I am a PhD Candidate at Saarland University and MPI-SWS under the supervision of Krishna P. Gummadi. I have recently submitted my thesis and am actively seeking opportunities in both industry and academia where I can contribute to impactful AI research and development. Please get in touch if you believe I am suitable for a position.

I have a strong track record in AI research, with expertise spanning fairness in machine learning, interpretability, and building responsible AI systems. During my PhD and internships, I not only conducted advanced research but also gained practical experience developing software solutions for AI systems. Throughout my work, I have contributed to three key areas of fair system design:

1. Evaluating existing approaches and systems for (un)fairness (AIES '23)
2. Updating deployed algorithmic systems fairly (AIES '19)
3. Designing new decision-making systems from scratch(AIES '21, Neurips '19, Neurips '22, ICDE '22, TKDE '21 and CSCW '24).

I have also worked as a research scientist intern at AWS-Tübingen, where I explored the bias in discriminative foundational models like CLIP. For further details, please refer to the publications below.

Throughout my academic and professional career, I have collaborated with leading AI researchers, gaining hands-on experience with a wide range of tools including neural networks, VAEs, GANs, and optimization algorithms. Some of my collaborators include, in alphabetical order: Abhijnan Chakraborty (IIT Dehli), Adish Singla (MPI-SWS), Baharan Mirzasoleiman (UCLA), Chris Russell (Oxford University), Jennifer Wortmann Vaughan (MSR), Manuel Rodriguez Gomez (MPI-SWS), Matthäus Kleindessner (Amazon), Muhammad Bilal Zafar (Ruhr University Bochum) and Volkan Cevher (EPFL).

During my masters at Saarland University, I have worked with Karol Myszkowski (MPI informatics) and Piotr Didyk (Università della Svizzera italiana). During this time I investigated the relationship between speed perception and frame-rates. I also proposed a method to set spatially and temporally variable frame-rate by adjusting per-pixel flicker. Please refer to my thesis below for more details.

My current interests include LLMs, generative models, and responsible applied AI.

For a more detailed over view of my work, please refer to my CV.

• PhD in Computer Science from Saarland University and MPI-SWS . (Thesis submitted)
• Masters in visual computing from Saarland University: Thesis
• BSc. honors in computer science from LUMS :Thesis

1. On the Fairness of Time-Critical Influence Maximization in Social Networks,

   by Junaid Ali, Mahmoudreza Babaei, Abhijnan Chakraborty, Baharan Mirzasoleiman, Krishna P. Gummadi and Adish Singla
   Got accepted at TKDE 21' (Transactions on Knowledge and Data Engineering) (A top tier journal in data mining)
   [ PDF] [ Poster] [ Code ]

1. (De)Noise: Moderating the Inconsistency of Human Decisions,

   by Nina Grgić-Hlača, Junaid Ali, Krishna P. Gummadi and Jennifer Wortman Vaughan
   in ACM Conference on Computer-Supported Cooperative Work and Social Computing (CSCW '24).
   [ Paper]

2. Evaluating the Fairness of Discriminative Foundation Models in Computer Vision,

   by Junaid Ali, Matthäus Kleindessner, Florian Wenzel, Volkan Cevher, Kailash Budhathoki and Chris Russel
   in AAAI/ACM Conference on AI, Ethics, and Society (AIES ’23). Acceptance Rate: 29%
   [ Paper]

3. Accounting for Model Uncertainty in Algorithmic Discrimination,

   by Junaid Ali, Preethi Lahoti and Krishna P. Gummadi
   in AAAI/ACM Conference on AI, Ethics, and Society (AIES ’21). Acceptance Rate: 37%
   [ PDF] [ Presentation ] [ Poster]

4. On the Fairness of Time-Critical Influence Maximization in Social Networks,

   by Junaid Ali, Mahmoudreza Babaei, Abhijnan Chakraborty, Baharan Mirzasoleiman, Krishna P. Gummadi and Adish Singla
   in International Conference on Data Engineering (ICDE '22) as Extended Abstract (An A* conference in data engineering)

5. Loss-Aversively Fair Classification,

   by Junaid Ali, Muhammad Bilal Zafar, Adish Singla and Krishna P. Gummadi
   in AAAI/ACM Conference on AI, Ethics, and Society (AIES ’19). Acceptance Rate: 32%
   [ PDF] [ Code ] [ Poster]

Workshop Publications

1. (De)Noise: Moderating the Inconsistency of Human Decisions,

   by Junaid Ali, Nina Grgić-Hlača, Krishna P. Gummadi and Jennifer Wortman Vaughan
   in Human Centered AI (HCAI) workshop at Neurips 2022. oral presentation
   [ Presentation]

2. On the Fairness of Time-Critical Influence Maximization in Social Networks,

   by Junaid Ali, Mahmoudreza Babaei, Abhijnan Chakraborty, Baharan Mirzasoleiman, Krishna P. Gummadi and Adish Singla
   Selected for oral presentation
   In Human-Centric Machine Learning (HCML) Workshop at NeurIPS '19
   

3. Unifying Model Explanability and Accuracy Through Reasoning Labels,

   by Vedant Nanada, Junaid Ali, Krishna P. Gummadi and Muhammad Bilal Zafar
   In Safety and Robustness in Decision Making (SRDM) Workshop at NeurIPS '19
   [ PDF]

• Human-Centered Machine Learning at Saarland University
• Distributed software systems design at LUMS
• Computational biology at LUMS