-aware AI (CHAI) as Associate Director. Labs & Teams: Heads the University of Rochester AI Initiative, collaborates with Paige.AI, and leads teams advancing AI in pathology and robotics. His lab’s KLab (klab.cis.rit.edu) focuses on vision and learning systems.
Animesh Garg is an Assistant Professor at the School of Interactive Computing at Georgia Tech, where he leads the People, AI, and Robotics (PAIR) research group . He holds a Senior Researcher position at Nvidia Research and has courtesy appointments at the University of Toronto and Vector Institute. Previously, he served as Chief Scientific Officer at Apptronik (2024-2025) and Senior Staff Research Scientist at Nvidia Research (2018-2024). Education : Ph.D. in Operations Research from UC Berkeley (2011-2016), MS in Computer Science and Industrial Engineering from Georgia Tech and University of Delhi. Research Focus : Building Generalizable Autonomy through Reinforcement Learning , Control Theory , and 3D Vision , with applications in Surgical Robotics , Self-Driving Labs , and Manufacturing . Key Article Themes : His recent work emphasizes Foundation Models for robotics, Differentiable Simulation , Language-Guided Autonomy , and Structured Inductive Biases in sequential decision-making. Scientific Awards : Stephen Fleming Early Career Professorship at Georgia Tech. Teaching : Courses on AI, Deep Reinforcement Learning, and Algorithmic Intelligence in Robotics at Georgia Tech. Labs & Collaborations : Affiliated with Institute for Robotics and Intelligent Machines (IRIM) and ML@GT at Georgia Tech; collaborates intensively with Nvidia Robotics.
Prof. Michael Moor is a tenure-track Assistant Professor for Medical AI at ETH Zurich's Department of Biosystems Science and Engineering in Basel. Previously, he conducted postdoctoral research at Stanford University under Prof. Jure Leskovec, focusing on medical foundation models. His work spans causal learning, multimodal AI, and sepsis prediction. Moor holds an MD from the University of Basel and a PhD from ETH Zurich's Machine Learning and Computational Biology Lab under Prof. Karsten Borgwardt. Research Interests: Generalist medical AI models Multimodal medical reasoning Zero-shot and few-shot learning Clinical causal inference Retrieval-augmented language models Sepsis prediction systems Key Achievements: Published foundational work in Nature (2023) on generalist medical AI Developed Med-Flamingo multimodal model (2023) Zero-shot causal learning framework accepted to NeurIPS 2023 (Spotlight) International sepsis prediction study with 156k ICU patients (2023) Lab Activities: Leads ETH's Medical Foundation Models group, collaborating with Stanford HAI and NASA Ames. Active in creating medical AI benchmarks like AgentClinic and developing retrieval-augmented systems like Almanac.
Baharan Mirzasoleiman is an Assistant Professor in the Department of Computer Science at the University of California, Los Angeles (UCLA), where she leads the BigML research group. Prior to joining UCLA, she was a postdoctoral research fellow in Computer Science at Stanford University working with Jure Leskovec. She received her Ph.D. in Computer Science from ETH Zurich advised by Andreas Krause. Her research focuses on addressing sustainability, reliability, and efficiency of machine learning, with particular emphasis on improving big data quality by developing theoretically rigorous methods to select the most beneficial data for efficient and robust learning. Her work spans several critical areas including data efficiency, robustness against label noise and data poisoning, and addressing spurious correlations in machine learning models. She has made significant contributions to understanding how neural networks exploit spurious features that correlate with certain categories during training but fail to generalize to minority groups. Professor Mirzasoleiman's research demonstrates how theoretically grounded approaches can lead to practical improvements in model robustness and efficiency across various applications including medical diagnosis and environmental sensing. Her work has resulted in the development of the SpuCo package, a Python library that provides modular implementations of state-of-the-art methods to address spurious correlations, along with controllable synthetic datasets like SpuCoMNIST and large-scale vision datasets like SpuCoAnimals. She has received numerous prestigious awards including the ETH medal for Outstanding Doctoral Thesis, being selected as a Rising Star in EECS by MIT, an NSF Career Award, a UCLA Hellman Fellows Award, and an Okawa Research Award. Her students have also received multiple fellowships and awards including Amazon Doctoral Student Fellowships and an OpenAI Superalignment Fast Grant. Professor Mirzasoleiman actively contributes to the academic community through invited talks at major conferences including ICML, ICLR, NeurIPS, and KDD, as well as co-organizing workshops on new frontiers in adversarial machine learning and sparsity in neural networks. She has developed educational resources including tutorials on Foundations of Data-efficient Learning presented at ICML 2024.
Angel Xuan Chang is an Associate Professor at Simon Fraser University's School of Computing Science, affiliated with labs including 3DLG, GrUVi, SFU NatLang, SFU AI/ML, and VINCI. He holds a Canada CIFAR AI Chair and was a TUM-IAS Hans Fischer Fellow (2018-2022). His research bridges natural language processing (NLP), 3D scene understanding, and embodied AI, focusing on language-grounded 3D generation and biodiversity monitoring via DNA barcodes. Recent work includes NuiScene (unbounded outdoor scene generation), ViGiL3D (3D visual grounding dataset), and CLIBD (vision-genomics biodiversity analysis). He advises students in projects like BIOSCAN-5M insect dataset and embodied AI navigation. His 2025 highlights include multiple ICCV and ICLR papers, workshops at ICML and CVPR, and a CRV invited talk. Education: Ph.D. in Computer Science from Stanford University (2014), advised by Chris Manning. Previous roles include visiting research scientist at Facebook AI Research and researcher at Eloquent Labs.
Sebastian Riedel is a Professor at University College London (UCL) and a Researcher at DeepMind, leading the UCL NLP Lab. His work focuses on teaching machines to read, reason, and write, integrating Natural Language Processing (NLP) with Machine Learning. He holds an Allen Distinguished Investigator award and has held roles at FAIR, UMass Amherst, Tokyo University, and the University of Edinburgh. Education: PhD in Computer Science from the University of Edinburgh (advisor: Ewan Klein), postdoctoral research at UMass Amherst (advisor: Andrew McCallum), and research at Tokyo University (advisor: Tsujii Junichi). Research Interests: NLP, machine learning, information extraction, and multimodal models like Gemini. He develops tools such as UCLEED (BioNLP event extractor), frontlets (Scala map wrappers), and thebibbrag (BibTeX to HTML converter). Awards: Allen Distinguished Investigator. Software contributions include GitHub repositories for NLP, machine learning, and data tools. Contact: s.riedel@ucl.ac.uk | Office: 1st Floor, 90 High Holborn, London WC1V 6LJ | Office Hours: Mondays 11 AM–12 PM.
Georgia Gkioxari is an Assistant Professor in the Division of Computing and Mathematical Sciences at Caltech , with a part-time affiliation at Meta AI . Her work focuses on extending visual perception models through advanced 2D and 3D representation learning, spatial reasoning, and generative models. Education: Not explicitly mentioned in the text Research interests span 3D perception , spatial reasoning , and vision-language integration , with projects like Visual Agentic AI for Spatial Reasoning and Token-by-Token Multimodal Alignment . Her publications emphasize 3D object detection , reconstruction , and generative modeling techniques including diffusion models and transformers . Scientific recognition includes the Meta LLM Evaluation Research Grant , Okawa Research Grant , Google Faculty Scholar Award 2024 , and Amazon Research Award . She teaches courses like Large Language & Vision Models (EE/CS 148) and Learning & 3D (CS 101) at Caltech. Labs & Teams: Leads Glab with members including Ilona Demler, Ziqi Ma, and Damiano Marsili
Professor Winston Hsu is a distinguished faculty member in the Department of Computer Science and Information Engineering at National Taiwan University, where he has served as a full professor since 2015. He is the co-director of the Communications and Multimedia Laboratory (CMLab) and founder of the MiRA (Multimedia indexing, Retrieval, and Analysis) research group. Additionally, he serves as the Founding Director for NVIDIA AI Lab at NTU, the first such lab in Asia. Professor Hsu received his Ph.D. in Electrical Engineering from Columbia University in 2007 under the supervision of Professor Shih-Fu Chang. Prior to his academic career, he was a founding engineer and research manager at CyberLink Corp., now a public image/video software company. National Taiwan University (2007-Present): Professor (2015-Present), Assistant/Associate Professor (2007-2015) MobileDrive (2021-2024): CTO and Vice President (Joint Venture between Foxconn and Stellantis) IBM TJ Watson Research Center (2016-2017): Visiting Scientist Microsoft Research Redmond (2014): Visiting Researcher Columbia University (2007): Ph.D. in Electrical Engineering Professor Hsu's research focuses on machine learning, computer vision, large-scale image and video search and recognition, and embedded AI. His work spans from fundamental research in visual recognition to practical applications in automotive systems, medical imaging, and e-commerce. He has pioneered work in disguised face recognition, low-resolution face hallucination, 3D model search, and virtual try-on systems. His current research emphasizes Embodied AI, integrating perception, action, and learning technologies for applications in automotive and robotics domains. His research group has produced numerous influential publications, particularly in top computer vision and multimedia conferences like CVPR, where they won first place in the Disguised Face Recognition competition in 2018. Their work spans diverse application areas including security, medical diagnostics, automotive systems, and e-commerce solutions, demonstrating strong translation from academic research to real-world impact. IBM Research Pat Goldberg Memorial Best Paper Award (2018) First Place, IARPA Disguised Faces in the Wild Competition (CVPR 2018) Best Brave New Idea Paper Award, ACM Multimedia 2017 NVIDIA AI LAB Award (First in Asia, 2016) First Place, MSR-Bing Image Retrieval Challenge (2013) World's Top 2% Scientists (2023) Professor Hsu actively mentors students and researchers, with his group consistently recruiting PhD students, postdocs, and research assistants. He has successfully bridged academia and industry through multiple collaborations, including his role as CTO at MobileDrive (a joint venture between Foxconn and Stellantis) from 2021-2024. His research has been supported by significant industry partnerships with Microsoft, IBM, and NVIDIA, as well as government grants from Taiwan's Ministry of Science and Technology. His laboratory, the Communications and Multimedia Laboratory (CMLab), maintains strong industry connections and focuses on cutting-edge research in visual AI. The lab has produced numerous award-winning projects and maintains active collaborations with global technology companies, particularly in the automotive and consumer electronics sectors.
Jeannette Bohg is an Assistant Professor of Computer Science at Stanford University, directing the Interactive Perception and Robot Learning Lab. Previously, she was a group leader at the Autonomous Motion Department (AMD) of the MPI for Intelligent Systems (2012-2017). She holds a PhD from KTH Royal Institute of Technology (Stockholm) and degrees from Chalmers University and TU Dresden. Her research focuses on perception, learning, and real-time multi-modal methods for autonomous robotic manipulation and grasping, aiming to bridge principles of human sensorimotor coordination with robotic implementation. Education: PhD in Robotics (KTH), MSc in Art & Technology (Chalmers), Diploma in Computer Science (TU Dresden) Research interests include developing goal-directed, real-time robotic systems capable of meaningful feedback for execution and learning. Key areas are dexterous manipulation, imitation learning, and cross-embodiment policy transfer. Notable contributions include the TidyBot platform and work on force-aware surgical robotics. Awards include the 2019 IEEE ICRA Best Paper Award, 2019 IEEE RA Early Career Award, and 2020 RSS Early Career Award. Her lab explores intersections of robotics, ML, and computer vision. Advising: Actively mentoring students/postdocs in manipulation, perception, and learning. Grants and collaborations span NSF, Stanford AI Lab, and industry partnerships. Future work emphasizes robust real-world deployment and human-robot collaboration. Labs/Teams: Leads the Interactive Perception and Robot Learning Lab, contributing to Stanford’s AI ecosystem. Previously managed the MPI AMD group, fostering interdisciplinary research in autonomous systems.
Katja Hose is a Full Professor of Data Management at TU Wien's DBAI research unit, heading the Data Management and Knowledge-Driven AI Lab. She previously held a Poul Due Jensen Foundation Professorship at Aalborg University. Her research focuses on data and knowledge engineering, including graph databases, knowledge graphs, querying, analytics, and machine learning, with interdisciplinary applications in bioscience, healthcare, and environmental assessment. Education: PhD in Computer Science (Ilmenau University of Technology, 2009), Postdoc at Max Planck Institute for Informatics (2009–2012). Academic roles include Program Co-Chair for ISWC 2024 and EDBT 2023, and editorial board membership at VLDBJ and TGDK. She leads projects like TARGET (health virtual twins) and ARMADA (data management). Research Interests: Knowledge Graphs, Semantic Web, Big Data, Machine Learning, Data Integration, and Provenance Systems. Key contributions include SHACL shape extraction, conversational data analytics, and environmental knowledge graphs. Awards include the 2025 Distinguished Meta-Reviewer Award and 2024 Manfred Paul Award. Advising and Grants: Supervised students including E. Pürmayr (Diploma Thesis 2025). Active in EU projects (TARGET, ARMADA) and grant coordination. Labs/Teams: DMKI Lab at TU Wien, collaborating with interdisciplinary teams in healthcare and environmental science.
