جست‌وجوی استاد و پژوهشگر

Manik Varmaمشاهده پروفایل

استاد مدعو

• Machine Learning
• Deep Learning
• Information Retrieval
• +۱۶ مورد دیگر

Manik Varma is a Distinguished Scientist and Vice President at Microsoft Research India, and an Adjunct Professor at the Indian Institute of Technology Delhi. He is a Fellow of the Indian Academies of Science (IASc, INSA, NASI), the Indian National Academy of Engineering (INAE), and the Association for Computing Machinery (ACM). He has received prestigious awards such as the Shanti Swarup Bhatnagar Prize and Microsoft Gold Star Award. Education : BSc in Physics from St. Stephen's College (David Raja Ram Prize) BA in Theoretical Physics from the University of Oxford (Rhodes Scholar) DPhil in Computer Vision and Machine Learning from the University of Oxford (University Scholar) Post-doctoral Fellow at the Mathematical Sciences Research Institute (MSRI), Berkeley Visiting Miller Professor at UC Berkeley His research focuses on Machine Learning (Extreme Classification, Resource-efficient ML, Supervised Learning), Information Retrieval (Computational Advertising, Dense Retrieval, Recommender Systems), and Computer Vision (Image Search, Object Recognition). Recent work includes graph-regularized encoders, label variance reduction, and multimodal classification frameworks. His publications span extreme classification algorithms like NGAME , SiameseXML , and DECAF , with applications in IoT, web search, and recommendation systems. He leads a research group at Microsoft Research India and advises PhD students at IIT Delhi. Scientific Awards : Shanti Swarup Bhatnagar Prize (Government of India) Microsoft Gold Star and Achievement Awards WSDM 2019 Best Paper Prize BuildSys 2019 Best Paper Runner-up Fellow of ACM, IASc, INSA, NASI, INAE He has supervised numerous PhD students, including Sonu Mehta and Suchith Prabhu, and collaborates with institutions like Microsoft Research India, IIT Delhi, and UC Berkeley. His research has led to scalable solutions for billion-label classification and resource-constrained IoT applications.

Amitabha Bagchiمشاهده پروفایل

استاد

• Data Algorithmics
• Probability and Networks
• Theoretical Computer Science
• +۷ مورد دیگر

Amitabha Bagchi is a Professor in the Department of Computer Science and Engineering at IIT Delhi. His research spans data algorithmics, probability, networks, and theoretical computer science, with applications in distributed systems, social networks, and AI-driven platforms. He has published extensively in leading venues such as SIGMOD, VLDB, ICDE, AAAI, and KDD, often collaborating with students and researchers on problems involving graph algorithms, fairness, and large-scale data analysis. Research Interests: His primary research interests include Data Algorithmics, Probability and Networks, Theoretical Computer Science, Distributed Algorithms, Graph Algorithms, and Machine Learning Theory. He investigates algorithmic foundations for real-world problems such as food delivery optimization, social network analysis, and efficient data structures for streaming and large graphs. Publication Trends: Recent publications focus on fairness in gig economy platforms, efficient solvers for graph Laplacians, generalization in neural networks, and temporal graph querying. His work combines theoretical rigor with practical impact, often involving GPU acceleration, distributed computing, and data-aware algorithm design. Scientific Service: Editor, Algorithms (2020–present) Editor, Journal of Discrete Algorithms , Elsevier (2006–2018) Guest Editor, special issue on Algorithms for Shortest Paths in Dynamic and Evolving Networks , Algorithms (2021) Volume Editor for proceedings of ESA, ATMOS, COCOON, and others Conference Leadership: He has served on numerous program committees and as chair for conferences including ESA (Engineering Track, 2016), ATMOS (2020), and ICALP (2019). His involvement spans algorithmic engineering, transportation optimization, and theoretical computer science forums. Teaching: He currently teaches COL863: Special Topics in Theoretical Computer Science on concentration inequalities and their applications. He has previously taught advanced courses in algorithms and data structures.

Vineeth N Balasubramanianمشاهده پروفایل

استاد

• Deep Learning
• Machine Learning
• Computer Vision
• +۸ مورد دیگر

Vineeth N Balasubramanian is a Professor in the Department of Computer Science & Engineering at the Indian Institute of Technology Hyderabad, with affiliate faculty status in the Department of Artificial Intelligence. His research focuses on the intersection of deep learning, machine learning, and computer vision, emphasizing explainability, robustness, and real-world applications. He leads Lab 1055, which investigates problems such as Explainable and robust AI/ML systems Lifelong learning in evolving environments Multimodal vision-language models Applications in agriculture, autonomous navigation, and human behavior analysis His recent work includes causal reasoning in transformers, vision-language model capabilities, and drone-based object detection. Funded by organizations like Google, Microsoft, Intel, and DST, he has received multiple awards including the World's Top 2% Scientists (2022-23), INSA/INAE Fellowships, and Best Paper recognitions. Lab 1055 collaborates with institutions like CMU, UBC, and Monash University, contributing to cutting-edge advancements in AI.

Christos Faloutsosمشاهده پروفایل

استاد

• Data Mining
• Graph Mining
• Fractals and Power Laws
• +۳ مورد دیگر

Christos Faloutsos is the Fredkin Professor of Computer Science at Carnegie Mellon University, with a courtesy appointment in Electrical and Computer Engineering. He holds a B.Sc. from the National Technical University of Athens and M.Sc./Ph.D. from the University of Toronto. His research focuses on data mining, graph analysis, fractals, and database systems. Notable contributions include foundational work on R-trees, graph mining laws (e.g., Kronecker graphs), and applications in medical imaging, network security, and fraud detection. Key projects include PEGASUS (petascale graph mining), fraud detection in online auctions (NetProbe), and tools for human trafficking analysis (TrafficVis). He has led NSF-funded projects on tensor mining, network anomaly detection, and bioinformatics. Over 300 refereed publications highlight his contributions across databases, data mining, and networks. Awards include the KDD Best Paper (2005, 2016), SIGMOD Test-of-Time Award, and recognition as a top nurturer in IT. His lab collaborations span the Parallel Data Lab (PDL), Machine Learning Department, and Computational Biology.

• Machine Learning
• Explainable AI
• Neural Networks
• +۳ مورد دیگر

Jonas Fischer is the head of the Explainable Machine Learning group at the Max Planck Institute for Informatics, Department of Computer Vision and Machine Learning. His research focuses on interpreting complex machine learning models, particularly in genomics and healthcare, aiming to enhance robustness and alignment with human decision-making. Prior to his role at MPI, he was a postdoctoral fellow at Harvard University's Department of Biostatistics, where he worked on interpretable models for gene regulatory systems in cancer. Education: PhD in Computer Science from Saarland University (2022), with a thesis titled More than the sum of its parts , exploring the intersection of pattern mining and deep learning. He has contributed to advancing methods in neural network pruning, federated learning, and low-dimensional embeddings (e.g., dtSNE, Mercat). His work bridges computational biology, data mining, and machine learning, with applications in DNA methylation analysis, graph-based differential networks, and biomedical informatics. Key research areas include: (1) Explainable AI and neural network interpretability, (2) Biomedical applications of machine learning (e.g., gene regulatory networks, cancer genomics), (3) Low-dimensional embeddings and visualization techniques, (4) Federated learning for privacy-preserving collaborative models, and (5) Pattern mining for error analysis in NLP and classification tasks. Publications span top venues like NeurIPS, ICLR, Bioinformatics, and Genome Biology. His group develops tools such as BONOBO for omics data integration and node2vec2rank for scalable graph analysis. He actively collaborates with biomedical researchers to address challenges in data-driven healthcare and precision medicine.

Han Liuمشاهده پروفایل

استاد

• Artificial Intelligence
• Computer Systems
• Statistical Machine Learning
• +۴ مورد دیگر

Han Liu is a Professor in the Department of Computer Science at Northwestern University's McCormick School of Engineering. He directs the MAGICS (Modern Artificial General Intelligible and Computer Systems) Lab and the Center for Foundation Models and Generative AI at Northwestern, with prior roles as director of the Deep Reinforcement Learning Center at Tencent AI Lab and professor at Princeton and Johns Hopkins Universities. PhD in Machine Learning and Statistics from Carnegie Mellon University (2012), advised by John Lafferty and Larry Wasserman Han Liu's research focuses on integrating artificial intelligence with computer systems, particularly through foundation models and probabilistic graphical models. His work aims to revolutionize science, engineering, and business by deploying statistical machine learning methods in edge and cloud computing environments. Recent research trends include transformer-based models, modern Hopfield networks, genomic foundation models, and theoretical analysis of attention mechanisms. His 2025 publications explore topics like species differentiation with DNA embeddings, universal approximation capabilities of transformers, and metaverse spatial reasoning. Alfred P Sloan Fellowship in Mathematics IMS Tweedie New Researcher Award ASA Noether Young Scholar Award NSF CAREER Award Presidential Early Career Awards for Scientists and Engineers Han Liu serves as associate editor for the Journal of American Statistical Association, Electronic Journal of Statistics, Technometrics, and the Journal of Portfolio Management. He has directed research centers at Northwestern and contributed to major conferences as area chair (NeurIPS, ICML, ICLR).

Jimeng Sunمشاهده پروفایل

استاد

• Artificial Intelligence
• Healthcare Analytics
• Drug Discovery
• +۵ مورد دیگر

Dr. Jimeng Sun is a Health Innovation Professor at the Siebel School of Computing and Data Science and Carle Illinois College of Medicine at the University of Illinois Urbana-Champaign. Co-founder of Keiji AI , he leads groundbreaking research at the intersection of artificial intelligence and healthcare, actively deploying clinical AI systems and developing frameworks like PyHealth and Therapeutics Data Commons . His research spans four major areas: Clinical AI Systems : Developing interpretable models (e.g., RETAIN) for patient similarity, temporal event prediction, medication recommendation, and clinical outcome forecasting Drug Discovery : Creating molecular optimization frameworks, drug-target interaction models, and AI-driven platforms Clinical Trials : Pioneering patient-trial matching, outcome prediction, and optimization frameworks using deep learning and graph neural networks Biosignal Analysis : Advancing sleep staging, seizure classification, and automated EEG/Cardiac monitoring systems With over 500 top-tier publications (including in Nature , NEJM AI , and leading AI conferences) and an h-index of 99, his work has been recognized with the Top 100 AI Leaders in Drug Discovery and Advanced Healthcare award. He maintains active collaborations with institutions like Massachusetts General Hospital , Medidata Solutions , and OSF Healthcare . His recent publications reveal a strong focus on: Reinforcement learning applications in medical data analysis Large language model adaptation for clinical tasks Knowledge graph integration with AI systems Synthetic data generation for healthcare Multi-modal learning in clinical contexts Explainable AI for medical applications Dr. Sun's lab ( Sunlab ) emphasizes practical impact over theoretical work, actively collaborating with hospitals and healthtech companies. He welcomes contributions from clinicians, researchers, and industry partners through initiatives like his AI for Health webinar series .

Philip S. Yuمشاهده پروفایل

استاد

• Data Mining
• Big Data
• Privacy-Preserving Data Publishing
• +۹ مورد دیگر

Philip S. Yu is a Distinguished Professor in the Department of Computer Science at the University of Illinois at Chicago and holds the Wexler Chair in Information Technology. Previously, he led the Software Tools and Techniques department at IBM Thomas J. Watson Research Center. Education: B.S. in Electrical Engineering, National Taiwan University M.S. and Ph.D. in Electrical Engineering, Stanford University M.B.A., New York University His research spans data mining , big data , social networks , privacy-preserving data publishing , graph/network mining , recommender systems , and deep learning . He has authored over 970 papers with 74,500+ citations and an H-index of 127. Recent work focuses on heterogeneous graph representation, quantum walks in network analysis, and federated unlearning. Scientific Honors: ACM SIGKDD 2016 Innovation Award IEEE Computer Society 2013 Technical Achievement Award IEEE ICDM 2003 Research Contributions Award IEEE Region 1 Award (1999) UIC Research of the Year (2013) IBM Master Inventor with 300+ patents AI 2000 Most Influential Scholar Honorable Mentions (2024-2025) He served as Editor-in-Chief for ACM Transactions on Knowledge Discovery from Data and IEEE Transactions on Knowledge and Data Engineering , and on steering committees for ACM KDD and IEEE Data Mining. His work bridges theoretical advances in graph neural networks , deep learning , and privacy-preserving systems with applications in healthcare, social media, and enterprise analytics.

• Mechanical Engineering
• Autonomous Vehicles
• Artificial Intelligence
• +۳ مورد دیگر

Professor Mohammad FARD is a faculty member at RMIT University's School of Engineering, specializing in Mechanical Engineering and Intelligent Systems. He leads research in autonomous vehicles, crash safety, and driver monitoring using AI. His industry experience includes six years at Nissan Technical Centre, focusing on vehicle body design. He holds a PhD from Tohoku University and has collaborated across Engineering, Health, and Science disciplines, achieving international media coverage for work on driver drowsiness and road safety. Research Interests: Autonomous Vehicles Advanced Crash Safety Driver State Monitoring AI in Noise/Vibration Human Factors/Ergonomics Teaching & Projects: Teaches Advanced CAE, Vehicle NVH, and research supervision in areas like crash simulation and vibration control. Current projects include Formula One safety barriers and driver education for autonomous vehicles. Awards & Labs: No awards listed. Active in cross-disciplinary teams and labs addressing automotive innovation and safety.

• Uncertainty Quantification
• Scientific Machine Learning
• Stochastic Optimization
• +۳ مورد دیگر

Hadi Meidani is a Clinical Associate Professor at the Carle Illinois College of Medicine , specifically within the Department of Biomedical and Translational Sciences at the University of Illinois at Urbana-Champaign . He teaches courses in Civil and Environmental Engineering, including topics like Systems Engineering & Economics , Machine Learning in CEE , and Uncertainty Quantification . Ph.D., Civil Engineering, University of Southern California (2012) M.S., Electrical Engineering, University of Southern California (2012) M.S., Structural Engineering, Sharif University of Technology (2005) B.S., Civil Engineering, K.N. Toosi University of Technology (2002) Dr. Meidani's research focuses on uncertainty quantification , scientific machine learning , and optimization under uncertainty for engineering systems. His work spans stochastic multiscale analysis , physics-informed machine learning , and model reduction techniques. His recent publications emphasize machine learning for infrastructure systems , graph neural networks , physics-informed models , and traffic assignment . Key trends include deep learning , multi-fidelity modeling , and neural operator transformers applied to metamaterial design , seismic reliability , and autonomous freight delivery .

• Machine Learning
• Artificial Intelligence
• Algorithms
• +۳ مورد دیگر

Zaid Harchaoui is an Adjunct Professor in the Department of Statistics at the University of Washington. His research focuses on machine learning, generative AI, and algorithmic optimization, with applications spanning ecology, neuroscience, and artificial intelligence. He explores learning under distributional shifts and develops tools for scalable generative models in language and vision domains. University: University of Washington Department: Statistics Research Focus: Learning from data with computational, inferential, and mathematical rigor; distributional shift adaptation; generative model scaling Email: zaid@uw.edu His recent work emphasizes generative AI applications in ecology and neuroscience, stochastic optimization for robustness, and algorithmic efficiency in large-scale learning. Key contributions include techniques for distributionally robust optimization, interpretable authorship obfuscation, and uncertainty quantification in behavior classification. Scientific awards and honors are not explicitly mentioned in the provided text. Collaborative efforts often intersect with nonlinear control algorithms, spectral analysis, and privacy-preserving machine learning frameworks.

Almut Sophia Koepkeمشاهده پروفایل

پژوهشگر

• Multimodal Learning
• Audio-Visual Processing
• Text-to-Audio Retrieval
• +۵ مورد دیگر

Almut Sophia Koepke is a junior research group leader at the Technical University of Munich and University of Tübingen, focusing on multimodal learning problems integrating sound, vision, and text. Her work bridges foundational research in audio-visual understanding with practical applications in few-shot learning, zero-shot translation, and cross-modal attention mechanisms.

• Computational Social Science
• Network Science
• Data Visualization
• +۷ مورد دیگر

Yu-Ru Lin is an Associate Professor at the School of Computing and Information, University of Pittsburgh, and serves as Research & Academic Director at the Institute for Cyber Law, Policy and Security (Pitt Cyber). She leads the Pitt Computational Social Dynamics Lab (PICSO Lab) and holds secondary appointments in Political Science, Computer Science, and the Intelligent Systems Program. PhD in Computer Science from Arizona State University Postdoctoral research at Harvard University and Northeastern University Her research focuses on computational approaches for: Networked social dynamics High-dimensional social information summarization Trust and distrust propagation Misinformation detection Policy diffusion analysis Recent publications span 2014-2020, covering: Social media crisis response Graph visualization techniques Policy diffusion patterns Misinformation mitigation Temporal topic modeling Scientific support includes: National Science Foundation (NSF) awards Minerva/ONR funding AFOSR grant for distrust modeling DARPA Understanding Group Biases program As director of PICSO Lab, she leads interdisciplinary research teams on: Digital accountability Urban mobility patterns Health informatics via crowdsourcing Trust-influence dynamics

Lexing Xieمشاهده پروفایل

استاد

• Machine Learning
• Social Media Analysis
• Computer Vision
• +۷ مورد دیگر

Lexing Xie is a Professor in Computer Science at the Australian National University. He leads the ANU Computational Media Lab ( http://cm.cecs.anu.edu.au ) and the ANU Integrated AI Network. His work focuses on the intersection of machine learning, social media analysis, and multimedia understanding. Dr. Xie's research broadly focuses on innovative design and use of machine learning algorithms, especially on large-scale graph data and collective behaviour. His recent work spans several key areas: Popularity in social media -- understanding, predicting, and optimization Multimedia knowledge graphs, vision and language integration Humanising machine intelligence through better understanding of social dynamics His publications reveal a strong trend toward understanding information diffusion patterns in social media, particularly through visual content. He has made significant contributions to the study of visual memes, popularity prediction using point processes, and multimodal learning that connects vision with language. His work often bridges theoretical machine learning with practical applications in social media analysis. Dr. Xie has received recognition for his research, including an Honourable Mention at CSCW 2019 for his work on attention flow in online video networks. His research has been supported by collaborations with major institutions including IBM Research and Columbia University. As an advisor, Dr. Xie has mentored numerous students who have gone on to contribute significantly to publications in top-tier conferences. His lab, the ANU Computational Media Lab, serves as a hub for interdisciplinary research connecting computer science with social sciences.

Oliver Kroemerمشاهده پروفایل

دانشیار

• Robot Learning for Manipulation
• Motion Planning for Manipulation Tasks
• Learning and Classification Algorithms
• +۴ مورد دیگر

Oliver Kroemer is an Associate Professor at Carnegie Mellon University's Robotics Institute (RI), affiliated with the Intelligent Autonomous Manipulation (IAM) Lab. His research focuses on enabling robots to learn versatile manipulation skills through lifelong frameworks, with applications in elder care, environmental maintenance, and hazardous operations. Developed methods for robot learning via physical interaction and reinforcement learning Created representations for contact states and motor primitives to improve skill generalization Research Interests: Spanning robot learning, tactile sensing, force-velocity control, and lifelong skill acquisition. Projects include Agile and Dynamic Interactions for Mobile Manipulation and Integrated Planning and Learning (Pillar project). Scientific Awards: Finalist, Georges Giralt Ph.D. Award (2015) Education: Masters & Bachelors in Engineering, University of Cambridge (2008) Ph.D., Technische Universitaet Darmstadt (2014) Students & Affiliates: Current PhD: Mark Lee, Sarvesh Patil, Saumya Saxena, Yunus Seker, Zilin Si Past PhD: Alex LaGrassa, Tabitha Lee, Qiao Liang, Shivam Vats, Kevin Zhang